Environment and Sustainable Development: Proceedings of the 2022 7th Asia Conference on Environment and Sustainable Development (Environmental Science and Engineering) 9819941008, 9789819941001

Citation preview

Environmental Science and Engineering

Keiji Ujikawa Mikio Ishiwatari Eric van Hullebusch   Editors

Environment and Sustainable Development Proceedings of the 2022 7th Asia Conference on Environment and Sustainable Development

Environmental Science and Engineering Series Editors Ulrich Förstner, Buchholz, Germany Wim H. Rulkens, Department of Environmental Technology, Wageningen, The Netherlands

The ultimate goal of this series is to contribute to the protection of our environment, which calls for both profound research and the ongoing development of solutions and measurements by experts in the field. Accordingly, the series promotes not only a deeper understanding of environmental processes and the evaluation of management strategies, but also design and technology aimed at improving environmental quality. Books focusing on the former are published in the subseries Environmental Science, those focusing on the latter in the subseries Environmental Engineering.

Keiji Ujikawa · Mikio Ishiwatari · Eric van Hullebusch Editors

Environment and Sustainable Development Proceedings of the 2022 7th Asia Conference on Environment and Sustainable Development

Editors Keiji Ujikawa Yokohama National University Yokohama, Kanagawa, Japan

Mikio Ishiwatari Japan International Cooperation Agency Tokyo, Japan

Eric van Hullebusch Institut de Physique du Globe de Paris University of Paris Paris, France

ISSN 1863-5520 ISSN 1863-5539 (electronic) Environmental Science and Engineering ISBN 978-981-99-4100-1 ISBN 978-981-99-4101-8 (eBook) https://doi.org/10.1007/978-981-99-4101-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

Conference Committees

International Advisory Chair Prof. Vincenzo Belgiorno, University of Salerno, Italy

Honorary Chair Prof. Richard Haynes, University of Queensland, Australia

General Chair Dr. Mitsuo Yoshida, International Network for Environmental and Humanitarian Cooperation, Nonprofit Inc., Japan

Conference Co-chairs Prof. Keiji Ujikawa, Yokohama National University, Japan Prof. Shane Snyder, Nanyang Technological University, Singapore

v

vi

Conference Committees

Program Chairs Prof. Eric van Hullebusch, University of Paris, France Prof. Mikio Ishiwatari, Japan International Cooperation Agency (JICA), Japan Prof. Makoto Usami, University of Kyoto, Japan

Publicity Chair Prof. Kei Nakagawa, Nagasaki University, Japan

International Program Committees Prof. Violeta Mugica Alvarez, Universidad Autonoma metropolitana-Azcapotzalco, Mexico Assoc. Prof. Mohamed Alwaeli, Silesian University of Technology, Poland Assoc. Prof. Muslum Arici, Kocaeli University, Turkey Dr. Nanjappa Ashwath, Central Queensland University, Australia Dr. Chodchanok Attaphong, King Mongkut’s Institute of Technology Ladkrabang, Thailand Prof. H. A. Aziz, Universiti Sains Malaysia, Malaysia Prof. Isabel Paula Lopes Bras, Polytechnic Institute of Viseu, Portugal Prof. Joe Dong, UNSW Sydney, Australia Dr. Fatine Ezbakhe, University of Geneva, Switzerland Prof. Izaskun Garrido, University of the Basque Country, Spain Dr. Luca Giupponi, University of Milan, Italy Dr. Radu Godina, Universidade Nova de Lisboa, Portugal Assoc. Prof. Yuk Feng Huang, Universiti Tunku Abdul Rahman, Malaysia Assoc. Prof. Sakul Hovanotayan, King Mongkut’s Institute of Technology Ladkrabang, Thailand Prof. Shiu-Wan Hung, National Central University, Taiwan Prof. Ganesh Raj Joshi, United Nations Center for Regional Development (UNCRD), Japan Prof. Dimitrios Karamanis, University of Patras, Greece Dr. Kosuke Kawai, National Institute for Environmental Studies, Japan Dr. Manoj Khandelwal, Federation University Australia, Australia Dr. Alban Kuriqi, University of Lisbon, Portugal Prof. Kevin Liu, Ming Chi University of Technology, Taiwan Assoc. Prof. Dina Matthew, Instituto Politecnico de Tomar, Portugal Assoc. Prof. Paulo Mendonca, University of Minho, Portugal Assoc. Prof. Gassan Hodaifa Meri, Pablo de Olavide University, Spain

Conference Committees

vii

Assoc. Prof. Maegala Nallapan Maniyam, University Selangor, Malaysia Prof. Evan K. Paleologos, Abu Dhabi University, Abu Dhabi, UAE Dr. Vinod Phogat, South Australian Research and Development Institute, Australia Assoc. Prof. Cheerawit Rattanapan, Mahidol University, Thailand Dr. Borja Gonzalez Reguero, University of California, USA Assoc. Prof. Marcello Ruberti, University of Salento, Italy Dr. Shehzar Shahzad Sheikh, National University of Science and Technology (NUST), Pakistan Dr. Yahya Sheikhnejad, University of Aveiro, Portugal Prof. Pierluigi Siano, University of Salerno, Italy Assoc. Prof. Małgorzata Szczepanek, UTP University of Science and Technology, Poland Dr. Caloiero Tommaso, National Research Council of Italy (CNR-ISAFOM), Italy Dr. Angel Torriero, Deakin University, Australia Dr. Wongkot Wongsapai, Chiang Mai University, Thailand Dr. Xinhua Yin, University of Tennessee, USA Dr. Jinsheng You, University of Nebraska, USA Prof. Ierotheos Zacharias, University of Patras, Greece

Foreword

It is our great pleasure to introduce this volume, the proceedings of 2022 7th Asia Conference on Environment and Sustainable Development (ACESD 2022), which was held in Kyoto, Japan during November 4 to 6, 2022. We would like to express our gratitude to all the participants, all our reviewers, speakers, chairpersons, and sponsors for their continuous support and contributions. We would also like to acknowledge with special appreciation to the following three keynote speakers: Prof. Vincenzo Belgiorno (University of Salerno, Italy), Prof. Eric D. van Hullebusch (University of Paris, France), and Prof. Makoto Usami (University of Kyoto, Japan), for their distinguished lectures about the latest information on environmental science and sustainable development issues. ACESD was initiated in 2016, which is an annual international conference covering research in the field of environment and sustainable development. It provides the researchers, engineers, administrators, academics as well as industry professionals with an international platform to share new ideas and research findings. The first ACESD was held in Hong Kong in 2016, followed by Tokyo (Japan) in 2017, Singapore in 2018, Yokohama in 2019. From 2020 to 2021, ACESD was held online. The scale of this ACESD conference has grown over time, covering more countries and regions, and that the quality of the papers presented has improved. Moreover, we were able to guarantee equality and free discussion for all participants, regardless of nationality, occupation, social status, age, or gender, with the aim of advancing scientific research. Indeed, we need a borderless debate on the environment and sustainable development. Since the outbreak of the global COVID-19 pandemic, it has been a challenge for the academic community to organize international conferences due to travel restrictions. ACESD 2022 has combined the in-person and virtual format as a hybrid-style conference, which always tries to feature a platform open for reputable scientists, researchers, administrators, academicians, industrial professionals and postgraduate students globally for scientific communications. This year ACESD received many submissions from members of universities, research institutes and industries; there are more than 80 participants from 26 different

ix

x

Foreword

countries/region over the world. All papers were subject to peer-reviewing by conference committee members and international experts. The acceptance of the papers is based on their quality and relevance to the conference. This volume of proceedings contains 35 papers. Those accepted papers are grouped into 8 chapters. Topics include wastewater treatment and water analysis, hydrology and water resources management, solid waste management, environmental pollution, climate change, renewable energy, and circular economy. We hope that this volume of the conference proceedings will serve as a valuable reference for researchers, educators, and practitioners. We would like to express our most sincere appreciation to conference co-chairs, program committee chairs, publication chair, and technical program committee members for their precious efforts. Without their contribution, we would not have achieved so much. Besides, thanks to the high level of international interest in the subject, the conference achieves a complete success. On behalf of the conference organization committee, we sincerely hope that you will think the ACESD2022 beneficial and fruitful for your professional development. Dr. Mitsuo Yoshida General Chair, ACESD2022 International Network for Environmental and Humanitarian Cooperation (iNehc) Tokyo, Japan

Contents

Part I 1

2

3

4

5

Progress in Biosynthesized of Silver Nanoparticles as Sustainable Approach for Photocatalytic Wastewater Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamad Aizad Mohd Mokhtar, Roshafima Rasit Ali, Nurfatehah Wahyuny Che Jusoh, Zhongfang Lei, Zatil Izzah Tarmizi, and Didik Prasetyoko Impact of Onshore Construction Activities on Sea Water Turbidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muhammad Salman Afzal, Furqan Tahir, and Sami G. Al-Ghamdi Characteristics of Natural Organic Matter and Trihalomethanes Formation in the Southern Part of Songkhla Lake Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kamonnawin Inthanuchit and Kochakorn Sukjan Inthanuchit Characterization and Statistical Multivariate Analysis of Potentially Toxic Elements Contamination of Groundwater in Chiniot Area, Punjab Plain, Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . Mitsuo Yoshida, Mirza Naseer Ahmad, and Rashida Sultana A Systematic Literature Review on Rainwater Quality Influenced by Atmospheric Conditions with a Focus on Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Md. Arif Hossen, M. Salauddin, and Mohammad A. H. Badsha

Part II 6

Wastewater Treatment and Water Analysis

3

17

25

35

53

Hydrology and Water Resources Management

Quantification of Flash Flood Runoff Volume Using Morphometric Parameters Towards Sustainability . . . . . . . . . . . . . . . Mahmoud M. Mansour, Mahmoud Nasr, Manabu Fujii, Chihiro Yoshimura, and Mona G. Ibrahim

79

xi

xii

7

Contents

Application of the Whale Optimization Algorithm (WOA) in Reservoir Optimization Operation Under Investigation of Climate Change Impact: A Case Study at Klang Gate Dam, Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vivien Lai, Y. F. Huang, C. H. Koo, Ali Najah Ahmed, and Ahmed El-Shafie

93

8

Development of Cleaner Production Alternatives in Water Management in a Slaughterhouse in Ecuador: A Case Study . . . . . . 105 Solange Tite Llerena, Mayra Llerena, and Lucrecia Llerena

9

Analyzing the Impact of Food-Energy-Water Nexus-Based Agricultural Patterns on Regional Water Resources . . . . . . . . . . . . . . 121 Rashi Dhanraj and Yogendra Shastri

10 Categorization of Urban Basin According to the Runoff Depth: Case Study of Katsushika Ward and Edogawa City Basin, Japan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Mohamed Wahba, Mahmoud Sharaan, Wael M. Elsadek, Shinjiro Kanae, and H. Shokry Hassan 11 ETSim: A Reference Evapotranspiration Estimator and Its Evaluation at the Southern Region of Japan . . . . . . . . . . . . . . . . . . . . . 143 Min Yan Chia, Yong Jie Wong, Yuk Feng Huang, Yoshihisa Shimizu, and Chai Hoon Koo Part III Solid Waste Management and Waste Valorization 12 Performance Evaluation of a Full-Scale Forced Aerated Municipal Solid Waste Composting System: A Case Study in Kalutara, Sri Lanka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Akifumi Kanachi, Naofumi Sato, Nayana Samaraweera, Layan Gunasekara, Rie Kawanishi, and Anurudda Karunarathna 13 A Systematic Bibliometric Analysis of Research on Hazardous Solid Waste Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Mohammed H. Alzard, Hilal El-Hassan, Ashraf Aly Hassan, Tamer El-Maaddawy, and Omar Najm 14 Pre-paid System for Waste Minimization and Cost Recovery—A Trial in Gaza Strip, Palestine . . . . . . . . . . . . . . . . . . . . . . 183 Ali Barhoum, Enas Qandeel, Hatem Abu Hamed, Rawan Tayeh, Suleiman Abu Mfareh, and Mitsuo Yoshida 15 Green Synthesis and Antibacterial Activity of Silver Nanoparticles Synthesized by Syzygium Aromaticum and Thymus Vulgaris Extracts Against Some Oral Pathogens . . . . . . 199 Abdullah T. Al-Fawwaz, Sajeda N. Al-Barri, Melad F. Al-Khazahila, and Nusaiba A. Al-Mashagbah

Contents

xiii

16 Institutional Pressure, Organizational Factors and E-Waste Management Practice: A Study in Telecommunication and Technology Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Hafizah Abd-Mutalib, Che Zuriana Muhammad Jamil, Rapiah Mohamed, Nor Atikah Shafai, and Saidatul Nurul Hidayah Jannatun Naim Nor-Ahmad 17 Life Cycle Assessment of Sugarcane Biorefinery Complex in the Indian Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Meghana Munagala and Yogendra Shastri Part IV Air Quality Assessment and Air Pollution Management 18 High-Performance Computing Urban Air Pollution 3D Simulation with CFD PALM4U . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Roberto San Jose and Juan L. Perez-Camanyo 19 Assessment and Policy Recommendations of School Ambient Air Quality During the COVID-19 Pandemic in Abu Dhabi, UAE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Evan K. Paleologos, Sherine Farouk, and Moza T. Al Nahyan 20 Particulate Matter Phytoremediation Capacity of Four Japanese Roadside Green Biofilters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Duha S. Hammad, František Mikšík, Kyaw Thu, and Takahiko Miyazaki Part V

Climate Change Adaptation and Natural Disaster Assessment

21 Resilience Assessment of Transportation Networks to Climate Change Induced Flooding: The Case of Doha Highways Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Mohammad Zaher Serdar and Sami G. Al-Ghamdi 22 Achievements, Difficulties and Challenges of Managing and Adapting to Drought and Saltwater Intrusion in the Vietnamese Mekong Delta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Nguyen Van Tho 23 Public Transportation Resilience Towards Climate Change Impacts: The Case of Doha Metro Network . . . . . . . . . . . . . . . . . . . . . . 297 Mohammad Zaher Serdar and Sami G. Al-Ghamdi 24 Social Vulnerability Assessment to Natural Hazards in East Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Nor Salsabila M. Sabri and Zulfa Hanan Ash’aari

xiv

Contents

25 Exploring the Significance of Resilience Qualities in the Context of the Middle East Built Environment . . . . . . . . . . . . . 319 Mohammed M. Al-Humaiqani and Sami G. Al-Ghamdi Part VI

Environmental Remote Sensing and Land Cover Change Monitoring

26 Vegetation Coverage Assessment for Smart Cities Based on the Sentinel Remote Sensing Data: The Case of Zhejiang Province (China) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Zhaoyu Wang 27 Monitoring of Agricultural Expansion Using Hybrid Classification Method in Southwestern Fringes of Wadi El-Natrun, Egypt: An Appraisal for Sustainable Development . . . . . 349 Ahmed M. Saqr, Mahmoud Nasr, Manabu Fujii, Chihiro Yoshimura, and Mona G. Ibrahim 28 Quantifying the Dynamics of Ecosystem Services Value in Response to Decentralization and Regional Autonomy in Indonesia: A Case Study of Southeast Sulawesi Province . . . . . . . 363 Gazali and Minoru Kumano Part VII

Environmental Health and Carbon Emission Management

29 Assessment of Human Health Impact of Particulate Matter Formation from Industry Textile Boiler in Cambodia . . . . . . . . . . . . . 385 Leakhena Hang, Palla Try, Srean Aun, Dalin Um, and Chanreaksmey Taing 30 Assessment of Heavy Metals Uptake by Carrot at Different Contamination Levels of Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 Syed Shabbar Hussain Shah, Tomomi Imura, and Kei Nakagawa 31 The Economic Impact of California’s Cap and Trade Program: An Interrupted Time Series Analysis with a Matching Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Tomás Baioni 32 Computing Digital Footprints: A New Model and Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Boukhalfa Zahout and Lionel Metivier

Contents

xv

Part VIII Clean Energy Technology and Energy Management 33 Comparisons of Organic Acid and Inorganic Acid Pretreatment for Production of Reducing Sugar and Ethanol Production from Coffee Shell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 Nichaphat Kitiborwornkul, Phakamas Jullsri, Prapakorn Tantayotai, Atittaya Tandhanskul, and Malinee Sriariyanun 34 Hydrogen Fuel Cell for Passenger Railway Transport and Its Deployment in Saudi Arabia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 Ahmed Ibrahim and Mohamed Abido 35 Deep Eutectic Solvent Pretreatment of Durian Peel for Enhanced Bioethanol Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463 Elizabeth Jayex Panakkal, Manvitha Theegala, Srihita Grashma Chaparla, Keerthi Katam, Nichaphat Kitiborwornkul, and Malinee Sriariyanun

Part I

Wastewater Treatment and Water Analysis

Chapter 1

Progress in Biosynthesized of Silver Nanoparticles as Sustainable Approach for Photocatalytic Wastewater Treatment Mohamad Aizad Mohd Mokhtar , Roshafima Rasit Ali , Nurfatehah Wahyuny Che Jusoh , Zhongfang Lei , Zatil Izzah Tarmizi , and Didik Prasetyoko

Abstract Severe effects of water pollution would need to be addressed by utilizing advanced water and wastewater treatment. Due to globalization, organic dyes and emerging contaminants are significant water pollutants that can bring harm to humans and the environment. Nevertheless, conventional treatment is not suitable for removing these pollutants. Photocatalysis is an eco-friendly, cost-effective, and efficient treatment by degrading organic pollutants into harmless by-products, making it a promising technology. Among photocatalysts available, silver nanoparticles (Ag NPs) show great catalytic and chemically stable nanomaterials. The synthesis of nanoparticles is shifting toward a biological synthesis that is environmentally friendly and avoids hazardous chemicals, leading to the discovery of biosynthesized Ag NPs. Various biomaterials such as plants, fruits, biopolymers, waste, and microorganisms were successfully synthesized Ag NPs via different techniques. The result shows remarkable and significant photocatalytic properties of biosynthesized Ag NPs towards the degradation of organic pollutants from previous studies. Further improving Ag NPs as photocatalysts by lowering the band gap energy and doping with support materials for better recovery are recommended. This review critically discusses the photocatalytic mechanism and the synthesis method of Ag NPs from various biomaterials with photocatalytic properties as a potentially

M. A. M. Mokhtar · R. R. Ali (B) · N. W. C. Jusoh · Z. I. Tarmizi Department of Chemical and Environmental Engineering, Malaysia-Japan, International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia e-mail: [email protected] M. A. M. Mokhtar · Z. Lei Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Ibaraki, Japan D. Prasetyoko Department of Chemistry, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh November, 6011 Surabaya, Indonesia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_1

3

4

M. A. M. Mokhtar et al.

sustainable approach to overcoming water pollution issues with its challenges and recommendations. Keywords Biosynthesis · Silver nanoparticles · Photocatalytic · Wastewater

1.1 Introduction Water security is an alarming call to achieve sustainable solutions and practices to provide clean water for daily use. Nevertheless, water pollution is the biggest threat to humankind with the fast development of industries and the increasing human population. Organic pollutants such as organic dyes, pharmaceutical drugs, personal care products, and herbicides in water and wastewater are harmful to humans and the environment. For instance, severe effects on eyes and skin irritation can happen when in contact with methylene blue and rhodamine B dye [1, 2]. Moreover, the pharmaceutical industries are booming with massive production of drugs during the pandemic COVID-19 that led to an increase in traces of pharmaceutical drugs in ground and surface water and wastewater effluents [3]. Hence, suitable treatment must be applied to effectively remove these persistent organic pollutants from the water medium. Conventional water and wastewater treatment such as biological treatment, adsorption, flocculation, chemical precipitation, and ion exchange are inefficient, time-consuming, high cost, and produce secondary pollution such as sludge in removing organic pollutants [4, 5]. For instance, bioremediation of methyl orange using Kocuria rosea (MTCC 1532) can only remove 40% of the pollutants for 120 h [6] compared to photocatalytic treatment with 83.4% removal for 90 min using biosynthesized silver nanoparticles (Ag NPs) for the same amount of concentration (100 mg/L) [7]. Furthermore, the adsorption technique shows considerably lower performance than photocatalytic treatment. For example, 87% removal of rhodamine B dye was achieved using the adsorption method [2] compared to 93% removal using photocatalytic treatment [8]. Hence, advanced, efficient, fast, cost-effective treatments and complete removal are needed to remove organic pollutants. Advanced oxidation processes (AOPs) produce hydroxyl radicals to react and degrade organic pollutants. Photocatalysis, Fenton-based, ozone-based, and sonolysis are AOPs mainly applied for pollutants purification [9]. Photocatalysis shows remarkable advantages compared to other AOPs technologies with carbon dioxide and water as final products. Photocatalysis is an attractive technology with highly efficient, low cost, and can degrade and mineralize organic pollutants into benign by-products [10]. Several reported studies on photocatalysis show an unprecedented degradation rate of organic pollutants. For instance, 95.89% removal of methyl orange via visible light irradiation had achieved using Ag NPs [11]. Photocatalysis is commonly associated with nanotechnology as it requires the presence of a photocatalyst or semiconductor. Generally, nanoparticle synthesis

1 Progress in Biosynthesized of Silver Nanoparticles as Sustainable …

5

follows two techniques: top-down and bottom-up. The physical synthesis of nanoparticles follows the top-down technique, while chemical and biological synthesis follows the bottom-up technique [12]. However, nanoparticles’ physical and chemical synthesis are energy intensive and use toxic chemicals as reducing agents. Meanwhile, the biological or green synthesis method is environmental-friendly, costeffective, and non-toxic [13]. Recently, the progress of biosynthesized nanoparticles attracted many reported studies on using biomaterials during the synthesis of nanoparticles. For example, Moringa oleifera [14], coffee waste [15], pullulan [16], and Clitoria ternatea [17] were used successfully in biosynthesized Ag NPs. This review critically examines biosynthesized Ag NPs with photocatalytic properties from various green sources for the first time as a sustainable approach for wastewater remediation with its challenges and way forward.

1.2 Photocatalytic Treatment The famous Fujishima-Honda effect of semiconductor electrochemical photolysis is the breakthrough of photocatalysis technology [18]. Photocatalysis works with the presence of a semiconductor and light sources, either visible light (sunlight) or ultraviolet (UV) light irradiation. Overall, the photocatalytic mechanism started with the catalyst receiving light energy. Subsequently, the electron in the valence band (VB) is photoexcited to the conduction band (CB) when the light energy is greater than the band gap (Eg) of the catalyst. Then, electron (e− )—hole (h+ ) pairs are generated and react with oxygen, water, and hydroxide ions to produce hydroxyl radicals (OH− ) [19]. Eventually, the produced hydroxyl radicals reacted with the pollutants to degrade and mineralize with the final products of carbon dioxide, water, and intermediates. Various metallic nanoparticles such as pristine metal, metal oxide, metal selenide, and metal sulfide were used as semiconductors for photocatalytic treatment. For instance, Hosny et al. (2022) reported a 72% photodegradation of malachite green dye via UV light using T. capensis leaves mediated gold (Au) nanoparticles [20], while Awad et al. (2021) presented Trigonella foenum-graecum mediated Ag NPs with 93% degradation of rhodamine B dye [8]. Moreover, metal oxides such as zinc oxide (ZnO), titanium dioxide (TiO2 ), and iron oxide (Fe2 O3 ) are famous in photocatalytic treatment. For example, Lal et al. (2022) reported 91% photodegradation of methylene blue dye for 180 min using Myrica esculenta fruits based ZnO [21]. The advantage of using metallic nanoparticles as photocatalysts is the flexibility of light absorption in broad wavelengths. Hence, metallic nanoparticles can efficiently degrade organic pollutants using photocatalytic treatment. Organic dyes are the most studied pollution model for photocatalytic treatment due to their wide application in the coloring sector, such as textile, leather, paper, and food manufacturing [10], which led to consistent water pollution issues. Previously reported studies show various organic dye degradation such as methylene blue [22–24], methyl orange [7, 11, 25], rhodamine B [4, 8, 26], and crystal violet dye

6

M. A. M. Mokhtar et al.

[27–29]. Even so, studies on emerging contaminants (EC) using photocatalytic treatment are fewer than on dye degradation. EC are organic compounds such as drugs, personal care products, hormones, pesticides, and plasticizers. Due to urbanization, EC compounds are in demand with large consumption globally, causing water pollution from improper discharge from industries and households [30]. Several reported studies of photodegradation of EC compounds, include paracetamol [19], amoxicillin [19, 31], and ciprofloxacin [32]. Hence, the photocatalytic treatment can efficiently degrade both organic dyes and EC compounds. Nevertheless, despite possessing significant advantages among water and wastewater technologies, several drawbacks of using photocatalytic treatment include the complexity and cost of photocatalyst preparation, difficulty in large-scale treatment, and photocatalyst recovery.

1.3 Biosynthesized Silver Nanoparticles Ag NPs have unique properties compared to other nanoparticles, such as antibacterial, chemically stable, a good conductor, and catalytic [33]. Hence, various applications of Ag NPs include imaging, bio-sensing, drug delivery, catalysis, bacteria, and water treatment [34]. Generally, the conventional techniques in synthesizing Ag NPs include laser ablation and evaporation–condensation techniques. However, these techniques are energy intensive despite having a sizeable yield and high purity of Ag NPs [35]. On the other hand, chemical techniques include reduction [36], precipitation [2], hydrothermal [37], solvothermal [38], electrochemical [39] and microwave radiation [16, 40]. Nevertheless, chemical synthesis utilized toxic chemicals such as ethylene glycol, sodium citrate, sodium borohydride, oleyl amine, and paraffin [35]. The biosynthesis method combines biological and chemical techniques by substituting hazardous solvents and reducing agents with green sources. Reduction is the most used technique in directly mixing biomaterial with Ag precursor. For example, Khan et al. (2020) reported using Petroselinum crispum plant extract that contains phenolic compounds for Ag reduction via silver nitrate (AgNO3 ) [41]. Previous studies reported various biomaterials for assisting the biosynthesized Ag NPs such as plant parts, fruits, waste, biopolymers, and microorganisms, as illustrated in Fig. 1.1. Aryan et al. (2021) reported synthesized spherical Ag NPs using Kalanchoe pinnata plant leaves with an average size of 38 nm [26]. Meanwhile, using coffee waste as the biomaterial produced a smaller size of 13.62 nm of spherical and hexagonal Ag NPs [42]. Hence, the biosynthesized Ag NPs using different biomaterials is one method to fabricate the size and morphology for different applications. The biosynthesis of Ag NPs is contributed by the presence of phytochemical compounds in the green sources, as shown in Fig. 1.2. For example, Thatikayala et al. (2019) reported a rich phenolic compound in Theobroma cacao extract which was responsible for reducing Ag NPs that is identified using Fourier-transform infrared spectroscopy (FTIR) [43]. This is also supported by Kumar et al. [24], who described the reduction of Ag NPs with capping properties of phenolic compounds, anthocyanin, and flavonoids [24].

1 Progress in Biosynthesized of Silver Nanoparticles as Sustainable …

7

Fig. 1.1 Biosynthesized Ag NPs from different biomaterials

Fig. 1.2 Proposed mechanism of biosynthesized Ag NPs

Several main characterization techniques of biosynthesized Ag NPs can be applied, such as UV–Visible Spectroscopy (UV–Visible), X-ray Diffraction (XRD), and Transmission Electron Microscopy (TEM). Several reported UV–Visible spectra of biosynthesized Ag NPs show peaks at 443 nm [40] and 430 nm [41], with dark brown observation confirming Ag NPs formation. Furthermore, the XRD analysis of biosynthesized Ag NPs was identified at lattice planes of (111), (200), (220), and (311) with face-centered cubic structures [41, 44, 45]. Seerangaraj et al. (2021) used TEM and reported the identification of biosynthesized Ag NPs whose morphology and size were ranging from 10 to 20 nm [27], with most of the reported studies also reporting spherical shape of Ag NPs [46, 47].

1.4 Photocatalytic Performance of Biosynthesized Silver Nanoparticles A photocatalyst is activated when the light energy received is higher than the band gap energy, either visible or UV light energy sources. The schematic diagram of the photocatalytic mechanism is shown in Fig. 1.3. Ramar et al. [48] reported biosynthesized Ag NPs using B. tomentosa Linn. leaf extract with different reaction times

8

M. A. M. Mokhtar et al.

Fig. 1.3 Ag NPs photocatalytic schematic diagram

of 1, 3, and 5 h, which increased the band gap energy to 1.92, 1.99 and 2.09 eV respectively. Subsequently, the photocatalytic performance used visible light with 100% degradation of Rose Bengal dye [48]. Moreover, Parthibavarman et al. [49] utilize apples and grapes extract for biosynthesized Ag NPs with a band gap of 2.97 and 2.71 eV, respectively. The grapes-mediated Ag NPs show better photodegradation of phenols and congo red dye with 95% and 98% degradation compared to apple-mediated Ag NPs [49]. The band gap energy can be calculated using Eq. 1.1, where α, h, ν, A, and Eg are the absorption coefficient, Plank’s constant, light energy, a constant, and band gap energy, respectively. The photocatalytic performance of biosynthesized Ag NPs is remarkable, with high photodegradation of organic pollutants, as shown in Table 1.1. The degradation percentage and Langmuir–Hinshelwood equation can be calculated using Eq. 1.2 and Eq. 1.3 below, where t, C, C o and k apps are time, concentration at any time, initial concentration and first order kinetic rate constant [19]. Visible light is the most used light source for the photocatalytic activity of biosynthesized Ag NPs due to the attractive properties of lower band gap energy. Previous studies show that methylene blue dye is the most reported organic pollutant in the photocatalytic performance of biosynthesized Ag NPs. For example, over 80% photodegradation of methylene blue dye using visible light was achieved [22, 23, 34, 50]. Even so, the photocatalytic performance of EC using biosynthesized Ag NPs is fewer than organic dyes. Hence, Jusoh et al. [39] reported an electrochemical synthesis of Durio zibethinus husksupported Ag NPs with 0.01197 mg/L.min degradation rate of paracetamol [39]. Different parameters were used to obtain suitable conditions during photocatalytic reactions. Generally, the parameters are photocatalyst dosage, initial concentration of pollutants, and pH of pollutants, aligned with the report by Mohamed Isa et al. [19]. Moreover, the reusability study is essential to avoid using photocatalyst once

Aniline blue Methylene blue

Spherical (8–25 nm) Spherical (12–20 nm) Spherical (4 nm)

Spherical (35–50 nm) Spherical (55.65 nm) Spherical (48.25 nm) Spherical (25.6 nm)

Bauhinia tomentosa Linn

Trichodesma indicum

Moringa oleifera

Sambucus ebulus

Ruellia tuberosa

Ocimum americanum

Banyan aerial

Rhodamine B

Spherical (5–8 nm) Methylene blue

Arisaema flavum

Rhodamine B

Spherical (35 nm) Spherical (82.53 nm)

Parkia speciosa

Trigonella foenum-graecum

Eosin yellow

Coomassie brilliant blue

Crystal violet

Methyl orange

75.41

Visible

Visible

UV

87

93

84

91.76

UV Visible

91.17

74

87

95.89

Visible

Visible

Visible

75

4-nitrophenol

81

82

100

80

83

Photodegradation (%)

82

Visible

Visible

Visible

UV

UV

Light irradiation

Orange red

Methylene blue

Methylene blue

Rose Bengal

Brilliant green dye

Spherical (38 nm) Spherical (25–90 nm)

Kalanchoe pinnata

Plants

Petroselinum crispum

Pollutants

Shapes and sizes

Biomaterials

Table 1.1 Photodegradation performance of biosynthesized Ag NPs

(continued)

[23]

[8]

[22]

[47]

[46]

[27]

[11]

[14]

[52]

[48]

[41]

[26]

Refs.

1 Progress in Biosynthesized of Silver Nanoparticles as Sustainable … 9

Waste

Fruit

Biomaterials

Spherical (2 nm) Spherical (15–25 nm) Spherical (20.5 nm) Spherical (35.4 nm) Spherical (6 nm)

Quasi-spherical (6–18 nm)

Orthosiphon stamineus

Grape

Vaccinium floribundum Kunth

Citrus X sinensis

Alpinia nigra

Theobroma cacao (husk)

Coffee waste

Phenols

Visible

94.6

24

Theobroma cacao (seed)

29 35

Visible

79.9

Methylene blue

Orange G

83.4

82.2

29.09

98

95

99.78

85.9

Visible

Visible

Visible

Visible

Visible

86

88

Photodegradation (%)

Rhodamine B

Methyl orange

Methylene blue

Methylene blue

Congo red

Phenols

2,4—Dicholorophenoxyacetic acid

Visible

Light irradiation

Theobroma cacao (pulp)

Spherical and hexagonal (13.62 nm)

Reactive blue 19

Spherical (5–20 nm)

Trigonella foenum-graecum Reactive yellow 186

Pollutants

Shapes and sizes

Table 1.1 (continued)

(continued)

[42]

[43]

[7]

[50]

[24]

[49]

[54]

[53]

Refs.

10 M. A. M. Mokhtar et al.

Microorganism/ Microalgae

Biopolymer

Biomaterials Methylene blue

Spherical (35.08 nm) Spherical (7–18 nm)

Cauliflower waste

Egg shell

Spherical (20 nm) Spherical (20 nm) Spherical (64 nm) Spherical (55.06 nm) Spherical (10–30 nm) Spherical (20–40 nm) Spherical (15.2–266.7 nm)

Chitosan

Lignin

Actinomycetes

Chlorella vulgaris

Mutant Bacillus licheniformis M09

Aeromonas sp. SNS

Synechococcus sp

Spherical (5–100 nm)

Cellulose

Methylene blue

Brilliant green

Methylene blue

Methylene blue

Methylene blue

Reactive yellow 4G

Methyl orange

Methylene blue

Methylene blue

Pomegranate peel

Spherical and agglomerate (60–150 nm)

Methylene blue

Durio zibethinus seed Spherical and rod (20–75 nm)

UV

Visible

Visible

Visible

18

92.62

94

96.51

71.3 11.25

Visible

100

81

90

89

73.49

UV

UV

Visible

Visible

Visible

Visible

97

Methylene blue

98.2

97.57

Photodegradation (%)

98.4

Visible

Visible

Light irradiation

Methyl violet 6B

Rose Bengal

Pollutants

Shapes and sizes

Table 1.1 (continued)

[61]

[60]

[5]

[45]

[13]

[59]

[58]

[34]

[57]

[56]

[55]

[51]

Refs.

1 Progress in Biosynthesized of Silver Nanoparticles as Sustainable … 11

12

M. A. M. Mokhtar et al.

in degrading pollutants. For instance, 4 cycles with over 80% photodegradation of methylene blue were achieved using cauliflower waste mediated Ag NPs [51]. Meanwhile, Kanagamani et al. (2019) successfully reused 5 cycles of over 80% degradation of norfloxacin using Kleinia grandiflora mediated Ag NPs. Hence, this shows that biosynthesized Ag NPs have good reusability and stability in photocatalytic activity. (αhν)2 = A(hν − Eg )

(1.1)

% Degradation = (Co − C) / Co  100%

(1.2)

Langmuir − Hinshelwood : kapps t = ln Co / C

(1.3)

1.5 Challenges and Recommendations In general, the biosynthesized Ag NPs and other nanomaterials support eco-friendly and avoid toxic chemicals compared to the conventional synthesis techniques. However, a large scale of biosynthesized nanomaterials is still a limitation in this promising technique. Moreover, the green sources must be readily available, abundant, and not inhibit any properties that can affect human and animal food chains. Meanwhile, photocatalyst recovery and separation, laboratory scale, and pollutants selectivity hinder the performance of photocatalytic water and wastewater treatment. Furthermore, high energy and complex process limit the application of UV light in photocatalytic treatment for most photocatalysts compared to visible or direct sunlight. Overcoming these challenges would require additional research on the biosynthesized Ag NPs for upscaling, industrialization and commercialization. Additionally, raw materials (green sources) are recommended for waste or other abundance sources that are always available. Different Ag NPs designs for recovery and separation have been studied, such as immobilizing the photocatalyst with membrane/polymer materials to act as a thin film synthesizing magnetic photocatalysts for simple separation and recovery. Furthermore, lower band gap designing of a photocatalyst is more significant in utilizing the visible light spectrum.

1.6 Conclusion In conclusion, this review examines current trends in biosynthesized Ag NPs with photocatalytic properties for wastewater treatment for the first time. Photocatalytic treatment is advanced water and wastewater treatment with a remarkable result over conventional treatment. The utilization of visible and UV light sources with the

1 Progress in Biosynthesized of Silver Nanoparticles as Sustainable …

13

final output of carbon dioxide, water, and intermediates is incredibly advantageous. Biosynthesized Ag NPs may result in the fabrication of size and morphology with the interaction of phytochemical compounds in the biomaterials. Various biomaterials such as plants, fruits, biopolymers, waste, and microorganisms have assisted in synthesizing Ag NPs with significant photocatalytic properties. Hence, using biosynthesized Ag NPs as photocatalysts is a promising technology as a sustainable approach to water and wastewater treatment. Nevertheless, green sources selection, laboratory scale, UV light sources, and recovery are the challenges of biosynthesized Ag NPs in photocatalytic treatment. These limitations can be minimized by designing Ag NPs that use abundant green sources and lower band gap and support materials for easier recovery and separation. Acknowledgements The authors would like to appreciate funding from Japan-ASEAN Science, Technology and Innovation Platform (JASTIP-Net 2022 Collaborative Research); Fundamental Research Grant Scheme (R.K130000.7843.5F403) and special gratitude to Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur and the University of Tsukuba, Japan.

References 1. Mohammadzadeh Kakhki R, Hedayat S, Mohammadzadeh K (2019) Novel, green and low cost synthesis of Ag nanoparticles with superior adsorption and solar based photocatalytic activity. J Mater Sci Mater Electron 30(9):8788–8795 2. Mohd Mokhtar MA, Ali RR, Mohamed Isa ED, Tarmizi ZI, Zhe JC, Kasmani RM et al (2022) Green synthesis of silver nanoparticles doped activated carbon for Rhodamine B dye adsorption. J Phys Conf Ser 2259(1):012010 3. Patel M, Kumar R, Kishor K, Mlsna T, Pittman CU, Mohan D (2019) Pharmaceuticals of emerging concern in aquatic systems: chemistry, occurrence, effects, and removal methods. Chem Rev 119(6):3510–3673 4. Sarathi Kannan D, Mahboob S, Al-Ghanim KA, Venkatachalam P (2021) Antibacterial, antibiofilm and photocatalytic activities of biogenic silver nanoparticles from Ludwigia Octovalvis. J Cluster Sci 32(2):255–264 5. Momin B, Rahman S, Jha N, Annapure US (2019) Valorization of mutant Bacillus licheniformis M09 supernatant for green synthesis of silver nanoparticles: photocatalytic dye degradation, antibacterial activity, and cytotoxicity. Bioprocess Biosyst Eng 42(4):541–553 6. Parshetti GK, Telke AA, Kalyani DC, Govindwar SP (2010) Decolorization and detoxification of sulfonated azo dye methyl orange by Kocuria rosea MTCC 1532. J Hazard Mater 176:503– 509 7. Baruah D, Yadav RNS, Yadav A, Das AM (2019) Alpinia nigra fruits mediated synthesis of silver nanoparticles and their antimicrobial and photocatalytic activities. J Photochem Photobiol, B 201:111649 8. Awad MA, A. Hendi A, Ortashi KM, Alzahrani B, Soliman D, Alanazi A, et al (2021) Biogenic synthesis of silver nanoparticles using Trigonella foenum-graecum seed extract: characterization, photocatalytic and antibacterial activities. SensS Actuators A: Phys 323:112670 9. Pandis PK, Kalogirou C, Kanellou E, Vaitsis C, Savvidou MG, Sourkouni G et al (2022) Key points of advanced oxidation processes (AOPs) for wastewater, organic pollutants and pharmaceutical waste treatment: a mini review. ChemEngineering 6(1):8

14

M. A. M. Mokhtar et al.

10. Zhang B, Liu F, Nie C, Hou Y, Tong M (2022) Photocatalytic degradation of paracetamol and bisphenol A by chitosan supported covalent organic framework thin film with visible light irradiation. J Hazard Mater 435:128966 11. Hashemi Z, Mizwari ZM, Mohammadi-Aghdam S, Mortazavi-Derazkola S, Ali EM (2022) Sustainable green synthesis of silver nanoparticles using Sambucus ebulus phenolic extract (AgNPs@SEE): optimization and assessment of photocatalytic degradation of methyl orange and their in vitro antibacterial and anticancer activity. Arab J Chem 15(1):103525 12. Dikshit PK, Kumar J, Das AK, Sadhu S, Sharma S, Singh S et al (2021) Green synthesis of metallic nanoparticles: applications and limitations. Catalysts 11(8):902 13. Mechouche MS, Merouane F, Messaad CEH, Golzadeh N, Vasseghian Y, Berkani M (2022) Biosynthesis, characterization, and evaluation of antibacterial and photocatalytic methylene blue dye degradation activities of silver nanoparticles from Streptomyces tuirus strain. Environ Res 204:112360 14. Mehwish HM, Rajoka MSR, Xiong Y, Cai H, Aadil RM, Mahmood Q et al (2021) Green synthesis of a silver nanoparticle using Moringa oleifera seed and its applications for antimicrobial and sun-light mediated photocatalytic water detoxification. J Environ Chem Eng 9(4):105290 15. El-Desouky N, Shoueir KR, El-Mehasseb I, El-Kemary M (2021) Bio-inspired green manufacturing of plasmonic silver nanoparticles/Degussa using banana waste peduncles: photocatalytic, antimicrobial, and cytotoxicity evaluation. J Market Res 10:671–686 16. Salleh MSN, Ali RR, Shameli K, Hamzah MY, Kasmani RM, Nasef MM (2021) Interaction insight of pullulan-mediated gamma-irradiated silver nanoparticle synthesis and its antibacterial activity. Polymers 13(20):3578 17. Mohamad Aizad Mohd M, Roshafima Rasit A, Eleen Dayana Mohamed I (2021) Silver nanoparticles loaded activated carbon synthesis using clitorea ternatea extract for crystal violet dye removal. J Res Nanosci Nanotechnol 3(1):26–36 18. Fujishima A, Honda K (1972) Electrochemical photolysis of water at a semiconductor electrode. Nature 238(5358):37–38 19. Mohamed Isa ED, Shameli K, Ch’ng HJ, Che Jusoh NW, Hazan R (2021) Photocatalytic degradation of selected pharmaceuticals using green fabricated zinc oxide nanoparticles. Adv Powder Technol 32(7):2398–2409 20. Hosny M, Fawzy M, El-Badry YA, Hussein EE, Eltaweil AS (2022) Plant-assisted synthesis of gold nanoparticles for photocatalytic, anticancer, and antioxidant applications. J Saudi Chem Soc 26(2):101419 21. Lal S, Verma R, Chauhan A, Dhatwalia J, Guleria I, Ghotekar S et al (2022) Antioxidant, antimicrobial, and photocatalytic activity of green synthesized ZnO-NPs from Myrica esculenta fruits extract. Inorg Chem Commun 141:109518 22. Ravichandran V, Vasanthi S, Shalini S, Shah SAA, Tripathy M, Paliwal N (2019) Green synthesis, characterization, antibacterial, antioxidant and photocatalytic activity of Parkia speciosa leaves extract mediated silver nanoparticles. Results Phys 15:102565 23. Rahman AU, Khan AU, Yuan Q, Wei Y, Ahmad A, Ullah S, et al (2019) Tuber extract of Arisaema flavum eco-benignly and effectively synthesize silver nanoparticles: photocatalytic and antibacterial response against multidrug resistant engineered E. coli QH4. J Photochem Photobiol B: Biol 193, 31–8. 24. Kumar B, Vizuete KS, Sharma V, Debut A, Cumbal L (2019) Ecofriendly synthesis of monodispersed silver nanoparticles using Andean Mortiño berry as reductant and its photocatalytic activity. Vacuum 160:272–278 25. Mohamed Isa ED, Che Jusoh NW, Hazan R, Shameli K (2021) Photocatalytic degradation of methyl orange using pullulan-mediated porous zinc oxide microflowers. Environ Sci Pollut Res 28(5):5774–5785 26. Aryan, Ruby, Mehata MS (2021) Green synthesis of silver nanoparticles using Kalanchoe pinnata leaves (life plant) and their antibacterial and photocatalytic activities. Chem Phys Lett 778:138760

1 Progress in Biosynthesized of Silver Nanoparticles as Sustainable …

15

27. Seerangaraj V, Sathiyavimal S, Shankar SN, Nandagopal JGT, Balashanmugam P, Al-Misned FA et al (2021) Cytotoxic effects of silver nanoparticles on Ruellia tuberosa: photocatalytic degradation properties against crystal violet and coomassie brilliant blue. J Environ Chem Eng 9(2):105088 28. Tavakoli-Azar T, Mahjoub A, Seyed Sadjadi M, Ghaznavi-Ghoushchi MB (2022) Enhanced photocatalytic activity of ZrO2-CdZrO3-S nanocomposites for degradation of Crystal Violet dye under sunlight. J Photochem Photobiol A 426:113746 29. Katouah HA (2021) Facile synthesis of Co3O4 and ZnO nanoparticles by thermal decomposition of novel Co(II) and Zn(II) Schiff base complexes for studying their biological properties and photocatalytic degradation of crystal violet dye. J Mol Struct 1241:130676 30. Sabouni R, Gomaa H (2019) Photocatalytic degradation of pharmaceutical micro-pollutants using ZnO. Environ Sci Pollut Res 26(6):5372–5380 31. Liu H, Wang L, Wei S, Wu Y, Zheng Y, Yuan F et al (2022) Study on photocatalytic degradation of amoxicillin in wastewater by Bi2WO6/nano-ZnO. Opt Mater 123:111835 32. Xu K, Shen J, Zhang S, Xu D, Chen X (2022) Efficient interfacial charge transfer of BiOClIn2O3 step-scheme heterojunction for boosted photocatalytic degradation of ciprofloxacin. J Mater Sci Technol 121:236–244 33. Ijaz I, Bukhari A, Gilani E, Nazir A, Zain H, Saeed R et al (2022) Green synthesis of silver nanoparticles using different plants parts and biological organisms, characterization and antibacterial activity. Environ Nanotechnol, Monit Manag 18:100704 34. Vijayakumar S, Chen J, Amarnath M, Tungare K, Bhori M, Divya M et al (2021) Cytotoxicity, phytotoxicity, and photocatalytic assessment of biopolymer cellulose-mediated silver nanoparticles. Colloids Surf A 628:127270 35. Vishwanath R, Negi B (2021) Conventional and green methods of synthesis of silver nanoparticles and their antimicrobial properties. Curr Res Green Sustain Chem 4:100205 36. Chand K, Cao D, Eldin Fouad D, Hussain Shah A, Qadeer Dayo A, Zhu K et al (2020) Green synthesis, characterization and photocatalytic application of silver nanoparticles synthesized by various plant extracts. Arab J Chem 13(11):8248–8261 37. Li YF, Gan WP, Zhou J, Lu ZQ, Yang C, Ge TT (2015) Hydrothermal synthesis of silver nanoparticles in Arabic gum aqueous solutions. Trans Nonferrous Metals Soc China 25(6):2081–2086 38. Wani IA, Khatoon S, Ganguly A, Ahmed J, Ganguli AK, Ahmad T (2010) Silver nanoparticles: large scale solvothermal synthesis and optical properties. Mater Res Bull 45(8):1033–1038 39. Jusoh NWC, Mustapha MH, Masudi A, Jaafar NF (2020) Photodegradation of paracetamol using Electrosynthesized silver supported on Durio Zibethinus husk. IOP Conf Ser Mater Sci Eng 736(4):042037 40. Tarmizi ZI, Burhanuddin MFB, Shahrul M, Mokhtar MAM, Zhe JC, Taib SHM, Sukri SNA (2022) Preparation and characterization of acetylated starch mediated silver nanoparticles: the effect of solution ratio and time-varying exposure. J Phys Conf Ser 2259:012005 41. Khan ZUH, Shah NS, Iqbal J, Khan AU, Imran M, Alshehri SM et al (2020) Biomedical and photocatalytic applications of biosynthesized silver nanoparticles: Ecotoxicology study of brilliant green dye and its mechanistic degradation pathways. J Mol Liq 319:114114 42. El-Desouky N, Shoueir K, El-Mehasseb I, El-Kemary M (2022) Synthesis of silver nanoparticles using bio valorization coffee waste extract: photocatalytic flow-rate performance, antibacterial activity, and electrochemical investigation. Biomass Convers Biorefinery 43. Thatikayala D, Jayarambabu N, Banothu V, Ballipalli CB, Park J, Rao KV (2019) Biogenic synthesis of silver nanoparticles mediated by Theobroma cacao extract: enhanced antibacterial and photocatalytic activities. J Mater Sci Mater Electron 30(18):17303–17313 44. Subramaniam S, Kumarasamy S, Narayanan M, Ranganathan M, Rathinavel T, Chinnathambi A et al (2022) Spectral and structure characterization of Ferula assafoetida fabricated silver nanoparticles and evaluation of its cytotoxic, and photocatalytic competence. Environ Res 204:111987 45. Rajkumar R, Ezhumalai G, Gnanadesigan M (2021) A green approach for the synthesis of silver nanoparticles by Chlorella vulgaris and its application in photocatalytic dye degradation activity. Environ Technol Innov 21:101282

16

M. A. M. Mokhtar et al.

46. Manikandan DB, Sridhar A, Krishnasamy Sekar R, Perumalsamy B, Veeran S, Arumugam M et al (2021) Green fabrication, characterization of silver nanoparticles using aqueous leaf extract of Ocimum americanum (Hoary Basil) and investigation of its in vitro antibacterial, antioxidant, anticancer and photocatalytic reduction. J Environ Chem Eng 9(1):104845 47. Eswaran SG, Narayan H, Vasimalai N (2021) Reductive photocatalytic degradation of toxic aniline blue dye using green synthesized banyan aerial root extract derived silver nanoparticles. Biocatal Agric Biotechnol 36:102140 48. Ramar K, Vasanthakumar V, Priyadharsan A, Priya P, Raj V, Anbarasan PM, et al (2018) Green synthetic approach of silver nanoparticles from Bauhinia tomentosa Linn. leaves extract for potent photocatalytic and in vitro biological applications. J Mater Sci: Mater Electron 29(13):11509–11520 49. Parthibavarman M, Bhuvaneshwari S, Jayashree M, BoopathiRaja R (2019) Green synthesis of silver (Ag) nanoparticles using extract of apple and grape and with enhanced visible light photocatalytic activity. BioNanoScience 9(2):423–432 50. Yadav J, Chauhan P (2022) Green synthesis of silver nanoparticles using Citrus X sinensis (Orange) fruit extract and assessment of their catalytic reduction. Mater Today Proc 51. Kadam J, Dhawal P, Barve S, Kakodkar S (2020) Green synthesis of silver nanoparticles using cauliflower waste and their multifaceted applications in photocatalytic degradation of methylene blue dye and Hg2+ biosensing. SN Appl Sci 2(4):738 52. Kathiravan V (2018) Green synthesis of silver nanoparticles using different volumes of Trichodesma indicum leaf extract and their antibacterial and photocatalytic activities. Res Chem Intermed 44(9):4999–5012 53. Singh J, Kumar V, Singh Jolly S, Kim K-H, Rawat M, Kukkar D et al (2019) Biogenic synthesis of silver nanoparticles and its photocatalytic applications for removal of organic pollutants in water. J Ind Eng Chem 80:247–257 54. Kamarudin NS, Jusoh R, Sukor NF, Jalil AA, Setiabudi HD (2020) Intensified photocatalytic degradation of 2, 4–dicholorophenoxyacetic acid using size-controlled silver nanoparticles: effect of pre-synthesis extraction. Adv Powder Technol 31(8):3381–3394 55. Sinha T, Ahmaruzzaman M (2015) High-value utilization of egg shell to synthesize silver and Gold-Silver core shell nanoparticles and their application for the degradation of hazardous dyes from aqueous phase-A green approach. J Colloid Interface Sci 453:115–131 56. Sumitha S, Vasanthi S, Shalini S, Chinni SV, Gopinath SCB, Anbu P et al (2018) Phyto-mediated photo catalysed green synthesis of silver nanoparticles using durio zibethinus seed extract: antimicrobial and cytotoxic activity and photocatalytic applications. Molecules 23(12):3311 57. Joshi SJ, S. J. G, Al-Mamari S, Al-Azkawi A (2018) Green synthesis of silver nanoparticles using pomegranate peel extracts and its application in photocatalytic degradation of methylene blue 13(3):67846 58. Nithya A, JeevaKumari HL, Rokesh K, Ruckmani K, Jeganathan K, Jothivenkatachalam K (2015) A versatile effect of chitosan-silver nanocomposite for surface plasmonic photocatalytic and antibacterial activity. J Photochem Photobiol B 153:412–422 59. Saratale RG, Cho S-K, Saratale GD, Kadam AA, Ghodake GS, Magotra VK et al (2022) Ligninmediated silver nanoparticle synthesis for photocatalytic degradation of reactive yellow 4G and in vitro assessment of antioxidant, antidiabetic, and antibacterial activities. Polymers 14(3):648 60. Gurme ST, Aware CB, Surwase SN, Chavan CS, Jadhav JP (2019) Synthesis of melanin mediated silver nanoparticles from aeromonas sp. sns using response surface methodology: characterization with the biomedical applications and photocatalytic degradation of brilliant green. J Polym Environ 27(11):2428–38 61. San Keskin NO, Koçberber KiliÇ N, Dönmez G, Tekinay T (2016) Green synthesis of silver nanoparticles using cyanobacteria and evaluation of their photocatalytic and antimicrobial activity. J Nano Res 40:120–127

Chapter 2

Impact of Onshore Construction Activities on Sea Water Turbidity Muhammad Salman Afzal , Furqan Tahir , and Sami G. Al-Ghamdi

Abstract Turbidity refers to the presence of suspended particles in water. The increased number of construction and development activities these current times is resulting in increased sediments in water, which reflects higher turbidity. Sunlight is frequently absorbed by turbid water, resulting in a rise in the water’s overall temperature and a decrease in dissolved oxygen concentration. All these conditions put aquatic life under stress. This research’s primary focus was sedimentation and suspension turbidity, which is more likely to occur due to onshore construction activities. In the study, samples were collected from various locations of one of Qatar’s onshore construction sites and analyzed for turbidity using a portable turbidity meter and a spectrophotometer. A comparison of the obtained data to Ministry of Municipality and Environment (MME) standards was conducted. The results indicated a significantly increased turbidity in a few sample locations, i.e., at the inlet point of the sedimentation tank, dewatering pipeline, and open excavations of the project site. The study also suggested using sedimentation tanks and silt traps in an onshore construction project to prevent turbid water from being discharged into the sea. Keywords Dewatering · Onshore construction activities · Sedimentation · Silt trap · Turbidity

M. S. Afzal · F. Tahir · S. G. Al-Ghamdi Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar e-mail: [email protected] F. Tahir (B) · S. G. Al-Ghamdi Environmental Science and Engineering Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia e-mail: [email protected] KAUST Climate and Livability Initiative, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_2

17

18

M. S. Afzal et al.

2.1 Introduction In the Arabian Gulf, coastal urbanization has proliferated in recent years, placing increased strain on crucial yet undervalued coastal ecosystems all around the region [1]. Because of the cumulative effects of coastal development, overfishing, industrial expansion, and other population-driven stressors, each system has been experiencing an acceleration in loss and degradation in recent years. One of the most damaged marine ecoregions in the world today is the Arabian Gulf. Although the Gulf has seen a rise in sustainable development in recent years [2], the majority of the conversation has centered on bettering construction codes and urban planning procedures, with cost savings, economic efficiency, fewer carbon footprints, use of recycled materials, and social benefits as the driving forces [1, 3, 4]. In contrast, the protection of ecologically sensitive natural resources has received insufficient attention [5]. One of the outcomes of the construction activities near the coastline is the rise in turbidity, which has adverse effects. Turbidity is an indicator of water transparency. The greater the number of embedded granules in the water, the more turbid it is, leading to higher turbidity values. Sediment is the main cause of turbidity, originating from construction and development practices [1, 6]. Turbidity may have various unwanted environmental consequences [7, 8]. High sediment concentration in water, or turbidity, interferes with the light penetration in water, thus reducing the amount of light that reaches the aquatic vegetation. Fine particulate matter can also block or destroy sensitive gill structures, decrease disease tolerance, prevent proper growth of eggs and larvae, and potentially interfere with the nutrition of aquatic life [1]. Large turbidity concentrations over a short duration or less turbidity over longer times may all have major impacts on marine species. Turbid water often absorbs sunlight, which increases temperature and decreases the amount of dissolved oxygen in the water. As a consequence, it stresses or kills living beings in water. High particulate matter concentrations affect ecosystem sustainability, leisure values, and habitat quality and make lakes fill faster [9]. In the sea, elevated sedimentation and siltation affect fish and other marine life habitats. Particulates in the water also include fasteners for other contaminants, particularly metals and bacteria. Turbidity readings may also be used to measure possible contamination in a water source [10]. The turbidity addresses multiple full gross regular load/construction programs. Building works such as grading and renovation create contaminants that can escape from the site, thus, causing harm to the water resources [11]. One of the major contaminants of concern is sediment. When it floods, floodwater washes off the loose dirt from the building site, and goods are kept outdoors. These turbidity-related issues are of major concern in marine projects since they disturb the quality of the sea ecosystem. Previously there have been many research works conducted regarding the quality of performance of green building projects during construction and operation [12]. However, marine construction is usually not the focus of most research and normally ignores marine quality and environmental aspects. A study by Rauf and Al-Ghamdi [13] used cost of quality (CoQ)

2 Impact of Onshore Construction Activities on Sea Water Turbidity

19

techniques with which the results from managerial-focused survey were statistically examined and evaluated with respect to the following specified fields: Quality Assurance/Quality Control (QA/QC) awareness; the complexity of evaluating CoQ components; and the relation between quality performance and sustainability traits. Another case study [14] by the same group illustrated the difficulties that design and build projects encounter regarding quality performance due to the contractual arrangement’s intrinsic nature. Efficient and environment-friendly materials are also needed, which all civil engineers should consider during any construction [15]. Sustainable measures can also help greatly reduce economic and environmental degradation for any project [16]. Onshore construction generally entails excavation, which is followed by onsite dewatering to prevent water inundation. Similarly, the dewatering process entails the installation of properly connected tubes to the excavated area, i.e., dewatering pumps are installed onsite, through which water is routed through sedimentation tanks and finally discharged into the sea. Additionally, water is allowed to pass through the silt screen to regulate sediment transport into the sea. Thus, it can be asserted that various onshore construction activities contribute significantly to turbidity. This study aims to ascertain the impact of onshore development on seawater turbidity. In short, it can be claimed that various onshore construction activities significantly contribute to turbidity. The essence and dilemma of the analysis were developed according to the main objective, that is, the effects of the onshore construction activities on the seawater’s turbidity values.

2.2 Materials and Method The study investigated the effect of onshore construction activities on the turbidity of seawater. Sampling was conducted along the coastline of an artificial island. The samples were collected, and turbidity readings for a few samples were taken onsite using a calibrated portable turbidity meter. At the same time, the rest were analyzed in the laboratory using a spectrophotometer to determine the study objectives. The model of the portable turbidity meter was Extech-TB 400, while that of the spectrophotometer was HACH DR6000.

2.2.1 Sampling Sites Figure 2.1 illustrates the sampling sites, i.e., various locations from which water samples were collected for analysis. The samples were collected in the following order; (a) from the construction site, a groundwater sample was collected at a depth of 5 m, (b) a second sample was again taken from the construction site at a depth of 3 m, (c) the third sample was taken from dewatering hose, (d) the fourth and the fifth one from sedimentation tank (inlet and outlet of the tank), (e) the sixth sample was

20

M. S. Afzal et al.

Fig. 2.1 Illustration of sampling sites of an artificial island construction and sea

collected from the discharge point, and the last four samples were collected from the sea at a distance of 20 m from the discharge point (before the silt trap), and after the silt trap at a distance of 30, 50 and 100 m from the discharge point. Samples obtained from groundwater depth, sedimentation tank, and the sea at 100 m from the discharge point were analyzed for turbidity values in the laboratory using a spectrophotometer. In contrast, the rest were analyzed onsite using a portable turbidity meter.

2.2.2 Analytical Approach Instruments Used. Turbidity is one of the most challenging factors to quantify as a measure of water’s optical quality. The clarity of the water can be determined directly with a turbidity device such as a turbidimeter or turbidity sensor. Turbidity sensors are also referred to as turbidimeters. A Secchi disc or a tube are two methods for determining the clarity of the water. This method is generally faster and less expensive, but its accuracy is contingent upon the user of the Secchi desk. Spectrophotometer or other optical scatter detection methods are also used to determine the turbidity of water samples rapidly and accurately. Turbidity sensors, like turbidity meters, use optical technology, but instead of using sample cells, they can be placed directly into the water source to determine turbidity. Seawater samples are collected, and the turbidity of the water is typically determined using a portable turbidity meter [17].

2.3 Results and Discussion The attained values from sampling are given in Table 2.1. Some of the samples were examined in the laboratory using a spectrophotometer. At the same time, the in-situ methodology used a portable turbidity meter to acquire the values for turbidity, while the Qatar standard of the Ministry of Municipality and Environment (MME) is taken as such. The unit of turbidity is taken as the Nephelometric Turbidity Unit (NTU).

2 Impact of Onshore Construction Activities on Sea Water Turbidity

21

Each sample is identified by its collection location, including groundwater, open excavation, dewatering pipeline, sedimentation tank, etc. As shown in Table 2.1, the turbidity values for the excavation site, dewatering pipeline, and sedimentation tank inlet are greater than the MME standard of 5 NTU. The aforementioned points have 38, 36, and 21 NTU readings, respectively. Figure 2.2 illustrates the sampling results graphically and compares them to standards established by the Ministry of Municipality and Environment (MME). As shown in the graph and previously discussed, the turbidity measurements for the dewatering pipeline, open excavation, and inlet of the sedimentation tank are significantly higher than the standards, while the rest are within the MME limits. Table 2.1 Turbidity monitoring data #

Method

Location

Results [NTU]

MME standard Qatar [NTU]

1

Laboratory sample

Groundwater

4.79

5

2

In-situ (Turbidity meter)

Open excavation

38

3

In-situ (Turbidity meter)

Dewatering pipeline

36

4

In-situ (Turbidity meter)

Sedimentation tank sample 1 (inlet point)

21

5

Laboratory sample

Sedimentation tank sample 2 (after settlement)

3.86

6

In-situ (Turbidity meter)

Discharge point

3

7

In-situ (Turbidity meter)

Discharge point (20 m away)

2.8

8

In-situ (Turbidity meter)

30 m after silt trap

2.3

9

In-situ (Turbidity meter)

50 m after silt trap

2.9

10

Laboratory sample

100 m after silt trap

3.77

Fig. 2.2 Comparison of turbidity values for different sites with MME standards

22

M. S. Afzal et al.

Fig. 2.3 Comparison of turbidity values for the sedimentation tank

Figure 2.3 illustrates the difference between sedimentation tank samples collected at the inlet and outlet points following sediment settlement. The results indicate that because water enters the sedimentation tank directly from the excavation site via a dewatering pipe without being treated, the inlet values for turbidity are significantly higher than the outlet, where a sample is taken after sediments settle at the bottom of the tank. Additionally, it demonstrates that the readings at the outlet point comply with the MME standard. The inlet point has a turbidity reading of 21 NTU, while the outlet has a reading of 3.86 NTU. Finally, Fig. 2.4 depicts the turbidity results for the discharge point as well as for samples taken 20, 30, 50, and 100 m ahead of the discharge point. Because the sediments were previously settled in a sedimentation tank, the discharge point value obtained was below the MME’s turbidity standards. The water passed through a silt trap, further reducing the turbidity reading for the sample taken 30 m downstream of the discharge point. The readings for the samples collected 50 and 100 m ahead of the discharge point were also well within MME standards. Still, the turbidity was slightly higher than the sample taken 30 m off the discharge due to interference of different environmental factors.

Fig. 2.4 Comparison of turbidity readings for discharge point and various distances ahead of the discharge point

2 Impact of Onshore Construction Activities on Sea Water Turbidity

23

The data analysis confirmed the hypothesis that the water quality of samples taken from various points on an onshore construction site varied significantly, with the highest turbidity values for the excavation point, dewatering pipeline, and sedimentation tank inlet. The survey results revealed significant deviations of turbidity values from Qatar’s MME standard, implying a significant impact of construction activities on the island’s sea water turbidity [6, 17, 18]. Therefore, it is of utmost importance that the turbid water must be well treated before its discharge into the sea to prevent the marine ecosystem and comply with the standards defined by the state. Furthermore, the turbidity should be monitored for a specific period after the construction phase.

2.4 Conclusion This study aimed to determine the effect of construction activities on the selected island’s seawater quality, particularly turbidity. The study was conducted by collecting water samples from various locations throughout the construction framework and analyzing them using a portable turbidity meter and spectrophotometer to obtain turbidity readings. The study then compared the results for the different sampling locations with Qatar’s MME standard. The comparison revealed significantly higher turbidity values at the inlet point of the sedimentation tank, dewatering pipeline, and open excavations. Therefore, to protect the marine ecosystem and adhere to state regulations, it is crucial that the turbid water be thoroughly cleansed before being discharged into the sea. Additionally, the study emphasized using sedimentation tanks and silt traps to prevent turbid water from being discharged into the sea, and such precautions must be taken in any onshore construction project. Acknowledgements This research was supported by a scholarship from Hamad Bin Khalifa University (HBKU), a member of the Qatar Foundation (QF). Any opinions, findings, conclusions, or recommendations expressed in this material are solely those of the author(s) and do not necessarily reflect the HBKU or QF.

References 1. Afzal MS, Tahir F, Al-Ghamdi SG (2022) Recommendations and strategies to mitigate environmental implications of artificial island developments in the gulf. Sustainability 14:5027. https://doi.org/10.3390/su14095027 2. Al-Humaiqani MM, Al-Ghamdi SG (2022) The built environment resilience qualities to climate change impact: concepts, frameworks, and directions for future research. Sustain Cities Soc 80:103797. https://doi.org/10.1016/j.scs.2022.103797 3. Tahir F, Sbahieh S, Al-Ghamdi SG (2022) Environmental impacts of using recycled plastics in concrete. Mater Today Proc. https://doi.org/10.1016/j.matpr.2022.04.593

24

M. S. Afzal et al.

4. Sbahieh S, Tahir F, Al-Ghamdi SG (2022) Environmental and mechanical performance of different fiber reinforced polymers in beams. Mater Today Proc. https://doi.org/10.1016/j. matpr.2022.04.398 5. Burt JA (2014) The environmental costs of coastal urbanization in the Arabian Gulf. City 18:760–770. https://doi.org/10.1080/13604813.2014.962889 6. Houser DL, Pruess H (2009) The effects of construction on water quality: a case study of the culverting of Abram Creek. Environ Monit Assess 155:431–442. https://doi.org/10.1007/s10 661-008-0445-9 7. Chen Y, Viadero RC, Wei X et al (2009) Effects of highway construction on stream water quality and macroinvertebrate condition in a mid-atlantic highlands watershed, USA. J Environ Qual 38:1672–1682. https://doi.org/10.2134/jeq2008.0423 8. Tahir F, Baloch AAB, Ali H (2020) Resilience of desalination plants for sustainable water supply in Middle East. In: Khaiter PA, Erechtchoukova MG (eds) Sustainability perspectives: science, policy and practice, strategies for sustainability. Springer Nature Switzerland AG, pp 303–329 9. Tahir F, Ajjur SB, Serdar MZ, et al (2021) Qatar climate change conference 2021: a platform for addressing key climate change topics facing Qatar and the world. Hamad bin Khalifa University Press (HBKU Press), Doha, Qatar 10. Selbig WR, Jopke PL, Marshall DW, Sorge MJ (1999) Hydrologic, ecologic, and geomorphic responses of brewery creek to construction of a residential subdivision 11. Purcell P, Bruen M, O’Sullivan J et al (2012) Water quality monitoring during the construction of the M3 motorway in Ireland. Water Environ J 26:175–183. https://doi.org/10.1111/j.17476593.2011.00274.x 12. Raouf AM, Al-Ghamdi SG (2020) Framework to evaluate quality performance of green building delivery: construction and operational stage. Int J Constr Manag 1–15. https://doi.org/10.1080/ 15623599.2020.1858539 13. Raouf AM, Al-Ghamdi SG (2020) Managerial practitioners’ perspectives on quality performance of green-building projects. Buildings 10:71. https://doi.org/10.3390/BUILDINGS100 40071 14. Raouf AM, Al-Ghamdi SG (2019) Framework to optimize cost of quality in delivering and operating green buildings. In: International conference on sustainable infrastructure 2019: leading resilient communities through the 21st century—proceedings of the international conference on sustainable infrastructure 2019. American Society of Civil Engineers (ASCE), pp 338–347 15. Al Rashid A, Khan SA, G. Al-Ghamdi S, Koç M (2020) Additive manufacturing: technology, applications, markets, and opportunities for the built environment. Autom Constr 118:103268. https://doi.org/10.1016/J.AUTCON.2020.103268 16. Al-Nuaimi S, Banawi A-AA, Al-Ghamdi SG (2019) Environmental and economic life cycle analysis of primary construction materials sourcing under geopolitical uncertainties: a case study of Qatar. Sustain 11:6000. https://doi.org/10.3390/SU11216000 17. Powers JP, Corwin AB, Schmall PC, Kaeck WE (2007) Construction dewatering and groundwater control: new methods and applications. Wiley 18. Millen JA, Jarrett AR, Faircloth JW (1997) experimental evaluation of sedimentation basin performance for alternative de-watering systems. Trans ASAE 40:1087–1095. https://doi.org/ 10.13031/2013.21361

Chapter 3

Characteristics of Natural Organic Matter and Trihalomethanes Formation in the Southern Part of Songkhla Lake Basin Kamonnawin Inthanuchit

and Kochakorn Sukjan Inthanuchit

Abstract The characteristics of natural organic matter (NOM) in water sources may be significant for regulating NOM and limiting the production of disinfection byproducts. NOM surrogate measures such as dissolved organic carbon (DOC), ultraviolet absorbance at a wavelength of 254 nm (UV-254), specific ultraviolet absorption (SUVA), and fluorescence excitation-emission (FEEM) were utilized to evaluate NOM qualities related to trihalomethanes (THMs) generation by chlorination from diverse water sources in the southern part of the Songkhla lake basin (SLB), such as the water sample of the reservoir (n = 3), the water well (n = 2), and the canal (n = 10). The canal had the highest DOC concentration (5.12–5.89 mg/L), followed by the reservoir (2.05–2.32 mg/L) and the well (2.12–2.23 mg/L). The results for the lowest SUVA values indicated that NOM was present in the water well and that aromatic proteins and SMP-like compounds predominated in the canal. The results of FEEM spectroscopy indicated that tryptophan-like chemicals (240 nmEx /360 nmEm and 260 nmEx /360 nmEm ) were the predominant DOM in community wastewater discharged into the SLB compared to humic and fulvic acid-like substances (280 nmEx /410 nmEm , 340 nmEx /410 nmEm , 330 nmEx /440 nmEm ). The highest concentration of THMs was found in the canal (560–736 µg/L), followed by the water sample from the reservoir (146–390 µg/L), and the lowest concentration was found in the water well (120–312 µg/L). The reservoir had a significantly higher THMs/ DOC ratio than the canal and the water well. The mixture of NOM originating from

K. Inthanuchit (B) Science in Environmental Science Program, Faculty of Science and Technology Management, Songkhla Rajabhat University, Songkhla 90000, Thailand e-mail: [email protected] Natural and Cultural Environmental Conservation Division of Songkhla Province, Office of Arts and Cultural, Songkhla Rajabhat University, Songkhla 90000, Thailand K. S. Inthanuchit Faculty of Traditional Thai Medicine, Prince of Songkla University, Hatyai, Songkhla 90110, Thailand © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_3

25

26

K. Inthanuchit and K. S. Inthanuchit

terrestrial and microbiological sources contributed to NOM fractions. Low molecular weight NOM, aromatic proteins, and water soluble microbial metabolites can all be used as reactants to make THM. Keywords Natural organic matter · Trihalomethane formation · Songkhla lake basin

3.1 Introduction Natural water sources such as rivers, reservoirs, and groundwater are used as raw water sources for the manufactured water supply. The Songkhla lake basin (SLB) in southern Thailand is one of the most important raw water sources. The SLB covers an area of 8,484,35 km2 and is comprised of 26 districts, 147 sub-districts/ municipalities, and 1,247 villages in the provinces of Phatthalung, Songkhla, and Nakhon Si-Thammarat. The SLB was separated into three broad regions based on the physical characteristics of the lagoon [1, 2]. The southern part of SLB is now facing water quality degradation issues. The U-Tapao Canal, a branch canal in SLB, found dissolved organic matter (DOM) and humic substances throughout the year [3, 4], mainly DOM from the watershed area close to the water source. By reacting with chlorine, which is often used to kill microorganisms during disinfection, DOM can make disinfection by-products (DBPs) that could cause cancer, such as haloacetic acids (HAAs) and trihalomethanes (THMs) [5]. Generally, the dissolved organic carbon (DOC) and trihalomethane formation potential (THMFP) values are used to evaluate the efficacy of a water supply treatment plant. However, procedures for detecting DOC and THMFP in water are somewhat complex. In addition, TOC analyzers and gas chromatography (GC) are extremely expensive. Therefore, they could not be employed online in water treatment facilities. In comparison to other DOC and THMFP analytical procedures, three-dimensional fluorescence spectroscopy analysis (the use of a fluorescent excitation-emission matrix) and ultraviolet photometry analysis (the use of ultraviolet adsorption at wavelength 254 nm or UV254) are more straightforward due to their minimal sample amount, pretreatment, and analysis time requirements. The purpose of this study was to evaluate NOM characteristics with trihalomethanes (THMs) generation by chlorination from diverse water sources, including reservoir (n = 3), well (n = 2), and canal (n = 10) water samples in the southern part of the Songkhla lake basin (SLB), Thailand. Understanding the properties of natural organic matter (NOM) in water sources could be the key to controlling NOM and reducing the formation of disinfection by-products. The results of this study can also tell us important things about the future effects of chlorinating water from different sources in SLB.

3 Characteristics of Natural Organic Matter and Trihalomethanes …

27

3.2 Material and Methods 3.2.1 Studied Songkhla Lake Basin and Sample Collection Songkhla lake basin (SLB): The SLB is divided into three distinct parts. The southern part opens with a 380 m wide strait to the Gulf of Thailand at the city of Songkhla. The eastern area is the Sathing Phra Peninsula, and the western area has large cities including Hat Yai city and Songkhla city and sample collection including the water sample of the reservoir, the water well, and the canal in the southern part of Songkhla lake basin (SLB), shown in Fig. 3.1. Reservoir: Klongla Reservoir (R1), Khlong ChumRai Reservoir (R2), and Sadao Reservoir (R3) are located in the southern part of SLB, Thailand. Water from these reservoirs is used to create the water supply for drinking, bathing, and household usage in the areas surrounding reservoirs. Water well: Both of W1 and W2 are employed in the production of the water supply. The produced water supply is delivered to towns, faculties, offices, and dormitories in the surroundings of Songkhla Rajabhat University for drinking, bathing, and home usage.

Fig. 3.1 Studied the southern part of Songkhla lake basin and sample collection

28

K. Inthanuchit and K. S. Inthanuchit

Canal: Klong Pum (C1 ), Bang Raing (C2 ), Bang Kram (C3 ), Hat Yai pumping station (C4 ), Klong Utapao (C5 ), Klong Hae (C6 ), Klong Toei (C7 ), Bang Not (C8 ), Pavong (C9 ), and Klong Samrong (C10 ) are in the southern part of SLB. There are a significant source of raw water supply for the SLB’s largest water supply manufacturing operation.

3.2.2 Water Sampling All sampling was done in Songkhla between February and May (dry season) and June and January (rainy season) [6]. On 8–9 March and 15–16 November 2020, as well as on 15–16 February and 18–19 October 2021, water samples were collected and kept. The Pearson’s correlation (r) test, with a value of p < 0.01, was used to figure out the relationship between two variables that measure important seasonal and commercial and administrative procedure changes along SLB.

3.2.3 Analytical Methods The collected water samples were filtered through a Whatman GF/F membrane (pore size 0.7 µm) prior to storage at 4 °C in the dark, added H2 SO4 to pH 2 in accordance with the standard method 1060B, and analyzed immediately. DOC were analyzed in accordance with Standard Method 5310D [7] for water using a TOC analyzer (O.I. analytical, College Station, Texas, USA). Milli-Q water (ELGA, Lane End, High Wycomebe, UK) was used with every sample to clean the system. UV-254 was analyzed in accordance with Standard Method 5910B [7] using a UV/VIS spectrometer, Jasco V-350 spectrophotometer (Jasco Corporation, Tokyo, Japan) at 253.7 nm, with matched quartz cells, that provided a path length of 10 mm. All water samples were adjusted to a pH of 7 by H2 SO4 and NaOH prior to UV-254 analysis. SUVA was calculated by dividing the UV absorbance of the sample (in cm−1 ) by the DOC of the sample (in mg/L) and then multiplying by 100 cm/M. SUVA is reported in units of L/mg-M. THMFP measurements were carried out according to Standard Method 5710B [7]. THMs were extracted with pentane in accordance with Standard Method 6232B [7] before injection to the gas chromatography (GC) system. Agilent Gas Chromatography-6890 with an electron capture detector (ECD) (Agilent technologies Inc., Wilmington, Delaware, USA) and chromatographic column (J&W Science DB-624, DE, USA), with 0.2-mm X 25 m 1.12 µm film was used to analyze THMs. At least two replications of each measurement of DOC, UV-254, and THMFP were performed.

3 Characteristics of Natural Organic Matter and Trihalomethanes …

29

FEEM spectroscopy was measured using Jasco FP-6200 and FP-750 Spectrofluorometers with a wavelength range of 220–600 nm for excitation and emission. FEEM spectra of all water samples were subtracted by the FEEM spectra of Milli-Q water and converted to quinine sulphate units (QSUs). Ten QSUs are equal to the fluorescence spectra of 10 µg/L quinine sulphate solution at 450 nm with an excitation wavelength of 345 nm. FEEM data were discarded when the excitation wavelength (Ex) was greater or equal to the emission wavelength (Em) or Ex × 2 was less than or equal to Em to eliminate the influence of primary and secondary scattered fluorescence and highlight the targeted peaks. Rayleigh and Raman scattering at peak Em ± 10–15 nm of each Ex was also separated from the FEEM spectra.

3.3 Results and Discussion 3.3.1 Variation of DOC, UV254, SUVA and THMFP in Water Sampling UV-254, DOC, and THMFP are frequently used as substitutes for DOM in water. These arguments may give significantly more precise information about DOM characteristics. DOC could be used to represent the levels of aromatic and aliphatic organic carbons in water. UV-254 represents the aromatic nature of the humic and fulvic acids. Compared to specific ultraviolet absorbance (SUVA), which is the ratio of how much light is absorbed to the amount of DOC (UV254/DOC), SUVA indicates the hydrophobic properties of the sample [8]. Commonly, the THMFP is used to determine the THMs at the end of the reaction between DOM and excess chlorine. Water with a high THMFP value can produce a high concentration of THMs, which has an active ability to produce THMs. THMFP was found to be a good indicator and was used to check the water for the highest possible THM levels. Table 3.1 shows the average of DOC, UV-254, SUVA, and THMFP during the dry and rainy seasons of 2020 and 2021. During the dry season, the average concentrations of DOC, UV-254, SUVA, and THMFP in the reservoir water were 2.05 mg/L, 0.64 cm−1 , 1.33 L/mg-M, and 146 µg/ L, respectively. During the rainy season, the average concentrations of DOC, UV254, SUVA, and THMFP in the reservoir water were 2.32 mg/L, 1.302 cm−1 , 3.02 L/ mg-M, and 390 µg/L, respectively. During the dry season, the average concentrations of DOC, UV-254, SUVA, and THMFP in the water well were 2.12 mg/L, 0.48 cm−1 , 1.02 L/mg-M, and 120 µg/L, respectively. During the rainy season, the average concentrations of DOC, UV-254, SUVA, and THMFP in the water well were 2.23 mg/ L, 0.551 cm−1 , 1.23 L/mg-M, and 312 µg/L, respectively. During a dry season, the average concentrations of DOC, UV-254, SUVA, and THMFP in canal water were 5.12 mg/L, 0.46 cm−1 , 2.38 L/mg-M, and 560 µg/L, respectively. In the canal water

30

K. Inthanuchit and K. S. Inthanuchit

Table 3.1 Variation of DOC, UV-254, SUVA, and THMFP in water sample Water sample

DOC (mg/L)

UV-254 (cm−1 )

SUVA (L/mg-M)

THMFP (µg/L)

Dry season

Rainy season

Dry season

Rainy season

Dry season

Rainy season

Dry season

Rainy season

2.05

2.32

0.649

1.302

1.33

3.02

146

390

Water Well 2.12 (n = 2)

2.23

0.481

0.551

1.02

1.23

120

312

Canal 5.12 Water (n = 10)

5.89

0.465

0.489

2.38

2.88

560

736

Reservoir (n = 3)

during the rainy season, the average concentrations of DOC, UV-254, SUVA, and THMFP were 5.89 mg/L, 0.489 cm−1 , 2.88 L/mg-M, and 736 µg/L, respectively. Table 3.1 demonstrates that the average of all parameters during the rainy season in 2021–2022 is higher than during the dry season. Based on land use type as described by [1, 2], upstream of a reservoir, water well, and canal, where there are numerous activities, followed by urban and developed land. It might identify contamination origins from non-point sources, such as agricultural and community activities, as well as point sources, such as industrial and community activities, with greater attention to detail. The residual organic matter in the reservoir, well, and canal water could still react with chlorine and produce slightly elevated THMFP levels. Therefore, water treatment plants that use reservoir water, well water, or canal water in the southern part of SLB should be cautious about the DBP concentration in their generated water supply. Figure 3.2 illustrates THMFP species in the reservoir and water well during the dry and rainy seasons. The average proportion of 90–93% chloroform, 4– 6% bromodichloromethane, and 3–4% dibromochloromethane, respectively. The proportion of THMFP species is comparable to other reservoirs and water wells in Thailand [9, 10], and [11]. There are THMFP species in the canal water throughout both the dry and rainy seasons. The proportion of chloroform, bromodichloromethane, dibromochloromethane, and bromoform was 79–82%, 13%, 4–6%, and 1%, respectively. The number of species at THMFP is about the same as at other canals in Thailand [12]. The WHO [13] gives suggested guidance values for maximum concentrations of particular THMs in human-consumable water. The guideline values describe the maximum concentration of a substance that poses no significant danger to human health when consumed over a lifetime. These concentrations are as follows: chloroform at 300 µg/L, bromodichloromethane (BDCM) at 60 µg/L, dibromochloromethane (DBCM) at 100 µg/L, and bromoform at 100 µg/L. According to the World Health Organization, the following equation can be utilized to calculate the combined toxicity of all THMs: (1)

3 Characteristics of Natural Organic Matter and Trihalomethanes …

CHCl3-FP

CHCl2Br-FP

CHCl2Br2-FP

31

CHBr3-FP

800

THMFP (ug/L)

700 600 500 400 300 200 100

0

Dry season

Rainy season

Reservoir (n=3)

Dry season

Rainy season

Water well (n=2)

Dry season

Rainy season

Canal water (n=10)

Fig. 3.2 THMFP and THMFP species in water sample

Cbromoform CDBCM CBDCM CChloroform + + + ≤1 GV bromoform GV DBCM GV BDCM GV Chloroform

(3.1)

where C = concentration and GV = guideline value. When the values of the four THMs compounds in water samples were compared to WHO as equation requirements (1), it was discovered that water samples from reservoir and water well in the dry season were 0.61 and 0.58, respectively, which followed WHO requirements, while water samples in the rainy season were 1.58 and 1.38, respectively. Furthermore, in both seasons, the water samples from the canals had levels of 3.14 and 3.97, which exceeded WHO standards (define compounds with a value of THMs, each type combined with guidelines of less than 1). Specific THMFP (THMFP/DOC or STHMFP) indicates the average potential of a sample’s carbon to generate THMs; it is a molar measure of the potential THM precursor concentration normalized to carbon [14]. Figure 3.3 demonstrated that THMFP/DOC was substantially higher during the rainy season than during the dry season. In rainy and dry season samples, aromatic chemicals and other types of dissolved organic matter may be significant THM precursors.

3.3.2 Characteristics of FEEM Spectroscopy in Water Sampling The FEEM was measured by the matrix of fluorescent intensity in coordinates for excitation and emission wavelength. Figure 3.4 shows the results of FEEM spectroscopy performed on the reservoir, water well, and canal water in the southern part

32

K. Inthanuchit and K. S. Inthanuchit

THMFP/DOC (ugTHMs/mgDOC)

Dry season

Rainy season

180 160 140 120

100 80 60 40 20 0 Reservoir (n=3)

Water well (n=2)

Canal water (n=10)

Fig. 3.3 THMFP/DOC in water sample

of SLB during the rainy and dry seasons of 2020 and 2021, with contour intervals of 10 QSU. As presented in Fig. 3.3, the fluorescent excitation-emission wavelengths (Ex/Em) exhibited large fluorescent emission intensities as fluorescence peaks. In the water samples, the peak locations A, B, C, and D of FEEM spectroscopy indicated the presence of natural organic matter and organic matter from non-point source pollution. As (i) humic/fulvic-like organic matter (Ex/Em = 330–350/420–480 nm), (ii) humic-like organic matter peak (Ex/Em = 250–260/420–480 nm), and (iii) proteinlike organic matter (Ex/Em = 250–260/250–280 nm) were identified as the three primary fluorescence peaks in water samples by [15, 16]. Figure 3.4 demonstrates that FEEM spectroscopy results can be divided into two regions. In region I, tryptophan-like compounds were detected by the FEEM peak at 240 nmEx /360 nmEm (peak A) and 260 nmEx /360 nmEm (peak B) (peak B). In region II, the FEEM peak was found in humic and fulvic acid-like compounds at 280 nmEx /410 nmEm (peak C), 340 nmEx /410 nmEm (peak D), 330 nmEx /440 nmEm (peak E), and 285 nmEx /460 nmEm (peak F); these peaks were similar to those observed in the previous work [4, 17]. The tyrosine-like and tryptophan-like chemicals as residues that predominantly contribute to protein-like fluorescence in wastewater [18] were identified as indicators for assessing the quality of stream water [19]. While tryptophan-like chemicals were the predominant DOM in community wastewater discharged into the downstream SLB, humic and fulvic acid-like substances were also present in significant amounts. This study showed that the wastewater from the lakeside community was the main source of DOM in SLB.

3 Characteristics of Natural Organic Matter and Trihalomethanes …

450

33

(A):240nmEx/360nmEm (B):260nmEx/360nmEm (C):280nmEx/410nmEm (D):340nmEx/410nmEm (E):330nmEx/440nmEm (F):285nmEx/460nmEm

Excitation (nmEx)

400

Region II 350

(D)

(F)

(C)

300

(B) 250

(E)

Region I

(A)

250 300 350 400 450 500 550 600 Emission (nmEm)

Fig. 3.4 Location of FEEM peak position in water sample

3.4 Conclusion The highest DOC concentration was found with canal, followed by reservoir, and water well. Results relative indicate lowest SUVA values that NOM have in the water well and the predominance of aromatic proteins and soluble microbial products (SMPs)-like compounds were clearly observed in the canal possibly because of the discharge of untreated wastewater. The FEEM spectroscopy results indicated tryptophan-like substances were the dominant DOM from community wastewater discharged into the canal water compared to humic and fulvic acid-like substances. The highest concentration of THMs was detected in canal, followed by reservoir water samples, whereas the lowest concentration of THMs was detected in the water well sample Then, specific treatment processes should be required to eliminate these NOM fractions in water treatment for controlling THMs formation. Acknowledgements This research is integrated with the subject of watershed and Songkhla Lake Basin (4463206) in the environmental science program, faculty of science and technology, Songkhla Rajabhat University. The author wishes to thank the Natural and Cultural Environmental Conservation Division of Songkhla Province for supporting this project.

34

K. Inthanuchit and K. S. Inthanuchit

References 1. Srichaichana J, Trisurat Y, Ongsomwang S (2019) Land use and land cover scenarios for optimum water yield and sediment retention ecosystem services in Klong U-Tapao Watershed, Songkhla, Thailand. Sustainability 11(10):2895. https://doi.org/10.3390/su11102895 2. Tippayawong S, Somboonsuke B (2013) Dynamics of songkhla lake basin research in the south of Thailand. J Agric Technol 9(5):1081–1096 3. Inthanuchit K, Kunpitak K, Podam N, Suwibul H, Yoyruroob S (2018) Monitoring of carbon and nitrogen loading of onsite wastewater treatment in Songkhla lake basin. EAU Herit J Sci Technol 13(2):225–239 4. Inthanuchit K, Inthanuchit KS (2022) Characterization of dissolved organic matter and humification index in filtered effluent water of lakeside communities around Songkhla Lake Basin, Thailand. J Res Unit Sci Technol Environ Learn 13(1):142–155 5. Marhaba TF, Washington MB (1998) Drinking water disinfection and by-product: history and current practice. Adv Environ Res 2(1):103–115 6. Climate center (2020) Climate of Songkhla. Thai Meteorology Department, Bangkok, pp 1–3 7. American Public Health Association (2017) Standard methods for the examination of water and wastewater, 23rd edn. American Water Works Association. Washington DC, USA, Water Environment Federation 8. Mattaraj S, Kilduff JE (2003) Effect of natural organic matter properties on nanofiltration fouling. The fourth regional symposium on infrastructure development in Civil Engineering. Kasetsart University, Bangkok, pp 1051–1060 9. Homklin S (2004) Removal of hydrophilic and hydrophobic dissolved organic matters in natural waters by alum coagulation. Master thesis. Graduated School, Chulalongkorn University, Thailand 10. Phumpaisanchai A (2005) Removal of hydrophobic and hydrophilic natural organic matters in reservoirs by alum coagulation. Master thesis. Graduated School, Chiang Mai University, Thailand 11. Musikavong C, Wattanachira S, Nakajima F, Furumai H (2007) Three dimensional fluorescent spectroscopy analysis for the evaluation of organic matter removal from industrial estate wastewater by stabilization ponds. Wat Sci Tech 55(11):201–210 12. Inthanuchit K (2008) Removal of hydrophilic and hydrophobic in raw water supply from Utapao canal, Thailand. Master thesis. Graduated School, Prince of Songkla University, Thailand 13. World Health Organization (2006) Guidelines for drinking water quality, 3rd edn. WHO, Geneva 14. Roger F, Anthony JR, George RA, Brian AB (1998) Dissolved organic carbon concentration and compositions, and Trihalomethane formation potentials in waters from agricultural peat soils, Sacramento-San Joaquin, California: Implications for drinking-water quality. U.S. Geological survey, Sacramento, California 15. Chen J, LeBoeuf EJ, Dai S, Gu B (2003) Fluorescence spectroscopic studies of natural organic matter fractions. Chemosphere 50(5):639–647 16. He W, Jung H, Lee JH, Hur J (2016) Differences in spectroscopic characteristics between dissolved and particulate organic matters in sediments: insight into distribution behavior of sediment organic matter. Sci Total Environ 547:1–8 17. Musikavong C, Inthanuchit K, Srimuang K, Suksaroj TT, Suksaroj C (2013) Reduction of fractionated dissolved organic matter and their trihalomethane formation potential with enhanced coagulation. Sci Asia 39:56–66 18. Mohapatra S, Sharma N, Mohapatra G, Padhye LP, Mukherji S (2021) Seasonal variation in fluorescence characteristics of dissolved organic matter in wastewater and identification of proteins through HRLC-MS/MS. J Hazard Mater 413:125453 19. Zhao Y, Song K, Li S, Ma J, Wen Z (2016) Characterization of CDOM from urban waters in Northern-Northeastern China using excitation-emission matrix fluorescence and parallel factor analysis. Environ Sci Pollut Res 23:15381–15394

Chapter 4

Characterization and Statistical Multivariate Analysis of Potentially Toxic Elements Contamination of Groundwater in Chiniot Area, Punjab Plain, Pakistan Mitsuo Yoshida, Mirza Naseer Ahmad, and Rashida Sultana

Abstract In the Punjab plain of Pakistan, rapid socio-economic development has occurred using groundwater resources, where the water contamination and deterioration of water quality are concerned. A total of 83 groundwater samples were collected from agricultural/domestic wells in the Chiniot area in the northern region of the Punjab Plain to analyze the trace element composition of the groundwater and to characterize the state of contamination. The groundwater samples were analyzed using inductively coupled plasma mass spectrometry. As a result, 29 elements (As, B, Ba, Br, Ca, Cd, Cl, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, S, Sb, Se, Si, Sr, U, V, W, and Zn) were detected above the detection limit, and 17 elements (As, B, Ba, Br, Cd, Cl, Cr, Cu, Fe, Mn, Ni, Pb, S, Sb, Se, U, and Zn) are potentially toxic elements (PTEs). The concentrations of PTEs are regulated by the criteria of the maximum contaminant level, the secondary maximum contaminant level, or the World Health Organization water guidelines. Among these trace elements, the concentrations of six potentially toxic elements (As, Br, Cd, Cl, Mn, and S) exceeded the reference values. Considering the statistical skewness and kurtosis diagram, 17 elements (Ba, Br, Ca, Cd, Cr, Li, Mg, Mn, Na, Ni, Pb, Rb, Sb, Se, Si, W, and Zn), have relatively low skewness. They are close to a normal distribution and may be produced by the natural equilibrium, without the impact of specific irregular, anthropogenic contaminations. However, the distributions of the other 12 elements deviated significantly from the M. Yoshida (B) Environmental Research Laboratory, International Network for Environmental and Humanitarian Cooperation (iNehc), Nonprofit Inc, Tokyo, Japan e-mail: [email protected]; [email protected] Global Environment Department, Japan International Cooperation Agency (JICA), Tokyo, Japan M. N. Ahmad Department of Earth Sciences, Abdus Salam School of Sciences, Nusrat Jahan College, RabwahPunjab Province, District Chiniot, Pakistan R. Sultana Department of Botany, Abdus Salam School of Sciences, Nusrat Jahan College, RabwahPunjab Province, District Chiniot, Pakistan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_4

35

36

M. Yoshida et al.

normal distribution and may have been affected by irregular contamination. Based on a multivariate analysis, the trace element compositions were divided into four clusters: Cluster-1 (Li, K, Sr, Si, Rb, Cr, and U), Cluster-2 (B, S, Mg, Pb, Se, W, Mo, and V), Cluster-3 (Na, As, Ca, Ba, Cl, Br, Ni, Cd, and Sb), and Cluster-4 (Mn, Fe, Co, Zn, and Cu). The sources of Clusters 1 and 2 are unknown; however, they are likely a mixture of long-term natural equilibrium in the groundwater as well as irregular natural and man-made contamination. Cluster-3 was mainly derived from arsenopyrite of meta-volcanic rocks and rock salt in the Precambrian basement. Cluster-4 was likely caused by local mineralization. Based on these analyses, further research on groundwater contamination and migration mechanisms in the study area is proposed. Some of the groundwater in the area are unsuitable for potable (drinking) water, as well as for the continued use as an agricultural water supply, and it is necessary to reconsider the use and development of groundwater resources in the Chiniot area. Keywords Groundwater contamination assessment · Potentially toxic elements · Multivariate analysis · Elements clusters · Hydrogeological model

4.1 Introduction The Punjab Plain is an alluvial plain of the Indus River and its five tributaries and has abundant groundwater resources. In this region, rapid socioeconomic development has occurred, largely promoted through the use of groundwater resources to supply most of the agricultural and municipal and domestic water. The Chiniot area, a typical agricultural and populated zone, is in the northern part of the Punjab Plain (Fig. 4.1) along the Chenab River, a tributary of the Indus Reiver, where Kirana Hills composed of Precambrian basement rocks are exposed [1, 2]. Fig. 4.1 Index map for the Chiniot area

4 Characterization and Statistical Multivariate Analysis of Potentially …

37

Under these conditions, there have been concerns about the deterioration of groundwater quality in the Chiniot area. According to previous studies, general water quality has gradually deteriorated Ahmad et al. [3] and the economic and social impact of water quality deterioration has been pointed out [4]. Geographic information system (GIS) mapping of the area was carried out by Ahmad et al. [5], using physiochemical parameters (pH, electrical conductivity (EC), alkalinity, total dissolved solids (TDS), water hardness, and oxidation reduction potential (ORP)), in addition to major elements analysis (Ca, Mg, Cl, S, Na, and K). Results showed extensive groundwater extraction is causing an increase in salt contamination and the subsequent deterioration of water quality. Although most groundwater samples collected in the area were within the permissible range of water quality for agricultural irrigation defined by the Food and Agriculture Organization (FAO) guidelines [6], the sulfate concentration showed a 94% fitting to the guideline value, and some samples were bordering the upper limits of the parameters (EC, TDS, Ca, and Mg), which could negatively impact agricultural activities. Moreover, the sodium adsorption ratio (SAR) [6] indicated that 38.88% of samples were classified as ‘poor’ to ‘very poor’ ranges [7] for agricultural irrigation water. Ahmad et al. (2022) pointed out the necessity of continuous monitoring and the further study of groundwater quality, including other quality parameters. Preliminary trace element analyses have been reported around the Kirana Hills, which are part of the study area, and groundwater contamination by potentially toxic elements (PTEs) such as arsenic (As) and manganese (Mn) have been reported above the quality standards [8]. However, previous studies have been limited to relatively small areas close to the Kirana Hills. Therefore, it is necessary to investigate the water quality of groundwater used by residents over a wide area. In this study, the survey area covered the entire Chiniot area, and the results of inductively coupled plasma mass spectrometry (ICPMS) analysis of major and trace elements in the groundwater are reported. Based on the results of chemical analysis, we interpreted the statistical characteristics of elemental concentration patterns, statistical characteristics of distribution, possible origins of PTEs in groundwater, and risks to agriculture as well as public health of the local community. The purpose of this study is to clarify the situation of groundwater pollution in this area.

4.2 Materials and Method 4.2.1 Groundwater Samples Groundwater samples were collected from 83 wells that residents of the Chiniot district use daily for domestic and agricultural purposes during March 2020, before the dry season. The locations of the sampling sites are shown in Fig. 4.2. All wells

38

M. Yoshida et al.

Fig. 4.2 Location map for sampled groundwater wells and surface water in Chiniot Area, Punjab, Pakistan

were shallow tube wells, with a depth of less than 60 m. Most wells were situated in villages and along public roads. They are pumped by hand or small electric pumps.

4.2.2 Trace Elements Analysis The collected groundwater samples were adjusted to pH of 2 with dilute nitric acid and elemental analysis was performed using the ICP-MS (Parkin Elmer) method. The precision of the analysis was verified using multiple measurements of the reference material and a blank test.

4.2.3 Statistical Analysis First, a general basic statistical analysis was performed, including the geometric mean, standard deviation, skewness, and kurtosis, which were calculated using the following methods: ⌜ | n | n Σ (xi − x)2 Standard deviation(s) = √ n − 1 i=1

4 Characterization and Statistical Multivariate Analysis of Potentially …

Skewness =

39

Σ ( xi − x )3 n (n − 1)(n − 2) i=1 s

Σ (xi − x)4 n(n + 1) 3(n − 1)2 Kurtosis = − s4 (n − 1)(n − 2)(n − 3) i=1 (n − 2)(n − 3) n

where x i = individual data; = geometric means = standard deviation, and n = number of samples. The Box-and-Whisker plots are based on the method of Hoaglin [9]. Subsequently, a multivariate analysis for the dataset of concentrations of 29 elements was performed on the 83 samples to classify the data into groups. The hierarchical cluster analysis using the agglomeration method was adopted. When there are m variables and n individual data points, the dissimilarity Dij between individual data x i and x j can be obtained from the following equation using the Euclidean distance: m ( )2 ( ) Σ xki − xk j Dissimilarity Di j = k=1

where Dij is the dissimilarity measure; m is the number of variants (here, the number of elements); n is the number of samples, and (x ki − x kj ) is the Euclidean distance. To process the hierarchical cluster analysis, the Ward method was applied, where all possible pairs of clusters were combined, and the sum of the squared distances within each cluster was calculated. When clusters u and v were combined to create a new cluster w, the dissimilarity Dwt between the newly created cluster w, and another arbitrary cluster t, was calculated using the dissimilarity Dut and Dvt between clusters u and v and cluster t before integration. The dissimilarity Duv between clusters u and v is shown below: Dissimilarity(Dwt ) =

{ } 1 2 2 2 + (n v + n t )Dvt + n t Duv (n u + n t )Dut nu + nv + nt

In calculating the correlation matrix, the correlation between two quantitative variables (concentrations of two elements) is indicated by the correlation coefficient (r xy ): Σn

rx y

− x)(yi − y) / 2 Σn 2 x) − (xi i=1 i=1 (yi − y)

= /Σ n

i=1 (x i

The software Bell Curve for Excel (version 4.02) of Social Survey Research Information Co., Ltd., Japan, was used for statistical calculations.

40

M. Yoshida et al.

4.3 Results The summary of the trace element analysis results and the statistical analysis of the trace element concentration distribution are presented as follows.

4.4 Analytical Results According to the ICP-MS analysis of groundwater samples, 29 elements (As, B, Ba, Br, Ca, Cd, Cl, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, S, Sb, Se, Si, Sr, U, V, W, and Zn) were detected above the limit (Fig. 4.3). Seven major elements, Na, Mg, Si, S, Cl, K, and Ca showed relatively higher concentration levels, and their mean concentrations ranged from 100 to 1,000 mg/L (Fig. 4..3), which reflects the general chemical composition of the Earth’s crust [10]. Figure 4.4 shows the statistical skewness and kurtosis of the concentration distribution of each element in the groundwater, which indicates the deviation of the sample population from the normal distribution. The skewness indicates the asymmetry of the distribution; a distribution can have a right-shifted peak (or positive), left-shifted peak (or negative), or no (zero) skewness (normal distribution). Kurtosis indicates the shape (fat or tail) of the distribution. When the kurtosis was less than 3, the peak was gentle, and the tail was short. However, if it was larger than 3, the peak was sharp, and the tail was long. When the statistical kurtosis is 3, it indicates a normal distribution. 1000000 100000 10000 1000 100 10 1 0.1 0.01 Li B Na Mg Si S Cl K Ca V Cr Mn Fe Co Ni Cu Zn As Se Br Rb Sr Mo Cd Sb Ba W Pb U

Fig. 4.3 Distribution of the concentration of each element in groundwater collected from Chiniot area, Punjab, Pakistan in March 2020. The box-and-whisker plots show that the lower quartile, medium, and upper quartile indicated by a box, 10 and 90% percentiles by upper and lower whiskers, and the outliers by cross marks

4 Characterization and Statistical Multivariate Analysis of Potentially … 40.0

41

Skewness Kurtosis

35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 -5.0

As B Ba Br Ca Cd Cl Co Cr Cu Fe K Li Mg Mn Mo Na Ni Pb Rb S Sb Se Si Sr U V W Zn

Fig. 4.4 Skewness and kurtosis of the sample population of each element

A normal distribution of an element concentration in groundwater indicates that a natural long-term equilibrium state has been established, and that irregular contamination from point sources is generally low. Conversely, deviation from the normal distribution implies that some irregular and/or local contamination is involved, which disturbs the long-term equilibrium of element concentration. Considering the statistical skewness and kurtosis diagram (Fig. 4.4), a total of 17 elements (Ba, Br, Ca, Cd, Cr, Li, Mg, Mn, Na, Ni, Pb, Rb, Sb, Se, Si, W, and Zn), have relatively low skewness, close to a normal distribution, and assumed to be caused by a long-term natural equilibrium, with a low disturbance of specific irregular contaminations. However, 12 elements (As, B, Cl, Co, Cu, Fe, K, Mo, S, Sr, U, and V), deviated significantly from the normal distribution with a statistically high skewness indicator. These may have been affected by irregular contamination, such as land use, wastewater, or migration from local geologic systems. Typical examples of the normal probability plot (P-P plot) are shown in Fig. 4.5; the non-linear plot of As concentration shows a deviation from normal distribution (Fig. 4.5a), and the linear plot of Ca concentration shows a normal distribution (Fig. 4.5b). The distribution of concentration generally exhibits positive skewness, but four trace elements (Cd, Ni, Sb, and Si) show negative skewness.

4.4.1 Descriptive Statistics of Trace Elements Table 4.1 summarizes the basic statistics for each element, including the mean, standard deviation, and minimum and maximum concentrations. Among these 29 elements, 17 (As, B, Ba, Br, Cd, Cl, Cr, Cu, Fe, Mn, Ni, Pb, S, Sb, Se, U, and Zn) are PTEs whose concentration level in water is regulated for water quality. This is achieved by using the threshold values of the maximum contaminant level (MCL) [11], the secondary maximum contaminant level (SMCL)

M. Yoshida et al.

1

predicted cumulative probability

predicted cumulative probability

42

0.8 0.6

As

0.4 0.2 0 0 0.2 0.4 0.6 0.8 observed cumulative probability

1

1

0.8 0.6

Ca

0.4 0.2 0

0 0.2 0.4 0.6 0.8 obsedved cumulative probability

1

Fig. 4.5 a Normal Probability (P-P) Plot for the concentration of As in the groundwater, showing a non-linear relationship. b Normal Probability (P-P) Plot for the concentration of Ca in the groundwater showing a generally linear relationship

[12], and the World Health Organization (WHO) drinking water guidelines [13], which indicate the criteria for assessing the environmental and public health risks. Here, the reference value for Br is the value when the chemical form is bromate (BrO3 − ). Among these, six PTEs (As, Br, Cd, Cl, Mn, and S) exceeded the reference values of the MCL, SMCL, and WHO guidelines. Moreover, although dissolved U concentration is below the MCL, its maximum concentration is close to the MCL. These results on PTEs contamination indicate that the groundwater in the Chiniot area is not always suitable for agricultural or drinking water, which poses public health risks for local communities.

4.4.2 Multivariate Analysis As the first step of the multivariate analysis, the correlation between the concentration of elements was examined by creating a correlation matrix (Table 4.2). Strong positive correlations (r > 0.7) were observed for the coupling of B-S, Br-Cl, Br-Na, Cd-Ni, Cd-Sb, and Sb-Ni, and a positive correlation (r > 0.5) was observed for the coupling of As-Cd, B-Cr, B-K, B-Na, B-U, B-Se, Br-Ca, Br-Cd, Br-Cr, BrNi, Br-S, Br-Sb, Ca-Fe, Ca-K, Ca-Ni, Ca-S, Ca-Sb, Ca-U, and Ca-Zn. A negative correlation was usually observed between As and the other elements, while Cu also showed many negative correlations. As a result of the hierarchical cluster analysis, the dataset was divided into four element clusters as follows: Cluster-1: Li, K, Sr, Si, Rb, Cr, and U Cluster-2: B, S, Mg, Pb, Se, W, Mo, and V Cluster-3: Na, As, Ca, Ba, Cl, Br, Ni, Cd, and Sb Cluster-4: Mn, Fe, Co, Zn, and Cu.

10

MCL

Threshold value

Criteria

MCL

2000

1496

10

0.5

93.3

Minimum

220

16.8

Standard deviation

Maximum

160

9.5

Average

PPB

83

PPB

70

Unit

n

W

0.5

Detection limit

B

As

Element

MCL

2000

227.5

18.81

42.03

80.57

83

PPB

0.05

Ba

WHO4

10

540

6

111

120

83

PPB

5

Br

W

331.16

19.88

52.58

89.57

83

PPM

0.05

Ca

MCL

5

5.72

0.05

2.06

3.42

27

PPB

0.05

Cd

SMCL

250

500

1

82

56

78

PPM

1

Cl

1.08

0.02

0.19

0.17

55

PPB

0.02

Co

MCL

100

69.6

0.7

15.6

21.4

83

PPB

0.5

Cr

MCL

1300

364

0.2

56.8

26.9

83

PPB

0.1

Cu

SMCL

300

318

11

45

57

70

PPB

10

Fe

62.26

1.81

10.24

10.17

83

PPM

0.05

K

98.2

2.6

18.5

23.5

83

PPB

0.1

Li

168.06

6.36

27.56

39.09

83

PPM

0.05

Mg

(continued)

SMCL

50

1105.54

0.05

249.60

174.86

83

PPB

0.05

Mn

Table 4.1 Summary of basic statistics of element concentration in groundwater in Chiniot area. MCL: maximum contaminant level [11], SMCL: secondary maximum contaminant level [12], WHO4: WHO Guidelines for drinking-water Quality (revision 4) [13]

4 Characterization and Statistical Multivariate Analysis of Potentially … 43

0.4

39

Minimum

Maximum

70

WHO4

Criteria

8.8

0.2

2.3

2.5

Threshold value

734.16

2.95

144.79

4.7

Standard deviation

58

83

155.87

83

3.8

n

Average

PPB

PPM

PPB

0.2

Unit

Ni

0.05

0.1

Na

Mo

Element

Detection limit

Table 4.1 (continued)

MCL

15

2.6

0.2

0.6

0.9

34

PPB

0.2

Pb

5

0.31

1.05

1.67

83

PPB

0.01

Rb

SMCL

250

392

3

65

68

83

PPM

1

S

MCL

6

1.2

0.05

0.34

0.38

64

PPB

0.05

Sb

MCL

50

18.1

0.5

3.7

4.0

55

PPB

0.5

Se

30,346

2368

7179

13,277

83

PPB

40

Si

7405

167

1286.06

1048.99

83

PPB

0.01

Sr

MCL

30

141.56

0.34

19.96

18.07

83

PPB

0.02

U

11.7

0.2

1.9

1.7

72

PPB

0.2

V

2.05

0.04

0.36

0.41

81

PPB

0.02

W

SMCL

5000

3737.4

0.9

854.8

655.4

82

PPB

0.5

Zn

44 M. Yoshida et al.

4 Characterization and Statistical Multivariate Analysis of Potentially …

45

Table 4.2 Correlation matrix showing correlation coefficients between variables (concentration of elements in groundwater). Black font indicates a positive correlation; red font indicates a negative correlation As B Ba Br Ca Cd Cl Cr Cu Fe K Mn Na Ni Pb S Sb Se U Zn

As B Ba Br Ca 1.000 -0.116 0.487 -0.030 -0.060 1.000 -0.030 0.452 0.406 1.000 -0.036 0.057 1.000 0.597 1.000

Cd Cl Cr Cu Fe K Mn Na Ni Pb S Sb Se U Zn 0.539 -0.021 -0.065 0.061 -0.118 -0.125 -0.091 -0.062 -0.149 -0.157 -0.039 0.002 -0.179 -0.254 -0.057 0.470 0.261 0.558 -0.024 0.266 0.659 -0.068 0.663 0.415 0.439 0.813 0.483 0.531 0.607 0.301 0.364 0.087 0.097 0.382 -0.015 0.032 0.059 -0.034 0.190 0.112 -0.159 0.139 0.126 -0.112 0.112 0.613 0.861 0.517 -0.037 0.037 0.407 0.254 0.823 0.590 0.132 0.589 0.712 0.116 0.267 0.373 0.685 0.403 0.384 0.038 0.556 0.538 0.404 0.466 0.687 0.098 0.707 0.684 0.072 0.614 0.597 1.000 0.396 0.463 -0.244 0.222 0.524 0.122 0.652 0.773 0.304 0.549 0.886 0.301 0.343 0.510 1.000 0.379 -0.016 -0.013 0.260 0.165 0.718 0.368 -0.011 0.319 0.543 0.012 0.142 0.183 1.000 0.072 0.144 0.506 0.136 0.675 0.610 0.215 0.402 0.638 0.332 0.351 0.418 1.000 0.075 0.077 -0.020 -0.080 0.093 0.418 -0.080 0.027 0.189 -0.026 0.134 1.000 0.399 0.087 0.088 0.290 -0.095 0.416 0.218 0.073 0.569 0.381 1.000 -0.058 0.432 0.557 0.231 0.598 0.488 0.244 0.644 0.513 1.000 0.196 0.167 -0.137 0.067 0.163 -0.274 0.070 0.354 1.000 0.542 0.239 0.642 0.711 0.304 0.406 0.294 1.000 0.083 0.472 0.859 0.132 0.338 0.680 1.000 0.302 0.340 0.617 0.134 0.076 1.000 0.573 0.381 0.686 0.363 1.000 0.181 0.376 0.574 1.000 0.429 0.022 1.000 0.319 1.000

The dendrogram of the cluster groups is show in Fig. 4.6. The composition and concentration of dissolved elements in groundwater are generally governed by the following four factors: Geology: Types of substances and minerals that make up the medium (stratum) that contains groundwater and basement rocks Physical/Chemical Properties: Physical and chemical properties and behavior of each substance in groundwater Inputs to the Groundwater Basin: Water quality of natural and anthropogenic water flowing into the groundwater basin Outputs from the Groundwater Basin: The process of water discharge from the groundwater basin and the water balance. 4 3.5

Similarity

3 2.5 2 1.5 1 0.5 0 Li K Sr Si Rb Cr U B S Mg Pb Se W Mo V Na As Ca Ba Cl Br Ni Cd Sb Mn Fe Co Zn Cu

Cluster-1

Cluster-2

Cluster-3

Cluster-4

Fig. 4.6 Dendrogram of the Cluster Analysis (Hierarchical Method) of major and trace elements concentration in the groundwater in Chiniot area

46

M. Yoshida et al.

These four factors; geology, physical/chemical properties of substance, inputs to groundwater basin, and outputs from the groundwater basin, combine to determine the temporal and spatial behavior of each element in the groundwater basin, and the result of cluster analysis indicates that there are four types of combinatorial similarity.

4.5 Discussions 4.5.1 Contamination Sources The sources of Cluster-1 (Li, K, Sr, Si, Rb, Cr, and U) and Cluster-2 (B, S, Mg, Pb, Se, W, Mo, and V) are unknown, but are likely a result of the mixture of irregular natural and man-made contamination, and long-term natural equilibrium in the groundwater basin (Li, Si, Rb, Cr, Mg, Pb, Se, and W), where the outputs and inputs to the groundwater basin are almost balanced. Cluster-3 (Na, As, Ca, Ba, Cl, Br, Ni, Cd, and Sb) consists of trace elements derived from arsenopyrite (FeAsS) in meta-volcanic rocks and halite in the Precambrian basement [1], in which the geological factor plays a key role. Cluster-4 (Mn, Fe, Co, Zn, and Cu) consists of base metals, derived from mineralization zones in the Precambrian basement, such as iron deposits and associated rocks [14]. One of the major origins of Mn is cryptomelane (K(Mn4+ , Mn2+ )8 O16 ) in Precambrian meta-sedimentary rocks [1], where local geology and inputs to the basin are important factors. Rapidly declining groundwater levels due to overexploitation induce oxidizing condition in the subsurface layers that promotes the formation of soluble complexes of U [15] and As [16], which can mobilize U and As and pollute the groundwater bodies. The Cl/Br ratio is known to be a useful indicator for assessing possible artificial influences on shallow groundwater in the range of 400–1,100 [17]. In this study, the Cl/Br ratio ranged from 90 to 1,022, while the average Cl/Br ration is 336. This suggests that some of the groundwater with a Cl/Br ratio above 400 has been contaminated, possibly by septic tanks, as these are commonly used in the study area.

4.5.2 Environmental Risks The environmental and public health risks of PTEs above the threshold values of the MCL, SMCL, and/or WHO4 are summarized below. Arsenic (As): 14.5% of the groundwater samples showed As concentrations above the MCL value (10.0 ppb). It is a widely known, persistent contaminant in groundwater in peri-Indian subcontinent countries, such as Bangladesh, Nepal, India, and Pakistan. Due to overexploitation of groundwater resources, groundwater levels

4 Characterization and Statistical Multivariate Analysis of Potentially …

47

decline and induce oxidizing condition that promotes the mobilization of As. Health problems have been reported to occur after 10–15 years of chronic exposure to elevated As levels of approximately 50 ppb [18]. As is carcinogenic, even at low levels. Bromine (Br): The total Br concentration ranged from 6 to 540 ppb. The WHO guideline value is 10 ppb when the chemical form of Br is bromate (BrO3 − ), an oxyanion of bromine. In the ICP-MS analysis, only total Br was analyzed, and not the chemical form. Bromine can be easily produced in different ways in municipal drinking water, but the most common is the reaction of ozone and bromide, as follows: Br− + O3 → BrO− 3 Therefore, any ozone treatment is risky for water disinfection of groundwater in present area. Cadmium (Cd): The total Cd concentration ranged from 5.72 to 0.05 ppb, where the maximum concentration is slightly above the MCL. The Cd contamination was also reported near the Kirana Hills by Iram et al. [19]. Cadmium (Cd) is one of the most toxic and mobile elements in the environment [20]. It can replace Ca in minerals due to its similar ionic radius and similar chemical behavior, therefore, Cd can enter the human body and accumulate to a high level in bone and several organs. Chronic Cd poisoning, termed itai-itai disease first discovered in Japan, causes osteomalacia, osteoporosis and renal tubular dysfunction [21]. Chlorine (Cl): The total chlorine concentration ranged from 1 to 500 mg/L, which is above the WHO guideline value, and slightly above the SMCL. The high-TDS groundwater is highly contaminated by Cl, as reported by Ahmad et al. [3]. Most of the salt in the soil and groundwater is inherent in the area, which is brought in by rivers [22] or the migration of subsurface rock salts from within the Precambrian basement. Manganese (Mn): The Mn concentration in the groundwater varied between 0.05 and 1105 ppb. Mn is an essential cofactor for antioxidant enzymes in humans, but it is also toxic when ingested or inhaled in large amounts over time [23–25]. Sulfur (S): The concentration of sulfur varied from 3 to 392 ppm. High-TDS groundwater (such as the study area) often shows a high concentration of sulfur [3]. Contamination is possibly caused by various irregular point sources from the land surface such as solid waste, wastewater, and septic tanks. Uranium (U): It is noted that the concentration of U is below the threshold value but shows a value close to the MCL. At high concentrations, U is toxic to human and ecological health due to its radioactivity and chemical toxicity [26, 27]. Recently, U contamination cases were reported in India, where declining groundwater levels due to overexploitation induce oxidizing condition in subsurface layers that generates soluble uranyl carbonate complexes [14], which can mobilize U and widely contaminate the water bodies.

48

M. Yoshida et al.

4.5.3 Hydrogeological Interpretation The water quality of groundwater in the Chiniot area, especially the trace elements, is determined by a combination of four factors: (i) water–rock interaction with the exposure of thrusting basement rock blocks; (ii) subsurface basement rocks intercalating rock salt and meta-sedimentary and metavolcanic rocks; (iii) water recharging and sediment supplies by infiltration and flooding of the Indus River and its tributaries; and (iv) inputs of wastewater and waste leachate from agricultural, industrial, and municipal activities. Figure 4.7 illustrates the effects of these factors, where they reach an equilibrium state, but are still partly disturbed, and are influenced by local hydrogeological differences. Factor (i): I oxidation of arsenic-bearing minerals, arsenopyrite (FeAsS), causes arsenic contamination of groundwater [16, 28] as follows: FeAsS(s) + 1.5H2 O + 2.75O2 (aq) → Fe2+ (aq) + H2 AsO3 (aq) + SO2− 4 (aq) + 2+ 8Fe(As, S)2 (s) + 13NO− 3 (aq) + 25H2 O + 10H (aq) → 8Fe (aq)+ 2− + 8HAsO2− 4 (aq) + 8SO4 (aq) + 13NH4 (aq)

where (s) is the solid phase and (aq) is the aquatic phase. The dissolution products were further oxidized from As(III) to As(V). The presence of arsenopyrite in groundwater aquifers affects the groundwater arsenic levels.

Fig. 4.7 Schematic geohydrological diagram of groundwater basin in Chiniot area, Punjab Plain in Pakistan

4 Characterization and Statistical Multivariate Analysis of Potentially …

49

Mn contamination in the groundwater is attributed to cryptomelane (K(Mn4+ , Mn2+ )8 O16 ), which is present in the basement rocks [4]. Most Mn is subjected to flooding and is easily bound to less crystalline phases and therefore easily mobilized into the groundwater [29]. Factor (ii): The leaching of Cl and Br from the underlying basement rocks, including rock salt, is typical. Groundwater pollution is accelerated by the drop in water levels due to overexploitation. Factor (iii): The groundwater sources are the Quaternary sediment layers that make up the aquifer, and the supply through infiltrating water from rivers and rainwater. Long-term geological processes have almost reached equilibrium for 17 elements (Ba, Br, Ca, Cd, Cr, Li, Mg, Mn, Na, Ni, Pb, Rb, Sb, Se, Si, W, and Zn). Factor (iv): The groundwater quality has deteriorated due to nitrate pollution from the intensive use of fertilizers in agriculture, excretion of livestock manure, release of untreated urban sewage and industrial wastewater, and atmospheric deposition. Groundwater is also increasingly polluted by sulfate owing to the release of domestic, municipal, and industrial wastewaters in the area. Human activities cause nitrogen, nitrate, sulfur, sulfide, and PTEs contamination [30]. This demonstrates the need to control human activities and to establish sustainable community practices and agriculture practices in the area.

4.6 Conclusions Simultaneous elemental analysis using ICP-MS is an effective method for evaluating pollution, through wide-area surveys of groundwater basins. The concentration of PTEs can be used to evaluate the suitability of the groundwater quality for drinking and agricultural water. However, it is also possible to estimate the cause and mechanism of pollution by statistically analyzing the concentrations of major elements and trace elements dissolved in the groundwater, and by performing a multivariate analysis. ICP-MS analysis allows rapid analysis of large numbers of groundwater samples, but it only analyzes elemental composition and cannot analyze chemical forms. In addition, there is a limitation in that it is not possible to analyze the combined effects of organic matters. Therefore, applying other water analysis methods to the same sample enables more advanced evaluation of the water quality. The groundwater samples used in this study were all collected from local community wells and are considered shallow groundwater, but not necessarily from a single aquifer. In this sense, detailed hydrogeological research is a future challenge. With rapid population growth and progress in development, Pakistan is heavily dependent on groundwater resources for advancement of the nation and country. However, the quality of the groundwater has not been fully investigated, and the region is facing public health problems, such as the diseases caused by the ingestion of PTEs-contaminated water, and water-borne diseases. In this study, groundwater was sampled from wells which are currently under use by local communities in the

50

M. Yoshida et al.

Chiniot area. Some of these wells are unsuitable for potable (drinking) water, as well as for the continued use as an agricultural water supply. Both natural and manmade contamination sources are likely, and it is necessary to reconsider the use and development of groundwater resources in the area. Acknowledgements Present study was carried out as a part of the Project entitled “Empowering the Local Community to Protect Groundwater for Health through a Participatory Approach in District Chiniot, Pakistan”, which was financially supported by the Japan Fund for Global Environment (JFGE) of Environmental Restoration and Conservation Agency of Japan (ERCA), Nasir Foundation, Pakistan, and International Network for Environmental and Humanitarian Cooperation (iNehc), Nonprofit Inc., Tokyo. The authors express special thanks to anonymous reviewer for its valuable comments to the paper manuscript. Conflicts of Interest The authors declare no conflicts of interest associated with this paper.

References 1. Shah SMI (1977) Stratigraphy of Pakistan. Geol Surv Pak Mem. 12:1–138 2. Khan ZK, Ahsan N, Mateen A, Chaudry MN (2009) Petrography and mineralogy of dolerites of Hachi Volcanics, Kirana Hills area, Pakistan. Geol Bull Punjab Univ 44:55–67 3. Ahmad MN, Sultana R, Salahuddin M, Ahmad JS (2016) Assessment of groundwater resources in Kirana Hills Region, Rabwah, District Chiniot, Pakistan. Int J Econ Environ Geol 7(2):54–58 4. Sultana R, Salahuddin M, Ahmad MN (2018) Economic impact assessment of Brackish groundwater in Kirana Hills Region, District Chiniot, Pakistan. Int J Econ Environ Geol 9(3):19–24 5. Ahmad MN, Sultana R, Uddin MS, Syed NA, Parvaiz RA, Ahmad M, Ahmad T (2022) Water quality mapping of district Chiniot, Pakistan by using GIS. Fuuast J Biol 12(1):1–8 6. Ayers RS, Westcot DW (1985) Water quality for agriculture. FAO irrigation and drainage paper, 29 Rev 1, ISBN 92-5-102263-1, Food and Agriculture Organization of the United Nations (FAO), Rome 7. Aboukarima AM, Al-Sulaiman MA, El Marazky MS (2018) Effect of sodium adsorption ratio and electric conductivity of the applied water on infiltration in a sandy-loam soil. Water SA 44(1):105–110 8. Yoshida M, Ahmad MN (2018) Trace element contamination of groundwater around Kirana Hills, District Chiniot, Punjab. Pakistan Int J Econ Environ Geol 9(4):12–19 9. Hoaglin DC, Mosteller F, Tukey JW (2000) Understanding robust and exploratory data analysis. Wiley-Interscience 10. Taylor SR (1964) Abundance of chemical elements in the continental crust: a new table. Geochim Cosmochim Acta 28(8):1273–1285 11. United States Environmental Protection Agency (USEPA) Homepage: national primary drinking water regulations. https://www.epa.gov/ground-water-and-drinking-water/nationalprimary-drinking-water-regulations#Inorganic 12. USEPA Homepage: Secondary Drinking Water Standards: Guidance for Nuisance Chemicals https://www.epa.gov/sdwa/secondary-drinking-water-standards-guidance-nuisance-che micals. Accessed 15 Aug 2022 13. World Health Organization (WHO) (2022) Guidelines for drinking-water quality, 4th edn. World Health Organization, Geneva 14. Akram MS, Mirza K, Ali U, Zeeshan M (2019) Geotechnical and hydrological characterization of subsurface for metallic minerals mining operations in Punjab, Pakistan. Open J Geol 9:752– 767

4 Characterization and Statistical Multivariate Analysis of Potentially …

51

15. Coyte RM, Jain RC, Srivastava SK, Sharma KC, Khalil A, Ma L, Vengosh A (2018) Largescale uranium contamination of groundwater resources in India. Environ Sci Technol Lett 5(6):341–347 16. Huq ME, Fahad S, Shao Z, Sarven MS, Khan IA, Alam M, Saeed M, Ullah H, Adnan M, Saud S, Cheng Q, Ali S, Wahid F, Zamin M, Raza MA, Saeed B, Riaz M, Khan WU (2020) Arsenic in a groundwater environment in Bangladesh: occurrence and mobilization. J Environ Manage 15(262):110318 17. Katz BG, Ebertsb SM, Kauffman LJ (2011) Using Cl/Br ratios and other indicators to assess potential impacts on groundwater quality from septic systems: a review and examples from principal aquifers in the United States. J Hydrol 397(3–4):151–166 18. Mandal BK, Suzuki KT (2002) Arsenic round the world. Talanta Review 58:201–235 19. Iram S, Sultana R, Salahuddin M, Ahmad MN, Shamrose Z (2018) Heavy Metal Concentration in Groundwater of Kirana Hill Region, Rabwah, District Chiniot, Pakistan. Int J Econ Environ Geol 9(1):21–26 20. Kubier A, Wilkin RT, Pichler T (2019) Cadmium in soils and groundwater: a review. Appl Geochem 108:1–16 21. Aoshima K (2016) Itai-itai disease: renal tubular osteomalacia induced by environmental exposure to cadm–um—historical review and perspectives. Soil Sci Plant Nutr 62:319–326 22. Qureshi AS, McCornick PG, Qadir M, Aslam Z (2008) Managing salinity and waterlogging in the Indus Basin of Pakistan. Agricul Water Manage 95:1–10 23. Ljung K, Vahter M (2007) Time to re-evaluate the guideline value for manganese in drinking water? Environ Health Perspect 115(11):1533–1538 24. Woolf A, Wright R, Amarasiriwardena C, Bellinger D (2002) A Child with chronic manganese exposure from drinking water. Environ Health Perspect 110(6):1–4 25. Khan K, Wasserman GA, Liu X, Ahmed E, Parvez F, Slavkovich V, Levy D, Mey J, van Geen A, Graziano JH, Factor-Litvak P (2012) Manganese exposure from drinking water and children’s academic achievement. Neurotoxicology 33(1):91–97 26. Gandhi P, Sampath PV, Maliyekkal SM (2022) A critical review of uranium contamination in groundwater: treatment and sludge disposal. Sci Total Environ 825:153947 27. Balaram V, Rani A, Rathore DPS (2022) Uranium in groundwater in parts of India and world: a comprehensive review of sources, impact to the environment and human health, analytical techniques, and mitigation technologies. Geosys Geoenviron 1:100043 28. Neil CW, Yang YJ, Schupp D, Jun Y-S (2014) Water chemistry impacts on arsenic mobilization from Arsenopyrite dissolution and secondary mineral precipitation: implications for managed aquifer recharge. Environ Sci Technol 48:4395–4405 29. Sracek O, Kˇríbek B, Mihaljeviˇc M, Ettler V, Vanˇek A, Penížek V, Veselovský F, Bagai Z, Kapusta J, Sulovský P (2021) Mobility of Mn and other trace elements in Mn-rich mine tailings and adjacent creek at Kanye, southeast Botswana. J Geochem Explor 220:106658 30. Torres-Martínez JA, Mora A, Knappett PSK, Ornelas-Soto N, Mahlknecht J (2020) Tracking nitrate and sulfate sources in groundwater of an urbanized valley using a multi-tracer approach combined with a Bayesian isotope mixing model. Water Res 182:115962

Chapter 5

A Systematic Literature Review on Rainwater Quality Influenced by Atmospheric Conditions with a Focus on Bangladesh Md. Arif Hossen , M. Salauddin , and Mohammad A. H. Badsha

Abstract Rainwater quality is often influenced by atmospheric conditions, roofing materials, meteorological parameters, and their interactions. Data and knowledge on rainwater quality are crucial for the sustainable management of water resources and safeguarding public health. Notwithstanding, while several studies investigated the potential application of rainwater harvesting, detailed investigations on rainwater quality are still limited in Bangladesh. This systematic literature review examines the source apportionment of physicochemical parameters and trace elements in pure rainwater, with a detailed focus on Bangladesh. For the reviewed literature, Mn, Fe, Cu, and Zn were primary heavy metals in rainwater, with their concentrations accounting for around 90% of the total. When examining the association among physicochemical parameters and trace metals, the reviewed works showed that nitrate, sulphate, and acidity of the rainwater samples showed a strong positive correlation with most trace metals, while NH4 + and Cl– mostly showed negative correlations with the metals. The results of this review study highlighted that further research on the influence of atmospheric conditions on rainwater quality, the presence of heavy metals in rainwater and the relationship between air quality and rainwater composition are still needed to provide a better assessment of the suitability of rainwater as a potable water source for the studied area. Keywords Rainwater · Atmospheric conditions · Physicochemical parameters · Heavy metals Md. A. Hossen Center for Environmental Science and Engineering Research (CESER), Chittagong University of Engineering and Technology (CUET), Chattogram, Bangladesh M. Salauddin (B) UCD Dooge Centre for Water Resources Research, UCD School of Civil Engineering, and, UCD Earth Institute, University College Dublin, Dublin, Ireland e-mail: [email protected] M. A. H. Badsha Department of Civil and Environmental Engineering, California Polytechnic State University (Cal Poly), San Luis Obispo, CA, USA © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_5

53

54

Md. A. Hossen et al.

5.1 Introduction Water scarcity is one of several important issues facing the world today. Recent studies have highlighted the significant economic, social, and environmental benefits of harvesting rainwater as an alternative water resource in regions struggling with potable water supply [1–3]. While rainwater is commonly considered a pure potable water source compared to surface and groundwater, its quality can be significantly influenced by some external sources, such as microbiological pathogens or chemical contaminants [4–6]. The cleanliness of various components of the rainwater harvesting (RWH) system and atmospheric conditions have potential impacts on harvested rainwater quality [7–9]. Harvested rainwater quality can be improved if the components of the rainwater harvesting system (Fig. 5.1), including catchments, gutters, pipe networks, and storage tanks, can be cleaned regularly, which are usually fabricated from non-toxic materials [9–11]. Additionally, atmospheric conditions can greatly influence the chemical composition of harvested rainwater. Rainwater acts as a scavenger of atmospheric pollutants. The chemical composition of rainwater has significantly changed due to the increased atmospheric pollutants [12–14]. Bangladesh has a humid subtropical climate, and frequently experiences heavy rainfall and tropical cyclones [15, 16]. The rainfall pattern of Bangladesh varies in

Fig. 5.1 Various components of a typical RWH system

5 A Systematic Literature Review on Rainwater Quality Influenced …

55

different seasons and locations. With the Himalayan mountains in the north and the Bay of Bengal in the south, Bangladesh’s unique geographical location has generated a wide range of rainfall patterns [17]. In the west-central region of Bangladesh, yearly rainfall averages 1500 mm, whereas, in the northeast and southeast part, it exceeds 3000 mm (Fig. 5.2). Winter, which lasts from November to February, is extremely dry, accounting for only around 4% of yearly rainfall. The westerly disturbances that approach the country from the northwestern region of India cause rain in this season, which varies from 20 mm in the west and south to 40 mm in the northeast part. The summer season (March–May) contributes approximately 10 to 25% of the total yearly rainfall. The rain during this season is produced by convective storms (locally called Kalbaishakhi). This season’s average rainfall ranges from 200 mm in the west-central portion of the country to 800 mm in the northeast. The rainy season, which lasts from June to October, accounts for more than 75% of the country’s yearly rainfall, ranging from 70% in the east to roughly 80% in the southwest and 85% in the northwest [18]. Rainfall varies from 1000 mm in the west-central region of the country to over 2000 mm in the south and northeast throughout this period. Weak tropical depressions are introduced into Bangladesh by the wet monsoon winds from the Bay of Bengal, causing rain during this period. Bangladesh receives plenty of rainfall (2,400 mm/year), whereas there is an arsenic and salinity problem in groundwater [19, 20], and the surface water quality is also gradually decreasing day by day [21]. Several studies have identified rainwater harvesting (RWH) as the most sustainable solution for Bangladesh’s urban water management system [22–24]. Potential site selection is critical for RWH adoption as an alternative water source, but the identification of a suitable site that meets technical, economic, and environmental requirements [25–27]. Rainwater has historically been used for drinking, domestic, and agricultural purposes in different places of the world [11, 28]. However, rainwater harvesting is not a prevalent practice in Bangladesh. Approximately 35% of households in coastal areas with substantial salinity problems use rainwater for drinking and domestic purposes during the rainy seasons [29, 30],

Fig. 5.2 Rainfall intensity in different seasons over Bangladesh (Rainfall periods: 1980–2021, Data source: Bangladesh Meteorological Department)

56

Md. A. Hossen et al.

highlighting the significance of rainwater in ensuring water supply to the people in these areas. Prior to using rainwater for any purpose (domestic, agricultural, drinking, etc.), assessment of the quality of pure rainwater should be given the utmost importance to ensure safeguarding of the public health. Pollutants deposited from the atmosphere by washout and particle fall from road traffic, construction activities, sea spray, industrial activities, and long transport from other regions can pollute pure rainfall [31–33]. Contaminants in the atmosphere of cities, and hence in rainfall, can have a wide range of regional variability [11, 34]. The quality of rainwater also depends on climatic parameters (such as wind speed, temperature, and humidity), particle properties (e.g., size and shape), which determine how far these particles can travel, and antecedent dry days because the settled particles are deposited within those days [35, 36]. Suitable site selection considering the available rainfall and atmospheric conditions is predominant for making rainwater a sustainable alternative source of water. It is, therefore, the chemical composition of rainwater and the factors that affect the composition of pure rainwater need to be investigated for sustainable and effective water management. However, to the best of the authors’ knowledge, no prior review study focused on the present state of knowledge on rainwater quality in Bangladesh. Furthermore, no global review study has been conducted on the impact of atmospheric conditions on pure rainwater quality. Thus, a clear research gap in the literature exists that would contribute to our understanding of the composition of physicochemical and trace metals in rainwater. This research gap has been attributed to this systematic literature review study. Herein, we confine the focus of this review to rainwater-related studies predominantly considered wet-only rainwater samples. Through performing a systematic review, we investigated “how different atmospheric conditions influence the rainwater quality” with a detailed focus on Bangladesh. In Sect. 5.2, the literature search method adopted in this study is explained. This is followed by a review of the sources and types of contaminants in rainwater in Sect. 5.3. The physicochemical and trace metals contamination of rainwater is reviewed in Sects. 5.4 and 5.5, respectively. Further, Sect. 5.6 reviews the correlation between the trace metal contents and general water quality parameters. The key research findings and suggestions for future research with a view to developing an effective rainwater management system are discussed in the final section of this paper.

5.2 Methods In this study, the review was designed and executed around search terms that would inevitably provide an overview of existing knowledge on physicochemical parameters and trace elements in rainwater. We conducted a systematic review using mainly two databases, i.e., Web of Science and Scopus. The search terms used for conducting this literature review were ‘rainwater’, ‘rainwater harvesting’, ‘rainwater quality’, ‘rainwater treatment’, ‘rainwater chemical composition’, ‘rainwater composition’,

5 A Systematic Literature Review on Rainwater Quality Influenced …

57

‘rainwater catchment’, and ‘rainwater quality in Bangladesh’. Based on the initial literature search, a total of 614 articles were subsequently identified, see Fig. 5.3. As shown in Fig. 5.3, the first criterion for inclusion was having common words such as ‘rainwater’ or ‘rainwater quality’ anywhere in the abstract, highlights, keywords, or methodology sections of the article. We considered only peer-reviewed published research papers for this study, as highlighted in criterion 2 in Fig. 5.3. The third criterion was that the search result had to focus on the water quality of harvested rainwater.

Fig. 5.3 Flowchart of systematic literature review

58

Md. A. Hossen et al.

Following an initial review of the title and abstract, further scrutiny was conducted a subsequent full-text review of the 154 articles using the search criterion demonstrated in Fig. 5.3. For instance, the search result had to consider only wet rainwater samples to be included within this work. In most of the studies, pH and electrical conductivity (EC) were measured on the spot, while major ions were determined by ionic chromatography. The trace metals were analyzed by ICP-MS and AAS. However, where multiple works investigated the rainwater quality for the same catchment, only the latest articles were included in this work. Finally, only peer-reviewed articles published between January 2000 and December 2021 were considered. These assessments yielded 35 search results which were critically examined within this study. In Fig. 5.4, the number of publications reviewed from specific countries is presented. As it is evident from Fig. 5.4, research was performed for these considered publications in 16 countries. The most significant number of investigations were conducted in the Republic of China (6 studies), followed by India (5 studies). Three investigations were carried out in Jordan, Spain, and Mexico. Overall, Africa, Europe, and North America were relatively poorly studied (see Fig. 5.4).

Fig. 5.4 Number of publications reviewed from specific countries: a projected in the world map and b projected in a bar chart

5 A Systematic Literature Review on Rainwater Quality Influenced …

59

5.3 Sources and Types of Contaminants The quality of rainwater is mostly affected in three different phases (Fig. 5.5). In the first phase, rainfall cleans up the urban atmosphere and scavenges aerosols, gases, and thin volatile particles. The catchment is the second phase when contamination occurs due to the wash-off of particles accumulated on the roof’s surface and scavenging of roofing materials. The first-flush, storage unit, and piping network are all included in the final phase. Different processes occur at each phase, adding various pollutants to the pure rainwater. Wet deposition (rainwater) acts as a sink for pollutants in the atmosphere. The removal of atmospheric contaminants during the entire precipitation process is dependent on two processes: in-cloud and below-cloud procedures [39]. Collisions between particles below the cloud base are vital methods for incorporating ambient particles into raindrops. The efficacy of the collision is determined by particle size distributions and raindrop size distributions [40]. Consequently, the percentage of contaminants exposed by rain varies, depending on rainfall characteristics (e.g., rainfall amount, rainfall duration, rainfall intensity, and raindrop size distribution). More importantly, as aforementioned, raindrop size distribution is crucial because it aids in capturing contaminants.

Fig. 5.5 Pathway of contaminants in rainwater quality (adopted from Martinson and Thomas [37]; Abbasi and Abbasi [38])

60

Md. A. Hossen et al.

The purity or impurity of raindrops varies by location since substances present in a specific region are picked up by raindrops over there [11]. The atmosphere, and thus the composition of raindrops, is affected by both natural and anthropogenic activities. Raindrops may collect everything from dust to sea spray, smoke, urban haze pollution, and even pathogens [41]. Rainwater is filled with increased pollutants in intensively industrialized areas, with a current or recent volcano eruption or high traffic density.

5.4 Physicochemical Parameters Significant variations in the physicochemical composition of rainwater are observed depending on the different atmospheric conditions (Table 5.1). The average concentration of all physicochemical parameters is found within the allowable limit of Bangladesh drinking water quality standards and WHO guidelines. pH is an essential physicochemical parameter to characterize water quality. The phenomenon of acid rain, which can lead to low pH levels in places dominated by vehicular emissions, industrial emissions, and other anthropogenic activities, is one of the most significant issues with the quality of pure rainwater. Except for one study conducted in an industrial coastal site in Southwestern Iran, every study in Table 5.1 reported an average pH value below 7.0. Sea has such a strong influence on coastal rainwater quality. Large ions originating from the sea salt aerosol can contribute significantly to the total ions and trace metal concentration, hence governing pH value [42]. Charlson and Rodhe [43] uncovered the factors that limit the acidity of natural rainwater, noting that rainwater acidification is predominantly controlled by anthropogenic activity. In all of the research analyzed, the average electrical EC is observed to be between 8.4 and 194 µS/cm. Elevated concentration of chloride is recorded in pure rainwater of oilproducing sites (740.8 µeq/L), industrial coastal sites (516.6 µeq/L), and typhooninduced urban sampling sites (186.6 µeq/L). The lowest concentration of chloride (4.98 µeq/L) is found in rural areas of Pahang, Malaysia. Elevated concentration of sulfate and nitrate ions in rainwater indicates vehicular, industrial, marine, and coal combustion activities adjacent to the sampling locations [32, 44]. The maximum concentration of SO4 2– and NO3 – is recorded in rainwater collected from the vicinity of oil-producing sites, industrial coastal sites, and urban areas where coal is used extensively for heating. In urban areas where coal is widely used for heating, the highest average concentration of NH4 + (229.8 µeq/L) is recorded. Ammonium, primarily associated with agricultural and biomass burning, reacts with sulfate and nitrate in the atmosphere to generate (NH4 )2 SO4 and NH4 NO3 , respectively [45]. Like other physicochemical parameters, elevated concentrations of cations (Na+ , K+ , Ca2+ , and Mg2+ ) are recorded in rainwater of oil-producing sampling sites and industrial coastal sites. Land-use patterns, coastal air masses, and other natural phenomena are all linked to the predecessors of Na+ and K+ in the atmosphere [46].

4.67–5.31

3.72–6.09

6.45

6.78

6.93

4.96

New Delhi, India

Tirupati, India

Northern India

Shanghai, China

5.6

Tainan, Taiwan

Gampaha, Sri 6.54 Lanka

4.49

Guangzhou, China

4.38–8.69

4.9–6.1

3.52–6.28

3.61–6.89

5.14–7.45

6.64

4.56

Xi’an, China

Shenzhen, China

4.99–6.80

4.38

6.08

Ya’an, China

Lijiang, China

6.85–7.21

6.13–7.74

4.04–8.27

47.7

–

–

–

82.8

14.2

–

–

22.3

–

167

–

16.2

4.51–7.68

4.9–6.5

5.66

5.5

Tezpur, India

Avg.

Avg. Range

4–241.2

–

–

–

21.9–142.8

9.01–28

–

–

19-27

–

3-687

–

12.6-158.5

Range

Conductivity

pH

Central Himalaya, India

Study location

2.7–1600.3

37.4–82.7

20.4–252.7

Range

24–56.5

2.52–106.5

9.6–0.1

54.8

65

53.4

21.9

129

3.63

71.4

39.9

33.4

42.3

112

10

79.9

BDL-148.75

20.3–183.9

26.3–102.3

3.9–66.9

59.6–214.5

0.65–7.58

12.1–275.2

30.06–64.15

9.21–55.33

18.47–81.9

0.1–1510.6

0.69–109.4

13.9–215.5

Avg. Range

NO3 -

BDL-329.03 18.8

186.8 17.2–419.7

86.8

20.6

38.7

11.56 0.85–32.7

20.89 14.1–52.7

21.75 17.31–27.11

59.38 42.31–118.5

34.45 20.67–62.01

92

70.5

72.2

Avg.

Cl-

Table 5.1 Global scenario of the physicochemical composition of pure rainwater

2.4–2182

2.74–105.4

18.8–210.4

Range

252

10

83.3

Avg.

NH4 +

54.36

128.4

163.3

64.7

489.7

32.6

138.4

72.35

22.59

–

BDL-255.18

7.9–353.9

39.2–223.5

8.3–147

145.7–939.1

16.7–79.8

31.2–344.6

23.5

–

70.6

33.5

230

11.4

170

55.36–116.59 66.7

10.41–33.31

135.12 50.07–262.35 21.1

214

15.9

60.8

Avg.

SO4 2-

(continued)

BDL-217.12

–

6.8–164.3

6.8–136.3

53.2–331.4

1.11–55

25.6-436.7

53.07-104.35

–

9.78-38.48

16.4-1205

0.06-62.8

31.4-200.2

Range

5 A Systematic Literature Review on Rainwater Quality Influenced … 61

3.4–5.8

3.89–8.00

4.31–7.42

4.98–5.15

5.5

6.4

5.08

5.62

4.51

6.74

Oleiros, Spain

Granada, Spain

Mexico City, Mexico

Limeira, Brazil

Amazonia, Brazil

Carpathians, Romania

Iasi, Romania 5.92

Southern Nigeria

5.06

4.99–6.90

6.62

Eshidiya, Jordan

4.11–6.92

5.8–7.0

4.69–6.57

5.33–7.90

4.81–8.24

6.65

Ghore El-Safi, Jordan

5.35–6.65

6.42–7.70

6

70.2

–

–

8.4

16.6

–

16.9

37.1

194

95

–

–

Avg.

Avg. Range

65.9-169.3

–

–

2.9-21.8

2.52-38.52

–

5.0-84.1

10.1-148

37-530

25.1-327.4

–

–

Range

Conductivity

pH

Southwestern 7.17 Iran

Pahang, Malaysia

Study location

Table 5.1 (continued)

1.08–11.1

Range

22.3–257.3

1.4–113.1

1.13–21.72

2.25–136.6

5.1–209.1

1.82–299.9

740.8 704–770.5

37.4

44.85 0.03–518.3

14.2

7.06

9.56

31.4

180.6 55.7–715

121.5 12.5–281.2

142

516.6 150–3000

4.98

Avg.

Cl-

218

48.5

30.7

11.8

14.7

42.6

26.5

31.5

63.7

67.3

70

6.5

169-258

3.0–261.6

0.16–782.2

BDL-76.8

2.1–71.16

5.71–202.8

5.7–128.5

11.8–111

1.3–167.2

2.7–103.6

30.08–164.2

1.39–16.6

Avg. Range

NO3 -

624.3

75.5

88.54

6.47

15.62

61.9

37.6

72.5

121.5

112

1211

8.06

Avg.

SO4 2-

582–707.6

3.2–106.4

0.02–3668

BDL-42.45

0.02–48.3

10.6–311.8

8.1–117.3

27.1–162

16.3–292.3

14.2–267.8

30–2800

1.51–19.6

Range

–

52.3

68.6

–

34.4

92.3

23.4

32.5

43

75.4

35.4

8.72

Avg.

NH4 +

– (continued)

1.27–319

0.78–3032

–

0.12–121.67

28.6-413.6

5.0–82.7

0.55–412

1.2–394.3

2.68–127.2

7.74–64.68

4.01–12.16

Range

62 Md. A. Hossen et al.

2.73-5.02

4.6-27.6

–

33

38.1

14.7

3.73

11.4

–

New Delhi, India

Tirupati, India

Northern India

Shanghai, China

Ya’an, China

Lijiang, China

6.4-34.53

14.92-73.78

0.2-664.4

15.4-61.5

39.6

Central Himalaya, India

2.54

16.5

16.9

38.5

36.39

54

51.9

47

Avg.

16.8-76.5

Avg. Range

22.5

Na+

K+

Study location

Tezpur, India

400

6.5–8.5

WHO guideline

–

6.5–8.5

Avg.

Avg. Range

0–11.7

7.8–45.8

13.65–22.03

7.61–78.3

15.27–74.7

0.4–560.4

22.6–65.2

18.4–56.6

Range

Range

Conductivity

pH

Bangladesh standards

Study location

Table 5.1 (continued)

44.9–166.8

Range

Range

10.5–4546

50.2

37.9 14–218.5

11.1–142.1

38.08 28.16–59.92

145.1 123–159.8

156.5 57.2–362

328

123.2 81.5–179.5

92.6

Avg.

Ca2+

7050

16900

Avg.

Cl-

7.7

8.53

9.15

23.3

60.9

32

41.6

18.5

Range

1.67–34.2

3.66–18.3

7.05–11.76

18.27–29.21

–

–

Avg.

NH4 +

[48]

[47]

References

Range

There is very little transport of pollutants from the adjacent industrialized cites.

Urban area

Urban area

Tropical urban area

(continued)

[54]

[53]

[52]

[51]

[50]

Sampling site is expected to the [49] influence of local air pollution sources.

The study site is devoid of any major pollution sources in its surrounding areas

Rural area

Site description

5200

8325

Avg.

SO4 2-

18.49–157.47 Urban area

0.1–604.8

0.26–125.2

10.1–36.9

Avg. Range

Mg2+

800

160

Avg. Range

NO3 -

5 A Systematic Literature Review on Rainwater Quality Influenced … 63

0.77–32.2

129.6

55

5.78–176.3

85.2

51.1

15.1

Ghore El-Safi, Jordan

Eshidiya, Jordan

Oleiros, Spain

1.02–99.5

0.8–161.2

10.0–30

Southwestern 15.4 Iran

10.5

188

85.1

130

1017

14.4

BDL-37.8

9.2

Tainan, Taiwan

1.1–57.3

11.2

Pahang, Malaysia

32.9

Guangzhou, China

0.7–21.6

31.1

BDL-106.46 221.2

1.75

Shenzhen, China

8.4-23

Gampaha, Sri 15.5 Lanka

13.8

Avg.

Avg. Range

Range

7.1–113.8

98.8

4.9–485.8

103.6 11.3–287.3

35.4

425.6 162.2–867.9

Avg.

Cl-

43.6

17

3.26

36.6

4.34–895

5.2–194.5

6.2–234.3

50–2660

9.4–16.7

31.3–285.3

100–1500

27.1–90.7

121.7 24.5–445

192.1 23.4–945.6

165

675

50.3

53.7

134

93

504

3.81

14.8–421

1.2–452.5

23.7-289.2

50–1700

BDL-3.86

BDL-137.07

2.5–148.9

2.1–36.1

0.6–35.6

13–66.6

Avg. Range

NO3 -

BDL-725.25 91.06 BDL-453.96 26

10.9–473.9

2.1–87.3

1.8–51.7

17.8–47.08

Range

Conductivity

pH

Xi’an, China

Study location

Table 5.1 (continued) Range

Avg.

NH4 +

Residential area immediate vicinity to agricultural lands, forests and the sea

Mine area

Study area can be classified as mixed between rural/industrial and agricultural surrounding

Industrial coastal site

Rural area

Industrialized and urbanized area

Rainwater samples collected from urban areas during four typhoon episodes

Sampling sites are in Urban area

There were no obvious pollution sources nearby sampling sites.

Urban area where Coal is used extensively for heating.

Avg.

SO4 2-

(continued)

[63]

[62]

[61]

[60]

[59]

[33]

[58]

[57]

[56]

[55]

Range

64 Md. A. Hossen et al.

0.43–1606.2

5.68

7.88

29.5

Limeira, Brazil

Amazonia, Brazil

Carpathians, Romania

179

310

510

Bangladesh standards

WHO guideline

8700

8700

565.2

38.1

32.73

74.4

22.39

7

23.6

43.5–1522

1.67–284.6

0.96–1194

5.4–210.5

0.87–67.83

0.03–73.7

4.3–87.0 0.25–437.9

7.5–299.4

Range

0.4–609.2

5000

3750

569

BDL-1397

107.5 1.77–645

170.6 2.25–7440

26.5

54.88 10.0–260

26.4

42.5

Avg.

Cl-

4100

2880

57.6

16.7

35.1

1.99

17.4

2.46

15.5

49.3–65.8

0.79–142.2

2.88–603.8

0.1–20.45

BDL-87.21

0.07–23.7

4.1–65.8

Avg. Range

NO3 -

Notes All units are in µeq/L, except pH and Conductivity (µS/cm); “—” used for not available data

0.72–125.7

51.2–460.4

Iasi, Romania 15.3

Southern Nigeria

0.7–53.1

0.13–38.11

0.08–16.83

2.16

Mexico City, Mexico

1.5–9.0

3.83

Avg.

Avg. Range

Range

Conductivity

pH

Granada, Spain

Study location

Table 5.1 (continued) Range

Avg.

NH4 +

Oil-producing site

Urban area

Mountainous area, thermal inversion often appears which effects on the dispersion of atmospheric pollutants.

Heterogeneous urban area with terrestrial dust, agriculture activities and biomass-burning aerosols.

Diversified industrial and agricultural activities

Green areas with moderate to high traffic density

Rural area

Avg.

SO4 2-

[72]

[71]

[70]

[69]

[68]

[67]

[66]

[65]

[64]

Range

5 A Systematic Literature Review on Rainwater Quality Influenced … 65

66

Md. A. Hossen et al.

5.5 Trace Metals Trace metals such as aluminium (Al), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), chromium (Cr), cadmium (Cd), and lead (Pb) are present in rainwater. These metals are a necessary part of nutrition and physiology in the animal cell; however, exposure to their excessive quantities can be toxic, causing severe health consequences [73]. As such, the removal of trace metals from rainwater is essential. Several sources potentially contaminate rainwater: some are related to atmospheric pollution, and others are related to the storage rainwater storage facilities. For example, in rainwater, Cu, Zn, Cd, and Pb predominately originate from anthropogenic sources, mostly related to industrial combustion and local traffic emissions [74]. Concentrations of trace metals in rainwater are derived mainly from long-range transport and traffic emissions [75]. The characteristic of rainwater varies due to geography; for example, the non-oil-producing region was less acidic than the oilproducing belts of Nigeria [70]. Moreover, the concentrations of Pb and Zn were reported different in urban, industrial, and rural sites [76]. In particular, the order of concentrations of the trace metals in Bangladesh is Zn > Cr > Cu > Fe > Mn > Pb > Cd (Table 5.2). Among the trace metals, Zn provided the highest contribution, followed by Cr, showing the association of industrial dust with the precipitation samples, which represents a major crustal influence [77]. High zinc and chromium intakes may cause headaches, nausea, vomiting, lack of appetite, stomach pain, and diarrhoea in the short term [78]. Sabin et al. [86] demonstrate: (1) atmospheric deposition potentially accounted for 57–100% of the trace metal loads in annual stormwater discharges in the highly impervious catchment; (2) dry deposition appears to be the dominant mechanism for the transfer of atmospheric pollutants to surfaces in a semi-arid catchment. Atmospheric emissions are probably the most preoccupancy to human health and the environment due to either the great quantity involved or their widespread dispersion [74]. If we closely look at Table 5.2, the influence of tropical typhoons on rainwater pollution becomes evident. For example, in the case of Taiwan, Cu and Pb concentrations were several hundred times higher than their concentrations observed on other days.

5.6 Relationship Among Physicochemical Parameters and Trace Metals To assess relationships among trace metals and physicochemical parameters, Pearson’s correlation was applied and tabulated in Table 5.3. Among all metals, sample pH correlates strongly with Aluminum (Al). Nitrate, sulphate, and acidity of the rainwater samples showed a strong positive correlation with most trace metals, while NH4 + and Cl– mostly showed negative correlations with the metals (Table 5.3). K+ and Mg2+ contents showed strong positive correlations with Zn, Cr, and Cd trace metals. A significant correlation is also observed between Na+ and Cd.

100 50 Cd Avg.

BDL-302.3

1.24–424

100-400

1042–4666

BDL-175.9

3.1–100

77.1

13.8

–

18.4

225

–

107.2

324

382

60

49.4

1705

26.5

15.3

200

200

Cr

Avg.

Gampaha, Sri Lanka

Tainan, Taiwan

Borneo, Malaysia

Kent Ridge, Singapore

Gangneung, South Korea

Central Mexico

Mexico City, Mexico

Ghore El-Safi, Jordan

Northern Jordan

Brisbane, Australia

Oleiros, Spain

Sevilla, Spain

Amazonia, Brazil

Mexico City, Mexico

Bangladesh standards

WHO guideline

Study location

Range

8.3–186

6.32–560

59–705

10.2–462

28.8-22

–

–

0.08–102

–

–

Chattogram, Bangladesh

8.34

4.2

29

6.4

8.7

2.11

–

7.6

83

115

2.78

30.5

9.94

4.75

8.58

Mn Avg.

Range

Al

Avg.

Study location

Table 5.2 Trace metals composition in pure rainwater

Range

0.23–40.3

BDL-38.26

17–64

0.95–21.3

0.82–140

0.55–3.7

–

1.9-15.4

2–727

70–170

0.19–54

16–45.5

0.47-48.5

BDL-21.08

1.0–38.0

Range

Fe

Avg.

Pb

300

300–1000

–

19.16

939

11.4

68

92

430

86.97

395

–

23.91

1093

–

10.5

44.22

Avg.

Range

–

BDL-74.56

505–2352

2.1–29.6

32–4400

24.5–1208

67-1320

24.23-449

110–1080

–

0.44–142

625–1705

–

BDL-78.68

BDL-440

Range

Cu

0.39–12.9

BDL-16.27

15-Sep

0.44–10.5

2.2–1600

0.32–14.8

3.82–267

–

10–124

70–120

0.52–76.3

3.5–26

0.22–8826

BDL-44.0

BDL-620

Range

Site Description

2000

1000

2.98

3.25

9

2.1

21

3.08

73

–

41.5

85

5.58

13.3

993

7.71

49.98

Avg.

Zn

3000

5000

–

22.19

20

55.7

770

6.52

210

–

873

160

7.23

16.7

64.2

30.32

206.8

Avg.

(continued)

References

–

BDL-125.4

Aug-75

15.5–145

4–26000

1.3–21.6

22–1087

–

Jan-90

120–280

0.63–33

1.5–33.8

4.87–299

BDL-70.1

8.0–630

Range

5 A Systematic Literature Review on Rainwater Quality Influenced … 67

2.0-94.0

–

0.03–53

0–10

0.11–5.3

0.21–4.3

0.08–1.9

81.7

–

0.4

–

1.62

4.5

41

1.5

3.1

0.77

0.53

0.28

Chattogram, Bangladesh

Gampaha, Sri Lanka

Tainan, Taiwan

Borneo, Malaysia

Kent Ridge, Singapore

Gangneung, South Korea

Central Mexico

Mexico City, Mexico

Ghore El-Safi, Jordan

Northern Jordan

Brisbane, Australia

Oleiros, Spain

BDL-48.2

0.66–3.7

20–200

–

0.17–0.86

Range

Al

Avg.

Study location

Table 5.2 (continued)

–

–

0.42

52

1.48

2

1.5

0.33

5.5

–

–

BDL

Avg.

Mn

–

–

0.21–3.2

0.56–217

0.17–3.7

18-Jan

0–4

0.01-3.1

2.0–9.5

–

–

BDL

Range

Fe

0.51

5.4

2.57

66

2.43

31

27

3.37

36.7

1710

5.2

8.3

Avg.

0.08–1.5

0.44–85

0.25–6.82

4.2–567.5

0.70–5.41

2–140

Oct-40

0.26–44.3

7.0–115

0.27–12260

BDL-19.04

BDL-42.0

Range

Cu Range

Zn Avg. [79]

Range

[81]

[56]

[80]

[58]

Residential area immediate vicinity to agricultural lands, forests and the sea

A subtropical urban area

Rainwater samples were collected in a rural region.

Study area can be classified as mixed between rural/industrial and agricultural surrounding

Sampling took place at two sites, one urban and one rural

(continued)

[63]

[76]

[84]

[61]

[83]

Urban, industrial and rural mixed [82]

The study site is mainly rural, harvested and pure both are collected.

Coastal and residential area

Rainwater samples collected from tropical rainforest region.

Rainwater samples collected from urban areas during four typhoon episodes

Industrialized and urbanized area [33]

Rural and urban mix

Avg.

68 Md. A. Hossen et al.

–

0.26

50

50

Amazonia, Brazil

Mexico City, Mexico

Bangladesh standards

WHO guideline

0.19–1.2

–

22-Apr

Range

3

5

0.37

–

0.1

Avg.

Mn

0.04–4.5

–

0.05-0.2

Range

Fe

10

50

1.58

0.51

6

Avg.

0.57–9.3

BDL-3.32

15-Apr

Range

Notes: All units are in µg/L; BDL stands for below detection limit; “—” used for not available data

9

Avg.

Al

Sevilla, Spain

Study location

Table 5.2 (continued) Cu Range

Zn Avg.

Green areas with moderate to high traffic density

Urban and rural

Samples were collected from an urban- trafficked site.

Avg.

[72]

[71]

[65]

[67]

[85]

Range

5 A Systematic Literature Review on Rainwater Quality Influenced … 69

70

Md. A. Hossen et al.

Table 5.3 Correlation matrix among physicochemical parameters and trace metals Parameters pH

EC

Cl

NO3

SO4

NH4

K

Na

Ca

pH

1

EC

0.620** 1

Cl

0.364** 0.603** 1

NO3

0.433** 0.611** 0.522** 1

SO4

0.438** 0.742** 0.735** 0.708** 1

NH4

0.377** 0.582** 0.277*

K

0.342** 0.774** 0.317** 0.721** 0.778** 0.972** 1

Na

0.352** 0.417*

Ca

0.410** 0.800** 0.466** 0.817** 0.883** 0.952** 0.957** 0.520** 1

Mg

0.462** 0.967** 0.901** 0.430** 0.786** 0.366** 0.372** 0.855** 0.529**

Al

0.737** 0.777** 0.500*

0.967** 0.516*

0.582*

Mn

0.259

0.027

0.625** 0.914** 0.659** 0.637*

0.495

0.918**

Fe

0.484*

0.338

0.771** 0.470

Cu

0.074

0.924** 0.065

Zn

0.283

0.636** −0.013

Cr

0.442** 0.724** −0.047

Cd Pb

0.735** 0.767** 1

0.868** 0.362** 0.750** 0.383** 0.441** 1

0.529*

0.587*

0.343

0.479

0.405

0.094

0.786** 0.009

0.185

0.460*

0.264

−0.127

−0.003

0.273

0.358

0.556*

0.617** 0.109

0.797** 0.174

0.337

0.251

0.615** −0.252

0.609*

0.518*

0.498** 0.766** −0.025

0.299

0.589*

−0.007

0.949** 0.692*

0.209

0.059

Parameters Mg

0.104

0.321

0.069

0.436*

0.205

0.058

0.003

0.270

0.354

Al

Mn

Fe

Cu

Zn

Cr

Cd

Pb

pH EC Cl NO3 SO4 NH4 K Na Ca Mg

1

Al

0.664** 1

Mn

0.372

0.793** 1

Fe

0.474

0.796** 0.441

1

Cu

0.176

−0.026

0.278

Zn

0.587*

0.731** 0.969** 0.386

−0.008

1

Cr

0.517

0.532*

0.795** 0.623*

−0.084

0.859** 1

Cd

0.994** 0.676*

0.827** 0.432

0.832** 0.117

Pb

0.180

−0.029

−0.004

−0.009

1

0.645** 0.997** −0.012

**Correlation is significant at the 0.01 level (2-tailed) *Correlation is significant at the 0.05 level (2-tailed)

0.162

1

−0.107

0.964** 1

5 A Systematic Literature Review on Rainwater Quality Influenced …

71

5.7 Summary and Concluding Remarks Rainwater collection, harvesting, and consumption are considered the most viable alternative water source for potable and non-potable uses, particularly in areas where high levels of arsenic present in groundwater or space suffer from salt intrusions [87]. Given that Bangladesh is one of the countries in the world with relatively high annual rainfall and significant consumption of water for domestic purposes [88], rainwater harvesting, and consumption are undoubtedly crucial for its inhabitants. Notwithstanding the potential opportunities of rainwater harvesting in Bangladesh, the quality of rainwater in regard to potable uses should be investigated. While much research effort have been made to optimize the RWH system, studies focusing on evaluating water quality parameters (including physiochemical and trace metal elements) remain less covered. This study carried out a systematic literature review of works relating to the source apportionment of physicochemical parameters and trace elements in rainwater. Source and types of contaminants that exist in rainwater and their pathway in rainwater were also covered. Based on the search criterion, 35 research articles covering 16 countries across the world were selected for the detailed review. It was observed that the atmospheric conditions overall have a significant impact on the quality of rainwater, as evident in the literature. When vehicular, industrial, marine, coal combustion, or other anthropogenic activities were observed near the sampling locations, elevated concentrations of sulfate and nitrate ions, as well as trace metals, were identified in rainwater. Although various studies identified rainwater contamination, epidemiological studies relating disease outbreaks and fatalities to rainwater consumption remain scarce worldwide. There is a strong evidence base outlining the potential of rainwater harvesting as the most sustainable alternative for developing countries like Bangladesh for the sustainable urban water management system [22–24]. Nevertheless, the research on rainwater quality is still at a rudimentary level. For example, the influence of atmospheric conditions on rainwater quality and the presence of trace metals in rainwater was not documented in any studies of Bangladesh, except by Hossen et al. [89]. Another research gap that appears through this study relates to the influence of the marine environment on the characteristics of rainwater quality. Further research investigating the potential interrelationship between air quality and rainwater composition would be desirable. Given the ongoing threat of global and regional climate change, rapid urbanization, groundwater depletion, increased water stress, and increased water treatment works, we must adopt a multi-stressor, multiscale perspective in investigating the suitability of rainwater and use the pulse-press framework to find how these stressors influence the rainwater quality. Disclosure Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

72

Md. A. Hossen et al.

References 1. Ali S, Zhang S, Yue T (2020) Environmental and economic assessment of rainwater harvesting systems under five climatic conditions of Pakistan. J Clean Prod 259:120829 2. Khanal G, Thapa A, Devkota N, Paudel UR (2020) A review on harvesting and harnessing rainwater: an alternative strategy to cope with drinking water scarcity. Water Supply 20(8):2951–2963 3. Pari L, Suardi A, Stefanoni W, Latterini F, Palmieri N (2021) Economic and environmental assessment of two different rainwater harvesting systems for agriculture. Sustainability 13(7):3871 4. Hamilton K et al (2019) A global review of the microbiological quality and potential health risks associated with roof-harvested rainwater tanks. NPJ Clean Water 2(1):1–8 5. Alim MA, Rahman A, Tao Z, Samali B, Khan MM, Shirin S (2020) Suitability of roof harvested rainwater for potential potable water production: a scoping review. J Clean Prod 248:119226 6. de Lambert JR, Walsh JF, Scher DP, Firnstahl AD, Borchardt MA (2021) Microbial pathogens and contaminants of emerging concern in groundwater at an urban subsurface stormwater infiltration site. Sci Total Environ 775:145738 7. Al-Batsh N et al (2019) Assessment of rainwater harvesting systems in poor rural communities: a case study from Yatta Area, Pllestine. Water 11(3):585 8. Hofman-Caris R et al (2019) Rainwater harvesting for drinking water production: a sustainable and cost-effective solution in the Netherlands? Water 11(3):511 9. Norman M, Shafri HZ, Mansor SB, Yusuf B (2019) Review of remote sensing and geospatial technologies in estimating rooftop rainwater harvesting (RRWH) quality. Int Soil Water Conserv Res 7:266–274 10. Domènech L, Heijnen H, Saurí D (2012) Rainwater harvesting for human consumption and livelihood improvement in rural Nepal: benefits and risks. Water Environ J 26:465–472 11. Campisano A et al (2017) Urban rainwater harvesting systems: research, implementation and future perspectives. Water Res 115:195–209 12. Gao B, Ouyang W, Cheng H, Xu Y, Lin C, Chen J (2019) Interactions between rainfall and fine particulate matter investigated by simultaneous chemical composition measurements in downtown Beijing. Atmos Environ 218:117000 13. Abdullah SN et al (2021) Estimation of rainwater harvesting by the reflectance of the purity index of rainfall. Environ Sci Pollut Res 28:35613–35627 14. Wang L et al (2021) A long-term chemical characteristics and source apportionment of atmospheric rainfall in a northwest megacity of Xi’an. China Environ Sci Pollut Res 28:31207–31217 15. Towfiqul Islam AR, Rahman MS, Khatun R, Hu Z (2020) Spatiotemporal trends in the frequency of daily rainfall in Bangladesh during 1975–2017. Theor Appl Climatol 141:869–887 16. Nashwan MS, Shahid S, Wang X (2019) Uncertainty in estimated trends using gridded rainfall data: a case study of Bangladesh. Water 11:349 17. Das S, Islam AR (2021) Assessment of mapping of annual average rainfall in a tropical country like Bangladesh: remotely sensed output vs. kriging estimate. Theor Appl Climatol 146:111–23 18. Mullick MR, Nur RM, Alam MJ, Islam KA (2019) Observed trends in temperature and rainfall in Bangladesh using pre-whitening approach. Glob Planet Change 172:104–113 19. Ayers JC et al (2016) Sources of salinity and arsenic in groundwater in southwest Bangladesh. Geochem Trans 17(1):1–22 20. Shamsudduha M, Joseph G, Haque SS, Khan MR, Zahid A, Ahmed KM (2020) Multi-hazard groundwater risks to water supply from shallow depths: challenges to achieving the sustainable development goals in Bangladesh. Expos Health 12:657–670 21. Uddin MJ, Jeong YK (2021) Urban River pollution in Bangladesh during last 40 years: potential public health and ecological risk, present policy, and future prospects toward smart water management. Heliyon 7:e06107

5 A Systematic Literature Review on Rainwater Quality Influenced …

73

22. Rahman S, Khan MT, Akib S, Din NB, Biswas SK, Shirazi SM (2014) Sustainability of rainwater harvesting system in terms of water quality. Sci World J 721357 23. Akter A, Ahmed S (2015) Potentiality of rainwater harvesting for an urban community in Bangladesh. J Hydrol 528:84–93 24. Islam MM, Afrin S, Tarek MH, Rahman MM (2021) Reliability and financial feasibility assessment of a community rainwater harvesting system considering precipitation variability due to climate change. J Environ Manage 289:112507 25. Aghaloo K, Chiu YR (2020) Identifying optimal sites for a rainwater-harvesting agricultural scheme in Iran using the Best-Worst Method and fuzzy logic in a GIS-based decision support system. Water 12:1913 26. Akter A, Ahmed S (2021) Rainwater harvesting potentials for a water-scarce city in Bangladesh. Proc Inst Civ Eng-Water Manag 174:84–98 27. Hashim HQ, Sayl KN (2021) Detection of suitable sites for rainwater harvesting planning in an arid region using geographic information system. Appl Geomat 13:235–248 28. Yannopoulos S, Giannopoulou I, Kaiafa-Saropoulou M (2019) Investigation of the current situation and prospects for the development of rainwater harvesting as a tool to confront water scarcity worldwide. Water 11:2168 29. Benneyworth L et al (2016) Drinking water insecurity: water quality and access in coastal south-western Bangladesh. Int J Environ Health Res 26(5–6):508–524 30. Rakib MA, Sasaki J, Matsuda H, Fukunaga M (2019) Severe salinity contamination in drinking water and associated human health hazards increase migration risk in the southwestern coastal part of Bangladesh. J Environ Manage 240:238–248 31. Tripathee L et al (2020) Measurement of mercury, other trace elements and major ions in wet deposition at Jomsom: the semi-arid mountain valley of the Central Himalaya. Atmos Res 234:104691 32. Garaga R, Chakraborty S, Zhang H, Gokhale S, Xue Q, Kota SH (2020) Influence of anthropogenic emissions on wet deposition of pollutants and rainwater acidity in Guwahati, a UNESCO heritage city in Northeast India. Atmos Res 232:104683 33. Liyandeniya AB, Deeyamulla MP, Priyantha N (2020) Source apportionment of rainwater chemical composition in wet precipitation at Kelaniya in Sri Lanka. Air Qual Atmos Health 13(12):1497–1504 34. Paul S, Ghosh S, Mathew M, Devanand A, Karmakar S, Niyogi D (2018) Increased spatial variability and intensification of extreme monsoon rainfall due to urbanization. Sci Rep 8(1):1– 10 35. Veremchuk LV, Yankova VI, Vitkina TI, Nazarenko AV, Golokhvast KS (2016) Urban air pollution, climate and its impact on asthma morbidity. Asian Pac J Trop Biomed 6(1):76–79 36. Sarkar S, Chauhan A, Kumar R, Singh RP (2019) Impact of deadly dust storms (May 2018) on air quality, meteorological, and atmospheric parameters over the northern parts of India. GeoHealth 3:67–80 37. Martinson B, Thomas T (2003) Improving water quality by design. In: 11th international conference on rainwater catchment systems. Mexico City, Mexico 38. Abbasi T, Abbasi SA (2011) Sources of pollution in rooftop rainwater harvesting systems and their control. Crit Rev Environ Sci Technol 41(23):2097–2167 39. Xiao HW, Xiao HY, Long AM, Wang YL, Liu CQ (2013) Chemical composition and source apportionment of rainwater at Guiyang, SW China. J Atmos Chem 70(3):269–281 40. Ma CJ, Kang GU (2019) The chemical nature of individual size-resolved raindrops and their residual particles collected during high atmospheric loading for PM 2.5. In: Rainfall-Extremes, Distribution and Properties, IntechOpen 41. Tao WK, Chen JP, Li Z, Wang C, Zhang C (2012) Impact of aerosols on convective clouds and precipitation. Rev Geophys 50:1–62 42. Yang L, Mukherjee S, Pandithurai G, Waghmare V, Safai PD (2019) Influence of dust and sea-salt sandwich effect on precipitation chemistry over the Western Ghats during summer monsoon. Sci Rep 9(1):1–13

74

Md. A. Hossen et al.

43. Charlson RJ, Rodhe H (1982) Factors controlling the acidity of natural rainwater. Nature 295:683–685 44. González CM, Aristizábal BH (2012) Acid rain and particulate matter dynamics in a mid-sized Andean city: The effect of rain intensity on ion scavenging. Atmos Environ 60:164–171 45. Parworth C et al (2015) Long-term measurements of sub-micrometer aerosol chemistry at the Southern Great Plains (SGP) using an Aerosol Chemical Speciation Monitor (ACSM). Atmos Environ 106:43–55 46. Vieira-Filho MS, Pedrotti JJ, Fornaro A (2013) Contribution of long and mid-range transport on the sodium and potassium concentrations in rainwater samples, São Paulo megacity, Brazil. Atmos Environ 79:299–307 47. Bhuyan P, Ahmed MS, Hopke PK, Hoque RR (2020) Understanding the chemistry and sources of precipitation ions in the mid-Brahmaputra valley of Northeastern India. Aerosol Air Qual Res 20:2690–2704 48. Bisht DS et al (2017) Chemical characterization of rainwater at a high-altitude site “Nainital” in the central Himalayas. India Environ Sci Pollut Res 24(4):3959–3969 49. Kumar P, Yadav S, Kumar A (2014) Sources and processes governing rainwater chemistry in New Delhi, India. Nat Hazards 74:2147–2162 50. Mouli PC, Mohan SV, Reddy SJ (2005) Rainwater chemistry at a regional representative urban site: influence of terrestrial sources on ionic composition. Atmos Environ 39(6):999–1008 51. Al Obaidy AH, Joshi H (2006) Chemical composition of rainwater in a tropical urban area of northern India. Atmos Environ 40(35):6886–6891 52. Meng Y et al (2019) Characterization of inorganic ions in rainwater in the megacity of Shanghai: spatiotemporal variations and source apportionment. Atmos Res 222:12–24 53. Li YC et al (2016) Chemical characteristics of rainwater in Sichuan basin, a case study of Ya’an. Environ Sci Pollut Res 23(13):13088–13099 54. Zhang N, He Y, Cao J, Ho K, Shen Z (2012) Long-term trends in chemical composition of precipitation at Lijiang, southeast Tibetan Plateau, southwestern China. Atmos Res 106:50–60 55. Lu X, Li LY, Li N, Yang G, Luo D, Chen J (2011) Chemical characteristics of spring rainwater of Xi’an city. NW China Atmos Environ 45:5058–5063 56. Hu GP, Balasubramanian R (2003) Wet deposition of trace metals in Singapore. Water Air Soil Pollut 144(1):285–300 57. Cao YZ, Wang S, Zhang G, Luo J, Lu S (2009) Chemical characteristics of wet precipitation at an urban site of Guangzhou. South China Atmos Res 94(3):462–469 58. Cheng MC, You CF (2010) Sources of major ions and heavy metals in rainwater associated with typhoon events in southwestern Taiwan. J Geochem Explor 105(3):106–116 59. Tay JH, Jaafar S, Tahir NM (2014) Ionic composition of rainwater at selected sites of Kuantan, Pahang, Malaysia: a preliminary study. Bull Environ Contam Toxicol 92:329–333 60. Mehr MR, Keshavarzi B, Sorooshian A (2019) Influence of natural and urban emissions on rainwater chemistry at a southwestern Iran coastal site. Sci Total Environ 668:1213–1221 61. Al-Khashman OA (2009) Chemical characteristics of rainwater collected at a western site of Jordan. Atmos Res 91(1):53–61 62. Al-Khashman OA (2005) Study of chemical composition in wet atmospheric precipitation in Eshidiya area, Jordan. Atmos Environ 39:6175–6183 63. Moreda-Piñeiro J et al (2014) Influence of marine, terrestrial and anthropogenic sources on ionic and metallic composition of rainwater at a suburban site (northwest coast of Spain). Atmos Environ 88:30–38 64. Calvo AI et al (2010) Chemical composition of wet precipitation at the background EMEP station in Víznar (Granada, Spain) (2002–2006). Atmos Res 96(2–3):408–420 65. Báez A, Belmont R, García R, Padilla H, Torres MD (2007) Chemical composition of rainwater collected at a southwest site of Mexico City, Mexico. Atmos Res 86:61–75 66. Martins EH, Nogarotto DC, Mortatti J, Pozza SA (2019) Chemical composition of rainwater in an urban area of the southeast of Brazil. Atmos Pollut Res 10:520–530 67. Honório BA, Horbe AM, Seyler P (2010) Chemical composition of rainwater in western Amazonia—Brazil. Atmos Res 98(2–4):416–425

5 A Systematic Literature Review on Rainwater Quality Influenced …

75

68. Keresztesi Á et al (2020) Assessing the variations in the chemical composition of rainwater and air masses using the zonal and meridional index. Atmos Res 237:104846 69. Arsene C, Olariu RI, Mihalopoulos N (2007) Chemical composition of rainwater in the northeastern Romania, Iasi region (2003–2006). Atmos Environ 41(40):9452–9467 70. Efe SI (2010) Spatial variation in acid and some heavy metal composition of rainwater harvesting in the oil-producing region of Nigeria. Nat Hazards 55(2):307–319 71. ECR (1997) Environmental Conservation Rules, Department of Environment. Ministry of Environment and Forest. People’s Republic of Bangladesh 72. WHO (2008) World Health Organization guidelines for drinking water quality, 197–209. World Health Organisation Publication, New York 73. Badsha MA, Khan M, Wu B, Kumar A, Lo IM (2021) Role of surface functional groups of hydrogels in metal adsorption: from performance to mechanism. J Hazard Mater 408:124463 74. Al-Khatib IA et al (2019) Health risk associated with some trace and some heavy metals content of harvested rainwater in Yatta area. Palestine. Water 11(2):238 75. Cheng MC, You CF, Lin FJ, Huang KF, Chung CH (2011) Sources of Cu, Zn, Cd and Pb in rainwater at a subtropical islet offshore northern Taiwan. Atmos Environ 45(11):1919–1928 76. Huston R, Chan YC, Chapman H, Gardner T, Shaw G (2012) Source apportionment of heavy metals and ionic contaminants in rainwater tanks in a subtropical urban area in Australia. Water Res 46(4):1121–1132 77. Kamani H, Hoseini M, Safari GH, Jaafari J, Mahvi AH (2014) Study of trace elements in wet atmospheric precipitation in Tehran, Iran. Environ Monit Assess 186(8):5059–5067 78. Sharma P, Rai V (2018) Assessment of rainwater chemistry in the Lucknow metropolitan city. App Water Sci 8(2):1–14 79. Hossen MA, Hoque A, Salauddin M, Pal SK, Muktadir MG, Jahan H (2021) Evaluation of physicochemical and trace metal qualities of rainwater in the south-eastern region of Bangladesh. J Water Supply Res Technol Aqua 70(5):757–772 80. Valappil NK, Viswanathan PM, Hamza V (2020) Chemical characteristics of rainwater in the tropical rainforest region in northwestern Borneo. Environ Sci Pollut Res 27(29):36994–7010 81. Lee JY, Yang JS, Han M, Choi J (2010) Comparison of the microbiological and chemical characterization of harvested rainwater and reservoir water as alternative water resources. Sci Total Environ 408:896–905 82. Rivera-Rivera DM, Escobedo-Urías DC, Jonathan MP, Sujitha SB, Chidambaram S (2020) Evidence of natural and anthropogenic impacts on rainwater trace metal geochemistry in central Mexico: a statistical approach. Water 12:192 83. García R, Belmont R, Padilla H, Torres MC, Báez A (2009) Trace metals and inorganic ion measurements in rain from Mexico City and a nearby rural area. Chem Ecol 25(2):71–86 84. Al-Momani IF (2003) Trace elements in atmospheric precipitation at Northern Jordan measured by ICP-MS: acidity and possible sources. Atmos Environ 37(32):4507–4515 85. Montoya-Mayor R, Fernández-Espinosa AJ, Seijo-Delgado I, Ternero-Rodríguez M (2013) Determination of soluble ultra-trace metals and metalloids in rainwater and atmospheric deposition fluxes: a 2-year survey and assessment. Chemosphere 92:882–891 86. Sabin LD, Lim JH, Stolzenbach KD, Schiff KC (2005) Contribution of trace metals from atmospheric deposition to stormwater runoff in a small impervious urban catchment. Water Res 39(16):3929–3937 87. Lo AG, Gould J (2015) Rainwater harvesting: global overview. Rainwater Harvest Agric Water Supply 213–33 88. Pal SK, Masum MMH, Salauddin M, Hossen MA, Ruva IJ, Akhie AA (2023) Appraisal of stormwater-induced runoff quality influenced by site-specific land use patterns in the southeastern region of Bangladesh. Environ Sci Pollut Res 30(13):36112–36126. https://doi.org/10. 1007/s11356-022-24806-8 89. Hossen A, Salauddin M, Hoque A, Pal SK (2021) Assessment of source apportionment and composition of trace elements in rainwater in the south-eastern region of Bangladesh. In EGU General Assembly Conference Abstracts 2021 Apr (pp. EGU21-635)

Part II

Hydrology and Water Resources Management

Chapter 6

Quantification of Flash Flood Runoff Volume Using Morphometric Parameters Towards Sustainability Mahmoud M. Mansour , Mahmoud Nasr , Manabu Fujii , Chihiro Yoshimura , and Mona G. Ibrahim

Abstract Although flash floods cause severe losses in different aspects of life, they represent a valuable source of fresh water for arid regions suffering from shortages and scarcity of safe water supply. Thus, the investigation of flash flood runoff volume (Rv) is worth more concern. This research is the first to aim at understanding the influence of basin morphometric parameters on generated runoff volume in the ungauged basin using the digital elevation model (DEM), geospatial techniques (ArcGIS), hydrological modeling and regression analysis. The study area is considered an arid region located in the eastern north of Egypt and consists of 56 basins. The derived data for all basins was divided into training and testing data sets. Regression analysis between Rv and each morphometric parameter using the training data set revealed that 12 parameters, with a considerable coefficient of determination (R2 ) ranging from 0.13 to 0.98 and a significant p-value lower than 0.05, were connected to the Rv data. These equations were used to calculate runoff volume for the testing data set and the result showed a significant agreement with the simulated runoff volume, suggesting reliable models for flash flood runoff volume predictions. Quantifying M. M. Mansour (B) · M. Nasr · M. G. Ibrahim Department of Environmental Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt e-mail: [email protected] M. M. Mansour Department of Civil Engineering, Faculty of Engineering, Menoufia University, Menoufia 32511, Egypt M. Nasr Department of Sanitary Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt M. M. Mansour · M. Fujii · C. Yoshimura Department of Civil and Environmental Engineering, School of Environment and Society, Tokyo Institute of Technology, Tokyo, Japan M. G. Ibrahim Department of Environmental Health, High Institute of Public Health, Alexandria University, Alexandria, Egypt © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_6

79

80

M. M. Mansour et al.

runoff volume facilitates optimal determination of water harvesting facilities’ capacities and enlarges freshwater resources for achieving sustainable development goal (SDG) 6. Keywords Runoff volume of flash flood · Morphometric parameters · Hydrological modeling · Regression analysis · Sustainability

6.1 Introduction Flash floods are usually connected to disasters, destruction and losses [1]. However, they are considered precious sources of fresh water in arid environments as well [2]. Major challenges for arid regions are managing and developing their natural resources and meeting the demands of a growing population [2]. Thus, the estimation of flash flood runoff volume (Rv) is a critical issue that requires more investigation. It could be estimated using hydrological modeling such as the hydrologic engineering centre hydrologic modeling system (HEC-HMS) based on the soil conservation service– curve number (SCS-CN) method, but this method consumes much time and computational effort in addition to the problem of insufficient data for ungauged basins [3–5]. Thus, morphometric parameters that are feasibly estimated using remote sensing (RS) and geographic information system (GIS) techniques, could be utilized for the appropriate quantification of Rv. The morphometric parameters of the drainage basin provide a proper understanding of the hydrological process [2]. They include a description of areal, linear and topographical features that could affect the rates and volumes of runoff [6]. Topography is identified as the first-order regulator of a catchment’s hydrological response to rainfall [2, 7]. The shapes, sizes and drainage lengths of the basin influence the runoff pattern as well [8]. In the absence of runoff records and other associated hydrological data, numerous strategies have been used to try to link runoff to basin features [2]. The mathematical description of a drainage basin’s attributes in terms of measuring linear features of channel networks as well as the basin’s areal and relief qualities provides essential information for understanding various elements of hydrology such as infiltration and runoff [9]. The drainage basin’s morphometric evaluation and its significance to hydrological response were widely discussed [10–16]. This study was aimed at investigating the characteristics of drainage basins related to runoff volume estimation. Thus, multivariate regression analyses were used to derive equations for runoff volume quantification using morphometric parameters. Based on runoff volume estimation, proper water harvesting facilities could solve the issue of fresh water shortage and scarcity and meet some targets of sustainable development goal (SDG) 6.

6 Quantification of Flash Flood Runoff Volume Using Morphometric …

81

6.2 Methodology Figure 6.1 shows the location of the study area in the Sinai Peninsula, Egypt and its digital elevation model (DEM). The DEM with a 30 m resolution was freely obtained from (https://earthexplorer.usgs.gov/). The size of the study area is greater than 12,500 km2 . Its elevations range from 0, adjacent to Suez Gulf, to 2625, at the eastern edge of the study area. The surface soil of the study area includes different types of rocks and deposits of gravel and sand [1]. The climate of this region is characterized by aridity as the average annual precipitation for the period between 1994 and 2014 accounted for around 20 mm [17]. ArcGIS version 14.1 was utilized to process the DEM, locate basins boundaries, delineate drainage networks based on 0.2 km2 as a stream threshold (St) and prepare the required maps. The study area consists of 56 basins with an area ranging from 18 km2 to below 2000 km2 , Fig. 6.1. The shapes of the basin are different, ranging from elongated to oval or near circular shapes (see Fig. 6.1). The primary features such as dimensions, area, perimeter and number of drainage streams and land surface elevation distribution of each basin were extracted from ArcGIS and exported to Fig. 6.1 The location and digital elevation model (DEM) of the study area

82

M. M. Mansour et al.

Microsoft Excel to calculate different morphometric parameters. The morphometric parameters include three main categories: areal properties, linear features and relief characteristics. Areal properties include basin area (A), basin width (W), basin length (Lb), alluvial area ratio (Ar), elongation ratio (Re), form factor ratio (Ff), circularity ratio (Rc), and compactness coefficient (Cc). Linear features include texture ratio (Rt), length of overland flow (Lo), drainage density (D), sinuosity index (SI), drainage intensity (Di), stream frequency (F) and mean bifurcation ratio (Rb). Relief characteristics include hypsometric integral (HI), mean basin slope (Sm), relief (Rf), relief ratio (Rr) and ruggedness number (Rn). All of these 20 parameters and time of concentration (Tc) were calculated by formulas and methods, as listed in Table 6.1, introduced by Kirpich [18], Horton [10], Miller [11], Schumm [19], Melton [12], Faniran [13], Gregory and Walling [14] and presented by Wahba et al. [6], Elsadek et al. [8], Mansour et al. [20]. The hydrologic engineering centre hydrologic modeling system (HEC-HMS) was used to simulate flash flood events and estimate runoff volume (Rv) from resulted hydrographs based on soil conservation service curve number (SCS-CN) method as the loss model, unit hydrograph of SCS as the transform method and Muskingum model as the routing method [3]. The applied rainfall was the 24-h SCS type II design storm with cumulative precipitation of 75 mm/day [21]. For subsequent multivariate regression analyses, the whole data set (56 basins) was divided into two data sets using an arbitrary method. The two data sets are the training data set representing 70% and the testing data set representing 30%. The multivariate regression analyses were carried out using Microsoft Excel, considering the coefficient of determination (R2 ) and p-value as judgmental factors.

6.3 Result and Discussion Table 6.2 illustrates the names, areas and estimated runoff volumes of the whole basins in addition to the classification into the training and testing data sets. It was discovered that the minimum Runoff volume of 0.09 Mm3 did not link to the smallest basin with an area of 18 km2 , and similarly, the maximum Runoff volume of 65.7 Mm3 did not match the largest basin with an area of 1907 km2 , indicating that other parameters had a significant influence on runoff volume quantification. Accordingly, Fig. 6.2 shows the correlation between runoff volume (Rv) and many related morphometric parameters, namely basin area (A), basin width (W), texture ratio (Rt), basin length (Lb), time of concentration (Tc), length of overland flow (Lo), hypsometric integral (HI), drainage density (D), sinuosity index (SI), alluvial area ratio (Av), mean basin slope (Sm) and drainage intensity (Di). The morphometric parameters with very weak to weak correlation were excluded, such as relief (Rf), relief ratio (Rr), ruggedness number (Rn), elongation ratio (Re), form factor ratio (Ff), circularity ratio (Rc), compactness coefficient (Cc), stream frequency (F) and mean bifurcation ratio (Rb). Runoff volume had a significant correlation with basin area because a larger basin could collect more rainfall than a smaller basin, meeting the results

6 Quantification of Flash Flood Runoff Volume Using Morphometric …

83

Table 6.1 The morphometric criteria used to quantify the runoff volume of flash floods No

Parameter

Formula or method

1

Basin area (A; km2 )

GIS software analysis

2

Basin length (Lb; km)

GIS software analysis

3

Basin width (W; km)

W = A/Lb

4

Alluvial area ratio (Ar)

5

Elongation ratio (Re)

Ar = Av/A; where Av = Alluvial area ) ( √ Re = 2 ( A/π ) /Lb

6

Form factor ratio (Ff)

F f = A/Lb2

7

Circularity ratio (Rc)

8

Compactness coefficient (Cc)

9

Texture ratio (Rt; km−1 )

10

Drainage density (D; km−1 )

Rc = 4π A/Pr 2 ( √ ) Cc = Pr/ 2 π A ; where Pr = Basin perimeter ∑ Rt = N u/Pr ; where Nu = Streams number of ‘u’ order ∑ D= Lu/A; where Nu = Streams length of ‘u’ order

11

Length of overland flow (Lo; km)

Lo = 1/(2D)

12

Sinuosity index (SI)

13

Stream frequency (F; km−2 )

S I = Lm/Lb; where Lm = Main channel length ∑ F= N u/ A

14

Drainage intensity (Di; km−1 )

15

Mean bifurcation ratio (Rb)

Di = F/D ∑ Rb = (Nu/N‘u + 1’)/(U – 1); where N ‘u + 1’ = Number of streams of next higher order ‘u + 1’ and U = Max stream order

16

Hypsometric integral (HI)

GIS software Analysis

17

Mean basin slope (Sm; °)

GIS software Analysis

18

Relief (Rf; m)

R f = Highestelevation − Lowestelevation

19

Relief ratio (Rr)

Rr = (R f /Lb) × 100

20

Ruggedness number (Rn)

Rn = R f × D

21

Time of concentration (Tc; hr)

T c = 0.00032(Lb)0.77 /(S)0.385 ; where S = Main channel slope

obtained by Mimikou [22]. According to Yousif and Bubenzer [9], higher W and Rt were connected to higher Rv, which was attributed to the existence of shorter flow paths and travel times and a decrease in infiltration capacities. There were direct relationships between Rv and both Lb and Tc with lower coefficients of determination, ranging from 0.70 to 0.75, than A and W because the infiltration rate might relatively increase due to the existence of longer flow distances and times for basins with higher Lb and Tc [9]. Figure 6.2 illustrates that the group of parameters including Lo, HI, Si, Sm, and Di had above weak to hardly moderate direct correlations to Rv because higher values of them might be less associated with and have a minor effect on increasing runoff removal rate and decrease in infiltration capacity [2, 23]. In contrast, Ar and D had inverse relationships with Rv because the alluvial deposits are characterized by higher permeability and higher infiltration

84

M. M. Mansour et al.

Table 6.2 The runoff volume for each basin along with data set type and basin name and area Basin area (km2 )

Runoff volume (Mm3 )

Data set type

11.4

Training

No

Basin name

1

El-Raha

469

2

Mabaoq

21

0.39

Training

3

Abu-Remt

82

2.06

Training

4

Rabbnh

182

4.74

Testing

5

B.R.M

31

0.76

Testing

6

Marbae

80

2.1

Training

7

B.M.L

26

0.7

Testing

8

Lahatih

281

7.41

Training

9

B.L.S.1

35

0.92

Training

10

B.L.S.2

21

0.09

Testing

11

Sudr

565

14.82

Training

12

B.S.W.1

114

2.92

Training

13

B.S.W.2

109

2.08

Training

14

Wardan

1157

29.03

Training

15

B.W.S

50

1.06

Training

16

Seaeda

80

2.12

Training

17

El-Qanawat

45

1.18

Training

18

El-Nekhelh

24

0.64

Training

19

Gharandel

867

23.26

Training

20

Abu-Mugrat

43

1.12

Training

21

Wasit

117

3.09

Testing

22

Tal

107

2.83

Testing

23

Tiba

356

10.12

Testing

24

El-Darrat

19

0.49

Training

25

El-Khabobh

121

3.68

Training

26

Nakhl

76

1.76

Training

27

Baebae

721

25.68

Testing

28

Sedry

1068

36.78

Testing

29

B.S.F

20

0.53

Testing

30

Feran

1781

31

B.F.A.1

32 33

65.7

Testing

48

1.06

Training

B.F.A.2

33

1.26

Training

B.F.A.3

123

4.01

Training

34

B.F.A.1

67

2.33

Training

35

El-Aawag

1907

47.27

Testing

36

B.A.S.1

85

0.89

Training

37

B.A.S.2

30

0.32

Training (continued)

6 Quantification of Flash Flood Runoff Volume Using Morphometric …

85

Table 6.2 (continued) No

Basin name

38

Sely

39

Abu-Jurf

40

B.J.M

41

El-Mrekh

42 43

Basin area (km2 )

Runoff volume (Mm3 )

Data set type

77

1.96

Testing

280

10.53

Training

73

1.28

Training

100

1.60

Training

B.M.A

37

0.39

Testing

Aman

158

4.73

Training

44

Rout

49

1.00

Training

45

B.R.M

34

0.36

Training

46

El-Mhash

170

6.41

Training

47

El-Rabod

56

0.82

Training

48

B.R.L

19

0.20

Training

49

Ltehy

73

1.77

Testing

50

Aghny

127

3.08

Testing

51

Marda

33

0.42

Training

52

Barkh

50

0.96

Training

53

Eqna

63

1.43

Training

54

A.E.1

22

0.26

Training

55

A.E.2

39

1.03

Training

56

A.E.3

18

0.28

Training

Min

–

18

0.09

–

Max

–

1907

65.7

–

capacity compared to other soil types, leading to a decrease in runoff volume, while higher D drainage density could be connected to lower flow path slopes and slower removal of runoff, allowing for an increased amount of infiltrated rainfall [8]. Because all the mentioned parameters in Fig. 6.2 had a considerable correlation to runoff volume with an individual p-value lower than 0.05, they were included in both multivariate linear regression (MVLR) and multivariate power regression (MVPR) analyses for runoff volume of flash flood quantification, as shown in Tables 6.3 and 6.4. Tables 6.3 and 6.4 illustrate iterations of both MVLR and MVPR analyses, respectively, using the 12 selected parameters, viz., A, W, Rt, Lb, Tc, Lo, HI, D, SI, Ar, Sm and Di. For each iteration, the parameter with the highest p-value was omitted. As for the first iteration in Table 6.3, the sinuosity index (SI) with a p-value (0.9) was excluded, and drainage intensity (Di) was skipped at the second iteration. In Table 6.4, the p-values of Lb, Tc, D and SI were displayed as ‘err’ in the initial two iterations. It was ascribed to collinearity issues for Lb and D as ‘err’ disappeared after their exclusion. Over 7 iterations for MVLR, 6 out of 12 variables were considered acceptable predictors, although two p-values were slightly higher than 0.05 due to the small

86 b

40

10

e

40

R² = 0.7491

10

20

h

40

20

R² = 0.7056

10

10 15 Tc (hr)

10 0 0.3

0.45

i

40

R² = 0.2175

10

k

30

2 2.5 D (km-1)

Rv (Mm3)

R² = 0.1409

10 0 0.25 0.5 0.75 Ar

1

0.3

20

R² = 0.1834

10

0.8

1.05

1.3

1.55

SI l

40

40 30

20

R² = 0.1343

10

20

R² = 0.1291

10 0

0 0

0.2 0.25 Lo (km)

40

3

30

20

10

0 1.5

HI 40

R² = 0.2528

30

20

0.6

20

0.15

0 0.15

10

40

20

Rv (Mm3)

Rv (Mm3)

R² = 0.2342

5 7.5 Rt (km-1)

0

5

30

20

2.5

30

0

30

Rv (Mm3)

0 f

40

40 60 Lb (km)

10

20

0 0

j

10 15 W (km)

Rv (Mm3)

Rv (Mm3)

Rv (Mm3)

5

30

20

R² = 0.9

20

0 0

0

Rv (Mm3)

10

300 600 900 1200 A (km2)

30

g

20

0 0

40 30

Rv (Mm3)

20

0

d

R² = 0.9145

30

R² = 0.9839

Rv (Mm3)

Rv (Mm3)

30

c

40

Rv (Mm3)

a

M. M. Mansour et al.

0

5

10 15 Sm ( )

20

0.5 0.75 1 1.25 1.5 Di (km-1)

Fig. 6.2 Relationships with a considerable coefficient of determination (R2 ) between Rv and the different morphometric parameters using the training data set: a basin area (A), b basin width (W), c texture ratio (Rt), d basin length (Lb), e time of concentration (Tc), f length of overland flow (Lo), g hypsometric integral (HI), h drainage density (D), i sinuosity index (SI), j alluvial area ratio (Ar), k mean basin slope (Sm), l drainage intensity (Di)

6 Quantification of Flash Flood Runoff Volume Using Morphometric …

87

Table 6.3 Multivariate linear regression analysis and adjusting p-values to appropriate values by omitting parameters with a higher p-value Parameter

A

W

Rt

Lb

Tc

Lo

HI

D

SI

Ar

Sm

Di

P-value

2.E-12

0.4

0.4

0.28

0.42

0.7

0.33

0.7

0.9

0.15

2.E-02

0.8

2.E-13

0.4

0.4

0.20

0.25

0.7

0.26

0.7

–

0.15

1.E-02

0.9

9.E-14

0.4

0.3

0.13

0.24

0.6

0.23

0.6

–

0.14

1.E-02

–

2.E-14

0.4

0.3

0.13

0.19

0.8

0.15

–

–

0.15

7.E-03

–

6.E-15

0.4

0.3

0.13

0.19

–

0.13

–

–

0.01

1.E-03

–

3.E-18

–

0.6

0.18

0.18

–

0.12

–

–

0.01

1.E-03

–

3.E-27

–

–

0.16

0.17

–

0.10

–

–

0.01

1.E-03

–

Table 6.4 Multivariate power regression analysis and adjusting p-values to appropriate values by omitting parameters with a higher p-value Parameter A P-value

W

Rt

Lb Tc

Lo

HI

D

SI

Ar

Sm

Di

1.E-08 0.2

0.1

err err

0.09 0.05 err err

4.E-03 1.E-03 0.85

8.E-09 0.2

0.2

err err

0.08 0.06 –

3.E-03 1.E-03 0.88

1.E-06 0.1

0.3

–

3.E-08 0.2

0.3

–

–

0.00 0.02 –

4.E-05 2.E-03 3.E-04 0.13

4.E-10 0.4

2.E-03 –

–

–

0.03 –

4.E-05 1.E-03 3.E-04 0.26

0.59 0.01 0.11 –

0.25

9.E-05 2.E-03 1.E-03 0.25

3.E-21 2.E-04 –

–

–

–

0.01 –

3.E-05 2.E-03 9.E-05 0.04

1.E-21 6.E-06 –

–

–

–

0.04 –

2.E-04 1.E-02 4.E-04 –

sample size of the training data set. Similarly, 6 out of 12 variables were considered acceptable parameters for MVPR (Table 6.4). For the final iteration of MVPR, Di was omitted despite its P-value being less than 0.05 to get a stream threshold-independent model and facilitate its application. Each group of the 6 selected parameters was used to build Eqs. (6.1) and (6.2) for flash flood runoff volume calculation. These parameters are not influenced by the defined stream threshold, which extends the applicability of these equations. In addition to each group of 6 parameters, the equations included a parameter to consider the relative change in rainfall amount. Equations (6.1) and (6.2) were examined using the testing data set and correlation analysis to illustrate their validity and performance quality, as shown in Fig. 6.3. MVLR − Rv = (0.02481 A + 0.02666 Lb − 0.10547 Tc + 1.77586 HI − 0.78587 Ar + 0.11125 Sm − 1.06252) × (P ÷ 75) for 50 ≤ P ≤ 100

(6.1)

MVPR − Rv = (0.0528A1.015 × Lb1.186 × HI−0.304 × SI1.291 × Ar−0.023 Sm0.121 ) × (P ÷ 75) for 50 ≤ P ≤ 100 (6.2)

88

M. M. Mansour et al. MVLR-Rv

MVPR-Rv

Linear (MVLR-Rv)

Linear (MVPR-Rv)

70 60

R² = 0.958

Rv (Mm3)

50

R² = 0.9542

40 30 20 10 0 0

10

20

30

40

50

60

70

Rvcal (Mm3)

Fig. 6.3 Correlation between Rvcal for both MVLR and MVPR and Rv for the testing data set

where, MVLR-Rv: calculated flash flood runoff volume based on MVLR (Mm3 ), MVPR-Rv: calculated flash flood runoff volume based on MVPR (Mm3 ), A: Basin area (km2 ), Lb: Basin length (km), Tc: Time of centration (hr), HI: Hypsometric integral (dimensionless), SI: Sinuosity index (dimensionless), Ar: Alluvial area ratio (dimensionless), Sm: Mean basin slope (°). P: Total precipitation of 24-h Soil Conservation Service (SCS) Type II design storm (mm). Figure 6.3 shows the correlation between flash flood runoff volume (Rv) and calculated flash flood runoff volume (Rvcal ) for both MVLR and MVPR. There were significant associations with high coefficients of determination (R2 s around 0.956) between both MVLR-Rv and MVPR-Rv and Rv, indicating reliable models or equations to estimate runoff volume generated during flash flood events for a certain basin. MVLR-Rv seemed to be slightly more accurate than MVPR-Rv. Nonetheless, using these models would save much time and computational effort currently spent by hydrological modeling to obtain flash flood runoff volume, which plays an important role in optimally sizing water harvesting facilities in such regions where freshwater resources are scarce, necessitating all available valid water for municipal,

6 Quantification of Flash Flood Runoff Volume Using Morphometric …

89

agricultural, and industrial purposes. Subsequently, the groundwater aquifer would be recharged, and the groundwater level would increase, resisting seawater intrusion in such coastal regions [24–28]. This figure represents an obvious achievement of SDG 6 “Clean water and sanitation” by providing safe and affordable drinking water for all and increasing water use efficiency by storing fresh water instead of draining it to the sea. Additionally, harvesting flash flood runoff would considerably decrease the social and economic losses due to the powerful and destructive flow of flash flood events, adding many benefits to other associated SDGs, particularly SDG 8 “Decent work and economic growth” and SDG 11 “Sustainable cities and community”. The derived equations could be properly applied to similar arid regions, including basins with a size of fewer than 2000 km2 . The total precipitation for the considered flash flood event to calculate the runoff volume should be 75 mm or in the range of 50 to 100 mm to get an accurate result. The latter limitation of the presented model was due to not addressing the effect of rainfall amount change in the present study. Thus, it was recommended in future studies to consider precipitation as a variable in addition to the identified significant parameters in this study to extend the application range of the presented models.

6.4 Conclusion In this study, morphometric parameters and hydrological modeling were utilized to estimate flash flood runoff volume for ungauged basins in the western south part of the Sinai Peninsula, which is considered an arid region. The areal, linear, and relief characteristics of the 56 basins were estimated and the flash flood runoff volumes were obtained by HEC-HMS models using a synthetic storm for the whole basins. The whole data set was randomly split into a training data set and a testing data set, representing 70 and 30%, respectively. The correlations between the morphometric parameters and the runoff volume were investigated for the training data set. For performing multivariate analyses, 12 out of the 21 considered parameters with the appropriate coefficient of determination and individual p-value were selected. It resulted in two models (e.g., MVLR and MVPR) including only 6 morphometric parameters for each with acceptable p-values in addition to correcting terms for slight variation in rainfall amount to quantify flash flood runoff volume. Validation of these equations was performed using the testing data set and illustrated their reliability for the calculation of flash flood runoff volume. Based on the predicted runoff volume, water harvesting facilities could be appropriately placed for optimal storage of fresh water in such regions suffering from a shortage of fresh water resources, meeting SDG 6 “Clean water and sanitation”. Acknowledgements The first author is very grateful to the Egyptian Ministry of Higher Education (MoHE) for providing financial support in the form of a Ph.D. scholarship. Also, thanks to the Japan International Cooperation Agency (JICA) for providing all the facilities and equipment to accomplish this research.

90

M. M. Mansour et al.

References 1. Eliwa HA, Murata M, Ozawa H (2015) Post Aswan High Dam flash floods in Egypt: causes, consequences and mitigation strategies. Bull Center Collab Commun Naruto Univ Educ 29(2):173–186 2. Negm AM (2020) Flash floods in Egypt. Springer, Cham. https://doi.org/10.1007/978-3-03029635-3 3. Khélifa WB, Mosbahi M (2021) Modeling of rainfall-runoff process using HEC-HMS model for an urban ungauged watershed in Tunisia. Model Earth Syst Environ. https://doi.org/10. 1007/s40808-021-01177-6 4. Wahba M, Hassan HS, Elsadek WM, Kanae S, Sharaan M (2022) Prediction of flood susceptibility using frequency ratio method: a case study of Fifth District, Egypt. In: 14th International conference on hydroscience & engineering (ICHE2022) on proceedings. Izmir, Turkey, pp 473–483 5. Mansour MM, Nasr M, Fujii M, Yoshimura C, Ibrahim MG (2023) Evaluation of a reliable method for flash flood hazard mapping in arid regions: a case study of the Gulf of Suez, Egypt. In: Chen X (ed) Proceedings of the 2022 12th International conference on environment science and engineering (ICESE 2022). Environmental science and engineering. Springer, Singapore (Forthcoming 2023) 6. Wahba M, Hassan HS, Elsadek WM, Kanae S, Sharaan M (2023) Novel utilization of simulated runoff as causative parameter to predict the hazard of flash floods. Environ Earth Sci 82(13):333 7. Wahba M, Mahmoud H, Elsadek WM, Kanae S, Hassan HS (2022) Alleviation approach for flash flood risk reduction in urban dwellings: a case study of Fifth District, Egypt. Urban Clim 42:101130. https://doi.org/10.1016/j.uclim.2022.101130 8. Elsadek WM, Wahba M, Al-Arifi N, Kanae S, El-Rawy M (2023) Scrutinizing the performance of GIS-based analytical Hierarchical process approach and frequency ratio model in flood prediction—case study of Kakegawa, Japan. Ain Shams Eng J 102453 9. Yousif M, Bubenzer O (2015) Geoinformatics application for assessing the potential of rainwater harvesting in arid regions. Case study: El Daba’a area, Northwestern Coast of Egypt. Arab J Geosci 8(11):9169–9191. https://doi.org/10.1007/s12517-015-1837-0 10. Horton RE (1945) Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geol Soc Am Bull 56(3):275–370 11. Miller VC (1953) A quantitative geomorphic study of drainage basin characteristics in the Clinch Mountain area, Virginia and Tennessee. Department of Geology Columbia University, New York 12. Melton MA (1957) An analysis of the relations among elements of climate, surface properties and geomorphology. Columbia University, Department of Geology, Office of Naval Research, New York 13. Faniran A (1968) The index of drainage intensity—a provisional new drainage factor. Aust J Sci 31:328–330 14. Gregory KJ, Walling DE (1973) Drainage basin form and process a geomorphological approach. Halsted Press, a division of John Wiley & Sons, New York 15. Mansour MM, Ibrahim MG, Fujii M, Nasr M (2022) Recent applications of flash flood hazard assessment techniques: case studies from Egypt and Saudi Arabia. Adv Eng For 47:101–110. https://doi.org/10.4028/p-03z404 16. Mansour MM, Nasr M, Fujii M, Yoshimura C, Ibrahim MG (2022) Identification of a practical method and a set of morphometric parameters for flash flood potential prioritization. In: 14th International conference on hydroscience & engineering (ICHE2022) on proceedings. Izmir, Turkey, pp 603–615 17. Dadamouny MA, Schnittler M (2016) Trends of climate with rapid change in Sinai, Egypt. J Water Clim Change 7(2):393–414 18. Kirpich ZP (1940) Time of concentration of small agricultural watersheds. Civ Eng 10(6):362 19. Schumm SA (1956) Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Geol Soc Am Bull 67:597–646

6 Quantification of Flash Flood Runoff Volume Using Morphometric …

91

20. Mansour MM, Ibrahim MG, Fujii M, Nasr M (2022) Sustainable development goals (SDGs) associated with flash flood hazard mapping and management measures through morphometric evaluation. Geocarto Int. https://doi.org/10.1080/10106049.2022.2046868 21. Abdel-Lattif A, Sherief Y (2012) Morphometric analysis and flash floods of Wadi Sudr and Wadi Wardan, Gulf of Suez, Egypt: Using digital elevation model. Arab J Geosci 5:181–195 22. Mimikou M (1984) Regional relationships between basin size and runoff characteristics. Hydrol Sci J 29(1):63–73 23. Zidan A, Abdalla M, Khalaf S, Saqr AM (2016) Kinetic energy and momentum coefficients for Egyptian irrigation canals. Mansoura Eng J 41(1):1–16. https://doi.org/10.21608/bfemu. 2020.99368 24. Mansour MM, Ellayn AF, Helal E, Rashwan IMH, Sobieh MF (2018) Delaying solute transport through the soil using unequal double sheet piles with a surface floor. Ain Shams Eng J 9(4):3399–3409. https://doi.org/10.1016/j.asej.2018.10.003 25. Allam A, Helal E, Mansour M (2019) Retarding contaminant migration through porous media using inclined barrier walls. J Hydrol Hydromech 67(4):339–348 26. Saqr AM, Ibrahim MG, Fujii M, Nasr M (2021) Sustainable development goals (SDGs) associated with groundwater over-exploitation vulnerability: geographic information system-based multi-criteria decision analysis. Nat Resour Res 30(6):4255–4276. https://doi.org/10.1007/s11 053-021-09945-y 27. Saqr AM, Ibrahim MG, Fujii M, Nasr M (2022) Simulation-optimization modeling techniques for groundwater management and sustainability: a critical review. Adv Eng For 47:89–100. https://doi.org/10.4028/p-50l1j1 28. Saqr AM, Nasr M, Fujii M, Yoshimura C, Ibrahim MG (2023) Optimal solution for increasing groundwater pumping by integrating MODFLOW-USG and particle swarm optimization algorithm: a case study of Wadi El-Natrun, Egypt. In: Chen X (ed) Proceedings of the 2022 12th International conference on environment science and engineering (ICESE 2022). Environmental Science and Engineering. Springer, Singapore, (Forthcoming 2023)

Chapter 7

Application of the Whale Optimization Algorithm (WOA) in Reservoir Optimization Operation Under Investigation of Climate Change Impact: A Case Study at Klang Gate Dam, Malaysia Vivien Lai , Y. F. Huang , C. H. Koo , Ali Najah Ahmed , and Ahmed El-Shafie

Abstract The effectiveness of analyzing large amounts of data that comes with engaging climate change scenarios, for planning advanced reservoir management can be achieved through the use of optimization algorithms. The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm derived following animalbehaviour-based concepts. In Malaysia, specifically at the Klang Gate Dam (KGD), very little organized information has been collected in investigating future reservoir operations considering such climate anomalies and complexities. Hence, this study at the KGD is to assist policymakers in gaining a better knowledge of reservoir operations, and to determine the optimal water releases, during the projected future climate forecasts. The analysis begins with the maximum water temperature demand from 2020 to 2099, in which the data is obtained from the Coupled Model Intercomparison Project 5 (CMIP5) under RCP 2.6, RCP 4.5, and RCP 8.5 simulations, which were then applied in this study. In the simulation process, an artificial neural network (ANN) was used. The results were then compared to the WOA in terms of V. Lai · Y. F. Huang (B) · C. H. Koo Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Jalan Sg. Long, Bandar Sg. Long, 43000 Kajang, Selangor, Malaysia e-mail: [email protected] A. N. Ahmed Institute of Engineering Infrastructures (IEI), Universiti Tenaga Nasional (UNITEN), 43000 Selangor, Malaysia A. El-Shafie Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia National Water and Energy Center, United Arab Emirates University, Al Ain P.O Box 1551, Abu Dhabi, United Arab Emirates © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_7

93

94

V. Lai et al.

reservoir risk evaluation performance. During the optimization phase, the average storage failure rate for all the RCPs was 34.93%, while during the simulation phase, the average storage failure rate was 97.29%. In terms of managing reservoir operation and storage failure, the WOA performed substantially better (50% more robust than the simulation procedure). In terms of periodic reliability, the shortage periods under RCP 2.6 and RCP 8.5 yield 1.15 and 9.58%, respectively. Keywords Reservoir operation · Global circulation models (GCMs) · Optimization

7.1 Introduction The primary function of a reservoir or dam is to provide essential water needs and then store any excess water to avoid floods downstream as well as for the preparation of a drought scenario [1, 2]. The activities of the reservoir are typically carried out in accordance with the circumstances at present. Operators and policymakers must follow the reservoir’s rules in order to satisfy the requirements of the system for operating the reservoir [3]. A thorough review of the reservoir operation optimization from traditional models to nature-inspired algorithms can be found in [4]. Whale Optimization Algorithm (WOA) is characterized as swarm intelligence and belongs to the animal-based concept [4]. Compared to heuristic algorithms (e.g., genetic algorithm, particle swarm optimization, etc.), WOA helps maintain a balance between exploration and exploitation during the search. Moreover, WOA was inspired by the nature concept of the humpback whales, and was firstly introduced by Mirjalili and Lewis [5]. Numerous studies on reservoir optimization have recently been performed by utilizing WOA. For example, few studies related to reservoir optimization operations at Klang Gate Dam (KGD) have been conducted. Hossain and El-Shafie were the first to initiate the study to conduct the reservoir optimization operation at KGD by utilizing heuristic approaches [6, 7]. The results attained in [6] demonstrated that Artificial Bee Colony (ABC) is capable of meeting the demand by achieving 61.36% from the years 1987 to 2008, at the same time to minimize the water deficit during the critical moment, especially in the low inflow period. The results contributed in [6, 7] were then compared with another study [8] by utilizing WOA and the enhancement technique namely Lévy flight of WOA (LFWOA). This study demonstrated the LFWOA was capable to reach the reliability by attaining 69.70%, whilst WOA obtained 56.06% of meeting the water demand from the year 1987 to 2008. In addition, WOA was applied in the Salman Farsi irrigation network in order to allocate the water in optimal conditions [9]. With WOA optimization of water allocation, the authors demonstrated that the net cropping benefit was escalated by 17%. Apart from this, WOA has also been used in other engineering fields such as hydropower reservoir system [9, 10] However, these studies have yet to involve the investigation under the climate scenarios impact on the reservoir operations.

7 Application of the Whale Optimization Algorithm (WOA) in Reservoir …

95

On the other hand, a few recent research on the management of reservoir operations in response to climate change events have been reported [11–13]. Using a mathematical model, for instance, to optimize the Shahrchay reservoir’s operation regulation, was recommended by Nourani et al. [14]. Three GCMs were introduced between 2020 and 2060 under the A1 B1 , RCP 4.5, and RCP 8.5. The authors claimed that the results benefited with managing and planning water resources. Silveira et al. demonstrated the seasonal and multi-annual changes in precipitation from different GCMs in CMIP5 under the RCP 8.5 scenario for Brazil’s hydropower sector [15]. The authors claimed that several GCMs were unable to deliver satisfactory results in hydropower estimations, especially under RCP 8.5. These studies, however, focused on reservoir optimization in the future using only a single GCM. Tan et al. [16] stated that an ensemble of GCMs will produce more reliable hydrological management outputs than a single GCM. There is also little information available on the investigation of the impacts of climate change on Klang Gate Dam’s (KGD) future reservoir operation utilizing WOA. The WOA implementation with an integration of the impact of climate change has yet to be explored at any reservoir in the past studies in tropical Malaysia, and definitely not at KGD. Thus, with the application of the WOA, the motivation to investigate the impact of climate change is set through implementing the RCP 2.6, RCP 4.5, and RCP 8.5 simulations and optimization for obtaining possibilities of attaining the optimal water releases at KGD for the years from 2020 to 2099. The outcomes of this study should be beneficially appropriate for policymakers in enhancing water resources management at the KGD.

7.2 Methodology 7.2.1 Study Area The Klang Gate Dam (KGD) is situated in Taman Melawati in the Gombak neighborhood of Kuala Lumpur (KL). It is also known as the Bukit Tabur Dam, and its rainfall station IDs are 3,217,002 and 3,217,004 (Source: Lembaga Urus Air Selangor, LUAS). The dam was built at latitude 3° 13' 58'' North and longitude 101° 45' 0'' East. The main purpose of KGD is to deliver water to the Bukit Nanas and Wangsa Maju water treatment plants in Kuala Lumpur. The KGD features can be found in [17].

7.2.2 Objective Functions of the Reservoir (a) Minimization of water deficit at KGD as defined in Eq. (7.1):

96

V. Lai et al.

Min Z =

12  (Dt − Rt )2

(7.1)

t=1

where t denotes the number of months in a year, while Dt and Rt denote monthly demand and release for that month, respectively. (b) Thresholds Every reservoir system must comply with the limitations and penalty functions in order to ensure optimal reservoir operation. In order to operate reservoirs optimally without compromising the objective function, the upper and lower boundary limits of the static penalty function, sometimes referred to as the threshold, were utilized [18]. (i) The Continuity threshold as defined in Eq. (7.2): St+1 = St + It − Rt − L t

(7.2)

in which St+1 and St are the final and initial storages, for time t (monthly), respectively; It represents the inflow to the reservoir; Rt represents the reservoir’s release information, and L t represents the evaporation. (ii) The Reservoir Storage Capacity Threshold is as follows: 6.24 MCM ≤ St ≤ 23.44 MCM (for t = January, February, . . . , December) (7.3) (iii) The following is the release threshold: 3.28 MCM ≤ Rt ≤ 5.22 MCM (for t = January, February, . . . , December) (7.4) (c) Penalty Functions The reservoir capacity constraint can be satisfied by penalizing the objective function using the following equation:  penalty1 =

(7.5)

0 i f St < Smax C2 (St − Smax )2 i f St < Smax

(7.6)

C1 (Smin 

penalty2 =

i f St < Smin − St ) i f St < Smin

0

2

in which C1 and C2 are penalty coefficients; St is expressed as storage while Smin and Smax are minimum and maximum storage values, respectively. (d) Finalized objective function.

7 Application of the Whale Optimization Algorithm (WOA) in Reservoir …

97

Equation (7.7) has been modified to reflect the final adjustment of the objective function for the optimization operation rule at the KGD on a monthly basis. Where Y is the fitness recursive objective function [19]. MinY = MinZ + penalty 1 + penalty 2

(7.7)

7.2.3 Whale Optimization Algorithm (WOA) The exploration and exploitation processes make up the Whale Optimization Algorithm (WOA). Consequently, it aids in keeping the balance, particularly during the search process. The first step in the construction of the WOA is to encircle the prey. This is followed by exploitation, which uses the idea of “bubble-net” attacking to accomplish and reduce the mechanism. The search for the prey has finally been conducted during the exploration. The WOA equations are described in [8] in further detail.

7.2.4 Risk Performance Evaluations Table 7.1 presents the common outline of the risk performance applied in reservoir optimization. The equations and desired ranges have been mentioned and highlighted. N is denoted as summation of the time consideration period; n is indicated as summation of period meeting the demand; m is represented as number of failure period; Nv is denoted as summation of the time period (month) in simulation. Table 7.1 Risk performance evaluations Risk performance criteria

Equations

Desired range/tolerable range

Periodic reliability

Periodic reliability,   R p = Nn X 100%

The higher the Rp , the more reliable the system

Storage failure (%)

Ratio of violating the boundary of The lower % indicates that the the storage (failed) to the total of reservoir can supply the number releases demand much better

Potential resilience (RES)

RES = max. (duration of water deficit)

The higher value of RES indicates the length of period facing water scarcity is longer

Vulnerability

V = N v 1 t=1 [max(0, Dt − Rt )] mX   100  annualde f icit SI = T annualdemand

The lower the vulnerability, the more robust the system

Shortage index (SI)

The higher value indicates the severity of the deficit

98

V. Lai et al.

7.3 Results and Discussion Table 7.2 shows the periodic reliability using WOA for the maximum temperature of water demand under various RCPs. There have been 960 releases in total, spanning the near to far future (2020–2099). The RCP 2.6 demonstrated the highest percentage for the same period (meeting the demand) at 37.60%, while the RCP 4.5 attained 13.96%. The surplus and shortage phase under RCP 2.6 performed considerably better than RCP 4.5. However, the reservoir reliability under RCP 8.5 is not demonstrated by satisfying any water demand with 100% of the surplus period, indicating that the inflow into KGD is in the high category. Table 7.3 shows the overall reservoir risk performances carried out at the maximum temperature of water demand by adopting WOA at KGD from the years 2020 to 2099. All RCPs suffered an overall average storage failure rate of 34.93% during the optimization process and an overall average storage failure rate of 97.29% during the simulation phase, respectively, at the maximum temperature of water demand. This suggests that relative to the reservoir optimization method, the reservoir simulation findings had the most critical storage failure. Consequently, optimization in reservoir operation is crucial for reservoir managers to comprehend the optimal operation of the reservoir and to eliminate any needless risk or loss during the operation period. The most remarkable findings in Table 7.3 were the resilience under RCP 8.5, when the highest water shortage persisted for 205 months. Table 7.2 Periodic reliability by utilizing WOA Reservoir reliability/RCPs

RCP 2.6

RCP 4.5

RCP 8.5

Maximum temperature water demand Surplus period

61.25% (588 times)

76.46% (734 times)

100.00% (960 times)

Exact period

37.60% (361 times)

13.96% (134 times)

0

Shortage period

1.15% (11 times)

9.58% (92 times)

0

Total no. of release

960

960

960

Table 7.3 Reservoir optimization performances in maximum temperature water demand Maximum temperature water demand Indices

RCP 2.6

RCP 4.5

RCP 8.5

Storage failure, in optimization–WOA (%)

28.44

45.42

30.94

Storage failure, in simulation only–ANN (%)

98.02

97.19

96.77

Resilience, max. number of water deficit (month)

11

92

205

Vulnerability

0.609

0.478

0.191

Shortage index

0.0018

0.0021

0.0003

7 Application of the Whale Optimization Algorithm (WOA) in Reservoir …

99

Release (MCM)

5.30 5.10 4.90 4.70 4.50

4.44 4.42 4.47 4.44

4.30

4.53 4.55 4.48 4.50 4.51 4.58 4.54 4.50

4.10 Jan

Feb

Mar

Apr

May

Jun

Optimisation RCP 2.6 (MCM)

Jul

Aug

Sep

Oct

Nov

Dec

Simulation RCP 2.6 (MCM)

RCP 2.6 Max Temp WD (MCM) Fig. 7.1 Average monthly water released (MCM) for maximum temperature water demand under RCP 2.6

Figure 7.1 shows the average monthly release at the maximum water demand temperature under RCP 2.6. The simulation procedure was conducted by utilizing an artificial neural network (ANN). The figure demonstrated that excessive releases were produced by both the simulation and optimization procedures, but the optimization process in the WOA produced a trend line that was nearer to the trend line for the maximum temperature of water demand. Figure 7.1 can be further investigated by concurrently reading Tables 7.2 and 7.3, which provide the respective tabulated results. In the ANN simulation, the storage failure reached 98.02% compared to optimization’s 28.44%, which led to a much lower storage failure rate in order to reduce the water deficit and reservoir release system performance. Figure 7.2 depicts the average monthly release under RCP 4.5 at the maximum water demand temperature. The graph illustrated that excessive releases were generated by both the simulation and optimization processes, but the optimization process in the WOA generated a trend line that was closer to the trend line for the maximum temperature of water demand. Table 7.3 demonstrated that the simulation procedures used the ANN approach, which resulted in a very high storage failure rate of 97.19%, however with the use of the reservoir optimization procedure, the percentage of failure was reduced to half of the simulation procedure, with 45.42%. This is the most threatening situation when compared to the other two scenarios under RCP 2.6 and RCP 8.5, as the reservoir optimization operation caused more than 40% storage failure. Therefore, it is vital for policymakers to consider future reservoir management, particularly in terms of reservoir optimization operations. The average monthly water release for the maximum water demand temperature under RCP 8.5 was shown in Fig. 7.3. The graph demonstrated that excessive releases were generated by both the simulation and optimization processes, but the optimization process in the WOA produced a trend line that was closer to the trend line for the maximum temperature of water demand. With the use of the reservoir optimization technique, the percentage of failure was decreased to 30.94%, as shown

100

V. Lai et al.

Release (MCM)

5.30 5.10 4.90 4.70 4.50

4.45 4.48

4.58 4.59

4.58 4.61 4.53 4.53 4.48 4.48 4.50 4.47

4.30 4.10 Jan

Feb

Mar

Apr

May

Jun

Optimisation RCP 4.5 (MCM)

Jul

Aug

Sep

Oct

Nov

Dec

Simulation RCP 4.5 (MCM)

RCP4.5 Max Temp WD (MCM) Fig. 7.2 Average monthly water released (MCM) for maximum temperature water demand under RCP 4.5

in Table 7.3. The simulation procedures used the ANN approach, which produced a very high storage failure rate of 96.77%. Due to an excessive inflow into the KGD, the periodic reliability obtained, however, only happened during the surplus period.

4.10

4.30

4.50

4.70

4.90

5.10

Feb

Mar

Apr May

RCP8.5 Max Temp WD (MCM)

Jun

Aug

Sep

Oct

Nov

Simulation RCP 8.5 (MCM)

Jul

Dec

4.55 4.57 4.55 4.53 4.51 4.51 4.52 4.57 4.59 4.55

Optimisation RCP 8.5 (MCM)

Jan

4.49 4.50

Fig. 7.3 Average monthly water released (MCM) for maximum temperature water demand under RCP 8.5

Release (MCM)

5.30

7 Application of the Whale Optimization Algorithm (WOA) in Reservoir … 101

102

V. Lai et al.

7.4 Conclusion The reservoir optimization operation has been studied by utilizing WOA to minimize water deficit for the future impacts of climate change. The comparison of the release at KGD under RCP 2.6, RCP 4.5, and RCP 8.5 using the simulation technique for ANN and the optimization procedure for WOA was then conducted. The standard assessment metrics, including periodic reliability, have been examined and discussed, as well as the risk performance metrics, including storage failure, vulnerability, shortage index, and resilience. According to an earlier discussion in the paper, under RCP 2.6, RCP 4.5, and RCP 8.5, the WOA performed significantly better in managing reservoir operation in terms of storage failure (50% more robust than the simulation procedure). The shortage periods under RCP 2.6 and RCP 8.5 produce 1.15 and 9.58%, respectively, in terms of periodic reliability. Acknowledgements This study was funded by the Universiti Tunku Abdul Rahman (UTAR), Malaysia, via its UTAR Research Publication Scheme (UTARRPS), under vote number 6251/H03. The authors are very sincerely grateful for the funding so graciously provided.

References 1. Il Eum H, Simonovic SP (2010) Integrated reservoir management system for adaptation to climate change: The Nakdong River Basin in Korea. Water Resour Manag 24(13):3397–3417. https://doi.org/10.1007/s11269-010-9612-1 2. Chen J, Shi H, Sivakumar B, Peart MR (2016) Population, water, food, energy and dams. Renew Sustain Energy Rev 56. https://doi.org/10.1016/j.rser.2015.11.043 3. Hossain MS, Mohd Sidek LB, Marufuzzaman M, Zawawi MH (2018) Passive congregation theory for particle swarm optimization (PSO): An application in reservoir system operation. Int J Eng Technol 7(4):383–387. https://doi.org/10.14419/ijet.v7i4.35.22767 4. Lai V, Huang YF, Koo CH, Ahmed AN, El-Shafie A (2022) A review of reservoir operation optimisations: from traditional models to metaheuristic algorithms. Arch Comput Meth Eng. https://doi.org/10.1007/s11831-021-09701-8 5. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw. https://doi.org/ 10.1016/j.advengsoft.2016.01.008 6. Hossain MS, El-Shafie A (2014) Evolutionary techniques versus swarm intelligences: application in reservoir release optimization. Neural Comput Appl 24(7–8):1583–1594. https://doi. org/10.1007/s00521-013-1389-8 7. Hossain MS, El-Shafie A (2013) Optimal operation of Klang gate dam using genetic algorithm. J Teknol Sci Eng 65(2):37–40. https://doi.org/10.11113/jt.v65.2188 8. Lai V, Huang YF, Koo CH, Ahmed AN, El-Shafie A (2021) Optimization of reservoir operation at Klang Gate Dam utilizing a whale optimization algorithm and a Lévy flight and distribution enhancement technique. Eng Appl Comput Fluid Mech 15(1):1682–1702. https://doi.org/10. 1080/19942060.2021.1982777 9. Saadati S, Eslamian S, Mousavi SZ, Akhondali AM, A. Naseri, “Evaluation of whale and particle swarm optimisation algorithms in optimal allocation of water resources of irrigation network to maximise net benefit case study: Salman Farsi,” Int. J. Hydrol. Sci. Technol., vol. 12, no. 3, 2021. https://doi.org/10.1504/ijhst.2021.10040163.

7 Application of the Whale Optimization Algorithm (WOA) in Reservoir …

103

10. Lü JX, et al (2021) An improved whale optimization algorithm and its application to power generation in cascade reservoir. In: Smart innovation, systems and technologies, vol 211. https:// doi.org/10.1007/978-981-33-6420-2_28 11. Zhang W, Li J, Liu P, Lei X, Chen J, Yeh WWG (2021) When to start an adaptation strategy in response to climate change in reservoir system management. J Hydrol 603. https://doi.org/10. 1016/j.jhydrol.2021.127111 12. Jaiswal RK, Lohani AK, Tiwari HL (2021) A decision support system framework for strategic water resources planning and management under projected climate scenarios for a reservoir complex. J Hydrol 603. https://doi.org/10.1016/j.jhydrol.2021.127051 13. Thomas T, Ghosh NC, Sudheer KP (2021) Optimal reservoir operation—a climate change adaptation strategy for Narmada basin in central India. J Hydrol 598. https://doi.org/10.1016/ j.jhydrol.2021.126238 14. Nourani V, Rouzegari N, Molajou A, Hosseini Baghanam A (2020) An integrated simulationoptimization framework to optimize the reservoir operation adapted to climate change scenarios. J Hydrol 587. https://doi.org/10.1016/j.jhydrol.2020.125018 15. Silveira CDS, et al (2019) Performance evaluation of AR5-CMIP5 models for the representation of seasonal and multi-annual variability of precipitation in Brazilian hydropower sector basins under RCP8.5 scenario. Hydrol Sci J 64(11). https://doi.org/10.1080/02626667.2019.1612521 16. Tan ML, Ficklin DL, Ibrahim AL, Yusop Z (2014) Impacts and uncertainties of climate change on streamflow of the johor River Basin, Malaysia using a cmip5 general circulation model ensemble. J Water Clim Chang 5(4). https://doi.org/10.2166/wcc.2014.020 17. Dashti Latif A, Najah Ahmed S, Sherif A, Sefelnasr M, El-Shafie A (2020) Reservoir water balance simulation model utilizing machine learning algorithm. Alexandria Eng J https://doi. org/10.1016/j.aej.2020.10.057 18. Zhai QH, Ye T, Huang MX, Feng SL, Li H (2020) Whale optimization algorithm for multiconstraint second-order stochastic dominance portfolio optimization. Comput Intell Neurosci 2020. https://doi.org/10.1155/2020/8834162 19. Allawi MF, Jaafar O, Mohamad Hamzah F, El-Shafie A (2019) Novel reservoir system simulation procedure for gap minimization between water supply and demand. J Clean Prod 206:928–943. https://doi.org/10.1016/j.jclepro.2018.09.237

Chapter 8

Development of Cleaner Production Alternatives in Water Management in a Slaughterhouse in Ecuador: A Case Study Solange Tite Llerena , Mayra Llerena , and Lucrecia Llerena

Abstract This study included the characterization of the stages involved in the productive process of slaughtering cattle and pigs using a flow diagram, as well as the analysis of the quality of the effluents. The objective of the research is to propose specific strategies focused on improving water resource management processes. For this, a situational diagnosis of the area was carried out to identify critical areas and the impacts were evaluated through a cause-effect matrix to determine the actions that have repercussions on the environmental components generating negative effects. The execution of the monitoring of the quality of the effluents demonstrated the evident deficiency in the treatment systems by the Wastewater treatment plant (WWTP). The parameters such as BOD5, COD and Nitrogen are the main indicators of water quality that demonstrate the limitations of purification systems. As a result, it was identified that the main stages that cause detrimental impacts on water quality were Bleeding and Evisceration. In addition, as part of the strategies of the cleaner production program, it was decided to implement a recirculation system for wastewater from the scalding phase for cleaning facilities, redesign treatment systems and implementation of regulatory water systems. In this last proposal, water consumption was reduced by 20%. Keywords Cleaner production · Environmental impact assessment · Slaughterhouse · Water quality

S. T. Llerena Chimborazo Polytechnic High School, Riobamba, Ecuador e-mail: [email protected] M. Llerena Ministry of Education, Quevedo, Ecuador e-mail: [email protected] L. Llerena (B) Quevedo State Technical University, Quevedo, Ecuador e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_8

105

106

S. T. Llerena et al.

8.1 Introduction Cleaner production is considered an integrated system of continuous improvement assignable to processes, products or services to promote eco-efficiency environmental policies, as well as to reduce expenses and risks associated with human beings and the environment [1]. At the level of Latin America, most of the slaughter facilities are found within the meat industries that present high rates of environmental contamination derived from inadequate waste management that causes serious environmental problems on environmental resources. The cleaner production proposal is aimed at the use of safe, efficient, and productive materials to generate less toxic waste that degrades the environment [2]. The Ecodesign as part of the context of environmental policies considers the cleaner production proposal an integral tool necessary for the manufacture of efficient, sustainable, socially responsible products and services with the environment and its surroundings [3]. Ecuador has approximately 300 cattle slaughter centers, most of them municipal, concentrated in urban areas, of which 70% are in the Sierra. According to the Ministry of Agriculture, Livestock, Aquaculture and Fisheries (MAGAP), the meat industry in Ecuador represents an important economic source, since it can self-satisfy its own demand for beef consumption [4]. The Municipal Farm of Pelileo is dedicated to the slaughter of cattle and pigs, its production capacity is around 600 bovines and 1,500 pigs per month, these processes generate highly contaminated effluents, as well as demand high water consumption whose average exceeds 500 m3 /day. The studies focused on the implementation of cleaner production in its activities, products and services have shown a significant reduction in the pollutant mass per unit of time that is discharged into wastewater streams subject to an insufficient purification process for its correct treatment [5]. Therefore, the main objective of the research is to propose strategies for water management, mainly redesigning the treatment processes in the wastewater treatment plant (WWTP) to implement effective processes such as ozonation. In addition, this research study contributes to compliance with the environmental regulations of Ecuador, promoting alternatives for the reduction of water consumption and the contaminant load [6]. Water management as a strategy for the productive slaughtering sector can become an excellent competitive advantage when promoting the circular economy in its processes [7]. The processes of implementing quality management systems for environmental components have become one of the priorities as part of the supply and demand of non-renewable resources. Also, constitutes a competitive advantage that favors economic re-entry by reducing costs in raw materials, water and energy [8]. Implementing a cleaner production program in the municipal slaughterhouse of the Pelileo canton, the measures adopted in this context will reduce the consumption of raw materials, supplies and energy. In addition, these measures minimize

8 Development of Cleaner Production Alternatives in Water Management …

107

the discharge of pollutants into the environment to promote environmental responsibility, and sustainable development and anticipate future sanctions by the competent environmental authority, mainly emphasizing those benefits related to the reduction of the water footprint by reducing costs and improving the company status [9]. Our research work seeks to reduce the risks of non-compliance with environmental legal requirements. The novelty of our research work is focused on the methodology used; the content of the research could be useful for future research aimed at the development of an ex-post environmental impact study for sectors dedicated to slaughtering. Likewise, the matrices elaborated as part of the environmental impact assessment instruments would contribute to generating a checklist on the cause-and-effect relationships in livestock processes such as beef and pork production. This document is organized as follows: Sect. 8.2 presents the methodology; Sect. 8.3 describes the results and discussion. Finally, Sect. 8.4 presents the conclusions and future research.

8.2 Methodology Next, the methodology is presented where the phases for the execution of activities related to the implementation of cleaner production strategies in the slaughtering processes are described. First, the situational diagnosis of the study area is developed through a direct approach to observe the facilities and recognize the processes, then the environmental diagnosis is presented, in this section all the information on the operation process is compiled, and the stages that generate the most contamination are established and the water resource is evaluated with the parameters established in the study. Then we present the environmental impact assessment using the Leopold matrix and finally, we propose cleaner production strategies for water management in the study area.

8.2.1 Situational Assessment The phases involved in the production process of bovine slaughter were evidenced, such as cattle reception, herding, resting, stunning, bleeding, disgorgement, cutting, skinning, evisceration, carcass washing, antemortem inspection, post-mortem control, airing and dispatch. Finally, the significant processes of slaughter in pigs are: scalding, depilated and flamed. The initial situational diagnosis of the slaughterhouse made it possible to identify the places with the highest water consumption and the production of wastewater with a high contaminant load. Figure 8.1 shows the cattle and pig slaughtering process, as well as the final destination of the water in all its stages. The normal slaughter average is 20 cattle/h. In addition, water is used for cleaning facilities, an auxiliary

108

S. T. Llerena et al.

Fig. 8.1 Slaughter operation flowchart

process that requires disinfectants that affects processes such as eutrophication in water, in such a way that it is an incident factor in water quality.

8.2.2 Environmental Diagnosis The lifting of the environmental diagnosis allowed to collect information about the quality of the water. To this end, samples were taken of the effluents resulting from each of the phases of the slaughter process to evaluate the physicochemical and microbiological parameters of the water. These parameters were evaluated with the current Ecuadorian environmental regulations, specifically Book VI Annex 1 referring to Table 8.1 corresponding to the discharge limits to a body of freshwater of the Unified Text of Secondary Legislation of the Ministry of the Environment (TULSMA). This annex consists of 12 parameters that correspond to the limits of discharge to a freshwater body, where only 5 parameters were considered in this research (are found in an asterisk in parentheses). Parameters involving organic matter degradation have been selected based on the high organic matter load resulting from the slaughtering production process. A total of 5 parameters were considered to be measured: Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5 ), Phosphorus, Nitrogen, Oils and Fats. The

8 Development of Cleaner Production Alternatives in Water Management …

109

Table 8.1 Limits of discharge to a body of fresh water Parameters

Represented

Oils and fats (*)

Substances soluble in hexane mg/l 0,3

Unit Limit maximum permitted

Chlorides

Cl−

mg/l 1 000

Coliforms fecal

MPN/100 ml

–

Removing >99,9%

Chemical oxygen demand (*) COD

mg/l 250

Biochemical oxygen demand BOD5 (*)

mg/l 100

Phosphorus (*)

P

mg/l 10

Nitrates + nitrites (*)

N

mg/l 10,0

Total nitrogen Kjedahl

N

mg/l 15

Solid settle able

SS

ml/l

Solid suspended totals

TSS

mg/l 100

Total solids

TS

mg/l 1 600

Temperature

°C

–

1,0

samples were collected, preserved and analyzed according to the standardized standard methods: APHA/AWWA/WPCF 17 ed, to evaluate the quality of the wastewater discharged into the Patate River, an important functional water resource for the direct and indirect areas of influence. The collection of the samples was carried out in October, November and December to cover the months with the highest demand. In addition to emphasizing the characterization of the critical phases that generate the greatest contaminant load in the wastewater from the process, it was considered to carry out the analyses corresponding to the discharge effluent to estimate the efficiency of the treatment systems in the Wastewater Treatment Plant (WWTP).

8.2.3 Assessment of Significant Environmental Impacts A Leopold matrix method oriented to the productive processes of slaughtering pigs and cattle (see Tables 8.3 and 8.4) was used to establish the phases that have the greatest impact on the environmental components (Table 8.2). Table 8.2 Assessment scale of environmental impacts in the Leopold matrix

Scale estimated values

Impact assessment

0

There is no cause-effect relationship

1

Slight

2

Moderate

3

High o severe

0

0

−1

0

2

1

4

0

−16

(+) Average

Arithmetic Average

0

0

0

0

0

0

1

2

−1

0

0

−1

−2

9

Noise

Odor

Rank

−1

1

1

(−) Average

Air

Expense

−1

−2

Rank

Water

0

−12

0

5 −11

0

5

3

−2

−3 3

0

0

0

0

1

−1

1

−2

0

0

Raised

0

0

0

0

1

−1

0

0

0

Daze

2

Rank

Soil

Repose

−1

Reception

Components

Stages of the bovine slaughtering process

−15

0

5

0

0

0

0

0

0

3

−2

1

−3

0

0

Blooded

Actions with possible effects of causing environmental impacts

Matrix for the assessment of environmental impacts

−8

0

5

3

−1

0

0

0

0

1

−1

1

−2

0

0

Deguelle

−5

0

4

0

0

0

0

0

0

1

−1

1

−2

0

0

Cut legs/ heads

−6

0

4

0

0

0

0

0

0

1

−2

1

−2

0

0

Bare less

−8

0

4

0

0

0

0

0

0

1

−2

1

−3

0

0

Evisceration

Table 8.3 The Leopold matrix for the identification of environmental impacts in the bovine’s slaughter

−6

1

3

0

0

0

0

0

0

1

–2

1

–2

0

0

Wash

5

49

3

2

2

10

11

1

(−) Average

−3 −86

3

3

0

0

2

–3

1

–1

3

0

1

–1

0

0

Ore

49

5

0

0

0

0

0

0

(+) Average

−86

−18

−6

−1

−21

−21

−2

Total

110 S. T. Llerena et al.

2

−6

Total

1

0

(+) Average

0

1

0

0

0

0

0

−1

0

0

0

1

0

2

1

2

−1

5

Noises

Odor

Rank

0

2

(−) Average

Air

Intake

−1

−2

Rank

Water

0

0

0

Rank

Soil

Daze

−1

Arrival

Environmental components

Stages of the porcine slaughtering process

−6

0

1 −12

0

3

3

−2

−2

3

0

0

0

0

1

−3

1

−3

0

0

Scalding

0

0

0

0

0

0

0

0

0

0

Raised

Actions with possible effects of causing environmental impacts

Matrix for the assessment of environmental impacts

0

0

0

0

0

0

0

0

0

3

0

2

0

0

0

Bare

-6

0

1

3

0

0

0

0

0

1

0

1

−3

0

0

Flame

−18

0

3

0

0

0

0

0

−2

0

4

0

−1

0

−2

0

1 0

3

−2

1

−2

0

0

Evisceration

−2

−3

1

−3

0

0

Wash

Table 8.4 The Leopold matrix for the identification of environmental impacts in pig slaughter

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

Oreo

−1

1

1

0

0

0

0

0

0

1

0

1

−1

0

0

Storage

−49

0

0

3

2

2

4

7

1

(−) Average

1

8

0

0

0

1

0

0

(+) Average

−49

−12

0

0

−14

−23

0

Total

8 Development of Cleaner Production Alternatives in Water Management … 111

112

S. T. Llerena et al.

The Leopold matrix assumes a qualitative assessment of the interaction of activities on environmental factors, recognizing the potential impact on the environment and its surroundings to a lesser and greater degree. This impact will be positive or negative depending on the activities of the operating process. The environmental impacts were evaluated on a scale of 1–3, where a value of 3 was assigned to the activity with high environmental impact, 2 with moderate environmental impact, 1 with slight environmental impact and 0 was established to indicate that there is no cause-effect relationship (see Table 8.2). In the rows, the environmental factors were listed and in the columns, the actions of the slaughtering process that influenced the factors evaluated were placed. From the resulting intersection between rows and columns, a valuation is established, the one that comes closest to the effect that can be caused. The assessment of the environmental impacts in the stages of the slaughter process considered the numerical criteria: 3 (high), 2 (moderate) and 1 (slight). The aforementioned criteria refer to the assessment of the risks presented by the environmental components of an activity. In summary, the most significant checked boxes are evaluated, and a number between 1 and 3 is placed in the upper left corner of each box to indicate the relative magnitude of the effects (1 represents the smallest magnitude, and 3 is the largest). Also, a number between 1 and 3 is placed in the lower right corner to indicate the relative importance of the effects. This tool has a much broader vision that involves the evaluation of other environmental systems; likewise, it allowed us to corroborate the information obtained from the direct observation of the slaughter stages in terms of the focal points of incidence in the management of water quality and use. In addition, it was considered to evaluate the soil and air environmental components in the environmental impact assessment matrices. The soil component was valued around quality and the air component was valued on odors, noise and vibrations. In the soil quality component, there were no interactions of cause and effect, therefore a value of zero was assigned. In the evaluation of noise and vibration, significant interactions were found in the Stunning and Hoisting stages of cattle slaughter. In the Stunning stage, a pneumatic gun is used to knock out the animals [10]. Meanwhile, in the Hoisting stage, a rail system is used to facilitate the mobility of the cattle to the other stages [11].

8.2.4 Formulation of Cleaner Production Alternatives for Water Management Following the quantitative and qualitative assessment of the environmental impacts around the water resource of the slaughterhouse, a series of alternatives were developed, such as the improvement of rapid blood collection processes, redesign of the systems in the blood treatment plant, wastewater among others, aimed at improving the quality management and use of water.

8 Development of Cleaner Production Alternatives in Water Management …

113

The quantitative assessment oriented to the monitoring of effluents provided relevant information focused on the determination of focal points that generate the greatest negative impact on water quality. Likewise, it allowed measuring the operational efficiency of the treatment systems to direct the proposal of cleaner production alternatives in the management of water quality and avoid future sanctions by the environmental authority. The identification matrix of environmental impacts in cattle slaughter (see Table 8.3) yielded a total of 132 interactions, of which 44 interactions evidenced the inadequate management of the water resource of the slaughter center. In the soil component, the assessment made on quality showed a total of 22 interactions, most of which were zero. The stage of receiving the cattle in the pens involves the generation of polluting waste that affects the soil. Wastes such as manure and urine that are produced during cattle reception are mixed on the floor. The soil contamination produced by slaughter residues affects soil characteristics, since when they accumulate, they affect the ecosystem and humans through the food chain [12]. The identification matrix of environmental impacts in the slaughter of pigs (see Table 8.4) yielded a total of 120 interactions, of which 40 significant interactions evidenced the impact of the activities. The activities carried out in the scalding, washing and evisceration stages generate greater contamination on the quality and consumption of water. In the assessment of the environmental impacts of the pig slaughter activities, the assessment of the air and soil components was carried out. The air component was evaluated on the quality, smells, noise and vibrations produced by the slaughter activity. The noise and vibration components presented significant interactions in the blanching, raising and flaming stages. In the scalding stage, tanks with large volumes of boiling water are used. In the process of slaughtering pigs, after finishing with the bleeding, we proceed to the next stage which is scalding, an operation that consists of loosening the pilosity of the skin by means of moist heat to facilitate the removal of the bristles by action mechanics [13]. The blanching can be done by immersion or spraying with hot water between 60 and 65 °C or by condensation, using steam. The scalding stage is carried out by immersion, the bled pigs are immersed in metal tubs of hot water for a period of 4 to 5 min. However, it is difficult to control the odors that permeate the carcass, as a result of the remains of natural secretions that the animal releases during death rattles [14]. On the other hand, cross-contamination due to impurities in the water affects contamination and the production of odours. Likewise, the poor management of water in the tubs, as there is no correct recirculation system, aggravates the situation of air and water quality.

114

S. T. Llerena et al.

8.3 Result and Discussion The first phase which involves the situational diagnosis revealed the quantitative profile of the use of water in the slaughterhouse, this analysis is shown in Table 8.5. The average flow of water consumption is around 800 m−3 day−1 . The values obtained from water consumption indicate that the washing phases in bovines and scalding in pigs require greater water consumption according to the estimation of the average daily flow, as shown in Table 8.5. The recognition of the stages in a critical state regarding water consumption showed that the washing phase requires enormous amounts of water to eliminate most of the remains of blood in the carcasses. On the other hand, the stage of scalding in pigs demonstrates the lack of maintenance by the mechanical personnel to periodically readjust the capacity of the tanks. The automation of equipment and systems in the slaughterhouse has an impact on the generation of environmental impacts, especially in the water component. Analyses aimed at measuring physicochemical parameters such as BOD5 , COD and nitrogen demonstrated the insufficiency of wastewater treatment methods. In the environmental diagnosis, the effluents from the slaughterhouse were characterized to determine the quality of the water. Table 8.6 presents the values concerning the sampling of the wastewater generated during the production process in October, November and December 2021. In this same Table 8.6, it was shown that the values obtained from the parameters (BOD5 , COD and Nitrogen) exceeded the permissible discharge limits per environmental regulations. The bleeding and evisceration stages turned out to be the most polluting according to the values obtained from the analyses carried out. In the same way, the deficiency in the wastewater treatment systems was evidenced, since the measured parameters (BOD5, COD and nitrogen) exceeded the values established in the permissible limits of the regulatory environmental regulations. This particular would induce the generation of future sanctions to the establishment due to non-compliance concerning the law that regulates environmental responsibility. The months of October, November and December were considered temporary references where there is a greater demand for water consumption. In addition, the parameters (BOD5, COD and nitrogen) were evaluated as indicators of water quality, considering the stages in a critical state that generate greater contamination of the water resource. Table 8.5 Main uses of water analyzed in the slaughterhouse

Process Bled

Flow m−3 day−1 80

Percentage (%) 10

Washed

500

63

Evisection

100

13

Scalding

120

15

8 Development of Cleaner Production Alternatives in Water Management …

115

Table 8.6 Results of the water quality parameters were analyzed in October, November and December 2021 Processes

October

Water quality indicators COD limit: BOD5 limit: Total Oils and 250 mg l−1 O2 100 mg l−1 O2 phosphorus fats limit: limit: 0,3 mg l−1 10 mg l−1

Total nitrogen limit: 15 mg l−1

500

140

8,3

0,1

10

80

50

2,0

0,02

8

Evisceration 310

12

Bled Cattle washing

115

9,0

0,1

100

65

3,1

0,1

2

525

165

9,0

0,2

18

76

63

2,0

0,1

3

Evisceration 345

15

Scalding November Bled Cattle washing

128

9,0

0,2

115

68

4,0

0,015

535

200

9,3

0,2

22

92

76

3,2

0,1

5

Evisceration 408

186

9.5

0,3

19

Scalding December Bled Cattle washing

2

Scalding

120

81

4,2

0,2

3

Treated wastewater

258

163

8,0

0,2

18

The COD values in the bleeding and evisceration stages exceed the estimate of the permissible limit, especially in December, as well as the BOD5 and total nitrogen, as can be seen in Table 8.6. In the environmental impact evaluation phase, a Leopold matrix was executed, validating the information obtained from the quantitative analysis of the water resource. The slaughter stages that generate the greatest negative impact on water management are scalding and carcass washing, due to the operating times to remove traces of blood from the cattle and the lack of equipment maintenance. The bleeding and evisceration stages affect the condition of the residual waters mainly around the physicochemical indicators of water quality. Finally, as part of the formulation of cleaner production strategies focused on managing the use and quality of water in critical areas, the following was proposed: (1) Install regulated sprinkler systems for carcass washing. (2) Incorporate hoses with automatic devices for closing valves when the operational processes of slaughter are finished. (3) Improve the water capacity tanks in the scalding area.

116

S. T. Llerena et al.

(4) Redesign the wastewater treatment processes in the wastewater treatment plant to achieve proper purification of contaminants. (5) Incorporate an anaerobic biological treatment system to degrade organic matter and reduce the levels of oxygen demand in the effluents. (6) Recirculate the water from the scalding stage after cooling for cleaning the facilities, since these waters exceed 60 °C in such a way that they favor the asepsis of the facilities. (7) Have chlorine or disinfectant dosing systems to maintain the microbiological quality of the water and prevent detergents from influencing eutrophication processes by depleting the oxygen in the water, which produces bad odors. (8) Install a rapid blood collection system to reduce the mixing of these materials with the effluents and prevent the accumulation of organic matter. (9) Carry out periodic inspections of the hydraulic network to establish maintenance and disinfection plans for facilities and equipment. (10) Carry out preventive and corrective maintenance on the equipment of the wastewater treatment plant to avoid damage to the mechanical systems.

8.4 Limitations Among the limitations of our study, we evidenced the lack of ethical commitment with those responsible for the administration of the slaughterhouse, since they did not have access to the documentation related to the design structure of the WWTP, to justify the poor water resource management that was evidenced during the environmental diagnosis phase, when performing the analysis of the discharge effluents after treatment. The limitations that we identified in our line of research in the use of environmental impact assessment matrices, since it is required to be extremely careful when evaluating the incidence of the activities carried out in the slaughtering processes on the environmental components, since most of them have a negative impact, so it is necessary to have the ability to justify and evidence each of the assessments made in the study and thus foresee that future environmental sanctions are generated.

8.5 Conclusion The quantitative and qualitative evaluation of the environmental impacts of the slaughter center showed the stages in a critical state that generate greater contamination and demand for water resources, thus emphasizing that most of the cleaner production strategies are highly efficient due to the nature of mitigation it possesses within its context.

8 Development of Cleaner Production Alternatives in Water Management …

117

The assessment carried out in the Leopold matrix demonstrated the interaction of negative impacts, mostly concentrated in the quality and consumption of water for the stages of bleeding, evisceration, cleaning and scalding. The study carried out in the slaughter center demonstrated the poor capacity of the technical maintenance staff to execute control, surveillance and recovery processes of operating equipment. Besides, the insufficient capacity of the wastewater treatment systems in the WWTP to generate a correct purification of contaminants in the effluents was evidenced. According to the parameters analyzed (COD, BOD5 and N), the deficiency of the effluent purification systems was reaffirmed. As a result of the analysis carried out, it was verified that the bad odors coming from the WWTP are due to the surplus material that is not being adequately treated. The implementation of an anaerobic biological reactor as part of the cleaner production proposals would reduce the levels of COD and BOD5. Anaerobic digestion processes in wastewater treatment systems with a high contaminant load increase the efficiency and performance of the processes by producing energy from the conversion of waste and biomass [15]. The possible integration of anaerobic processes in the treatment of effluents with organic matter is closely linked to the principles of the circular economy, generating value-added inputs such as biogas to produce electricity [16]. Consequently, generates inputs for the agricultural sector such as biofertilizers (See Fig. 8.2). Finally, as part of the cleaner production strategies, the adoption of dry-cleaning procedures, control of the water used, improvement of rapid blood collection processes, automation of water distribution systems and the redesign of wastewater treatment systems. In summary, the redesign of the WWTP systems to reduce BOD5 and COD involves the implementation of more sophisticated and rigorous processes such as ozonification, this process generates an effective treatment to reduce these Fig. 8.2 Diagram of the anaerobic system as a proposal for sustainable improvement for wastewater treatment in the WWTP of the slaughterhouse

118

S. T. Llerena et al.

parameters. Ozonation is considered an aerobic process, so it involves oxidative processes. Ozone, having a high redox potential, decomposes organic matter in wastewater [17]. However, ozonation alone does not completely degrade organic pollutants, since ozone is selective in attacking some organic compounds [18]. Sometimes the ozonation processes produce toxic intermediate products because it requires inorganic compounds to improve their removal efficiency. The application of metallic ions such as Mn(II), Al(III), Fe(II), Fe(III), Cu(II), Co(II) and Ni(II) improve the effectiveness of ozone in the oxidation of pollutants organic [15].

Appendix List of Abbreviations WWTP Wastewater treatment plant BOD5 Biochemical Oxygen Demand COD Chemical Oxygen Demand MAGAP Ministry of Agriculture, Livestock, Aquaculture and Fisheries TULSMA Text of Secondary Legislation of the Ministry of the Environment APHA-AWWA-WPCF Standard Methods for the examination of water and wastewater N Nitrogen

References 1. Balboa CH, Somonte MD (2014) Circular economy as an ecodesign framework: the ECO III model. Inf Técnico 78(1):82. https://doi.org/10.23850/22565035.71 2. Domínguez J, Guamán S (2014) Sensitivity analysis of the Ecuadorian livestock sector: prices and tax scheme. Rev Mex Agroneg 34(6):655–664 3. Galdeano MC, et al (2018) Effect of water temperature and pH on the concentration and time of ozone saturation. Brazilian J Food Technol 21. https://doi.org/10.1590/1981-6723.15617 4. Gutema FD et al (2021) Assessment of hygienic practices in beef cattle slaughterhouses and retail shops in bishoftu, ethiopia: implications for public health. Int J Environ Res Public Health 18(5):1–13. https://doi.org/10.3390/ijerph18052729 5. Iromi T et al (2021) Comparison of meat quality traits of scalded and non-scalded broiler breast meat. Anim Ind Technol 8(2):87–94. https://doi.org/10.5187/ait.2021.8.2.87 6. Le NT, et al (2021) Design and implement a monitoring and early warning system of water quality for cage fish culture on Chava river. AIP Conf Proc 2420. https://doi.org/10.1063/5.006 8352 7. Ma S et al (2020) Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries. J Clean Prod 274:123155. https://doi.org/10.1016/j.jclepro. 2020.123155 8. Majedi H et al (2020) Integrated surface and groundwater resources allocation simulation to evaluate effective factors on greenhouse gases production. Water Sci Technol Water Supply 20(2):652–666. https://doi.org/10.2166/ws.2019.194

8 Development of Cleaner Production Alternatives in Water Management …

119

9. Nurfarhana T, et al (2021) Enhancement of bioreactor performance using acclimatised seed 10. Nyam YS et al (2020) Towards understanding and sustaining natural resource systems through the systems perspective: a systematic evaluation. Sustain 12(23):1–20. https://doi.org/10.3390/ su12239871 11. Pazmiño ML, Ramirez AD (2021) Life cycle assessment as a methodological framework for the evaluation of the environmental sustainability of pig and pork production in Ecuador. Sustain 13:21. https://doi.org/10.3390/su132111693 12. Pepin B et al (2021) Survival of swine pathogens in compost formed from preprocessed carcasses. Transbound Emerg Dis 68(4):2239–2249. https://doi.org/10.1111/tbed.13876 13. Plummer PJ et al (2018) Management of Coxiella burnetii infection in livestock populations and the associated zoonotic risk: a consensus statement. J Vet Intern Med 32(5):1481–1494. https://doi.org/10.1111/jvim.15229 14. Sevillano CA et al (2021) Anaerobic digestion for producing renewable energy-the evolution of this technology in a new uncertain scenario. Entropy 23(2):1–23. https://doi.org/10.3390/ e23020145 15. Shewa WA, Dagnew M (2020) Revisiting chemically enhanced primary treatment of wastewater: a review. Sustain 12:15. https://doi.org/10.3390/SU12155928 16. Sim SJ et al (2004) Removal of natural organic matter (NOM) by ozone oxidation and biological filtration using a rope-type biofilter. J Ind Eng Chem 10:349–353 17. Wotton SB et al (2000) Electrical stunning of cattle. Vet Rec 147(24):681–684. https://doi.org/ 10.1136/vr.147.24.681 18. Yu L et al (2020) Discussion on urban black odor water body treatment and long-term management and maintenance. IOP Conf Ser Earth Environ Sci 428(1):8–13. https://doi.org/10.1088/ 1755-1315/428/1/012010

Chapter 9

Analyzing the Impact of Food-Energy-Water Nexus-Based Agricultural Patterns on Regional Water Resources Rashi Dhanraj

and Yogendra Shastri

Abstract The role of agriculture is crucial in India’s food, energy and water (FEW) nexus. The agricultural sector consumes 80% of the total available water. The potential utilization of agricultural residue as feedstock for biofuel has brought concerns over India’s water crisis. A study was performed where the optimization model was formulated to generate a trade-off between profit earned by people engaged in biofuel production from the agriculture stage and water consumption. That study generated three cropping patterns CP1 , based on minimizing irrigation consumption; CP2 , based on optimization of ethanol production cost; CP3 , based on maximum profit earned by farmers. The current study used the hydrological model (SWAT) to compare three cropping patterns from the previous study with the region’s actual cropping pattern (Base case). The aim was to verify the trend shown in the earlier study and gain information that could be used to improve the optimization model. The Upper Bhima river basin within the Maharashtra border was used for the case study. The Upper Bhima River basin selection was made as the districts covered in this region are major sugarcane producers in Maharashtra. The SWAT model showed how the presence of different crops impacts the basin’s overall evapotranspiration and thus the groundwater aquifer storage. The average annual percolation for the base case was 22.7% higher than the CP1 . The irrigation water consumption was highest for the base case; the irrigation water withdrawal was 2196 m3 /ha. Keywords Optimization · Hydrological model · Lignocellulosic biofuels · Water resources

R. Dhanraj · Y. Shastri (B) Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_9

121

122

R. Dhanraj and Y. Shastri

9.1 Introduction Studies on the prospect of biofuel generation from agricultural residue and its socioenvironmental impact have been carried out by various researchers [1, 2]. In 2018, the Government of India (GOI) set the target of meeting 20% ethanol blending under the Ethanol Blended Petrol (EBP) Programme by 2030 [3]. In the coming years, biofuel consumption has been projected to increase. The increased biofuel consumption could lead to growing crops with higher income potential. Insufficient agricultural planning is of grave concern in water-stressed regions like India [2, 4]. Around 80% of the total available water is consumed by the agriculture sector [5]. In 2018, the water stress threshold was 1667 m3 per capita per year, while water availability per capita per year in India was around 1458 m3 [6]. The interlink between agriculture, bio-energy, and water comes under the food, energy, and nexus category. Studies have been carried out where the FEW nexus approaches have been applied in agricultural planning to help analyze the optimum water utilization under safe water threshold limits [7]. Researchers formulated multi-objective optimization to analyze the trade-off between economic and environmental objectives when different feedstocks were used for biofuel production [8, 9]. Keeping in mind the primary goal of agriculture is to supply food, it is crucial to understand the interaction between food, energy and water. An optimization model for biomass to an energy system based on the economic benefits and regional resources helps analyze the impact of land allocation to crops on regional water resources [9]. However, precise quantification of water resources requires a hydrological model. Soil water analysis tool (SWAT) is a hydrological model that has been utilized to analyze the quality of water, crop yield, and soil erosion and apply this information to lignocellulose-based biorefinery [10]. This work focuses on the impact of different agricultural patterns on water resources utilizing the soil water analysis tool. The agriculture pattern was obtained from the optimization model. The article has been divided as follows: Section second details the method used for the study. Section third covers the case study, followed by section fourth which covers results and discussion. The article concludes in section fifth.

9.2 Methodology The work was divided into parts; first, an optimization model was formulated where agricultural land allocated to crops was the decision variable, and regional land and water resources were constraints. The description of the model is not part of this paper. This paper focuses on the second part, where the soil water analysis tool was applied to analyze the impact of agricultural patterns on water resources.

9 Analyzing the Impact of Food-Energy-Water Nexus-Based Agricultural …

123

9.2.1 Optimization Model The mixed-integer linear programming problem was formulated to solve the optimization problem in General Algebraic Modelling System (GAMS) version 24.9.2 and solved using the CPLEX® MILP solver [11]. The model was run for three objectives: maximization of farmer’s profit, minimization of ethanol cost, and minimization of irrigation water requirement. The land was allocated to crops to meet each objective; agricultural land, rainwater, and groundwater were the constraints. The model would stop the land allocation once the ethanol demand was met. The model had certain limitations as it did not consider different factors that impact percolation in a region and the impact on surface water. Thus, we used the hydrological model to study cropping patterns’ impact on percolation and surface water. The three agricultural patterns obtained from running the model were used in the hydrological model.

9.2.2 Hydrological Model The hydrological model used in this work was the soil water analysis tool (SWAT). SWAT is a semi-distributed physically based hydrological model used for planning water resource management from watershed level to basin level. The SWAT could give satisfactory results without calibration and has been used in cases where data is insufficient [12]. The work plan is shown in the flowsheet in Fig. 9.1. In this study, the hydrological cycle, crop yield, and auto-irrigation from agricultural management have been used. The water balance for the sub-basin is based on Eq. (9.1). SW t = SW 0 +

t 

(RW i − R Oi − E T i − P E Ri − GW Qi )

(9.1)

(i=1)

where i is the time in days, SWt is the soil water content at time t (mm), SW0 is the initial soil water content (mm), RWi is the amount of precipitation on the day i (mm), ROi is the amount of surface runoff on the day i (mm), ETi is the amount of evapotranspiration on the day i (mm), PERi is the amount of water entering the vadose zone from the soil profile on the day i (mm) and GWQi is the amount of return flow on the day i (mm). The agricultural management component of SWAT has two options for irrigation. One irrigation option is when the user manually enters the timing and amount of irrigation, while the other is an auto-irrigation option. The auto-irrigation function in SWAT is based on a water stress identifier of either (1) plant water demand or (2) soil water content. When the plant water demand command is used, the water stress is calculated using Eq. 9.2.

124

R. Dhanraj and Y. Shastri

Fig. 9.1 Flow chart of the SWAT model application

WS = 1 −

ET P ET

(9.2)

where WS stands for water stress, PET stands for the maximum plant transpiration (mm), and ET is the actual amount of transpiration (mm). The auto-irrigation based on soil water content becomes active when the total soil water content falls below field capacity by more than the user-defined soil water deficit threshold. The SWAT model requires a geographic information system as an interface. The information on the location under study and data used as input for SWAT is discussed in the next section.

9.3 SWAT Application to Maharashtra River Basins The location selected for the study was the Upper Bhima river basin, as shown in Fig. 9.2. A large portion of this lies in Maharashtra, while some part is present in Karnataka. As this study is only concerned with Maharashtra, the Bhima basin region in Karnataka has been ignored. The location of the Bhima basin is 19°14' north, 17°44' south, 75°54' east and 73°65' west. The area covered by the Sina River was not included in this work. The catchment area of the Bhima River under study is around 30,260 km2 . According to the report published by the Water Resource Department Government of Maharashtra, India, around 72% of the basin area is

9 Analyzing the Impact of Food-Energy-Water Nexus-Based Agricultural …

125

Fig. 9.2 The land use map of Maharashtra state shows the catchment area selected to study the hydrological response for different cropping patterns

under cultivation. The maximum area is under the water scarcity zone, while the western area partially comes under the assured rainfall zone and partially under the transition zone. The average rainfall received in the region ranges from 415 to 4240 mm, and the temperature ranges from 33.4 to 39.1 °C. Approximately 71% of irrigation water comes from surface water. The Upper Bhima river basin lies in the Deccan trap. The soil types found in the region are sandy clay loam, clay loam and clay [13]. The Quantum Geographic Information Service (QGIS) version 3.14 has been used in this work [14]. The interface used for integrating the SWAT model with QGIS 3.14 is QSWAT3_64 version 1.1 [15, 16]. The digital elevation model (DEM) used to create a stream network and delineate the watershed area under study was obtained from CGIAR (2008) [17]. The spatial resolution of DEM was 30 m, and the horizontal datum was WGS84. The land use map was obtained from DAAC (2016) [18], while the soil use map used the Harmonized World Soil Database raster file obtained from the Harmonized World Soil Database v 1.2 [19]. SWAT utilizes TauDEM to create stream networks and delineate watersheds. The minimum drainage area that is required to maintain the existence of the river permanently is the drainage area threshold value [20]. The threshold value of the drainage area ascertains that the simulated drainage network is in agreement with the real drainage network. The threshold value for the watershed was 900 km2 . The length of the main channels varies from 32 to 184 km, with the

126

R. Dhanraj and Y. Shastri

depth varying from 3.75 to 8.06 m. The spatial data are input for the QSWAT model while weather data is fed to the SWAT editor. In this study, the reservoir was not selected. The focus was not on analyzing the trend that the cropping pattern could have on the water resources. The model is not used to study the region’s water availability and water withdrawal. Thus, the model was calibrated for crop yield and actual evapotranspiration. The crops selected for the study were wheat, sorghum, paddy, cotton and sugarcane. Amongst these, wheat, sorghum (grown in rabi season), and sugarcane are required to pass through two calendar year periods. The model was run from 2012 to 2013, and the annual average value was reported in the results section. The management option in the SWAT editor was edited, and the date for the crop plantation, the date for initiation of auto irrigation, auto fertilization and kill harvest have been tabulated in Table 9.1. The optimization model was run for the whole Maharashtra state for the three objectives. The land allocated by the model at the state level was normalized, and the percentage of land distribution for each cropping pattern was applied to the Upper Bhima River basin. Here agricultural land is used more than once; thus, the percentage of agricultural land is compared for two cropping seasons. The period between June and September is termed Kharif season, and the period between October and February is termed Rabi season. The highest percentage of agricultural land is considered as agricultural land utilized, and the rest of the land is unutilized. The distribution has been tabulated in Table 9.2. • Base case: The actual percentage of cropland area occupied in the Bhima basin by the crops under study was considered, and the rest of the land is under the unused land category. • Cropping pattern 1 (CP1 ): The first objective, minimization of irrigation water requirement, has only two crops, sorghum and sugarcane. Sorghum is generally rainfed, while sugarcane, due to its high ethanol yield, was selected. • Cropping pattern 2 (CP2 ): The land allocation to crops for the objective minimization of ethanol cost has the most unused land. In spite of the large land area is not used for cultivation, all the crops were allocated land. The crops with low biomass cost and model ethanol yield were selected here. Table 9.1 Management operation schedule for paddy Management operation

Cotton

Sugarcane

Rice

Sorghum (Kharif)

Wheat

Sorghum (Rabi)

Plant

7-May

10-June

15-May

7-June

24-Oct

15-Nov

Irrigation

7-May

10-June

15-May

7-June

24-Oct

15-Nov

Fertilization

7-July

10-Sep

10-June

7-July

15-Dec

15-Dec

Kill

21-Oct

15-Oct (the growth period passes to next year)

7-Oct

30-Sept

15-Feb (the growth period passes to next year)

7-March (the growth period passes to next year)

9 Analyzing the Impact of Food-Energy-Water Nexus-Based Agricultural …

127

Table 9.2 The percentage distribution of agricultural land allocated to crops in different scenarios adapted from the results of the optimization model [9] Objective function

Minimization of ethanol cost

Cotton

5

Sorghum (Kharif) + Wheat

5

Minimization of irrigation water requirement

Maximization of farmer’s profit

Actual land pattern

19

3

Wheat

4

Sorghum (Kharif)

2

Paddy

1

Cotton + Sorghum (Rabi)

4

28

12 2 12

Paddy + Sorghum (Rabi)

1

Sorghum (Rabi) Sugarcane

7 21

1

Unused agricultural 82 land

1

5

17

71

61

36

• Cropping pattern 3 (CP3 ): The cropping pattern that was obtained for maximization of farmer’s profit was used during the generation of the HRU step of the SWAT model. Here the unused land is less, as the model allocated enough land to maximize farmers’ profit within the regional resource constraint. The following section discusses the results generated for all the cropping patterns.

9.4 Results and Discussion The result of the three agricultural patterns obtained from the optimization model and actual agricultural pattern has been discussed here. As shown in Table 9.3, the water stored in the deep aquifer (confined aquifer) was compared, and the results show that the actual land pattern has the lowest value, 14,272 m3 /ha, while the minimum irrigation water requirement has the highest value, 19,470 m3 /ha. That was due to the water being withdrawn for irrigation. The actual evapotranspiration was low for the cropping patterns for minimum irrigation requirements due to 28% of agricultural land being allocated to sorghum, primarily rainfed. The base case has high irrigation demand due to sugarcane cultivation. As shown in Table 9.3, the irrigation water requirement for the minimum irrigation requirement case was 70% less than the base case. Amongst all the scenarios, the case minimum ethanol cost had the least agricultural land allocated (18%) to the crop under consideration; however, it was the case of minimum irrigation water where the ground flow

128

R. Dhanraj and Y. Shastri

Table 9.3 Comparison of results generated for each scenario (Annual average values with units in m3 /ha) Minimum irrigation Minimum ethanol Maximum 3 framer’s profit requirement (m3 /ha) cost (m /ha) (m3 /ha)

Actual land pattern (m3 / ha)

Deep aquifer water storage

19,470

19,038

18,520

14,272

Irrigation water consumed

658

990

1758

2196

Actual evapotranspiration

5172

5346

5590

6432

Groundwater flow

286.1

422.6

844.8

1592.5

was lowest at 32% less compared to the former. The reason was that in the case of minimum ethanol cost, agricultural land was allocated to crops like cotton and wheat, which required irrigation, while Kharif sorghum is primarily rainfed. The irrigation efficiency was 60 to 70%; the extra irrigation water which was not taken up by the crops contributed to the return flow. The groundwater flow leaves the sub-basin, and the water level in the aquifer decreases, thus leading to water stress in the region.

9.5 Conclusion The study was done to look into the impact of crops that have shown to potential to meet biofuel demand in India, thus making it part of the food, energy and water nexus study. The hydrological model selected here was the Soil and Water Assessment Tool (SWAT), as it could be used for different spatial and temporal scales. SWAT also is helpful in studying the interaction between crops and water resources. This study was used to understand the balance between precipitation, water withdrawal and water reaching the deep aquifers. The data generated from the SWAT model for different cropping patterns could be used to avoid over-exploitation of groundwater. The inclusion of the hydrological model could play an important role in the study of techno-ecological synergies of value-added products generated from agricultural residue.

References 1. Hiloidhari M, Das D, Baruah DC (2014) Bioenergy potential from crop residue biomass in India. Renew Sustain Energy Rev 32:504–512. https://doi.org/10.1016/j.rser.2014.01.025 2. Purohit P, Dhar S (2018) Lignocellulosic biofuels in India: current perspectives, potential issues and future prospects. AIMS Energy 6(3):453–486

9 Analyzing the Impact of Food-Energy-Water Nexus-Based Agricultural …

129

3. Sarwal R (2021) Ministry of Petroleum & Natural Gas. Road map for Ethanol blending in India 2020–25 : report of the expert committee. NITI Aayog, India 4. Lee JY, Naylor RL, Figueroa AJ, Gorelick SM (2020) Water-food-energy challenges in India: political economy of the sugar industry. Environ Res Lett 15(8) 5. Rakitskaya K (2021) Master thesis in sustainable development 2021/45 Examensarbete i Hållbar utveckling Water-energy-food nexus in India: a review of interlinkages and challenges for a sustainable development 6. Rasul G, Neupane N, Hussain A, Pasakhala B (2021) Beyond hydropower: towards an integrated solution for water, energy and food security in South Asia. Int J Water Resour Dev 37(3):466–490. https://doi.org/10.1080/07900627.2019.1579705 7. Li M, Fu Q, Singh VP et al (2019) An optimal modelling approach for managing agricultural water-T energy-food nexus under uncertainty. Sci Total Environ 651:1416–1434 8. Cobuloglu HI, Büyüktahtakin IE (2015) A stochastic multi-criteria decision analysis for sustainable biomass crop selection. Expert Syst Appl 42(15–16):6065–6074. https://doi.org/10.1016/ j.eswa.2015.04.006 9. Dhanraj R, Punnathanam V, Shastri Y (2021) Multi objective optimization of ethanol production based on regional resource availability. Sustain Prod Consum 27:1124–1137. https://doi.org/ 10.1016/j.spc.2021.02.021 10. Kreig JAF, Ssegane H, Chaubey I, Negri MC, Jager HI (2019) Designing bioenergy landscapes to protect water quality. Biomass and Bioenergy 128(November 2018):105327. https://doi.org/ 10.1016/j.biombioe.2019.105327 11. Robichaud V (2010) An introduction to GAMS 1. Matrix. 1–14 12. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development. J Am Water Resour Assoc 34(1):73–89 13. Water Resources Department Government of Maharashtra, India (2013) Upper Bhima Subbasin DRAFT report. https://wrd.maharashtra.gov.in/Site/Upload/PDF/booklet-Upper% 20Bhima.pdf. Accessed July 2022 14. Quantum geographic information service (2019) Geographic information service (QGIS) version 3.14. https://www.qgis.org/en/site/. Accessed July 2019 15. Soil & Water Analysis Tools (2019) QSWAT3_64 version 1.1 16. https://swat.tamu.edu/software/qswat/. Accessed July 2019 17. Jarvis A, Reuter HI, Nelson A, Guevara E (2008) Hole-filled seamless SRTM data V4. In: International Centre for Tropical Agriculture (CIAT). https://srtm.csi.cgiar.org 18. Roy PS, Meiyappan P, Joshi PK, Kale MP, Srivastav VK, Srivasatava SK, Behera MD, Roy A, Sharma Y, Ramachandran RM, Bhavani P, Jain AK, Krishnamurthy YVN (2016) Decadal land use and land cover classifications across India, 1985, 1995, 2005. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1336 19. Fischer G, Nachtergaele F, Prieler S, van Velthuizen HT, Verelst L, Wiberg D (2008) Global agro-ecological zones assessment for agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy 20. Dile YT, Daggupati P, George C, Srinivasan R, Arnold J (2016) Introducing new open source gis user interface for the swat model. Environ Modell Softw 85:129–138

Chapter 10

Categorization of Urban Basin According to the Runoff Depth: Case Study of Katsushika Ward and Edogawa City Basin, Japan Mohamed Wahba, Mahmoud Sharaan, Wael M. Elsadek, Shinjiro Kanae, and H. Shokry Hassan

Abstract Climate change has a tenacious impact on a large portion of the earth. As a result of that, many urban areas have experienced unrivalled scale of flooding. This research targets classifying an urban basin according to the depth of runoff. In order to estimate the runoff depth, a digital elevation model (DEM) has been delineated using ArcMap to identify the basins, sub-basins, flow direction, and streamlines. Furthermore, the delineated layers were essential to generate a hydrodynamic model via HEC-RAS of the studied area. Accordingly, the exported spatial data together with the precipitation values were utilized to develop a 2D-unsteady flow calculation of the adopted watershed. Consequentially, the runoff depth has been calculated and classified into 10 categories. The results concluded that nearly two-thirds of the study area has been saturated by the estimated runoff. In addition, less than a tenth of the basin has endured more than 500 mm of runoff depth. However, approximately M. Wahba (B) · M. Sharaan · H. S. Hassan Environmental Engineering Department, School of Energy Resources, Environment, Chemical and Petrochemical Engineering, Egypt-Japan University of Science and Technology, E-JUST, Alexandria 21934, Egypt e-mail: [email protected] M. Wahba Civil Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt M. Sharaan Civil Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, Egypt W. M. Elsadek Civil Engineering Department, Faculty of Engineering, South Valley University, Qena, Egypt M. Wahba · S. Kanae Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, Japan H. S. Hassan Electronic Materials Researches Department, Advanced Technology and New Materials Research Institute, City of Scientific Researches and Technological Applications (SRTA-City), New Borg El-Arab City, Alexandria 21934, Egypt © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_10

131

132

M. Wahba et al.

a quarter of the region has experienced nearly 165 mm of the runoff. Eventually, this categorization will significantly help the decision makers to intensify protective measures on the most accumulating points of runoff in the studied watershed. Keywords Climate change · Urban · Flooding · DEM · ArcMap · HEC-RAS · Runoff

10.1 Introduction Numerous natural and man-made hazards, such as flash floods, landslides, and earthquakes, result in a significant number of casualties and loss of property across the world every year [1–4]. After the dramatic change in climate, flash floods might be regarded as one of the most devastating effects on urban dwellings [5]. Meanwhile, flash floods represent the most traditional environmental peril, since it affects millions across the world [6]. Moreover, the Intergovernmental Panel on Climate Change [7] has stated that Southeast and East Asia will suffer from deleterious consequences of flash floods, particularly in the most populated and low-level areas. Thus, to mitigate the negative impacts of flash flooding, it is essential to identify the most accumulating points of runoff. In addition, abundant deleterious results of severe flooding can be observed, including disruptions in people’s lives, destroy urban traffic, the collapse of homes, and the dispersal of pollution [8]. On that caveat, some researchers have linked the sustainable development goals (SDGs) with the alleviation approaches of flash flooding. This linkage has promised a strong connection between the protective measures and the economic and social sustainability [9]. The studied watershed is located in Tokyo, Japan. According to [10] Tokyo is scheduled to have the highest population density among all the Japanese prefectures in October 1st 2020. Furthermore, since many regions witness a growing tendency in the population and impairment of flood-controlling system, the susceptibility to flash floods is high. Moreover, gathering the inhabitance and properties in a small area may worsen the situation [11]. Thus, calculating the runoff is an imperative step to set protective measures aftermath [12]. Meanwhile, the runoff depth can be estimated by generating a hydrodynamic model using HEC-RAS or by utilizing the Maximum Entropy Model and frequency analysis [13]. Ultimately, this research resembles an essential step in getting the most prone areas to flash floods since the research aims to generate a floodplain map and then to be classified according to the runoff depth. The generated map could be utilized after that to develop the flood hazard map.

10 Categorization of Urban Basin According to the Runoff Depth: Case …

133

Fig. 10.1 Elucidation of the studied basin

10.2 Study Area This research has been conducted on a sub-basin in Katsushika ward, this region is in the far east of Tokyo prefecture. Figure 10.1 illustrates the studied area. The adopted basin area is nearly 41 km2 . The lowest elevation value in the basin is 12 m whereas the highest one is 80 m. Moreover, the lowest elevation is located mostly in the west of the delineated basin as it represents the bed of the Naka River. Furthermore, the basin broadens to contain parts of both Katsushika and Edogawa cities, since it occupies nearly half of the Katsushika ward. However, the basin takes around 47% of the Edogawa city area. Figure 10.2 depicts the borders of the selected basin, Katsushika ward, Edogawa city.

10.3 Material and Tools 10.3.1 Digital Elevation Model The terrain elevation data has been employed using a Digital Elevation Model (DEM) with an accuracy of 12.5 m. The DEM was obtained using ALOS-PALSAR (Advanced Land Observing Satellite-Phased Arrayed L-band Synthetic Aperture Radar) from the Earthdata website. This elevation layer is significantly imperative [14] since it is used to delineate the studied basin. Likewise, the accuracy of DEM

134

M. Wahba et al.

Fig. 10.2 The borders of basin, Katsushika ward, Edogawa city

has a vital role in generating the flood-plain map [15]. Moreover, it resembles an essential tool to estimate the direction of flow, slope of terrain, streamlines, basins, and sub-basins.

10.3.2 Precipitation Data The rainfall data has been collected during a certain event which started on 11 October 2019 at 10:00 PM until 12 October 2019 at 01:00 PM. Figure 10.3 sketches a bar chart of the adopted rainfall event. The rainfall data was extracted from the Jaxa Global Rainfall Watch website (https://sharaku.eorc.jaxa.jp/GSMaP/).

10.4 Methodology A DEM has been utilized to identify the elevation values of the studied area. Likewise, elevation values have an inverse relation with the flooding as the higher elevation value, the lesser the potential of floods [16]. The layer of DEM has been exported to ArcMap. In addition, the data on elevation has been filled to eliminate minor errors

10 Categorization of Urban Basin According to the Runoff Depth: Case …

135

Rainfall (mm/hr)

Rainfall data 20 18 16 14 12 10 8 6 4 2 0 22:00 23:00 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 Time (hr)

Fig. 10.3 Rainfall intensity during the studied event

from the data. Consequentially, the filled layer was used to determine the direction of flow. This layer is substantially important as it has been utilized to estimate the flow accumulation, and then to identify the streamlines of flow. Furthermore, the flow direction is also used to delineate the basins in the region. Ultimately, the outlet points were selected on the streamlines and used as input with the flow direction to estimate the watersheds (sub-basins). A hydrodynamic model has been conducted to estimate the flood-plain map. This model was carried out using HEC-RAS software. The previously delineated basin has been exported to HEC-RAS in addition to the flow streamlines. This research used a 2D hydrodynamic model since such a two-dimensional model promises more precise results than the one-dimensional model, especially for the study of floodplains. However, 1D modeling is better for studying a lengthy river since it requires less time and less precision [17]. Moreover, the selected basin has been surrounded by 2D mesh with an accuracy of 10m. Subsequently, the boundary conditions necessary for the model consist of two types, downstream and upstream. The downstream boundary conditions were drawn at the end of major streamlines with a friction slope of 0.01, whereas the upstream boundary conditions were considered as rain-on-grid. Moreover, applying a rain-on-grid method has been utilized recently by [18] in HEC-RAS and used to generate the flood-plain map. The precipitation data was used as input data for the 2D-unsteady flow model. Eventually, the infiltration rate has been neglected since the majority of the basin is an urban area with paved roads. Figure 10.4 shows the procedure of the methodology. Furthermore, it must be noted that the urban delineated area has a drainage system which can help to alleviate the amount of runoff on the streets. However, the result of the hydrodynamic model illustrates the floodplain map in case of neglecting the existence of the drainage system during the difficulty in identifying the locations of the manholes in the streets. Accordingly, since the manholes are usually distributed

136

M. Wahba et al.

Fig. 10.4 The methodological framework

uniformly and the main target of the research is to classify the runoff depth spatially, the result will not be affected in case of neglecting the drainage system.

10.5 Delineation of the Study Basin The DEM has been exploited to identify the basins and thus the sub-basins of the selected basin. Figure 10.5 demonstrates basins and delineated watersheds in the adopted basin.

10 Categorization of Urban Basin According to the Runoff Depth: Case …

137

Fig. 10.5 The delineated basins and watersheds

10.6 Results and Discussion 10.6.1 Flood Plain Map After conducting a hydrodynamic model on the identified basin using the former conditions, the runoff map has been generated as described in Fig. 10.6. The model has estimated the runoff depth after using continuous 16 h of rainfall events. The result shows that the flow accumulates with the highest depth in the Naka River as it has the lowest elevation in the basin. Moreover, the maximum runoff depth in the river reached nearly 2.00 m. Furthermore, the study shows that nearly 17 Km2 has been exposed to runoff less than 300 mm. However, the flood depth is ranged between 33 cm to about half a meter in less than a fifth of the adopted basin. In addition, as the runoff depth increases, the corresponding area of the basin decreases. Since the other runoff categories have been included in nearly 7% of the study area. Table 10.1 and Fig. 10.7 illustrate the classification of runoff in the selected basin.

138

M. Wahba et al.

Fig. 10.6 Runoff depth (m)

10.6.2 Runoff Velocity The velocity of flow represents an essential tool in studying the risk of flash floods, particularly in urban regions. Since the more speed of runoff, the more damage is expected. Additionally, the moving flow can carry debris of the damaged structures and properties. Also, as the flow velocity increases, the potential risk of spreading pollution from the runoff is high. Figure 10.8 depicts the estimated flow velocity in the basin. The higher velocity values can be located in most of the Naka River which mounted in some parts of the river more than 40 cm/s. However, the major values of the velocities are less than 20 cm/s in the basin.

10 Categorization of Urban Basin According to the Runoff Depth: Case …

139

Table 10.1 Runoff depth categorization Runoff depth (mm)

No. of pixels

Area*103 (m2 )

Percentage (%)

0

100,670

15,720.30

37.56

>0–166

60,982

9522.71

22.75

167–331

47,510

7418.98

17.72

332–505

40,098

6261.55

14.96

506–762

12,130

1894.17

4.52

762–1037

2459

383.99

0.92

1038–1221

432

67.46

0.17

1222–1395

717

111.96

0.27

1396–1744

2993

467.38

1.12

1745–2349

10

1.56

0.01

0.92

0.17

Percentage of Runoff (%) 0.27 1.12

4.52

0.01

Runoff (mm)

0

>0 - 166 167 - 331

14.96

37.56

332-505

506-762 762-1037

17.72

1038-1221 1222-1395 22.75

1396-1744 1745-2349

Fig. 10.7 Classification of runoff depth according to the area proportion

140

M. Wahba et al.

Fig. 10.8 Flow velocity (m/s)

10.7 Conclusion This research has conducted a hydrodynamic model on a basin in Tokyo, Japan. The goal of this model is to estimate the runoff depth in the basin and classify the flood depth according to the area of the delineated basin. This study is an essential step to determine the more prone areas to flash floods. Moreover, to carry out this research a DEM with a resolution of 12.5 m has been utilized. In addition, the DEM was delineated using ArcMap to identify the basins, sub-basins, and flow streamlines. The delineated layers have been exported to HEC-RAS to generate the flood-plain map of the study area. Furthermore, a rainfall event has been adopted and used as an input in the model. Likewise, the downstream boundary conditions were positioned at the end of major

10 Categorization of Urban Basin According to the Runoff Depth: Case …

141

streamlines. Moreover, the model has calculated the spatial runoff depth as well as the velocity across the study area. The result demonstrates that less than a tenth of the basin has experienced the highest runoff depth which ranged between about half a meter to approximately 2 m. Nevertheless, nearly a quarter of the region has been exposed to low values of runoff which is estimated as less than about 16 cm. Similarly, the flow velocity has been mapped. The maximum flow speed is located at the Naka River with values approaching 40 cm/s. Ultimately, this study can guide the decision makers to point out protective solutions in the most susceptible areas to flash floods. Acknowledgements The first author would like to deliver thanks to the Egyptian Ministry of Higher Education (MoHE) for giving him the Ph.D. scholarship. Likewise, he would like also to appreciate the E-JUST and JICA for awarding him the required equipment and software for this research.

References 1. WHO (2003) Disaster data-key trends and statistics in world disasters report. WHO, Geneva, Switzerland. http://www.ifrc.org/PageFiles/89755/2003/43800-WDR2003_En.pdf. Accessed 5 Apr 2017 2. Youssef AM, Pradhan B, Hassan AM (2011) Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environ Earth Sci 62:611–623 3. Tehrany MS, Shabani F, Jebur MN, Hong H, Chen W, Xie X (2017) GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomat Nat Hazards Risk. https://doi.org/10.1080/19475705. 2017.1362038 4. Du J, Fang J, Xu W, Shi P (2013) Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province China. Stoch Environ Res Risk Assess 27(2):377–387 5. Wahba M, Hassan HS, Elsadek WM, Kanae S, Sharaan M (2022) Prediction of flood susceptibility using frequency ratio method: a case study of fifth District, Egypt. In: The 14th International conference on hydroscience & engineering (ICHE2022). Izmir, Turkey 6. Mansour MM, Ibrahim MG, Fujii M, Nasr M (2022a) Recent applications of flash flood hazard assessment techniques: case studies from Egypt and Saudi Arabia. In: Advanced engineering forum, vol 47. Trans Tech Publications, Ltd., pp 101–110 7. IPCC (2014) Climate change 2014: impacts, adaptation and vulnerability. In: Barros VR, Field DJ, Dokken MD, Mastrandrea KJ, Mach TE (eds) Contribution of working group 2 to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK, New York, NY, USA 8. Elsadek WM, Wahba M, Al-Arifi N, Kanae S, El-Rawy M (2023) Scrutinizing the performance of GIS-based analytical Hierarchical process approach and frequency ratio model in flood prediction–Case study of Kakegawa, Japan. Ain Shams Engineering Journal, 102453 9. Mansour MM, Ibrahim MG, Fujii M, Nasr M (2022b) Sustainable development goals (SDGs) associated with flash flood hazard mapping and management measures through morphometric evaluation. Geocarto Int 10. Tokyo Metropolitan Government (2020) https://www.metro.tokyo.lg.jp/english/about/history/ history03.html

142

M. Wahba et al.

11. Wisner BP, Blaikie PM (2004) At risk: natural hazards, people’s vulnerability and disasters, 2nd edn. Routledge, London, UK 12. Wahba M, Mahmoud H, Elsadek WM, Kanae S, Hassan HS (2022) Alleviation approach for flash flood risk reduction in urban dwellings: a case study of Fifth District, Egypt. Urban Clim 42:101130 13. Munna GM, Alam MJB, Uddin MM, Islam N, Orthee AA, Hasan K (2021) Runoff prediction of Surma basin by curve number (CN) method using ARC-GIS and HEC-RAS. Environ Sustain Indic 11:100129 14. Bui DT, Pradhan B, Nampak H, Bui QT, Tran QA, Nguyen QP (2016) Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibility modeling in a high-frequency tropical cyclone area using GIS. J Hydrol 540:317–330 15. Xu K, Fang J, Fang Y et al (2021) The importance of digital elevation model selection in flood simulation and a proposed method to reduce DEM errors: a case study in Shanghai. Int J Disaster Risk Sci. https://doi.org/10.1007/s13753-021-00377-z 16. Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR (2016) A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weightsof-evidence bivariate statistical models with multi-criteria decision-making technique. Nat Hazards 83(2):947–987 17. Gharbi M, Soualmia A, Dartus D, Masbernat L (2016) Comparison of 1D and 2D hydraulic models for floods simulation on the Medjerda Riverin Tunisia. J Mater Environ Sci 7(8):3017– 3026 18. Wahba M, Hassan HS, Elsadek WM, Kanae S, Sharaan M (2023) Novel utilization of simulated runoff as causative parameter to predict the hazard of flash floods. Environ Earth Sci 82(13):333

Chapter 11

ETSim: A Reference Evapotranspiration Estimator and Its Evaluation at the Southern Region of Japan Min Yan Chia, Yong Jie Wong, Yuk Feng Huang, Yoshihisa Shimizu, and Chai Hoon Koo

Abstract Machine learning models have been applied extensively for reference evapotranspiration estimation (ETo ) for replacing empirical models which are data intensive. However, most of the trained and calibrated machine learning models are only capable of performing local estimation. Therefore, the spatial robustness and applicability of these models have remained major research gaps. In light of these, in this investigation, the authors applied the ETSim estimator, an ETo estimating framework based on a machine learning ensemble (developed in Peninsular Malaysia) to the southern region of Japan to assess its spatial robustness and applicability. Two models, namely the local and global model of the ETSim estimator were used to estimate the daily and monthly mean ETo at Ishigaki, Naha and Miyako located on three different islands in southern Japan. The local model could not estimate the daily ETo of the three testing stations, with the reported mean absolute percentage error (MAPE) of more than 100% when meteorological variables other than solar radiation (Rs ) were fed as input. On the other hand, the global model performed much better using the input combination with four meteorological variables. The ETSim estimator was also applied to estimate the monthly mean ETo with a coarser temporal resolution. Despite achieving tremendous improvement, the local model was still unable to provide the monthly mean ETo estimation satisfactorily. The global model, however, estimated the monthly mean ETo decently, with the registered MAPE generally below 20%. At the same time, the goodness-of-fit of the global model was maintained above 0.7 for most of the cases. The results suggest that the ETSim estimator framework has suitable components that allow it to be used elsewhere other than at its original location. M. Y. Chia · Y. F. Huang (B) · C. H. Koo Department of Civil Engineering, Faculty of Engineering and Science, Lee Kong Chian, UniversitiTunku Abdul Rahman, Petaling Jaya, Malaysia e-mail: [email protected] Y. J. Wong · Y. Shimizu Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, Kyoto, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_11

143

144

M. Y. Chia et al.

Keywords Reference evapotranspiration · Spatial robustness · Machine learning ensemble

11.1 Introduction Reference evapotranspiration (ETo ) has always been an emerging research topic due to its simple conversion into actual crop evaporation (ET) using a crop coefficient (ETc ) [1]. However, the calculation of the ETo requires a number of meteorological variables for the Penman–Monteith (PM) equation, endorsed by the United Nations Food and Agriculture Organization (FAO) of the United Nations to be used [2]. Hence, scholars worldwide have shifted their attention towards the use of black-box machine learning models for the estimation of ETo . To list a few, Bellido-Jiménez et al. [3] used the multilayer perceptron (MLP), extreme learning machine (ELM), generalized regression neural network (GRNN), support vector machine (SVM), random forest (RF) and the extreme gradient boosting (XGBoost) to estimate ETo in the region of Andalusia using various input combinations. While performing the cross-station testing, the authors concluded that the MLP had the best performance despite reporting high variability and uncertainty. Mokari et al. [4] employed the ELM, genetic programming (GP), RF and SVM for similar purposes in regions with varying climate zones of New Mexico. In this case, the ELM and SVM were reported as the models with the best stability. Gong et al. [5] hybridized the ELM using particle swarm optimization (PSO) and genetic algorithm (GA). In another study, the GA-ELM achieved better “acceptable and reliable” performance across 96 stations with different climates in China. Recently, Kushwaha et al. [6] tested the additive regression, random subspace (RSS), M5 pruning tree (M5P) and their combinations for ETo estimation. The additive regression was regarded as the best performer in this study. Despite many studies reporting the efficacy of different machine learning models and algorithms as promising alternatives for the empirical models, however, very few of them looked at the spatial robustness to deploy their developed models in other regions [7]. Many of the solutions were just developed and calibrated for local use instead of widespread application. At present, only studies involving differing climate zones in the same region are available in the literature [8, 9]. A universal solution that covers different regions with distinctive climate conditions serves the need for a new state-of-the-art global solution [10]. In this study, the authors presented an ETo estimating framework known as the ETSim estimator. The ETSim estimator was trained in Peninsular Malaysia and consisted of two models, namely the local and global models [11]. The local model was trained using data span from the year 2000 to the year 2019 at Sitiawan (4.22°N, 100.70°E), whereas the global model was trained over a wider region, using data collected from 12 meteorological stations with different geographical characteristics in Peninsular Malaysia of the same period. Based on cross-station testing, the ETSim estimator had achieved an average mean absolute percentage error (MAPE)

11 ETSim: A Reference Evapotranspiration Estimator and Its Evaluation …

145

of 0.0395 mm/day to 0.1532 mm/day as well as 0.0411 mm/day to 0.1527 mm/ day for the local and global model, respectively. The excellent performance of the ETSim estimator for local use motivated the authors and developers to investigate its usability and applicability in other regions. This manuscript reports the performance of the ETSim estimator when it was deployed at three islands located in the southern region of Japan. The three stations are located at Ishigaki, Naha and Miyako. According to the Köppen-Geiger classifications, Japan is classified with the Cfa (humid subtropical) climate, which is different from Peninsular Malaysia (Af , tropical rainforest) [12, 13]. This makes the current investigation more interesting and meaningful.

11.2 Materials and Methods 11.2.1 Study Area and Data The ETSim estimator was tested for its robustness in the southern region of Japan. Specifically, stations located on three southern islands of Japan, namely Ishigaki, Naha and Miyako were selected as the testing stations. The southern region of Japan was selected as the region of interest as it has the shortest distance away from Peninsular Malaysia, where ETSim estimator was trained. The geographical details of the testing stations are tabulated in Table 11.1. The actual locations of the testing stations are depicted in Fig. 11.1. At the three testing stations, daily data of six meteorological variables dated from 1st January 2001 to 31st December 2021 were obtained from the Japan Meteorological Agency (JMA). This period was chosen to match the training period of the ETSim estimator (1st January 2001 to 31st December 2020). The six meteorological variables were maximum temperature (T max , °C), minimum temperature (T min , °C), mean temperature (T mean , °C), average relative humidity (RH, %), average wind speed (U, m/s) and solar radiation (Rs , MJm−2 day−1 ). Table 11.1 Geographical details of the testing stations

Testing station Latitude (°N) Longitude (°E) Altitude (m) Ishigaki

24.34

124.17

6

Naha

26.21

127.69

28

Miyako

24.78

125.30

100

146

M. Y. Chia et al.

Fig. 11.1 Locations of the testing stations

11.2.2 Penman–Monteith Equation The Penman–Monteith (PM) equation is regarded as the standard for the calculation of the reference evapotranspiration (ETo ) [1]. Hence, it was used to calculate the actual ETo for the three testing stations so that the estimation by the ETSim estimator can be compared. Mathematically, the PM equation can be expressed as shown in Eq. (11.1). ET o =

900 0.408Δ(Rn − G) + γ T +273 U2 (es − ea )

Δ + γ (1 + 0.34U2 )

(11.1)

where Rn is net radiation (MJm−2 day−1 ), G is soil heat flux (MJm−2 day−1 ), T is daily mean temperature (°C), U 2 is wind speed at 2 m height (m/s), es is mean saturation vapor pressure (kPa), ea is actual vapor pressure (kPa), Δ is slope of vapor pressure curve (kPa/°C) and γ is psychrometric constant.

11.2.3 ETSim Estimator The ETSim estimator is an ETo estimating software that is made up of an ensemble of various machine learning models. It was deployed based on the published work of

11 ETSim: A Reference Evapotranspiration Estimator and Its Evaluation …

147

Table 11.2 Preferred input combinations of the local and global models of ETSim estimator Number of meteorological variables

Preferred input combination Local model

Global model

6

T max , T min , T mean , RH, U, Rs (C6)

5

T max , T min , RH, U, Rs (C5)

4

T max , RH, U, Rs (C4)

3

T max , U, Rs (C3)

2

U, Rs (C2L)

1

Rs (C1)

RH, Rs (C2G)

Chia et al. [9, 11]. Essentially, the ETSim comprises three different machine learning algorithms, namely a multilayer perceptron (MLP), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) hybridized using an extreme learning machine integrated with whale optimization algorithm (WOA-ELM) [14]. The contribution of each of the base models towards the final estimation remains unknown as the ETSim was built on the basis of the black-box non-linear neural ensemble. Within the ETSim, there are two variants of models, the local model and the global model. The local model was trained at Sitiawan (4.22°N, 100.70°E), whereas the global model was trained using data obtained from 12 meteorological stations across the whole of Peninsular Malaysia. It should be noted that the local and global models of the ETSim estimator have different sets of preferred input combinations with different numbers of meteorological variables, as shown in Table 11.2. Both the local and global models are able to cater to different numbers of input meteorological variables. Both models under the ETSim estimator were used to estimate the ETo at the Ishigaki, Naha and Miyako stations. The current version of the ETSim was developed on the MATLAB platform using the MATLAB App Designer.

11.2.4 Performance Evaluation Metrics The suitability of the ETSim estimator in the southern region of Japan was evaluated based on several performance evaluation metrics that measure its accuracy as well as the goodness-of-fit. The satisfactory performance of the ETSim estimator indicates that this framework is robust spatially.

148

M. Y. Chia et al.

11.3 Mean Absolute Error The mean absolute error (MAE) was used to measure the deviation between the estimations from the actual values. In other words, lower MAE corresponds to a model with high accuracy, and vice versa. The MAE can be calculated using Eq. (11.2). M AE =

N 1 ∑ |yi − y i | N i=1 ∆

(11.2) ∆

where N is the number of samples, y is the estimated value and y is the actual value.

11.4 Root Mean Square Error The root mean square error (RMSE) also determines the difference between the actual and estimated values. However, as compared to the MAE, the RMSE provides more emphasis on errors with larger magnitude. Ideally, the RMSE should have a similar value as the MAE, and both shall be as close to zero as possible. RMSE larger than MAE means that there exists a significant number of large errors or outliers in the estimation. The RMSE was calculated using Eq. (11.3). RMSE =

N 1 ∑ (yi − y i )2 N i=1 ∆

(11.3)

11.5 Mean Absolute Percentage Error The mean absolute percentage error (MAPE), on the other hand, normalizes the MAE to percentage scale to ease comparison. The absolute scale of the MAE is sometimes vague and could not accurately represent the accuracy of the model, and therefore MAPE is usually used as a complementary metric. Equation (11.4) was used to calculate the MAPE for this study. M AP E =

N | 1 ∑|| yi − y i | × 100% N i=1 ∆

(11.4)

11 ETSim: A Reference Evapotranspiration Estimator and Its Evaluation …

149

11.6 Coefficient of Determination To determine the goodness-of-fit of a model, the coefficient of determination (R2 ) was used. The value of R2 represents the percentage of data or samples that can be explained by the current model. It can be calculated using Eq. (11.5). ∑n

(y i − y)(xi − x) )2 n 2 ∑n 2 i=1 (x i − x) i=1 (y i − y)

R 2 = ( /∑

i=1

(11.5)

All four performance evaluation metrics were used to evaluate the ETSim estimator when it was developed in Peninsular Malaysia, and hence fair comparisons can be made when the ETSim estimator was tested in the southern region of Japan.

11.7 Results and Discussion 11.7.1 Daily Estimation of ETo Table 11.3 summarizes the performance of the local as well as the global model at the three testing stations, using different input combinations. From Table 11.3, it is shown that the ETSim estimator is not applicable at Ishigaki, Naha and Miyako for daily ETo estimation. For the local model, the registered MAPE at the three testing stations was at least 211.11% when meteorological variables other than Rs were included in the input combinations. This could be due to the fact that the meteorological conditions at the training spot of the local model of the ETSim estimator were very different from those at these three testing stations in Japan. Nevertheless, for Sitiawan and the testing stations, the Rs is found to be the main driver for the ETo process as it is the sole energy provider to the water molecules [15]. However, when the global model of the ETSim estimator was used to estimate the daily ETo of the three testing stations, it was found that the accuracy experienced tremendous improvement. The highest MAPE incurred was 53.89% when C5 was used as the input combination at Naha. Contradictory to the local model, C1 was not the best input combination. Instead, the preferred input combination was C4 when the global model was used. The reported MAPE at Ishigaki, Naha and Miyako were 19.72%, 22.39% and 19.20%, respectively. However, the R2 values were low to suggest that the goodness-of-fit was less satisfactory. Using C2L/C2G and C1 could substantially improve the R2 at the expense of slightly higher MAPE than using C4 as the input combination. The better performance of the global model could be attributed to the higher variability of training data when the global model was trained. In fact, the training of the global model involved one station located at a highland position, with lowtemperature environment [2]. For C4 (T max , RH, U, Rs ), the four input meteorological

150

M. Y. Chia et al.

Table 11.3 Performance of ETSim estimator in estimating daily ETo Testing station

Input Local model combination MAEa RMSEa

Global model MAPEb

R2

MAE RMSE MAPE R2

Ishigaki C6

47.13

288.13

2610.31 0.005 0.40

2.77

40.89

0.183

C5

52.62

291.34

3293.97 0.016 0.58

2.05

34.26

0.345

C4

4.63

26.88

762.51 0.022 0.36

1.03

19.72

0.617

C3

Naha

79.31

236.26

4098.05 0.005 0.65

1.36

23.73

0.512

C2L/C2G

9.83

17.46

264.36 0.087 0.53

0.73

22.80

0.778

C1

0.81

1.02

23.39 0.786 0.78

1.00

23.82

0.784

C6

35.10

210.78

1657.14 0.000 0.45

2.88

38.54

0.142

C5

46.42

220.87

3088.49 0.023 1.15

3.25

53.89

0.142

25.68

659.47 0.013 0.59

1.59

22.39

0.361

C4 C3

Miyako

a b

4.700 67.42

195.24

3494.74 0.003 0.76

1.44

24.17

0.438

C2L/C2G

9.25

17.73

241.00 0.076 0.62

0.84

23.09

0.676

C1

0.88

1.14

23.86 0.700 0.86

1.12

24.03

0.699

C6

22.86

58.78

1495.73 0.002 0.42

2.85

45.66

0.149

C5

32.76

146.42

2608.51 0.039 0.63

2.18

31.73

0.261

C4

3.88

24.56

650.33 0.020 0.41

1.15

19.20

0.522

C3

54.95

171.36

3773.69 0.001 0.63

1.00

22.76

0.613

C2L/C2G

6.50

12.00

211.11 0.013 0.47

0.66

18.57

0.786

C1

0.66

0.89

20.08 0.758 0.65

0.88

20.62

0.757

MAE and RMSE were measured in mm/day MAPE was measured in %

variables that were recorded at that highland in Peninsular Malaysia, had higher similarity with those collected at these testing stations, making the global model more suitable for the estimation of daily ETo despite being trained at a place remote from the Japanese testing stations. When comparing the local and global model of the ETSim, it can also be seen the RMSE was closer to the MAE for the global model’s estimation. Large errors could have been incurred by the local model as the meteorological conditions were very different from the case of Japan, especially during the cold season which was not experienced by Peninsular Malaysia. The variability of the training data of the global model enabled it to be more robust and could be applied elsewhere in the globe.

11.7.2 Monthly Estimation of ETo Despite the fact that the ETSim estimator was trained using data of daily timescale, the authors were interested in the performance of the ETSim estimator when the temporal

11 ETSim: A Reference Evapotranspiration Estimator and Its Evaluation …

151

resolution was changed to the monthly interval. The results of the performance of ETSim estimator (local and global models) in estimating monthly mean ETo are tabulated in Table 11.4. As compared to estimating daily ETo , the ETSim estimator performed better when it was used to estimate the monthly mean ETo . For the local model, the MAPE had improved tremendously. In spite of that, the performance of the local model still indicates that it is unsuitable for application in the Japan region (except for using C1 as an input combination). However, the global model could estimate the monthly mean ETo at the testing stations with good accuracy (lower than MAPE of 20%). Apart from improving the accuracy, the goodness-of-fit of the global model was much better than when it was used for daily ETo estimation. In fact, the value of R2 was consistently maintained above 0.7, which was not observed for the case of daily ETo estimation. Nevertheless, an optimum input combination can be selected at each of the testing stations. For Ishigaki, the best input combination was C4, whereas for Naha and Miyako it was C6. The comparison between the actual and estimated time series of the monthly mean ETo at the three testing stations is provided in Fig. 11.2. Table 11.4 Performance of ETSim estimator in estimating monthly mean ETo Testing station

Input Local model combination MAEa RMSEa

Global model MAPEb

R2

MAE RMSE MAPE R2

Ishigaki C6

42.70

73.61

995.17 0.154 0.28

0.56

7.23

0.788

C5

35.19

61.52

924.42 0.015 0.46

0.71

13.47

0.848

C4

3.34

6.08

94.14 0.023 0.24

0.38

7.11

0.888

C3

52.16

79.31

1340.28 0.001 0.40

0.50

10.64

0.877

C2L/C2G

8.38

10.97

191.44 0.290 0.41

0.48

9.92

0.847

C1

0.71

0.79

17.76 0.874 0.65

0.74

16.60

0.874

C6

32.15

54.90

763.21 0.108 0.31

0.55

8.28

0.720

C5

32.23

51.57

868.50 0.034 1.00

1.63

29.30

0.652

C4

2.88

4.92

80.14 0.019 0.46

0.81

13.75

0.327

C3

41.23

55.82

1102.50 0.010 0.55

0.65

14.79

0.825

C2L/C2G

7.54

9.70

174.84 0.280 0.51

0.59

13.19

0.570

C1

0.79

0.89

20.01 0.789 0.74

0.85

18.89

0.791

C6

21.65

27.03

617.10 0.002 0.26

0.50

7.22

0.719

C5

26.52

39.24

808.92 0.172 0.49

0.83

15.31

0.759

C4

2.75

5.53

87.66 0.032 0.29

0.50

9.33

0.719

C3

40.93

57.48

1269.79 0.199 0.40

0.48

11.26

0.866

C2L/C2G

5.16

6.19

135.50 0.054 0.37

0.44

10.63

0.782

C1

0.52

0.63

14.34 0.831 0.48

0.60

13.56

0.831

Naha

Miyako

a b

MAE and RMSE were measured in mm/day MAPE was measured in %

152

M. Y. Chia et al.

Fig. 11.2 Comparison of actual and estimated monthly mean ETo at Ishigaki, Naha and Miyako

11 ETSim: A Reference Evapotranspiration Estimator and Its Evaluation …

153

From the results, it can be said that the ETSim estimator is capable of estimating daily ETo using its global model, while monthly mean ETo can also be estimated with a better accuracy.

11.8 Conclusion The ETSim estimator was developed in Peninsular Malaysia and tested at three stations located in the southern region of Japan. The local model of the ETSim estimator incurred large estimation errors regardless of the testing stations, whereas the global model provided better estimation even with only four meteorological variables (T max , RH, U, Rs ). This was attributed to the higher variability of the training data during the development stage of the global model. When the ETSim was used to estimate the monthly mean ETo with a coarser temporal resolution, the local model’s estimations were still unsatisfactory despite achieving tremendous improvement. Meanwhile, the estimations of the global model became more accurate, with the model achieving better goodness-of-fit. The ETSim estimator has been proven to be applicable in regions outside of its origin (Peninsular Malaysia). Data from other regions of the world are welcomed so that the spatial robustness of the ETSim estimator can be further assessed and extended. Acknowledgements This research was funded by Universiti Tunku Abdul Rahman (UTAR), Malaysia, through the Universiti Tunku Abdul Rahman Research Fund (UTARRF) under project number IPSR/RMC/UTARRF/2018-C2/K03. The meteorological data for this study were provided by the Japan Meteorological Agency (JMA). The authors highly appreciate and sincerely thank the respective organizations for their assistance.

References 1. Xiang K, Li Y, Horton R, Feng H (2020) Similarity and difference of potential evapotranspiration and reference crop evapotranspiration—a review. Agric Water Manag 232:106043 2. Allen R, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements 3. Bellido-Jiménez JA, Estévez J, García-Marín AP (2021) New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain. Agric Water Manag 245:106558 4. Mokari E, DuBois D, Samani Z, Mohebzadeh H, Djaman K (2021) Estimation of daily reference evapotranspiration with limited climatic data using machine learning approaches across different climate zones in New Mexico. Theoret Appl Climatol 147(1–2):575–587 5. Gong D, Hao W, Gao L, Feng Y, Cui N (2021) Extreme learning machine for reference crop evapotranspiration estimation: model optimization and spatiotemporal assessment across different climates in China. Comput Electron Agric 187:106294 6. Kushwaha NL, Rajput J, Sena DR, Elbeltagi A, Singh DK, Mani I (2022) Evaluation of datadriven hybrid machine learning algorithms for modelling daily reference evapotranspiration. Atmos-Ocean 1–22

154

M. Y. Chia et al.

7. Wong YJ, Shimizu Y, Kamiya A, Maneechot L, Bharambe KP, Fong CS et al (2021) Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia. Environ Monit Assessment 193(7):438 8. Raza A, Shoaib M, Faiz MA, Baig F, Khan MM, Ullah MK, Zubair M (2020) Comparative assessment of reference evapotranspiration estimation using conventional method and machine learning algorithms in four climatic regions. Pure Appl Geophys 177(9):4479–4508 9. Dong J, Zhu Y, Jia X, Shao M, Han X, Qiao J, Bai C, Tang X (2022) Nation-scale reference evapotranspiration estimation by using deep learning and classical machine learning models in China. J Hydrol 604:127207 10. Wong YJ, Shimizu Y, He K, Nik Sulaiman NM (2020) Comparison among different ASEAN water quality indices for the assessment of the spatial variation of surface water quality in the Selangor river basin, Malaysia. Environ Monit Assessment 192(10):644 11. Chia MY, Huang YF, Koo CH (2022) Resolving data-hungry nature of machine learning reference evapotranspiration estimating models using inter-model ensembles with various data management schemes. Agric Water Manag 261:107343 12. Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF (2018) Present and future Koppen-Geiger climate classification maps at 1-km resolution. Sci Data 5:180214 13. Wong YJ, Nakayama R, Shimizu Y, Kamiya A, Shen S, Muhammad Rashid IZ, et al (2021) Toward industrial revolution 4.0: development, validation, and application of 3D-printed IoTbased water quality monitoring system. J Clean Prod 324:129230 14. Chia MY, Huang YF, Koo CH (2021) Improving reference evapotranspiration estimation using novel inter-model ensemble approaches. Comput Electron Agric 187:106227 15. Mokhtari A, Noory H, Vazifedoust M (2018) Performance of different surface incoming solar radiation models and their impacts on reference evapotranspiration. Water Resour Manage 32(9):3053–3070

Part III

Solid Waste Management and Waste Valorization

Chapter 12

Performance Evaluation of a Full-Scale Forced Aerated Municipal Solid Waste Composting System: A Case Study in Kalutara, Sri Lanka Akifumi Kanachi, Naofumi Sato, Nayana Samaraweera, Layan Gunasekara, Rie Kawanishi, and Anurudda Karunarathna

Abstract This paper presented the outcome of a full-scale forced aerated municipal solid waste composting trial conducted to eliminate several issues such as offensive odor, slow degradation and excessive leachate generation often seen in conventional municipal solid waste windrow composting systems. The experiment was carried out in the Mihisaru integrated Municipal Solid Waste (MSW) management facility at Kalutara, Sri Lanka. The waste composition showed that the partially segregated waste contained approximately 85% organic matter with 75% water, which exerts a higher oxygen requirement during the early decomposition stage. Thus, a full-scale forced aeration system was designed to aerate large piles with approximate dimensions of 15 m long, 2 m height and 4 m width. Two perforated HDPE aeration pipes at 1 m distance were placed along the bottom of each pile and the air was supplied to the waste piles through an industrial blower. The system was installed in three piles. The variations of moisture, flow volumes, air pressure, temperature were daily monitored while the volatile solids changes and compositions of air inside the waste piles were monitored every other week for 3 months. The results revealed that force aeration reduces the amount of anaerobic odor gases and leachate generation. Moreover, it was found that force aeration can reduce the windrow turning requirement by 75%, and the total time required for decomposition is reduced to 50%. The drawback of the static pile aeration system was the larger particle size of matured compost; thus, it is recommended to mechanically shred the waste after the thermophilic decomposition phase and allow it to stabilize under natural convective aeration until maturation. Keywords Municipal solid waste · Forced aeration · Full-scale composting

A. Kanachi · N. Sato (B) · N. Samaraweera · L. Gunasekara · R. Kawanishi EX Research Institute, Tokyo, Japan e-mail: [email protected] A. Karunarathna University of Peradeniya, Peradeniya, Sri Lanka © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_12

157

158

A. Kanachi et al.

12.1 Introduction Municipal Solid Waste (MSW) is commonly disposed of at the final disposal site in a manner that does not protect the environment in most local authorities in Sri Lanka. The total MSW generated is 10,768 tones per day, of which only 3,458 tones are collected and disposed of at authorized and unauthorized final disposal sites [1]. The amount of waste generated is expected to increase in proportion to economic development and population growth. Open dumping generates sanitation and environmental issues surrounding the dump sites, as well as contributions to global warming through emissions of landfill gases such as methane. Unfortunately, waste reduction has not been successful, and unsafe open dumping continues. Since 60% of MSW generated in Sri Lanka is biodegradable waste, MSW composting may be a good way to reduce the amount of MSW that ends up in landfill sites. There have been a number of government-led projects to establish as many as 137 compost plants. Most of them are small to medium-sized conventional windrow composting systems and operated by local authorities. However, due to a lack of technology and operational know-how, many composting plants are struggling to manage their windrow systems, resulting in an offensive odor, slow decomposition, and excessive leachate generation, which has led to social issues with neighboring residents. Several authors [2, 3] have suggested the positive effects of mixing/turning compost piles. Compost piles should be turned every other day to maintain aerobic activity in the phase of stabilization. There is also evidence that small static piles or simple embankments, 1.52 m high and 2.44 m wide, deteriorate under passive aeration conditions. These static piles were improved by incorporating horizontal aeration pipes at the base of protruding chimneys and piles [4]. This case study presents the outcome of a full-scale forced aerated municipal solid waste composting trial conducted to shorten the period of producing composting and eliminate social issues such as offensive odor and fly infestation which are often seen in conventional municipal solid waste windrow composting systems.

12.2 Materials and Method 12.2.1 Materials The experiments were conducted at the Mihisaru integrated MSW management facility in Pohorawatta, Kalutara District, Western Province, Sri Lanka. The segregated waste was treated at the windrow compost plant operated by the Western Province Waste Management Authority. The waste originated from two local authorities, namely Kalutara Pradeshya Sabha and Kalutara Urban Council, collected from residential, markets, street sweeping, commercial institutions, and industries. The intake waste, 20–25 tones per day, was

12 Performance Evaluation of a Full-Scale Forced Aerated Municipal Solid …

159

segregated from non-biodegradable waste at the source. The waste composition showed that the partially segregated waste contained approximately 85% organic matter with 75% water which exerts a higher oxygen requirement during the early decomposition stage.

12.2.2 Installation of Forced Aeration System A forced aeration system was established by installing a series of parallel 110mm diameter HDPE perforated pipe networks into the windrow compost piles. The HDPE pipe network was placed on the 600mm height windrow compost pile and the pipes are connected to each other using a 110 mm diameter HDPE STUB Flange neck with a steel flange including a rubber gasket. After laying pipe networks further compost pile (1200 mm) was stacked on the perforated pipe network leaving the manifold pipe area. The pattern of the aeration holes in the pipe is 10 mm diameter and two holes in a raw, each hole in HDPE pipe at 4 O’clock & 8 O’clock and are repeated along at 1/2 ft distance intervals. The pattern of the Leachate holes in the pipe is 10 mm in diameter at the bottom of the pipe at the 1 m distance intervals. The air escapes from the holes of HDPE pipes and enters the compost pile through the holes. One compost pile has two /parallel rows of pipes at a 1.83 m distance and the end of each pipe is closed by an end cap to prevent air from escaping. The end of the manifold HDPE pipe is connected to Centrifugal Blower. The pipe networks are aerated by coupling a Centrifugal Blower to the manifold pipe end. The system was installed in three piles. The general formula to calculate hole diameter is shown below [5]: H ole ∅ = S Q RT [(D 2 ∗ S)/(L ∗ 12)] The view of the pipe network is illustrated in Fig. 12.1. The full-scale forced aeration system was designed to aerate large trapezoidal piles with approximate dimensions of 15 m long, 2 m high, and 4 m wide at the bottom with 2 m top width. The cross-section of a compost pile is shown in Fig. 12.2.

12.2.3 Physical Monitoring and Chemical Analysis The variations of flow volumes, air pressure, and temperature were daily monitored while the moisture content, volatile solids changes and compositions of air inside the waste piles were monitored every other week for 3 months. The Odor emission and fly abundance were qualitatively assessed using the Citizen science (CS) protocol [6], where citizens voluntarily participated in scaling the odor levels and abundance of flies adjacent to the composting site by walking along a path

160

A. Kanachi et al.

Fig. 12.1 Structure of pipe network

Fig. 12.2 Cross section of a compost pile

50 m away from the site border. The same assessment was done 30 days after the installation of the aeration system.

12.3 Results and Discussion 12.3.1 Gas Composition and Gas Concentration with Time There are four differences in gas emissions over the period of monitoring. Firstly, in the forced aerated pile, CO2 emission was stable at a lower level from 1.20 to 4.51% while, in the normal windrow pile, CO2 started at 51.83%, decreased to around 30% on the 15th and 20th days, increased to 46.15% on the 30th day then sharply decreased to 11.84% on the 60th day. Secondly, CH4 was almost stable in the range of 0.13 to 0.73% in the forced aerated pile while it fluctuated at the timing of turning the

12 Performance Evaluation of a Full-Scale Forced Aerated Municipal Solid …

161

normal windrow pile on the 15th day. Thirdly, H2 was not seen in the forced aerated pile but it was seen in conventional windrow piles at a rate of 3.83% on the 1st day and decreased to 0.00% on the 60th day. Fourthly, O2 was stable at a higher level of 17.19 to 19.34% in the forced aerated pile whereas it began at 0.90% on the 1st day and increased to 4.73% on the 30th day in the normal windrow pile. CO and CN HM were stable at a lower level over the period both in the forced aerated pile and normal windrow pile. The variation of gas composition in void spaces of windrow piles is shown in Table 12.1. The temperature of the forced aeration pile went up slightly from 53.28 to 64.60 °C over the monitoring period while there was a drop in the temperature of the normal windrow pile during the turning period around the 15th day as shown in Fig. 12.3. As shown in Fig. 12.4, the forced aerated windrow piles dried faster than conventional windrow piles. The moisture content of aerated piles dropped to 44% on the 60th day which was below the optimum moisture content for microbial activities. However volatile solid analysis revealed that compost feedstock in forced aerated files had achieved near stable volatile solid content (37%). The solid samples from feedstocks were extracted and analyzed for pH and EC. As shown in Fig. 12.5, pH increases from slightly acidic levels (4.3–4.8) on 1st day to a more neutralized pH level (7.3–7.8) by 60th day. Though there is a marked difference, it was observed that feedstock in forced aerated piles attained a stable Table 12.1 Variation of gas composition in void spaces of windrow piles Days

Forced aerated pile

Normal windrow pile

CO

CO2

CH4

Cn Hm

H2

O2

CO

CO2

CH4

Cn Hm

H2

O2

1

0.00

1.20

0.13

0.00

0.00

19.34

0.04

51.83

0.41

0.01

3.83

0.90

15

0.03

4.51

0.28

0.02

0.00

17.19

0.04

32.84

0.43

0.01

1.38

0.29

20

0.04

3.04

0.42

0.04

0.00

19.11

0.03

34.52

9.97

0.03

0.04

0.62

30

0.01

3.44

0.73

0.06

0.00

17.68

0.02

46.15

7.98

0.05

0.61

4.73

45

0.01

2.02

0.16

0.00

0.00

17.91

0.03

43.26

6.34

0.04

0.50

4.14

60

0.00

1.52

0.14

0.00

0.00

18.78

0.09

11.84

0.60

0.05

0.00

5.64

Fig. 12.3 Variation of temperature

80.00 60.00 40.00 20.00 0.00 1

15

20

30

45

60

Temperature of Forced aerated pile (°C) Temperature of Normal windrow pile (°C)

162

A. Kanachi et al.

Fig. 12.4 Variation of moisture content and volatile solids Fig. 12.5 Variation of pH, EC, Conductivity

neutral pH level around the 20th day while it took approximately 40 days in normal windrow piles. However, the electrical conductivity (EC) of normal windrow piles was higher than the forced aerated piles after the 20th day. The elevated EC in normal windrow composting could be due to elevated levels of anaerobic metabolic products that developed due to anerobic activities in piles.

12.3.2 Property Changes In overall assessment, it was found that forced aerated windrow piles attained initial stabilization (rapid decomposition) approximately by the 20th day while the normal windrow piles achieved initial stabilization after the 40th day. The forced aeration thus reduces the initial stabilization time by 50%. Also, the number of windrowing during the forced aeration was only two times (30th day and 60th day) whereas normal windrowing was done 8 times. The reduction of turning (75%) was an advantage

12 Performance Evaluation of a Full-Scale Forced Aerated Municipal Solid …

163

because turning required heavy types of machinery which incurs higher operation costs than electricity cost for air blowers. The drawback in static pile forced aeration was the larger particle sizes compared to normal windrow piles.

12.3.3 Fly and Odor Control The odor intensity felt by the participants in the citizen science assessment prior to the aeration survey was significantly higher than the condition felt after the installation of the aeration system. estimation prior to the installation. Further participants commented that the forced aerated piles did not generate a strong odor but there was a little anaerobic odor emission throughout the process. The conventional windrow compost is likely to generate an offensive odor. Although the conventional windrow compost is an aerobic system by turning, anaerobic space can be produced inside the pile and anaerobic respiratory substances mainly methane is likely to be emitted into the atmosphere. This phenomenon is marked in static piles where the offensive odor comes out. The fly was attracted to the foul smell generated from both piles during the first week. However, after the aeration to accelerate the decomposition, the fly infestation was not observed. On the other hand, it takes windrowing five times (18 days) to prevent flies from increasing their number.

12.3.4 Social Consideration The compost plant is located next to the existing open dump and is close to the residential area. Currently, there are complaints from the local community. According to the results of a socio-economic survey carried out in February 2020, odor from landfills and compost plants is a major problem. As a result, acceptance of these two facilities is very low, even among people living far from them. To address these problems, technical interventions to improve waste management facilities were implemented. Although technical improvement of the compost plant has been implemented, it is necessary to build consensus through close communication with the neighborhood and community from the planning stage of technical improvement of the compost plant in order to resolve the dissatisfaction. Specifically, this means constant sharing of information on the planned content of the pilot project, the schedule and method of construction, and discussion on the establishment of a monitoring system and its implementation after the facility is operational. To be more specific, (1) a monitoring committee will be established, (2) monitoring items, frequency, and implementer will be decided, and (3) monitoring will be conducted (see Fig. 12.6). * The total number of respondents is 250 households. The total is not 100% as there were blank responses.

164

A. Kanachi et al.

100 80

10

15.6

15.6

24.8

35.6

25.6

14 11.6

32

17.6

24.8

%

60 40

33.6

39.6

20

0

14.8 Offensive odor

30.8

25.2

21.6

Animals

Vermin

Acceptable

Medium

View

Terrible

19.6 24.4

26 31.6

17.2

49.6

Entry of waste pickers

38.8 Health issue related to air pollution

No answer

Fig. 12.6 Community opinions of environmental effects related to compost plant. Source Socioeconomic survey, February 2020

12.4 Conclusions The results revealed that forced aeration reduces the amount of anaerobic odor gases and leachate generation. Moreover, it was found that forced aeration can reduce the windrow turning requirement by 75%, and the total time required for decomposition is reduced to 50%. The drawback of the static pile aeration system was the larger particle size of matured compost; thus, it is recommended to mechanically shred the waste after the thermophilic decomposition phase and allow it to stabilize under natural convective aeration until maturation. Social research methods are being used to examine the effects of social consideration.

References 1. Basnayake BFA, Ariyawansha RTK, Karunarathna AK, Werahera SM, Mannapperuma N, Pariatamby A, Bhatti MS (ed) (2020) Sustainable waste management challenges in Sri Lanka. sustainable waste management challenges in developing countries, pp 352–381. IGI Global. https://doi.org/10.4018/978-1-7998-0198-6.ch015 2. Vaverková AD, Elbl J, Vobˇerková S, Koda E, Adamcová D, Gusiatin ZM, Rahman AA, Radziemska M, Mazur Z (2020) Composting versus mechanical–biological treatment: does it really make a difference in the final product parameters and maturity. Waste Manage 106:173–183, ISSN 0956–053X. https://doi.org/10.1016/j.wasman.2020.03.030 3. Siles-Castellano AB, López-González JA, Jurado MM, Estrella-González MJ, Suárez-Estrella F, López MJ (2021) Compost quality and sanitation on industrial scale composting of municipal solid waste and sewage sludge. Appl Sci 11:7525. https://doi.org/10.3390/app11167525

12 Performance Evaluation of a Full-Scale Forced Aerated Municipal Solid …

165

4. Kumar S, Negi S, Mandpe A, Singh RV, Hussain A (2018) Rapid composting techniques in Indian context and utilization of black soldier fly for enhanced decomposition of biodegradable wastes—a comprehensive review. J Environ Manage 227:189–199.ISSN 0301–4797. https:// doi.org/10.1016/j.jenvman.2018.08.096 5. Rynk R (1992) On-farm composting handbook. northeast regional agricultural engineering service pub. no. 54. Cooperative Extension Service. Ithaca, N.Y. 6. Brax C, Sironi S, Capelli L (2020) How can odors be measured? An overview of methods and their applications. atmosphere 11:92. https://doi.org/10.3390/atmos11010092

Chapter 13

A Systematic Bibliometric Analysis of Research on Hazardous Solid Waste Management Mohammed H. Alzard , Hilal El-Hassan, Ashraf Aly Hassan , Tamer El-Maaddawy , and Omar Najm

Abstract The continuous global development in multiple sectors and rapid urbanization have increased hazardous solid waste production rates. Numerous research studies have been conducted over the years to examine and identify scientific and effective management schemes that can significantly reduce this type of waste, creating a need for additional literature analysis to provide further insight into this area. This paper uses bibliometric analysis to collect and analyze the publications on hazardous solid waste management (HSWM) from 1973 to 2021. The web-based bibliometric analysis tool Biblioshiny was used to analyze the bibliometric information of 1585 publications retrieved from Scopus. The analysis included general characteristics of the research, the contribution of the different research constituents (authors, countries, institutions, and publications) and their social structure, and the authors’ keywords analysis. The results show that 1585 articles were published between 1974 and 2021 in 422 scientific journals covering many subject areas. The contributions to the current research were mainly from developed countries, as they are among the top hazardous solid waste generators. The authors’ keyword analysis showed a significant focus on recycling to manage such waste and minimize the reliance on landfills. The findings of this study provide researchers with an understanding of the current research works and identify the most prolific research constituents, which might prove helpful to scholars seeking collaboration opportunities or simply looking for a starting point in this research field. Additionally, the results would provide practitioners and governments with an evaluation of the relative performance for equitable decision-making. Keywords Hazardous solid waste · Waste management · Bibliometric analysis · Biblioshiny M. H. Alzard · H. El-Hassan (B) · A. A. Hassan · T. El-Maaddawy United Arab Emirates University, Al Ain, United Arab Emirates e-mail: [email protected] O. Najm Al Ain University, Abu Dhabi, United Arab Emirates © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_13

167

168

M. H. Alzard et al.

13.1 Introduction In the past few decades, the development of the industrial sector and rapid urbanization led to the production of a large quantity of toxic and hazardous waste. Hazardous waste is a type of waste with properties that can cause illness, death, and harm to humans, plants, animals, and ecosystems. It is generated from many sources, from industrial manufacturing process wastes to batteries, and may come in many forms, including liquids, solids, gases, and sludges [1]. In a year, almost 60 kg of hazardous waste is generated per capita globally, and the amount is increasing [2]. Therefore, hazardous waste should be disposed of properly. Landfills are one of the most used management solutions to handle hazardous solid waste. However, they are far from ideal, owing to their high cost and environmental footprint associated with waste disposal, emissions control, and proper rehabilitation, their poor design, the possibility of leaching materials into aquifers or nearby environments, and their low capacity [3, 4]. Another traditional way to handle hazardous waste is incineration. Incineration, in general, is more popular and acceptable than landfilling since it is characterized by volume reduction and energy trapping for power generation. However, it is not sustainable since it requires high energy to operate and generates by-products that must also be dealt with [4]. Recent literature revealed that hazardous solid waste management (HSWM) is not yet sustainable. As such, more focus should be placed on enhancing the technology of the current solutions while also creating novel, innovative ones [3, 4]. Research on HSWM expanded drastically in recent years. With the extensive research efforts devoted to it, there is a need for a systematic analysis that will provide an idea about its current situation and potential future trends. Several review articles have been published to reveal the development of HSWM [3–5]. However, despite their significant and valuable output, they mainly analyze literature based on subjective evaluation and use a small sample size, making them less comprehensive in coverage. Also, the process of selecting the papers is not adequately described. To overcome these shortcomings, the use of bibliometric reviews has become more predominant in different disciplines. Bibliometric research is a variant of systematic literature reviews that involves applying quantitative and statistical techniques to bibliographic databases [6]. The use of bibliometric research has experienced notable growth in recent decades. Unlike other manually conducted literature review variants, which might be subjective and less comprehensive, bibliometric review techniques benefit from using quantitative and statistical measures and technology to analyze a large number of articles (hundreds to thousands of articles). Consequently, bibliometric studies tend to be more objective and extensive than other types of review articles. This paper aims to employ bibliometric analysis to determine the key contributors, research themes, and critical publication outlets and map the social patterns and structures to understand the social processes supporting knowledge development in HSWM research. Despite the benefits it offers, such work has not yet been carried out in this area. The outcomes of this paper will help improve scientific communication

13 A Systematic Bibliometric Analysis of Research on Hazardous Solid …

169

and aid in future information retrieval processes for scholars and experts by providing insight into the current research situation and possible future development directions for HSWM research. Additionally, effective decisions to further develop this research field can be taken by decision-makers through these outcomes.

13.2 Methodology This research aims to conduct a bibliometric analysis of the published research on HSWM by collecting and synthesizing the available research on the topic. Various manuscripts have been published on the topic under study. Therefore, it is essential to select a database source that is comprehensive, objective, and trustworthy. The methodological approach adopted in this study is summarized in Fig. 13.1. The data used for the analysis in this research were bibliographic information extracted from Scopus. Scopus is one of the most extensive academic abstracts and citation databases, covering nearly 50 million pieces of literature published since 1823 [7]. Data retrieval resulted in obtaining 2903 documents published between 1973 and 2021 worldwide using the research query “TITLE-ABS-KEY (management AND of AND hazardous AND solid AND waste)”. Since this analysis was carried out between May and June 2022, all the history publications on the topic up to 2021 were included in the time frame of the analysis. The database comprised 66% journal articles, 16% conference papers, 8% reviews, 5% books and book chapters, and the remaining 5% includes notes, editorials, and conference reviews. This research will focus only on journal articles to ensure the high quality of the data source [8]. The retrieved database contained documents written in 15 different languages. Most of the retrieved documents in the bibliometric database were written in English (96%); therefore, the language was limited to English. After applying these three

Fig. 13.1 Flowchart of the analytical method and analysis

170

M. H. Alzard et al.

conditions, the research query was updated (Fig. 13.1), and the database comprised 1747 research articles. Data refinement approaches were employed to ensure high quality. Usually, great effort is dedicated to reducing the technical issues in bibliometric databases, yet they can still exist. These issues include missing data columns, spelling mistakes, name changes, clerical errors, and changes in citation databases over time [9]. To avoid such issues, screenings and checks were conducted to ensure that only sources with complete and correct bibliographical information were included. These identified errors were removed before running the analysis using Scopus itself. Entries that did not have an author name, undefined countries, undefined affiliation, or if they were not published yet, were all removed by updating the research query. This led to obtaining a database of 1585 articles. The database was then loaded and converted to a BibTeX format. Bibliometric analysis can be performed using many tools and software packages to provide the analysis in a visualization format, quantitative format, or both. Some of the most well-known tools are CiteSpace, VOSviewer, and Bibliometrix. This study used the tool Biblioshiny to carry out the bibliometric analysis. Biblioshiny is a web-based accessible application version of the open-source tool Bibliometrix that can provide rapid analysis and establish data matrices for performance analysis and science mapping of the bibliographic database [10]. The retrieved BibTeX database file was imported to Biblioshiny, which examined it in a few steps while ensuring data uniformity and consistency. The bibliometric analysis in this paper included quantitative and qualitative analyses. The quantitative analysis covers main information, research areas, scientific production over the studied period, writing language, journal distribution, source types, and information related to authors, countries, universities/ institutes, and publications. In contrast, the qualitative analysis considers the thematic areas and keyword/term mapping.

13.3 Results and Discussion 13.3.1 Characteristics of Research The evolution of interest over time in HSWM is presented in Fig. 13.2. By analyzing the yearly distribution of the retrieved 1585 documents/articles, it can be seen that the interest in the HSWM field has steadily increased starting from 1973, with an annual average growth rate of 10.76% with some fluctuations. This shows that research in the field of HSWM is still growing and is expected to grow further in the upcoming years. One possible reason for such an increase in this research field is the continuous advocacy for better and more sustainable waste management, especially for hazardous wastes. The output of the research on HSWM was covered in 25 subject areas. The top 15 subject areas in terms of the percentage of publications are presented in Fig. 13.3.

171

120 100 80 60 40 20 2020

2018

2016

2014

2010

2012

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

0 1974

Number of publications

13 A Systematic Bibliometric Analysis of Research on Hazardous Solid …

Year

Fig. 13.2 Annual scientific production of publications on hazardous solid waste management

The number of publications in each subject area can be calculated by multiplying the number of articles in the database (1585) by the percentage. It can be seen that the topic has been studied and published in subject areas primarily related to science, with almost 60% being published in environmental science and engineering. This is because waste management is extensively studied in areas of environmental science and managed using engineering schemes and methods. It is also worth noting that the subject areas covered here are known for the high production rates of solid hazardous waste. For example, according to the World Health Organization (WHO), 15% of the global waste generated in the healthcare sector is hazardous waste, which may be infectious, radioactive, or harmful to the environment. As these numbers are expected to increase, there is a need for proper medical waste management which will help in the segregation, storage, and safe disposal of waste that is harmful to people’s health [11]. This same mitigation is required in other subject areas. Environmental Science

55.53 6.73 5.13 4.96 4.42 3.62 3.49 2.36 2.02 1.85 1.77 1.56 1.39 1.39 0.76

Subject area

Medicine Chemistry Energy Economics, Econometrics and Finance Business, Management and Accounting Biochemistry, Genetics and Molecular Biology Physics and Astronomy 0

10 20 30 40 50 60 70 80 90 100 Publication percentage

Fig. 13.3 Publication percentage of the different subject areas

172

M. H. Alzard et al.

13.3.2 Analysis of Sources The journal articles or publications were distributed across a range of sources. The analysis of the database obtained from Scopus presented a total of 422 journals for studies related to HSWM between the years 1973 to 2021. A maximum of 210 publications and a minimum of 1 publication were recorded from the sources. Table 13.1 presents a list of the top ten sources by total number of publications (TP). The Waste Management Journal recorded the highest number of publications, followed by the Journal of Hazardous Materials with 210 and 183 publications, respectively. The top ten sources’ impact factors ranged from 4.5 (Journal of The Air and Waste Management Association) to 17.9 (Resources Conservation and Recycling). The listed source in Table 13.1 appears to be critical platforms for publications related to HSWM; therefore, scholars should target these sources if they are interested in the topic and aim for high visibility and citations. Further analysis revealed that most sources originate from the United Kingdom, the Netherlands, and the United States. This shows that developed countries have a dominant status in the sources.

13.3.3 Authors’ Contribution and Collaboration Analysis A total of 4884 authors have contributed to HSWM research during the analysis period. The number of authors of multi-author publications was 4698, whereas the number of authors of a single-author publication was 186. The top ten authors ranked based on the total number of publications are presented in Table 13.2. The productivity of each author over time (starting with the year they first published their first article on the topic) is also presented. It should be noted that the number of publications listed in the table only reflects the number of publications each author contributed to HSWM research. Juan Liu was the most contributing author with 13 publications on the topic, followed by Yuan Chen and Xiaomin Li with 12 and 9 publications each. The total number of citations is another important metric to study. While the total number of publications is essential in determining an author’s productivity, some argue that the number of citations is more significant since it measures the influence of an author [12–14]. Based on the total number of citations, Jiafu Li ranks first with 914 citations, Xiaomin Li with 695 citations, and Yuan Chen with 560 citations. This information can be helpful to scholars interested in research collaboration opportunities. Since the authors listed in Table 13.2 are well-established in the field, collaboration with them will lead to valuable input. To reveal the social structure and the interaction between the authors, a coauthorship analysis was conducted using Biblioshiny. The co-author analysis reveals the influential authors and how closely they are related. In this study, a co-authorship network with 40 nodes, each reflecting an author connected with multiple links, is presented in Fig. 13.4. Each node in the network represents an author. The node size indicates each author’s number of publications in HSWM. The nodes are

13 A Systematic Bibliometric Analysis of Research on Hazardous Solid …

173

Table 13.1 Top ten sources in hazardous solid waste management and their characteristics Sources

TP

CiteScore

SJR

SNIP

Waste management

210

13.5

1.74

2.16

IF 8.81

Elsevier

Publisher

Journal of hazardous materials

183

14.7

1.99

2.06

14.22

Elsevier

Waste management and research

119

5.9

0.75

1.32

4.43

SAGE

Journal of environmental management

50

11.4

1.48

1.90

8.91

Elsevier

Journal of the air and waste management association

47

4.5

0.62

0.92

2.63

Taylor & Francis

Resources conservation and recycling

41

17.9

2.58

2.94

13.71

Elsevier

Science of the total environment

33

14.1

1.80

2.17

10.75

Elsevier

Environmental science and technology

29

14.8

2.63

2.04

11.35

ACS

Journal of cleaner production

25

15.8

1.92

2.44

11.07

Elsevier

Chemosphere

24

11.7

1.50

1.60

8.94

Elsevier

TP: Total number of publications CiteScore measures the average citations received per document published in the serial SJR measures weighted citations received by the serial. Citation weighting depends on the subject field and the prestige of the citing serial SNIP (Source Normalized Impact per Paper) measures actual citations received relative to citations expected for the serial’s subject field The above metrics were obtained from [7]

connected with links with different thicknesses, demonstrating the author’s collaboration, whereas the thickness of the line connecting the nodes demonstrates the collaboration rate (i.e., the thicker the line, the higher the collaboration rate) [15– 17]. Clearly, a strong collaboration exists between the authors listed in Table 13.2, e.g., Xiaomin Li (li x) and Colin D. Hills (hills cd). It should be noted that authors with no links are omitted from the network by Biblioshiny, which explains why some authors are missing from the network. Connected authors seem to have more citations, which translates to more influence, than those with weak or no links. This means that the more collaborations between the authors, the higher the readability, visibility, and citations they will receive.

174

M. H. Alzard et al.

Table 13.2 Top ten contributing authors based on the total number of publications R

Author

Year

TP

TC

TCY

R

Author

Year

TP

TC

TCY

1

Juan Liu

2005

1

45

2.50

6

2010

1

18

1.39

2010

1

157

12.08

Issam A Al-Khatib

2013

2

40

4.00

2013

2

13

1.30

2015

1

51

6.38

2014

1

46

5.11

2016

2

38

5.43

2016

2

54

7.71

2018

1

0

0.00

2017

1

59

9.83

2020

1

4

1.33

2018

1

7

1.40

–

–

–

–

2019

1

78

19.50

–

–

–

–

2020

2

72

24.00

–

–

–

–

2021

1

3

1.50

–

–

–

–

2

3

4

Yuan Chen

Xiaomin Li

Colin D. Hills

Total

13

534

2002

1

96

4.57

–

Total

8

151

–

2010

1

15

1.15

2012

1

13

1.18

2016

2

65

9.29

2013

1

24

2014

1

28

2.40

2017

1

35

5.83

3.11

2018

1

8

2015

1

141

17.63

2019

2

110

2017

1

2018

1

59

9.83

2021

1

0

0.00

60

12.00

–

–

–

2019

–

1

78

19.50

–

–

–

–

2020

2

36

12.00

–

–

–

–

2021

2

25

12.50

–

–

–

–

Total

12

560

–

Total

8

233

–

2004

1

144

7.58

2018

3

62

12.40

2007

1

142

8.88

2019

2

37

9.25

2012

2

213

19.36

2021

2

4

2.00

2016

1

30

4.29

–

–

–

–

2017

2

111

18.50

–

–

–

–

2018

1

23

4.60

–

–

–

–

10.67

–

–

–

–

Total

7

103

–

1984

1

5

0.13

7

8

Jianchao Wang

Yong Liu

1.60 27.50

2020

2

32

Total

10

695

1994

2

39

1.35

1995

1

5

0.18

1997

1

55

2.12

1996

3

114

4.22

1999

1

12

0.50

2004

1

144

7.58

2001

1

32

1.46

2007

1

142

8.88

2006

2

15

0.88

2016

1

13

1.86

2020

1

16

5.33

Total

9

457

–

Total

7

135

– 9

Chi-Sun Poon

–

(continued)

13 A Systematic Bibliometric Analysis of Research on Hazardous Solid …

175

Table 13.2 (continued) R

Author

Year

TP

TC

TCY

5

Jiafu Li

2007

1

9

0.56

R

Author

Year

TP

TC

TCY

10

Margarida J. Quina

2008

1

64

4.27

2010

1

45

3.46

2013

1

667

66.70

2010

1

63

4.85

2011

3

64

2015

1

98

5.33

12.25

2014

1

39

4.33

2016

2

2017

1

25

3.57

2020

1

2

0.67

40

6.67

–

–

–

2021

–

2

30

15.00

–

–

–

–

Total

9

914

Total

7

232

–

–

R: Rank, TC: Total number of citations, TCY: Total citations per year (publishing year to 2021)

Fig. 13.4 Authors’ collaboration network

13.3.4 Countries and Institutions Contributions and Collaboration Analysis Table 13.3 presents the top ten most productive countries, the number of citations each country accumulated, and the top ten most productive institutes in HSWM. Ninety-one countries have been interested in the topic over the years. Of these, 33 countries have published more than 20 articles. Results show that the output of the top 10 most productive countries was more than the remaining 81 countries. Also, most countries listed in Table 13.3 are developed countries, which again shows that they have a high dominance in this research topic in terms of contribution. This can be attributed to the fact that these countries are among the top hazardous solid waste contributing countries and constantly searching for means to manage and dispose of this waste [18]. In terms of the total number of citations, the top three ranks belonged to the top three most productive countries, followed by Italy and Spain. Analysis of the institutes based on authors’ affiliation shows that a total of 1920 institutions

176

M. H. Alzard et al.

Table 13.3 Top ten contributing countries and institutions based on the total number of publications R

Country

TP

TC

1

USA

583

6216

R 1

Institute

TP

Technical University of Denmark

16

2

China

430

5877

2

Tongji University

16

3

India

254

3325

3

Tsinghua University

15

4

UK

141

1693

4

Birzeit University

14

5

Italy

140

2451

5

Central South University

14

6

Brazil

99

732

6

University of California

13

7

Iran

99

535

7

Chinese Academy of Sciences

12

8

France

85

1250

8

University of Florida

12

9

Canada

80

1620

9

University of Malaya

11

10

Spain

74

1793

10

Chongqing University

10

worldwide have contributed at least one publication to the body of HSWM research in collaborations and as single institutions. It should be noted that each institution’s information appeared at least once in the 1585 articles included in the obtained bibliometric database. The intellectual interactions and structural connections among countries and institutions are presented in Figs. 13.5 and 13.6 as a collaboration network. From Fig. 13.5, it can be seen that high collaboration rates exist between the top ten contributing countries listed in Table 13.3. Generally, most collaborations were between the developed countries, especially the USA and China with the rest of the world. The high collaboration rates between these two countries can be attributed to the fact that they produce vast quantities of hazardous solid waste with similar properties; therefore, sharing the knowledge of the best way to handle such waste is expected. Some collaborations existed within the European Union and with Asian countries. The collaboration network between 24 institutes, presented in Fig. 13.6, shows a high collaboration rate between institutes located in the same country (or territory).

13.3.5 Publication Analysis Information related to the citations of the analyzed bibliometric database was extracted, including the total number of citations for each article (TC) and the total number of citations per year (TCY). Table 13.4 presents the top ten most cited papers in the HSWM research field, with TC ranging from 265 to 667. Most papers listed in Table 13.4 present management regimes to deal with hazardous waste, with recycling being considered the most sustainable and effective means of disposing of this waste [19, 20]. The most cited paper with the highest TCY among the top ten papers was “Recent development in the treatment of oily sludge from petroleum industry: A review”

13 A Systematic Bibliometric Analysis of Research on Hazardous Solid …

177

Fig. 13.5 Countries’ collaboration network

Fig. 13.6 Institutes’ collaboration network

authored by Guangji Hu, Jianbing Li, and Guangming Zeng, and published in the Journal of Hazardous Materials. Oily sludge is arguably one of the most significant solid wastes generated in the petroleum industry. In this review, the authors introduced the origin, characteristics, and environmental impacts of oily sludge. This shows that this paper significantly impacted scholars interested in the field despite being published in 2013. “Properties of concrete containing scrap-tire rubber—an overview” by Rafat Siddique and Tarun R. Naik, published in Waste Management, was the second most cited paper and had a high total citation per year. In this paper, the authors presented an overview of the use of scrap tires in Portland cement concrete in

178

M. H. Alzard et al.

Table 13.4 Top ten cited papers in hazardous solid waste management research R

Paper

PY

TC

TCY

1

Recent development in the treatment of oily sludge from petroleum industry: A review [5]

2013

667

66.7

2

Properties of concrete containing scrap-tire rubber—an overview [21]

2004

660

34.7

3

The solids retention time—a suitable design parameter to evaluate the capacity of wastewater treatment plants to remove micropollutants [22]

2005

598

33.2

4

Solid wastes generation in India and their recycling potential in building materials [23]

2007

451

28.2

5

Recycling utilization patterns of coal mining waste in China [24]

2010

428

32.9

6

Novel and innovative pyrolysis and gasification technologies for energy-efficient and environmentally sound MSW disposal [25]

2004

385

20.23

7

Possible applications for municipal solid waste fly ash [26]

2003

363

18.2

8

Waste to energy—a key element for sustainable waste management [27]

2015

307

38.4

9

Health risk assessment of BTEX emissions in the landfill environment [28]

2010

279

21.5

10

Characterization of the bottom ash in municipal solid waste incinerator [29]

1999

265

11.0

PY: Publication year

the form of rubberized concrete. It also highlighted the benefits of using magnesium oxychloride cement as a binder for rubberized concrete mixtures. The paper details the potential uses of rubberized concrete. One would argue that both publications had a similar impact on scholars based on the total number of citations, given that both had accumulated an almost similar count. However, a quick look at the total citations per year would show that the publication by Hu et al. [5] has a higher impact since it was cited 667 times over almost ten years, whereas Siddique and Naik [21] accumulated 660 citations in 18 years. This shows that judging the impact of a publication should not solely depend on the citation count but should be used on the total citations per year, which reveals how impactful the publication has been since it was published.

13 A Systematic Bibliometric Analysis of Research on Hazardous Solid …

179

13.4 Keyword Analysis An analysis of the keywords in any bibliometric analysis is especially vital since it will reveal the current scenario and the evolution of the knowledge analyzed and outline its future. In the 1585 analyzed documents, 3665 authors’ keywords were identified. Some of the most frequently used keywords by authors, with at least 20 occurrences, were hazardous waste (111), waste management (110), solid waste (73), heavy metals (56), leaching (55), recycling (53) municipal solid waste (52), fly ash (44), landfill (44), solid waste management (34), incineration (31), medical waste (25), leachate (23), bottom ash (20) and management (20). Figure 13.7 shows the co-occurrence of the 49 main keywords, with a minimum number of occurrences of 5. The sizes of the text and circles highlight the number of occurrences, while the thickness of the lines presents the strength of the co-occurrence between the keywords, i.e., the thicker the line, the stronger the co-occurrence. Generally, a higher frequency of a keyword reflects a hotspot in the field [30]. Figure 13.7 shows that the closely associated terms are structured into clusters with the same color. Five clusters are identified, each representing the research area under investigation during the analyzed time period. It can be seen that recycling is one apparent method of management in addition to solidification and stabilization. Toxic materials such as heavy metals, lead, and chromium are included in cluster 2 with terms like leaching. Indeed, the leaching of such materials has been proven to cause harm to the environment [19]. Hot topics in HSWM include research on innovative ways to deal with the waste to replace the current methods (landfills, incineration), studying the effect of the different elements released by hazardous waste and determining their impact on the environment, and providing an accurate

Fig. 13.7 Co-occurrence network of authors’ keywords

180

M. H. Alzard et al.

description of the waste and its composition to better determine the best way to manage it. These outcomes provide future directions to scholars interested in the research on HSWM as well as decision-makers. The distances among the keywords can also be utilized to determine possible gaps in knowledge. For example, it can be seen from Fig. 13.7 that there is a weak link between the keywords landfill and leaching. This means insufficient research on the leaching of hazardous waste materials stored in landfills. The same applies to recycling and landfills. More research on management schemes (e.g., recycling) capable of reducing socio-environmental problems, reducing environmental impacts, and having a substantial economic potential should be investigated. Furthermore, research is lacking on landfills adopting modern technology, such as those that can accelerate waste degradation and offer means to recover gases from these landfills and repurpose them for energy sources. The utilization of modern technology in incineration and recycling activities will reduce the environmental impacts, generate energy, and achieve a circular economy.

13.5 Conclusions Hazardous solid waste production and subsequent management are worth noticing in developed and under-developed countries. The production of waste materials might not be hindered completely, but comprehensive knowledge of their products, harmful environmental and social impacts, and control should be prioritized. Using bibliometric analysis, this paper analyzed the literature published between 1973 and 2021 on hazardous solid waste management (HSWM). It included analyses of the general characteristics of the collected literature, the research constituents, their social structure, and the authors’ keywords. A total of 1585 journal articles were published during the analyzed time period. These were published in 422 scientific journals with impact factors ranging from 4.5 (Journal of The Air and Waste Management Association) to 17.9 (Resources Conservation and Recycling), covering a wide range of subject areas and authored by 4884 authors affiliated with different countries and institutions. The contribution to the body of knowledge in HSWM is mostly from developed countries. Co-occurrence analysis showed a high collaboration rate between authors located in the same regions. The authors’ keyword analysis showed a significant focus on management methods, waste composition identifications, and the environmental impact of hazardous solid waste. The analysis also revealed that the future prospective for HSWM is disposal and reuse. Proper hazardous waste management can help achieve economic growth, achieve a resilient environment, and improve the health and safety of the global human population.

13 A Systematic Bibliometric Analysis of Research on Hazardous Solid …

181

References 1. US EPA, O. Hazardous Waste Available online: https://www.epa.gov/hw/learn-basics-hazard ous-waste. Accessed 8 Aug 2022 2. The World Counts Hazardous Waste Statistics Available online: https://www.theworldcounts. com/challenges/planet-earth/waste/hazardous-waste-statistics. Accessed 8 Aug 2022 3. Sanito RC, Bernuy-Zumaeta M, You S-J, Wang Y-F (2022) A review on vitrification technologies of hazardous waste. J Environ Manage 316:115243. https://doi.org/10.1016/j.jenvman. 2022.115243 4. Bello AS, Al-Ghouti MA, Abu-Dieyeh MH (2022) Sustainable and long-term management of municipal solid waste: a review. Bioresour Technol Rep 18:101067. https://doi.org/10.1016/j. biteb.2022.101067 5. Hu G, Li J, Zeng G (2013) Recent development in the treatment of oily sludge from petroleum industry: a review. J Hazard Mater 261:470–490. https://doi.org/10.1016/j.jhazmat. 2013.07.069 6. Mukherjee D, Lim WM, Kumar S, Donthu N (2022) Guidelines for advancing theory and practice through bibliometric research. J Bus Res 148:101–115. https://doi.org/10.1016/j.jbu sres.2022.04.042 7. Elsevier About Scopus - Abstract and Citation Database | Elsevier Available online: https:// www.elsevier.com/solutions/scopus. Accessed 28 Aug 2021 8. Li Y, Li M, Sang P (2022) A bibliometric review of studies on construction and demolition waste management by using CiteSpace. Energy Build 258:111822. https://doi.org/10.1016/j. enbuild.2021.111822 9. Holden G, Rosenberg G, Barker K (2005) Tracing thought through time and space. Soc Work Health Care 41:1–34. https://doi.org/10.1300/J010v41n03_01 10. K-Synth Team Biblioshiny Available online: https://www.bibliometrix.org/home/index.php/ layout/biblioshiny. Accessed 8 Aug 2022 11. Ugalmugle S, Swain R (2022) Medical waste management market size by type of waste, service, and waste generator, industry analysis report, regional outlook, growth potential, price trends, competitive market share & forecast, 2022–2030; Global Market Insights, p 140 12. de Sousa FDB (2021) Management of plastic waste: a bibliometric mapping and analysis. Waste Manage Res 39:664–678. https://doi.org/10.1177/0734242X21992422 13. Bonilla CA, Merigó JM, Torres-Abad C (2015) Economics in Latin America: a bibliometric analysis. Scientometrics 105:1239–1252. https://doi.org/10.1007/s11192-015-1747-7 14. Montalván-Burbano N, Pérez-Valls M, Plaza-Úbeda J (2020) Analysis of scientific production on organizational innovation. Cogent Bus Manage 7:1745043. https://doi.org/10.1080/ 23311975.2020.1745043 15. Xie H, Zhang Y, Choi Y, Li F (2020) A scientometrics review on land ecosystem service research. Sustainability 12:2959. https://doi.org/10.3390/su12072959 16. Goksu I (2021) Bibliometric mapping of mobile learning. Telematics Inform 56:101491. https:// doi.org/10.1016/j.tele.2020.101491 17. de Sousa FDB (2022) A simplified bibliometric mapping and analysis about sustainable polymers. Mater Today: Proceedings 49:2025–2033. https://doi.org/10.1016/j.matpr.2021. 08.210 18. Sensoneo Global Waste Index 2022 Available online: https://sensoneo.com/global-wasteindex/. Accessed 5 Aug 2022 19. US EPA, O. Managing and Reducing Wastes: A Guide for Commercial Buildings Available online: https://www.epa.gov/smm/managing-and-reducing-wastes-guide-commercial-bui ldings. Accessed 5 Aug 2022 20. Zhang Z, Malik MZ, Khan A, Ali N, Malik S, Bilal M (2022) Environmental impacts of hazardous waste, and management strategies to reconcile circular economy and ecosustainability. Sci Total Environ 807:150856. https://doi.org/10.1016/j.scitotenv.2021.150856 21. Siddique R, Naik TR (2004) Properties of concrete containing scrap-tire rubber—an overview. Waste Manage 24:563–569. https://doi.org/10.1016/j.wasman.2004.01.006

182

M. H. Alzard et al.

22. Clara M, Kreuzinger N, Strenn B, Gans O, Kroiss H (2005) The solids retention time—a suitable design parameter to evaluate the capacity of wastewater treatment plants to remove micropollutants. Water Res 39:97–106. https://doi.org/10.1016/j.watres.2004.08.036 23. Pappu A, Saxena M, Asolekar SR (2007) Solid wastes generation in India and their recycling potential in building materials. Build Environ 42:2311–2320. https://doi.org/10.1016/j. buildenv.2006.04.015 24. Haibin L, Zhenling L (2010) Recycling utilization patterns of coal mining waste in China. Resour Conserv Recycl 54:1331–1340. https://doi.org/10.1016/j.resconrec.2010.05.005 25. Malkow T (2004) Novel and innovative pyrolysis and gasification technologies for energy efficient and environmentally sound MSW disposal. Waste Manage 24:53–79. https://doi.org/ 10.1016/S0956-053X(03)00038-2 26. Ferreira C, Ribeiro A, Ottosen L (2003) Possible applications for municipal solid waste fly ash. J Hazard Mater 96:201–216. https://doi.org/10.1016/S0304-3894(02)00201-7 27. Brunner PH, Rechberger H (2015) Waste to energy – key element for sustainable waste management. Waste Manage 37:3–12. https://doi.org/10.1016/j.wasman.2014.02.003 28. Durmusoglu E, Taspinar F, Karademir A (2010) Health risk assessment of BTEX emissions in the landfill environment. J Hazard Mater 176:870–877. https://doi.org/10.1016/j.jhazmat. 2009.11.117 29. Chimenos JM, Segarra M, Fernández MA, Espiell F (1999) Characterization of the bottom ash in municipal solid waste incinerator. J Hazard Mater 64:211–222. https://doi.org/10.1016/ S0304-3894(98)00246-5 30. Yunfeng G, Youning X, Yaxuan Z, Jianghua Z (2018) An analysis of the hotspot and frontier of mine eco-environment restoration based on big data visualization of VOSviewer and CiteSpace. Dizhi Tongbao 37:2144–2153

Chapter 14

Pre-paid System for Waste Minimization and Cost Recovery—A Trial in Gaza Strip, Palestine Ali Barhoum, Enas Qandeel, Hatem Abu Hamed, Rawan Tayeh, Suleiman Abu Mfareh, and Mitsuo Yoshida

Abstract During the past year, MoLG-JICA Project for Capacity Development in Solid Waste Management in Palestine (Phase-III) conducted a series of pilot projects for solid waste reduction in alliance with the national strategy for solid waste management that aims at implementing sustainable waste management through collaboration with different stakeholders. This paper focuses on the results of an initiative carried out in the southern governorates of Gaza Strip, Palestine, where there was a need for an urgent intervention to solve the problems of a gradual increase of daily generated quantities of municipal waste besides the problem of the irregular payments of the costs of the collection, transporting and disposal services of municipal solid waste, which in turn affects cost recovery by the service provider. The first phase of the initiative targeted waste generation sources; the households, using a simple technique to encourage them to watch the daily generated quantities using designated plastic bags. Along with the public awareness campaign, using different tools and software to control and track the results for 10 months of pilot implementation. The outputs showed that the monthly quantities were slightly reduced but the cost A. Barhoum (B) Joint Service Council for Solid Waste Management for the Local Authorities in the Governorates of Khan Younis, Rafah and Middle Area (JSC-KRM), Gaza Strip, Palestine e-mail: [email protected] E. Qandeel Communication and Community Awareness Consultant for Pre-Paid System for Waste Minimization and Cost Recovery, Gaza Strip, Palestine H. Abu Hamed · R. Tayeh MoLG-JICA Project for Capacity Development in Solid Waste Management in Palestine, Phase-III, Ramallah, Palestine S. Abu Mfareh Directorate of Joint Service Council, Ministry of Local Government, Ramallah, Palestine M. Yoshida Global Environment Department, Japan International Cooperation Agency, Tokyo, Japan

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_14

183

184

A. Barhoum et al.

recovery objective couldn’t be achieved in this short period. Recommendations for changing the approaches and widening the targeted circle of households to enhance the outcomes of the project are given for achieving the proposed indicators. Keywords Waste reduction · Pre-paid · Cost recovery · Designated plastic bags

14.1 Introduction The Gaza Strip is a coastal area along the eastern Mediterranean Sea. The area of the Gaza Strip is 365 km2 . The Gaza Strip is home to 2.11 million people [1], divided into five governorates and it is considered one of the highest population densities in the world. The Gaza Strip is suffering from the lack of efficient SWM services due to technical and financial struggles that face the service providers (municipalities and JSC’s) over the last 15 years. Adding to this is the scarcity of sanitary landfills for the final disposal of solid waste, since it is currently the only implemented method of waste disposal in Palestine. In 2019, the Al-Fukhary sanitary landfill in the south of the Gaza Strip started to receive waste from 46% of the area of the Gaza Strip on a daily basis, this landfill was prepared to receive the waste of three governorates and planned to serve 5 governorates after many years. This landfill currently receives about 600 tons of solid waste per day [2]. It was noticeable by the end of 2019, that the average daily generation of solid waste per capita is estimated at around 0.9 kg/capita/day [3] in the whole occupied Palestinian territory (oPt) as, in principle, the total waste generated constantly increases, each year, following the increase of the population and the evolution of lifestyle and livelihood conditions. Hence, the total generated solid waste in the Gaza Strip increased by about 21% in March and April 2020 concurrently with the declaration of the Palestinian Government of a State of Emergency when the first positive case of COVID-19 in the State of Palestine was diagnosed on 5th March 2020. On the other hand, the cost recovery rate of the solid waste management service was less than 30% in most of the Gaza Strip municipalities in 2017 [4], but it got worse during the COVID-19 pandemic (March–July 2020). Estimations of the cost recovery of the service of this period did not exceed 10% in most municipalities, which led municipalities to decrease the waste collection shifts and sometimes not transfer collected waste to the sanitary landfill but to the random dumping sites.

14 Pre-paid System for Waste Minimization and Cost Recovery—A Trial …

185

14.1.1 SWM Service Providers in the Gaza Strip There are different actors who provide SWM services for the population in the Gaza Strip. The local authorities (municipalities and Joint Service Councils) cover the urban and rural areas. In some areas, the service is divided into primary collection (from the waste generation source to the container) and secondary collection (from the container to the transfer station or to the landfill/dumpsite). The United Nations Relief and Works Agency for Palestine Refugees (UNRWA) is responsible for the services in the refugee camps. The UNRWA uses its own equipment but normally uses disposal sites that are operated by the local authorities. The Joint Service Councils in Khan Younis, Rafah, and Middle governorates (JSCKRM) are responsible for the secondary collection and disposal of solid waste in three governorates. The JSC implements the collection of waste from the areas of member municipalities (17 municipalities) within schedules and an organized follow-up from the administrative unit of the JSC. The containers located in the higher density areas are emptied on a daily basis, while containers in areas with lower population density are emptied once every three days throughout the years taking into account holidays and events. Since this JSC is the only responsible entity to operate the new sanitary landfill, the management is continuously searching for creative solutions to achieve sustainable waste management which can in turn, extend the life span of the landfill.

14.2 Waste Reduction Initiative Municipalities have been facing problems to maintain the balance of SWM services with respect to financial aspects. The increase in both population and waste generation resulted in a noticeable increase in the costs of these services. In order to cope with high costs, a new system for prepaid is introduced, and many municipalities are applying it worldwide. This system is about prepaid charging of services called “pay as you throw” (PAYT), which is a proportional system in which the total amount varies according to the waste generated individually [5, 6]. The Prepaid Garbage Bag Collection System introduced by the Project is a Pay-As-You-Throw (PAYT) system. It is one of the economic instruments for reducing waste generation and achieving financial sustainability of Solid Waste Management (SWM). Similar approaches were implemented on the international level concerning the same concept of using economic instruments for encouraging the recycling practices, such as volume-based waste fee (VWF) targeting households and small businesses, and such systems have been tested and investigated in South Korea. The evaluation of the system’s effectiveness is carried out by comparing the recycling performance rate of the pre-intervention and the post-intervention period through empirical analysis. The analysis of the intervention in South Korea revealed that such a system has a positive impact on the MSW recycling rate in a temporary manner rather than constant, accordingly, it was suggested to use other recycling policy measures for

186

A. Barhoum et al.

enhancing the recycling rate. Furthermore, analyzing for adoption unit pricing system shall be considered in other countries for getting more overview evaluation [7]. Ministry of Local Governorates (MoLG) in Palestine played a vital role in implementing a capacity-building program for the JSCs in West Bank and Gaza Strip, and in this MoLG-JICA Project [8], JSC-KRM was one of the beneficiaries, which received financial support to implement an initiative aiming to reduce the generated wastes [9, 10] as effective management for the SW problem in the three governorates targeted by the council, through building a pre-paid system for solid waste management service. This system was expected to address mainly three problems as follows: ● Waste Generation: The new pre-paid system was created to push the residents to pay the cost of the SWM service in advance by buying monthly packages of designated plastic bags. Clear and simple criteria for selling and using the bags were declared so the people could monitor and control the generated quantities at the households. ● SWM Service Cost Recovery: The second purpose of the system was to increase the cost recovery rate for the SWM services, so the JSC-KRM can efficiently operate the landfill. This would be possible by collecting the fees (the tariff of designated bags) before providing the service, collecting the bags. ● Environmental Conditions: The system included supplying JSC-KRM with new waste containers for streets, this would contribute to cleaning the streets and avoiding the random waste accumulation points inside the cities or towns.

14.2.1 Target Areas As shown in Fig. 14.1, the pre-paid initiative was designed to be implemented in three neighborhoods at three municipalities (one neighborhood for each municipality) as pilot locations and those neighborhoods were chosen by the municipalities in cooperation with JSC-KRM, taking into consideration that those municipalities represent different clusters of the society, so the responses and the reactions towards it that shall be generated by the residents (the targeted) will be studied for the ten months (pilot project period). So, the selection of the three areas took into consideration to select a neighborhood from a city in Khan Yunis Municipality, the second one from a town in Abasan AlKabira Municipality and the other from a refugee camp (municipal neighborhood) in Al-Nusirat Municipality, the demography, culture, educational status and financial status are different on those three areas. Table 14.1 shows the distribution of The Pre-Paid Initiative’s Targeted People Per Municipality.

14 Pre-paid System for Waste Minimization and Cost Recovery—A Trial …

187

Fig. 14.1 Targeted neighborhood for each municipality Table 14.1 Distribution of the pre-paid initiative’s targeted people per municipality Municipality

Total no. of residential units in the targeted neighborhood

Targeted residential units

% Targeted residential units (%)

Population of the targeted neighborhoods

Khan Younis (City)

30,490

3,577a

11

20,988

Al-Nusirat (Refugee camp)

9,155

1,991

22

13,410

Abasan Al-Kabira (Town)

5,500

1,005

18

6,300

a

Including 500 shops

188

A. Barhoum et al.

14.2.2 Methodology and Approaches The methodology of the pilot initiative, which is intended to achieve the desired objectives of this initiative by providing the following: Building a systematic approach to collect the cost of the SWM services in a regular manner and reducing the amount of solid waste at the same time required providing different tools to reach enhancement of the infrastructure as a first step. Increase the capacity building for the distinct players and raise the awareness of the targeted people to ensure the proper operation of the system. 1. Infrastructure and Equipment The equipment and tools were developed to be integrated with the existing system in both the municipalities and JSC-KRM. The decision was to use designated plastic bags to collect the daily waste generated from the households. a. Designated Plastic Bags Fully recyclable plastic bags with dimensions of 70*50 cm were packaged to be sold for the targeted households (see Fig. 14.2). Bags were sold by the municipalities through identified selling points inside the neighborhoods; 30 bags/month were sold for each residential unit with a price almost equal to the fee of waste collection service (about 10 – 20 NIS/month in Gaza Strip). b. Street Containers As shown in Fig. 14.3, new containers (capacity: 4m3 ) were manufactured to collect the plastic bags from the households, those containers were designed with the same Fig. 14.2 Designated plastic bag

14 Pre-paid System for Waste Minimization and Cost Recovery—A Trial …

189

Fig. 14.3 Street containers

specifications approved by the collection vehicles (compactors) owned and operated by JSC-KRM. It was distributed in the neighborhoods and then merged into the daily collection schedule to be weighed before disposing the compare the waste quantities. c. Administrative Software A management software was established and installed to link all the project components, to collect daily data regarding the received weights and the collected fees from the neighborhood. The software provided the necessary comprehensive and support to the JSC-KRM and the targeted municipalities, in documentation and recording management of the municipality’s subscription data (Residential Units/ Shops), distribution of the plastic bags for each residential unit/shop/institution (free bags and sold bags), follow-up waste quantity in the proposed areas (Total and for each municipality), follow-up fee collection measurements (JSC-KRM and municipalities), generate dynamic reports which included: customize financial reports, dashboard for indicators with data visualization (info-graphic), number & name of residential unit/shop who purchased the bags more than once for the same month, in addition, to customize search and filtering feature by municipality name, month, subscription and more. (see Fig. 14..4. that demonstrate the software functions). 2. Capacity Building Tens of employees (administrative staff and workers) were involved in implementing the pre-paid system, and all of them were trained and oriented to ensure the best operation for the collection, transferring, and disposal of waste. In addition to training on best methodologies to sell the designated bags and collect the fees. 3. The Method of Cost Recovery Objective Improving the cost recovery by distributing plastic bags to the beneficiaries in the targeted areas in the first month and then distributing the bags to them in the following month with a sum of money, provided that the municipality deducts the prescribed cleaning fees for citizens benefiting from the pilot initiative, which are issued monthly in the municipal service bill.

190

A. Barhoum et al.

Fig. 14.4 Diagram for software functions

The amount collected by the municipalities is allocated to provide the necessary plastic bags after the number of plastic bags provided by the project runs out to ensure the continuity of this pilot initiative. As well as to enable the targeted municipalities to pay their bills regularly. Solid Waste Management Council, which provides the service of secondary collection and solid waste disposal for these municipalities with other municipalities.

4. Public Awareness A comprehensive communication plan was prepared to classify and identify the different stakeholders of the initiative. This was used to design the awareness tools and messages. (See Figs. 14.5, 14.6 and 14.7 which shows some of the awareness activities). This plan aimed to raising awareness to influence the targeted citizens’ attitudes, behaviors, and beliefs towards the SWM positively in order to achieve the project’s goals. A module of 5-stages for positive behavior change was used: ● Precontemplation: It is the stage at which there is no intention to change behavior in the foreseeable future. Generally, the people in the Gaza Strip are unaware of the problems of increasing solid waste quantities and improper disposal practices, and most of them have kind of denial that paying service fees would enhance and ensure the continuity of the services. ● Contemplation: It is the stage where people become aware that a problem exists and are seriously thinking about overcoming it but have not yet made a commitment to take any action. Due to JSC-KRM annual reports of 2019 and 2020, many

14 Pre-paid System for Waste Minimization and Cost Recovery—A Trial …

191

Fig. 14.5 Community awareness raising activities

Fig. 14.6 Awareness raising seminar by JSC-KRM

people benefited from previous awareness campaigns and outreach activities. So, it was expected that there were a number of people who understood the importance of finding solutions for SWM problems especially the uncontrolled increment of the waste quantities. ● The Preparation: Preparing the targeted people to change their behaviors and to contribute to achieve the project objectives, was the first action of the pilot. Hence, an awareness team worked on raising the awareness of the residents about the best practices of solid waste amount reduction, and the importance of paying for the services. The awareness materials such as brochures, fact sheets and radio spots were used.

192

A. Barhoum et al.

Fig. 14.7 Awareness raising lecture by JSC-KRM

● Action: This is the stage in which targeted people start to modify their attitudes and behaviors and use plastic bags on a daily base to dispose of their wastes, and pay for the service by buying new packages of bags. Throwing the bags inside the containers to keep the streets clean and commit to the instructions of the municipalities and the awareness team to reduce the daily quantities. ● Maintenance: Monitoring the daily actions and the project progress to prevent any relapse.

14.3 Results Based on the issues addressed by the project, it can be noted that: ● The issue of reducing the daily generation of solid waste from the houses in the targeted areas in the three neighborhoods, and was achieved according to Table 14.2 despite the short period in which plastic bags were distributed to citizens. ● SWM Service recovery issues: Due to the inaccuracy of the data and information provided by the partner municipalities about the targeted neighborhoods, which includes the number of water subscriptions registered with the municipalities and Table 14.2 Waste quantities records before and after the distribution of designated plastic bags Municipality

Before distribution of designated plastic bags (tons)

After distribution of designated plastic bags (1 month) (tons)

Khan Yonis

13.08

11.3

Al-Nusirat

9.83

8

Abasan Al-Kabira

2.92

2.27

14 Pre-paid System for Waste Minimization and Cost Recovery—A Trial …

193

Before distribution of designated plastic bags After distribution of designated plastic bags (1 month) 13.08

11.30

9.83 8.00 2.92

Khan Yonis Municipality

Al-Nusirat Municipality

2.27

Abasan Al-Kabira Municipality

Fig. 14.8 Waste quantities records before and after the distribution of designated plastic bags

the name or names of the beneficiaries of these subscriptions, and thus identifying the names of the homeowners in order to deal with them by the project management benefiting from the waste collection service. This requires more effort and work by the project management and the municipalities to get the most accurate data and information, and thus this topic led to the lack of a sufficient period of time to measure the impact of this goal. ● Through the experience in this field, most of the municipalities in Palestine, given the situation in which they live, adopt the system of in-kind or financial incentives and with each other at intervals as a tool for improving the cost recovery for all services provided by the municipalities. Encourage the project management to make this system one of the objectives of this pilot initiative. (see Table 14.2 and Fig. 14.8). The initial results of the pilot project, after 10 months of implementation (Aug 2021–May 2022) showed that there was progress towards achieving the project objectives. (Table 14.2), It shows the differences between the estimated average of waste quantities per day in the three neighborhoods before and after the distribution of the designated plastic bags. The data shows slight differences in the waste reduction quantities, as the duration of the implementation period was not long enough (only 10 months) and those results were measured after one month of distributing the plastic bags (Jan 2022). However, it is apparent if designated plastic bags are introduced, the waste quantities are more or less decreased (Table 14.2). Table 14.3, shows the baseline data used to measure the average generation per capita. Using the total waste quantities received at the Al-Fukhari landfill in 2021 (column 4) and dividing it by the total number of working days per year (column 3), the average daily waste generation amounts for each governorate (column 5) is calculated. Depending on the official statistics of the population for each governorate (column 2), the average waste generation per capita in the Middle area is the highest

194

A. Barhoum et al.

at 0.64 kg/day, while the average of Khan Younis is 0.62 kg/day, and the average of Rafah is 0.50 kg/day. The estimated generation amount of municipal solid waste per day for each targeted neighborhood is shown in Table 14.4. It was found that the estimated generation quantity per day is the main indicator for the project’s progress, since the purpose of the project was to achieve waste reduction. The efforts of the awareness campaign done by the awareness team during the implementation period (Aug 2021- May 2022), resulted in increasing the knowledge of the neighborhood residents about the project objectives, the importance of waste reduction, how to reduce the daily generated waste quantities and how to use the designated plastic bags introduced by the Project. JSC-KRM used an indicator to follow up on the progress of delivering the awareness messages for the targeted group. Table 14.5 and Fig. 14.9, show the number of targeted households and people in each area and the number of households during the project implementation phase. Table 14.3 Baseline data for quantity of waste disposed of in landfill and received from the council’s jurisdiction during 2021 Governorate

Population for 2021

Number of working days

Total waste quantities (Tons)

Average uaily waste generation (Tons)

Average of generated waste per capita (kg/ day)

Rafah

260,118

344

45,116

131

0.50

KhanYounis

381,352

344

81,764

237

0.62

Middle Area

334,883

344

71,679

214

0.64

Table 14.4 Baseline data for solid waste generation/capita/day for each target neighborhood Municipality

Target neighborhood’s population

Estimated generation quantity/day

Khan Yonis

20,988

20,988 *0.62 = 13.01 tons

Al-Nusirat

13,410

13,410*0.64 = 8.58 tons

Abasan Al-Kabira

6,300*0.62 = 3.9 tons

6,300

Table 14.5 Number of people directly targeted by the awareness campaign Municipality

Number of targeted households

Number of targeted people (directly targeted by the campaign)

Khan Yonis

3,145

4,237

Al-Nusirat

2,232

3,264

Abasan Al-Kabira

1,090

1,124

14 Pre-paid System for Waste Minimization and Cost Recovery—A Trial …

195

Number of targeted households Number of targeted people (directly targeted by the campaign) 4,237 3,264

3,145 2,232

1,090

Khan Yonis Municipality

Al-Nusirat Municipality

1,124

Abasan Al-Kabira Municipality

Fig. 14.9 Number of people directly targeted by the awareness campaign

14.4 Challenges and Lessons Learned This initiative revealed many lessons and challenges that must be addressed to succeed and fully achieve the objectives, those challenges were as follows: ● The inability of the three municipalities in the project to provide accurate data for the targeted neighborhoods, which is represented by the correct number of residents, the number of shops, and the number of housing units, which led the JSC- KRM to exert more effort to reach the correct data as much as possible. ● There was a significant gap in the relationship between the residents and the municipalities, which led to the existence of an unhealthy environment in the relationship between the residents and the municipalities. ● There were many trials in order to strengthen the relationship with residents, and these trials were insufficient for a number of reasons, the most important of which is the lack of social participation policy within the work of the municipality, in addition to the complicated political and economic situations in Gaza Strip. ● The inability of the three municipalities to provide a team to work on the awareness campaign, so the number of the team on the field wasn’t sufficient. ● The resident’s existing debts to the municipalities due to non-payment against the provided SWM services for a long time, so many of them couldn’t afford to buy the designated plastic bags.

196

A. Barhoum et al.

14.5 Conclusions The pilot project of the prepaid system faced many challenges; the most important problem was the inaccuracy of the information provided by the targeted municipalities which greatly affected the outputs of the project, by following up on the implementation of the pilot initiative activities, the following conclusions can be made: ● A significant achievement has been made in reducing the quantity of daily waste generation in the targeted areas, despite the short implementation period in which plastic bags were used by citizens, as shown below: ● As for cost recovery: Due to the shortage of time to obtain sufficient information on this subject, we were unable to measure this objective, it is worth to highlight that the municipalities in Palestine use the system of financial and in-kind incentives to improve the level of collection of financial dues to the citizens. Accordingly, this mechanism was chosen within the objectives of the project, but due to the lack of accurate information and time, we were unable to measure this objective. ● The Pilot initiative has made municipalities more aware of the importance of taking measures to raise their waste collection rate and narrow the gap between citizens and municipalities and update and develop their information systems. ● This system contributed to the promotion of the culture of using plastic bags of a specified size, which encouraged people to reduce their daily waste production. This was a positive step in promoting the culture of reducing the volume of municipal solid waste generated daily and benefiting from it as well, and this is in line with what is mentioned in the national strategy for solid waste management in Palestine, objective (3) policy (6). [11, 12] ● Since the implementation of the first phase of the system was only 10 months excluding the preparation stage, the results were not as expected, this required JSC-KRM to work hard to complete the work focusing on the awareness and providing accurate data about the people who live in the neighborhoods so all of them can be reached easily and continuously until most of them totally change their behaviors and commitment to the system. Acknowledgements This project was financially supported by the MoLG-JICA Project for Capacity Development in Solid Waste Management in Palestine Phase-III (2020-2024), a technical cooperation between Palestine and Japan. The authors thank all the members of the Project team, cooperating municipalities (Khan Younis, Al-Nusirat, Absan Al Kabeela), JSC-KRM, Ministry of Local Government (MoLG), and Japan International Cooperation Agency (JICA). The views presented in this paper do not necessarily represent the official views of MoLG, JSC, or JICA to which the authors belong. Contributions of the Authors Ali Barhoum and Enas Qandeel for implementation of the Project and drafting of the manuscript; Hatem Abu Hamed and Rawan Tayeh for technical supporting the Project and reviewing the manuscript; Suleiman Abu Mfarreh for supervising the Project; and Mitsuo Yoshida supervising, reviewing, and revising the manuscript.

14 Pre-paid System for Waste Minimization and Cost Recovery—A Trial …

197

References 1. Palestinian Central Bureau of Statistics (PCBS). Statistical Yearbook of Palestine 2021, p 19. https://www.pcbs.gov.ps/Downloads/book2593.pdf 2. The Joint Service Council for Solid Waste Management of Local Authorities in the Governorates of Khan Younis, Rafah and the Middle Area Annual Report 2021, p 26. http://jsc-krm. ps/Data/Images/bb9d5640-c1db-4a44-8be0-d9d6f482bad0.pdf 3. Thöni V, Matar SKI (2019) Solid waste management in the occupied Palestinian territoryoverview report, CESVI. https://www.cesvi.eu/wp-content/uploads/2019/12/SWM-in-Palest ine-report-Thoni-and-Matar-2019_compressed-1.pdf 4. EconConserv and Ma’alem (2017) Studies for Optimization of Waste Collection. https://drive. google.com/drive/folders/1FQ-kbXXZktVmgpER4gla7mSH6KtEH47E 5. Batllevell M, Hanf K (2008) The fairness of PAYT systems: some guidelines for decisionmakers. Waste Manage 28:2793–2800. https://www.sciencedirect.com/science/article/abs/pii/ S0956053X08002298?via%3Dihub 6. Alzamora BR, de Barros RTV (2020) Review of municipal waste management charging methods in different countries. Waste Manage 115:47–55. https://www.sciencedirect.com/sci ence/article/abs/pii/S0956053X2030386X 7. Park S, Lah TJ (2015) Analyzing the success of the volume-based waste fee system in South Korea. Waste Manage 43:533–538. https://doi.org/10.1016/j.wasman.2015.06.011 8. MoLG-JICA (2019) Project for technical assistance in solid waste management in Palestine (2015–2019): final report : a technical cooperation between Palestine and Japan. Japan International Cooperation Agency: Ministry of Local Government (MoLG), Palestine https://ope njicareport.jica.go.jp/618/618/618_317_1000041684.html 9. MoLG-JICA Project for Capacity Development in Solid Waste Management in Palestine PhaseIII (2022) http://swm.link 10. Studies for optimizing waste recovery in in Gaza Strip—Palestine, Assessment of existing waste recovery activities https://drive.google.com/file/d/1A6v8N43tJ3uhVtDohPa5ssxaZ7Xeh3iR 11. National Strategy for Solid Waste Management in Palestine 2017–2022. https://drive.google. com/file/d/1nuRYuD0BI2pco9XE8gYGD8k4IKeTTX5G 12. Abu Mfarreh S, Yoshida M (2021) National solid waste management strategy and challenges for waste minimization in Palestine. In: Proceedings of the 3rd international e-conference on engineering, technology and management—ICETM 2020, pp 49–55. https://doi.org/10.15224/ 978-1-63248-190-0-08. https://www.seekdl.org/conferences/file/paper/20201023_013003.pdf

Chapter 15

Green Synthesis and Antibacterial Activity of Silver Nanoparticles Synthesized by Syzygium Aromaticum and Thymus Vulgaris Extracts Against Some Oral Pathogens Abdullah T. Al-Fawwaz, Sajeda N. Al-Barri, Melad F. Al-Khazahila, and Nusaiba A. Al-Mashagbah

Abstract A variety of bacterial flora inhabit the oral cavity. It is known that some of these bacteria species can lead to oral illnesses in people. Toothpaste eliminates odor and removes stains. This study investigated the impact of different toothpastes containing silver nanoparticles produced by extracts of medicinal plants on dental caries causing bacteria isolated from human teeth. Clove Syzygium aromaticum was chosen as the plant. Sesquiterpenes (a- and b-calyophyllenes), phenols (eugenol with about 3% of acetyl eugenol and trace amount of esters, alcohols, and ketones make up 81–95% of its chemical composition. Thymus vulgaris contains 1,2 Benzene dicarboxylic acid, 3 nitro (CAS), thymol, phenol, 2 methyl 5 (1 methylethyle), and these components in that order (48.75%, 32.42% and 8.12%, respectively). The produced AgNPs were examined using FTIR and UV–Vis (ultraviolet–visible spectroscopy), and their antibacterial activity was confirmed when tested on oral Gram-positive and Gram-negative microorganisms that were present in the oral cavity. The Syzygium aromaticum-AgNPs, with 21.0 ± 1.64 and 17.0 ± 2.5 mm, respectively, and T. vulgaris-AgNPs with 17.0 ± 2.5 and 16.0 ± 1.17 mm, respectively, had the highest inhibition zone, both of these had higher activity when compared to NPs mixed with three different commercial toothpaste. Our findings imply that AgNPs made with clove and thyme can function as potent growth inhibitors in a variety of oral microorganisms. Future studies will reveal the routes of administration and cytotoxic assays, which can help in the medicines for oral health or medical usage. Keywords Antibacterial activity · Cariogenic pathogens · Nanoparticles · Plant extract A. T. Al-Fawwaz (B) · S. N. Al-Barri · N. A. Al-Mashagbah Department of Biological Sciences, Faculty of Science, Al Al-Bayt University, Mafraq, Jordan e-mail: [email protected] S. N. Al-Barri · M. F. Al-Khazahila Al-Shifa Center for Medicinal Plants, Mafraq, Jordan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_15

199

200

A. T. Al-Fawwaz et al.

15.1 Introduction A lot of attention has been paid to nanotechnology over the past few years, Characterization, design, and implementation of systems, strategies, and structures at the nanometer scale are all considered to be part of nanotechnology (1–100 nm). Nanoparticles are created with certain characteristics that produce valuable materials for biology and research [1]. Green synthesis is a process for making nanomaterial that is safe, efficient, cost-effective, and friendly to the environment. For the green synthesis of nanomaterials, microorganisms like bacteria, algal, yeast, fungi species, and the majority of plants serve as substrates. The final size and morphology of the nanoparticle are regulated by a variety of active compounds and precursors. Moreover, nanomaterial assistance from green production includes antibacterial characteristics [2]. In comparison, green synthesis which uses plant resources or other microorganisms is rated as both economical and environmentally friendly. Nanoparticles produced using green synthesis are consistent and biocompatible [3]. Stability and biocompatibility are just two of the benefits of green nanoparticles, they also have stronger antibacterial properties than nanoparticles made chemically or physically [4]. In the natural environment, several processes are available for the synthesis of nano- and micro-scaled materials that have subsidized the development of this moderately new and largely unfamiliar area of research on the biosynthesis of nanomaterials. Nevertheless, in green synthesis, reducing and maintenance agents are generally present within the bioextracts [5]. The oral cavity harbors a rich number of pathogens that may cause different oral diseases [6], including dental caries, periodontal diseases, and oral candidiasis with dental caries usually affecting humans worldwide [7]. Currently, there are a wide variety of therapies for the control and prevention of dental caries standing on the use of fluoride-based products and the most effective tool to maintain adequate oral health. However, the prevalence of dental caries has significantly increased. For this reason, alternative agents should be used with advanced physicochemical properties [8]. The chemical compounds found in traditional medicinal plants, also referred to as phytochemicals promote a number of physiological and biochemical processes in the human body. These non-nutritive compounds are used to treat a variety of infectious disorders and have anti-disease characteristics. In numerous research conducted in various parts of the world, medicinal plant extracts and their active ingredients have been utilized to combat bacteria, fungi, algae, and viruses [9]. The diameter of silver nanoparticles, which typically have a diameter of less than 100 nm, ranges from 20 to 15,000 silver atoms. Even at low concentrations, silver nanoparticles exhibit extraordinary antibacterial activity due to their high surface-tovolume ratio [10]. They also have minimal costs, cytotoxicity, and immunological reactions [11]. The strong antibacterial properties of silver nanoparticles improve the effectiveness of medicinal treatment [12].

15 Green Synthesis and Antibacterial Activity of Silver Nanoparticles …

201

In fact, the use of silver ions and compounds for sanitary and therapeutic objectives has been widespread. But, as time went on, the use of their anti-infection properties reduced as a result of the development of antibiotics and other disinfectants [13–15]. In addition to fighting viruses and fungi, AgNPs are effective against both Grampositive and Gram-negative bacteria, including some strains that are resistant to antibiotics. Gram-negative bacteria like Acinetobacter, Pseudomonas, Escherichia, and Salmonella are susceptible to AgNPs antimicrobial effects when tested in vitro [16]. However, these NPs work against Gram-positive bacteria such as Bacillus [17] Staphylococcus, Listeria, and Enterococcus [18]. The size of the NP affects the anti-bacterial effectiveness of AgNPs. The smaller AgNPs can more easily penetrate biological surfaces because they have a larger surface area to volume ratio [19]. The lipid bilayer is disorganized by these tiny AgNPs working with cell membranes, increasing membrane permeability and causing bacterial lysis. AgNPs with sizes ranging from 5 to 20 nm have a strong antimicrobial activity against Staphylococcus aureus (S. aureus) and Klebseilla pneumonia (K. pneumonia), whereas AgNPs smaller than 30 nm established a strong antimicrobial activity against S. aureus and K. pneumonia [20]. As a result, small AgNPs are more hazardous than big elements, and their oxidation increases their toxicity even further. The results of this study will contribute to determining the capacity of medicinal plant extracts AgNPs as a successful alternative in the prevention and control of dental caries.

15.2 Materials and Methods 15.2.1 Sampling of Cariogenic Dental Pathogen Teeth with dental caries were collected from a dental clinic in Mafraq city. The samples were collected and transferred to the lab after being refrigerated.

15.2.2 Isolation and Identification of Cariogenic Pathogen Teeth samples were firstly washed with 97% ethanol for 3 min. then were opened by breaking them down into small fractures, then inoculated into nutrient broth (Nb) and incubated at 37 °C for 5 days until the broth became turbid. Then 0.1 mL of Nb was cultured on agar plates to obtain a pure culture. The bacteria were isolated by comparing bacterial colony size, color, and bacterial morphology. Four bacterial isolates were identified as A1, A2, B1, and B2. In the end, all isolates were further identified via molecular biological identification [21]. The genes were amplified using the PCR method with two rounds of processing using the appropriate universal primer set if repeats were necessary. Enzymatic purification of crude PCR product was performed then. Gene was sequenced from both forward and reverse directions

202

A. T. Al-Fawwaz et al.

on a 3730xl DNA Analyzer. The highest quality data for each sample is then used for final delivery. The seq assembly was performed using cap3, and then the blast was sequenced on NCBI using the Internal Transcribed Spacer region (ITS) from the reference material database.

15.2.3 Plant Extract Preparation Cloves (Syzygium aromaticum) and thyme (Thymus vulgaris), two aromatic and medicinal plants, were examined. Cloves were purchased a nearby market in the form of dried flower buds, and they were later stored in a dry, tightly closed bottle. Thyme was brought to a lab in a laboratory and dried at room temperature after being collected in March from north Jordan. First, 20 g of dried plant materials and extracted in 80 mL ethanol for 24 h. The mixtures were centrifuged at 4500 rpm for 7 min, supernatants were collected and the solvent was evaporated using a rotary evaporator. Extracts were stored at 4 °C until further used.

15.2.4 Silver Nano Extract Preparation and Evaluation To synthesize 0.01 M of the aqueous solution of silver nitrate (AgNO3 ), in 100 ml sterile deionized distilled water, 0.169 g of sliver nitrate was dissolved (ddw). 80 mL of 0.1 M AgNO3 solution was added to 20 ml of the prepared extract, 3 h were spent stirring continuously with a magnetic stirrer at 60 °C at a ratio of 1:4 (v/ v). The solutions hue changed when silver nitrate was converted into AgNPs (see Fig. 15.1). The mixture was centrifuged twice for 25 min at 5000 rpm to produce pellets, which were then dried in an oven for 48 h at 90 °C. For further analysis and characterizations, the dried silver nanoparticles were subsequently re-suspended in methanol solvent. The creation of silver nanoparticles was confirmed by spectral analysis and visual observation of color changes in the solutions using a laser beam. When the laser entered the nanoparticle solution, it dispersed. However, no scattering was noticed when the laser traveled through the extract solutions or the methanol solvent (Fig. 15.1). The experiment was repeated three times. Using UV–Vis spectroscopy, the produced silver nanoparticles, AgNPs, were examined (Specord S 600-Molecular Spectroscopy-UV–Vis Diode-array Spectrophotometers, Germany). By monitoring the UV–visible spectra of the reaction mixture with a quartz cuvette at a scanning speed of 210–480 nm and using methanol as a reference, the reduction of silver nitrate was monitored at various time intervals (0.5, 1.0, 1.5, 2.0, 2.5, 3.0, and 3.5 h) [22].

15 Green Synthesis and Antibacterial Activity of Silver Nanoparticles …

203

Fig. 15.1 Silver nanoparticles UV–visible (UV–vis) spectra as a function of time at intervals of Syzygium aromaticum extract T1 (0.5 h) to T7 (3.5 h), with the transformation of the reaction mixture’s color

15.2.5 Antibacterial Activity of Plant Extracts and Silver Nanoplant Extracts According to the agar well diffusion approach, agar plates were streaked over the surface of the media using a sterile cotton swab to guarantee confluency after being infected with 0.1 mL of the microbial suspensions. Then, a 6 to 8 mm hole diameter was cut aseptically with a sterile cork borer, and a volume of 75 μL of the tested solution was hosted into the well. Afterwards, agar plates were incubated for 24 h at 37 °C. Eventually, the antibacterial activity was evaluated by measuring the zones of inhibition in mm. The test was repeated three times. [22].

15.2.6 Effects of Mixing Toothpaste with Nanoextracts on Antibacterial Activity Three different types of commercially available toothpaste were selected (TP1, TP2, and TP3). Afterwards, the antibacterial efficacy of toothpaste was tested against the bacterial isolates using the well diffusion method, and the wells were filled with 250 mg of toothpaste using micropipettes and incubated for 24 h at 37 °C. Toothpastes were mixed with two of plant extracts. Each of these has three extracts, including methanol and silver nanoparticles at a ratio of 1:1 respectively. 250 mg of toothpaste dissolved in 1 mL of distilled water, and then mixed with 5 ml of each plant extract. The mixture was used on bacteria that were isolated from human teeth.

204

A. T. Al-Fawwaz et al.

15.3 Results 15.3.1 Isolation and Identification of Oral Pathogen The plate cultural technique was used to isolate four bacteria (A1, A2, B1, B2) from samples. Based on their morphological traits and the outcomes of the ITS 1&2 gene sequencing the identification. The A1 and A2 strains were identified as Lysinibacillus fusiformis strain NBRC 15,717, B1 was Planomicrobium okeanokoites strain NBRC 12,536, and B2 was Lysinibacillus fusiformis strain DSM 2898 2898 isolated from each sample that was available for testing.

15.3.2 Silver Nano Extract Preparation and Evaluation Given that SPR (surface plasmon resonance) exists for the metal nanoparticles, the ultraviolet–visible (UV–Vis) spectrum was used for the preliminary characterization of the biosynthesized silver nanoparticles (AgNPs), which is the easiest and most indirect method capable of indicating the formation of metal nanoparticles, The extracts’ variations in color proved that AgNPs had been synthesized. An increase in SPR between 275 and 330 nm was detected during a spectroscopic investigation of the colored solution created using a Shimadzu UV–Vis spectrophotometer, which proved that AgNPs were formed (Fig. 15.1). At various time intervals, plant-silver nanoparticles UV–Vis spectra were recorded as a function of time (0.5, 1.0, 1.5, 2.0, 2.5, 3.0, and 3.5 h). After 3.5 h, the AgNPs from the plant Syzygium aromaticum showed a peak under UV–Vis spectroscopy at 290 nm. The rise in intensity over time, AgNP synthesis was observed as an increase in the quantity of nanoparticles produced. Spectrum analysis and the visual observation of color changes in the solutions using a laser beam were used to confirm the formation of silver nanoparticles. The laser scattered when it made contact with the nanoparticle solution. Yet, when the laser passed through the extract solutions, there was no sign of scattering (see Fig. 15.2).

15.3.3 Biosynthesis of Silver Nanoparticles(AgNPs) Silver nanoparticles (AgNPs) were created in the current work from two common Jordanian folk medicinal plants: Syzygium aromaticum and Thymus vulgaris. After looking into the AgNPs’ antibacterial properties, UV–Vis spectroscopy was used to identify them. Production of AgNPs started after mixing the plant extracts with the silver nitrate solution (Figs. 15.1 and 15.2).

15 Green Synthesis and Antibacterial Activity of Silver Nanoparticles …

205

Fig. 15.2 A laser pointer is shown in the image of two separate solutions: (a) a solution containing AgNPs from Thymus vulgaris extract (b) and Thymus vulgaris extract

15.3.4 Antibacterial Activity of Plant Extracts and Ag Nano Plant Extracts Plant extracts and AgNPs solutions were tested for their antibacterial efficacy against the strains NBRC 15,717 and 12,536 of Lysinibacillus fusiformis and Planomicrobium okeanokoites, respectively. The inhibition zone was discovered in Syzygium aromaticum- AgNPs to be 21.0 ± 1.64 and 17.0 ± 2.5 mm, respectively, demonstrating that the antibacterial activities of AgNPs from Syzygium aromaticum were larger than those of AgNPs from T. vulgaris, whereas T. vulgaris-AgNPs with 17.0 ± 2.5 and 16.0 ± 1.17 mm, as shown in Fig. 15.3. Also, the antibacterial activities of AgNPs of Syzygium aromaticum and AgNPs of T. vulgaris against Lysinibacillus fusiformis strain were higher than Planomicrobium okeanokoites strain, and it higher than AgNPs of T. vulgaris. Figure 15.4 demonstrates that the AgNPs antibacterial properties of Syzygium aromaticum against Lysinibacillus fusiformis strain NBRC 15,717, and Planomicrobium okeanokoites strain NBRC 12,536 mixing with different commercial toothpaste, TP1 was higher than TP2and TP3, whereas there was no activity of AgNPs of Syzygium aromaticum with TP3 against Lysinibacillus fusiform strain NBRC 15,717. In addition, AgNPs of Syzygium aromaticum with TP2 showed the highest antibacterial activities against Lysinibacillus fusiformis strain NBRC 15,717. Figure 15.5 demonstrates that Thymus vulgaris with TP1 had better antibacterial activity than TP2 and TP3 against Lysinibacillus fusiformis strain NBRC 15,717 and

206

A. T. Al-Fawwaz et al.

Fig. 15.3 A stacked bar chart showing the antibacterial activity of AgNPs of Syzygium aromaticum and Thymus vulgaris against Lysinibacillus fusiformis strain NBRC 15,717 and Planomicrobium okeanokoites strain NBRC 12,536

Fig. 15.4 A stacked bar chart showing the antibacterial activity of AgNPs of Syzygium aromaticum against Lysinibacillus fusiformis strain NBRC 15,717,and Planomicrobium okeanokoites strain NBRC 12,536, mixing with three commercial toothpaste (TP1, TP2, TP3)

15 Green Synthesis and Antibacterial Activity of Silver Nanoparticles …

207

Fig. 15.5 A stacked bar chart showing the antibacterial activity of AgNPs of Thymus vulgaris against Lysinibacillus fusiformis strain NBRC 15,717, and Planomicrobium okeanokoites strain NBRC 12,536, mixing with three commercial toothpaste (TP1,TP2,TP3)

Planomicrobium okeanokoites strain NBRC 12,536, while Thymus vulgaris with TP3 had lower antibacterial activities against both strains. Result show adding commercial toothpaste to AgNPs of plant extract lead to a decrease in antibacterial activity.

15.4 Discussion Two indigenous kinds of aromatic and therapeutic plants, namely Syzygium aromaticum and Thymus vulgaris were chosen in view of these factors to produce silver nanoparticles. Since there was no study investigating in the literature. Plant extract derived silver nanoparticles were evaluated for antimicrobial activity and characterized using UV–Vis spectroscopy. Previous studies showed that various plants, including Leaves of Svensoniahyderobadensis and the stem barks of Boswellia, Shoreaspecies, propolis, miswak, and chitosan [23]. Because of the Tyndall effect and the presence of AgNPs in the solution, the sharp peak of silver nanoparticle SPR absorption in the area of 250–300 nm was created by scattering laser. Light beam refraction and scattering by a medium containing small suspended particles is known as the Tyndall effect. The Tyndall effect is caused by particles that are roughly the same size as the wavelength of light. However, Light passes through

208

A. T. Al-Fawwaz et al.

without being scattered when the size of the particles in the solution is less than 1 nm [24]. Due to an increase in resistant bacterial strains, alternative treatments for various illnesses have included nanoparticles and medicinal plants. Enhancing the antibacterial activity of biosynthesized nanoparticles is the outcome of the interaction between natural chemicals found in plant extracts and nanoparticles [25]. This study found that the AgNPs of Syzygium aromaticum and Thymus vulgaris extracts used in this work can significantly inhibit two Gram-positive bacteria, Lysinibacillus fusiformis and Planomicrobium okeanokoites. Several bacterial strains were used to determine The extent of the inhibitory effects on bacterial growth with Lysinibacillus fusiformis strain NBRC 15,717, showed greater sensitivity to Syzygium aromaticum-AgNP solution treatment. Syzygium aromaticum is used in dentistry as an anodyne (painkiller) in case of emergencies. It is also used in medicine [26, 27]. As clove oil contains a significant amount of eugenol, which has strong biological and antibacterial properties [28], it was investigated here as a stabilizing and reducing agent in the production of AgNPs. Therefore, it is crucial to create dental materials that are antibacterial and have better mechanical qualities so that they may be produced and used in upcoming clinical applications. Our findings show that when plant extract with AgNPs were mixed with three different commercial toothpastes, their antibacterial activity decreases, whereas Wassel et al. observed that varnishes containing natural products combined with NaF have a higher antibacterial effect [23]. In dentistry, antibacterial silver has been used in conjunction with fluorides to prevent and treat caries. It has been proposed that silver ions primarily target cariogenic bacteria, while fluoride ions aid in tooth structure reconstruction [29]. The antimicrobial properties of AgNPs included in orthodontic appliances and conventional microbiocidal assays against S. mutans, Lactobacillus casei, Staphylococcus aureus, and Escherichia coli were reported, as well as in vitro biofilms using cariogenic bacteria (S. mutans), have also been reported in other studies, suggested the potential used to prevent the dental biofilm, and reduce the frequency of demineralization factors associated with caries during traditional dental treatments [30]. In Addition, AgNPs can destroy the permeability of bacterial membranes by creating many gaps, implying that AgNPs can damage the structure of the bacterial cell membrane [31].

15.5 Conclusions AgNPs are considered among the most important and desirable nanomaterials when compared to other nanoparticles made of various metals and are employed in a variety of life applications. In the current study, the traditional Jordanian plants green Thymus vulgaris and Syzygium aromaticum were employed in the green production of silver nanoparticles. Bioactive substances found in plant extracts are what cause AgNO3 to be reduced and capped into AgNPs. Both of the bacterial strains isolated from teeth

15 Green Synthesis and Antibacterial Activity of Silver Nanoparticles …

209

with dental caries were resistant to the produced silver nanoparticles antibacterial properties. By using a laser beam and UV–vis spectroscopy, silver nanoparticles were identified. Further applications of these AgNPs as antibacterial in biotechnology, medicine and other fields may be possible due to their non-toxic, affordability and environmental friendliness. Comparatively to AgNPs-plant extracts alone, the addition of AgNPs plant extracts reduces antibacterial efficacy against oral pathogens. In order to properly define the cytotoxic activities of silver nanoparticles and other metalNPs against anticancer cells as well as the consequences of these particles on the environment, more research is therefore required.

References 1. Kesharwani P, Gorain B, Low SY, Tan SA, Ling ECS, Lim YK et al (2018) Nanotechnology based approaches for anti-diabetic drugs delivery. Diabetes Res Clin Pract 136:52–77 2. Sivaraj A, Kumar V, Sunder R, Parthasarathy K, Kasivelu G (2020) Commercial yeast extracts mediated green synthesis of silver chloride nanoparticles and their anti-mycobacterial activity. J Clust Sci 31:287–291 3. Jain AS, Pawar PS, Sarkar A, Junnuthula V, Dyawanapelly S (1993) Bionanofactories for green synthesis of silver nanoparticles: toward antimicrobial applications. Int J Mol Sci 2021:22 4. Singh P, Mijakovic I (2022) Rowan berries: a potential source for green synthesis of extremely monodisperse gold and silver nanoparticles and their antimicrobial property. Pharmaceutics 14:82 5. Inbakandan D, Venkatesan R, Khan SA (2010) Biosynthesis of gold nanoparticles utilizing marine sponge Acanthella elongata. Colloid. Surface B 812:634–639 6. Mira A, Simon Soro A, Curtis M (2017) Role of microbial communities in the pathogenesis of periodontal diseases and caries. J Clin Periodontol 44:S23–S38 7. Frencken JE, Sharma P, Stenhouse L, Green D, Laverty D, Dietrich T (2017) Global epidemiology of dental caries and severe periodontitis–a comprehensive review. J Clin Periodontol 44:S94–S105 8. Nedeljkovic I, De Munck J, Vanloy A, Declerck D, Lambrechts P, Peumans M et al (2020) Secondary caries: prevalence, characteristics, and approach. Clin Oral Invest 24(2):683–691 9. Casciaro B, Calcaterra A, Cappiello F, Mori M, Loffredo MR, Ghirga F et al (2019) Nigritanine as a new potential antimicrobial alkaloid for the treatment of Staphylococcus aureus-induced infections. Toxins 11(9):511 10. Oves M, Aslam M, Rauf MA, Qayyum S, Qari HA, Khan MS et al (2018) Antimicrobial and anticancer activities of silver nanoparticles synthesized from the root hair extract of Phoenix dactylifera. Mater Sci Eng, C 89:429–443 11. Samuel MS, Jose S, Selvarajan E, Mathimani T, Pugazhendhi A (2020) Biosynthesized silver nanoparticles using Bacillus amyloliquefaciens; application for cytotoxicity effect on A549 cell line and photocatalytic degradation of p-nitrophenol. J Photochem Photobiol, B 202:111642 12. Schmalz G, Hickel R, van Landuyt KL, Reichl F-X (2017) Nanoparticles in dentistry. Dent Mater 33(11):1298–1314

210

A. T. Al-Fawwaz et al.

13. McShan D, Ray PC, Yu H (2014) Molecular toxicity mechanism of nanosilver. J Food Drug Anal 22 14. Souto EB, Silva GF, Dias-Ferreira J, Zielinska A, Ventura F, Durazzo A, Lucarini M, Novellino E, Santini A (2020) Nanopharmaceutics: Part I—Clinical trials legislation and good manufacturing practices (GMP) of nanotherapeutics in the EU. Pharmaceutics 12:146 15. Yeung AWK, Souto EB, Durazzo A, Lucarini M, Novellino E, Tewari D, Wang D, Atanasov AG, Santini A (2020) Big impact of nanoparticles: analysis of the most cited nanopharmaceuticals and nanonutraceuticals research. Curr Res Biotechnol 2(53–63):116–127 16. Sotoodehnia P, Mazlan N, MohdSaud H, Samsuri WA, Habib SH, Soltangheisi A (2019) Minimuminhibitory concentration of nano-silver bactericides for beneficial microbes and its effect on Ralstonia solanacearum and seed germination of Japanese Cucumber (Cucumis sativus). PeerJ 7:e6418 17. Pulit-Prociak J, Banach M (2016) Silver nanoparticles—a material of the future … ? Open Chem 14:76–79 18. Banach M, Tymczyna L, Chmielowiec-Korzeniowska A, Pulit-Prociak J (2016) Nanosilver biocidal properties and their application in disinfection of hatchers in poultry processing plants. Bioinorg Chem Appl 2016:5214783 19. Qasim M, Udomluck N, Chang J, Park H, Kim K (2018) Antimicrobial activity of silver nanoparticles encapsulated in poly-N-isopropylacrylamide-based polymeric nanoparticles. Int J Nanomed 13:235–249 20. Qais FA, Shafiq A, Khan HM, Husain FM, Khan RA, Alenazi B, Alsalme A, Ahmad I (2019) Antibacterial effect of silver nanoparticles synthesized using Murraya koenigii (L.) against multidrug-resistant pathogens. Bioinorg Chem Appl 2019:4649506 21. Banas JA, Takanami E, Hemsley RM, Villhauer A, Zhu M, Qian F, Marolf A, Drake DR (2020) Evaluating the relationship between acidogenicity and acid tolerance for oral streptococci from children with or without a history of caries. J Oral Microbiol 12:1688449. https://doi.org/10. 1080/20002297.2019.1688449 22. Siddhant J, Mohan MS (2017) Medicinal plant leaf extract and pure flavonoid Mediated green synthesis of silver nanoparticles and their enhanced antibacterial property Sci Rep 7(1) 23. Wassel MO, Khattab MA (2017) Antibacterial activity against Streptococcus mutans and inhibition of bacterial induced enamel demineralization of propolis, miswak, and chitosan nanoparticles based dental varnishes. J Adv Res 8(4):387–392 24. Huang J, Mo X, Fu H, Sun Y, Gao Q, Chen X et al (2021) Tyndall-effect-enhanced supersensitive naked-eye determination of mercury (II) ions with silver nanoparticles. Sens Actuators, B Chem 344:130218 25. Durán N, Durán M, De Jesus MB, Seabra AB, Fávaro WJ, Nakazato G (2016) Silver nanoparticles: a new view on mechanistic aspects on antimicrobial activity. Nanomed: Nanotechnol, Biol Med 12(3):789–99 26. Mystrioti C, Xanthopoulou T, Tsakiridis P, Papassiopi N, Xenidis A (2016) Comparative evaluation of five plant extracts and juices for nanoiron synthesis and application for hexavalent chromium reduction. Sci Total Environ 539:105–113 27. Yee MS-L, Khiew P-S, Chiu WS, Tan YF, Kok Y-Y, Leong C-O (2016) Green synthesis of graphene-silver nanocomposites and its application as a potent marine antifouling agent. Colloids Surf B Biointerfaces 148:392–401 28. Miyoshi JH, Castro JC, Fenelon VC, Garcia FP, Nakamura CV, Nogueira AC, Ueda-Nakamura T, de Souza HM, Mangolim CS, Moura-Costa GF (2022) Essential oil characterization of Ocimum basilicum and Syzygium aromaticum free and complexed with β-cyclodextrin. Determination of its antioxidant, antimicrobial, and antitumoral activities. J Incl Phenom Macrocycl Chem 102:117 29. Mei ML, Lo EC, Chu C (2018) Arresting dentine caries with silver diamine fluoride: what’s behind it? J Dent Res 97(7):751–758

15 Green Synthesis and Antibacterial Activity of Silver Nanoparticles …

211

30. Hernández-Gómora AE, Lara-Carrillo E, Robles-Navarro JB, Scougall-Vilchis RJ, HernándezLópez S, Medina-Solís CE et al (2017) Biosynthesis of silver nanoparticles on orthodontic elastomeric modules: evaluation of mechanical and antibacterial properties. Molecules 22(9):1407 31. Zhang X-F, Liu Z-G, Shen W, Gurunathan S (2016) Silver nanoparticles: synthesis, characterization, properties, applications, and therapeutic approaches. Int J Mol Sci 17(9):1534

Chapter 16

Institutional Pressure, Organizational Factors and E-Waste Management Practice: A Study in Telecommunication and Technology Industries Hafizah Abd-Mutalib, Che Zuriana Muhammad Jamil, Rapiah Mohamed, Nor Atikah Shafai, and Saidatul Nurul Hidayah Jannatun Naim Nor-Ahmad

Abstract The digitalisation era has demonstrated the environmental threat resulting from poor e-waste management. Therefore, this study aims to examine if institutional pressure and organisational factors can be used as determinants of good e-waste management practice. A questionnaire was distributed to the telecommunication and technology industries listed firms of Bursa Malaysia using the survey method. The results reveal that institutional pressure from customers positively and significantly impacts firms’ e-waste management practices. For organisational factors, top management and staff play a significant role in shaping firms’ e-waste management practices. The results from this study may be used to shed some light on the betterment of e-waste management practice, which will lead to achieving environmental sustainability. Keywords E-Waste · Institutional pressure · Organisational factors

16.1 Introduction The use of electrical and electronic appliances in this era is inevitable [1]. The usage is undeniable, especially when the COVID-19 pandemic hit the world, and technology has taken its place in the life of the global community and has been accepted as the new norm of living. Electronic appliances or gadgets have replaced the traditional way of communication and doing daily activities. This new norm of living has triggered the question of how these gadgets and appliances will be treated after they have reached the end of their useful lives. H. Abd-Mutalib (B) · C. Z. M. Jamil · R. Mohamed · N. A. Shafai · S. N. H. J. N. Nor-Ahmad Tunku Puteri Intan Safinaz School of Accountancy, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_16

213

214

H. Abd-Mutalib et al.

The wastes from these gadgets and appliances, referred to as e-waste (waste from electrical and electronic appliances and their components), have recently become the buzz talk of the global community. The Basel Convention has identified e-waste as hazardous as it contains dangerous materials and chemicals such as mercury, lead or brominated flame retardants [2]. Exposure to these materials and chemicals in the air through open burning or by permeating the soil will impact the lives of humans and the environment. Some studies demonstrate that inappropriate treatment of e-waste may result in serious health issues, such as changes in thyroid function, respiratory problems, changes in temperament and behaviour, decreased lung function, DNA damage and cancer [3, 4]. Despite the horror fact of e-waste as stated above, studies found that global ewaste generation has devastatingly increased year by year. According to a series of reports by the United Nations, global e-waste is documented to be as high as 53.6 million tonnes in 2019 [5–7]. The reports estimated that every world population will generate 9 kg of e-waste in the year 2030, which is equivalent to 74.7 million tonnes aggregately. The reports also highlighted the danger of illegal transboundary movement of e-waste for illegal dumping, which may result in a life-threatening scenario for the receiving countries [2, 8]. The alarming numbers demonstrate the urgency of having proper e-waste management practices to record and manage the e-waste generation. This study aims to examine factors that may contribute to the e-waste management practice of listed firms in Malaysian telecommunication and technology industries, whose activities are highly related to the generation of e-waste. Generally, this study examines the perception of these firms concerning their management of e-waste. Specifically, this study believes that external institutional pressures from the government, customers, suppliers and competitors act as important indicators to shape a firm’s environmental practices [9–12]. Consequently, internal organisational factors such as the commitment from top management, firms’ resources and staff capabilities are also identified as positively impacting environmental practices in past studies [9, 10, 13, 14]. The findings from this study will contribute to the effort made by environmental regulatory bodies to enhance awareness of proper e-waste management among business organisations. Enhanced awareness of good e-waste management will ensure the nation achieves one of the Sustainable Development Goals (SDG), SDG11, which is on achieving sustainable cities and communities. The results will also enhance the literature on e-waste management and practices, which has now become the limelight in environmental-related research.

16 Institutional Pressure, Organizational Factors and E-Waste …

215

16.2 Literature Review and Development of Hypotheses 16.2.1 Electrical and Electronic Waste (E-Waste) E-waste is defined as “waste from any electrical or electronic equipment including all components, sub-assemblies and consumables, which are part of the product at the time of discarding” [2]. Other definition includes “waste from electrical and electronic assemblies containing components, such as accumulators, mercury-switches, glass from cathode-ray tubes and other activated glass or polychlorinated biphenyl capacitors, or contaminated with cadmium, mercury, lead, nickel, chromium, copper, lithium, silver, manganese or polychlorinated biphenyl” [15]. E-waste is hazardous as it contains toxic materials such as mercury, lead and brominated flame retardants [2]. If not properly disposed of, these toxic components will eventually end up in landfills. Toxic and hazardous chemicals will be generated through open burning, or by permeating the soil, thus endangering human health and the environment. Improperly managed e-waste results in soil, atmospheric and aquatic contamination [3, 16], which poses a threat to humans, animals and plants [3]. Within humans, exposure to e-waste leads to health problems such as changes in thyroid function, respiratory problems, changes in temperament and behaviour, decreased lung function, DNA damage and cancer [3, 16]. The Global E-waste Monitor Reports by the United Nations show that global e-waste has increased rapidly [5–7]. In 2014, e-waste generated globally was documented as much as 41.8 million tonnes. This figure has increased to 53.6 million tonnes in 2019 and is expected to increase to 74.7 million tonnes in the year 2030, or 9 kg per person [7], unless some drastic actions are taken to prevent the escalating numbers. Asia marks the highest producer of e-waste, with 24.9 million tonnes generated in 2019; from this figure, only about 12% is documented to be properly collected and recycled, whereas the others have been dumped in local waste landfills [7]. The latest development on e-waste monitoring activities revealed that there exist transboundary movements of e-waste from higher-income nations to poorer nations. In 2019, about 5.1 million tonnes of e-waste had made their way through cross-borders [8]. From this figure, 3.3 million tonnes were moved in an uncontrolled manner, which involved illegal movements that posed threats to the receiving nations [8]. As a developing country with fast economic growth and massive urbanisation, Malaysia could significantly increase electrical and electronic equipment use. The usage has contributed to e-waste generation in Malaysia, which also shows an alarming increase. According to a study, e-waste is one of the top six waste streams generated in Malaysia, whereby 4.5% of total waste generation in 2012 [17]. Statistics by the United Nations revealed that in 2014, Malaysia generated 232 kilotonnes of e-waste, or 7.6 kg per person [5]. The figures increased to 364 kilotonnes or 11.1 kg per person in 2020 [7]. Furthermore, a substantial increment of 60.3% in industrial e-waste generation has been observed from 2015 to 2017 [18]. To date, the Malaysian Department of Environment (DOE) [19] envisaged the total amount of discarded e-waste to be increased to 24.5 million units in 2025.

216

H. Abd-Mutalib et al.

Figure 16.1 illustrates several devices’ volumes (in units) that contributed to the e-waste generation [19]. From the figure, mobile phones and personal computers mark the highest contributors of e-waste compared to household appliances such as televisions, washing machines, refrigerators and air-conditioners. Smartphones and personal computers have a shorter lifespan compared to other appliances. Therefore, the life cycle would be shorter, while e-waste generated would be higher. The alarming statistic is that within ten years (from 2016 to 2025), e-waste generation in Malaysia (in units) will double from around 12 k units to almost 25 k units in 2025. The main question would be how these e-wastes be treated, as failing to address this will lead to the waste being dumped in local landfills. The above explanation seems alarming, and something needs to be done to curb the problem of Malaysia becoming the e-waste landfills. Studies on e-waste management practices are among the steps that can be taken to increase the awareness of the nations, not only on its hazardous impacts but also on how to have good e-waste management practices so that the negative impacts can be minimised.

(Source: Department of Environment, Malaysia)

Fig. 16.1 Estimated e-waste generation in Malaysia. (Source Department of Environment, Malaysia)

16 Institutional Pressure, Organizational Factors and E-Waste …

217

16.2.2 E-Waste Management Practice Literature on e-waste management practice is seen as growing in this decade. As e-waste is a part of the environmental concern, the role of government in creating awareness among individuals and organisations is crucial, including the responsibility for promoting law enforcement and regulation and the promotion of e-waste recovery activities [20]. Comprehensive regulations focussing on proper e-waste handling and stakeholders’ accountability will raise awareness of the importance of proper e-waste management [21]. With public awareness, contamination from e-waste can be reduced [22]. Past literature also documents methods of good e-waste management practices. The most popular method of handling e-waste is through recycling [23]. A past study suggests that attitude and subjective norms as predictors of intention to recycle e-waste [23]. Besides recycling, e-waste management practice also concerns how e-waste is being reused, reduced, substituted, treated and phased out [24]. Although the literature shows promising activities, proper e-waste management faces several challenges for implementation, such as unregulated e-waste recycling operations [25, 26] and illegal cross-boundary transportation of e-waste [25].

16.2.3 Institutional Theory and Institutional Pressures Institutional theory suggests that organisations do not operate in their own environment or in a vacuum [27]. Instead, organisations are being institutionalised by a set of beliefs, rules, roles, and symbolic elements that can affect organisational activities. Such beliefs, rules, roles, and symbolic elements can be in the forms of regulative (required/enforced by law), normative (enforced by a shared sense of what is appropriate), or cognitive (enforced by the mental models of how work should be done) [28]. In practising good environmental commitments, organisations are seen to be institutionalised by some external factors, such as government regulations and customer orientations [9–12]. For instance, Mohamad Zailani et al. [11] examined if government regulations and incentives motivate firms to adopt eco-designs that influence environmental performance. The results from this study revealed that institutional drivers of government and incentives influence firms’ environmental performance both directly and indirectly through their internal proactive environmental strategy. A similar result was found in the Taiwan textile and apparel industry [10], where government involvement positively impacted green supply chain management. Meanwhile, Menguc et al. [9] suggest that the effect of entrepreneurial orientation on a proactive environmental strategy would be more assertive with the intensity of government regulations. These past findings thus justify the role played by the government in imposing environmental rules and regulations, which in turn motivate firms to adopt environmental strategies, which leads to environmental performance.

218

H. Abd-Mutalib et al.

Besides the government regulations, external and institutional pressure in the form of customer environmental sensitivity was also found to positively impact environmental strategy and performance. In Menguc et al. [9], the study suggests that the effect of entrepreneurial orientation on a proactive environmental strategy would be more substantial when there is higher customers’ sensitivity towards environmental issues. A similar result was also found in a study by Mohamad Zailani et al. [11], where customer pressure encourages firms to adopt environmental strategies that stimulate environmental performance. Meanwhile, Tatoglu et al. [12] found that firms with customer focus, differentiation strategy and pressure from stakeholders will likely engage with voluntary environmental management practices. The scenario signals the critical role played by external or institutional factors in shaping business firms’ decisions to adopt environmental strategies. Based on the above discussion, it is prevalent that institutional pressures may drive firms to engage in environmental sustainability practices. Findings from past and recent research show that institutional pressures from government regulations, customer orientation, and other stakeholders’ expectations positively impact business firms’ good engagement with the environment [9–12, 29, 30]. Based on these justifications, this study believes that e-waste management practices of business organisations are highly impacted by institutional pressures such as by the government or other stakeholders who are highly sensitised towards environmental sustainability. Therefore, this study hypothesises: H1 : There is a positive impact of institutional pressures on e-waste management practice. H1a : There is a positive impact of government regulations on e-waste management practice. H1b : There is a positive impact of customers on e-waste management practice. H1c : There is a positive impact of suppliers on e-waste management practice. H1d : There is a positive impact of competitors on e-waste management practice.

16.2.4 Resource-Based View and Organisational Factors The resource-based view suggests how business firms exploit and utilise their resources to achieve competitive advantage [31, 32]. Past studies justify the positive impact of firms’ resources on firms’ environmental commitment. For instance, the commitment from top management was found to positively impact firms’ environmental performance [9, 13]. Top management who are aware of the potential benefits of specific environmental initiatives might communicate that environmental issues are critical for firms’ sustainability. Therefore, they will have initiatives on environmental programmes and will be committed to environmental sustainability, thus will have a good impact on firms’ environmental practices [9, 13], including the e-waste management practice. Meanwhile, firms’ resources, such as financial capabilities and adequate human resources to conduct environmental activities, are also found to be impacting the

16 Institutional Pressure, Organizational Factors and E-Waste …

219

firm’s environmental commitment [10]. Firms with adequate financial resource allocations can get environmental certifications, which may enhance awareness of proper environmental and e-waste management practices [10, 14]. Meanwhile, proper environmental knowledge and trained staff are also seen to positively impact firms’ environmental practices [14]. A study by Liyin and Hong [14] revealed that staff with adequate training on environmental practices would be more empowered to perform their tasks and thus be more proactive towards environmental management practices. Therefore, this study hypothesises: H2 : There is a positive impact of organisational factors on e-waste management practice. H2a : There is a positive impact of top management commitment on e-waste management practice. H2b : There is a positive impact of organisational resources on e-waste management practice. H2c : There is a positive impact of knowledgeable staff on e-waste management practice.

16.3 Methodology The population of the study is the firms listed on Bursa Malaysia, specifically in the telecommunication and technology industries, listed in the Main Market and the Ace Market. These industries are chosen as samples as their activities are highly related to e-waste generation; thus, they are required to disclose their e-waste information in their annual reports [33]. Altogether, there are 112 firms. The study utilises a survey method, using a questionnaire as the research instrument. This method is considered the most preferred research instrument for the survey approach [34, 35]. The questionnaires were distributed through visits to the respective firms. Most of the responses were received immediately during the visits, while some were sent later by email. The final number of responses is 31, contributing to 28% of the response rate. The dependent variable is e-waste management practice (EPR), while the independent variables are institutional pressure (INP) and organisational factor (ORF). INP is further classified into pressures from the government (INP_GOV), customers (INP_CUST), suppliers (INP_SUPP) and competitors (INP_COMP), while OFR is further classified as top management (ORF_TOP), firm resources (ORF_RES) and staff (ORF_STF). The dependent and independent variables are measured by a Likert scale of 1–5, where 1 indicates Strongly Disagree, while 5 indicates Strongly Agree. The study also controls for several firm characteristics such as firm performance (FP), firm size (FS) and the board where the firms are listed (BT). The details of the measurement of variables are indicated in Table 16.1. Two (2) regression models are utilised in this study. The models are represented as follows:

220

H. Abd-Mutalib et al.

Table 16.1 Measurement of variables Variable

Abb

No of items

Source

E-waste management practice

EPR

17

[1, 24] [10–12]

Institutional pressure

INP

19

Government

INP_GOV

5

Customer

INP_CUST

5

Suppliers

INP_SUPP

5

Competitors

INP_COMP

4

Organisational factor

ORF

15

Top management

ORF_TOP

5

Firm resources

ORF_RES

5

Firm staff

ORF_STF

5

[9, 10, 12, 13]

Control variables

Abb

Measurement

Source

Firm performance

FP

Return on assets

[1, 24]

Firm size

FS

Log total assets

Board type

BT

1 = listed on the main board 1 = listed on the ace board

EPR = α + β1INP + β2ORF + β3FP + β4FS + β5BT + e EPR = α + β1INP_GOV + β2INP_CUST + β3INP_SUPP + β4INP_COMP + β5ORF_TOP + β6ORF_RES + β7ORF_STF + β3FP + β4FS + β5BT + e

(16.1)

(16.2)

16.4 Findings The data were firstly checked for normality and internal consistency. Each variable has a kurtosis and skewness level of ±10 and ±3, indicating the univariate normality. Furthermore, statistics show the results of less than 0.8 for correlations between variables and VIF of less than 10. All these findings indicate that the data is normal. The items were also checked for internal consistency, and the Cronbach alpha score indicates scores of more than 0.7, indicating excellent internal consistency.

16 Institutional Pressure, Organizational Factors and E-Waste …

221

Table 16.2 Descriptive statistics Variable EPR

Min 2.240

Max 4.650

Mean 3.742

SD 0.557

INP

1.000

4.290

2.857

0.995

ORF

1.000

5.000

3.387

0.956

INP_GOV

1.000

4.800

3.058

1.093

INP_CUST

1.000

4.800

2.890

1.143

INP_SUPP

1.000

4.400

2.639

1.073

INP_COMP

1.000

4.750

2.839

0.945

ORF_TOP

1.000

5.000

3.600

1.095

ORF_RES

1.000

5.000

3.226

0.955

ORF_STF

1.000

5.000

3.336

0.960

0.280

−0.015

0.139

18.103

1.230

0.293

0.461

ROA

−0.450

TA

13.900

Board_Type

0.000

20.67 1.000

See Table 16.1 for variables definition N = 31

16.4.1 Descriptive Statistics The descriptive results are tabled in Table 16.2. The mean score for EPR indicates a score of 3.742 (between slightly agree and agree), showing that the firms under study somehow agree that they perform e-waste management practices. The activities include e-waste recycling, reusing, reducing, proper disposal, adhering to regulations and disclosure of e-waste information.

16.4.2 Regression Results The objective of this study is to examine if INP and ORF have an impact on EPR. The results are shown in Table 16.3. In the first model, with an R2 of 75.3% (p < 0.01; F = 6.550), ORF positively impacts EPR, signalling that organisational factors highly influence e-waste management practice in the sampled firms; thus, H2 is accepted. The results, however, found no evidence of the impact of INP on EPR; thus, H1 is rejected. In the second model, INP and ORF are further classified into different types of pressures and internal factors. With the R2 of 71.4% (p < 0.01; F = 4.990), the results indicate that the e-waste management practice of the sampled firms is impacted by the pressure from its customers (INP_CUST). Besides the customers, firms’ ewaste management practice is also positively impacted by the top management’s commitment (ORF_TOP) and its staff (ORF_STF). Therefore, H1b , H2a and H2c are

222

H. Abd-Mutalib et al.

Table 16.3 Regression results Model 1 DV = EPR B-value Constant

2.842

INP

0.040

ORF

0.404

Model 2 DV = EPR Sig **

B-value 2.778

Sig **

*** −0.256

INP_GOV INP_CUST

0.337

INP_SUPP

−0.108

INP_COMP

0.232

ORF_TOP

0.291

ORF_RES

−0.342 0.320

ORF_STF FP

0.497

0.962

FS

−0.029

−0.031

MT

−0.143

−0.145

R2

0.753

0.714

F-value

6.550

4.990

Sig

***

***

*

* *

See Table 16.1 for variables definition. N = 31

accepted. Furthermore, none of the control variables indicates any influence on firms’ e-waste management practice.

16.5 Discussion and Conclusion The above results show several significant findings that are crucial in promoting good e-waste management practices. Above all, the sample firms’ mean score for e-waste management practice is only 3.742, indicating that the firms under study have yet to implement good practices for e-waste management. Secondly, the government does not put enough pressure on firms to engage in proper e-waste management practices, despite the role of the government as the regulatory body for environmental sustainability. More awareness programmes need to be conducted on business firms, particularly those contributing to the high generation of e-waste, such as in the telecommunication and technology industries. Enhanced awareness and proper regulations by the government will lead to good practices of e-waste management, thus ensuring the nation achieves SDG, particularly SDG11.

16 Institutional Pressure, Organizational Factors and E-Waste …

223

Consistent with the expectations, external institutional pressure from customers and internal forces of the commitments from top management and staff have proven to be significant factors towards a good practice of e-waste management. Since business nowadays is without geographical barriers, telecommunication and technology firms have been involved in international business transactions. The demands for socially and environmentally responsible business can be observed nowadays, not only from the stakeholders but also from the customers. As business firms need to respond to these demands, the commitment from top management will be an essential indicator to ensure that the demands are being addressed. Therefore, it is not surprising that pressure from customers and commitment from top management have significantly and positively impacted e-waste management practices in the firms under study. The commitment from top management will also ensure that responsible staff are well equipped with proper knowledge and training in ensuring the proper practice of ewaste management. On the other hand, pressures from suppliers and competitors and firm resources are not substantial determinants of the dependent variable. Above all, the findings from this study highlighted the practice of e-waste management in industries that are inclined to the generation of e-waste. Although the statistic is not so encouraging, this small finding may provide an early indication that proper e-waste management may not be seen as necessary by the sampled firms. Understanding the factors that can lead to better e-waste management practices, such as the commitment from top management and customer expectations, might be a significant factor in enhancing the good practice of e-waste management. On the other hand, the government may use this finding as a plan for the way forward, such as creating awareness among the industries for the betterment of their e-waste management practice. The study is not without limitations. It only focuses on listed firms in telecommunication and technology industries, and involves a small sample size. Future studies might want to examine e-waste management practices in small and medium enterprises (SMEs) and other sectors that might contribute to significant e-waste generators, such as the banking and financial sectors. Acknowledgements We would like to thank the Ministry of Higher Education Malaysia (MOHE) and Universiti Utara Malaysia (UUM) for supporting this study through the Fundamental Research Grant Scheme (FRGS/1/2020/SS01/UUM/02/14).

References 1. Abd-Mutalib H, Muhammad Jamil CZ, Mohamed R, Shafai NA, Nor-Ahmad SN (2021) Firm and board characteristics, and E-waste disclosure: a study in the era of digitalization. Sustainability 13(18) 2. Basel Convention (2020) Controlling transboundary movements of hazardous wastes and their disposal. http://www.basel.int/TheConvention/Overview/tabid/1271/Default.aspx

224

H. Abd-Mutalib et al.

3. Alabi OA, Adeoluwa YM, Huo X, Xu X, Bakare AA (2021) Environmental contamination and public health effects of electronic waste: an overview. J Environ Health Sci Eng 19(1):1209– 1227 4. Singh K, Arora G, Singh P, Gupta A (2021) IoT-based collection vendor machine (CVM) for E-waste management. J Reliab Intell Environ 7:35–47 5. Baldé C, Wang F, Kuehr R, Huisman J (2015) The global E-waste monitor 2014. https://i.unu. edu/media/unu.edu/news/52624/UNU-1stGlobal-E-Waste-Monitor-2014-small.pdf. Accessed 1 Feb 2020 6. Balde CP, Forti V, Gray V, Kuehr R, Stegmann P (2017) The global E-waste Monitor 2017. https://www.itu.int/en/ITU-D/Climate-Change/Documents/GEM2017/Global-E-waste. Accessed 1 Feb 2020 7. Forti V, Baldé CP, Kuehr R, Bel G (2020) The global E-waste monitor 2020: quantities, flows and the circular economy potential. Bonn/Geneva/Rotterdam. https://www.invest-data.com/ eWebEditor/uploadfile/2020071100243938206180.pdf 8. Baldé CP, D’Angelo E, Luda V, Deubzer O, Kuehr R (2022) Global transboundary Ewaste flows monitor 2022. https://ewastemonitor.info/wp-content/uploads/2022/06/GlobalTBM_webversion_june_2_pages.pdf 9. Menguc B, Auh S, Ozanne L (2010) The interactive effect of internal and external factors on a proactive environmental strategy and its influence on a firm’s performance. J Bus Ethics 94:279–298 10. Wu GC, Ding JH, Chen PS (2012) The effects of GSCM drivers and institutional pressures on GSCM practices in Taiwan’s textile and apparel industry. Int J Prod Econ 135:618–636 11. Mohamad Zailani SH, Eltayeb TK, Hsu CC, Choon K, Tan CH (2012) The impact of external institutional drivers and internal strategy on environmental performance. Int J Oper Prod Manag 22(6):721–745 12. Tatoglu E et al (2020) Why do emerging market firms engage in voluntary environmental management practices? A strategic choice perspective. Br J Manag 31:80–100 13. Latan H, Jabbour CJC, Jabbour ABLS, Wamba SF, Shahbaz M (2018) Effects of environmental strategy, environmental uncertainty and top management’s commitment on corporate environmental performance: the role of environmental management accounting. J Clean Prod 180:297–306 14. Liyin S, Hong Y (2006) Improving environmental performance by means of empowerment of contractors. Manag Environ Qual Int J 17(3):242–257 15. DOE (2020) E-waste has serious impact on human health and environment. http://www.doe. gov.my/hhew/serious-impact-on-human-health-environment/. Accessed 31 Jan 2020 16. Grant K et al (2013) Health consequences of exposure to E-waste: a systematic review. Lancet Glob Health 1(6) 17. Jamin NC, Mahmood NZAMR (2015) Scheduled waste management in Malaysia: an overview. Adv Mater Res 1113:841–846 18. Yong YS, Lim YA, Ilankoon IMSK (2019) An analysis of electronic waste management strategies and recycling operations in Malaysia: challenges and future prospects. J Clean Prod 224:151–166 19. DOE (2022) What is E-waste? https://ewaste.doe.gov.my/index.php/what-is-e-waste/ 20. Suja F, Abdul Rahman R, Yusof A, Masdar MS (2014) E-waste management scenarios in Malaysia. J Waste Manag 1–7 21. Rasnan MI, Mohamed AF, Choo TG, Watanabe K (2016) Sustainable E-waste management in Asia: analysis of practices in Japan, Taiwan and Malaysia. J Environ Assess Policy Manag 18(4) 22. Soo VK, Featherston C, Doolan M (2013) E-waste assessment in Malaysia. Presented at the 20th CIRP international conference on life cycle engineering, Singapore 23. Tan CH, Ramayah T, Yeap JAL, Ooi SK (2017) Examining residents’ receptiveness towards e-waste recycling in Penang, Malaysia. Glob Bus Manag Res 9(4s):374–390 24. Nik Azman NAN, Mohd Salleh S (2020) Finding the link between corporate financial performance and E-waste reporting. Glob Bus Manag Res Int J 12(4):122–129

16 Institutional Pressure, Organizational Factors and E-Waste …

225

25. Tansel B (2017) From electronic consumer products to E-wastes: global outlook, waste quantities, recycling challenges. Environ Int 98:35–45 26. Alias A-F, Ishak MB, Mohamad Zulkifli SNA, Abdul Jalil R (2014) E-waste management: an emerging global crisis and the Malaysian scenario. Int J Environ Sci 4(4):444–457 27. Berthod O (2016) Institutional theories of organizations. Public Policy, and Governance, Global Encyclopedia of Public Administration 28. Scott W (2013) Institutions and organizations: ideas, interests, identities. Sage, Thousand Oaks 29. de Grosbois D (2016) Corporate social responsibility reporting in the cruise tourism industry: a performance evaluation using a new institutional theory-based model. J Sustain Tour 24(2):245– 269 30. Font X (2019) Trade associations as corporate social responsibility actors: an institutional theory analysis of animal welfare in tourism. J Sustain Tour 27(1):118–138 31. Rodrigue M, Magnan M, Boulianne E (2013) Stakeholders’ influence on environmental strategy and performance indicators: a managerial perspective. Manag Account Res 24(4):301–316 32. Wernerfelt B (1984) A resource-based view of the firm. Strateg Manag J 5(2):171–180 33. Bursa Malaysia Sustainability Guide 2nd Edition (2018) https://bursa-malaysia.s3.amazon aws.com/reports/Bursa%20Malaysia%20Sustainability%20Reporting%20Guide%20(2ndEdition).pdf 34. Ajzen I Constructing a theory of planned behaviour questionnaire. https://www.researchgate. net/publication/235913732_Constructing_a_Theory_of_Planned_Behavior_Questionnaire 35. Lee J, Cerreto FA, Lee J (2010) Theory of planned behavior and teachers’ decisions regarding use of educational technology. J Educ Technol Soc 13(1):152–164

Chapter 17

Life Cycle Assessment of Sugarcane Biorefinery Complex in the Indian Context Meghana Munagala and Yogendra Shastri

Abstract Sugarcane biorefinery complex has enormous potential to diversify the current product portfolio of the sugar mill, thereby contributing to resiliency against market fluctuations. It also promotes a circular economy and sustainable handling of the waste streams generated during sugar production. This work has developed five configurations of sugarcane biorefinery complex aimed at the production of sugar, ethanol, and a bagasse valorization product (electricity in Configuration-1, Bio-CNG in Configuration-2, lactic acid in Configuration-3, succinic acid in Configuration-4, and xylitol in Configuration-5). The process data for the bagasse valorization route are adapted from both experimental and Aspen Plus® simulation studies. Life cycle assessment framework using OpenLCA 1.10 software and Ecoinvent® database is employed to assess the environmental impacts associated with these configurations. 1 tonne of cane processed in the biorefinery configuration is used as a functional unit. For cradle-to-gate scope, ReCipe(H) methodology and economic allocation method are chosen to evaluate the LCA results. Results showed that Configuration-2 performed better with a GHG impact of 111.08 kg CO2 eq./t of cane. Configuration3 that aimed at lactic acid production resulted in higher GHG emissions. Waste valorization strategies envisaged in the current study displayed significant GHG benefits compared to the business-as-usual case. Upon comparison with the businessas-usual scenario that co-produced sugar-ethanol and burned excess bagasse, GHG impact was reduced by 246%, 58%, 79%, 23%, and 56% for Configurations-1, 2, 3, 4, and 5, respectively. Keywords Sugarcane biorefinery · Life cycle assessment · Sugar complex · Indian sugar mill · Waste valorization

M. Munagala (B) · Y. Shastri Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_17

227

228

M. Munagala and Y. Shastri

17.1 Introduction Sugarcane, mainly cultivated for sugar production is one of the major commercial crops cultivated globally, with India alone estimated to record an all-time high production at 500 Million tonnes for the year 2021–22 [1]. However, sugar production results in the generation of vast amounts of waste that can cause adverse effects on soil, air and water. For 1 tonne of sugarcane processed, 100 kg of sugar is produced in addition to 300 kg bagasse, 45 kg molasses, and 23 kg press mud [2]. Bagasse, a major lignocellulosic residue generated is currently underutilized by burning in the mill boilers to ensure the sugar mill is energy self-sufficient. Around 55% of the bagasse generated remains excess even after meeting the energy demands of a sugar mill. Molasses obtained are generally diverted for ethanol production, resulting in 0.22 m3 spent wash as an effluent [3]. Press mud and spent wash generated are mostly utilized for land applications. From a circular economy point of view, the study of a sugar mill as an integrated complex that sustainably uses various waste streams to produce a plethora of value-added products seems to be a promising option. This can also contribute to a paradigm shift that perceives agro-industrial waste as a potential valorization resource. Further, these valorization pathways have to be evaluated from an environmental perspective to determine the hotspots and associated benefits. In this regard, life cycle assessment (LCA) [4], an assessment framework widely used by various researchers to estimate the environmental impacts throughout the life cycle of a product/process can be employed for the integrated complex. In India, 100 Million tonnes of bagasse is generated annually that can be harnessed to produce biofuels and biochemicals [5]. Diversifying the sugar mill to produce multiple products by bagasse transformation also contributes to the resiliency of the sugar industry. In the current study, in addition to the existing sugar-ethanol product scenario of Indian sugar mills, a sugarcane biorefinery complex considered valorization of surplus bagasse left out after meeting the sugar mill’s energy needs. As a result, five different configurations to produce five alternative products were developed. The five valorization products of the various configurations were chosen to include electricity, biofuel, i.e., Bio-CNG and biochemicals such as lactic acid, succinic acid, and xylitol. Additionally, other mill wastes generated such as press mud and spent wash were also valorized. Environmental assessment of these five biorefinery configurations for the Indian sugar industry context was carried out by employing the LCA framework. Moreover, these configurations are compared with the status quo of sugar mills that produce ethanol-sugar and divert excess bagasse for burning. LCA results will be instrumental in identifying the benefits associated with valorization opportunities and also enable decision-makers to make an informed choice.

17 Life Cycle Assessment of Sugarcane Biorefinery Complex in the Indian …

229

17.2 Methodology 17.2.1 Sugar Industry Integrated Complex: Configuration Development In the current study, an Indian sugar mill with a cane crushing capacity of 181 tonnes per hour is considered. Considering the variations in excess bagasse utilization strategy and to ensure the energy self-sufficiency of sugar mills, altogether, five configurations are developed for the LCA study. They are: five different configurations that co-produced sugar and ethanol, and also utilized surplus bagasse to produce one of the five main products such as electricity, Bio-CNG, lactic acid, succinic acid, and xylitol. Therefore, the three main units in the integrated sugar complex are: (i) sugar mill that produces sugar (ii) ethanol distillery that uses molasses for ethanol production, and (iii) bagasse valorization unit that transforms surplus bagasse into a value-added product. Moreover, a combined heat and power (CHP) plant that utilizes bagasse as fuel is considered to be annexed to the sugar mill. It is to be noted that the CHP plant powered by bagasse ensures the energy self-sufficiency of both the sugar mill and valorization unit. Therefore, bagasse is split between the CHP plant and valorization unit, and this split ratio varies for each configuration based on the energy requirements of the valorization unit. In addition to these units, each configuration additionally utilizes press mud and spent wash generated to produce biogas anaerobically. This biogas is diverted to the boiler to meet the energy requirements of the ethanol distillery unit. Hence, the configurations considered are as follows: Configuration-1: In addition to sugar and ethanol, electricity is generated from surplus bagasse that can be sold to the national grid. Configuration-2: In addition to sugar and ethanol, Bio-CNG is produced from bagasse for utilization as a transportation fuel. Configuration-3: In addition to sugar and ethanol, lactic acid is produced from bagasse. Configuration-4: In addition to sugar and ethanol, succinic acid is produced from bagasse. Configuration-5: In addition to sugar and ethanol, xylitol is produced from bagasse. Burning of bagasse in a sugar mill boiler for electricity production is already a well-established process and hence not described here in detail. Bio-CNG production from bagasse [6] in the current study involves hydrodynamic cavitation of bagasse after which pretreated bagasse is anaerobically digested to produce biogas. This biogas is upgraded using water scrubbing to produce Bio-CNG that meets the requirements of transportation fuel. The bagasse valorization production routes (Configurations-3,4 and 5) aimed at value-added chemicals such as lactic acid [7], succinic acid [8], and xylitol [9], mainly consist of four major stages: pretreatment, hydrolysis, fermentation, and downstream separation. For lactic acid production, bagasse is pretreated using sodium hydroxide

230

M. Munagala and Y. Shastri

followed by hydrolysis, fermentation, and downstream separation (reactive distillation). For succinic acid and xylitol, bagasse pretreated with dilute sulfuric acid is subjected to detoxification, fermentation, and downstream separation.

17.2.2 Life Cycle Assessment The LCA is conducted as per the International Standard Organization (ISO 14040/44) standard using OpenLCA 1.10 software (www.openlca.org). The goal is to perform a cradle-to-gate LCA of sugar and ethanol production along with one main product for each of the five sugar-complex configurations considered in Maharashtra state, India. The targeted audiences for this assessment include policymakers, sugar industries, and LCA practitioners. The functional unit is the processing of 1 tonne (t) of cane to produce sugar, ethanol, and five main products depending on the configuration of the biorefinery complex. The system boundary consists of: (i) sugarcane cultivation, (ii) sugarcane transportation, (iii) sugarcane milling, (iv) ethanol distillery, (v) bagasse-based CHP plant, (vi) biogas generation from press mud and spent wash, and (vii) bagasse valorization to one of the five main products. (Fig. 17.1). The life cycle inventory (LCI) data compiled are based on the following sources: (i) data gathered from Vasantdada Sugar Institute, (ii) discussions with domain experts, (iii) Ecoinvent 3.3 database [10], and (iv) peer-reviewed literature. The LCI inventories of sugarcane cultivation [11, 12], sugarcane transportation for a round trip of 100 km [7], milling, ethanol distillery [3], and bagasse valorization stages [6–9, 13] are taken from the literature. Emissions related to bagasse combustion in boilers are adapted for the Indian context [14]. Life cycle impact assessment (LCIA) translates the input and output flows of the inventory phase into relevant environmental indicators. In this study, the ReCiPe 2016 midpoint (H) LCIA method is used to evaluate 18 midpoint indicators. However, the climate change impact indicator is discussed in more detail in the results section. Impacts among multiple products are divided by employing economic allocation throughout the study. Further, to evaluate the environmental benefits of efficient waste management strategies adopted in the five configurations of the current study, the resultant climate change impacts are compared with respect to the business-as-usual scenario. The business-as-usual scenario considered a typical Indian sugar mill that produced sugarethanol and subjected excess bagasse to open burning. However, due to the lack of emissions data on the open burning of bagasse, the same emissions data set [14] adapted for bagasse burning in Indian sugar mill boilers is used. For this comparison, the system scope is extended to include the avoided emissions due to the production of valorization products that can replace the conventional counterparts. For example, 1 kWh of bagasse-based electricity produced from Configuration-1 can displace 1 kWh of electricity produced from the Indian production mix; thereby, resulting in a saving of GHG emissions. Similarly, avoided emissions are calculated for all other configurations.

17 Life Cycle Assessment of Sugarcane Biorefinery Complex in the Indian …

231

Fig. 17.1 Schematic representation of the sugarcane biorefinery complex considered in the study

17.3 Results and Discussion The LCA results (Table 17.1) of the five configurations are discussed below and these results are further compared with the business-as-usual scenario of Indian sugar mills.

17.3.1 Configuration-1: Sugar, Ethanol and Electricity Production The life cycle impacts of configuration that processed 1 tonne of cane to produce electricity from bagasse in addition to sugar and ethanol are tabulated in Table 17.1. The total climate impact associated with Configuration-1 of the biorefinery complex that processed 1 t of cane to produce 100 kg sugar, 11.25 kg ethanol, and 225.69 kWh electricity was 119.97 kg CO2 eq. Sugar production contributed to 78% of these total impacts, followed by ethanol (6%) and electricity (16%) production. The breakdown of these impacts revealed that the production of sugar, ethanol, and electricity (from bagasse) resulted in 0.936 CO2 eq./kg sugar, 0.658 kg CO2 eq./kg ethanol, and 0.084 kg CO2 eq./kWh, respectively. Further analysis revealed that the sugarcane cultivation stage with GHG impacts of 93.03 kg CO2 eq./kg cane emerged as an environmental hotspot. Sugarcane farming also contributed majorly to all midpoint categories tabulated in Table 17.1, in particular, freshwater eutrophication and water depletion.

1.33e−05

0.12 1.44e−05

kg P eq

kg CFC-11 eq

kg 1,4 DB eq

Freshwater eutrophication

Ozone depletion

Human toxicity

14.53

0.11

309.63

m3

Configuration 2

13.45

286.7

111.08

Water depletion

Configuration 1 119.97

Unit

kg CO2 eq

Impact category

Climate change

17.89

1.83e−05

0.12

342.54

141.80

Configuration 3

13.80

1.36e−05

0.11

294.14

113.95

Configuration 4

13.62

1.35e−05

0.11

289.88

112.46

Configuration 5

Table 17.1 LCA results for processing 1 tonne of cane through the five configurations of the Indian sugarcane biorefinery complex to produce sugar, ethanol, and bagasse valorization product

232 M. Munagala and Y. Shastri

17 Life Cycle Assessment of Sugarcane Biorefinery Complex in the Indian …

233

17.3.2 Configuration-2: Sugar, Ethanol and Bio-CNG Production For 1 tonne of cane processed in Configuration-2 that produced 9.79 kg Bio-CNG in addition to 100 kg sugar and 11.25 kg ethanol, climate change impact was 111.08 kg CO2 eq. A similar trend as Configuration-1 was observed with sugar production alone accounting for 85% of the overall impacts. Bio-CNG production from bagasse resulted in 0.927 kg CO2 eq./kg and ethanol production led to 0.658 kg CO2 eq./ kg ethanol. Sugarcane farming stage remained the major contributor to the overall GHG impacts. For all mid-point categories reported in Table 17.1, Configuration-2 has lower impact values compared to Configuration-1. This was mainly because of the difference in bagasse handling practices. Configuration-1 involved bagasse burning that resulted in higher boiler emissions whereas bagasse was subjected to anaerobic digestion in Configuration-2. As a result, Configuration-1 displayed relatively higher impact values.

17.3.3 Configuration-3: Sugar, Ethanol, and Lactic Acid Production Configuration-3 produced 100 kg sugar, 11.25 kg ethanol, and 24.87 kg lactic acid from 1 t of cane resulting in an overall GHG impact of 141.8 kg CO2 eq. The distribution of these impacts is as follows: 66% attributable to sugar production, 29% to lactic acid, and the remaining 5% to ethanol production. An increased share of bagasse valorization production to the overall impacts was observed compared to Configurations 1 and 2. This rise in GHG value can be attributed to the chemicals used in the lactic production route. In particular, sodium hydroxide (NaOH) used for bagasse pretreatment in lactic acid production dominated the overall GHG impact of lactic production (1.532 kg CO2 eq./kg LA). Sodium hydroxide production from the chlor-alkali process was highly energy intensive. Therefore, associated higher life cycle impacts of NaOH were reflected in all the impact categories of Configuration-3.

17.3.4 Configuration-4: Sugar, Ethanol and Succinic Acid Production The life cycle GHG impacts associated with Configuration-4 that produced 100 kg sugar, 11.25 kg ethanol, and 10.64 kg succinic acid was 113.95 kg CO2 eq. (Table 17.1). Production of 1 kg succinic acid and 1 kg ethanol led to 1.217 and 0.67 kg CO2 eq., respectively. Thereby, leading to a contribution of 12% to the overall GHG impact by the succinic acid route and 6% by the ethanol route. Whereas, the remaining share of 82% was attributed to the sugar production route. Configuration-4

234

M. Munagala and Y. Shastri

displayed environmentally better performance compared to Configurations-1 (electricity) and 3 (lactic acid). Bagasse burning in Configuration-1 and energy-intensive chemicals (NaOH) usage in Configuration-3 resulted in relatively higher impacts compared to Configuration 4. A similar trend was observed in all other mid-point categories evaluated in the study.

17.3.5 Configuration-5: Sugar, Ethanol, and Xylitol Production The climate change impact of Configuration-5 that produced 100 kg sugar, 11.25 kg ethanol, and 14.61 kg xylitol was 112.46 kg CO2 eq. (Table 17.1). 83% of this value was attributed to the sugar production route that has an impact of 0.936 CO2 eq./kg sugar. Ethanol route resulted in 7% of the total GHG impact, followed by xylitol at 10% due to 0.784 kg CO2 eq./kg impacts. Among all the biochemical configurations (3, 4, and 5), Configuration-5 resulted in a lower impact value for all midpoint categories. This was due to the higher energy consumption of Configuration-4 and the energy-intensive chemical (NaOH) used in Configuration3. Compared to Configuration-1 which burned bagasse and led to higher boiler emissions, Configuration-5 displayed lower impacts. On the other hand, chemicals involved (sulfuric acid and lime) in xylitol production led to higher impacts compared to Configuration-2. From these LCA results, it is clear that the GHG impacts of Configuration-2 that produced Bio-CNG displayed lower environmental impacts in all midpoint categories. Whereas, Configuration-3 lactic acid has emerged as the valorization route with higher GHG impact mainly due to sodium hydroxide usage in the pretreatment.

17.3.6 Comparison with Respect to the Business-As-Usual Scenario 1 kg ethanol produced from the business-as-usual scenario can replace 0.66 kg petrol and contribute to GHG savings of 6.22 kg CO2 eq./t cane. Therefore, the net GHG impact associated with the business-as-usual scenario after considering avoided emissions was 113.56 kg CO2 eq./t cane. For Configuration-1, GHG savings from ethanol, electricity, and digestate that can displace petrol, production mix-based electricity, and synthetic fertilizers were -285.81 kg CO2 eq./t cane. Therefore, the net GHG impact of Configuration-1 was −165.84 kg CO2 eq./t cane and this negative GHG value represents overall GHG benefits due to avoided emissions. Similarly, upon replacement of the conventional counterpart by the bagasse valorization products formed in Configurations-2, 3,4, and 5, avoided GHG benefits were 5.51, 4.5, 1.9,

17 Life Cycle Assessment of Sugarcane Biorefinery Complex in the Indian …

235

and 3.839 kg CO2 eq./kg of product replaced, respectively. Therefore, after considering avoided emissions due to ethanol, digestate, and valorization product, the net GHG impact of Configuration-2,3,4, and 5 were 47.81, 23.48, 87.33, 49.97 kg CO2 eq./t cane, respectively. We can observe a significant GHG reduction of 246%, 58%, 79%, 23%, and 56% upon comparison with the business-as-usual scenario. It is interesting to note that lactic acid with the highest GHG impacts turned out to be a benign route compared to Configurations-2, 4, and 5 after considering avoided emissions. These results highlight the greater GHG benefit of adapting the waste valorization strategies discussed in the current study.

17.4 Conclusion To adopt circular economy practices and achieve diversification of sugar mill products, an integrated biorefinery complex with five different configurations was proposed for the Indian context. For 1 tonne of sugarcane processed in these five configurations of the sugar complex, GHG impacts varied from 111.08 to 141.81 kg CO2 eq./kg cane. The sugarcane farming stage is attributed majorly to the total GHG emissions in Configurations 1–5. Configuration with Bio-CNG as a product emerged as an environmentally sustainable route. Configuration-3 displayed the highest GHG impact, with the lactic acid production alone contributing to 41.12 kg CO2 eq. The impacts associated with lactic acid production can be improved by recycling the sodium hydroxide used in the pretreatment stage. Moreover, all the configurations displayed substantial savings compared to the business-as-usual scenario with the open burning of bagasse.

References 1. Ministry of Agriculture & Farmer’s Welfare, Government of India (2022). https://pib.gov.in/ PressReleasePage.aspx?PRID=1865320 2. Meghana M, Shastri Y (2020) Sustainable valorization of sugar industry waste: status, opportunities, and challenges. Bioresour Technol. https://doi.org/10.1016/j.biortech.2020. 122929 3. Soam S, Kumar R, Gupta RP, Sharma PK, Tuli DK, Das B (2015) Life cycle assessment of fuel ethanol from sugarcane molasses in northern and western India and its impact on Indian biofuel programme. Energy 83:307–315. https://doi.org/10.1016/j.energy.2015.02.025 4. Guinée JB, Heijungs R, Huppes G, Zamagni A, Masoni P, Buonamici R, Ekvall T, Rydberg T (2011) Life cycle assessment: past, present, and future. Environ Sci Technol 45:90–96. https:// doi.org/10.17654/JPHMTFeb2015_029_042 5. Konde KS, Nagarajan S, Kumar V, Patil SV, Ranade VV (2021) Sugarcane bagasse based biorefineries in India: potential and challenges. Sustain Energy Fuels 5:52–78. https://doi.org/ 10.1039/d0se01332c 6. Munagala M, Shastri Y, Nagarajan S, Ranade V (2022) Production of Bio-CNG from sugarcane bagasse: commercialization potential assessment in Indian context. Ind Crop Prod 188:115590

236

M. Munagala and Y. Shastri

7. Munagala M, Shastri Y, Nalawade K, Konde K, Patil S (2021) Life cycle and economic assessment of sugarcane bagasse valorization to lactic acid. Waste Manag 126:52–64. https://doi.org/ 10.1016/j.wasman.2021.02.052 8. Shaji A, Shastri Y, Kumar V, Ranade VV, Hindle N (2021) Economic and environmental assessment of succinic acid production from sugarcane bagasse. ACS Sustain Chem Eng 9(38):12738–12746. https://doi.org/10.1021/acssuschemeng.1c02483 9. Shaji A, Shastri Y, Kumar V, Ranade VV, Hindle N (2022) Sugarcane bagasse valorization to xylitol techno-economic and life cycle assessment. Biofuels Bioprod Bioref 16:1214–1226 10. Wernet G, Bauer C, Steubing B, Reinhard J, Moreno-Ruiz E, Weidema B (2016) The ecoinvent database version 3 (part I): overview and methodology. Int J Life Cycle Assess 21:1218–1230. https://doi.org/10.1007/s11367-016-1087-8 11. Mandade P, Bakshi BR, Yadav GD (2016) Ethanol from Indian agro-industrial lignocellulosic biomass: an emergy evaluation. Clean Technol Environ Policy 18:2625–2634. https://doi.org/ 10.1007/s10098-016-1179-y 12. Murali G, Shastri YS (2019) Biofuels Life cycle assessment based comparison of different lignocellulosic ethanol production routes production routes. Biofuels 13(2):237–247 13. Hiloidhari M, Banerjee R, Rao AB (2021) Life cycle assessment of sugar and electricity production under different sugarcane cultivation and cogeneration scenarios in India. J Clean Prod 290:125170. https://doi.org/10.1016/j.jclepro.2020.125170 14. Sahu SK, Ohara T, Beig G, Kurokawa J, Nagashima T (2015) Rising critical emission of air pollutants from renewable biomass based cogeneration from the sugar industry in India. Environ Res Lett 10.https://doi.org/10.1088/1748-9326/10/9/095002

Part IV

Air Quality Assessment and Air Pollution Management

Chapter 18

High-Performance Computing Urban Air Pollution 3D Simulation with CFD PALM4U Roberto San Jose and Juan L. Perez-Camanyo

Abstract This is a numerical simulation work of the air flow and the pollution in an urban area of 4 km2 of Madrid (Spain) by taking into account real 3D geographical information and applying the PALM4U computational fluid dynamics model on a 10 m resolution grid using meteorology and air pollution data from WRF/ Chem simulation as boundary conditions. The computational domain includes four high-rise buildings (around 250 m) two tilted towers and a very important street with high traffic flow. These types of numerical simulations have large computing demands, so High-Performance Computing (HPC) architecture is needed to have reasonable results within a manageable time frame. The very high resolution of the simulation reveals a detailed geographical dispersion pattern of air pollution. The heterogeneity of three-dimensional urban elements strongly impacts the spatial distribution of pollutants. High-rise buildings have a large impact on urban wind patterns. The model results are compared against observed data obtained from one air quality monitoring station that is included inside the computational domain. The model results agree well with the measurements. This study demonstrates the potential of CFD simulations in urban air quality modeling to provide detailed information to urban planners. Keywords Urban pollution · High-rise building · CFD PALM4U and numerical simulation

18.1 Introduction High-rise buildings have a major influence on the dispersion of pollutants in cities, which is why it is important to have modelling tools capable of simulating these effects. The basis of these tools is computational fluid dynamics (CFD) models, R. S. Jose (B) · J. L. Perez-Camanyo Environmental Software and Modelling Group, Computer Science School, Technical University of Madrid (UPM), Madrid, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_18

239

240

R. S. Jose and J. L. Perez-Camanyo

which can actually be used thanks to the existence of high-performance computational infrastructures that have emerged as a result of advances in the computing performance of computers. CFD modelling tools allow the simulation of energy, wind and concentration flows with a high spatial resolution (several metres) on a three-dimensional grid incorporating obstacles that represent urban elements such as buildings and trees’ turbulent flows. The main two groups of CFD models that are currently used in research works are: RANS which implements the Reynoldsaveraged Navier–Stokes equations and the turbulence is parameterized. LES that explicitly solve for large eddies. There are differences between them. In RANS-type CFD models, it is assumed that in a turbulent flow the transport is non-convective, and therefore the turbulence can be parameterized as a stochastic phenomenon with a broadband spectrum with no distinction between various frequencies and therefore does not distinguish the different types of eddies. CFD RANS models have some limitations for simulating the exchange of pollutants within street canyons in cities as they assume gradient transport. The CFD LES models, although requiring more CPU hours than RANS, explicitly resolve most of the energy transport eddies. Therefore, LES models have a high capacity to reproduce the large eddies that represent intermittent fluctuations of the steady-state transport regime, but their high computational cost makes it more efficient to use a RANS model in most studies, as in this study, which also focuses more on the transport and dispersion of atmospheric pollutants. There are already studies where CFD-type models have been applied to analyze the impact of urban buildings on the dispersion of some atmospheric pollutants [1]. There are several types of effects or impacts of buildings on air quality, on the one hand, there are the aerodynamic effects, i.e. buildings can hinder the wind flow and therefore the transport and dispersion of pollutants, creating areas of accumulation of pollutants by the obstruction exerted by the buildings on the wind flow. Also, a building in other areas can create new wind flows that help to clean the contaminated area. A priori it is not possible to know the effects of buildings in an area, so it is necessary to use modeling tools such as the one used in this study. Our simulation exercise allows us to analyze and quantify the impacts on the air quality of four highrise buildings; these impacts will be positive in some areas and negative in others. The buildings are located in a central area of Madrid (Spain). The great innovation of our work is that we take into account the atmospheric conditions (meteorological and air quality) of the environment of the study area, in addition we are simultaneously simulating the micrometeorology, the energy balance, the release of emissions, the deposition of pollutants and some simple chemical reactions of some pollutants (O3 and NOx), all of them on a complex 3D environment where buildings and trees appear with obstacles to the wind flow. Concentrations of pollutants in cities are not only dependent on local emissions but also on concentrations in the surrounding areas, as pollutants are transported over medium and long distances. Therefore, when modelling urban air quality, we also have to model the transport of pollutants from the outside to the inside. The most optimal way is to use a mesoscale model to simulate the situation in the surrounding areas and the outdoor conditions are passed to the microscale or CFD model as boundary conditions, known as domain nesting. This has been implemented in this

18 High-Performance Computing Urban Air Pollution 3D Simulation …

241

work, which gives it a novel aspect compared to other studies that take outdoor information from observations. Any modelling of pollutant dispersion in urban environments can be clarified as a multi-scale problem. It could actually range from a global scale through several mesoscales to an urban scale. In our case, we have combined a 3D Eulerian model to model the background concentrations together with a 3D CFD microscale model to model the concentrations in the city taking into account the urban topography of the city itself. The CFD models allow us to simulate the turbulent flows and solve the pollutant transport equations with a computational complexity that is manageable given the available computational resources. Furthermore, CFD models allow us to take into account all the specific characteristics of urban environments such as the presence of trees and buildings that affect the atmospheric flow. After completing a CFD simulation, we can obtain the concentrations of the main pollutants at a very high spatial resolution (metres), which will allow us to distinguish some areas more polluted than others even along the same street, since CFD models simulate in detail the atmospheric processes that take place in urban layers. The information provided by this type of tool can be very useful for urban planners and other decision-makers as it allows them to have detailed information on the air quality situation in their city and to simulate various mitigation strategies to select the most optimal one without having to apply them all in reality [2]. Some research work has already shown the advantages of nesting a mesoscale meteorological model to a microscale model so that the mesoscale model provides boundary conditions for wind and temperature, with improved performance of the microscale model in terms of wind prediction [3]. Sufficient computational power and specific models for fluid dynamics are now available to increase research in air quality modelling in urban environments. Already in the literature, CFD models have been used to analyse air pollution in urban environments, showing results of NO2 concentrations with a high spatial resolution that allow the high variability of this pollutant within a city to be observed. In many studies of this type, instead of producing initial and boundary conditions with a mesoscale model, theoretical conditions are simulated based on more frequent meteorological scenarios. The advantage of coupling the mesoscale model is that it produces boundary conditions adjusted to the reality of the domain, which is one of the advances shown in this work. Also in other urban air quality simulation works an idealized bi-dimensional street or simple urban canopy (ideal representation of the buildings, not 3D real buildings) is considered, in our case the real 3D buildings with their heights and trees have been simulated to know the evolution of pollutant concentrations in the real streets of the simulated area. Therefore, we consider that this research follows an innovative approach because we are nesting mesoscale and microscale numerical air quality models with traffic simulation tools to calculate emission data, regional and urban meteorology and various chemical and physical mechanisms to simulate air pollution concentrations at different spatial scales over a real 3D urban canopy. This contribution focuses on to estimate the impacts of high-rise building on wind patterns and pollutant spatial distribution. In this experiment, we model a 2 km × 2 km domain with 10 m spatial resolution using a CFD model (PALM4U model, Hannover University, Germany), including a high-rise building zone located in Madrid (Spain)

242

R. S. Jose and J. L. Perez-Camanyo

city downtown area. Two different scenarios are considered: BAU (Business As Usual) with the four tallest buildings (towers) in Madrid city and NOTOWERS scenario without the four tallest buildings. The impacts of the towers are calculated as BAU-NOTOWERS for the simulation period is 12/06/2017–18/06/2017. The numerical simulation is performed with the WRF/Chem meteorological and air quality model (NCAR, US) (using nesting approach) and PALM4U CFD model using detailed emission data from the combination of the SUMO traffic model (German Aerospace Center) and EMIMO emission model (UPM).

18.2 Experiment The CFD PALM4U has been used to simulate two different scenarios. The first scenario is called BAU (Business As Usual), it is a reference scenario, which includes all buildings inside of the simulation zone or domain. In the second scenario, the four highest buildings have been removed, it is called NOTOWERS scenario. The four towers are shown in Fig. 18.1. The differences (BAU-NOTOWERS) between the two scenarios/simulations are the impacts of the four buildings on the wind patterns and air quality of the zone. This experiment allows us to improve our knowledge about the effects and how to interact with the buildings and the atmosphere in an urban environment. The simulations have been run with a multi-scale configuration, where the domains have been nested by the boundary conditions. The basic configuration of each of the

Fig. 18.1 Four towers will be simulated to know their impacts on urban pollution

18 High-Performance Computing Urban Air Pollution 3D Simulation …

243

Table 18.1 Table of the computational domains Domain identifier

Spatial resolution (km)

Area

D1

25

Iberian Peninsula

D2

5

Madrid Community

D3

1

Madrid city

D4

0.01

Plaza Castilla Zone

domains is shown in Table 18.1. The first three domains are simulated with the WRF/ Chem mesoscale model and the last domain (D4) is simulated with the microscale or CFD model. The WRF/Chem domains are centred on the geographical coordinate point 40.478ºN, 3.704ºW corresponding to the centre of Madrid (Spain) and have a dimension of 45 (x-direction) by 50 (y-direction) cells in horizontal and 33 (z-direction) vertical layers distributed heterogeneously until reaching a height corresponding to 50 hPa. The first layers are closer together and therefore thinner than the layers at higher levels in order to best represent the state of the atmosphere in the vicinity of the surface. For the nesting between the mesoscale model and the microscale or CFD model to work from a numerical point of view, it is necessary to introduce a certain level of turbulence at the start of the CFD simulation, for which a synthetic turbulence generator is used, which is included in the model itself. This turbulence is applied to the boundary conditions of the vector wind components, which are generated by the mesoscale model before entering the microscale model. To apply the fluctuations and to be in the safe range the model modulates the amplitude based on the existing atmospheric stability. Because of the high computational cost of this type of simulation, a week has been selected to apply the models. A week with high levels of ozone pollutants has been selected. Specifically, a time period between 12 June 2017 and 18 June 2017 was simulated. The area of Madrid (Spain) selected includes the four tallest buildings in Madrid, where an air quality monitoring station is located in order to compare the results of the modelling system with the data observed by the station. The 3D grid of the CFD model has a size of 10 × 10 × 10 m, with 60 vertical layers covering the simulated tall buildings.

18.2.1 Models The WRF/Chem model simulates meteorology (WRF part) and air quality (Chem part), which includes equations for the emission, transport and chemistry of the simulated gases and aerosols. Both the meteorology and pollutant parts are solved at the same time (this is called an on-line model) so that iterations of particles in the meteorology (radiation and cloud formation) can be included, known as feedback effects. Furthermore, no interpolation between meteorology and air quality is

244

R. S. Jose and J. L. Perez-Camanyo

necessary because both parts are running at the same time on the same computational domain. It is an open-source model used worldwide in air quality simulations, which has already been validated in many previous experiments [4]. The model allows different parameters to be configured, generating different settings that the modeler must choose, in this experiment we have chosen a configuration already validated in an international multiple experiments such as the AQMEII (Air Quality Model Evaluation Initiative International) Phase 2 [5]. The main parameters are shown in Table 18.2. For the microscale simulations, we use a CFD model called PALM4U (Parallelized Large-Eddy Simulation Model for Urban) [14]. PALM4U calculates threedimensional values of vector variables such as wind and scalar variables such as temperature and pollutant concentrations. It is a new model but has been validated in a multitude of experiments with wind tunnels and by comparing its results with observed data [15]. In the model, buildings are modeled as solid obstacles that generate drag and friction forces, thus modifying the dynamic flow. The surface layer is modeled with a multi-layer model and two types of surfaces can be used: natural and artificial. Natural surfaces have a strong influence on the energy exchange between the natural surface and the atmosphere [16], causing reductions in air temperature, for example. Vegetation can partially absorb radiation, transforming that energy into sensible and latent heat flow. In natural surfaces we can find trees, which are actually 3D elements that hinder the dynamic flow, this is simulated through canopy leaf area density (LAD) and basal area density (BAD). All 3D elements, both trees and buildings, produce shadows that are calculated and integrated into the radiation model. Finally, for the chemical reactions part, the PALM4U model incorporates a chemical module that represents the CBM4 Carbon Bond Mechanism [17] chemical scheme where 32 different chemical compounds are simulated that react with 81 different types of chemical reactions. Finally, part of the pollutants in the atmosphere end up deposited on the surfaces, in this case the physical phenomenon of deposition is done through the DEPAC module [18]. Table 18.2 WRF/Chem model configuration Module

Option

References

Gas phase chemistry

Carbon bond mechanism version Z (CMBZ)

Zaveri and Peters [6]

Aerosol phase chemistry

Model for simulating interactions and aerosol chemistry (MOSAIC)

Zaveri et al. [7]

Dry aerosol deposition

Binkowski and Shankar

Binkowski and Shankar [8]

Wet deposition

Easter

Easter [9]

Photolysis rates

Fast-J

Williams et al. [10]

Radiation

Rapid radiative transfer model (RRTM)

Mlawer et al. [11]

Cloud microphysics

Lin

Lin et al. [12]

Cumulus

Grell 3D ensemble

Grell and Dévényi [13]

18 High-Performance Computing Urban Air Pollution 3D Simulation …

245

Emissions are key inputs to every pollutant dispersion, and in particular traffic emissions that are released into the atmosphere, as transport is the main activity of pollution in urban and populated areas. Since we are working with cities, in order to estimate the traffic emissions as much as possible we need first to estimate the traffic flows in the streets and roads of the city. There are specific tools to calculate traffic flow data, in our work, we have used a traffic microsimulation model, called SUMO (Simulation of Urban Mobility) traffic model that allows us to know the traffic flows and the speed of vehicles [18]. Like the WRF/Chem and PALM4U models, it is an open-source model. The model is indicated to carry out traffic simulations in continuous space and discrete time, with a maximum temporal resolution of 1 s. It is a microscopic type model and the traffic flow is simulated through the individualized and explicit simulation of each vehicle that makes up the flow, each vehicle is labeled to identify it and at any time we can know of any vehicle, the route it is taking, its departure time and its speed. The speed is determined by the speed of the vehicle that precedes it and so on. In traffic models such as SUMO, it is necessary to define the network of streets or highways to be simulated where the vehicles will circulate and which routes the vehicles will follow, which is traditionally done through origin–destination matrices. In our case we have used data provided by the vehicle detectors that are installed in the streets of the city of Madrid and from them the traffic demand matrices have been produced, the whole process is automated within the SUMO model, and the basic fundamentals part of the process is to generate an initial random traffic that is adjusted according to the data measured by the detectors that act as calibrators, specifically 2/3 of the 3000 detectors located on the streets of Madrid have been used. The rest of the detectors (1/3) are used to evaluate the results of the traffic simulation. To generate the network of links through which the vehicles circulate, the information provided by OpenStreetMap (OSM) has been used, since it contains all the necessary information (number of lanes, direction, roundabouts, traffic lights…), in this experiment we have worked with 100,000 street segments. Although the input data generation process in SUMO is automated and relatively simple, when performing the first simulations, realistic results are not obtained since bottlenecks occur at some points that prevent the normal movement of vehicles. Then the next phase is the refinement of the input data, it is an iterative process, to eliminate the bottlenecks at the intersections, which is a great challenge. As we have mentioned, it is an iterative process where the areas that cause blockages are manually determined since no vehicles are arriving on the connected streets. The main outputs we are interested in from the SUMO model to calculate traffic emissions are: the number of vehicles passing through each cell, average passing speed, and the distance traveled by passing vehicles. With this data, the detailed methodology (Level 3) described in the EMEP/EEA Air Pollution Emissions Inventory Guide [19] can be applied to estimate the emissions to be injected into the atmosphere. The methodology defines emission calculation formulas for each vehicle category (passenger cars, light commercial trucks, heavy vehicles, including buses and motorcycles) and emission factors per km traveled that depend on speed. Vehicle types are also classified based on fuel type, engine capacity and age. In our experiment, we had detailed information on the type of vehicles registered in Madrid,

246

R. S. Jose and J. L. Perez-Camanyo

specifically the data on the types of vehicles registered in Madrid as of December 2016 were used.

18.3 Results Once the first simulation corresponding to the BAU scenario is completed, the hourly results of the modelling system are compared with the values measured by an air quality station (identified as P. Castilla) located in the domain. Specifically, we have focused on the pollutant NO2 which is measured by the station. For the analysis of model performance, we usually use graphical techniques through time series (model and observation) as shown in Fig. 18.2 and statistical techniques through a series of typical parameters such as Normalized Mean Bias (NMB); Root Mean Square Error (RMSE) and Pearson’s time correlation (R2). With respect to the statistical parameters, an R2 value of 0.6 was obtained (which is acceptable), an NMB value of 45% (which, being less than 50%, is adequate) and an RMSE of 30 ug/m3 (acceptable value). Based on the graph in Fig. 18.2 and the statistical values, we can conclude that the simulation results are acceptable, although future work will try to improve them. The results of the WRF/Chem model of 1 km spatial resolution were also compared with the observed data and slightly lower values were obtained, but very similar to those of the CFD, since the values of the contour of the studied area are very influential in the values produced by the CFD. After the evaluation process of the BAU simulation, we can use both simulations: BAU and NOTOWERS to evaluate the impacts of the four high-rise buildings (towers) on concentration levels of NO2 and O3 concentrations Fig. 18.3 shows the changes in NO2 and Fig. 18.4 in O3 concentrations (%) if the four towers weren’t present. In Fig. 18.3, we can see how the four tall buildings (towers) produce increases and

Fig. 18.2 Time series of NO2 measured concentrations and modeled with WRF/Chem-PALM4U 10 m. spatial resolution

18 High-Performance Computing Urban Air Pollution 3D Simulation …

247

decreases in NO2 concentrations. The southernmost tower and the northernmost tower decrease NO2 concentrations to −15% (purple area) on the right side of the towers but just on the opposite side of the street, they increase concentrations up to 10% (red area). The 4 towers cause NO2 increases along a street perpendicular to the buildings. There are also increases in the area near the north tower. Figure 18.4 shows that the effects of the four buildings on O3 concentrations are the inverse of those observed for NO2 . That is, in the areas where the buildings generated increases in NO2 , decreases are now observed. For example, the buildings have caused a strong reduction in O3 concentrations of up to −14% in the street perpendicular to the buildings, the impacts are observed up to 500 m away from the buildings. The northernmost tower causes the largest increase in O3 concentrations of up to +20%. Figure 18.5 shows how the NO2 concentrations are distributed vertically in height around the first two towers. It also shows the configuration of the wind flows through vectors. The upper image shows the wind and concentrations with the towers and the lower image shows the same 3D representation with the towers removed. Figure 18.5 shows how the second building has generated a vortex that causes pollutants to

Fig. 18.3 Spatial distribution with 10 m. spatial resolution of the effects of the four high-rise buildings (BAU-NOTOWER %) on NO2 hourly averaged concentrations for the period 12–18/06/ 2017. Arrows shows the four towers

248

R. S. Jose and J. L. Perez-Camanyo

Fig. 18.4 Spatial distribution with 10 m. spatial resolution of the effects of the four high-rise buildings (BAU-NOTOWER %) on O3 hourly averaged concentrations for the period 12–18/06/ 2017. Arrows show the four towers

accumulate on the right side of the building, especially in the middle and upper parts of the building. If the building were not present, NO2 concentrations would be 74 µg/m3 but the building causes concentrations to rise to 83 µg/m3 . We can also observe that in the first building, the concentrations accumulate on the left side; while between the 2 buildings, there is less pollution due to the difficulty for the wind to penetrate this area.

18 High-Performance Computing Urban Air Pollution 3D Simulation …

249

Fig. 18.5 Vertical cross-section (YZ) around the two first towers of NO2 concentrations and wind vectors in the BAU simulation (with towers) and in the NOTOWERS simulation (without towers) for 15/06/2017 22:00 GMT

18.4 Conclusion This work has shown a novel urban air quality modelling system, an integrated system composed of several tools: EMIMO for the estimation of emissions injected into the atmosphere which in turn includes a traffic flow model (SUMO), WRF/Chem mesoscale meteorological and chemical model and finally a CFD type microscale model such as PALM4U. The last animation level (1 km) of the WRF/Chem model provides the boundary conditions to the PALM4U model which has been run with a spatial resolution of 10 m over a computational domain of 4 km2 . In the area simulated by the PALM4U model, there were buildings, trees, vegetated and artificial surfaces that had been modelled. After the evaluation phase of the results, we have concluded that the results of the integrated air quality system are adequate and that it is able to reproduce the spatial pattern of the distribution of pollutants along the streets. The experiment included the study of the impacts of four very tall buildings on the atmospheric flow and therefore on the distribution of pollutants in the area. To obtain the data on the impacts, two scenarios were designed, one including the tall towers (BAU) and the other without the four towers (NOTOWERS), the differences between the two simulations showed that there was a 5% increase in temperature in the areas near the towers studied. Regarding the impacts of buildings on pollution, the results of both simulations are that the impacts can be varied depending on the meteorology at any given time, but impacts (increases or decreases of pollutants) were observed in areas close to the

250

R. S. Jose and J. L. Perez-Camanyo

buildings, but these impacts also extended to areas further away from the buildings. The 3D modelling system presented can produce very detailed information at the meter level on meteorological variables such as wind and temperature as well as at the air quality level through concentrations of the main pollutants. The information can be used to implement the most effective mitigation measures to reduce pollutant concentrations, since it allows simulating the strategies before implementing them and therefore having information about their possible effectiveness. It is recommended to use CFD-LES models to analyse the air quality around highrise buildings to simulate the different effects on the wind environment around the buildings. The simulations must include realistic conditions from the urban environment (buildings, trees, …) and meteorological environment (real winds, air temperature) of a mesoscale model. Larger periods of time are recommended and it will be explored in the next research works. CFD-LES tools with realistic conditions can be an important and useful tool for the urban planners to get precise information about the effects and impacts of high buildings because CFD_LES can be used to investigate the impacts of urban layout on urban ventilation and pollutant dispersion, the methodology can be extended to the assessment of exposure and translate the results into health impacts. Acknowledgements The UPM authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Centro de Supercomputación y Visualización de Madrid (CESVIMA).

References 1. Abhijith K, Kumar P, Gallagher J, McNabola A, Baldauf R, Pilla F et al (2017) Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments–a review. Atmos Environ 162:71–86. https://doi.org/10.1016/j.atmosenv.2017. 05.014 2. Caplin A, Ghandehari M, Lim C, Glimcher P, Thurston G (2019) Advancing environmental exposure assessment science to benefit society. Nat Commun 10(1). https://doi.org/10.1038/ s41467-019-09155-4 3. Miao Y, Liu S, Chen B, Zhang B, Wang S, Li S (2013) Simulating urban flow and dispersion in Beijing by coupling a CFD model with the WRF model. Adv Atmos Sci 30(6):1663–1678. https://doi.org/10.1007/s00376-013-2234-9 4. Fast J, Gustafson W, Easter R, Zaveri R, Barnard J, Chapman E et al (2006) Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model. J Geophys Res 111(D21). https://doi.org/10.1029/200 5jd006721 5. San José R, Pérez J, Balzarini A, Baró R, Curci G, Forkel R et al (2015) Sensitivity of feedback effects in CBMZ/MOSAIC chemical mechanism. Atmos Environ 115:646–656. https://doi. org/10.1016/j.atmosenv.2015.04.030 6. Zaveri R, Peters L (1999) A new lumped structure photochemical mechanism for large-scale applications. J Geophys Res Atmos 104(D23):30387–30415. https://doi.org/10.1029/1999jd 900876 7. Zaveri R, Easter R, Fast J, Peters L (2008) Model for simulating aerosol interactions and chemistry (MOSAIC). J Geophys Res 113(D13). https://doi.org/10.1029/2007jd008782

18 High-Performance Computing Urban Air Pollution 3D Simulation …

251

8. Binkowski F, Shankar U (1995) The regional particulate matter model: 1. model description and preliminary results. J Geophys Res 100(D12):26191. https://doi.org/10.1029/95jd02093 9. Easter R (2004) MIRAGE: model description and evaluation of aerosols and trace gases. J Geophys Res 109(D20). https://doi.org/10.1029/2004jd004571 10. Williams J, Landgraf J, Bregman A, Walter H (2006) A modified band approach for the accurate calculation of online photolysis rates in stratospheric-tropospheric Chemical Transport Models. Atmos Chem Phys 6(12):4137–4161. https://doi.org/10.5194/acp-6-4137-2006 11. Mlawer E, Taubman S, Brown P, Iacono M, Clough S (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res Atmos 102(D14):16663–16682. https://doi.org/10.1029/97jd00237 12. Lin Y, Farley R, Orville H (1983) Bulk parameterization of the snow field in a cloud model. J Clim Appl Meteorol 22(6):1065–1092. https://doi.org/10.1175/1520-0450(1983)022%3c1 065:bpotsf%3e2.0.co;2 13. Grell G, Dévényi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res Lett 29(14):38-1–38-4. https://doi. org/10.1029/2002gl015311 14. Maronga B, Gryschka M, Heinze R, Hoffmann F, Kanani-Sühring F, Keck M et al (2015) The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives. Geosci Model Dev 8(8):2515–2551. https://doi.org/10.5194/gmd-8-2515-2015 15. Park S, Baik J, Lee S (2015) Impacts of mesoscale wind on turbulent flow and ventilation in a densely built-up urban area. J Appl Meteorol Climatol 54(4):811–824. https://doi.org/10.1175/ jamc-d-14-0044.1 16. Dupont S, Brunet Y (2009) Coherent structures in canopy edge flow: a large-eddy simulation study. J Fluid Mech 630:93–128. https://doi.org/10.1017/s0022112009006739 17. Gery M, Whitten G, Killus J, Dodge M (1989) A photochemical kinetics mechanism for urban and regional scale computer modeling. J Geophys Res 94(D10):12925. https://doi.org/10.1029/ jd094id10p12925 18. Oostendorp R, Krajzewicz D, Gebhardt L, Heinrichs D (2019) Intermodal mobility in cities and its contribution to accessibility. Appl Mobil 4(2):183–199. https://doi.org/10.1080/23800127. 2018.1554293 19. DEPAC opens North American branch (1998), 1998(381), 4. https://doi.org/10.1016/s02621762(98)90336-4

Chapter 19

Assessment and Policy Recommendations of School Ambient Air Quality During the COVID-19 Pandemic in Abu Dhabi, UAE Evan K. Paleologos, Sherine Farouk, and Moza T. Al Nahyan

Abstract The quality of ambient air has improved in many cities as a result of lockdowns during the COVID-19 pandemic. The current study utilized hourly averages of five ambient (outdoors) air quality indicators (PM10 , PM2.5 , NO2 , SO2 , and O3 ) to assess the impact on air quality of the reduced mobility, during the first 8 months of 2020, at two schools in Abu Dhabi, UAE. The first school is located in a busy downtown area with significant commercial activities and traffic, whereas the second is in a suburban, purely residential area of low population density. The 2021 WHO global air quality guideline levels (AQG) and the lowest level target, that of interim target 1 of the WHO Global Update 2005 guidelines, were used to evaluate our data. Workplace and mobility restriction measures were taken very early on in UAE and included the closure of educational institutions on March 8, 2020, the subsequent mandate on distance education, the hold on public transportation until the third week of April 2020, restrictions in workplace capacity, etc. Despite these measures, there appeared very little improvement in the five studied indicators at the two schools, both in terms of the WHO 2021 and the interim target 1 guidelines. The analysis of the Air Quality Index (AQI) during the first 31 days of 2022 at these two schools confirmed that the poor quality of air is more or less a permanent situation at these two school zones, putting at risk the health of the sensitive group of students. Keywords Outdoors air pollution · School zones · COVID-19 pandemic

19.1 Introduction The measures to combat the health effects of the COVID-19 pandemic have revealed in many cases the environmental impacts of modern economic and social activities. Thus, imposed lockdowns have led to a reduction in atmospheric pollution, as well E. K. Paleologos (B) · S. Farouk · M. T. Al Nahyan Abu Dhabi University, Abu Dhabi, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_19

253

254

E. K. Paleologos et al.

as other improvements in various environmental indicators in many cities [1–3]. In addition, the pandemic has led to an unprecedented review of infrastructure systems to provide safe services, such as water and wastewater [4], and of solid waste collection, transportation, disposal, and operational systems [5], which may prove to be significant for upcoming national or global crises [6]. In that respect, the pandemic also provided an opportunity to analyze outdoor air pollution data during the first and last phases of the pandemic and by associating this to specific economic activities that ceased or slowed down during lockdowns, to identify dominant sources of air pollution in urban environments. Industrial facilities and transport vehicles discharge harmful gases, such as CO, NO2 , SO2 , as well as small particles of different sizes (PM10 and PM2.5 ), that can penetrate deep into the lungs [7]. Nitrogen oxides from vehicle exhaust fumes produce O3 under strong sunlight at a city’s air level, which can create respiratory problems. Ore treatment, such as in cement factories, gives out heavy metals, such as cadmium, zinc, and lead. The use of nitrogen fertilizers in farming generates nitric oxide (N2 O), which is a greenhouse gas (GHG), as well as ammonia (NH3 ), which is involved in the acidification of soils. Heavy metals and persistent organic pollutants (POP) can be transported in the air for long distances and can contaminate waters and soils far from their origin [7, 8]. Outdoor air contains substances of both natural and anthropogenic origin. For example, particulate matter (PM) may include dust from the earth’s surface, sea salt in coastal areas, or small biological matter from plant and animal debris. Especially in the Gulf Region, extensive sandstorms are a major source of PM and chemical pollution as the sand particles provide surfaces for the attachment of chemical substances on them. Air pollution can affect public health, especially the respiratory system, but can also lead to productivity losses through hospital visits and sick leaves, and may be responsible for even premature deaths. At the same time, it can affect the environment across many countries in many ways, such as, for example, with “transboundary haze pollution,” which diminishes agricultural crops by injuring plants, reducing yields, or even leading to plants’ death [9], as well as deteriorate the built environment by eroding the surfaces of buildings and monuments [7]. The World Health Organization (WHO) has stated that “outdoor air pollution is a major environmental health problem affecting everyone in developed and developing countries alike,” estimating that “air pollution had caused 3 million premature deaths worldwide in 2012” [10]. The WHO has argued that by reducing air pollution levels “countries can reduce the burden of disease from stroke, heart disease, lung cancer, and both chronic and acute respiratory diseases, including asthma.” The WHO air quality guidelines were updated in 2021 and have set thresholds and limits to key air pollution components that pose health risks [11]. These guidelines were utilized in our study to assess the ambient air quality at two Abu Dhabi schools. In addition, data reported by the World Bank Group [12] show that in developing countries, exposure to air pollution has increased at an alarming rate and has become the main environmental threat to health, with ambient concentrations of PM2.5 becoming multiple times higher than the health-based guideline values for ambient (outdoor) air quality established by WHO. Lower-middle-income countries

19 Assessment and Policy Recommendations of School Ambient Air …

255

(LMICs) in particular because of their drive for economic development, which has led to population congregation in urban centers lacking basic infrastructure, and the increased mobility and energy use, are characterized by a significant deterioration of their ambient air. Thus, from the estimated 4.1 million people worldwide who died prematurely in 2016 because of exposure to ambient PM2.5 , about 90% of them were in LMICs. Two-thirds of those deaths occurred in East Asia, the Pacific, and South Asia with China and India, which accounted for 52% of global deaths from ambient PM2.5 , leading the list of affected countries [12]. The aim of this article is to analyze the ambient air quality at two schools in Abu Dhabi, UAE, during the first phase in 2020, and in 2022, a later phase of the COVID-19 pandemic, respectively. Thus, a comparison of the data from the COVID19 lockdown period, with current data, when students have returned to face-to-face teaching and mobility have largely returned to Abu Dhabi can potentially identify pollution sources, whose cessation or decrease of activities during the lockdown contributed to outdoor air quality improvement. Equivalently, this identification can assist government organizations in devising measures for controlling emissions from such sources, especially in critical school zone areas.

19.2 Background Outdoor air pollution originates from natural and anthropogenic sources. Six pollutants (SO2 , NO2 , PM 10 and 2.5, O3 , CO, and Pb) are prevalent in the air and have been documented to have harmful effects on public health and the environment. They are designated as “criteria pollutants” and the US EPA has set NAAQS (National Ambient Air Quality Standards) to regulate them by setting permissible levels based on human health and environmental criteria. The units of measure for the NAAQS are “parts per million (ppm) by volume, parts per billion (ppb) by volume, and micrograms per cubic meter of air (µg/m3 )” [13]. In Abu Dhabi a recent report [13] by the Environment Agency-Abu Dhabi (EAD) has found that SO2 , NO2 , and CO are within UAE limits, while tropospheric ozone exceeds UAE limits, exhibiting a gradual increase in the past years. Background PM10 is significantly high in the Abu Dhabi Emirate with measurements above WHOrecommended guidelines, and high concentrations correlating with dust events. Dust storms happen naturally and frequently in North Africa, the Middle East, and Australia, all of which have extensive deserts and semi-arid areas. Dust storms reduce air quality and visibility, and have adverse effects on health, especially for people with respiratory problems. Dust particles vary in size from coarse (non-inhalable) to fine (inhalable), to very fine (respirable). Coarse dust particles reach in general the inside of the nose, mouth, or throat. Smaller or fine particles can get deeper into the lungs with fine dust particles being able to reach the alveoli sacs where the exchange of gases with the blood takes place. “People who may be more vulnerable than others to PM are infants, children, and adolescents; the elderly; people with respiratory conditions, such as asthma,

256

E. K. Paleologos et al.

bronchitis, and emphysema; people with heart disease, and people with diabetes. For these people, exposure to a dust storm may trigger allergic reactions and asthma attacks, cause serious breathing-related problems or contribute to cardiovascular or heart disease, and eventually result in reduced life span” [10, 11]. Aerosols and desert dust contribute to poor air quality in the UAE and the Gulf region and assist in the transport of pollutants over a long range of land and sea masses. Desert dust also serves as a reactive surface for air pollutants. In the UAE, the desert environment has many soil fines and together with the meteorological conditions of the region can develop jet streams that can carry fine particles over long distances, decreasing visibility and air quality substantially. In the UAE, the air quality monitoring stations are spread throughout all seven Emirates and they are operated separately by each Emirate within each area. In the Abu Dhabi Emirate, there exist a total of 19 air-monitoring stations and these are operated by EAD. The urban area of Abu Dhabi city has the most stations, with eight monitoring stations. The remaining stations are scattered around the Emirate, with the bulk of these spaced out in the Western Region [14].

19.3 Methodology Our current study analyzes five common ambient air quality indicators (PM10 , PM2.5 , NO2 , SO2 , and O3 ) that were measured at two school zones in the city of Abu Dhabi, UAE. The period of our study included the beginning of 2020 until the month of August 2020, for which hourly averages were available. In addition, hourly and daily data are reported by the Environment Agency-Abu Dhabi (EAD) through internet sources for the early months of 2022, at a time when the number of COVID-19 cases had subsided in the city and the country, and economic and social activities had returned largely to normal. The first station (depicted as Station 2: St2) is located at the bus parking lot of a major school (Khadeeja Great School), on a main downtown road, where there exists a large shopping center (on the opposite side of the monitoring station), a gas station, and a high concentration of small business and residential buildings. St2 was selected in our study to represent the ambient air quality in a downtown school area. The second station (depicted as Station 3: St3) is located in a vehicle’s school parking lot in a suburban residential area (Khalifa School station), far from the Abu Dhabi downtown area. The station is next to a mangrove forest area, where trees are found in large numbers, and the population density is low as the residential area consists of villas surrounding the school zone. The concentration of plants and trees is much larger than what is typically found in downtown Abu Dhabi. St3 monitors the ambient air quality at a suburban, residential school zone. The following 2021 WHO global air quality guideline levels (AQG) [11] were utilized to analyze the results of our study. The lowest interim target towards the 2021 WHO AQG, Interim Target 1, refers to the WHO Global Update 2005 guidelines document.

19 Assessment and Policy Recommendations of School Ambient Air …

257

PM 10 guideline: “The recommendation is an annual PM10 AQG level of 15 µg/m3” with the lowest interim target (Interim Target 1) being that of 70 µg/m3 . PM 2.5 guideline: “The recommendation is an annual PM2.5 AQG level of 5 µg/m3 ” with the lowest interim target (Interim Target 1) being that of 35 µg/m3 . O3 guideline: “The recommendation is a peak season ozone AQG (air quality guideline) level of 60 µg/m3 (the average of daily maximum 8-h mean ozone concentrations). The peak season is defined as the six consecutive months of the year with the highest six-month running-average ozone concentration. In regions away from the equator, this period will typically be in the warm season within a single calendar year (northern hemisphere) or spanning two calendar years (southern hemisphere). Close to the equator, such clear seasonal patterns may not be obvious, but a runningaverage six-month peak season will usually be identifiable from existing monitoring or modelling data.” The lowest interim target (Interim Target 1) for O3 is 100 µg/m3 . NO2 guideline: “The recommendation is an annual nitrogen dioxide AQG level of 10 µg/m3 ,” with the lowest interim target (Interim Target 1) for NO2 being 40 µg/m3 . The “short-term (24-h) recommendation for nitrogen dioxide AQG level is 25 µg/m3 ” and the Interim Target 1 is 120 µg/m3 . SO2 guideline: “The recommendation is a short-term (24-h) sulfur dioxide AQG level of 40 µg/m3 , defined as the 99th percentile (equivalent to three to four exceedance days per year) of the annual distribution of 24-h average concentrations. An interim target 1 of 125 µg/m3 … [is] proposed.”

19.4 Results and Discussion The timeline of the restriction measures in Abu Dhabi during the initial phase of the COVID-19 pandemic is given in Table 19.1. The five outdoor air pollution indicators measured during 2020 (until the end of August 2020) at the downtown school area are shown in Fig. 19.1. The five outdoor air pollution indicators measured during the first 8 months of 2020 at the suburban school area are shown in Fig. 19.2. In terms of the PM10 and PM2.5, there appear some well-defined peaks in Figs. 19.1 and 19.2 that coincide temporally in both school air-monitoring stations (in particular the one on June 1, 2020), which are the outcome of regional sandstorms, which affect the air quality in both school zones. In terms of PM10 and PM2.5 , the 2021 WHO AQG and the Interim Targets are not met, although the lowest, Interim Target 1 of 35 µg/m3 for PM2.5 may be attainable. Clearly, the desert environment, assisted by the massive construction activities that have taken place in UAE to increase the built environment and transform previously small towns, which had populations in the tens of thousands in the 1950s, to multi-million people cities does not help in the attainment of the PM10 and PM2.5 AQG. In terms of O3 , the “six consecutive months of the year with the highest 6-month running-average ozone concentration (peak season)” is seen to exceed in both school zones 2021 WHO O3 AQG, with the lockdowns of 2020 not appearing to have affected O3 levels. Regarding the NO2 annual level both the 2005

258

E. K. Paleologos et al.

Table 19.1 Major restrictions in Abu Dhabi during the first phase of the COVID-19 pandemic March 8, 2020

The UAE Ministry of Education announced that educational institutions would be closed for 4 weeks to lower the chances of spreading COVID-19 among students and faculty

March 15, 2020 March 16, 2020

Jobs were moved to become remote for 2 weeks Prayers at places of worship were suspended

March 25, 2020 March 26, 2020

Malls and fresh food markets closed for two weeks across the country while pharmacies, supermarkets, and restaurants were allowed to operate; remote working was implemented on 80% of the staff The UAE launched the National Sterilization Programme from 8 p.m. until 6 a.m. Permits were required for emergencies to travel during these hours

April 1, 2020 April 5, 2020 April 20, 2020

The Ministry of Human Resources and Emiratization changed the workplace capacity to 30% Public transportation was put on hold until the 18th of April The AlHosn mobile application was launched allowing for PCR and vaccination data to be immediately accessed and checked

June 6, 2020

Moving between cities in the Emirate of Abu Dhabi was prohibited

June 24, The National Sterilization Program ended. Entering or exiting all Emirates except 2020 Abu Dhabi was allowed July 3, Public beaches and parks reopened while maintaining COVID-19 guidelines 2020

Interim Target 1 of 40 µg/m3 and even more the new lower 2021 WHO AQG of 10 µg/m3 are not met despite the much reduced mobility of the city’s population during 2020, and the distant education mode applied to the educational system of the country. The short-term (24-h) NO2 AQG level is violated most of the days of the year at both schools. In terms of SO2 , the AQG level of 40 µg/m3 , “defined as the equivalent to three to four exceedance days per year”, was exceeded at the downtown school zone and was met, borderline, at the suburban school. With regards to the first days of 2022, the air quality situation at both schools is summarized, for reasons of expedience, through the Air Quality Index (AQI), rather than the five studied air pollutants. The measurements of the first 31 days in 2022 indicate that the downtown school station (St2) exhibits AQI that is found in the high yellow (moderate, AQI: 51 to 100), orange (unhealthy to sensitive groups, AQI: 101– 150), and even in the red range (unhealthy, AQI: 151–200) (see Fig. 19.3). Station St2 (Fig. 19.3, left), which samples ambient air in a downtown school area, has 3 days where the quality of air is unhealthy to all members of the population, 12 days when the air is unhealthy to sensitive groups, which includes the young children at the

19 Assessment and Policy Recommendations of School Ambient Air …

259

Fig. 19.1 SO2 , NO2 , PM10 , PM2.5, and O3 air quality indicators for Station 2 (St2): downtown school zone, from January to the end of August 2020

school, and no days when the air is good. It is interesting to note that St3 (Fig. 19.3, right), which is also located in a school zone, but in a vegetated, suburban, purely residential area, exhibits perhaps worse characteristics than St2. For this period, St3 has 3 days in the red range, and of the 17 days that are found in the orange zone, 10 days exhibit AQI values over 130, some of which are borderline between the orange and red classification (February 2, AQI: 150; February 3, AQI: 145; February 11, AQI: 149; and February 23, AQI: 147) (Fig. 19.2, left). No days were found in the green AQI color classification (good, AQI: 0–50).

19.5 Summary and Conclusions Many cities in the world have reported that the quality of outdoor air had improved during the lockdowns imposed during the COVID-19 pandemic. Our study utilized hourly averages of five common ambient air quality indicators (PM10 , PM2.5 , NO2 , SO2 , and O3 ) to evaluate whether the COVID-19 measures taken in Abu Dhabi had improved the outdoor air quality at two school zones during the first eight months of 2020. Restrictions in the Emirate of Abu Dhabi included workplace and mobility

260

E. K. Paleologos et al.

Fig. 19.2 SO2 , NO2 , PM10 , PM2.5, and O3 air quality indicators for Station 3 (St3): suburban, residential school zone, from January to end of August 2020

St2: Air Quality, Jan 31-Mar 2, 2022

St3: Air Quality, Jan 31-Mar 2, 2022

200

200

150

150

100

100

50

50

0

0

Fig. 19.3 Stations St2 and St3 AQI from January 31 to March 2, 2022 (left: St2, and right: St3) [15, 16]

curtailments, closure of educational institutions and distant education, places of worship and public transportation restrictions, and entry in public places only with the demonstration of negative PCR tests through a mobile application. We studied air quality indicators at a school in a busy downtown area, where large and smallscale commercial activities dominate and consequently heavy vehicle movement exists. The second school studied is located at a suburban, purely residential area of

19 Assessment and Policy Recommendations of School Ambient Air …

261

low population density. Our study utilized the 2021 WHO global air quality guideline levels (AQG) and the lowest level target, of interim target 1, which refers to the WHO Global Update 2005 guidelines. Despite the COVID-19 restrictions, very little improvement in the five ambient air quality indicators was observed at the two schools, both in terms of the WHO 2021 and the interim target 1 guidelines. An explanation of this unabated air pollution lies in the existence of very fine particles in the air, which are due to the desert environment of the country and the city, and the frequent onset of sandstorms. Additional sources of air pollution are the extensive construction activities, and the location of cement factories and industrial facilities in close proximity to the city, all of which contribute to and exacerbate the natural poor quality of the air over Abu Dhabi. The analysis of the Air Quality Index (AQI) during the first 31 days of 2022 at these two schools confirmed that the poor quality of air is more or less a permanent situation at these two school zones, putting at risk the health of the students. Given that the health impact of sandstorms cannot be affected, despite the extensive “greening” of the city of Abu Dhabi, measures should be taken toward more environmentally friendly modes of transportation by the general public, as well as restrictions on the emissions of the many construction and labor vehicles, and of the air pollution generated by construction activities, especially in the proximity of schools. Acknowledgements The first author would like to acknowledge the support of the Office of Research & Sponsored Programs (ORSP) at Abu Dhabi University through research grants 19300540 and 19300614.

References 1. Bao R, Zhang A (2020) Does lockdown reduce air pollution? Evidence from 44 cities in northern China. Sci Total Environ 731 2. Siciliano B, Dantas G, da Silva CM et al (2020) Increased ozone levels during the COVID-19 lockdown: analysis for the city of Rio de Janeiro, Brazil. Sci Total Environ 737 3. Teixidó O, Tobías A, Massagué J et al (2021) The influence of COVID-19 preventive measures on the air quality in Abu Dhabi (United Arab Emirates). Air Qual Atmos Health 14(7):1071– 1079 4. Paleologos EK, O’ Kelly BC, Tang C-S et al (2021) Post COVID-19 water and wastewater management to protect public health and geoenvironment. Environ Geotech 8(3):193–207 5. Vaverková MD, Paleologos EK, Dominijanni A et al (2021) Municipal solid waste management under COVID-19: challenges and recommendations. Environ Geotech 8(3):217–232 6. Tang C-S, Paleologos EK, Vitone C et al (2021) Environmental geotechnics: challenges and opportunities in the post COVID-19 world. Environ Geotech 8(3):172–192 7. Mohamed A-MO, Paleologos EK, Howari F (eds) (2020) Pollution assessment for sustainable practices in applied sciences and engineering. Elsevier, Butterworth-Heinemann, Oxford 8. EEA (European Environmental Agency) (2013) European Union emission inventory report 1990–2011 under the UNECE Convention on Long-range Transboundary Air Pollution (LRTAP). European Environmental Agency Technical Report 10/2013. Office for Official Publications of the European Union, Luxembourg. http://www.eea.europa.eu/publications/euemission-inventory-report-lrtap. Accessed 14 March 2022

262

E. K. Paleologos et al.

9. Ontario Ministry of Agriculture, Food, and Rural Affairs Effects of air pollution on agricultural crops. http://www.omafra.gov.on.ca/english/crops/facts/01-015.htm. Accessed 13 Feb 2021 10. WHO (World Health Organization) (2016) Ambient air pollution: a global assessment of exposure and burden of disease. World Health Organization, p 121 11. WHO (World Health Organization) (2021) WHO global air quality guidelines. Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization, Geneva, p 273 12. World Bank Group (2020) The global health cost of ambient PM2.5 air pollution. World Bank Group, Pollution Management & Environmental Health, Washington D.C. 13. U.S. EPA NAAQS table. https://www.epa.gov/criteria-air-pollutants/naaqs-table. Accessed 10 Feb 2022 14. Mohamed R (2017) Abu Dhabi State of environment report 2017: air quality. Environment Agency-Abu Dhabi report. https://www.soe.ae/wp-content/uploads/2017/10/environmentalreport-air-quality.pdf. Accessed 10 Feb 2022 15. IQAir Air quality near Khadeeja School, Al Danah. https://www.iqair.com/united-arab-emi rates/abu-dhabi/al-danah/khadeeja-school. Accessed 02 March 2022 16. IQAir Air quality near Khalifa School, Al Mushrif, https://www.iqair.com/united-arab-emi rates/abu-dhabi/al-mushrif/khalifa-school. Accessed 02 March 2022

Chapter 20

Particulate Matter Phytoremediation Capacity of Four Japanese Roadside Green Biofilters Duha S. Hammad, František Mikšík, Kyaw Thu, and Takahiko Miyazaki

Abstract Particulate matter (PM) accumulation on the leaves of two trees and two shrubs were examined for 14 days to study the ability of broad leaves to capture particulate matter in Japan. Two healthy mature leaf samples of each specimen were carefully collected and analyzed through the gravimetric analysis method (four filtration steps). PM in different divisions can be captured and deposited inside the leaf foliage. Fine particles were the highest portion of the PM content captured by the analyzed trees. Leaf features such as hair and wax have been associated with high PM accumulation as presented in Toxicodendron succedaneum (waxy glossy leaves) which was the most effective species among the analyzed species for all PM fraction divisions (PM10, PM 2.5, and PM 0.2), and Prunus × yedoensis (hairy leaves) that captured the largest portion of ultra-fine PM. The surface area has no effect on the accumulation of particulate matter since the Ficus erecta shrub has the largest surface area, but the lowest portions of PM among of the investigated species. Keywords Remediation · Leaves · Particulate matter

20.1 Introduction Air pollution threatens the health of people in numerous cities of the world. WHO revealed that 9 out of 10 people breathe air containing high levels of pollutants which can kill 7 million deaths globally per year with noncommunicable diseases (NCDs), causing approximately one-quarter (24%) of all adult deaths from heart disease, D. S. Hammad (B) · F. Mikšík · K. Thu · T. Miyazaki Department of Advanced Environmental Science and Engineering, Faculty of Engineering Sciences, Kyushu University, Kasuga-koen 6-1, Kasuga 816-8580, Japan e-mail: [email protected] F. Mikšík · K. Thu · T. Miyazaki Research Center for Next Generation Refrigerant Properties (NEXT-RP), International Institute of Carbon-Neutral Energy Research (I2CNER), Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka 819-0395, Japan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_20

263

264

D. S. Hammad et al.

(25%) from stroke, (43%) from chronic obstructive pulmonary disease, and (29%) from lung cancer [1, 2]. The primary sources of air pollution are mobile sources, stationary sources, and natural sources, which contain several toxic compounds, including intense organic compounds, toxic gases, trace elements (TE) which are considered the most dangerous component, and particulate matter (PM) [3]. Particulate matter (PM) is heterogeneous solid or liquid atmospheric aerosol suspended in the air for long periods and transported over long distances. PM includes four types: total suspended particles (TSP) with a diameter ≤ 100 μm, coarse particulate matter (PM10) which is the airborne particulate matter with an aerodynamic diameter ≤ 10 μm, fine particulate matter (PM2.5) which is the inhalable particulate matter with an aerodynamic diameter equal to or less than 2.5 μm and ultrafine particulate matter (PM1) with a diameter of ≤ 1.0 μm which also recognized as the most harmful size fraction [4–8]. Different sizes of PM have variable effects on human health [9–14]. For instance, in Japan, PM 2.5 which has a high concentration exceeds the Japanese environmental quality standards for 24 h of 35 μg.m−3 [2]. Moreover, containing 29 heavy metals such as Se, Mo, Pb, As, Zn, W, Sb, Cu, V, Cr, Ni, and Cs can be correlated with nasal, ocular, skin symptoms and severe non-carcinogenic risk to moderate carcinogenic risks for healthy school children [15, 16]. Many protection policies and actions are focused on the reduction of particulate matter, especially near busy roads in urban areas, but the removal of particulate matter and air pollutants, in general, is very complicated. Vegetation can be used as green biofilters, and bio-monitors of anthropogenic pollutants, such as heavy metals (HM) in the urban environment [17]. Many studies investigated the efficiency of PM capturing in plants, for example, Sæbø et al. [18] proved the ability of 22 trees and 25 shrubs to accumulate particulate matter inside their foliage. This work aims to study the accumulation ability of PM in 4 types of Japanese trees and shrubs that have not been fully studied in previous studies and investigate the role of the leaf features and leaf surface area in enhancing PM accumulation.

20.2 Materials and Methods 20.2.1 Plant Types and Experimental Sites Four broad-leaf types of trees and shrubs were included in this study, camphor tree (Cinnamomum camphora), Yoshino cherry (Prunus × yedoensis), Japanese fig (Ficus erecta Thunb.), and Japanese Hazenoki tree (Toxicodendron succedaneum) (more information shown in Table 20.1), which were located in two nearby sites near parking and busy road (33°31' 24” N 130°28' 40"E, 33°31' 25"N 130°28' 43’E and 33°31' 21"N 130°28' 44’E) in Kyushu university campus (Fukuoka, Japan) (shown in Fig. 20.1) with a distance between road and the analyzed species ranges from 2 to

20 Particulate Matter Phytoremediation Capacity of Four Japanese …

265

3 m. The weather conditions during those two weeks were standard for the spring season in Fukuoka: warm, and sunny with low rainfall, and the monthly temperature ranged from 16.8–24.9 °C. Fukuoka WMO on-site station (ID:47,807) recorded monthly total precipitation in May 2022 with 45 mm in this location, according to the Japan Meteorological Agency [19]. Therefore, two young mature healthy leaf samples were collected from every specimen with a time gap of 2 weeks, when there was no previous rainfall for more than 5 days. Sampling height varied from 1.5 to 1.7 m above ground level depending on plant structure. All leaf samples were placed in plastic bags, sealed, labeled with numbers, and stored at 4 °C in a clean laboratory refrigerator prior to analysis. Table 20.1 The plants species were used in this experiment to assess the capturing efficiency, (North Carolina Extension Gardener Plant Toolbox) No

Botanical name

Common name Leaf characteristics

(1)

Cinnamomum camphora

Camphor tree

• • • •

Simple Ovate, obovate Entire margin Hairless

(2)

Prunus × yedoensis

Yoshino cherry • • • •

Simple Oval, veined Serrate margin Hairy

(3)

Ficus erecta

Japanese fig

• • • •

Simple Broadly ovate Entire margin Hairless

(4)

Toxicodendron succedaneum

Japanese Hazenoki tree

• Compound (Pinnately, Bipinnately, Palmately), • Elliptical • Entire margin • Hairless

Image

266

D. S. Hammad et al.

Fig. 20.1 The distribution of sampling sites of the analyzed species

20.2.2 Quantitative Gravimetric Analysis of PM The quantity of PM was analyzed using Dzier˙zanowski et al. [20] method (Table 20.2) which divided PM analysis into two categories: (i) PM content from water washing and (ii) PM content from chloroform washing [20]. In this study, Sodium dodecyl sulfate (SDS)(CH3 (CH2 )11OSO3 Na) (Merck, Germany) with a 5% concentration (more than 5% concentration can produce more bubbles and impedes the filtration process as the bubbles can occupy the filter holes instead of particles) was used instead of chloroform to achieve the best extraction of PM and deep washing of plants stomata as it is a strong anionic surfactant. Thus, the sampled leaves of plants were washed with 250 mL of distilled water for 60 s and with 150 ml of SDS for 60 s, thereafter the washing solutions from both steps were applied to 4 sieving and filtration tools through (i) polypropylene sieve (retention 106 μm, Itoh Seisakusho Manufacturing, Japan) then, (ii) through a 10 μm Omni pore membrane filter (Merck Millipore Ltd., Ireland), (iii) through a 2.7 μm glass microfiber filter (Whatman filter, Cytiva, Japan) then using, (iv) a 0.2 μm PTFE membrane filter (Merck Millipore Ltd., Ireland), the filtration was conducted using a 25 mm vacuum filter holder with stopper support assembly connected to a vacuum pump to have three fractions of PM: (i) 10–100 (coarse), (ii) 2.5–10 (fine), and (iii) 0.2–2.5 μm (ultra-fine). The filters were dried before and after filtration for 30 min at 60 °C, stabilized in the weighing

20 Particulate Matter Phytoremediation Capacity of Four Japanese …

267

Table 20.2 Condition table of quantitative gravimetric analysis of PM Parameter

Condition /type

Water washing phase

250 mL, DW, 60 s

SDS washing phase

150 mL, SDS, 60 s

Filtration phase

100 μm sieve, 10 μm filter, 2.5 μm filter, 0.1 μm filter, vacuum filter holder, pump

Flow rate

4 ml/min

Run time

90 min/sample

Weighting room temperature

Conditioned room/ 23 °C

Oven temperature

60 °C

Weighting unit

Gram

room for 30 min, and weighed using (electronic balance HT124R, ViBRA, Japan). The total leaf area of plant leaves was calibrated and measured using ImageJ software (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://imagej.nih.gov/ij/, 997–2012) using the same method of Osunkoya et al. 2010. Allowing the amount of PM to be expressed as μg.cm−2 , further analysis was conducted using LEXT OLS4000 3D Laser Measuring Microscope (Olympus, Japan) and a scanning electron microscope (SEM) (JSM-7900F, Japan Electronics Co., Ltd., Japan) to test the relationships between leaf traits in each selected species and PM accumulation. Calculations The particulate matter quantity was measured through equations [20, 21]:   PM Deposition µg. cm−2 = W f − W i /A

(20.1)

Wf : Weight of filters after filtration in μg. Wi : Weight of filters before filtration in μg. A: Total surface area of leaf in cm2 .

20.3 Results The extracted PM accumulation differed considerably between species and periods of time, but the analyzed species showed significant ability to capture PM. Most species presented an increase in PM deposition throughout the experiment period (14 days). In total the fine PM fraction (2.5–10 μm) recorded the highest portion, whereas the ultra-fine size fraction is the lowest (Fig. 20.2). Among the analyzed species, Toxicodendron succedaneum (wax tree) had the highest PM deposition between 15 μg.cm−2 and 26.4 μg.cm−2 , respectively, in terms of the different sampling times (Fig. 20.3). While Ficus erecta, which has the largest surface area (large boundary layer) among

268

D. S. Hammad et al.

the four species (37–98 cm2 ) showed the lowest PM deposition (6.68 μg.cm−2 ) for the first sample (blank sample) and (5.9 μg.cm−2 ) for the second sample (after 14 days). In Toxicodendron succedaneum species, coarse size fraction (10–100 μm) made up the greatest proportion of accumulated PM mass and the ultra-fine size fraction (0.1–2.5 μm) made up the smallest proportion, it is worth mentioning that fine size fraction (2.5–10 μm) showed a higher portion more than the ultra-fine size fraction. Expressed as a percentage, the PM fractions as 40.3%, 33.3%, and 26.3% for coarse, fine and ultra-fine, respectively (Fig. 20.4). Prunus × yedoensis had the highest ultra-fine size fraction (0.1–2.5 μm) deposition with 48.3% among the four species, then Toxicodendron succedaneum species with 42.1%, followed by Ficus erecta with 5% and finally Cinnamomum camphora with 4.5%. For fine size fraction (2.5–10 μm), the highest portion was for Toxicodendron succedaneum with 13.79 μg.cm−2 , followed by Prunus × yedoensis which presented clusters of fine particles in a cross-section of 2.5 μm filter membrane

PM depostion (µg. cm -2 )

45 40 35 30 25 20 15 10 5 0 Coarse PM

Fine PM

Ultra fine PM

PM fractions

The analyzed species

Fig. 20.2 Total PM content in analyzed species

Sample(1)

Ficus erecta

Sample(2)

Cinnamomum camphora Prunus × yedoensis Toxicodendron succedaneum 0

5

10

15

20

25

Total PM depostion (µg. cm -2)

Fig. 20.3 The total PM deposition of the four species

30

35

PM depostion (µg. cm -2)

20 Particulate Matter Phytoremediation Capacity of Four Japanese … 45 40 35 30 25 20 15 10 5 0

269

PM10 µg. cm -2 PM2.5 µg. cm -2 PM0.2 µg. cm -2

Toxicodendron succedaneum

Prunus × yedoensis

Cinnamomum camphora

Ficus erecta

The analyzed species

Fig. 20.4 PM fractions deposition (μg.cm−2 ) in the analyzed four plants

(Fig. 20.5) as a high content with 11.95 μg.cm−2 , then Cinnamomum camphora with 6.16 μg.cm−2 , and the last for Ficus erecta with 4.81 μg.cm−2 . Coarse size fraction (10–100 μm) was the highest in Toxicodendron succedaneum deposition which has the smallest leaf among the four species with surface area of (2–23) cm2 followed by Cinnamomum camphora which has the second smallest surface area with 8–21 cm2 , then Cinnamomum camphora and Ficus erecta, respectively (Fig. 20.6).

Fig. 20.5 SEM photomicrographs of fine PM fraction (2.5–10 μm) clusters in Prunus × yedoensis 2.5 μm membrane filter

270

D. S. Hammad et al.

Fig. 20.6 PM fractions divisions deposition in the four species, a Toxicodendron succedaneum, b Prunus × yedoensis, c Cinnamomum camphora, d Ficus erecta

20.4 Discussion Trees and shrubs can reduce human exposure to air pollution through the interception of aerosol particles or through the uptake of air pollution toxic gases via leaf stomata on the plant surface [22–24]. Significantly, trees and shrubs can capture large-size particles (coarse and fine particles), but not ultra-fine particles as it is more sensitive to air turbulence and rain which can distract continuous stable deposition as a result of their small size. This was confirmed by Dzier˙zanowski et al. [20] and Sæbø et al. [18], which approved that coarse and fine size fractions covered the largest proportion of all accumulated PM [18, 20]. On the contrary, Freer-Smith et al. [25] study recorded a greater accumulation of ultra-fine PM compared with large-size PM [25]. The amount of natural PM on the leaf surface of Ficus erecta which has smooth inner structural surface, and large surface area was lower than the amount of PM in other specimens, Ficus erecta can capture an unpretentious amount of coarse, fine, ultra-fine PM fractions. It is related that a larger leaf area (LA) may contribute to a larger boundary layer (refers to the still air surrounding the leaf) which increases its resistance to the deposition of particles around leaf surfaces, which may lead to a

20 Particulate Matter Phytoremediation Capacity of Four Japanese …

271

negative correlation to PM deposition [24]. While Toxicodendron succedaneum (wax tree) that has rough dense inner structural surface (even though the leaves’ external surface is smooth and glossy), compound leaves type and the smallest leaf area among the investigated types can capture coarse, fine, and ultra-fine PM effectively, with the highest portion for fine particles. This supports that the leaf size or area does not influence PM capturing capacity. The same conditions are applied for Prunus × yedoensis, which specifically has serrated margin and rough surface(hairs), this structure can be the reason to capture ultra-fine and fine PM as the same for the coarse PM with a large portion for the ultra-fine PM, as the presence of the leaf hair has been associated with positive PM accumulation in many studies [20, 26, 27]. Cinnamomum camphora has smooth and glossy surface leaves, but the microscopic image shows a circular hollow surface, which allows capturing the coarse and fine PM in a middle range, it is not the same for ultra-fine particles which can be washed off with rain very quickly (Fig. 20.7). Leaf microstructural features such as rough surface and hair presence in Toxicodendron succedaneum and Prunus × yedoensis can create different contact angles (θ) between water droplet and leaf surface, as a result, rough leaf surfaces can increase the contact time between rainwater and the leaf surface to remove PM from leaves, this helps leaves to keep particles more than the smooth surface leaves like Ficus erecta and Cinnamomum camphora, which in same weather conditions, raindrops with low-intensity influence can quickly cross the water-repellent leaf surface and take away particulates [28]. Remarkably, the surface roughness can influence the transition from a laminar to a turbulent airflow [29, 30]. Thus, the deposition velocity can be larger across rough surface compared to smooth surface [31]. Tree or shrub location does not have any significant influence on the particulate matter deposition capacity, two types of locations (busy roads and parking) were chosen in this study. Toxicodendron succedaneum and Ficus erecta trees are on the same busy road, but they recorded high difference in PM deposition, while Prunus × yedoensis and Cinnamomum camphora are near car parking and busy road, Prunus × yedoensis recorded PM deposition more than Cinnamomum camphora, which is supposed to have a significant PM deposition percentage as it is very near to two PM main resources. It is worth mentioning, that SDS was a sufficient remover, the particles removed using SDS were more than the DW, it is related that some particulate matter organic compounds are Hydrophobic such as polycyclic aromatic hydrocarbons (PAHs) [32].

20.5 Conclusions Investigating new plant species for phytoremediation is an essential part of research to enhance the understanding of the effects of air pollution. Whereas chemical and analytical experiments which help to quantify the contributing effects of leaf traits can improve the understanding of the mechanisms of PM deposition.

272

D. S. Hammad et al.

Fig. 20.7 3D Laser Measuring Microscope image for the four species leaves, a Ficus erecta, b Toxicodendron succedaneum, c Prunus × yedoensis, d Cinnamomum camphora

In this study, the analyzed species captured 93.299 μg.cm−2 of ambient particulate matter, most of these particles accumulated in the plant’s foliage belonged to fine size fraction (2.5–10 μm) followed by coarse size fraction, and finally ultra-fine size fraction. The leaf surface area or leaf size has no effect on PM accumulation, as the highest amount of PM (41.4 μg.cm−2 ) was captured by Toxicodendron succedaneum, which has the smallest surface area among the analyzed species while Ficus erecta which has the largest surface area, recorded the lowest portion of PM. Leaf features such as hair presence and rough surfaces were traits with a positive correlation with PM accumulation which is similar to the same results obtained from previous studies [9, 21, 23, 26, 28]. For instance, Prunus × yedoensis which has hairy rough surface leaves can capture the ultra-fine and fine PM as the same for the coarse PM with a large portion for the ultra-fine PM. The best types of trees to capture PM among

20 Particulate Matter Phytoremediation Capacity of Four Japanese …

273

the analyzed species are Toxicodendron succedaneum and Prunus × yedoensis. In conclusion, leaf that has surface traits, such as hair and surface roughness can retain more particulate matter than the smooth hairless leaf.

References 1. WHO Air pollution (2018) https://www.who.int/news/item/02-05-2018-9-out-of-10-peopleworldwide-breathe-polluted-air-but-more-countries-are-taking-action. Accessed 12 Aug 2022 2. Joint WHO et al (2006) Health risks of particulate matter from long-range transboundary air pollution. WHO Regional Office for Europe, Copenhagen 3. Unger N et al (2010) Attribution of climate forcing to economic sectors. Proceed Nat Acad Sci 107(8):3382–3387 4. Chirino YI et al (2015) Sampling and composition of airborne particulate matter (PM10) from two locations of Mexico City. Data in Brief 4:353–356 5. Environmental Protection Agency, Particulate Matter (PM) Basics. https://www.epa.gov/pmpollution/particulate-matter-pm-basics#PM. Accessed 7 Sept 2022 6. Pan M et al (2019) Determination of the distribution of infectious viruses in aerosol particles using water-based condensational growth technology and a bacteriophage MS2 model. Aerosol Sci Technol 53(5):583–593 7. University of Idaho, Emissions and Smoke Portal. https://www.frames.gov/smoke/tutorial/mod ule-1/particulate-matter. Accessed 8 Sept 2022 8. Zhu C et al (2021) Role of atmospheric particulate matter exposure in COVID-19 and other health risks in human: a review. Environ Res 198:111281 9. Dockery DW et al (1993) An association between air pollution and mortality in six US cities. New Engl J Med 329(24):1753–1759 10. European Environment Agency (EEA) Air pollution in Europe 1990–2004. Report No 2/2007. Official Publications of the European Communities, Copenhagen 11. Nemmar A et al (2002) Passage of inhaled particles into the blood circulation in humans. Circulation 105(4):411–414 12. Przybysz A et al (2014) Accumulation of particulate matter and trace elements on vegetation as affected by pollution level, rainfall and the passage of time. Sci Total Environ 481:360–369 13. Strassman A et al (2021) NO 2 and PM 2.5 exposures and lung function in swiss adults: estimated effects of short-term exposures and long-term exposures with and without adjustment for short-term deviations. Environ Health Perspect 129(1):017009 14. World Health Organization (WHO) Particulate matter air pollution: how it harms health. Factsheet EURO 2005/04/05. Berlin, Copenhagen, Rome 15. Sugiyama T et al (2020) Health effects of PM2. 5 sources on children’s allergic and respiratory symptoms in Fukuoka, Japan. Sci Total Environ 709:136023 16. Zhang X, Eto Y, Aikawa M (2021) Risk assessment and management of PM2. 5-bound heavy metals in the urban area of Kitakyushu, Japan. Sci Total Environ 795:148748 17. Escobedo FJ et al (2008) Analyzing the cost-effectiveness of Santiago, Chile’s policy of using urban forests to improve air quality. J Environ Manag 86(1):148–157 18. Sæbø A et al (2012) Plant species differences in particulate matter accumulation on leaf surfaces. Sci Total Environ 427:347–354 19. Japan Meteorological Agency https://www.jma.go.jp/bosai/#pattern=forecast&area_type=cla ss20s&area_code=4021900. Accessed 2 Aug 2022 20. Dzier˙zanowski K et al (2011) Deposition of particulate matter of different size fractions on leaf surfaces and in waxes of urban forest species. Int J Phytoremediation 13(10):1037–1046 21. Viecco M et al (2018) Potential of particle matter dry deposition on green roofs and living walls vegetation for mitigating urban atmospheric pollution in semiarid climates. Sustainability 10(7):2431

274

D. S. Hammad et al.

22. Brantley HL et al (2014) Field assessment of the effects of roadside vegetation on near-road black carbon and particulate matter. Sci Total Environ 468:120–129 23. Chen L et al (2016) Experimental examination of effectiveness of vegetation as bio-filter of particulate matters in the urban environment. Environ Pollut 208:198–208 24. Chen L et al (2017) Variation in tree species ability to capture and retain airborne fine particulate matter (PM2. 5). Sci Rep 7(1):1–11 25. Freer-Smith PH, Beckett KP, Taylor G (2005) Deposition velocities to Sorbus aria, Acer campestre, Populus deltoides× trichocarpa ‘Beaupré’, Pinus nigra and× Cupressocyparis leylandii for coarse, fine and ultra-fine particles in the urban environment. Environ Pollut 133(1):157–167 26. Hwang H-J, Yook S-J, Ahn K-H (2011) Experimental investigation of submicron and ultrafine soot particle removal by tree leaves. Atmos Environ 45(38):6987–6994 27. Paull NJ et al (2020) Airborne particulate matter accumulation on common green wall plants. Int J Phytoremediation 22(6):594–606 28. Neinhuis C, Barthlott W (1998) Seasonal changes of leaf surface contamination in beech, oak, and ginkgo in relation to leaf micromorphology and wettability. New Phytol 138(1):91–98 29. Vadlamani NR, Tucker PG, Durbin P (2018) Distributed roughness effects on transitional and turbulent boundary layers. Flow Turbul Combust 100(3):627–649 30. Zhang L et al (2019) An investigation on the leaf accumulation-removal efficiency of atmospheric particulate matter for five urban plant species under different rainfall regimes. Atmos Environ 208:123–132 31. Fowler D, Cape JN, Unsworth MH (1989) Deposition of atmospheric pollutants on forests. Philos Trans Royal Soc London. B Biol Sci 324(1223):247–265 32. Ali DC, Wang Z (2021) Biodegradation of hydrophobic polycyclic aromatic hydrocarbons. Microb Biosurfactants 117–146

Part V

Climate Change Adaptation and Natural Disaster Assessment

Chapter 21

Resilience Assessment of Transportation Networks to Climate Change Induced Flooding: The Case of Doha Highways Network Mohammad Zaher Serdar and Sami G. Al-Ghamdi

Abstract Over the past decades, the rate and intensities of natural hazards have increased significantly, attributed to the impacts of climate change. Simultaneously, the population living in urban areas has increased rapidly, converting cities into vibrant economic hubs. However, this rapid expansion led to ill-planned developments, which are expensive to maintain, let alone to enhance. The emergence of unprecedented challenges accompanied by climate change has paved the way for a resilience-oriented design approach. Resilience thinking focuses on reducing the impact and streamlining the recovery process. This paper aims to address the impacts of climate change on the transportation network through flooding. We conducted the study using a graph-theoretic approach based on betweenness centrality as a metric to assess several flooding scenarios. The results show extensive damage to the network in all scenarios, which is expected considering rain and storms unusual to the region in the records used as a reference during the development. This study highlights the need to re-evaluate stormwater management plans urgently and take all the necessary mitigation measures to improve the resilience of the Doha highways network, which is crucial for the FIFA World Cup 2022 which will be held during the winter season. Keywords Climate change resilience · Complex networks · Flooding hazard · FIFA World Cup 2022 · Geographic information System (GIS) · Urban transportation resilience

M. Z. Serdar · S. G. Al-Ghamdi Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar S. G. Al-Ghamdi (B) Environmental Science and Engineering Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia e-mail: [email protected] KAUST Climate and Livability Initiative, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_21

277

278

M. Z. Serdar and S. G. Al-Ghamdi

21.1 Introduction Over the past decades, the annual cost of natural disasters has increased drastically; such an increase can be attributed to several factors but mainly climate change [1, 2]. Climate change impacts include many phenomena (e.g., elevated temperatures, change in precipitation patterns). These impacts have been reflected in increased intensity and rates of stressors and disasters, such as heat waves that led to a series of disastrous wildfires in California [3] and the unprecedented rains and floods in the Gulf Cooperation Council (GCC) region [4, 5]. Cities rely on an extensive network of interconnected infrastructures (e.g., transportation, electrical, water, communication networks, etc.) to ensure their population’s proper functionality and prosperity [6, 7]. Among these infrastructures, transportation networks play a central role in contributing to the population’s well-being and facilitating recovery efforts in the post-disaster period. The reliability of these infrastructures requires achieving robustness and flexibility while addressing their vulnerability, especially in the face of emerging and unprecedented events; in other words, it requires resilience [8]. System resilience has many definitions and can be expressed through a combination of several characteristics such as “vulnerability and robustness”; generally, most of the resilience definitions focus on disturbance impact tolerance and speeding up the recovery process [9, 10]. Several metrics (e.g., travel time, betweenness) and approaches (e.g., graph theory, simulation) are mentioned in the literature on transportation resilience. Moreover, choosing the suitable ones depends on the scale of the assessment, data and resources availability, disturbance category (e.g., natural hazard), and the stage of interest (e.g., recovery phase) [8, 11]. Graph theory, also known as complex networks, is one of the most used approaches to assess transportation networks’ resilience due to its simplicity and capacity to assess largescale networks (e.g., urban, regional, and international trade routes) by employing connectivity metrics such as node degree and betweenness [12, 13]. Graph theory simplifies the transportation network by developing an abstract of the network, where its roads are represented as links (or edges) and the intersections as nodes. This abstract can then be used to calculate connectivity metrics (e.g., node degree, betweenness), which is widely used to assess the network’s resilience [14]. For example, betweenness centrality is used to assess the resilience of different types of transportation networks such as the metro [15] and roads [16, 17]. Betweenness measures the importance of a specific node or link in connecting other nodes in the network by calculating the number of shortest paths crossing through it. Considering the rapid development of Doha city, mainly due to preparation for FIFA World Cup 2022, in this study, we want to assess the resilience of the Doha road network to climate change impact. The addressed climate change impacts focus on floodings, an emerging threat in the region [4, 18], using several scenarios developed by the Ministry of Municipality and Environment (MME). ArcGIS software is used to process the road data and flooding scenarios, which yield the damaged states of the network. The network abstract is then developed using Gephi software [19],

21 Resilience Assessment of Transportation Networks to Climate Change …

279

and network centrality metrics (mainly betweenness) are extracted to conduct the resilience assessment, as explained later.

21.2 Methodology The development of a resilience assessment methodology requires several components, such as defining the disturbance, choosing a suitable metric, and the assessment approach. Our study focuses on climate change-boosted floodings and uses the betweenness as a metric.

21.2.1 Simulating Flooding Under Climate Change Scenarios The impacts of climate change could include many aspects (e.g., increased precipitation), and we need to reflect these aspects through simulations, including the base case, on the road network. The Ministry of Municipality and Environment (MME) in Qatar has developed a flood hazard map that considers all the related aspects (such as design hyetographs and urban creep) and reflected it over several Average Recurrence Intervals (ARI) (e.g., 10-year ARI, 100-year ARI). Moreover, the provided layers also include hazard categories base on climate change scenarios. These layers and the supporting scientific background, detailed calculations, and manuals are provided through the MME website [20]. The hazard categories have been defined based on the results of depth and velocity products and the associated means to egress as in Table 21.1. In our case, we will consider the road closed when covered by medium hazard level as it prevents sedan vehicles from crossing. For this study, three hazard scenarios were selected to highlight the impact of climate change. These scenarios are the base case defined as 100-year ARI and two climate change scenarios (Scenarios 1 and 2) used by MME for project evaluation, demonstrated in Table 21.2. Table 21.1 Flood hazard categories Low hazard

Medium hazard

High hazard

Extreme hazard

Depth [m]

1.2

Velocity [m/s]

1.5

Depth x Velocity [m2 /s]

High hazard

Typical means to egress

Sedan

Sedan early, but 4WD or trucks later

4WD or large trucks

Large trucks

280

M. Z. Serdar and S. G. Al-Ghamdi

Table 21.2 Adopted climate change scenarios Scenario

Future climate era

Change in rainfall intensity (%)

Tide conditions relative to Qatar National Datum (QND95)

Sea level rise [m]

Storm surge [m]

Resultant sea condition ([m] + QND95)

Scenario 1

2050

+10

MHHW

+0.37 m

+0.5 m

MHHW + 0.87

Scenario 2

2100

+20

MHHW

+0.98 m

+0.5 m

MHHW + 1.48

21.2.2 Choosing Suitable Assessment Approach and Metric Due to the large scale of the application, it is more efficient and suitable to adopt a graph-theoretic resilience assessment approach. However, the graph theory can be accompanied by several centrality metrics (e.g., node degree, betweenness, and clustering coefficient); each centrality metric is suitable for a specific purpose or disturbance. Several studies have suggested using the betweenness metric for city-scale applications [15–17]. Betweenness centrality measures the role of certain nodes/ edges in bridging the connections between different pairs of nodes in the network, highlighting the importance of the node/element and the connectivity of the network. The mathematical expression of node betweenness is given by Eq. (21.1) [21], as follows:    σod (i ) (21.1) C Bn (i) = o=i=d∈V σod where o,d: origin–destination couples (nodes) in the network. σod : the number of shortest paths between all origin–destination couples. σod (i): the number of shortest paths that pass through (i ). The use of betweenness can detect the impact of disturbance on the network and quantify it, thus providing an effective metric for resilience assessment; the change in the betweenness values also reflects the traffic flow redistribution in road networks due to the disruption of shortest paths [17].

21.3 Results and Discussion Based on the classification of the scenarios and since the MME clarified that 100years ARI aimed for long-term developments, the assessment was conducted on the Doha highways network, as presented in Fig. 21.1. Also, the resulting damage to the network is presented in Fig. 21.2.

21 Resilience Assessment of Transportation Networks to Climate Change …

281

Fig. 21.1 Study area and disturbance scenarios. Hazard categories are low, medium, high, and extreme are presented on the map as Turkuaz, yellow, orange, and red, respectively

Fig. 21.2 Doha highways network before and during different scenarios

The previously defined hazard category (medium level), which means sedans cannot egress, showed an alarming fragmentation of the network, where betweenness centrality has dropped to less than 4% even during no climate change consideration, as shown in Table 21.3. While these results correlate with a previous study conducted on the Chicago transportation network by Kermanshah 2014, where the betweenness dropped to Table 21.3 Betweenness change and impacted lost edges under different scenarios Scenario

Number of Percentage of Before unaffected edges damaged edges [%] maxC Bn Ref. (15,359)

After maxC Bn

Change [%] maxC Bn (%)

100-year ARI 7488

51.2%

8,784,536 321,649.5 −96.3

Scenario 1

7358

52%

8,784,536 308,517.6 −96.5

Scenario 2

6754

56%

8,784,536 263,444

−97

282

M. Z. Serdar and S. G. Al-Ghamdi

less than 15% during an extreme flooding scenario [17], nevertheless, these results show that the flooding threat was generally ignored in the early development and design of the transportation network in Doha. The extensive damage resulting in the network highlights this critical vulnerability, especially since the precipitation patterns changed radically in recent years, with a tremendous increase in intensity as one of the climate change impacts [4]. Therefore, there is an urgent need to reevaluate storm management plans especially considering the upcoming FIFA World Cup 2022, which will be held in late November, a month associated with rains in the Middle East.

21.4 Conclusion The rapid increase in population in cities over the past decades has triggered an accelerated urbanization trend, creating vibrant cities with high economic value. However, ill-planned expansion exposed many vulnerabilities, further exploited under unprecedented extreme events caused by climate change hazards. In this paper, we conducted a resilience assessment for the road network of Doha under an emerging threat attributed to climate change, flooding. Due to the large scale of the assessment, we used a graph-theoretic approach with betweenness centrality as a metric. To simulate the impact of climate change and flooding, flood hazard maps predeveloped by the Ministry of Municipality and Environment (MME) were used. The results show an extensive decline in the network connectivity under all scenarios, where more than 50% were rendered inaccessible by sedans. Moreover, the betweenness centrality dropped by a considerable magnitude highlighting the weak connectivity of the Doha highways network. This study builds on MME plans and efforts to develop the infrastructure in the city, which will host the FIFA World Cup 2022, by evaluating the resilience of the transportation network under flooding and climate change scenarios. However, it also highlights the urgent need to re-evaluate stormwater management and take all necessary preparations to mitigate such disturbance, especially as the landmark event will be held in the winter season. Acknowledgements We would like to thank the Ministry of Municipality and Environment of Qatar (MME), especially the Infrastructure Planning Department (IPD) and The Centre for Geographic Information Systems (CGIS), for providing training and Data crucial for this study. Funding This publication was made possible by the National Priorities Research Program (NPRP) grant (NPRP12S-0212–190073) from the Qatar National Research Fund (QNRF), a member of Qatar Foundation (QF). This research was supported by a scholarship from Hamad Bin Khalifa University (HBKU), a member of Qatar Foundation (QF). Any opinions, findings, and conclusions or recommendations expressed in these materials are those of the authors and do not necessarily reflect the views of QNRF, HBKU or QF.

21 Resilience Assessment of Transportation Networks to Climate Change …

283

References 1. Coronese M, Lamperti F, Keller K, Chiaromonte F, Roventini A (2019) Evidence for sharp increase in the economic damages of extreme natural disasters. Proc Natl Acad Sci 116:21450– 21455. https://doi.org/10.1073/pnas.1907826116 2. Tahir F, Ajjur SB, Serdar MZ, Al-Humaiqani M, Kim D, Al-Thani SK, et al. (2021) Qatar climate change conference 2021. Hamad bin Khalifa University Press (HBKU Press). https:// doi.org/10.5339/conf_proceed_qccc2021 3. Hall P, Vanderbeck R, Triano M (2019) Electric utilities: an industry guide to enhancing resilience. Resilience Primer. https://doi.org/10.4324/9781315095912-7 4. Salimi M, Al-Ghamdi SG (2020) Climate change impacts on critical urban infrastructure and urban resiliency strategies for the Middle East. Sustain Cities Soc 54:101948. https://doi.org/ 10.1016/j.scs.2019.101948 5. Ajjur SB, Al-Ghamdi SG (2021) Seventy-year disruption of seasons characteristics in the Arabian Peninsula. Int J Climatol:joc7160. https://doi.org/10.1002/joc.7160 6. Liu W, Song Z (2020) Review of studies on the resilience of urban critical infrastructure networks. Reliab Eng Syst Saf 193:106617. https://doi.org/10.1016/j.ress.2019.106617 7. Serdar MZ, Koc M, Al-Ghamdi SG (2021) Urban infrastructure resilience assessment during mega sport events using a multi-criteria approach. Front Sustain 2. https://doi.org/10.3389/ frsus.2021.673797 8. Serdar MZ, Al-Ghamdi SG (2021) Preparing for the unpredicted: a resiliency approach in energy system assessment. In: Ren J (ed), Green energy technol., Cham: Springer International Publishing; pp 183–201. https://doi.org/10.1007/978-3-030-67529-5_9 9. Hosseini S, Barker K, Ramirez-Marquez JE (2016) A review of definitions and measures of system resilience. Reliab Eng Syst Saf 145:47–61. https://doi.org/10.1016/J.RESS.2015. 08.006 10. Serdar MZ, Koç M, Al-Ghamdi SG (2022) Urban transportation networks resilience: indicators, disturbances, and assessment methods. Sustain Cities Soc 76:103452. https://doi.org/10.1016/ j.scs.2021.103452 11. Sun W, Bocchini P, Davison BD (2020) Resilience metrics and measurement methods for transportation infrastructure: the state of the art. Sustain Resilient Infrastruct 5:168–199. https:// doi.org/10.1080/23789689.2018.1448663 12. Sun W, Bocchini P, Davison BD (2018) Resilience metrics and measurement methods for transportation infrastructure: the state of the art. Sustain Resilient Infrastruct, 1–32. https://doi. org/10.1080/23789689.2018.1448663 13. Serdar MZ, Al-Ghamdi SG (2021) Resiliency assessment of road networks during mega sport events: the case of FIFA World Cup Qatar 2022. Sustainability 13:12367. https://doi.org/10. 3390/su132212367 14. Reggiani A, Nijkamp P, Lanzi D (2015) Transport resilience and vulnerability: the role of connectivity. Transp Res Part A Policy Pract 81:4–15. https://doi.org/10.1016/j.tra.2014.12.012 15. Derrible S (2012) Network centrality of metro systems. PLoS ONE 7:e40575. https://doi.org/ 10.1371/journal.pone.0040575 16. Kermanshah A, Derrible S (2017) Robustness of road systems to extreme flooding: using elements of GIS, travel demand, and network science. Nat Hazards 86:151–164. https://doi. org/10.1007/s11069-016-2678-1 17. Kermanshah A, Karduni A, Peiravian F, Derrible S (2014) Impact analysis of extreme events on flows in spatial networks. 2014 IEEE international conference big data (Big Data) IEEE, pp 29–34.https://doi.org/10.1109/BigData.2014.7004428 18. Serdar MZ, Ajjur SB, Al-Ghamdi SG (2022) Flood susceptibility assessment in arid areas: a case study of Qatar. Sustainability 14:9792. https://doi.org/10.3390/su14159792 19. Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. BT—International AAAI conference on weblogs and social. International AAAI conference weblogs social media, pp 361–2.

284

M. Z. Serdar and S. G. Al-Ghamdi

20. MME. MME Flood Mapping Portal 2018. https://aldeera.gisqatar.org.qa/mmeflood/. Accessed April 29, 2021 21. Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Networks 1:215– 239

Chapter 22

Achievements, Difficulties and Challenges of Managing and Adapting to Drought and Saltwater Intrusion in the Vietnamese Mekong Delta Nguyen Van Tho

Abstract The Vietnamese Mekong Delta is a fairly flat area with a low average elevation relative to the mean sea level. It is dissected by the Tien and Hau Rivers, two main tributaries of the Mekong River and has a dense network of natural rivers and canals connecting to the sea. It is the largest center of agricultural production and aquaculture in Vietnam and plays a particular role in ensuring food security and economic development of the country, providing rice, vegetables, fruits and aquatic products not only for the country but also for export. In recent years, climate change and extreme climatic events like drought and severe saltwater intrusion have seriously affected the lives and agricultural cultivation of people in the region. Local people have taken many adaptation measures, such as changing the planting schedule to avoid drought and salinity, while the Government has built large saline prevention sluices in some provinces to control saltwater intrusion at a regional level. These measures have had some success. However, there are still many difficulties and challenges for the future. This paper discusses how people in this region adapt to drought and saltwater intrusion in agricultural and aquaculturral farming systems, the solutions issued by the Government to deal with drought and saltwater intrusion, the results achieved, and the difficulties and challenges that are still faced. Keywords Vietnamese Mekong delta · Saline intrusion · Drought

N. Van Tho (B) Mien Tay Construction University. 20B, Pho Co Dieu Street, Ward 3, Vinh Long City, Vinh Long Province, Vietnam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_22

285

286

N. Van Tho

22.1 Introduction The Vietnamese Mekong Delta (VMD) is located in the southernmost part of the country. It is bordered by Cambodia to the north-west, Ho Chi Minh City to the north-east and its northeast part borders Ho Chi Minh City. The southern and eastern area of VMD borders the East Sea (South China Sea) to the south and south-east, and the West Sea (Gulf of Thailand) to the west. It has 12 provinces (Long An, Tien Giang, Ben Tre, Dong Thap, Vinh Long, Tra Vinh, Soc Trang, Hau Giang, An Giang, Kien Giang, Bac Lieu and Ca Mau) and the central city of Can Tho, and it has two large low-lying areas, Dong Thap Muoi (Plain of Reeds) and the Long Xuyen Quadrangle (Fig. 22.1). The VMD is one of the largest and most fertile plains in Southeast Asia and the world. The hydrological regime of the VMD is quite complicated and influenced by rainfall, by the flow of freshwater down the Mekong River and by the tides of the East Sea and West Sea.

Fig. 22.1 The VMD map and its provinces (adapted from [1])

22 Achievements, Difficulties and Challenges of Managing and Adapting …

287

Fig. 22.2 Map of river systems and canal networks in the VMD (adapted from [5])

The VMD has an area of 40,816 km2 and a population of over 17 million, which in turn account for 12% total area and 19% population of the country, respectively [2]. It is fairly flat [3] and has an average elevation of only 0.8 m above sea level [4]. There are 2 main tributaries of the Mekong River flowing through Vietnamese territory, namely the Tien River and the Hau River. In addition, there is a dense network of natural rivers and canals connecting to the sea (Fig. 22.2). The climate in the region is affected by two seasons, the dry season and the wet season. The dry season is usually from December of the previous year to May of the following year and the wet season is usually from June to November. Vietnam is one of the countries most affected by climate change, largely due to its location and its economic dependence on sectors such as agriculture [6], and especially its extreme vulnerability to natural disasters and climate change [7, 8]. The VMD plays an important role in Vietnam’s economy, being the largest producer and exporter of rice, fruit and aquaculture products in the country. It is well known as Vietnam’s largest center of agricultural production and aquaculture and plays a particular role in ensuring food security and economic development of the country. The region contributes 50% of the rice crop, 65% of aquaculture, 70% of fruit, 95% of exported rice and 60% of exported fish [8], contributing about 15.4% of GDP [2]. In recent years, the VMD has been seriously affected by the impacts of climate change such as sea level rise, drought and saltwater intrusion, which seriously affect agricultural production and the lives of people in the region. If there are no adaptation

288

N. Van Tho

measures, around 45% of the VMD area will be affected by salinity by 2030, at an economic cost of about 17 billion USD [9]. Serious saline intrusion in 2016 affected about 22% of the rice growing area, causing a loss of 12% of national rice production and 8% of national agricultural GDP, directly affecting the lives of more than 3 million farmers [10]. However, saline intrusion in 2020 was even more serious than saltwater intrusion and sea level rise in 2016. It is considered to have been the worst saline intrusion in the VMD so far. Sea water intruded further inland than before, affecting agriculture, aquaculture and the lives of millions of people in 10 of the 13 provinces of the VMD. In 2020, seawater intruded into estuaries in the VMD for a distance of 45–66 km, 6–17 km deeper than in 2016. In the coastal areas of the West Sea, seawater intruded 48 km inland, 6 km deeper than in 2016 [11]. One particular examples is that many inland localities, for example, the villages of Dong Phu, Hoa Ninh, Binh Hoa Phuoc and An Binh (on a Tien river island near Vinh Long City, Vinh Long province) had fresh surface water of less than 1 g/l all year round over the past century, experienced salinities of up to 4 g/l in 2020. An evaluation and comparative study of saline intrusion in 1998, 2010, 2016 and 2020 was carried out by [12] and shows the extent of saline intrusion far inland in 2020 compared to that in previous years (Fig. 22.3). To cope and adapt to the above impacts, people in the VMD, along with local and central governments, have been constantly searching for solutions to adapt to and reduce damage from the impacts of climate change. The results achieved, difficulties faced and challenges will be discussed in the next part of this paper.

22.2 Adaptive Solutions and Achievements Many solutions have been carried out by farmers in the VMD to adapt to drought and saline intrusion. First, sowing rice seeds or planting rice crops earlier than the usual rice planting time to avoid drought and saltwater intrusion is one of the innovations adopted by farmers in the VMD. Each year, two main rice crops are grown in the VMD, namely the Summer-Autumn crop (from the beginning of April and harvested around the end of August) and the Winter-Spring crop (from sowing rice in about the last days of November to the first days of December and harvested in early April of the next year). In addition, farmers in this area can plant another rice crop, the Autumn– Winter crop or the 3rd rice crop. To avoid drought and saltwater intrusion, farmers sow rice seeds earlier than the normal crop schedule. For example, in the WinterSpring crop, farmers plant rice earlier, from mid-October to late-October, especially in coastal provinces such as Long An, Ben Tre, Tien Giang, Tra Vinh, Soc Trang, Bac Lieu and Kien Giang which are at risk of freshwater shortage at the end of the crop. They also do not plant a 3rd crop (Autumn–Winter crop), or during this period they switch to growing vegetables which use less water. One of the other examples is that many farmers living in areas affected by saline intrusion have switched from cultivating one rice crop per year, or a rice farming system combined with fish

22 Achievements, Difficulties and Challenges of Managing and Adapting …

289

Fig. 22.3 Areas affected by the saline intrusion of four drought years: 1998, 2010, 2016 and 2020 in the VMD (adapted from [12])

farming to an integrated farming system that combines rice farming with brackish or saltwater shrimp farming such as Penaeus monodon and Litopenaeus vannamei during the dry season and rice cultivation in the rainy season. This is a fairly new and innovative solution implemented by farmers adapting to drought and saltwater intrusion to increase farming productivity in conditions of shortage of freshwater and saline intrusion in the dry season. Second, freshwater storage in rice fields or orchards is also an effective way that farmers in the VMD usually implement to adapt and reduce the effects of drought and saline intrusion. In rice farming, to adapt to long droughts and saltwater intrusion, farmers often dig an internal drainage system throughout the rice field both to drain salt water and to store freshwater. This solution has proven to be effective in maintaining soil moisture and preventing salt deposition [13]. Farmers also pump freshwater into ditches or small ponds lined with plastic or tarpaulins, or in long

290

N. Van Tho

tubular plastic bags put in orchards and vegetable gardens. These plastic bags can be rolled up and reused in the future. This provides a better means of storing and using freshwater to irrigate crops in conditions of prolonged heat or scarcity of freshwater. Third, drip irrigation or fine spray irrigation is applied to orchards or vegetable cultivation. This method has been gradually applied to vegetable and fruit trees when water is scarce in the dry season and during periods of saline intrusion. Modern drip irrigation methods controlled by a computer system or remotely by other control devices are widely applied in countries in the world like China, India, Japan, Israel and European countries etc., contributing to saving water and limiting pollution, soil erosion, preventing water loss, increasing productivity and incoming for farmers [14, 15]. In Vietnam, drip irrigation in agricultural cultivation is not common, and has usually only been applied on large farms. However, affected by drought and increasingly severe saline intrusion in recent years, more farmers have started to switch to drip irrigation systems by watering through pipes placed close to the ground or buried underground fitted with drippers, especially for high-value fruit gardens such as mango, durian, longan, rambutan and so on, or applied to vegetable cultivation. Apart from drip irrigation, farmers also apply fine-spraying irrigation with automatic sprinklers, which can save water and still retain the necessary moisture for the soil to help plants and vegetables grow when the VMD is seriously affected by the shortage of freshwater during periods of drought and saltwater intrusion. In addition to the solutions discussed above, farmers also use locally available natural things such as nipa leaves, straw, water hyacinth and hay to cover the ground around the base of the crop to limit water evaporation and keep the soil moisture. They also convert unproductive rice planting lands in coastal areas that are frequently threatened by saline intrusion to brackish or saline aquaculture or use salinity-tolerant varieties of rice, fruits and vegetables to cope with saltwater intrusion. Apart from solutions taken by farmers to adapt to drought and saltwater intrusion with initial positive results, local and central governments have also issued decisions or strategies, and applied many other solutions to help people in the VMD reduce damage from drought and saline intrusion, help stabilize the development of people’s lives, ensure agricultural production and supply food for the region and for the country. At a community level, before salinity intrusion occurs, local authorities usually carry out community extension programmes to inform people on measures and tools to store freshwater, in order to ensure sufficient freshwater to sustain daily life and agricultural production during the period of drought or saline water intrusion. The authorities also encourage farmers to actively convert areas of inefficient agricultural farming land that are threatened by saline intrusion into planting crops that use freshwater more efficiently, or are salt-tolerant to ensure income for farmers. A network of salinity monitoring stations has already been established throughout the VMD supported by state and local budgets. The government has also built sluice gates at many strategic locations in the VMD to control the intrusion of saline water. Under current policy, these are closed when salt levels reach 1.5 parts per thousand so that farmers do not use water affected by saline intrusion, which can cause damage to agricultural crops.

22 Achievements, Difficulties and Challenges of Managing and Adapting …

291

Over the past years, the Central Government has issued many policies and implemented many solutions to promote the socio-economic development in the VMD in the context of the region affected much by climate change. One of the most prominent policies is Resolution No. 120/NQ-CP on sustainable development of the VMD to adapt to climate change issued by the Prime Minister on November 17, 2017 [16]. As a result, many sluice gates have been upgraded to regulate the amount of saline water to serve agricultural farming and aquaculture in the VMD. Recently, the Cai Lon-Cai Be saltwater prevention irrigation system has been constructed. This is considered the largest saltwater prevention project in Vietnam, providing saline intrusion control in 5 provinces in the VMD, namely Ca Mau, Bac Lieu, Soc Trang, Hau Giang and Kien Giang. This project is in phase 1, started for the construction in November 2019, with a total investment of more than 3,300 billion VND (equivalent to about 142 million USD) and the inauguration ceremony to put into use in March 2022. This project has contributed to ensuring the safety of domestic water sources and taking the initiative in production in the upstream areas of the sluices and localities in the region, which do not have to build temporary riverside dams every year to prevent saline intrusion, contributing to savings for the state budget. It is clear that these solutions help people mitigate the impact of drought and saltwater intrusion which is becoming more and more severe in the VMD.

22.3 Difficulties and Challenges Although many initial positive results have been achieved by farmers and governments, there are still many difficulties and challenges that the VMD will face from the consequences of climate change, such as drought, saline intrusion and sea level rise in the future. They will be discussed in this part. Local authorities encourage farmers to proactively convert inefficient agricultural land threatened by saline intrusion into cultivation of drought- and salt-tolerant crops and vegetables, or aquaculture as well as diversification of crops and animal livestock that can be produced efficiently, in order to adapt to climate change and to ensure income for farmers. Climate change situations such as drought and saltwater intrusion in the VMD are happening faster than forecast, with increasing severity and with a greater frequency. The study of salt-tolerant plant varieties in general or viable salt-tolerant rice varieties in particular, that have quality and bring economic benefits to farmers is not easy, and requires a lot of time. This field is not strongly developed in Vietnam. In reality, what to plant, what animals to raise, or how to change crops to adapt to drought and saline intrusion are mainly done according to farmers’ spontaneity and experience, and not based on any robust scientific or technical advice. One of the difficulties in practice is that many crops recommended by authorities that can be easily grown to adapt to drought (jackfruit, dragon fruit, etc.) and saline intrusion (coconut, etc.) have low economic value and are mainly consumed in the domestic market or exported to the Chinese market. When the Chinese market closed or restricted imports due to concerns about the spread of the COVID-19 epidemic,

292

N. Van Tho

the price of those kinds of fruit in the domestic market in Vietnam fell dramatically to about 3,000 VND/kg (equivalent to about 0.13 USD/kg) for a long time, while the price of input materials such as fertilizer increases, making the life of farmers very difficult. Management of freshwater sources for agricultural cultivation and aquaculture within the VMD in a balanced way is also one of the big difficulties and challenges. The VMD is facing growing competition for rapidly depleting water resources [6]. The competition for freshwater use in the upstream provinces of the VMD such as An Giang and Dong Thap during the dry season is one of the reasons for saline intrusion becoming more serious. It is clear that in the dry season, water evaporation in the region is high, while the amount of water coming from the Mekong River flowing through the VMD decreases. Combined with the use of water for domestic purposes, agriculture and aquaculture lead to the encroachment of sea water further inland in the dry season. In addition, ensuring national water security for the VMD is one of the most difficult challenges. The VMD depends largely on the Mekong River’s water source for freshwater. A country located in the lower Mekong River, Vietnam faces more difficulties than other member countries of the Mekong River Commission and the 2 upper states (China and Myanmar) using the Mekong River’s water source to ensure national water security, especially when the countries are yet to reach agreement on the equitable sharing of water resources. The six countries in the Mekong River Basin are continuing to promote economic development, taking full advantage of the river’s water and other resources. However, the exploitation of natural resources, although bringing many benefits to one country can have serious consequences for other countries. The difference of interests in water use between upstream and downstream countries is also widening, creating increasing challenges for the management, use and protection of the natural resources of this river. Specifically, there is a difference in benefit sharing when building hydroelectric dams on the Mekong River. China has already built six dams in the Upper Mekong and a further 14 are either in construction or planned. This is especially problematic because about 20% (up to 70% in the dry season) of the water flow in the lower Mekong River originates in China, resulting in big impacts in downstream countries [17]. In addition to present and planned dams in China, the neighbouring countries of Laos, Thailand and Cambodia have also constructed dams on the Mekong and its tributaries as shown in Fig. 22.4 [18, 19]. Currently, there are a total of about 130 dams on the Mekong River, seven of which are on the main stem of the river, but a further 150 dams are either currently in construction or being planned [20]. It is clear that the hydropower dams that have already been built and are planned to be built in the future are big. They will use very large amounts of water from the Mekong River for electricity production, agricultural farming and for the human life of countries outside Vietnam. These hydroelectric dams have already and will increasingly restrict water flow to the lower Mekong River, especially the VMD during the dry season, inevitably leading to more severe saltwater intrusion events

22 Achievements, Difficulties and Challenges of Managing and Adapting …

293

Fig. 22.4 Location of hydropower projects and Mekong River Basin [18, 19]

[12, 17, 20, 21]. Therefore, strengthening the international cooperation of the Government of Vietnam with the countries upstream of the Mekong River in order to share common interests and to ensure the security of freshwater sources for use in the VMD is still a big challenge for the future.

294

N. Van Tho

22.4 Conclusions The VMD is the country’s key agricultural and aquaculture farming region, providing rice, vegetables, fruits and aquatic products not only for the country but also for export. With a low elevation with respect to sea level and a dense network of rivers connecting to the sea, the region has experienced increasingly serious salinity intrusion in recent years, seriously affecting the lives, agricultural and aquaculture activities of the people in the region. In addition, the VMD is also one of the regions in the world that will be severely affected by sea level rise in the future due to the effects of global climate change. Local people and authorities have taken many measures to adapt to saline intrusion, with some degree of success. However, the planning of areas used for the cultivation or breeding of high-economic-value varieties to adapt to saltwater intrusion in the future is based mainly on the experience of farmers, rather than on sound scientific and technical advice. The construction of sluices to prevent salinity has also achieved positive results in preventing saline intrusion in the dry season, but this is not an effective long-term strategy to avoid the impact of sea level rise in the next few decades. Therefore, the construction of sea dykes combined with road construction to both protect the VMD from rising water and develop future transport infrastructure is an important long-term strategy that the authorities should think about. In addition, the authorities also should have a plan for the relocation of people whose livelihoods have become unsustainable in the face of unmanageable saltwater intrusion and sea level rise in the future.

References 1. Tran DD, Halsema GV, Hellegers PJGJ, Hoang LH, Ludwig F (2019) Long-term sustainability of the Vietnamese Mekong Delta in question: an economic assessment of water management alternatives. Agric Water Manag 223:1–12 2. GSO (General Statistics Office of Viet Nam): Statistical Yearbook of Vietnam 2019. Statistical Publishing House, Ha Noi (2020) 3. Fujihara Y, Hoshikawa K, Fujii H, Kotera A, Nagano T, Yokoyama S (2016) Analysis and attribution of trends in water levels in the Vietnamese Mekong Delta. Hydrol Process 30:835– 845 4. Minderhoud PSJ, Coumou L, Erkens G, Middelkoop H, Stouthamer E (2019) Mekong delta much lower than previously assumed in sea-level rise impact assessments. Nat Commun 10(3847):1–13 5. MRC (Mekong River Commission): overview of the hydrology of the Mekong Basin. Mekong River Commission, Vientiane (2005) 6. WB (World Bank): Vietnam-Mekong Delta Water Management for Rural Development Project. Washington, D.C: World Bank (2018) 7. Clark PU, Shakun JD, Marcott SA, Mix AC, Eby M et al (2016) Consequences of twenty-firstcentury policy for multi-millennial climate and sea-level change. Nat Clim Chang 6:360–369 8. WB (World Bank): mobilizing financing for climate smart investments in the mekong delta: an options note. World Bank (2020) 9. WB (World Bank): Vietnam Country climate and development report. Washington, DC: World Bank (2022)

22 Achievements, Difficulties and Challenges of Managing and Adapting …

295

10. Rentschler J, de Vries Robbé S, Braese J, Huy Nguyen D, van Ledden M, Pozueta Mayo B (2020) Resilient shores: Vietnam’s coastal development between opportunity and disaster risk. The World Bank, Washington D. C 11. MARD (Ministry of Agriculture and Rural Development): report on the drought and saltwater intrusion in Mekong Delta. MARD (2020) 12. Park E, Loc HH, Binh DV, Kantoush S (2021) The worst 2020 saline water intrusion disaster of the past century in the Mekong Delta: impacts, causes, and management implications 13. Tran TA, Nguyen TH, Vo TT (2019) Adaptation to flood and salinity environments in the Vietnamese Mekong Delta: empirical analysis of farmer-led innovations. Agric Water Manag 216:89–97 14. Abioye EA, Abidin MSZ, Mahmud MSA, Buyamin S, Ishak MHI, Rahman MKIA, Otuoze AO, Onotu P, Ramli MS (2020) A review on monitoring and advanced control strategies for precision irrigation. Comput Electron Agric 173:105441 15. Bansal G, Mahajan A, Verma A, Singh DB (2021) A review on materialistic approach to drip irrigation system. Mater Today Proc 46:10712–10717 16. The Vietnamese Government: The Government’s Resolution No. 120/NQ-CP on sustainable development of the Mekong Delta to adapt to climate change. Government of Vietnam (in Vietnamese language) (2017) 17. Soutullo J (2019) The Mekong River: geopolitics over development, hydropower and the environment. Policy Department for External Relations. European Parliament. ISBN: 978-92-846-6065-0. https://doi.org/10.2861/718814 | QA-04–19–745-EN-N 18. Hoang LP, van Vliet MTH, Kummu M, Lauri H, Koponen J, Supit I, Leemans R, Kabat P, Ludwig F (2018) The Mekong’s future flows under multiple drivers: how climate change, hydropower developments and irrigation expansions drive hydrological changes. Sci Total Environ 19. Triet NVK, Dung NV, Hoang LP, Duy NL, Tran DD, Anh TT, Kummu M, Merz B, Apel H (2020) Future projections of flood dynamics in the Vietnamese Mekong Delta. Sci Total Environ 742:140596 20. Bussi G, Darby SE, Whitehead PG, Jin L, Dadson SJ, Voepel HE, Vasilopoulos G, Hackney CR, Hutton C, Berchoux T, Parsons DR, Nicholas A (2021) Impact of dams and climate change on suspended sediment flux to the Mekong delta. Sci Total Environ 75:1–12 21. Hecht JS, Lacombe G, Arias ME, Dang TD, Piman T (2019) Hydropower dams of the Mekong River basin: a review of their hydrological Impacts. J Hydrol 568:285–300

Chapter 23

Public Transportation Resilience Towards Climate Change Impacts: The Case of Doha Metro Network Mohammad Zaher Serdar and Sami G. Al-Ghamdi

Abstract Since the mid of the past century, accelerated urbanization and rapid city expansion have driven the development of extensive public transportation networks to facilitate mobility throughout cities. However, these developments disregarded climate change’s threat, which gained momentum in recent decades with the unusual rate of extreme disasters. These unprecedented challenges motivated the thinking about preparing these networks and ensuring their resilience. This paper highlights the impacts of climate change on flooding hazards and the resulting damage to the Doha metro network. We applied a complex network approach to assess the resilience of the network under different scenarios. The results show no damage suffered under the base case; however, several stations and sections of the network are impacted under climate change scenarios. Thus, as the impacts of climate change are becoming more evident, it is critical to revisit development plans and precautions to reinforce or redesign critical infrastructures to avoid catastrophic events and substantial economic losses. Therefore, this study promotes the efforts to prepare cities for the impacts of climate change and support the development of resilient critical infrastructures to ensure the prosperity of its inhabitants, as in the case of Doha and the preparation for the FIFA World Cup 2022. Keywords Climate change resilience · Doha metro network · Flooding hazard · FIFA World Cup 2022 · Geographic Information System (GIS) · Public transportation resilience

M. Z. Serdar · S. G. Al-Ghamdi Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar S. G. Al-Ghamdi (B) Environmental Science and Engineering Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia e-mail: [email protected] KAUST Climate and Livability Initiative, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_23

297

298

M. Z. Serdar and S. G. Al-Ghamdi

23.1 Introduction After the end of the Second World War, the number of people living in urban areas has grown in an accelerated manner, attracted by the promise of better opportunities and services; however, this rapid urbanization incorporates vulnerabilities exploited by climate change impacts in recent years [1, 2]. Since the mid of the previous century, accelerated urbanization has spread across the globe, with the percentage of people living in urban areas rising from 30% in the 1960s to more than 50% by 2020 and projected to reach almost 70% by 2050 [3]. A high level of urbanization means a high concentration of economic and demographical values, so any disturbance to a city costs substantially more than a rural area. The high quality of life characterizes urban areas provided through extensive infrastructure networks supporting them [4, 5]. Climate change introduces disturbing changes to precipitation patterns associated with extreme flooding that has increasingly affected the critical infrastructures in high-density urban areas [6]. Public transportation is an essential part of the critical infrastructure in any city, and its state reflects the impact of disturbances on the city [7, 8]. A well-designed critical infrastructure should enjoy flexibility and robustness to address its vulnerabilities under disturbances; these qualities can be reflected under the concept system of resilience [9, 10]. Many resilience assessment approaches are suggested in the literature, such as big data, simulation, and complex networks [11]. Statistical analysis of previous events (a big data approach) can reflect system performance and resilience. Meanwhile, simulation of the transportation network interaction helps predict performance under unprecedented events. The complex networks method is a widely adopted approach due to its simplicity and effectiveness in large-scale assessments [9, 12]. The complex network’s approach uses a graph abstract that resembles the system by expressing the relation between its components, focusing mainly on its connectivity. The developed graph abstract consists of nodes and links, where nodes can be metro stations and links represent the tunnels and tracks connecting them. Moreover, to increase the accuracy of the assessment, a weighting (for nodes or links) can be introduced based on specific considerations such as length, capacity, or the number of users [13]. In addition, several topological metrics can be used during the assessment, such as betweenness and clustering coefficient. Betweenness is widely employed for road networks [14, 15] and metro system assessments [16], where it represents the role of nodes and links in connecting different nodes in the graph; this allows expressing resilience in terms of preserving this functionality during the disturbances [15]. Within the scope of FIFA World Cup 2022, Doha city is undergoing a transformational revolution in all infrastructures, but prominently in public transportation infrastructures. The development of Doha metro lines and the expanding bus lines, and the direction to use electrical buses during the mega event are incited by the commitments to deliver the most compact version of the event and adhering to strict sustainability goals [17, 18]. However, taking into account that the event will be conducted during winter and the series of unusual extreme rainfall intensities, there

23 Public Transportation Resilience Towards Climate Change Impacts: The …

299

is a necessity to assess the resilience of these public transportation networks toward the threat of flooding, which is believed to be caused by climate change [6, 19, 20]. In this study, we assess the resilience of the Doha Metro Network under several climate change scenarios. The study uses flood hazard data of these scenarios developed and used by the Ministry of Municipality and Environment (MME). Both ArcGIS and Gephi are used in this study to conduct the assessment and identify impacted parts under each scenario, as will be explained in the following section. The rest of the paper will address the methodology, present the results and discuss them, and finally provide concluding remarks and recommendations.

23.2 Methodology In this paper, we want to assess the resilience of the Doha metro network toward climate change impacts. For this purpose and considering the scale and the characteristics of this mode of public transportation, we will identify the sensitive components and apply a complex network approach for the assessment using betweenness as the metric. Meanwhile, the impact of climate change on flooding hazards will be considered in each scenario.

23.2.1 Flood Hazard Due to Climate Change Climate change can cause several impacts, including elevated temperature and increased precipitation intensities. Subsequently, high-intensity rainfall in underprepared areas causes devastating floods. Within the scope of climate change preparedness and mitigation efforts, the Qatari Ministry of Municipality and Environment (MME) has conducted an extensive study on the impact of climate change on flooding hazards. This study resulted in map layers showing the different hazard categories in Qatar, which will guide planning and development efforts; these layers and full details of the study can be accessed through [21]. Nevertheless, the approach used in their development relied on 2D simulation using the hydrological properties of the state and rainfall records with an acceptable level of exceedance reflected in the form of once every 5, 10 or 100 years, for example. This paper will use these layers to simulate the impact of floods under different climate change scenarios. However, other methods could be adopted from the literature, like using flood susceptibility, which reflects several characteristics and overlay their maps based on multiple weighting factors using the multi-criteria-decision-making concept, as presented in [20]. Hazard categories were defined in MME Map layers into four categories derived from the risk matrix based on depth and velocity. These hazard categories were associated with different types of vehicles based on their capacity to egress during such events and to be used for evacuation and disaster management operations. In our study, since metro lines generally run underground, we will consider the hazard based

300

M. Z. Serdar and S. G. Al-Ghamdi

Table 23.1 Flood hazard categories [21] Low hazard

Medium hazard

High hazard

Extreme hazard

Depth [m]

1.2

Velocity [m/s]

1.5

Depth*Velocity [m2/s]

High hazard

Typical means to egress

Sedan

Sedan early, but 4WD or trucks later

4WD or large trucks

Large trucks

Table 23.2 Adopted climate change scenarios [21] Scenario

Future climate era

Change in rainfall intensity (%)

Tide conditions relative to Qatar National Datum (QND95)

Sea level rise [m]

Storm surge [m]

Resultant sea condition ([m] + QND95)

Scenario 2

2100

+20

MHHW

+0.98 m

+0.5 m

MHHW + 1.48

Scenario 3

2100

+30

MHHW

+0.98 m

+0.5 m

MHHW + 1.48

on stations that are subjected to high-hazard levels and that the failure propagates to lines connected to these stations. Table 23.1 shows the defined hazard categories and all related limits. During this study, we will use three hazard layers to emphasize the effect of climate change. The base scenario will be represented as a 100-year ARI (Average Recurrence Intervals) and another two scenarios (scenarios 2 and 3) based on climate change forecasts; these scenarios are developed according to the Fifth Assessment Report (AR5) by Intergovernmental Panel on Climate Change (IPCC). These three cases are currently used to assess future development projects by the MME. Table 23.2 shows the main characteristics of scenarios 2 and 3.

23.2.2 Large-Scale Assessment Using Complex Networks Method The complex networks method is widely applied in social and data science due to its capacity to identify the relationship between a large number of elements, the element’s influence, and the nature of their bonds [22]. These characteristics allowed its application in the resilience assessment for both road and public transportation networks (e.g., metro system and bus lines), using various topological metrics but most prominently betweenness [14–16]. Betweenness expresses the importance of an element (node or edge) by calculating the number of shortest paths between origin– destination couples in the network that passes through that element. The relative

23 Public Transportation Resilience Towards Climate Change Impacts: The …

301

betweenness can be calculated by Eq. (23.1) [23], as follows: C Bn (i) =



 o=i=d∈V

σod (i ) σod

 (23.1)

where o,d: origin–destination couples (nodes) in the network. σod : the number of shortest paths between all origin–destination couples. σod (i): the number of shortest paths that pass through (i ). Since betweenness reflects the connectivity between different parts of the network, changes in its value reflect the state of the network and its resilience to different disturbances [15].

23.3 Results and Discussion The study area is set to include the currently operated lines of the Doha Metro network (phase 1), which is also planned to serve during the FIFA World Cup 2022; Fig. 23.1 shows the extent of the metro network and the different flooding scenarios and associated hazard categories as previously defined in Table 23.1 and Table 23.2. The base flooding scenario is based on 100-year ARI, used by MME for strategic infrastructure [21]. Additionally, Fig. 23.2 shows the impact of different scenarios on the metro network, according to our previously defined approach. Doha metro network (phase 1) plans consist of 80.35 km (per track), connecting 37 stations with a maximum betweenness of 49 (these data are based on the online ArcGIS library, with minor differences from reality). Table 23.3 shows the impacts of the implemented scenarios and the extent of the damage. We can notice no damage is suffered during the base case of 100-year ARI, and there is no difference in the damage in both scenario two and scenario three.

Fig. 23.1 Study area and disturbance scenarios. Hazard categories are low, medium, high and extreme are presented on the map as Turkuaz, yellow, orange and red, respectively

302

M. Z. Serdar and S. G. Al-Ghamdi

Fig. 23.2 Doha metro network before and during different scenarios. Green operational, Yellow indirectly affected, and red directly damaged by the flood

Table 23.3 Floods-induced impacts on stations and lines under different scenarios Scenario

Number of the affected stations Directly

Indirectly

Lost length (km)

Post-disaster maxC Bn (Change%)

100-year ARI

–

–

–

49 (0%)

Scenario 2

2

7

26.31(32.5%)

35 (-28.6%)

Scenario 3

2

7

26.31(32.5%)

35 (-28.6%)

It is notable that under 100-year ARI, no station is damaged or threatened by flooding hazards, proving the design was conducted according to the best practices during its development (in 2009). However, the introduction of climate change impacts two critical stations directly (Education City and Hamad International Airport stations), and their failure will also affect related lines extending the impact to another seven stations (either by line failure or discontinuity). These results show relatively higher resilience of metro networks compared to highway networks, where such extreme flooding can cause almost 85% betweenness loss [15]. Nevertheless, considering the amount of lost length, the technical difficulties, and the sensitive equipment, the resulting repair cost could be much higher than that of a highway network. It is also very important to consider the exact role of the damaged stations, which is not captured by the unweighted complex network. For example, in our study, the damaged stations play a critical role in the network; Hamad International Airport station facilitates the arrival and departure of tourists and fans during FIFA World Cup 2022, on the other hand, Education City station provides access to one of the World Cup stadiums and link to another one. These aspects highlight the importance of considering weighting conducting resilience assessment to ensure the accuracy of such a process. It is quite alarming that the original design, which can withstand the hazards of the maximum considered design flood (100-year ARI) without suffering any damage, is subjected to such high impacts under climate change scenarios. Additionally, the

23 Public Transportation Resilience Towards Climate Change Impacts: The …

303

abnormal rainfall pattern and intensity witnessed in the past few years, and is an urgent need to revisit the design of such critical infrastructure to improve its resilience to climate change, especially with its vital role in hosting mega sports events such as the FIFA World Cup 2022 [6, 8]. Moreover, it is important to consider the possible impacts caused by failure propagation from the supporting infrastructures, mainly the electrical network. The impact of such failure propagation could extend to several aspects depending on the investigated assets. On the one hand, the metro station will face a critical challenge in providing proper ventilation while running the HVAC system by backup generators, which could also be inundated, in the worst-case scenario, rendering it unusable. On the other hand, the loss of main network support may paralyze the network, a problem that cannot be accommodated by local backup generators. Such a threat can even worsen if the water enters the tube, which may require a lengthy and careful recovery operation to avoid damaging the whole MEP system and ensure safe evacuation for the commuters. Nevertheless, such scenarios require proper evaluation, which can be addressed in future studies.

23.4 Conclusion As a result of the accelerated urbanization witnessed since the middle of the past century, the need for efficient public transportation increased significantly. Metro networks provide a fast, traffic-neutral, and eco-friendly alternative to other modes of transportation. However, with much of these networks built regardless of the impacts of climate change, it is vital to evaluate their resilience under such emerging threats. This paper conducts a resilience assessment of the Doha metro network using a complex network approach based on betweenness centrality as the metric and flooding hazards as the disturbance. Subsequently, three flooding scenarios were used in the base case of 100-year ARI and two cases simulating climate change impacts. The results show the network suffers no damage under the 100-year ARI (typically used in such development design). However, under the climate change cases, two metro stations are impacted directly and seven more indirectly, and while the damage is relatively limited, the introduction of weighting based on importance would have provided far better accuracy for the assessment. With the impacts of climate change becoming more evident, it is critical to revisit development plans and precautions to reinforce or redesign critical infrastructures to avoid catastrophic events and substantial economic losses. Therefore, this study promotes the efforts to prepare cities for the impacts of climate change and support the development of resilient critical infrastructures to ensure the prosperity of its inhabitants, as in the case of Doha and the preparation for the FIFA World Cup 2022. Acknowledgements We would like to thank the Ministry of Municipality and Environment of Qatar (MME), especially the Infrastructure Planning Department (IPD) and the Centre for Geographic Information Systems (CGIS), for providing training and data crucial for this study.

304

M. Z. Serdar and S. G. Al-Ghamdi

Funding This publication was made possible by the National Priorities Research Program (NPRP) grant (NPRP12S-0212-190073) from the Qatar National Research Fund (QNRF), a member of Qatar Foundation (QF). This research was supported by a scholarship from Hamad Bin Khalifa University (HBKU), a member of the Qatar Foundation (QF). Any opinions, findings, and conclusions or recommendations expressed in these materials are those of the authors and do not necessarily reflect the views of QNRF, HBKU or QF.

References 1. Panwar V, Sen S (2019) Economic impact of natural disasters: an empirical re-examination. Margin J Appl Econ Res 13:109–139. https://doi.org/10.1177/0973801018800087 2. Ajjur SB, Al-Ghamdi SG (2021) Variation in seasonal precipitation over gaza (Palestine) and its sensitivity to teleconnection patterns. Water (Switzerland) 13:1–17. https://doi.org/10.3390/ w13050667 3. United Nations, Department of Economic and Social Affairs PD. World Urbanization Prospects: The 2018 Revision. New York: 2019. https://doi.org/10.18356/b9e995fe-en 4. Liu W, Song Z (2020) Review of studies on the resilience of urban critical infrastructure networks. Reliab Eng Syst Saf 193:106617. https://doi.org/10.1016/j.ress.2019.106617 5. Serdar MZ, Koc M, Al-Ghamdi SG (2021) Urban infrastructure resilience assessment during mega sport events using a multi-criteria approach. Front Sustain 2. https://doi.org/10.3389/ frsus.2021.673797 6. Salimi M, Al-Ghamdi SG (2020) Climate change impacts on critical urban infrastructure and urban resiliency strategies for the Middle East. Sustain Cities Soc 54:101948. https://doi.org/ 10.1016/j.scs.2019.101948 7. Azolin LG, Rodrigues da Silva AN, Pinto N (2020). Incorporating public transport in a methodology for assessing resilience in urban mobility. Transp Res Part D Transp Environ 85:102386. https://doi.org/10.1016/j.trd.2020.102386 8. Serdar MZ, Al-Ghamdi SG (2021) Resiliency assessment of road networks during mega sport events: the case of FIFA World Cup Qatar 2022. Sustainability 13:12367. https://doi.org/10. 3390/su132212367 9. Serdar MZ, Al-Ghamdi SG (2021) Preparing for the unpredicted: a resiliency approach in energy system assessment. In: Ren J (ed), Green energy technology, Cham: Springer International Publishing; pp 183–201. https://doi.org/10.1007/978-3-030-67529-5_9 10. Al-Humaiqani MM, Al-Ghamdi SG (2022) The built environment resilience qualities to climate change impact: concepts, frameworks, and directions for future research. Sustain Cities Soc 80:103797. https://doi.org/10.1016/j.scs.2022.103797 11. Serdar MZ, Koç M, Al-Ghamdi SG (2022) Urban transportation networks resilience: indicators, disturbances, and assessment methods. Sustain Cities Soc 76:103452. https://doi.org/10.1016/ j.scs.2021.103452 12. Sun W, Bocchini P, Davison BD (2018) Resilience metrics and measurement methods for transportation infrastructure: the state of the art. Sustain Resilient Infrastruct, 1–32. https://doi. org/10.1080/23789689.2018.1448663 13. Sun W, Bocchini P, Davison BD (2020) Resilience metrics and measurement methods for transportation infrastructure: the state of the art. Sustain Resilient Infrastruct 5:168–199. https:// doi.org/10.1080/23789689.2018.1448663 14. Kermanshah A, Derrible S (2017) Robustness of road systems to extreme flooding: using elements of GIS, travel demand, and network science. Nat Hazards 86:151–164. https://doi. org/10.1007/s11069-016-2678-1 15. Kermanshah A, Karduni A, Peiravian F, Derrible S (2014) Impact analysis of extreme events on flows in spatial networks. 2014 IEEE international conference big data (Big Data), IEEE, pp 29–34.https://doi.org/10.1109/BigData.2014.7004428

23 Public Transportation Resilience Towards Climate Change Impacts: The …

305

16. Derrible S (2012) Network centrality of metro systems. PLoS ONE 7:e40575. https://doi.org/ 10.1371/journal.pone.0040575 17. The Supreme Committee for Delivery and Legacy. The supreme committee for delivery and legacy (sustainability policy) 2020. https://www.qatar2022.qa/en/about/sustainability. Accessed October 10 2020 18. Al-Thawadi FE, Weldu YW, Al-Ghamdi SG (2020) Sustainable urban transportation approaches: life-cycle assessment perspective of passenger transport modes in Qatar. Transp Res Procedia 48:2056–2062. https://doi.org/10.1016/j.trpro.2020.08.265 19. Tahir F, Ajjur SB, Serdar MZ, Al-Humaiqani M, Kim D, Al-Thani SK, et al. (2021) Qatar climate change conference 2021. Hamad bin Khalifa University Press (HBKU Press). https:// doi.org/10.5339/conf_proceed_qccc2021 20. Serdar MZ, Ajjur SB, Al-Ghamdi SG (2022) Flood susceptibility assessment in arid areas: a case study of Qatar. Sustainability 14:9792. https://doi.org/10.3390/su14159792 21. MME. MME Flood Mapping Portal 2018. https://aldeera.gisqatar.org.qa/mmeflood/. Accessed April 29 2021 22. Yong S, Xiangming W, Zhenmin Z, Yuan L (2012) Using complex network theory in the Internet engineering. 2012 7th international conference computer science education, IEEE, pp 390–4. https://doi.org/10.1109/ICCSE.2012.6295099 23. Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Networks 1:215– 239

Chapter 24

Social Vulnerability Assessment to Natural Hazards in East Malaysia Nor Salsabila M. Sabri and Zulfa Hanan Ash’aari

Abstract Malaysia is particularly vulnerable to natural disasters such as flooding and drought. These events can result in life-altering consequences, particularly for vulnerable communities. In this study, the social vulnerability index (SoVI) has been utilized to identify the highly vulnerable areas to natural disasters in East Malaysia. Sensitivity and adaptive capacity were evaluated using 13 indicators for every 57 districts in East Malaysia. The SoVI score was generated by summing up the factor score from multivariate analysis. Findings from this study indicated that the critical factors contributing to the high SoVI score in East Malaysia are population density and the number of sensitive populations such as the elderly and children. However, the vulnerability scores were reduced if the adaptive capacity scores increased. This study’s findings can assist in identifying the most effective strategies for hazard prevention and mitigation. Keywords Natural disaster · Sensitive population · Adaptive capacity · Principal component analysis (PCA) · Sabah · Sarawak

24.1 Introduction Globally, there has been a noticeable increase in the occurrence of natural disasters in recent years [1, 2]. Although there is no universally accepted definition of a disaster, they can be classified into categories like natural, man-made, and hybrid disasters. The common element of disasters is their severity; other than that, they have different characteristics and impacts [3]. In Malaysia, climate change has had a significant impact on natural disasters, leading to rising sea-levels, increased rainfall, heightened flooding risks, secere droughts, and intense heat waves [4]. Flood, for example, affects over 4.82 million people (22% of the population) and threatens 29,800 km2 (9% area) of land. Of the 189 rivers in Malaysia, 85 are prone to flooding [5]. In N. S. M. Sabri · Z. H. Ash’aari (B) Department of Environment, Faculty of Forestry and Environment, Universiti Putra Malaysia, Putrajaya, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_24

307

308

N. S. M. Sabri and Z. H. Ash’aari

the East Malaysia region, a high tendency of increased drought and increased dry months were reported in Sarawak [6]. Furthermore, about five flood events occurred in Sarawak between 2010 and 2018 [7]. In Sabah, the southwest monsoon season causes weather changes that often bring prolonged and extreme drought [8]. The natural hazard could affect the human population and the environment [9]. Individuals and societies are vulnerable in conditions, where they are exposed to social, economic, and cultural risks as well as hazards that might cause harm to them [10]. Risk and harm cannot be avoided; thus, the best method is to prepare for adaptation and prevent it in every situation [11]. According to the Intergovernmental Panel on Climate Change (IPCC), climate vulnerability can be defined as “the degree to which geophysical, biological, and socio-economic systems are susceptible to and unable to cope with, adverse impacts of climate change” [12]. People can manage the hazards and disasters that occur in vulnerable communities because of their understanding, as the idea of vulnerability has been discussed for decades [11]. From that, sustainable development can be achieved as vulnerability can help people prepare for any worst-case scenario in the future. Therefore, quantifying social vulnerability is necessary for hazard mitigation [13, 14]. The definition of social vulnerability has varied in many ways. It can be defined as a group or individuals that are exposed to unexpected conditions and changes to their livelihoods [15]. In addition, social and geographic inequalities can also be categorized as social vulnerability [16]. In addition, social vulnerability can be affected by several individual variables such as age, race, gender, income, ethnicity, residence, job, disability, and level of education [16–18]. Furthermore, income allocation, various economic assets, and informal social security have affected social vulnerability [15]. Motivated by various potential impacts of the natural disaster on social vulnerability, this research aims to evaluate the spatial variations of social vulnerability towards natural hazards, particularly in East Malaysia, which is lacking in the existing literature. This study’s findings could help find appropriate ways to prevent and mitigate the hazards. Besides, this study is also aligned with Sustainable Development Goal 11, which makes cities and human settlements inclusive, safe, resilient, and sustainable.

24.2 Methods 24.2.1 Study Area East Malaysia lies to the east of Peninsular Malaysia, also known as West Malaysia. The two areas were separated by the South China Sea (Fig. 24.1). East Malaysia consists of Sabah, Sarawak, and the Federal Territory of Labuan. Sabah’s total population is 3.90 million people per year, with 2.78 million citizens and the total area is 73,623 km2 [19, 20]. The total population of Sarawak in 2019 is 2.81 million per year, with a total area of 124,450 km2 while Labuan has 418,000 people per year and a total area of 92 km2 . A total of 57 Sabah, Sarawak, and Labuan districts

24 Social Vulnerability Assessment to Natural Hazards in East Malaysia

309

Fig. 24.1 Study Area. East of Malaysia (Sabah, Sarawak, and Federal Territory of Labuan)

were selected to analyze the social vulnerability assessment to natural hazards. East Malaysia is in the tropical zone, and the climate is typically defined as having dry and wet seasons throughout the year. The average annual rainfall is 2500 mm, and the average temperature ranges from 23 °C as minimum temperature to 33 °C as maximum temperature during hot weather ([20–22], with a relative humidity of 70% to 80%. The wet season (northeast monsoon) lasts from October to March, and the dry season (southwest monsoon) lasts from April to September [20].

24.2.2 Data This study utilized census data from the 2019 census, retrieved from the Department of Statistics Malaysia (DOSM). Thirteen sociodemographic variables were chosen considering the social demographic of the study area (Table 24.1). Access to health care is one of the critical variables considered in this study. Natural disasters significantly impact medical services, health concerns, and distance from the hospital are crucial aspects to consider when determining social vulnerability [23]. Being healthy and maintaining a solid public healthcare system is critical for vulnerable populations to disasters or hazards. A lack of healthcare systems can create serious issues, resulting in more accidents and disruptions. Other than that, due to social and economic factors, women and men populations are exposed to disasters differently.

310

N. S. M. Sabri and Z. H. Ash’aari

Table 24.1 Summary of variables used in Social Vulnerability Index Assessment (SOVI) Variables

References

Year

Data source

Male population

[28]

2019

Female population

[11]

Department of Statistics Malaysia (DOSM)

Non-citizen population

[9]

Population density

[29]

Population age 65 years above

[9]

Population under 14 years

[30]

High-income population (T20)

[31]

Low-income population (B40)

[29]

Access to water supply

[32]

Access to electricity

[33]

Access to healthcare

[34]

Access to internet

[35]

Access to telephone

[35]

Women in the same social group/class are poorer and more susceptible than men in the same category due to pre-existing gender relations [24]. The elderly are among the most susceptible groups to natural disasters. The impact of an unforeseen calamity may be overwhelming since an elder already feels vulnerable due to chronic health concerns, diminished cognitive abilities, and decreased sensory awareness [25]. Low household-income populations are more vulnerable than high-income populations because they tend to live in hazard-prone areas [26]. They have fewer resources to mitigate hazards or deal with the impact when it occurs [27].

24.2.3 Social Vulnerability Index (SoVI) Among methods used in social vulnerability assessment, the Social Vulnerability Index (SoVI) is one of the most well-known methodologies. SoVI provides a critical basis for spatial analysis [9, 28]. Furthermore, the data were convenient to retrieve. The SoVI framework, developed by [16], uses place-based indicators to quantify and identify the most significant determinants of social vulnerability. Given a deep understanding of the nature and drivers of vulnerability, the SoVI has continuously evolved. It has been applied in a variety of geographical and social contexts, including Africa [36], Asia [37], the Caribbean Islands [38], and Latin America [39]. This approach can be used to identify the location of socially vulnerable groups [40]. However, the SoVI approach can only identify variations related to social vulnerability and cannot be used to find the root causes of social vulnerability [41]. Principal Component Analysis (PCA) is used to quantify social vulnerability. Only components with eigenvalues more than 1.0 were selected [28]. The selected component scores were

24 Social Vulnerability Assessment to Natural Hazards in East Malaysia

311

normalized using Eq. (24.1), with a mean of 0 and a standard deviation of 1 Z=

X −μ σ

(24.1)

Note: Z = Z-score or standardized score μ = mean x = variable σ = standard deviation The scores for each component were summed together with equal weights to create the final SoVI score. The score then was mapped across East Malaysia to reveal the spatial variation of social vulnerability.

24.3 Results and Discussions The KMO and Bartlett’s tests were conducted to assess the suitability of the data for PCA. As the KMO test is used to examine the strength of the partial correlation between variables, the value that is closer to 1.0 is considered ideal, and Table 24.2 shows the value of KMO is 0.817, which is ≥0.6 and is accepted [42]. Bartlett’s test of sphericity was used to test the null hypothesis. As a result, the p-value of 1.5 SD) to low vulnerable (< -1.5 SD). The two components were summed using vulnerability Eq. (24.2): SOVI = PC1 − PC2

(24.2)

The total score of SoVI (Fig. 24.2) reveals that the regions facing the highest vulnerability are concentrated in Sabah, specifically in Sandakan, Tawau, Kota Kinabalu, and Lahad Datu. On the other hand, the low vulnerable areas are located in Kuching, Miri, and Sibu. The factors that affect the SoVI scores for each district can be explained by the social sensitivity and adaptive capacity components towards natural disasters, as shown in Fig. 24.3 and Fig. 24.4. Comparison scores for sensitivity and adaptation capacity can conclude that higher sensitivity scores tend to elevate the SoVI scores. When the sensitivity analysis shows high values and the adaptive capacity has low values, the areas become highly vulnerable as their resilience towards natural disasters weakens Fig. 24.3 reveals that the scores of social sensitivities were high in all areas that exhibit high SoVI scores. The low adaptive capacity, particularly in Lahad Datu and Tawau, is influenced by the lower percentage of people getting access to resources and the high population of children under 14 years. The children tend to be exposed to the risk as they have low adaptation toward hazards. Nonetheless, the high value of adaptive capacity can also explain the low SOVI score because according to [44], the score of SoVI becomes lower when the adaptive capacity is higher, which can reduce recovery time. Figure 24.4 shows that the high adaptive capacity is located in all areas with low SoVI scores: Miri, Kuching, and

24 Social Vulnerability Assessment to Natural Hazards in East Malaysia

313

Fig. 24.2 Map of social vulnerability Index (SOVI) score of East Malaysia

Sibu. This has caused a reduction in vulnerability score despite having a high population density and a high population of people aged above 65. It is vital to access these resources, especially in this modern world, as information is spreading fast at our fingertips. On the other note, the effect of natural disasters not only affects populations with low income but also on populations with high income as their loss of property is much higher. However, the low-income population will greatly impact the time required for them to recover from a caused by natural disaster occurrence [11]. In addition, the district that has a higher population of elderly indicates the society is more prone to disaster as they require loss of support and emotional care. Non-citizen populations can be categorized as special needs populations because most are not fluent in Bahasa Melayu. This gives a great risk to them accessing resources, especially during natural disasters, because of the language barrier. Appropriate adaptation approaches should be targeted toward social group areas with high sensitivity values and low adaptive capacity [45].

314

N. S. M. Sabri and Z. H. Ash’aari

Fig. 24.3 Map of sensitivity analysis toward natural hazards in East Malaysia

Fig. 24.4 Map of adaptive capacity of society towards natural hazards in East Malaysia

24 Social Vulnerability Assessment to Natural Hazards in East Malaysia

315

24.4 Conclusion This study conducted a social vulnerability assessment of natural hazards in East Malaysia by utilizing sociodemographic data and Geopsatial analysis. The findings from this study reveal that population density, elderly population, and children population contributed to the high vulnerability of SOVI in a selected area. Besides, low-vulnerable areas mostly have full access to all resources. Social vulnerability measurement as a tool for disaster risk reduction continues to gain importance as people become increasingly exposed to risk from natural calamities. A better understanding of how social vulnerability indicators and indices correspond to real-world disaster outcomes could benefit both model development decisions and how social vulnerability indicators are perceived during decision-making and public policy creation processes. The SoVI model can assist state and local authorities during natural disasters. The time taken can be shortened to help people in need during the occurrence of disaster by the information of location and community from the model. The study’s limitations are acknowledged. First, the availability of sociodemographic data is limited because the sources are not completely updated, and not all variables can be analyzed. Second, important variables such as the disabled and unemployed populations, which have been cited in many earlier studies, were not included in this study due to a lack of data in that year. However, this study is one of a few studies in Malaysia that assess the social vulnerability of natural hazards in East Malaysia. It still can provide a reference for preparedness and mitigation the natural disaster, especially for social and economic sectors. Acknowledgements The author would like to acknowledge the Ministry of Higher Education Malaysia for the funding of this research under the Fundamental Research Grant Scheme (Grant reference code: FRGS/1/2019/WAB05/UPM/02/8) and the Department of Statistics Malaysia (DOSM) for the data.

References 1. De Haen H, Hemrich G (2007) The economics of natural disasters: implications and challenges for food security. Agric Econ 37:31–45 2. Huggel C, Raissig A, Rohrer M, Romero G, Diaz A, Salzmann N (2015) How useful and reliable are disaster databases in the context of climate and global change? A comparative case study analysis in Peru. Nat Hazard 15(3):475–485 3. Mohamed Shaluf I (2007) Disaster types. Disaster prevention and management. Int J 16(5):704– 717. https://doi.org/10.1108/09653560710837019 4. The World Bank Group and the Asian Development Bank (2021) Climate Risk Country Profile: Malaysia 5. Abdullahi MG (2014) Floods in Malaysia: historical reviews, causes, effects and mitigations approach. Int J Interdiscip Res Innov 2:59–65 6. Bong CHJ, Richard J (2019) Drought and climate change assessment using Standardized Precipitation Index (SPI) for Sarawak River Basin. J Water Climate Change 11(4):956–965

316

N. S. M. Sabri and Z. H. Ash’aari

7. Abid SK, Sulaiman N, Wei CS, Nazir U (2021) Flood vulnerability and resilience: exploring the factors that influence flooding in Sarawak. In: IOP conference series: earth and environmental science (Vol 802, No 1, p 012059). IOP Publishing 8. Payus C, Huey LA, Adnan F, Rimba AB, Mohan G, Chapagain SK, Order G, Gasparatos A, Fukushi K (2020) Impact of extreme drought climate on water security in north Borneo: case study of Sabah. Water 2020 12(4):1135. https://doi.org/10.3390/w12041135 9. Aksha SK, Juran L, Resler LM, Zhang Y (2018) An analysis of social vulnerability to natural hazards in Nepal using a modified social vulnerability index. Int J Disaster Risk Sci 10(1):103– 116. https://doi.org/10.1007/s13753-018-0192-7 10. Hilhorst T (2004) Mapping vulnerability: disasters, development, and people: Routledge 11. Nor Diana MI, Muhamad N, Taha MR, Osman A, Alam MM (2021) Social vulnerability assessment for landslide hazards in Malaysia: a systematic review study. Land 10(3):315. https://doi.org/10.3390/land10030315 12. Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CEE (2007) IPCC. Climate change :impacts, adaptation, and vulnerability; contribution of working group ii to the fourth assessment. Retrieved from Cambridge University Press: Cambridge, UK 13. Solangaarachchi D, Griffin AL, Doherty MD (2012) Social vulnerability in the context of bushfire risk at the urban-bush interface in Sydney: a case study of the Blue Mountains and Ku-ring-gai local council areas. Nat Hazards 64(2):1873–1898 14. Tate E (2013) Uncertainty analysis for a social vulnerability index. Ann Assoc Am Geogr 103(3):526–543 15. Adger WN (1999) Social vulnerability to climate change and extremes in coastal Vietnam. World Dev 27(2):249–269 16. Cutter SL, Boruff BJ, Shirley WL (2003) Social Vulnerability to Environmental Hazards*. Soc Sci Q 84(2):242–261 17. Rygel L, O’sullivan D, Yarnal B (2006) A method for constructing a social vulnerability index: an application to hurricane storm surges in a developed country. Mitig Adapt Strat Glob Change 11(3):741–764 18. Holand IS, Lujala P, Rød JK (2011) Social vulnerability assessment for Norway: a quantitative approach. Norsk Geografisk Tidsskrift-Norwegian J Geogr 65(1):1–17 19. Department of Statistics Malaysia (2020) My Local Stats: Malaysia, Sabah and Sarawak. Retrieved from https://newss.statistics.gov.my/newss-portalx/ep/epProductFreeDownloadSea rch.seam 20. Suparta W, Yatim ANM (2017) An analysis of heat wave trends using heat index in East Malaysia. J Phys Conf Ser 852:012005. https://doi.org/10.1088/1742-6596/852/1/012005 21. Sa’adi Z, Shahid S, Ismail T, Chung E-S, Wang X-J (2017) Distributional changes in rainfall and river flow in Sarawak, Malaysia. Asia-Pac J Atmos Sci 53(4):489–500 22. Rahman HA (2018) Climate change scenarios in Malaysia: engaging the public. Int J MalayNusantara Studies 1(2):55–77 23. Kumar D, Bhattacharjya RK (2020) Study of integrated social vulnerability index SoVI of Hilly region of Uttarakhand. India. Environ Climate Technol 24(1):105–122 24. Ashraf MA, Azad MAK (2015) Gender issues in disaster: understanding the relationships of vulnerability, preparedness and capacity. Environ Ecol Res 3(5):136–142. https://doi.org/10. 13189/eer.2015.030504 25. Pekovic V, Seff L, Rothman M (2007) Planning for and responding to special needs of elders in natural disasters. Generations 31(4):37–41 26. Hoeven A, Benn H, Brown M, Chino T, Johnston D, Kabbaj O, … Zhang S (2015) Poverty and climate change; reducing the vulnerability of the poor through adaptation 27. Hallegatte S, Vogt-Schilb A, Rozenberg J, Bangalore M, Beaudet C (2020) From poverty to disaster and back: a review of the literature. Econ Disasters Climate Change 4(1):223–247. https://doi.org/10.1007/s41885-020-00060-5 28. Mavhura E, Manyangadze T (2021) A comprehensive spatial analysis of social vulnerability to natural hazards in Zimbabwe: driving factors and policy implications. Int J Disaster Risk Reduct 56:102139

24 Social Vulnerability Assessment to Natural Hazards in East Malaysia

317

29. Kamal NIA, Ash’aari ZH, Abdullah AM, Kusin FM, Mohamat Yusuff F, Sharaai AH, … Mohd Ariffin NA (2021) Extreme heat vulnerability assessment in tropical region: a case study in Malaysia. Climate Development, 1–15 30. Dong J, Peng J, He X, Corcoran J, Qiu S, Wang X (2020) Heatwave-induced human health risk assessment in megacities based on heat stress-social vulnerability-human exposure framework. Landsc Urban Plan 203:103907 31. Méndez-Lázaro P, Muller-Karger FE, Otis D, McCarthy MJ, Rodríguez E (2018) A heat vulnerability index to improve urban public health management in San juan, Puerto Rico. Int J Biometeorol 62(5):709–722. https://doi.org/10.1007/s00484-017-1319-z 32. Azhar G, Saha S, Ganguly P, Mavalankar D, Madrigano J (2017) Heat wave vulnerability mapping for India. Int J Environ Res Public Health 14(4):357. https://doi.org/10.3390/ijerph 14040357 33. Zuhra SS, Tabinda AB, Yasar A (2019) Appraisal of the heat vulnerability index in Punjab: a case study of spatial pattern for exposure, sensitivity, and adaptive capacity in megacity Lahore, Pakistan. Int J Biometeorol 63(12):1669–1682. https://doi.org/10.1007/s00484-019-01784 34. Kim D-W, Kwon C, Kim J, Lee J-S (2020) Characteristics of heat waves from a disaster perspective. J Prev Med Public Health 53(1):26–28. https://doi.org/10.3961/jpmph.19.315 35. Zhang W, McManus P, Duncan E (2018) A raster-based subdividing indicator to map urban heat vulnerability: a case study in Sydney, Australia. Int J Environ Res Public Health 15(11):2516. https://doi.org/10.3390/ijerph15112516 36. Letsie MM, Grab SW (2015) Assessment of social vulnerability to natural hazards in the Mountain Kingdom of Lesotho. Mountain Res Dev 35(2):115–125 37. Zhou Y, Li N, Wu WX, Wu JD, Shi PJ (2014) Local spatial and temporal factors influencing population and societal vulnerability to natural disasters. Risk Anal 34(4):61 38. Boruff BJ, Cutter SL (2007) The environmental vulnerability of Caribbean Island nations. Geogr Rev 97(1):24–45 39. Hummell BMD, Cutter SL, Emrich CT (2016) Social vulnerability to natural hazards in Brazil. Int J Disaster Risk Sci 7(2):111–122 40. Fekete A (2009) Validation of a social vulnerability index in context to river-floods in Germany. Nat Hazard 9(2):393–403 41. Wood NJ, Burton CG, Cutter SL (2010) Community variations in social vulnerability to Cascadia-related tsunamis in the US Pacific Northwest. Nat Hazards 52(2):369–389 42. Nethery RC, Sandler DP, Zhao S, Engel LS, Kwok RK (2018) A joint spatial factor analysis model to accommodate data from misaligned areal units with application to Louisiana social vulnerability. Biostatistics 20(3):468–484. https://doi.org/10.1093/biostatistics/kxy016 43. Burton C, Rufat S, Tate E (2018) Social vulnerability: conceptual foundations and geospatial modeling, pp 53–81 44. Mortreux C, Barnett J (2017) Adapative capacity: exploring the research frontier. WIREs Climate Changehttps://doi.org/10.1002/wcc.467 45. Yu J, Castellani K, Forysinski K, Gustafson P, Lu J, Peterson E, … Brauer M (2021) Geospatial indicators of exposure, sensitivity, and adaptive capacity to assess neighbourhoodvariation in vulnerability to climate change-related health hazards. Environ Health 20(1):1–20

Chapter 25

Exploring the Significance of Resilience Qualities in the Context of the Middle East Built Environment Mohammed M. Al-Humaiqani and Sami G. Al-Ghamdi

Abstract Despite applying green urban development concepts and technologies to uphold legacies, the resilience of the urban built environment (UBE) remains a significant challenge. A robust system with high flexibility to climate change can withstand different climate variations with the lowest possible degradation of its performance indicators. An urban system could be designed to be responsive, absorptive, and adaptive to the potential shocks and stresses and utilized to meet particular needs that help rapidly restore the system’s functionality. This paper reviews the major impacts of climate change and investigates the resilience challenges to the cities in the Middle East (ME) from the perspective of resilience qualities. The findings of this study indicate that many cities in the ME are projected to suffer from severe climate change impacts. However, there is an argument that there is still room to develop the necessary risk management plans and invest in building resilient infrastructure and utilities. Considering the resilience qualities and strategies of the built environment components and urban infrastructure can result in robust, flexible, redundant, resourceful, inclusive, and integrated systems. The paper identifies directions of needed work to improve the adaptation of the built environment to climate change. Keywords Climate change impacts · Middle east · Resilience qualities · Urban built environment · Urban resilience

M. M. Al-Humaiqani · S. G. Al-Ghamdi Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar S. G. Al-Ghamdi (B) Environmental Science and Engineering Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia e-mail: [email protected] KAUST Climate and Livability Initiative, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_25

319

320

M. M. Al-Humaiqani and S. G. Al-Ghamdi

25.1 Introduction Urban areas are increasingly vulnerable to climate change impacts mainly due to direct exposure to the different climate hazards. The hazards are shaped and configured by several parameters, including urbanization and environmental degradation [1]. The common knowledge of weather and climate generally concentrates on the variables that directly impact daily life, including temperature, humidity, solar radiation, wind speed, and changes in precipitation patterns [2]. The climate change impacts could be slightly different from one region to another. In the ME, temperature and wet-bulb temperature increases, changes in precipitation patterns, flooding, storm surges, and sea-level rise (SLR) are considered critical impacts [3]. Climate change is a severe issue in the ME, specifically in the Gulf region, as it impacts the luxury and wealth of people. The temperature increases lead to more energy and water consumption [4, 5], marine life and land quality will be affected, biodiversity degradation will appear, and groundwater salinity will increase [6]. As per [7], with the growing water demand and water supply deterioration, most countries in the ME may suffer from severe water shortages in the near future. On the other hand, any impact on marine life will become a problem for the water desalination plants, significantly affecting many Gulf Cooperation Council countries (GCC) [8]. In response to the above discussion about the potential climate change shocks and stresses, cities should be managed to reduce the potential current and future hazards. They should also establish strategies to respond to the disaster and recover after being gone under the disaster [9]. This is, however, the definition of resilient cities that accommodate resilience approaches that make the city systems less prone to disturbances, enable quick responses to cope with the disturbance, and allow flexible responses to deal with such events [10]. The concept of disaster and climate change resilience is becoming more broadly known as one of the essential means that characterize communities, organizations, and individuals’ ability to recover after a natural or climatic disaster. Therefore, resilience in critical infrastructure, including electrical, renewable energy, water, and transportation, is of great importance [11, 12].

25.2 Materials and Methods This paper investigates the UBE and relevant climate change impacts in the ME region and highlights the urban resilience directions. It primarily focuses on identifying and determining the significance of incorporating resilience in planning and assessing the built environment.

25 Exploring the Significance of Resilience Qualities in the Context …

321

25.2.1 Research Method The data presented in this paper were sourced from the published research work, including peer-reviewed journals (as a primary source), international and government policy reports using the keywords given at the beginning of this paper, and relevant published articles. The paper highlights the research trends of climate change resilience performed in the ME region, specifically in the GCC countries.

25.2.2 Climate Change Impacts in the Middle East The increase of highly vulnerable urban systems and communities in many countries to a high risk of climate change is mainly due to rapid urbanization. For the informal settlement, the IPCC has emphasized the need to learn and build a more sustainable and resilient future to climate change to protect the population, urban systems, and at-risk groups [13]. The cities in the ME are also estimated to suffer from high warming of more than 4 K by 2100 [14]. The future projected temperatures in the GCC countries have been studied by Pal & Eltahir [15], concluding that extreme wet-bulb temperatures could exceed the critical threshold. Hence, with sea breeze circulation and business-as-usual GHG emissions, high wet-bulb temperatures will be witnessed in the GCC countries’ coastal cities, making them more vulnerable [3]. In addition to the temperature increase, floods due to heavy precipitation and SLR also critically impact the region. The annual precipitation has changed in many areas in the ME. For example, over the past few years, the heavy rains in Sudan caused destructive flooding, damaged homes and infrastructure, killed hundreds of people, and displaced thousands across the country [16, 17]. A dust storm is another hazard that could cause a major disaster, especially in arid and semi-arid regions. The study by Shi et al. [18]; showed that the drivers of dust emission trends are essential and must be understood. It also confirms that the prediction of dust storm evolutions under future climate change could be improved through the variation of such drivers under different warming periods, which eventually helps mitigate its impacts [19]. Furthermore, the interacting stresses due to the overuse of energy and water and longer growing seasons result in direct and indirect effects on the environment. Communities, businesses, and individuals must be protected from interacting stresses [20]. For example, heat stress should not be allowed to exacerbate so that it has less influence on the health and comfort of people. Figure 25.1 highlights the categorization of the main climate change impacts in the Middle East.

322

M. M. Al-Humaiqani and S. G. Al-Ghamdi

Fig. 25.1 Categorization of the main climate change impacts in the Middle East

25.2.3 Resilience in the Urban Built Environment (UBE) Resilience aims to improve a particular system’s coping, adaptive, and restorative capacities to withstand and fairly respond to the external changing condition and recover rapidly from the subject disruption. It has several definitions; most are related to the ability of the system to prepare for adverse impacts, absorb, resist, adapt, and rapidly recover from disruptive events [21]. In resilient cities, the impacts of shocks on a system are reduced, and the adaptive capacity is improved [2]. Incorporating resilience requirements into planning and assessment and ensuring the robustness of the delivered infrastructures in the face of climate change is essential [22]. Therefore, disaster management and emergency policies and strategies are needed to capture hazards’ dynamics, exposure, and vulnerability [23]. Figure 25.2 summarizes the most potential climate change impacts in the ME (a) [3, 24–26]. It also indicates the groups of the UBE systems that could undergo stress due to climate change impacts

25 Exploring the Significance of Resilience Qualities in the Context …

Climate Change Impacts Categories: •Extreme Weather Events • Environment •Water Scarcity & Quality •

UBE Systems: •Shelter Systems •Life Support Systems •Movement Systems •Open Space Systems

323

UBE Resilience : • •Robustness •Redundancy •Flexibility •Resourcefulness •Rapidity •Inclusion •

Fig. 25.2 Categorization of the a potential climate change impacts in the ME, b urban built environment (UBE) systems, c relevant UBE resilience qualities

(b) [27–30]. At the same time, it outlines the UBE resilience qualities as suggested by the city resilience index (c) [11, 31]. There are four common elements of resilience: context, disturbance type, and capacity to withstand and react to the disturbance. The capacity of a system to resist, absorb, respond, adapt, and restore from the effect of a climate change hazard in a timely and efficient manner is essential. It is commonly achieved by preserving and restoring the system and its functions. The resilience strategies should be defined to help prioritize the relevant climate change issues [32]. Overall, urban resilience has three generalizable elements: systems, agents, and institutions. Resilience has become crucial for cities facing potential climate change impacts [33, 34]. Urban resilience aims to improve cities’ capacity to function regardless of the encountered climatic stresses or shocks, enabling people and businesses to survive and thrive safely [35]. The urban system must be able to maintain or recover quickly from a disruptive event, adapt to change, and rapidly transform systems with limited adaptive capacity [33]. The concept of urban resilience qualities is still growing, requiring further study to develop plans and set policies to respond to climate and natural effects. For building a resilient city, the essential qualities must be ensured to enable a system to respond promptly and prevent the failure or breakdown that could occur due to external disruption. These qualities include reflectiveness, robustness, redundancy, flexibility, resourcefulness, rapidity, inclusiveness, and integration [11, 36, 37]. In addition, more qualities such as modularity and controllability may be explicitly included in industrial engineering [38].

324

M. M. Al-Humaiqani and S. G. Al-Ghamdi

25.3 Resilience to Sea-Level Rise (SLR): Selective Cities In the ME region, natural hazards continue to disturb built environment development. Flooding is considered the most significant disaster in the region, forming more than 50%, followed by earthquake disasters and land droughts [39, 40]. Such disasters and disturbances require more focus on the long-term operational demands of structures and assets to reduce vulnerability to such impacts. For instance, flood risks increase significantly on dried and hardened ground surfaces where rainwater infiltration is reduced [39]. Also, inadequate drainage and the destruction of ecosystems lead to more flood events [41, 42]. For example, in 2010, hundreds of Yemenis died and were displaced from low-lying flooding areas due to inadequate drainages [42]. Sea-level rise (SLR) is a risk to many countries in the region. Rising levels have been publicized as the major threat that will affect small islands, increase the scale of erosion, lead to land loss, and increase the intensity of storm surges [43]. The SLR predicted consequences would vary from one place to another based on the nature of the urban development, natural barriers, and coastal structures [44]. The sea level trends in the Arabian Sea continuously rise by 1–2 mm per year under RCP 2.6 and RCP 4.5 [43]. SLR can place airports at risk leading to direct economic losses [45]. In the Arabic Gulf, Kuwait’s coastal line is highly vulnerable to SLR. The Kuwait and Hawalli cities are the most severely impacted due to the residential sector’s economic losses and the number of affected people. In addition, at a 1 m SLR, beaches would be entirely eroded, and ecosystems would be damaged in the natural environment [44]. In the State of Qatar, critical urban areas are located along its coast, in which SLR due to global warming was pointed out by the Interim Coastal Development Guidelines as a threat and could cause areas inundation [46]. SLR is a major threat to mangrove ecosystems along the Qatari coastal line [47]. The inland flooding may cover 18% of Qatar’s land under a 5-m rise in sea level, adversely impacting the population and environment. In Egypt, the Nile Delta region is highly vulnerable to SLR [48]. Similarly, the assessment of the susceptibility of Alexandria city to SLR showed that there would be severe consequences in the case of a tsunami event, and they cannot be stopped. The middle and east of the city would be submerged in water, but it is expected that natural and artificial barriers would restrain the flooding [49]. The western part is expected to be minimally stressed due to the existence of the barriers. As a result, the inhabitants and economy of the city will be influenced by many sectors. Similarly, more than 60% of the Nile Delta urban areas are vulnerable to SLR inundation. The scale of areas and infrastructure, environmental quality, and socioeconomic characteristics influence the resilience of the urban areas. Accordingly, the areas showed low resilience under three SLR scenarios [50]. Table 25.1 summarizes a sample of the effects caused by SLR on land, people, and economy in discussed selective cities. It outlines the different aspects of SLR, including submerged levels, people at risk, economic losses, impacts, magnitude, and main findings.

25 Exploring the Significance of Resilience Qualities in the Context …

325

Table 25.1 Sample of the effects of SLR on land, people, and economy in sample cities in the ME City

Sea-Level Rise (SLR) Aspect

Kuwait—Hawalii

Sea Level Rise [m] 0.5 1 1.5 2

Egypt—Alexandria City part

Main findings

References

Submerged People Economic The [44] area [km2 ] at risk losses governmental [x10k] [$M] authorities must consider the long-term 5 60 2700 consequences of 7.5 84 3900 SLR to develop a proactive 9 135 6200 adaptation 12 180 8400 strategy

The research [49] emphasizes that the Eastern [low & medium]: Natural and decision-makers Part artificial defense lines exist must raise Middle [low]: Slight impact if hit by sea awareness, Part flooding implement [medium]: Population relevant displacement management plans and Western [low & medium]: Tiny stress regulations, and part due to the existence of sand be ready for dunes emergency City as [medium]: Population evacuations a whole displacement [high]: Tsunami will cause loss of lands

Qatar

[magnitude]: Impact

More than 85% of all coastal agricultural and cropland in Qatar land is prone to inundation due to SLR. Overall, 75% of Qatar’s coastal wetland is projected to be affected by storm surges and a 30% increase in the storm surge zone

[51]

The strategies that reduce disaster risks and losses and make the built environment resilient to climate change impacts are significant for a safe future. So understanding the multi-hazard risks of increasing resilience strategies is crucial [52]. The study by Mastroianni et al. emphasized the need for a proactive disaster policy, asset management policy, risk management effectiveness, and asset management practices planning [39]. For example, integrating the experts’ knowledge through applying the appropriate techniques would help obtain the trade-offs among indicators [53]. Also, collaboration and exchange among diverse management strategies contribute to disturbance impact reduction [54]. Also, learning, self-organization, robustness, capacity, coherence, flexibility, efficiency, and resourcefulness are essential resilience dimensions [55]. In addition, the characteristics, including robustness, redundancy, resourcefulness, and rapidity are the main characteristics of urban system resilience [56–59]. According to the conceptual framework defined by Bruneau et al., four dimensions are considered; organizational, technical, economic,

326

M. M. Al-Humaiqani and S. G. Al-Ghamdi

and social [56] based on three quantifiable objectives: reducing failure’s probability, reducing consequences, and reducing the recovery time [60]. In the ME region, mitigation strategies might be insufficient and necessary to be cost-effective. Subsequently, the alternatives are limited; therefore, improving the absorptive and adaptation capacities is feasible to increase the resilience of the built environment against climate change impacts including SLR.

25.4 Conclusion The resilience of the UBE against climate change impacts is crucial. The results from this study show that many countries in the ME lack the strategies to build environmental resilience, such as community and country resilience strategies, risk and resilience governance, risk and safety plans, disasters and crises management plans, and resilience frameworks. The research emphasizes that communities and households should be able to transform living standards in the face of climate change shocks and stresses. The cities have to adopt resilience strategies to withstand such climate change stresses. In addition to the optimal engineering design, the strategy would include adopting a robust approach to projected risks and potential shocks and stresses by incorporating different resilience qualities and indicators. Understanding climate change effects on the UBE helps develop the appropriate strategies to combat recent and future climate-based risks. The resulting options would incorporate more flexibility, robustness, and redundancy into the design and bring elements and ideas into concrete actions. It will also allow for accepting the inherent uncertainty and emerging evidence to feed current standards and regulations and imply the ability to rapidly find alternatives to achieve goals and meet needs during the shock. Furthermore, identifying the gaps in the existing systems and relevant future challenges is also essential. The identification will feed the planning for transforming into a resilient environment and help improve preparedness and the risk communication strategy, warning and evacuation systems, and recovery planning. Ultimately, there is a lack of studies estimating the resilience qualities in the ME, specifically in the hot countries under projected future climate conditions. Acknowledgements The authors have no acknowledgments to state. Funding This publication was made possible by the National Priorities Research Program (NPRP) grant (NPRP12S-0212-190073) from the Qatar National Research Fund (QNRF), a member of the Qatar Foundation (QF). In addition, this research was supported by a scholarship from Hamad Bin Khalifa University (HBKU), a member of the Qatar Foundation (QF). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of QNRF, HBKU or QF.

25 Exploring the Significance of Resilience Qualities in the Context …

327

References 1. Campbell-Lendrum D, Corvalán C (2007) Climate change and developing-country cities: implications for environmental health and equity. J Urban Heal 84(SUPPL. 1):109–117. https://doi. org/10.1007/s11524-007-9170-x 2. Bazrkar MH, Zamani N, Eslamian S, Eslamian A, Dehghan Z (2015) Urbanization and climate change—handbook of climate change adaptation. Handb Clim Chang Adapt, pp 619–655. Accessed 10 July 2021. [Online]. Available: https://doi.org/10.1007/978-3-642-38670-1_90 3. Salimi M, Al-Ghamdi SG (2020) Climate change impacts on critical urban infrastructure and urban resiliency strategies for the Middle East. Sustain Cities Soc 54:101948. https://doi.org/ 10.1016/j.scs.2019.101948 4. Andri´c I, Koc M, Al-Ghamdi SG (2019) A review of climate change implications for built environment: Impacts, mitigation measures and associated challenges in developed and developing countries. J Clean Prod 211:83–102. https://doi.org/10.1016/j.jclepro.2018.11.128 5. Andric I, Al-Ghamdi SG (2020) Climate change implications for environmental performance of residential building energy use: the case of Qatar. Energy Rep 6:587–592. https://doi.org/ 10.1016/j.egyr.2019.09.030 6. Fragaszy SR et al (2020) Drought monitoring in the Middle East and North Africa (MENA) region: participatory engagement to inform early warning systems. Bull Am Meteorol Soc 101(7):E1148–E1173. https://doi.org/10.1175/BAMS-D-18-0084.1 7. Djoundourian SS (2021) Response of the Arab world to climate change challenges and the Paris agreement. Int Environ Agreements Polit Law Econ, 0123456789. https://doi.org/10.1007/s10 784-021-09524-9 8. Salimi M, Al-Ghamdi SG (2019) Climate change impacts on critical urban infrastructure and urban resiliency strategies for the Middle East. Sustain Cities Soc, 101948. https://doi.org/10. 1016/J.SCS.2019.101948 9. Wamsler C, Brink E, Rivera C (2013) Planning for climate change in urban areas: from theory to practice. J Clean Prod 50:68–81. https://doi.org/10.1016/j.jclepro.2012.12.008 10. Wardekker JA, de Jong A, Knoop JM, van der Sluijs JP (2010) Operationalising a resilience approach to adapting an urban delta to uncertain climate changes. Technol Forecast Soc Change 77(6):987–998. https://doi.org/10.1016/j.techfore.2009.11.005 11. Al-Humaiqani MM, Al-Ghamdi SG (2022) The built environment resilience qualities to climate change impact: concepts, frameworks, and directions for future research. Sustain Cities Soc 80:103797. https://doi.org/10.1016/j.scs.2022.103797 12. Al-Humaiqani MM, Al-Ghamdi SG (2023) Assessing the built environment’s reflectivity, flexibility, resourcefulness, and rapidity resilience qualities against climate change impacts from the perspective of different stakeholders. Sustainability 15:5055. https://doi.org/10.3390/su1 5065055 13. O’Brien K, Pelling M, Patwardhan A, Hallegatte S, Maskrey A, Oki T, Oswald-Spring U, Wilbanks T, Yanda PZ (2015) Toward a sustainable and resilient future. https://www.ipcc.ch/ report/managing-the-risks-of-extreme-events-and-disasters-to-advance-climate-change-ada ptation/toward-a-sustainable-and-resilient-future/. Accessed 10 July 2021 14. Zhao L et al (2021) Global multi-model projections of local urban climates. Nat Clim Chang 11(2):152–157. https://doi.org/10.1038/s41558-020-00958-8 15. Pal JS, Eltahir EAB (2016) Future temperature in southwest Asia projected to exceed a threshold for human adaptability. Nat Clim Chang 6(2):197–200. https://doi.org/10.1038/nclimate2833 16. United Nations (2022) Dire impact from floods in South Sudan as new wet season looms. https://news.un.org/en/story/2022/03/1114992. Accessed 8 September 2022 17. IFRC (2021) Sudan: 2020 floods. https://www.ifrc.org/emergency/sudan-2020-floods. Accessed 8 September 2022 18. Shi Y, Zhai G, Xu L, Zhou S, Lu Y, Liu H, Huang W (2021) Assessment methods of urban system resilience: from the perspective of complex adaptive system theory. Cities 112103141. https://doi.org/10.1016/j.cities.2021.103141

328

M. M. Al-Humaiqani and S. G. Al-Ghamdi

19. Shi L, Zhang J, Yao F, Zhang D, Guo H (2021) Drivers to dust emissions over dust belt from 1980 to 2018 and their variation in two global warming phases. Sci Total Environ 767:144860. https://doi.org/10.1016/j.scitotenv.2020.144860 20. Zevenbergen C, Gersonius B, Radhakrishan M (2020) Flood resilience. Philos Trans Royal Soc A: Math Phys Eng Sci 378(2168):20190212. https://doi.org/10.1098/rsta.2019.0212 21. Mcallister T (2013) Developing guidelines and standards for disaster resielince of the built environment: a research needs assessment. Natl Inst Stand Technol Publ, pp 1–142, [Online]. Available: https://nvlpubs.nist.gov/nistpubs/TechnicalNotes/NIST.TN.1795.pdf 22. Kanno Y, Ben-Haim Y (2011) Redundancy and robustness, or when is redundancy redundant? J Struct Eng 137(9):935–945. https://doi.org/10.1061/(asce)st.1943-541x.0000416 23. Etinay N, Egbu C, Murray V (2018) Building urban resilience for disaster risk management and disaster risk reduction. Procedia Eng 212(2017):575–582. https://doi.org/10.1016/j.pro eng.2018.01.074 24. Raouf M (2008) Climate change threats, opportunities, and the GCC countries. Middle East Institute. https://www.mei.edu/publications/climate-change-threats-opportunities-andgcc-countries. Accessed 31 May 2021 25. Laurent CD, Lambert A (2019) Climate change, sea level rise, and sustainable Urban adaptation in Arab coastal cities. Middle East Institute. https://www.mei.edu/publications/climate-changesea-level-rise-and-sustainable-urban-adaptation-arab-coastal-cities#_ftn17. Accessed 31 May 2021 26. US EPA (2021) Climate change indicators: weather and climate. https://www.epa.gov/climateindicators/weather-climate. Accessed 31 May 2021 27. Maxwell KB, Julius SH, Grambsch AE, Kosmal AR, Larson E, Sonti N (2018) Chapter 11 : built environment, urban systems, and cities. impacts, risks, and adaptation in the United States: the fourth national climate assessment, Volume II. Washington, DC. https://doi.org/10. 7930/NCA4.2018.CH11 28. Designing Buildings Wiki (2020) Elements of the built environment. https://www.designing buildings.co.uk/wiki/Elements_of_the_built_environment. Accessed 31 May 2021 29. Hoornweg D (2012) The Utilities of Cities. World bank. https://blogs.worldbank.org/sustainab lecities/the-utilities-of-cities. Accessed 31 May 2021 30. JICA (2008) 7 7.1 Urban Infrastructure and Utilities Plan 31. Arup (2014) Understanding the city resilience index. https://www.arup.com/projects/city-res ilience-index. Accessed 31 May 2021 32. Asian Development Bank (2014) Urban climate change resilience: a synopsis 33. Meerow S, Newell JP, Stults M (2016) Defining urban resilience: a review. Landsc Urban Plan 147:38–49. https://doi.org/10.1016/j.landurbplan.2015.11.011 34. Tahir F et al. (2021) Qatar climate change conference 2021: a platform for addressing key climate change topics facing Qatar and the world. In: Qatar climate change conference 2021, Sep. 2021, p 44. https://doi.org/10.5339/conf_proceed_qccc2021 35. ADB (2014) Urban climate change resilience: a synopsis. Asian Development Bank, p 28, 2014. [Online]. Available: http://www.adb.org/sites/default/files/publication/149164/urban-cli mate-change-resilience-synopsis.pdf 36. Silva B, Morera J (2014) City resilience framework. Rockefeller foundation & ARUP. [Online]. Available: http://publications.arup.com/Publications/C/City_Resilience_Framework.aspx 37. The Prairie Climate Centre (2017) Building a climate-resilient city: the built environment. [Online]. Available: https://prairieclimatecentre.ca/publications/. Accessed 6 March 2022 38. El-Halwagi MM, Sengupta D, Pistikopoulos EN, Sammons J, Eljack F, Kazi M-K (2020) Disaster-resilient design of manufacturing facilities through process integration: principal strategies, perspectives, and research challenges. Front. Sustain. 1:1–17. https://doi.org/10. 3389/frsus.2020.595961 39. Mastroianni E, Lancaster J, Korkmann B, Opdyke A, Beitelmal W (2021) Mitigating infrastructure disaster losses through asset management practices in the Middle East and North Africa region. Int J Disaster Risk Reduct 53:102011. https://doi.org/10.1016/j.ijdrr.2020.102011

25 Exploring the Significance of Resilience Qualities in the Context …

329

40. Below R, Grover-Kopec E, Dilley M (2007) Documenting drought-related disasters. J Environ Dev 16(3):328–344. https://doi.org/10.1177/1070496507306222 41. Sharifi F, Samadi SZ, Wilson CAME (2012) Causes and consequences of recent floods in the Golestan catchments and Caspian Sea regions of Iran. Nat Hazards 61(2):533–550. https://doi. org/10.1007/s11069-011-9934-1 42. Banerjee A, Bhavnani R, Burtonboy CH, Hamad O, Linares-Rivas Barandiaran A, Safaie S, Tewari D, Zanon A (2014) Natural disasters in the Middle East and North Africa : a regional overview (English). [Online]. Available: http://documents.worldbank.org/curated/en/211811 468106752534/Natural-disasters-in-the-Middle-East-and-North-Africa-a-regional-overview 43. Jennath A, Krishnan A, Paul SK, Bhaskaran PK (2021) Climate projections of sea level rise and associated coastal inundation in atoll islands: Case of Lakshadweep Islands in the Arabian Sea. Reg Stud Mar Sci 44:101793. https://doi.org/10.1016/j.rsma.2021.101793 44. Al-Mutairi N, Alsahli M, El-Gammal M, Ibrahim M, Samra RA (2021) Environmental and economic impacts of rising sea levels: a case study in Kuwait’s coastal zone. Ocean Coast Manag 205:105572. https://doi.org/10.1016/j.ocecoaman.2021.105572 45. Yesudian AN, Dawson RJ (2021) Global analysis of sea level rise risk to airports. Clim Risk Manag 31:100266. https://doi.org/10.1016/j.crm.2020.100266 46. MMUP (2013) Interim coastal development guidelines. Ministry of Municipality and Urban Palnning, p. 85, 2013. [Online]. Available: https://www.mme.gov.qa/QatarMasterPlan/Downlo ads-qnmp/Regulation/English/Group3-Guideline/036_Interim Coastal Development Guidelines August 2017.pdf 47. Shehadi MA (2015) Vulnerability of mangroves to sea level rise in Qatar: assessment and identification of vulnerable mangroves areas. Clim Chang 48. El Raey ME (2016) Impact of sea level rise on the Arab region impact of sea level rise on the Arab region Mohamed El Raey University of Alexandria Regional Center for Disaster Risk Reduction 49. Abou-Mahmoud MME (2021) Assessing coastal susceptibility to sea-level rise in Alexandria, Egypt. Egypt J Aquat Res 47(2):133–141. https://doi.org/10.1016/j.ejar.2021.04.002 50. Abdrabo MA, Hassaan MA (2015) An integrated framework for urban resilience to climate change—case study: sea level rise impacts on the Nile delta coastal urban areas. Urban Clim 14:554–565. https://doi.org/10.1016/j.uclim.2015.09.005 51. Wheeler D, Dasgupta S, Laplante B, Murray S (2009) Sea-level rise and storm surges: a comparative analysis of impacts in developing countries. World, pp 1–43 52. Dabbeek J, Silva V, Galasso C, Smith A (2020) Probabilistic earthquake and flood loss assessment in the Middle East. Int J Disaster Risk Reduct 49:101662. https://doi.org/10.1016/j.ijdrr. 2020.101662 53. Moghadas M et al (2019) A multi-criteria approach for assessing urban flood resilience in Tehran, Iran. Int J Disaster Risk Reduct 35101069. https://doi.org/10.1016/j.ijdrr.2019.101069 54. Wardekker JA, Wildschut D, Stemberger S, van der Sluijs JP (2016) Screening regional management options for their impact on climate resilience: an approach and case study in the VenenVechtstreek wetlands in the Netherlands. SpringerPlus 5(1). https://doi.org/10.1186/s40064016-2408-x 55. Dastjerdi S et al (2021) A conceptual framework for resilient place assessment based on spatial resilience approach: an integrative review. Urban Clim 36100794. https://doi.org/10.1016/j. uclim.2021.100794 56. Bruneau M, Chang SE, Eguchi RT, Lee GC, O’Rourke TD, Reinhorn AM, Shinozuka M, Tierney K, Wallace WA, von Winterfeldt D (2003) A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq Spectra 19(4):733–752. https://doi.org/ 10.1193/1.1623497 57. Curt C, Tacnet JM (2018) Resilience of critical infrastructures: review and analysis of current approaches. Abs Risk Anal 38(11):2441–2458. https://doi.org/10.1111/risa.2018.38.issue-11, https://doi.org/10.1111/risa.13166 58. Zobel CW (2011) Representing perceived tradeoffs in defining disaster resilience. Decis Support Syst 50(2):394–403. https://doi.org/10.1016/j.dss.2010.10.001

330

M. M. Al-Humaiqani and S. G. Al-Ghamdi

59. Chang SE, Shinozuka M (2004) Measuring improvements in the disaster resilience of communities. Earthq Spectra 20(3):739–755. https://doi.org/10.1193/1.1775796 60. Tamvakis P, Xenidis Y (2013) Comparative evaluation of resilience quantification methods for infrastructure systems. Procedia Soc Behav Sci 74339–74348. https://doi.org/10.1016/j.sbs pro.2013.03.030

Part VI

Environmental Remote Sensing and Land Cover Change Monitoring

Chapter 26

Vegetation Coverage Assessment for Smart Cities Based on the Sentinel Remote Sensing Data: The Case of Zhejiang Province (China) Zhaoyu Wang

Abstract As China’s urbanization development gradually enters a period of transition, the issue of ecological and environmental management is receiving increasing attention. As a model of urbanization and development transformation in recent years, Zhejiang Province has received much attention for its ecological civilization. This paper takes Zhejiang Province as the research object, and observes the change trend of land cover classification in Zhejiang Province from 2015 to 2019 through the classification method of remote sensing statistics based on the Sentinel satellite, then assesses the development of an ecological environment in the construction of smart city in Zhejiang Province. The results show a significant increase in the area of herbaceous wetlands (+118.2%) in the study area, with some herbaceous cover restoring wetland morphology, in line with government mapping. Overall, remote sensing satellite data is effective as a means of observing the ecological environment in the smart city sector and could be useful in future applications. Keywords Smart city assessment · Remote sensing · Vegetation cover

26.1 Introduction Smart cities are cities that operate in an intelligent and sustainable manner. To improve the quality of life of citizens by using various information technologies or innovative concepts to connect and integrate urban systems and services in order to enhance the efficiency of the use of resources and optimize urban management and services [1]. Due to the extensive nature of urban construction and the complexity of land information elements in China, smart cities as an effective means of urban observation, Z. Wang (B) The University of Sheffield, Sheffield S10 2TN, UK e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_26

333

334

Z. Wang

governance and reflection will become an inevitable trend to promote high-quality urban development. As China’s urbanization rate rises from 17.9% in 1978 to 63.89% in 2020 [2], urban development is shifting from growth-oriented to sustainable. The focus of urbanization has gradually shifted to ecological solutions to the environmental problems of radical urbanization, including atmospheric problems [3], water exploitation [4], land use [5], carbon emissions [6] and other vital problems. These problems are to a certain extent an obstacle to high-quality urbanization [7], so realtime monitoring of the basic ecological situation is an important part of the initial construction of a smart city, especially the green vegetation of the ecological environment, which is closely related to human survival, i.e. urban green spaces. Urban green space is an important environmental factor in the sustainable development of cities. A scientific and reasonable urban green space landscape layout can enhance the management of urban green space systems and guide the development of urban land-use layout toward a healthy and comfortable living environment. The key to assessing and solving the above problems is to obtain basic feature information. Mapping and detecting urban dynamics on a large scale is very laborintensive and time-consuming due to the numerous work constraints imposed by the environment. Some smart tools, such as remote sensing, are becoming important tools for describing and classifying information about features on a large scale. Remote sensing technology has the facility to acquire information over large areas quickly, repeatedly and dynamically, providing technical support and accuracy for large-scale, real-time, cyclical ecological monitoring. At present, satellite remote sensing technology is developing in the direction of high precision and multi-spectrum. Remote sensing applications are gradually changing from single data to multi-temporal multisource fusion and from static analysis to dynamic monitoring [8]. The above features are adapted to the needs of ecological monitoring. With innovations in applications in recent decades, a large number of research and application scenarios have been realized, providing important support for ecological protection, including natural disaster monitoring [9], atmospheric monitoring [10], urban expansion [11] and urban green space surveys [12]. The Yangtze River Delta, one of the most economically dynamic and technologically innovative regions in China, has a clear requirement to collectively protect the ecological environment in the Yangtze River Delta Regional Comprehensive Development Outline released in 2019. The relevant government departments at all levels have incorporated environmental management into planning outlines at different levels and implemented them in accordance with the rules and regulations, and have now achieved considerable results. A relevant study [13] was completed by establishing a digital-based urbanization and ecological environment indicator system, as well as extracting vegetation cover and heat island intensity indicators from remote sensing images to complete a comprehensive evaluation, and then analyzing the degree of coordination between the two and the barriers. Most of the existing studies

26 Vegetation Coverage Assessment for Smart Cities Based on the Sentinel …

335

related to the construction of ecological areas in smart cities are based on statistical data. However, there is a relative lack of large-scale ecological and environmental monitoring based on the integration of feature classification and land cover in remote sensing technology, which could provide richer and more updated information to support relevant research and be used as a database for future smart city construction. Feature classification and fraction of vegetation cover are two branches of application based on remote sensing impact classification techniques. Feature classification refers to the application of remote sensing satellites to capture feature information and then classify it by analyzing its spectral characteristics in order to identify features [14]. For example, Global Land Aquatic Coverage [15] (GLAC) can monitor surface life types (trees, shrubs or wetland plants). Regular monitoring and analysis of feature classification and surface vegetation cover provides a comprehensive understanding of urbanization and ecological changes, which is important for building smart cities and sustainable development research. This paper uses the ecological development of Zhejiang Province from 2015 to 2019 as a case study, to argue the significance of land cover type and the percentage of greenery for ecological monitoring. By extracting feature classification information and changes in the surface vegetation cover from Sentinel remote sensing satellite data and analyzing the changes in the quantity and spatial distribution of four types of surface cover––permanent water bodies, aquatic vegetation, herbaceous vegetation, and shrubs––the comprehensive progress of smart ecological construction in the city is assessed.

26.2 Study Area The study area is Zhejiang Province, China, with a total area of 101,800 km2 and a population of 64.68 million [2]. Zhejiang Province is located in the Yangtze River Delta Plain on the eastern coast of China, between 118° and 123° East longitude and 27°12' and 31°30' North latitude, with typical subtropical monsoon climate characteristics. The surface cover, apart from urban built-up land, is dominated by forests and agricultural land, with a very small amount of wetland vegetation, shrubs, and herbaceous plants (Fig. 26.1). The topography of the province is predominantly hilly, with undulations inland to the southwest and down towards the coast to the northeast (Fig. 26.2), and the proportion of mountains, plains, and water bodies is approximately 70%, 20%, and 10%. Most of the land in Zhejiang Province is mountainous. The water bodies account for the least 10% and the other is the plain where the city was built and developed.

336

Z. Wang

Fig. 26.1 The geomorphology and regional areas of the Zhejiang Province

26.3 Method 26.3.1 Land Cover Classification of Remote Sensing Images Remote sensing is a technology that receives information from electromagnetic radiation emitted or reflected from an object by means of a detector at a certain distance, without direct contact with the object to be measured, and processes, classifies, and identifies it. Remote sensing image classification is a form of application of remote sensing technology, and in this paper, the application is land cover classification. A land cover map is a classification of remote sensing images that illustrates the different types of physical cover on the Earth’s surface. These images assist in identifying different types of land, such as forests, vegetation, agricultural land, water bodies, and wetlands. The dynamic land cover map demonstrates the process of land cover type variation through history. Classification and coverage are recorded as changes in a 3-year cycle to ensure continuity and consistency across different epochal years. The processing and time series detection included which are shown below: (1) Geometric correction, atmospheric correction, and specific pre-processing were performed on the Sentinel-2 satellite with a tiled grid in 110*110 km units. (2) When cleaning data, use specific sensor status masks and outlier detection techniques. (3) Calculate the density indicator of input data. (4) Fuse the input data using Kalman filtering between 5/day 100 m and 1/day 300 m.

26 Vegetation Coverage Assessment for Smart Cities Based on the Sentinel …

337

Fig. 26.2 The topography of the Zhejiang Province

(5) Extracting the necessary reference index to increase accuracy: base reflectance, time-series harmonics, vegetation indicators, descriptive over the 3-year epoch period. (6) Prepare a training database containing about 168,000 points to calibrate the classification. The database can be acquired from Geo-WIKI crowd-sourcing. (7) Use external data to accurately calibrate the feature classification. Including but not limited to shoreline masking, ecological regionalization, urban cover, permanent and seasonal water cover, arctic vegetation, weather, and topography et cetera [16].

26.3.2 FCOVER (Green Vegetation Coverage) Fraction of vegetation cover (FCover) represents the portion of the land surface covered by green vegetation. This value quantifies the spatial extent of vegetation

338

Z. Wang

more precisely, as it is not correlated with light direction and is more sensitive to the amount of vegetation. This makes FCover a better alternative to the classical vegetation index for monitoring ecosystems. 300 m resolution images are obtained with FCOVER by the following processing. (https://land.copernicus.eu/global/products/fcover) instantaneous Top-ofCanopy reflectance from Sentinel-3 OLCI (v1.1 products), or daily Top-of-Aerosol input reflectance from PROBA-V (v1.0). Temporal smoothing and small gap filling are applied to the instantaneous LAI estimates, discriminating Evergreen Broadleaf Forest (EBF) and no-EBF pixels.

26.3.3 Data Land cover and land cover change Data were downloaded from Copernicus Global Land Service (https://land.copernicus.eu/global/products/lc). This paper uses the average statistics recorded by the Sentinel-2 satellite from 2015 to 2019. The data used for the study consisted mainly of remote-sensing images and statistical data. To ensure validity and comparability of the satellite data extraction results, images and statistics are subjected to data filtering, geometric and atmospheric calibration, image stitching, and other processes on the platform. The statistics are used to show the proportion of each type of land cover in the smart city system and its changes, so as to assess the development of urban greening in the course of Zhejiang’s smart city construction. FCOVER Data were downloaded from Copernicus Global Land Service(https:// land.copernicus.eu/global/products/lc). This data temporal compositing is adapted to provide a near-real-time (10-daily) estimate and successive updated estimates until a consolidated value is reached after about 2 months. In order to correspond to the land cover years, the study used data from 2015 to 2019, and downloaded data with a spatial resolution of 333 m, which was increased to 90 m using XX interpolation during the mapping process. The Sentinel-3 OLCI mainly focuses on green vegetation and has the advantages of short delay and wider coverage, and can provide data support for urban greening assessment, which is a good data assistance. (https://land.copernicus.vgt.vito.be/PDF/portal/Application.html#). FCOVER is a complementary or detailed study of the landcover classification in order to further determine the fragmentation and distribution of green vegetation, which is an important indicator and basis for the rational spatial layout of urban greenery.

26 Vegetation Coverage Assessment for Smart Cities Based on the Sentinel …

339

26.4 Result and Analysis 26.4.1 Annual Result and Analysis Vegetation cover and green vegetation index and analysis in 2015. The vegetation classification and fragmentation of green vegetation in Zhejiang Province in 2015 are shown in Figs. 26.3 and 26.4. As shown in the figure, the overall greenland area in Zhejiang Province in 2015 was 3526.72 km2 , of which the predominant permanent water accounted for 58.72%. The area of herbaceous vegetation was 594.62 km2 , slightly larger than the area of herbaceous wetland (563.87 km2 ). Shrubland accounted for the smallest proportion, only 8.43%. The overall fragmentation and layout distribution of greenery in mountainous areas accounted for a larger proportion, while the fragmentation and layout distribution of greenery in corresponding urban areas were serious, scattered, and accounted for a smaller proportion. Vegetation cover and green vegetation index and analysis in 2016. The vegetation classification and fragmentation of green vegetation in Zhejiang Province in 2016 are shown in Figs. 26.5 and 26.6.

Fig. 26.3 Land cover classification in 2015 In Zhejiang Province. The green part represents vegetation

340

Z. Wang

Fig. 26.4 FCOVER in 2015 in Zhejiang Province

Overall green space in Zhejiang Province rose to 3,711.25 km2 , in 2016, with permanent water dominating it (56.63%). Among the other vegetation surface cover types, herbaceous wetland area almost doubled to 861.18 km2 , significantly overtaking herbaceous vegetation as the dominant type in the vegetation category. The area of shrubland, on the other hand, decreased by a small margin and remained almost unchanged. The overall fragmentation and layout distribution remained almost unchanged. Vegetation cover and green vegetation index and analysis in 2017. The vegetation classification and fragmentation of green vegetation in Zhejiang Province in 2017 are shown in Figs. 26.7 and 26.8. In 2017, the overall green space in Zhejiang Province rose to 3,813.77 km2 , with the predominant type of permanent water showing little change compared to last year. Among the other vegetation-based surface cover types, herbaceous wetland area increased steadily, showing the largest increase in recent years (+164.03 km2 ). On the other hand, herbaceous vegetation and shrubland declined by a similar magnitude, by 51.26 and 20.50 km2 , respectively, while overall fragmentation and distribution remained almost unchanged.

26 Vegetation Coverage Assessment for Smart Cities Based on the Sentinel …

341

Fig. 26.5 Land cover classification in 2016 in Zhejiang Province. The green part represents vegetation

Fig. 26.6 FCOVER in 2016 in Zhejiang Province

342

Z. Wang

Fig. 26.7 Land cover classification in 2016 in Zhejiang Province. The green part represents vegetation

Fig. 26.8 FCOVER in 2017 in Zhejiang Province

26 Vegetation Coverage Assessment for Smart Cities Based on the Sentinel …

343

Vegetation cover and green vegetation index and analysis in 2018 The vegetation classification and fragmentation of green vegetation in Zhejiang Province in 2018 are shown in Figs. 26.9 and 26.10. Figures 26.9 and 26.10 show that in 2018, the overall green area of Zhejiang Province was 3915.55 km2 , of which the dominant permanent water showed a negative growth trend for the first time, decreasing to 2081.17 km2 . Among the other vegetation types, herbaceous wetlands maintained a significant growth trend of 1158.49 km2 . The area of herbaceous vegetation decreased steadily to 420.34 km2 , while the area of shrubland showed a small increase to 255.55 km2 . The overall fragmentation and layout distribution remained almost unchanged. Vegetation cover and green vegetation index and analysis in 2019 The vegetation classification and fragmentation of green vegetation in Zhejiang Province in 2019 are shown in Figs. 26.11 and 26.12. Figures 26.10 and 26.11 show that in 2019, the area of urban green space in Zhejiang Province reached 3967.54 km2 , of which permanent water occupies more than half of the area, reaching 2122.17 km2 . Other plant-based surface cover types occupy the other half. The area of herbaceous wetland increased to 1,230.25 km2 , accounting for nearly two-thirds (31%) of the remaining area, showing a dominant position in the vegetation surface cover. herbaceous vegetation and shrubland continued to decrease in area. Overall fragmentation and layout distribution remained almost unchanged.

26.4.2 Annual Trend Analysis The study of urban development requires a comparative analysis of changes in individual features over time, and the changes in shrubland, herbaceous vegetation, and herbaceous wetlands of interest in this study are shown in Fig. 26.13. On the time scale, herbaceous wetlands showed a steady upward trend, increasing from 0.55 to 1.20% between 2015 and 2019. Herbaceous vegetation and shrubland, on the other hand, showed a decreasing trend, with herbaceous vegetation decreasing more, from 0.58% in 2015 to 0.39% in 2019. In contrast, shrubland declined more slowly, from 0.29 to 0.21%. Further annual analysis is shown in Fig. 26.14. Through year-by-year analysis, shrubland was consistently less than herbaceous wetland and herbaceous vegetation. As the percentage of herbaceous wetlands increases, the difference between shrubland and herbaceous wetlands increases, from 0.26% in 2015 to 0.99% in 2019. Similarly, herbaceous wetlands had a lower percentage than herbaceous vegetation in 2015. However, as the area of herbaceous wetland increases and the area of herbaceous vegetation decreases, the difference between the two increases, from −0.03% in 2015 to 0.81% in 2019. The above changes have led us to observe an important phenomenon, namely the doubling of herbaceous wetlands in Zhejiang Province within five years. This is based on the concept of ecological civilization put forward in the 13th and 14th

344

Z. Wang

Fig. 26.9 Land cover classification in 2018 in Zhejiang Province. The green part represents vegetation

Fig. 26.10 FCOVER in 2018 in Zhejiang Province

26 Vegetation Coverage Assessment for Smart Cities Based on the Sentinel …

345

Fig. 26.11 Land cover classification in 2018 in Zhejiang Province. The green part represents vegetation

Fig. 26.12 FCOVER in 2019 in Zhejiang Province

346

Z. Wang

Fig. 26.13 Trend chart of land cover classification

Fig. 26.14 Overall weighting trend graph

Five-Year Plans, and the spatial program of the provincial government in response to the requirements of comprehensive environmental management and ecological protection and restoration. With the innovation and application of smart cities in agriculture and water, cultural tourism and community-level applications, efficient monitoring and governance restores and even enhances urban and rural biodiversity. As we can see from the news reports, many large urban wetland parks and wild wetlands in Zhejiang Province have increased in size and have been restored. The results of this paper have been tested to be consistent with those given in Exploring the Relationship between Urbanization and Ecological Environment Using Remote Sensing Images and Statistical Data: A Case Study in the Yangtze River Delta, China, published by Zhenfeng Shao et al.

26 Vegetation Coverage Assessment for Smart Cities Based on the Sentinel …

347

26.5 Conclusion This study used satellite remote sensing image data of Zhejiang Province for the past 4 years from 2015–2019, analyzed the statistics of feature classification, green vegetation fragmentation, and the rationality of green space layout in Zhejiang Province, and concluded the following. Zhejiang Province has a large proportion of greenery in the feature classification, and the construction of smart ecological cities is a relatively successful case, mainly related to its geographical location and climatic characteristics, the sea and humid climate have a good promotion effect on the growth of greenery, but a large proportion of greenery is on top of mountains, the proportion of greenery and distribution in the city from the FCOVER, need to further improve, improve the layout of greenery landscape. On the time scale, the big change in greenery from 2015 to 2019 is herbaceous wetlands, with the proportion changing to 1.20%. This change is mainly encouraged by national policy, which shows how important national policy or government support is to the development of the whole smart eco-city. Remote sensing imagery is a potential and helpful database for urban development and assessment of related indicators, especially for studies on long time scales and real-time observations.

26.6 Outlook Remote sensing imagery is a potential tool/database for future urban construction assessment. This study is not long enough in terms of time scale, and future research needs to further extend the time range to provide a stronger basis for future smart city construction in Zhejiang Province. Acknowledgements Thanks to the European Space Agency for the free downloadable Sentinel image data. The topographic maps in this paper were created using Generic Mapping Tools (GMT) software and the FCOVER maps were created using NCAR Command Language (NCL) software.

References 1. Kashef M, Visvizi A, Troisi O (2021) Smart city as a smart service system: human-computer interaction and smart city surveillance systems. Comput Hum Behav 124:106923. https://doi. org/10.1016/j.chb.2021.106923 2. National Bureau of Statistics of the People’s Republic of China (2021) China Statistical Yearbook. China Statistics Press, Beijing, China 3. Ding L et al (2015) Research on the coupling coordination relationship between urbanization and the air environment: a case study of the area of Wuhan. Atmosphere 6(10):1539–1558. https://doi.org/10.3390/atmos6101539

348

Z. Wang

4. Ma H, Chou N-T, Wang L (2016) Dynamic coupling analysis of urbanization and water resource utilization systems in China. Sustainability (Basel, Switzerland) 8(11):1176. https://doi.org/10. 3390/su8111176 5. Du X, Huang Z (2017) Ecological and environmental effects of land use change in rapid urbanization: the case of Hangzhou, China. Ecol Ind 81:243–251. https://doi.org/10.1016/j. ecolind.2017.05.040 6. Sun J et al (2018) Nexus among energy consumption, economic growth, urbanization and carbon emissions: heterogeneous panel evidence considering China’s regional differences. Sustainability (Basel, Switzerland) 10(7):2383. https://doi.org/10.3390/su10072383 7. Li B et al (2016) Spatio-temporal assessment of urbanization impacts on ecosystem services: case study of Nanjing City China. Ecol Ind 71:416–427. https://doi.org/10.1016/j.ecolind.2016. 07.017 8. Zhang M, Cao T, Zhao X (2017) Applying sensor-based technology to improve construction safety management. Sensors (Basel, Switzerland) 17(8):1841. https://doi.org/10.3390/s17 081841 9. Psomiadis E et al (2019) Synergistic approach of remote sensing and gis techniques for flash-flood monitoring and damage assessment in Thessaly plain area, Greece. Water (Basel) 11(3):448. https://doi.org/10.3390/w11030448 10. Huang AHL, Society of Photo-optical Instrumentation Engineers Content Provider (2005) Atmospheric and environmental remote sensing data processing and utilization : numerical atmospheric prediction and environmental monitoring, San Diego, California, USA. Place of publication not identified: SPIE, 1–4 Aug 2005 11. Zhong Q et al (2019) Assessing spatial-temporal dynamics of urban expansion, vegetation greenness and photosynthesis in megacity Shanghai, China during 2000–2016. Remote Sens Environ 233:111374. https://doi.org/10.1016/j.rse.2019.111374 12. Liu X et al (2020) High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nat Sustain 3(7):564–570. https://doi.org/10.1038/s41893-020-0521-x 13. Shao Z et al (2020) Exploring the relationship between urbanization and ecological environment using remote sensing images and statistical data: a case study in the Yangtze River Delta, China. Sustainability (Basel, Switzerland) 12(14):5620. https://doi.org/10.3390/su12145620 14. 2019 5th International conference on geoinformation science of the international archives of the photogrammetry, remote sensing and spatial information sciences, vol XLII-4/W12, GeoAdvances 2018, Casablanca, Morocco, 10–11 Oct 2018 15. Xu P et al (2021) Assessing a prototype database for comprehensive global aquatic land cover mapping. Remote Sensing (Basel, Switzerland) 13(19):4012. https://doi.org/10.3390/rs1319 4012 16. Copernicus Global Land service web site. https://land.copernicus.eu/global/products/lc. Accessed 20 June 2022

Chapter 27

Monitoring of Agricultural Expansion Using Hybrid Classification Method in Southwestern Fringes of Wadi El-Natrun, Egypt: An Appraisal for Sustainable Development Ahmed M. Saqr, Mahmoud Nasr, Manabu Fujii, Chihiro Yoshimura, and Mona G. Ibrahim

Abstract Agricultural development activities are essential for sustainable development in arid and semiarid regions. Therefore, a hybrid image classification methodology was proposed to monitor agricultural expansion and identify its linkages to sustainable development goals (SDGs). It was applied in the southwestern fringes of Wadi El-Natrun, Egypt, which is considered a promising area for reclamation projects via groundwater. Based on the inspection of the existing land cover (LC) types, spectral indices, i.e., normalized difference vegetation index (NDVI), bare soil index (BSI), and dry built-up index (DBI), were used to get training samples for the obtained Landsat images. Then, the maximum likelihood algorithm followed by a change detection procedure was applied to delineate LC trends between 2007 and 2022. The resulting LC maps exhibited high accuracy, with a Kappa coefficient equal to ~0.90. In addition, the agricultural regions grew from nearly 7.64% of the whole area to about 43.72%, accompanied by a severe shortage in the desert land of roughly 36.30%. However, the urban areas witnessed a small gain of approximately A. M. Saqr · M. Nasr · M. G. Ibrahim Environmental Engineering Department, Egypt-Japan University of Science and Technology, Alexandria, Egypt A. M. Saqr (B) · M. Fujii · C. Yoshimura Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, Japan e-mail: [email protected]; [email protected] A. M. Saqr Irrigation and Hydraulics Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt M. Nasr Sanitary Engineering Department, Alexandria University, Alexandria, Egypt M. G. Ibrahim Environmental Health Department, High Institute of Public Health, Alexandria University, Alexandria, Egypt © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_27

349

350

A. M. Saqr et al.

0.23%. It was observed that barren land was the major contributor to new cultivation land by ~37.42%. The study also demonstrated that the significant encroachment of arable land might have reinforcing linkages to SDG targets focusing on ending desertification, poverty, and hunger. However, conflicting linkages existed with SDG targets concerning affordable drinking water and aquifer protection from water deterioration. Consequently, decision-makers should adopt management policies like the utilization of greenhouse farming systems to mitigate the negative consequences of LC alteration. Keywords Agricultural expansion · Sustainable development goals (SDGs) · Normalized difference vegetation index (NDVI) · Bare soil index (BSI) · Dry built-up index (DBI) · Maximum likelihood algorithm

27.1 Introduction Land cover (LC) is identified as the biological and physical cover of the ground surface, including artificial buildings, water bodies, barren land, and plants [1]. The pace of change in LC has accelerated globally in recent decades due to many anthropogenic activities [2]. Among LC alteration patterns, agricultural expansion remains a key milestone in the national renaissance of most countries, especially in arid and semi-arid areas [3]. A great majority of people there rely on agriculture to satisfy their socioeconomic needs [4]. However, this agrarian extension can put stress on environmental resources, including land and water [5]. So, surveillance of the encroachment in cultivation areas is considered a substantial step toward understanding the repercussions of human activities on ecological conditions and the entire ecosystem [6]. Therefore, it could be essential to avoid a contradiction with the goals of sustainability through efficient mapping and quantifying of LC trends. In 2015, the United Nations General Assembly adopted the sustainable development goals (SDGs) through the 2030 agenda. This agenda provides a roadmap and a coordinated platform of action for achieving sustainable socio-economic development while limiting environmental degradation and natural resource depletion [7]. The agenda framework includes 17 goals with 169 targets that explicitly combine the economic, environmental, and social aspects of sustainability. In separate goals, the SDGs address a wide range of concerns, including food (SDG2), water (SDG6), and land (SDG15). Besides, these goals and their corresponding targets do not exist in isolation. They all interact at a deeper level through inextricable linkages [8]. For instance, any alteration in LC, such as agricultural expansion, could affect SDG targets of food and water. Unfortunately, the relationship pattern between cultivation encroachment and SDG targets was not conceptualized in previous studies. In the past decades, different reliable methods have been applied to map LC patterns. Moreover, remote sensing techniques exhibited effective performance in comparison with traditional methods like field-based procedures [9]. Besides, they include the implementation of satellite data to quantify LC geospatial and temporal

27 Monitoring of Agricultural Expansion Using Hybrid Classification …

351

changes. Furthermore, they are characterized by relatively high accuracy as well as effort, money, and time savings [2]. In most studies, remote sensing analysis of satellite data was based on a supervised classification method through the maximum likelihood algorithm. It resulted in promising results with high accuracy compared to other algorithms [10]. However, researchers employed conventional indices to recognize training sites required for the classification process. These indices include visual interpretation of satellite images and unsupervised computerized techniques, such as the ISODATA tool [11]. Recently, several spectral indices have been derived to effectively distinguish LC types. For instance, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), dry built-up index (DBI), and bare soil index (BSI) were developed to indicate vegetation, water, built-up, and barren LC categories, respectively [12–14]. They were implemented using a combination of various satellite wavelengths (bands) to efficiently assist in the separation of LC categories and direct classification of mono-class domains as reported elsewhere [15]. However, their application as a training tool to get the spectral signatures of different LC types has not been elucidated yet. This study’s objective was to develop an integrated strategy formed of spectral indices and the maximum likelihood algorithm to enhance the output quality of supervised classification in any domain. This approach was applied in the southwestern fringes of the Wadi El-Natrun basin, Egypt to monitor spatial and temporal LC changes between 2007 and 2022. This region was chosen to perform the current research as it has been considered a promising location for sustainable development through agricultural expansion. Spectral satellite indices were applied to prepare training samples for different LC classes. Then, LC maps were derived by employing the maximum likelihood classification algorithm. Post-classification steps were adopted to get different statistics for the LC change. Besides, the types of linkages between agricultural expansion and SDG targets were discussed to guide policymakers in the future planning of reclamation activities.

27.2 Materials and Methods 27.2.1 Study Area The domain under consideration is situated in the southwestern extension of the floodplain of the Nile Delta, Egypt, as displayed in Fig. 27.1. It especially lies in the Behera governorate, occupying a total area of approximately 1715 km2 . It extends between longitudes (29°46' 00'' E–30°30' 27'' E), and latitudes (30°07' 00'' N– 30°31' 31'' N). It is bounded by the Wadi El-Natrun basin and by El-Dabaa Road in the northeastern and southwestern directions, respectively. It is characterized by a semi-arid climate. Moreover, the mean rainfall reaches about 48 mm/year, while the mean annual temperature is nearly 25 °C [16]. Gentle slopes exist in almost all zones

352

A. M. Saqr et al.

Fig. 27.1 Location map of the domain under study

of the study domain. In addition, groundwater, which is pumped from the Miocene aquifer, is considered the only feeding source of water for people there. Besides, the groundwater in this aquifer is mainly recharged from the adjacent Quaternary aquifer of the Nile delta [17].

27.2.2 Step-Wise Methodology The objectives of the current research study were fulfilled by performing the following steps: Data. Two types of data were collected: • Satellite data: Two different sets of satellite Landsat bands for the years 2007 and 2022 were downloaded from the website of the United States Geological Survey for free [18]. Moreover, the obtained spectral bands were blue, green, red, thermal infrared, short-wavelength infrared, and near-infrared. They were acquired to cover a time span of about 15 years with relatively little effective cloud cover. The details of the satellite data, including acquisition date and spatial resolution, were demonstrated in Table 27.1. • Ancillary data: 65 reference points were identified from the Google Earth Pro archive and Geographic Information System (GIS) database, as illustrated in Table 27.1 Details of the satellite imageries for performing the current research Spacecraft ID/sensor

Acquisition date

Spatial resolution (m)

LANDSAT 7/ETM +

12-April-2007

30

LANDSAT 8/OLI

28-March-2022

30

27 Monitoring of Agricultural Expansion Using Hybrid Classification …

353

Fig. 27.1. These points could be used as guides for rectifying and evaluating the accuracy of satellite images of the study area [1]. They represented road geometry, study area borders, and dominant LC classes. The identified LC classes were vegetation, desert, and urban. Besides, they were verified during the field trips. The urban class represented land populated by industrial, commercial, and residential activities, while the vegetation and desert categories were indications of cultivated and unused areas, respectively. Satellite Data Preprocessing. In this study, the tools and techniques of Environment for Visualization Images (ENVI) 4.8 software were used to process data while the Arc GIS 8 platform was applied to display the outputs. Moreover, preprocessing operations were individually performed to geometrically and radiometrically correct the spectral bands of each year. Firstly, the bands were georeferenced to the Universal Transverse Mercator (UTM) projection system following the reference points. The projection system is based on the 1984 World Geodetic System (WGS) Datum and Zone 36 North. Then, these bands were clipped using the borders depicted in Fig. 27.1 to represent the study area. Finally, the ‘FLAASH’ technique was applied to remove any existing atmospheric effects. Spectral Indices Analysis. Following the preprocessing of satellite bands, three different spectral indices were applied to highlight and differentiate a specific aspect of the earth’s cover. These indices were NDVI, BSI, and DBI, which technically describe the existing LC classes, i.e., vegetation, desert, and urban, respectively. Moreover, they could be estimated using the ‘Map Algebra’ technique based on superimposing certain satellite bands over each other as reported elsewhere [14]: NDVI = BSI =

NIR − RED NIR − RED

(27.1)

(SWIR + RED) − (NIR + BLUE) (SWIR + RED)+(NIR + BLUE)

(27.2)

BLUE − TIR − NDVI BLUE + TIR

(27.3)

DBI =

where: NIR = Near-infrared Landsat band; RED = Red Landsat band; SWIR = Short-wavelength infrared Landsat band; BLUE = Blue Landsat band; and TIR = Thermal infrared Landsat band. The spatial distribution maps obtained from the aforementioned indices were presented in Figs. 27.2, 27.3 and 27.4. It was evident from the figures that NDVI, BSI, and DBI were in the ranges of (−0.069 → 0.397), (−0.152 → 0.297), and (−0.708 → 0.186), respectively, in 2007. By 2022, these ranges reached (−0.157 → 0.562), (−0.367 → 0.194), and (−0.667 → 0.227). It should be noted that the normal range of these spectral indices is from −1 (low value) to 1 (high value). Moreover, high values of the spectral indices mean a dense distribution or high occurrence of the LC category, while lower values indicate a low existence of the LC class [12]. Consequently, the location of high values

354

A. M. Saqr et al.

Fig. 27.2 Normalized difference vegetation index (NDVI) map in 2007 and 2022 for the domain under study

Fig. 27.3 Bare soil index (BSI) map in 2007 and 2022 for the domain under study

Fig. 27.4 Dry built-up index (DBI) map in 2007 and 2022 for the domain under study

27 Monitoring of Agricultural Expansion Using Hybrid Classification …

355

Fig. 27.5 Composition of red, green, and blue bands for 2007 and 2012 Landsat images

could represent a guide for getting the training samples required for the classification process. Supervised Classification and Accuracy Assessment. At this stage, the red, green, and blue bands of each year were combined to form one composite image using the ‘Band Stacking’ tool, Fig. 27.5. This composite image physically represented a naturally colored one for the domain under study. Moreover, it corresponds to how we could see the LC categories from space. Then, the colored image was employed for supervised LC classification using the maximum likelihood algorithm based on the training samples obtained from spectral indices. This algorithm applies some discriminant estimations for each pixel to classify Landsat images based on a powerful toolbox in ENVI software [19]. After obtaining the LC maps, about 100 random pixels based on the reference points following Radwan [3] were used for accuracy assessment. The accuracy assessment measures the quality degree of the outputs [20–24]. Consequently, it could aid in determining the degree of map suitability for a given application. The Kappa coefficient is considered an effective index to get the accuracy percentage for satellite rasters [19]. It can be computed based on the following formula using a statistical tool in ENVI software [25]: Kappa coefficient =

PCC − PCA 1 − PCA

(27.4)

where: PCC = The relative actual matching among the classified raster; and PCA = A measure of agreement degree due to chance between the reference pixels and model predictions. It should be noted that the Kappa coefficient is a statistical index that measures the agreement between the classified map and the pixels used for verification. It lies between 0 and 1. The value of 0 represents no agreement while 1 indicates perfect agreement [19].

356

A. M. Saqr et al.

Change Detection Evaluation. Once the two images of 2007 and 2022 were separately classified, a post-classification detection procedure was implemented to evaluate the differences that occurred in the assessment period. It was effectively applied in many case studies [2, 3], giving reliable results. Besides, more quantitative results could be obtained through the change matrix. This matrix could give quantitative information about the most considerable changes in the LC classification as reported elsewhere [19].

27.3 Results and Discussion 27.3.1 Spatio-Temporal Patterns of Land Cover (LC) The results of the accuracy assessment for the integrated classification technique of spectral indices and maximum likelihood algorithm indicated that the kappa coefficient was 0.92 and 0.90 for the years 2007 and 2022, respectively. These values, which approached 1, illustrated the robust ability of the proposed method to classify the LC categories into urban, vegetation, and desert. Moreover, Jones [26] stated that the score of Kappa is considered acceptable if it exceeds 0.70. In addition, the applied technique exhibited satisfactory accuracy compared to many previous LC studies depending on visual inspection and unsupervised computerized tools for selecting training samples. For instance, the deduced LC map of 2005 for Hunter Wine Country Private Irrigation District, Australia exhibited a Kappa estimate of 0.83 obtained by Manandhar et al. [27]. Following the classification accuracy evaluation, Fig. 27.6 showed the resultant maps of LC patterns in the study area for 2007 and 2022. It was obvious from the figure that the desert regions occupied most of the regions in the study area, followed by the vegetation and urban classes during the period of assessment. Furthermore, quantitative statistics were performed for the LC geospatial and temporal distribution. In 2007, urban, vegetation, and desert classes accounted for about 0.004%, 7.64%, and 92.35% of the total area, respectively. By 2022, the area percentages of vegetation and desert regions became ~43.72% and ~56.05%, successively. Moreover, a dramatic gain of nearly 36.08% was recorded for the vegetation cover while the desert land declined by roughly 36.30%. However, the urban class witnessed a slight increase, reaching approximately 0.23% of the total area. Similarly, many previous classification studies applied remote sensing techniques to monitor agriculture expansion globally, including the research performed by Knauer et al. [12] in Burkina Faso country. They described the effectiveness of LC classification to compute the increase in agricultural regions by about 20.37% of the total valley area in the period between 2001 and 2014.

27 Monitoring of Agricultural Expansion Using Hybrid Classification …

357

Fig. 27.6 Land cover (LC) maps for the study area in 2007 and 2022

27.3.2 Dynamics of Land Cover (LC) Change The pattern of LC change, over the period from 2007 to 2022, was depicted in Fig. 27.7 based on the post-classification procedure. Furthermore, the change matrix was constructed as seen in Table 27.2 following the map of change detection. It was detected that the change from desert to vegetation was the most dominant LC change mode. Moreover, new reclamation land accounted for about 37.42% of the total area, at the expense of desert regions. In addition, agricultural expansion was distributed in all parts, especially the northeastern regions close to the Wadi El-Natrun basin. In contrast, the northwestern fringes of the study area were still undeveloped, offering promising locations for future reclamation projects. Approximately 1.34% of the total area was altered from cultivation to be in the form of bare land. At the same time, vegetation and desert classes with percentages equal to ~6.30% and ~54.70% of the total area remained unchanged during the evaluation period. Besides, the LC units under the urban category registered relatively small areas with slight variation over other classes throughout the assessment epoch. The findings of the LC change model are in agreement with several recent studies conducted to track the transition from desert to green land in similar places in Egypt. For instance, Ezzeldin et al. [2] investigated the changes in LC between 1984 and 2015 that occurred in the Eastern Nile Delta, Egypt. They found that the agricultural land rose by 8.60%, resulting in a significant loss in the desert areas, especially in the southern regions of the studied domain. The encroachment of arable land in places such as the study area is influenced by governmental reclamation initiatives. The Egyptian authorities have started development programs to encourage people to rehabilitate the barren areas far from the narrow Nile valley. This could also aid to meet Egypt’s vision [28] by 2030 to increase the percentage of cultivated land. However, this agricultural expansion may negatively affect the existing natural resources, especially groundwater. Therefore, the effect of this LC trend should be assessed from a sustainable perspective to discuss its pros and cons, which would be illustrated in the following section.

358

A. M. Saqr et al.

Fig. 27.7 Change detection map of land cover (LC) classes in the time interval (2007–2022)

Table 27.2 Change matrix of the changes in land cover (LC) classes from 2007 to 2022 Area (%)

Land cover *Area (%)

Urban

Vegetation

Desert

Urban

0.001

0.001

0.002

Vegetation

0.003

6.296

1.343

Desert

0.217

37.424

54.714

*

Note the matrix diagonal represents the unchanged areas, while the off-diagonal elements demonstrate the changed regions

27.4 Linkages of Study Outputs with Sustainable Development Goals (SDGs) Addressing the contribution of any research study to SDGs is essential to promote economic development while guaranteeing social inclusion and environmental sustainability, as reported elsewhere [29]. By analyzing the correlation between the existing trend of cropland expansion and the targets of SDGs, two types of linkages, i.e., reinforcing and conflicting, existed, as demonstrated in Fig. 27.8. Moreover, reinforcing linkage means positive outcomes from agricultural expansion, while conflicting relationships are associated with negative consequences, which need governing policies to be overcome. A brief discussion of the resulting linkages could be given as:

27 Monitoring of Agricultural Expansion Using Hybrid Classification …

359

Fig. 27.8 Linkages pattern between agricultural expansion and targets of sustainable development goals (SDGs)

• Reinforcing linkages: On the one hand, the enlargement of agricultural areas in the study domain could result in the achievement of several SDG targets. For instance, the alteration of bare soil into cultivation zones can strongly match Target 15.3: End desertification and restore degraded land. As a result, many socioeconomic benefits can be attained. Moreover, food security can be preserved for the residents, viz., Target 2.1: End hunger for people. Besides, the obtained crops, fruits, and vegetables may be exported to international markets with many benefits to the country’s economy to comply with Target 8.1: Sustain economic growth. In addition, agricultural productivity will enhance the standard of living of farmers to maintain Target 1.1: Eradicate extreme poverty. Also, agricultural activities can aid in creating new jobs for many people, i.e., Target 8.3: Offer job opportunities. • Conflicting linkages: On the other hand, the rapid increase of reclamation projects in the study area could put extreme pressure on groundwater, considered the only feeding source. Wassef [6] reported that the study area and its fringes witnessed severe drawdowns in groundwater levels. A similar situation of continuous lowering of groundwater heads also existed in the neighboring area, i.e., the Wadi El-Natrun basin [16, 30]. Consequently, conflicts may emerge with some SDG targets. To illustrate, sufficient groundwater quantities for human use can be threatened. In addition, the Miocene aquifer of the study area will face further deterioration regarding a continuous shortage in groundwater storage. This situation of groundwater condition does not support Target 6.1: Access to affordable drinking water, and Target 6.6: Protect water-related ecosystems. Therefore, sustainable policies and urgent actions should be implemented to guarantee the long-term viability of the study area’s aquifer. These initiatives include enhancing the efficiency of the irrigation process by generalizing the latest technology in hi-tech irrigation methods and subsurface drainage systems. Also, greenhouse farming can be introduced to reduce water consumption. Besides, awareness programs should be raised among stakeholders to rationalize water use and encourage the cultivation of the least water-consuming plants. In addition, laws of land reclamation should be updated by policymakers to regulate future horizontal activities.

360

A. M. Saqr et al.

27.5 Limitations and Future Perspectives Despite all the promising results obtained from the current research study, it exhibited minor drawbacks concerning the use of medium-resolution Landsat pictures. The application of purchased high-quality images from satellites like SENTINEL, SPOT, and IKONOS is highly recommended in future studies. In addition, artificial intelligence algorithms should be adopted to predict future scenarios of LC trends to support planning, management, and decision-making processes.

27.6 Conclusions and Recommendations An integrated classification approach formed of spectral indices and the maximum likelihood algorithm was suggested to observe the agricultural expansion and its correlation with SDGs in arid and semi-arid areas. The results indicated the high accuracy of the approach for application in the southwestern fringes of Wadi ElNatrun, Egypt with a Kappa index equal to about 0.90. The statistics of LC showed the extensive growth of cultivation areas from ~7.64% to ~43.72% of the whole area in the assessment period (2007–2022). In contrast, a decrease from ~92.35% to ~56.05 was recorded for desert land, while urban land slightly increased by nearly 0.23%. Moreover, new arable zones accounted for approximately 37.42% of the total area, at the expense of desert regions. This study also highlights the role of existing agricultural development in meeting SDG targets relevant to desertification eradication, food security, and poverty elimination through reinforcing correlations. However, management policies are recommended to avoid conflicting with SDG targets related to affordable drinking water and the protection of aquifers from deterioration. These policies mainly include the generalized use of modern irrigation systems and low-water-consumption plants, in addition to the implementation of greenhouse farming. Future research studies should focus on the application of high-resolution satellite images to enhance the quality of results. In addition, the use of artificial intelligence techniques is highly recommended for the prediction of future LC variations to guide policymakers in planning activities and the fulfillment of SDGs. Acknowledgements The first author is very grateful to the Egyptian Ministry of Higher Education (MoHE) and Fujii-Sensei for providing the financial support to carry out this research. Also, thanks to the Tokyo Institute of Technology for their generous hospitality during the research period.

27 Monitoring of Agricultural Expansion Using Hybrid Classification …

361

References 1. Masria A, Nadaoka K, Negm A, Iskander M (2015) Detection of shoreline and land cover changes around Rosetta promontory, Egypt, based on remote sensing analysis. Land 4(1):216– 230 2. Ezzeldin MM, El-Alfy KS, Abdel-Gawad HA, Abd-Elmaboud ME (2016) Land use changes in the Eastern Nile Delta Region; Egypt using multi-temporal remote sensing techniques. Int J Sci Eng Res 7(12):78–98 3. Radwan TM (2019) Monitoring agricultural expansion in a newly reclaimed area in the western Nile Delta of Egypt using Landsat imageries. Agriculture 9(7) 4. Mansour MM, Ibrahim MG, Fujii M, Nasr M (2022) Recent applications of flash flood hazard assessment techniques: case studies from Egypt and Saudi Arabia. Adv Eng Forum 47:101–110. https://doi.org/10.4028/p-03z404 5. Saqr AM, Ibrahim MG, Fujii M, Nasr M (2022) Simulation-optimization modeling techniques for groundwater management and sustainability: a critical review. Adv Eng Forum 47:89–100. https://doi.org/10.4028/p-50l1j1 6. Wassef RS (2010) Development of a groundwater flow model for water resources management at the development area west of the Rosetta branch, Egypt. Ph.D. Thesis, Faculty of Natural Science, Martin Luther University, Germany 7. Nations U (2015) Transforming our world: the 2030 agenda for sustainable development. NY, USA. https://doi.org/10.1891/9780826190123.ap02 8. Stafford-Smith M et al (2017) Integration: the key to implementing the sustainable development goals. Sustain Sci 12(6):911–919 9. Priyankara P, Ranagalage M, Dissanayake DMSLB, Morimoto T, Murayama Y (2019) Spatial process of surface urban heat island in rapidly growing Seoul Metropolitan area for sustainable urban planning using landsat data (1996–2017). Climate 7(9) 10. Kaiser EA et al (2022) Spatiotemporal influences of LULC changes on land surface temperature in rapid urbanization area by using Landsat-TM and TIRS images. Atmosphere 13(3) 11. Maina J, Wandiga S, Gyampoh B, Charles K (2020) Assessment of land use and land cover change using GIS and remote sensing: a case study of Kieni, Central Kenya. J Remote Sens GIS 9(1) 12. Knauer K, Gessner U, Fensholt R, Forkuor G, Kuenzer C (2017) Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: the role of population growth and implications for the environment. Remote Sens 9(2) 13. Osgouei PE, Kaya S, Sertel E, Alganci U (2019) Separating built-up areas from bare land in Mediterranean cities using Sentinel-2A imagery. Remote Sens 11(3) 14. Shahfahad et al (2021) Indices based assessment of built-up density and urban expansion of fast-growing Surat city using multi-temporal Landsat data sets. GeoJournal 86(4):1607–1623 15. Diallo Y, Hu G, Wen X (2010) Assessment of land use cover changes using NDVI and DEM in Puer and Simao Counties, Yunnan Province China. Report Opin 2(9):7–16 16. Saqr AM, Ibrahim MG, Fujii M, Nasr M (2021) Sustainable development goals (SDGs) associated with groundwater over-exploitation vulnerability: geographic information system-based multi-criteria decision analysis. Nat Resour Res 30(6):4255–4276. https://doi.org/10.1007/s11 053-021-09945-y 17. El-Boghdady M (2017) Integrated hydrogeophysical and environmental studies on groundwater aquifers in Wadi El-Natrun area, Egypt. Ph.D. Thesis, Ain Shams University 18. USGS Homepage. https://earthexplorer.usgs.gov/ 19. Yacouba D, Guangdao H, Xingping W (2009) Applications of remote sensing in land use/land cover change detection in Puer and Simao Counties, Yunnan Province. J Am Sci 5(4):157–166 20. Zidan A, Abdalla M, Khalaf S, Saqr AM (2016) Kinetic energy and momentum coefficients for Egyptian irrigation canals. Mansoura Eng J 41(1):1–16. https://doi.org/10.21608/bfemu. 2020.99368

362

A. M. Saqr et al.

21. Zidan A, Abdalla M, Khalaf S, Saqr AM (2017) Resistance equations for Egyptian irrigation canals (case study: ‘Dakahliya Governorate’). Int Water Technol J 7(2):73–90. https://www.researchgate.net/publication/362876063_Resistance_equations_for_ Egyptian_irrigation_canals_Case_study_Dakahliya_Governorate 22. Zidan A, Abdalla M, Khalaf S, Saqr AM (2018) Regime equations for Egyptian irrigation canals (case study: ‘Dakahliya Governorate’). Int Water Technol J 8(4):129– 141. https://www.researchgate.net/publication/363196196_Regime_equations_for_Egyptian_ irrigation_canals_Case_study_Dakahliya_Governorate 23. Mansour MM, Nasr M, Fujii M, Yoshimura C, Ibrahim MG (Forthcoming 2023) Evaluation of a reliable method for flash flood hazard mapping in arid regions: a case study of the Gulf of Suez, Egypt. In: Environmental science and engineering (ICESE 2022). Springer 24. Mansour MM, Ellayn AF, Helal E, Rashwan IMH, Sobieh MF (2018) Delaying solute transport through the soil using unequal double sheet piles with a surface floor. Ain Shams Eng J 9(4):3399–3409. https://doi.org/10.1016/j.asej.2018.10.003 25. McHugh ML (2012) Interrater reliability: the kappa statistic. Biochemia Med 22(3):276–282 26. Jones B (2018) Deforestation surges in Virunga National Park in the wake of violence. Mongabay 27. Manandhar R, Odehi IOA, Ancevt T (2009) Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement. Remote Sens 1(3):330– 344 28. https://mcit.gov.eg/Publication/Publication_Summary/1020/ 29. Mansour MM, Ibrahim MG, Fujii M, Nasr M (2022) Sustainable development goals (SDGs) associated with flash flood hazard mapping and management measures through morphometric evaluation. Geocarto Int. https://doi.org/10.1080/10106049.2022.2046868 30. Saqr AM, Nasr M, Fujii M, Yoshimura C, Ibrahim MG (Forthcoming 2023) Optimal solution for increasing groundwater pumping by integrating MODFLOW-USG and particle swarm optimization algorithm: a case study of Wadi El-Natrun, Egypt. In: Environmental science and engineering (ICESE 2022). Springer

Chapter 28

Quantifying the Dynamics of Ecosystem Services Value in Response to Decentralization and Regional Autonomy in Indonesia: A Case Study of Southeast Sulawesi Province Gazali

and Minoru Kumano

Abstract The decentralization system that started in Indonesia in 1999 has resulted in prominent changes in many sectors: politics, economics, society, and the environment. However, most of the previous research about decentralization focused on political, governance, and economic aspects. Accordingly, this paper tried to analyze the implication of the decentralization and regional autonomy policy in Indonesia from an environmental point of view, which is analyzing the dynamics of Land Cover Changes and the dynamics of Ecosystem Service Values (ESV) in Southeast Sulawesi Province after this policy was applied. The results show that there are significant changes in the growth/loss rate of the land cover types after decentralization was put into practice. The growth rate of Agriculture Land, Built-up Areas, and Bare Land increase significantly after decentralization, while Forest Areas got a significant loss rate. Secondly, this study reveals a significant loss of ESV after decentralization. Before decentralization (from 1990 to 2000), the decrease of ESV accounted for about US$91.5 Million, whereas in the period 2000 to 2010 (after decentralization) the decline of ESV was much higher at about US$166.6 million. From this perspective, there is an indication that decentralization has a negative implication for the degradation of ecosystem services. Keywords Decentralization · Ecosystem services value · Land cover changes · Regional autonomy

Gazali (B) Development Planning Agency of Kolaka Regency, Southeast Sulawesi, Kolaka, Indonesia e-mail: [email protected] M. Kumano Faculty of Regional Innovation, Miyazaki University, Miyazaki, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_28

363

364

Gazali and M. Kumano

28.1 Introduction 28.1.1 Background and Research Problem Decentralization is a governance system that has emerged in the last several decades and became a trend in many countries, both developing and developed countries. This concept has various terms, aspects, and interpretations, but all of the definitions refer to the process of redistributing power, function, or authority of the central government to the sub-layer level of government [1–3]. Decentralization is believed can overcome the major problems of the nation by encouraging effectiveness, efficiency, and competitiveness in many aspects such as politics and democratization, infrastructures and public services, economic growth, and natural resources management. It is because local governments are close to the people and can well-understand local needs and preferences [2, 4, 5]. After the enactment of Law No. 22/1999, the Indonesian Central Government delegated most of the responsibilities to sub-layers of government (regional autonomy). There are two types of autonomy in this policy: (a) Full Autonomy for the regency and municipality levels and (b) Limited Autonomy for provincial levels. Full autonomy means regencies/municipalities have the authority to create and implement local policies as long as it does not contradict national law and upset public interests [1]. One of the implementation forms of regional autonomy in Indonesia is the regional proliferation (regional split) that was regulated by Government Regulation No. 29/ 2000 (revised by Government Regulation No. 78/2007). Regional proliferation means creating new autonomous regions by splitting one core region into two regions. The notion of this policy is to improve public welfare which is believed will be more effective when managed locally. There is a great expectation that regional proliferation can reform and improve the equality of economic performance as well as the provision of basic services which became one of the major problems in the previously centralized era [6]. It then appears to be a trend in the Indonesian governance system where since 1999 the number of the province increased from 26 to 34, and the number of regencies/municipalities increased from 303 to 514 in 2015. The regional proliferation trend in Indonesia is likely to continue in the future. Nowadays there are more than 200 regions officially registered their application to become the new autonomous regions in the Ministry of Internal Affairs. Therefore, the assessment for addressing the impact and the effectiveness of regional proliferation must be analyzed carefully so it can be used as an essential consideration for the improvement of this policy. There is an indication that the regional proliferation process is mostly encouraged by political and rent-seeking motivation [6, 7]. Several researchers also argue that public welfare and poverty reduction as the main goal of this policy seems to fail to be achieved because regional autonomy leads to new problems at the local levels, such as corruption [3], or high-level exploitation of natural resources [7].

28 Quantifying the Dynamics of Ecosystem Services Value in Response …

365

Since decentralization and regional proliferation are applied, new autonomous regions try to enforce their fiscal condition and improve their regional economic performance through optimizing their natural resources management. As a result, at the local level of governments (cities and regencies), there is a tendency to exploit natural resources as the best and easiest option for gaining revenue to cover the development cost [7, 8]. Undoubtedly, this condition could lead to the spatial and environmental dynamics of the region, which until now there were not many kinds of research trying to discuss this phenomenon. Many studies have been conducted to capture the dynamics of decentralization and proliferation in Indonesia, but mostly from social and economic perspectives, such as economic growth [9, 10], fiscal and budgeting [11, 12], political and governance, and also social, community, participation, health, and education [13–15]. However, in addition to that two factors (social and economic), there is one more aspect that is essential to be considered in the context of a sustainable development framework, namely the environmental dimension. There are several studies about decentralization within the latter dimension but mostly are forest-based researches that discussed the impact of decentralization on deforestation. Therefore, this research tries to contribute to discussing decentralization and regional proliferation from this environmental perspective by analyzing the spatial dynamics and the ecosystem-service value of the region which is still rarely discussed.

28.1.2 Research Objective The main objective of this research is to determine the implication of decentralization and regional proliferation in Indonesia from the environmental point of view, particularly in the context of spatial dynamics and the ecosystem service value of the region.

28.2 Literature Review 28.2.1 Remote Sensing and GIS for Land Cover Classification The main process of analyzing spatial dynamics of the region is collecting spatial data and information related to land use/land cover situations. Ground-based surveys could be carried out for relatively limited areas, but it could be difficult for large-scale study areas. Remote sensing is one of the effective sources of data for mapping the condition over large areas. Remote sensing is generally stated as a tool and technology for analyzing an area or object without direct contact with the subject [16]. The advancement of remote

366

Gazali and M. Kumano

sensing and GIS using computer technology makes the analyzing process of spatial dynamics relatively easier and cheaper. It can rapidly cover large areas including the areas that are difficult to be accessed, possible for temporal/repetitive measurement, and usually in digital form that is easier to analyze and manipulate [17]. The most popular remote sensing data is satellite imagery data. There is various type of satellite imagery systems, which are used for observing our earth’s condition. Each system has its sensor and resolution that should be considered with the scale of the observed area. To analyze spatial dynamics on a regional scale, LANDSAT imagery data is the most appropriate and accessible system. This data can be downloaded from the USGS website for free and has relatively complete time-series data from the 1990s even from the 1970s in some areas. Generally, there are two methods of interpreting the remote sensing data for land classification: (1) manual visual interpretation and (2) digital classification. Manual interpretation can produce a better result but it needs much more resources if the objective area is too large. Some scholars argue that visual interpretation is the best approach in term of spatial dynamics detection because human as the operator has better knowledge to recognize and categorize the pattern from the image. Hence, this research uses manual interpretation for analyzing the LANDSAT data, especially for the built-up area, and then combined it with digital classification from the land cover maps from the Indonesian Ministry of Forestry (MoF).

28.2.2 Ecosystem Service Value (ESV) The discussion about ecosystem services is one of the important baseline concepts in the context of the environmental field. Although it has been discussed by scholars for decades, it started to be discussed more often since Daily [18] mentioned it in his book in 1997. After that this concept began to become more popular after the establishment of the Millennium Ecosystem Assessment (MEA) in 2001. From this assessment, ecosystem service defines as all of the benefits that humans obtain from the ecosystem. These benefits are grouped into 4 main categories: provision services, regulating services, cultural services, and supporting services. Accordingly, it is undebatable that ecosystem services are an essential part of human well-being. It needs global effort to maintain an adequate level of services for supporting our life, which means it should be managed and used sustainably. Many scholars discussed and developed various tools and approaches to raise public and policy makers’ understanding of ecosystem services. One of them was developing a global valuation system for the ecosystem service in monetary units called Ecosystem Service Value (ESV) [19, 20]. Ecosystem services value (ESV) is the method to tackle such a challenge. ESV is the process of assessing the contributions of ecosystem services to sustainable scale, fair distribution, and efficient allocation. It is a tool that (1) provides for comparisons of natural capital to physical and human capital regarding their contributions to human welfare; (2) monitors the quantity and quality of natural capital over

28 Quantifying the Dynamics of Ecosystem Services Value in Response …

367

time concerning its contribution to human welfare; and (3) provides for evaluation of projects that will affect natural capital stocks [21]. This valuation does not mean that the services can be traded as commodities in the market. This monetary value is just a proxy to estimate the benefits, an appropriate tool for communication, and as valuable input in the decision-making process [19, 22].

28.3 Methods 28.3.1 Study Area This study was conducted in a selected region, The Province of South East Sulawesi as a case study. There are 34 provinces in Indonesia and South East Sulawesi Province is one of the most active provinces in terms of the regional proliferation process since the decentralization era was started. In 2001 (before decentralization), this province only had 6 regencies/municipalities, and then increased to 17 regencies/ municipalities after decentralization. South East Sulawesi geographically lies on 2º45’–6º15’ S and 120º25’–124º– 45’E. The topographic condition of this region varies from the flat area to the slope area. About 40% of the region is laid on the slope of 0–15%, 32% of the land is located on the 15–40% of slope, and 27% is more than 40% of the slope (see Fig. 28.1).

28.3.2 Land Cover Changes Analysis Land cover changes in the region were analyzed using GIS as the tool. In this study, we identified land cover changes by analyzing the land cover maps from 3 different times (spatial–temporal map) which are 1990, 2000, and 2010. These time points were selected based on the data availability and the objective of this study to identify the spatial dynamics of the region regarding the implication of decentralization policy. Those 3 periods represent 10 years before and 10 years after the implementation of decentralization in Indonesia. Land cover map as well as land cover classification generated from the combination of on-screen digitation of time-series LANDSAT data and forest map dataset from the Ministry of Forestry of Indonesia.

28.3.3 ESV Changes Analysis One of the most cited concepts for ESV valuation is The Benefit Transfer Method which was developed by Costanza [20] and De Groot [22]. They developed the tool

368

Gazali and M. Kumano

Fig. 28.1 Study area orientation map

for calculating the ESV of the area using biomes and land cover as the proxy. The formula for valuating ESV is as follows: ESV k = Ak × V Ck ESV t =



Ak × V Ck

(28.1) (28.2)

ESVk and ESVt refer to ESV for land use/land cover type k and ESV for the total ecosystem, respectively. VCk is the value coefficient for land use/land cover k ($/ha/ year). The global coefficient for this concept was developed from various studies, research, and literature which were stored and collected into one big system called ESDV (Ecosystem Services Value Database). This study chooses several biomes from the global coefficient of ESV as the proxy for calculating ESV using the land cover classification. ESV approach was used as a tool for determining the implication of decentralization policy to the environment

28 Quantifying the Dynamics of Ecosystem Services Value in Response …

369

since this tool is already accepted in the global society as an appropriate approach for assessing the environmental condition.

28.4 Result and Discussion 28.4.1 Land Cover Classification Analysis of the spatial dynamics of the region before and after decentralization was conducted by multi-temporal analysis on the land cover map of the study area for the years 1990, 2000, and 2010. Since the decentralization policy started to apply in 2000, the years 1990 and 2010 were chosen as the reference year for the analysis. 1990 is 10 years before decentralization, and 2010 means 10 years after decentralization. It is expected that by identifying and comparing the land dynamics between 1990 to 2000 and 2000 to 2010, this study can reveal the significant different patterns which can be concluded as a spatial implication of decentralization policy in the study area. The land cover classification used in this analysis was produced by using the combination of time-series Landsat Image (1990, 2000, and 2010) and the dataset map from the Ministry of Forestry (MoF). The LANDSAT images were used to correct the land cover map from the MoF, which only focuses on forest and vegetation areas. The combination of this process resulted in the complete time series land cover maps of the study area for the next analysis. Land covers were classified into eight types: Agriculture, Built-up area, Forest, Grassland, Wetland, Mangrove, Bare land, Water Body, and cloud/unidentified (see Table 28.1 and Fig. 28.2). In 1990, forest-covered about 62% area of the region and followed by agricultural area with more than 22% area. Grassland occupied about 10.9% and the rest is swamp/ mangrove and build-up area with 3.7% and 0.7% respectively (see Table 28.2 and Fig. 28.3). Table 28.1 Land cover of South East Sulawesi in 1990

No

Land cover

Area (ha)

1

Forest

2,299,373

% 62.2

2

Swamp/mangrove

135,525

3.7

3

Grassland

403,679

10.9

4

Build-up

24,642

0.7 22.1

5

Agriculture

817,803

6

Waterbody

6,137

0.2

7

Cloud/unidentified

98

0.0

8

Bare land Total

9,854

0.3

3,697,113

100.0

370

Gazali and M. Kumano

Fig. 28.2 Land cover map–1990

Table 28.2 Land cover of South East Sulawesi in 2000

Land cover

Area (ha)

1

Forest

2,154,564

58.3

2

Swamp/mangrove

137,406

3.7 13.4

No

%

3

Grassland

494,304

4

Build-up

25,821

0.7

5

Agriculture

864,306

23.4

6,138

0.2

0

0.0

14,573

0.4

3,697,113

100.0

6

Waterbody

7

Cloud/unidentified

8

Bare land Total

28 Quantifying the Dynamics of Ecosystem Services Value in Response …

371

Fig. 28.3 Land cover map–2000

In 2000, the forest was still dominant in this region with a percentage of 58.3%. The agricultural area accounted for 23.4% and followed by grassland for 13.8%. Swamp/mangrove, build-up area, and water body covered 3.7%, 0.7%, and 0.2% respectively (see Table 28.3 and Fig. 28.4). In 2010, while the number is decreased, the forest still dominantly covered the region by 53.4% and followed by agriculture at 25.2%. Grassland, Swamp/ mangroves, and build-up areas accounted for 16%, 3.7%, and 0.8%.

28.4.2 Land Cover Changes Analysis The analysis of land cover changes was conducted in two parts. The first part was analyzing the changes from 1990 to 2000 and the second part was analyzing the changes from 2000 to 2010. The changes in the land cover can be determined by comparing the results of land cover classification.

372 Table 28.3 Land cover of South East Sulawesi in 2010

Gazali and M. Kumano

No

Land cover

Area (ha)

1

Forest

1,974,867

53.4

2

Swamp/mangrove

137,636

3.7 16.0

3

Grassland

591,025

4

Build-up

28,443

0.8

5

Agriculture

933,481

25.2

6,124

0.2

0

0.0

25,538

0.7

3,697,113

100.0

6

Waterbody

7

Cloud/unidentified

8

Bare land Total

Fig. 28.4 Land cover map—2010

%

28 Quantifying the Dynamics of Ecosystem Services Value in Response …

373

Land Cover Changes 1990–2000. During this period, major changes happened in the forest area. It decreased by more than 140 thousand hectares or 6.3%. On the other hand, grassland/bare land, agriculture, and build-up area increased within this period by 22%, 5.7%, and 4.8% respectively (see Table 28.4). Land Cover Changes 2000–2010. For this period, the main changes still happened to the forest area. The decrement rate accounted for 8.3% or a decrease of about 179 thousand hectares. However, the increasing rate of the grassland, agricultural area, and build-up area grew up within this period. These areas increased by 19.6%, 8%, and 10.2% respectively (see Table 28.5). The Changes Pattern. The pattern of land cover changes was identified using confusion matrix analysis which examined the pattern of the changes between each type of land. Table 28.6 shows the crosstab matrix for identifying the change from 1990 to 2000, and Table 28.7 is the matrix for the changes from 2000 to 2010. As shown in Table 28.6, there is a decrease in the forest area within the period 1990 to 2000. Based on the confusion matrix above, it is clear that forest area is mostly converted to bare land/grassland and agriculture area. Furthermore, from this data, we can see that the increasing number of agricultural land comes from forest and grassland/bare land. In addition, for the build-up area, most of the additional build-up area came from agricultural land. For the period 2000–2010, in Table 28.7, the decreasing number of forests is the main change in the land cover. Forest conversion significantly increased the number of agricultural land by about 66.244 hectares within this decade. The changes pattern (growth rate and spatial distribution) of each land classification before decentralization (1990 to 2000) and after decentralization Table 28.4 Land cover changes 1990–2000 Land cover

1990

2000

Changes (ha)

Changes (%)

Forest

2,299,373.3

2,154,564.0

−144,809.3

−6.3

Swamp/mangrove

135,525.1

137,406.5

1881.4

1.4

Grassland

403,678.9

494,304.2

90,625.3

22.4

Build-up

24,642.3

25,820.8

1178.5

4.8

817,802.8

864,306.2

46,503.4

5.7

9854.4

14,573.4

4719.0

47.9

Agriculture Bare land

Table 28.5 Land cover changes 2000–2010 Land Cover

2000

2010

Changes (ha)

Changes (%)

Forest

2,154,564.0

1,974,866.6

−179,697.4

−8.3

Swamp/mangrove

137,406.5

137,635.8

229.3

0.2

Grassland

494,304.2

591,024.8

96,720.6

19.6

Build-up

25,820.8

28,442.7

2621.9

10.2

864,306.2

933,480.8

69,174.6

8.0

14,573.4

25,538.2

10,964.8

75.2

Agriculture Bare land

Year 1990 (ha)

Land cover

0

0

0

0

0

Build-up

Agriculture

Cloud

Water body

Bare land

2,154,564

0

Grassland

Total

0

2,154,564

Swamp/ mangrove

Forest

Forest

Year 2000 (ha)

137,407

0

0

0

0

0

0

133,593

3,814

Swamp/ mangrove

494,304

578

0

98

1,244

0

398,230

1,283

92,871

Grassland

Table 28.6 Crosstab matrix of the land cover changes 1990–2000

25,821

0

0

0

898

24,642

85

176

20

Build-up

864,306

399

0

0

815,661

0

5,112

243

42,891

Agriculture

0

0

0

0

0

0

0

0

0

Cloud

6,138

0

6,137

0

0

0

0

1

0

Water body

14,573

8,877

0

0

0

0

252

230

5214

Bare land

3,697,113

9,854

6,137

98

817,803

24,642

403,679

135,526

2,299,374

Total

374 Gazali and M. Kumano

Year 2000 (ha)

Land cover

0

6

0

0

44

Build-up

Agriculture

Cloud

Water body

Bare land

1,974,866

1866

Grassland

Total

0

1,972,950

Swamp/ mangrove

Forest

Forest

Year 2010 (ha)

137,637

0

11

0

366

0

160

137,100

0

Swamp/ mangrove

591,024

2768

0

0

2,936

0

483,167

0

102,153

Grassland

Table 28.7 Crosstab matrix of the land cover changes 2000–2010

28,443

11

3

0

2180

25,821

297

99

32

Build-up

933,481

201

0

0

858,701

0

8,201

134

66,244

Agriculture

0

0

0

0

0

0

0

0

0

Cloud

6,124

0

6,124

0

0

0

0

0

0

Water body

25,538

11,549

0

0

117

0

613

74

13,185

Bare land

3,697,113

14,573

6,138

0

864,306

25,821

494,304

137,407

2,154,564

Total

28 Quantifying the Dynamics of Ecosystem Services Value in Response … 375

376

Gazali and M. Kumano

Table 28.8 Comparison of the changes in the land cover before and after decentralization No

Land cover

Aspect

1

Forest area

Loss rate

Before (%)

After (%)

6,3

8,3

2

Agricultural land

Growth rate

5,7

8,0

3

Build-up area

Growth rate

4,8

10,2

4

Grassland

Growth rate

22,4

19,6

5

Bare land

Growth rate

47,9

75,2

(2000 to 2010) were descriptively compared to identify the dynamics of the region before and after decentralization. This session will focus on five types of land classification: Forest, agriculture, build-up area, bare land, and grassland which have significant roles based on the land cover changes analysis (see Table 28.8).

28.4.3 Ecosystem Service Value (ESV) Calculation The benefit Transfer Method was used to calculate the ESV in the study area. This approach used 17 ecosystem functions coefficient by Constanza, et al. (2014) that was adjusted by Yi et all (2017) which was using land cover as the proxy for calculating the ESV. There are 17 ecosystem functions. The detailed result of ESV calculation for the study area for each year is shown in Tables 28.9, 28.10, 28.11, and 28.12. Based on the calculation, the value of ecosystem services in South East Sulawesi Province thinned out from 1990 to 2010. In 1990, the value of ecosystem services accounted for 22,231 million US$, while in 2000 reduced to 22,140 million US$ and then went down to about 21,973 million US$ in 2010. The reduction is mostly influenced by the loss of forest areas that have the biggest service function as climate regulation. Table 28.9 List of ecosystem functions

No.

Ecosystem function

No.

Ecosystem function

1

Gas regulation

10

Pollination

2

Climate regulation

11

Biological control

3

Disturbance regulation

12

Habitat/refugia

4

Water regulation

13

Food production

5

Water supply

14

Raw material

6

Erosion control

15

Genetic resources

7

Soil formation

16

Recreation

8

Nutrient cycling

17

Cultural

9

Waste treatment

0.0

Swamp

0.0

0.0

11.1 0.0

0.0 0.0

0.0 0.0

0.0 0.0

5.6 0.0

0.0 0.0

0.0

13

14

92.0

0.0

0.0

0.0

0.0

852.2

15

16 0.0

67.1

0.0

0.7

83.2

481.2

0.0

0.0

73.0

21.8

0.0

0.0

13.4

490.1

0.0

0.0

13.3

64.7

4552.8

Total

67.4

0.0

0.0

0.0

76.8

3480.4

1681.7

4.6 12,375.2

64.7

17

299.6 270.0

10.5

459.9 193.1 3488.1 1993.6

0.0

0.0 1899.7 179.1 0.0

12.5 490.1

25.3

0.0

26.2

12

0.0 128.5 332.7

14.1

69.0

0.0

18.0

11

31.2 5118.2 556.4 825.5 479.8 1233.5 468.1 239.1 1045.1 101.1 192.5 914.8 2924.7 467.0 4843.8 2384.1 406.7 22,231.6

0.0

46.1

408.6

30.3

275.9

0.0

324.7

10

Total

0.0

0.0

6.9 0.0

9

0.0

0.0

0.0 0.0

0.0 232.2

0.8

32.2

0.0

8

0.0

353.3

17.8

774.9

0.0

87.5 435.1

7

Water body

55.3

24.2

62.1

0.0

6

Bare land

66.1 404.7 759.8

1.2

18.4

3.6

0.0

27.6 4699.9 151.7

16.1

5

0.0 327.1

Forest

0.0

4

Grassland

0.0

336.1

3

0.0

2

0.0

0.0

0.0

Agriculture

1

Ecosystem function

Build-up

Land class

Table 28.10 ESV of South East Sulawesi—1990 (unit: million US$)

28 Quantifying the Dynamics of Ecosystem Services Value in Response … 377

0.0

0.0

Water body

Bare land

0.0

1.5

0.0

0.0

0.0

0.0

0.0

46.1

67.1 410.3 770.3

19.8

0.0

11.1

56.0

29.7

58.2

0.0

7 0.0

0.0

0.0

358.2

21.7

8 0.0

0.0

6.5

0.0

0.0

0.0 0.0

0.0

0.0 235.3

1.0

30.2

0.0

92.5 459.8 726.1

6

0.0

5.6

414.3

37.1

258.5

0.0

343.1

9

15.3

23.7

0.0

27.7

11

0.0

0.0 0.0

0.0

0.0 130.3

17.3

64.6

0.0

19.0

10

13

14

86.2

0.0

0.0

337.3

0.0

0.0

900.6

15

16 0.0

70.9

0.0

0.7

84.4

589.2

0.0

0.0

74.1

26.7

0.0

0.0

13.6

600.1

0.0

0.0

13.3

67.8

4811.6

Total

82.5 0.0 0.0

0.0

76.8

3528.7

2059.3

4.3 11,595.9

67.8

17

303.8 273.7

12.9

430.9 181.0 3268.5 1868.0

0.0

0.0 2007.8 189.3 0.0 600.1

12

30.3 4846.0 552.5 835.1 500.7 1198.5 491.0 241.8 1058.6 100.9 197.0 1023.6 3113.0 471.1 4782.8 2268.9 428.3 22,140.1

0.0

Swamp

Total

4.4

5

0.0 345.7

17.2

0.0

4

25.9 4403.9 142.2

Grassland

Forest

3

0.0

0.0

355.2

2

0.0

0.0

0.0

Agriculture

1

Ecosystem function

Build-up

Land class

Table 28.11 ESV of South East Sulawesi—2000 (unit: million US$)

378 Gazali and M. Kumano

0.0

0.0

Water body

Bare land

0.0

1.8

0.0

0.0

0.0

0.0

0.0

46.0

67.2 411.0 771.6

23.6

0.0

11.1

56.2

35.5

53.3

0.0

7 0.0

0.0

0.0

358.8

26.0

8 0.0

0.0

5.9

0.0

0.0

0.0 0.0

0.0

0.0 235.8

1.2

27.6

0.0

99.9 496.6 665.5

6

0.0

5.6

415.0

44.3

237.0

0.0

370.6

9

18.3

21.7

0.0

29.9

11

0.0

0.0 0.0

0.0

0.0 130.5

20.7

59.3

0.0

20.5

10

13

14

79.0

0.0

0.0

337.9

0.0

0.0

972.7

15

16 0.0

76.5

0.0

0.6

84.5

704.5

0.0

0.0

74.2

31.9

0.0

0.0

13.6

717.5

0.0

0.0

13.3

74.7

5196.7

Total

98.7 0.0 0.0

0.0

76.6

3534.8

2462.2

3.9 10,628.6

74.7

17

304.3 274.2

15.4

395.0 165.9 2995.9 1712.2

0.0

0.0 2168.5 204.4 0.0 717.5

12

29.0 4511.1 541.3 835.2 529.5 1150.2 525.4 241.7 1072.5 100.5 200.4 1134.4 3353.1 476.4 4699.7 2121.7 451.5 21,973.6

0.0

Swamp

Total

5.3

5

0.0 373.4

15.8

0.0

4

23.7 4036.6 130.3

Grassland

Forest

3

0.0

0.0

383.7

2

0.0

0.0

0.0

Agriculture

1

Ecosystem function

Build-up

Land class

Table 28.12 ESV of South East Sulawesi—2010 (unit: million US$)

28 Quantifying the Dynamics of Ecosystem Services Value in Response … 379

380

Gazali and M. Kumano

Table 28.13 shows the comparison of the calculation of the value of ecosystem service in the study area listed by the function of the ecosystem in 1990, 2000, and 2010. Genetic resources, climate regulation function, and food production are the top three dominants of the value function in this region. The first two functions have a negative trend within the periods while the food production function shows an upward trend. Generally, the number of reduction of ES value increase after the decentralization period. Before decentralization, the ES value decreased to about US$ 91.5 million while after decentralization it went down significantly to about US$166.6 million. Table 28.13 Comparison of ESV changes (∆ ESV) before and after decentralization Ecosystem function

∆ ESV

ESV (million US$) 1990

2000

2010

Before decentralization

31.2

30.3

29

−0.9

5,118.3

4846

4511.1

−272.3

Disturbance regulation

556.4

552.5

541.3

−3.9

Water regulation

825.5

835.1

835.2

9.6

479.6

500.7

529.4

21.1

1198.6

1150.2

−34.9

Gas regulation Climate regulation

Water supply Erosion control

1,233.5

After decentralization −1.3 −334.9 −11.2 0.1 28.7 −48.4

Soil formation

468.1

491

525.4

22.9

34.4

Nutrient cycling

239.1

241.8

241.7

2.7

−0.1

1058.7

1072.5

13.6

13.8 −0.5

Waste treatment

1,045.1

Pollination

101.1

101

100.5

−0.1

Biological control

192.5

196.9

200.4

4.4

Habitat/ refugia

914.8

1023.6

1134.4

108.8

110.8

2,924.7

3112.9

3353.1

188.2

240.2

Food production Raw material

471

476.4

3.9

Genetic resources

4,843.8

4782.8

4699.7

−61

Recreation

2,384.1

2268.8

2121.7

−115.3

428.4

451.5

21.7

22,140.1

21,973.5

−91.5

Cultural Total

467.1

406.7 22,231.6

3.5

5.4 −83.1 −147.1 23.1 −166.6

28 Quantifying the Dynamics of Ecosystem Services Value in Response …

381

The most influenced depreciation of ESV occurred for climate regulation, erosion control, genetic resources, and recreation function.

28.5 Conclusions The first step of this paper is to assess the land cover dynamics in the area. Our results show that for each land classification, Forest area is the type that decreased within the study period, while agricultural land, build-up area, bare land, and grassland increased. Comparing the dynamic of the land before and after decentralization, there is a significant difference in growth/loss rate before and after decentralization. For the forest area, it lost 6.3% before decentralization, and then it lost 8.3% after decentralization. It indicates that the deforestation rate grew up compared to the period before decentralization. On the other hand, the growth rate of agricultural land and build-up area increases within the period after decentralization. For the agriculture area, it increased from 5.6 to 8.0%, while the growth rate for the buildup area increased significantly from 4.8% before decentralization to 10.2% after decentralization. Regarding regional proliferation/regional split policy, there is an indication that in newly proliferated regions, the area of built-up area, agricultural land, and bare land increases significantly compared to the parent region. Regional proliferation could accelerate the dynamics in that type of land cover. Secondly, from those land cover dynamics data, this paper tries to analyze the Ecosystem Service Values (ESV) of the region. The results indicate that there is a significant loss in terms of ESV. From 1990 to 2000 the value of ecosystem services lost about US$ 91.5 Million and then from 2000 to 2010, the loss became higher accounting for about US$ 166.6 Million. The biggest loss comes from ecosystem function value for climate regulation which is related to the decrease of forest area within the study area. From this perspective, it can be said that there is an indication decentralization policy harm the degradation of the ecosystem.

References 1. Alm J, Martinez-Vazquez J, Indrawati SM (eds) (2005) Reforming intergovernmental fiscal relations and the rebuilding of Indonesia: the big bang program and its economic consequences. Edward Elgar Publishing 2. Rondinelli DA (1981) Government decentralization in comparative perspective: theory and practice in developing countries. Int Rev Adm Sci 47(2):133–145 3. Hadiz VR (2004) Decentralization and democracy in Indonesia: a critique of neo-institutionalist perspectives. Dev Chang 35(4):697–718 4. Rodríguez-Pose A, Sandall R (2008) From identity to the economy: analysing the evolution of the decentralisation discourse. Eviron Plann C Gov Policy 26(1):54–72 5. Saito F (2008) Decentralization and local governance: introduction and overview. In: Foundations for local governance. Physica-Verlag HD, pp 1–24

382

Gazali and M. Kumano

6. Imron MA (2011) Regional autonomy proliferation of region and pseudo local government in Indonesia. Jurnal Kawistara 1(2) 7. Prasojo E, Holidin D (2013) Rethinking problematic governance of local government in new proliferated regions and their impacts. BISNIS & BIROKRASI: Jurnal Ilmu Administrasi dan Organisasi 19(2) 8. Matsui K (2005) Post-decentralization regional economies and actors: Putting the capacity of local governments to the test. Dev Econ 43(1):171–189 9. Aritenang A (2008) A study on Indonesia regions disparity: post decentralization. Available at SSRN 1737977 10. Pepinsky TB, Wihardja MM (2011) Decentralization and economic performance in Indonesia. J East Asian Stud 11(3):337–371 11. Brodjonegoro B, Asanuma S (2000) Regional autonomy and fiscal decentralization in democratic Indonesia. Hitotsubashi J Econ:111–122 12. Arze del Granado FJ, Martinez-Vazquez J, Simatupang RR (2008) Local government fiscal competition in developing countries: the case of Indonesia 13. McCarthy JF (2004) Changing to gray: decentralization and the emergence of volatile sociolegal configurations in Central Kalimantan, Indonesia. World Develop 32(7):1199–1223 14. Usui N, Alisjahbana AS (2005) Local development planning and budgeting in decentralized Indonesia: missing links in the bottom-up approach. Kansai Univ Rev Econ 7:71–97 15. Beard VA (2005) Individual determinants of participation in community development in Indonesia. Eviron Plann C Gov Policy 23(1):21–39 16. Campbell JB, Wynne RH (2011) Introduction to remote sensing. Guilford Press 17. Giri CP ed (2012) Remote sensing of land use and land cover: principles and applications. CRC Press 18. Daily GC (1997) Introduction: what are ecosystem services. Nat Serv Soc Depend Nat Ecosyst 1(1) 19. De Groot RS, Alkemade R, Braat L, Hein L, Willemen L (2010) Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol Complex 7(3):260–272 20. Costanza R et al (2014) Changes in the global value of ecosystem services. Glob Environ Chang 26:152–158 21. Liu S, Costanza R, Farber S, Troy A (2010) Valuing ecosystem services: theory, practice, and the need for a transdisciplinary synthesis. Ann N Y Acad Sci 1185(1):54–78 22. De Groot R et al (2012) Global estimates of the value of ecosystems and their services in monetary units. Ecosyst Serv 1(1):50–61

Part VII

Environmental Health and Carbon Emission Management

Chapter 29

Assessment of Human Health Impact of Particulate Matter Formation from Industry Textile Boiler in Cambodia Leakhena Hang, Palla Try, Srean Aun, Dalin Um, and Chanreaksmey Taing

Abstract Fine particulate matter with a diameter of 2.5 µm and smaller (PM2.5 ) is a significant air pollutant that affects human health. This present study aims to assess the human health impact of particulate matter formation from boiler operations in the textile industry in Cambodia. The human health impact is evaluated within the life cycle assessment (LCA) methodological framework. The damage impacts on human health were assessed by using the Recipe 2016 method. Emission inventory was built from selected six factories located in Phnom Penh city. The emission of pollutants (NOx, SO2 , PM2.5 ) was assessed using European Environmental Agency (EMEP/EEA) air pollutant emission inventory guidebook 2019. Therefore, this article presents the result of industrial textile-specific boiler operation. The result of Health impact damage is mainly from the emission of boilers operating in the textile industry in 2014 causing 3205.34 disability-adjusted life year (DALYs). PM2.5 contributed 91.16% of the total impact, which was mainly released from wood burning. NOx contributes 4.96%, and SO2 contributes 3.88%, respectively. Thus, NOx and SO2 emissions were relatively small impacts if compared to PM2.5 . However, there are still necessary to further investigate since their relative damage affected human health. The results of this study can be used as the first step in performing the scenario to propose the alternative of substitute material

L. Hang (B) Faculty of Hydrology and Water Resources Engineering, Institute of Technology of Cambodia, Russian Federation Blvd., P.O. 86, Phnom Penh, Cambodia e-mail: [email protected] L. Hang · S. Aun · D. Um Research and Innovation Center, Institute of Technology of Cambodia, Russian Federation Blvd., P.O. Box 86, Phnom Penh, Cambodia P. Try · S. Aun · C. Taing Faculty of Chemical and Food Engineering, Institute of Technology of Cambodia, Russian Federation Blvd., P.O. 86, Phnom Penh, Cambodia C. Taing General Department of Science Technology and Innovation, Ministry of Industry Science Technology and Innovation, Norodom Boulevard, Phnom Penh, Cambodia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_29

385

386

L. Hang et al.

boiler feeding to reduce the human health impact from potential particulate matter formation. Keywords Biomass · Particulate matter · DALY · Air pollution · Human health impact

29.1 Introduction Particulate matter (PM) can be defined as a mixture of solid and liquid particles suspended in the air [1]. It is noticed that PM produced through photochemical reactions involved pollutants that are a by-product of fuel combustion from motor vehicles, power plants, and industrial boilers [2]. Particulate matter with a diameter below 2.5 µm (PM2.5 ), (NO2 ), and ground-level ozone (O3 ) are considered by the World Health Organization (WHO) as the most harmful pollutants of health risk assessment studied by the long-term exposure. Evidently, its health effect is the strongest [3]. Obviously, epidemiology studies expressed that various adverse health effects and reductions in life expectancy including chronic and acute respiratory and cardiovascular morbidity, chronic and acute mortality, lung cancer, diabetes, and adverse birth outcome were associated with exposure to PM2.5 [4,5]. According to the Global Burden of Disease Study in 2013, the negative impact on human health as exposure to ambient PM pollution is ranked 12th among the global DALYs. DALYs expressed per 100 000 population. DALYs for a disease or health condition are the sum of the years of life lost to due to premature mortality (YLLs) and the years lived with a disability (YLDs) due to prevalent cases of the disease [6]. So far, health risk assessments caused by air pollution have been continued investigating. Clearly, PM2.5 was chosen to provide international recommendations regarding the consistent integration of its health effects into life cycle impact assessment (LCIA) because it might best describe the component of particulate matter responsible for adverse health effects [5]. In Cambodia, textile industry is one of the fastest growing industries if compared to the textile industry all over the world. It constituted of around 80 percent of total exports and was considered as an immensely contributed to the Cambodian economy. It has been estimated that the textile industry supports around one-fifth of the total Cambodian women to be employed and independent who can support themselves and their families [7]. Despite of its profitable contribution, the textile industry was known as one of the biggest global polluters and its high consumption of energy sources [8]. In Cambodia, there are studies about boiler operation impact to climate change but there is no study related to health impact of particulate matter that is released from boiler operation. Many related air pollution publications from boiler are most focused and concerned on the Environmental problem, mainly climate change. Nitkiewicz and Sekret [9] have compared the life cycle impacts of three heating plant systems with the consideration of the input and output data of each type with only CO2 , CO, and NOx emission data output investigated. Also, [10]

29 Assessment of Human Health Impact of Particulate Matter Formation …

387

have identified a proper drying system practice in Vietnam, Cambodia, the Philippines, and Myanmar. In terms of environmental concerns, GHG emission is the most concerned pollutant they are investigating. Moreover, [11] reviewed on comparative study of heat pump system and biomass boiler system to a tertiary building using the Life Cycle Assessment (LCA) with careful concerned of CO2 emission to environment from the impact of the system. Therefore, this study aims to investigate human health impact from boiler operation in the textile industry caused by particulate matter formation in Cambodia. The human health impact was assessed by applying life cycle impact assessment (LCIA) method using the ReCipe 2016.

29.2 Methodology Particulate Matter (PM) was considered as primary pollutant when it is directly emitted from such as construction site, fields, smokestack, or fire, and it was considered as secondary pollutant when it forms in atmosphere due to secondary chemical reaction from other airborne substances such as NOx and SO2 which emitted mostly form power plant, industries, and automobile [12,13]. In this study, four pollutants of PM2.5 , PM10 , SO2 , and NOx were investigated from the emission emitted from six textile factories located in Phnom Penh city, Cambodia. The factories were wellknown for their production of approximately 400 thousand pieces (shoes & clothes) per day and no less than 9.5 million pieces per year to suppliers such as PUMA, Adidas, Nike, Levi’s C&A, Next, and other brand name customers. Still, the production varied from factory to factory. The targeted data have been collected from the 2013 UNIDO report, published by Mr. Jerome Stucki which were listed in Table 29.1 and Fig. 29.1.

29.2.1 Calculation of Human Health Impact The human health impact is evaluated within the life cycle assessment methodological framework. The damage impacts on human health were assessed by using the Table 29.1 Presented annual recorded activity data in 2013 from six factories were reported by the UNIDO report [14]

Factory

Employee

Wood (m3 )

A

1203

929.95

B

4000

64,644

C

2530

22,371

D

3165

25,516.6

E

2000

3090.66

F

1518

35,708

388

L. Hang et al.

Fig. 29.1 Illustrate of location of factories; six target area for the investigation of air pollution emissions

ReCipe 2016 method. DALYs (disability-adjusted life years) have been used as the unit for human health damage, representing the year that is lost or that a person is disabled due to disease or accident [15, 16]. Thus, health impact (HI) can be calculated as the following formation: HI = E × CFs

(29.1)

where: HI = human health impact in-unit DALY. CF = characterization factors (ReCipe 2016 method). E = emission inventory can be calculated by using EMEP/EEA air pollutant emission inventory guidebook 2019 as the following formulation: E = Activities data × EFs

(29.2)

where: EF = emission factor obtained from EMEP/EEA tier 1 The EF of PM2.5 140 g/ GJ, NOx 91 kg/GJ, SO2 11 g/GJ table 3.5 page 17 in 1.A.2 EMEP/EEA 2019. Activities data = the amount of fuel burnt of source categories.

29 Assessment of Human Health Impact of Particulate Matter Formation …

389

29.3 Result and Discussion 29.3.1 Emission Inventory Figure 29.2 presents about the emission from combustion feeding boiler operation. The result of inventory shows that PM2.5 emission released is the highest, compared to NOx and SO2 . In the air quality management, the development of Emission Inventory (EI) database is very important. The development of EI database helps us to find out the main sources of pollution [17 18]. As in this study, EI was calculated by applying EMEP/EEA air pollutant emission inventory guidebook 2019 method to reveal the main pollutant from the sources of combustion of the textile boiler. Remarkably, the study proclaims that the common material used to burn boilers in the textile factory in Phnom Penh is wood. The result illustrated that wood highly produced particulate matter (PM2.5 ) as shown in Fig. 29.2. A similar discovery of high concentration of particulate matter released from wood-burning also found in the study of impact of stove use patterns and outdoor air quality on household air pollution and cardiovascular mortality in southwestern China by [19]. They found that PM2.5 concentration (106 µg/m3 ) was the highest if compared to other stoves used. In addition, in the assessing the effects of household wood burning on particulate matter in Rawand by [20], showed that households which used wood for cooking were vulnerable to moderately high concentration levels of PM2.5 and PM10 , while low concentration of these particulate matter was found in the households using charcoal for cooking. Also, [21] investigated the characteristics of particulate emissions from co-firing in the industrial boiler. He aimed to measure the total concentration of particulate matter and emission factor emitted from two sources: 100% coal burning and a mixture of coal fuel with 10% of biomass coal fuel (BCF). The result of his investigation revealed that high concentration of particulate matter was found in a mixture of coal fuel with 10% of biomass coal fuel (BCF) if compared to the emission released from 100% coal. These are caused by low heating value and high ash content which affect the efficiency of boiler combustion. It’s obvious that PM2.5 is the major pollutant that emitted from wood burning compared to other combustion sources.

29.3.2 Health Impact Caused by Particulate Matter Formation The human health criteria-related effect include cancer, non-cancer, and particulate pollutants, the letter issue include respiratory health issue from various-sized particulate matter (PM) [22]. The result of this study illustrated that health impact damage is mainly from the emission of boilers operating in the textile industry in 2014 causing a total of 3205.34 DALYs. PM2.5 was the most contributed of the total impact (90.16%), which mainly releases from wood burning. NOx and SO2

390

L. Hang et al.

Emission inventory from textile-boiler combustion per one employee 25

emission [kg]

20 15 10 5 0

Nox

SOx

PM2.5

Wood Fig. 29.2 Emission from boil combustion of Cambodia textile factory per one employee

contributed 5.31% and 4.53%, respectively. It’s noticeable that NOx and SO2 emissions were relatively small impacts if compared to PM2.5 produced from the woodburning. Adamkiewicz et al. [23] conducted research on disability-adjust life years in the assessment of health effects of traffic-related air pollution. In his study, measurements of traffic-related parameters were determined in the assessment of the impact as well as of traffic-related air pollutants on humans. The study is also conducted by using the ReCipe model of life cycle impact assessment (LCIA) methodology. The annual loss of more than 1600 DALY of PM10 and nitrogen oxides was found of the traffic-related air pollution. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015 by Cohen et al. [24] has reported that over the past 25 years, ambient air pollution contributed greatly to the global burden of disease in 2015. Ambien PM2.5 alone was ranked fifth as mortality risk factor. It caused approximately 103.1 million DALYs as estimated 4.2% of global DALYs, of which 59% of these were in east and South Asia. Savolahti et al. [25] investigated PM2.5 emissions from residential wood combustion and discovered that disease burden attributable to PM2.5 emitted from residential wood combustion was estimated to be 3400 disability-adjusted life years (DALY) and 200 deaths. The discovery number of DALYs from PM2.5 estimation, resulting from wood burning is found to be relatively high. High result of DALYs estimation could be a concern matter for policymakers to carefully take attention since their relative impact damage to human health.

29 Assessment of Human Health Impact of Particulate Matter Formation …

391

29.4 Conclusion The emission of pollutants emitted from selected textile-specific boilers was found to be high in concentration of PM2.5 and NOx. Seemingly, they caused a total of 3205.34 DALYs which were contributed by 90.16% of PM2.5, 5.31% of NOx, and 4.53% of SO2. Alternative technology to reduce emission of PM is to implement control technology name, Cyclone, electrostatic precipitator, or baghouse. In the future, the research can improve on updated activity data to ensure data quality. Acknowledgements This paper was funded by Cambodia Higher Educational Improvement Project (credit No.6221-kh). The author would like to thank the Institute of Technology of Cambodia for all the facilities. And thanks to all blind reviewers for the valuable comments and suggestions.

References 1. World Health Organization: Health-effects-of-particulate-matter-final-Eng (2013). https:// www.euro.who.int/_data/assets/pdf_file/0006/189051/Health-effects-of-particulate-matterfinal-Eng.pdf. Accessed 11 Apr 2022 2. Martuzzi M (2002) World Health Organization, Regional Office for Europe: health impact assessment of air pollution in the eight major Italian cities. World Health Organization Europe, Rome, Italy 3. World Health Organization: REVIHAAP-Final-technical-report-final-version. https://www. euro.who.int/_data/assets/pdf_file/0004/193108/REVIHAAP-Final-technical-report-final-ver sion.pdf. Accessed 27 Apr 2022 4. Xie P, Liu X, Liu Z, Li T, Zhong L, Xiang Y (2011) Human health impact of exposure to airborne particulate matter in pearl river delta, China. Water Air Soil Pollut 215:349–363. https://doi.org/10.1007/s11270-010-0483-0 5. Fantke P, Jolliet O, Evans JS, Apte JS, Cohen AJ, Hänninen OO, Hurley F, Jantunen MJ, Jerrett M, Levy JI, Loh MM, Marshall JD, Miller BG, Preiss P, Spadaro JV, Tainio M, Tuomisto JT, Weschler CJ, McKone TE (2015) Health effects of fine particulate matter in life cycle impact assessment: findings from the Basel Guidance workshop. Int J Life Cycle Assess 20:276–288. https://doi.org/10.1007/s11367-014-0822-2 6. Mukherjee A, Agrawal M (2017) A global perspective of fine particulate matter pollution and its health effects. In: de Voogt P (ed) Reviews of environmental contamination and toxicology, vol 244. Springer International Publishing, Cham, pp 5–51 7. Cambodian Textile Industry—Cambodia Clothing Sector. http://www.fibre2fashion.com/ind ustry-article/7357/cambodia-rising-through-the-horizon. Accessed 22 June 2022 8. Lellis B, Fávaro-Polonio CZ, Pamphile JA, Polonio JC (2019) Effects of textile dyes on health and the environment and bioremediation potential of living organisms. Biotechnol Res Innovat 3:275–290. https://doi.org/10.1016/j.biori.2019.09.001 9. Nitkiewicz A, Sekret R (2014) Comparison of LCA results of low temperature heat plant using electric heat pump, absorption heat pump and gas-fired boiler. Energy Convers Manage 87:647–652. https://doi.org/10.1016/j.enconman.2014.07.032 10. Nguyen-Van-Hung, Tran-Van-Tuan, Meas P, Tado CJM, Kyaw MA, Gummert M (2019) Best practices for paddy drying: case studies in Vietnam, Cambodia, Philippines, and Myanmar. Plant Prod Sci 22:107–118. https://doi.org/10.1080/1343943X.2018.1543547 11. Lozano Miralles JA, López García R, Palomar Carnicero JM, Martínez FJR (2020) Comparative study of heat pump system and biomass boiler system to a tertiary building using the Life

392

12. 13.

14.

15.

16.

17. 18.

19. 20.

21. 22.

23.

24.

25.

L. Hang et al. Cycle Assessment (LCA). Renew Energy 152:1439–1450. https://doi.org/10.1016/j.renene. 2019.12.148 US EPA O (2022) Particulate Matter (PM) Basics. https://www.epa.gov/pm-pollution/partic ulate-matter-pm-basics. Accessed 14 July 2022 World Health Organization (2021) WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization. https://apps.who.int/iris/handle/10665/345329. Accessed 14 July 2022 Hot-Spot and TEST in Cambodia|UNIDO, https://saro.org.za/what-we-do/energy-and-enviro nment/resource-efficient-and-low-carbon-industrial-production/water-management/test/hotspot-and-test-in-cambodia/ van Zelm R, Preiss P, van Goethem T, Van Dingenen R, Huijbregts M (2016) Regionalized life cycle impact assessment of air pollution on the global scale: damage to human health and vegetation. Atmos Environ 134:129–137. https://doi.org/10.1016/j.atmosenv.2016.03.044 Huijbregts MAJ, Steinmann ZJN, Elshout PMF, Stam G, Verones F, Vieira M, Zijp M, Hollander A, van Zelm R (2017) ReCiPe2016: a harmonised life cycle impact assessment method at midpoint and endpoint level. Int J Life Cycle Assess 22:138–147. https://doi.org/10.1007/s11 367-016-1246-y Bang HQ, Khue VHN (2019) Air emission inventory. IntechOpen Nguyen TKO, Lai NH, Permadi DA, Nguyen NHC, Sothea K, Chitaporpan S, Kanabkaew T, Rattanarat J, Sichum S (2020) Emission inventories for air pollutants and greenhouse gases with emphasis on data management in the cloud. In: Laffly D (ed) TORUS 3–toward an open resource using services. Wiley, pp 41–71 Snider G (2018) Impacts of stove use patterns and outdoor air quality on household air pollution and cardiovascular mortality in southwestern China. Environ Int 9 Irankunda E, Gasore J (2021) Assessing the effects of household wood burning on particulate matter in Rwanda. Int J Sustain Energy Environ Res 10:29–37. https://doi.org/10.18488/jou rnal.13.2021.101.29.37 Rudianto IS (2021) Characteristics of particulate emissions from Co-firing in an industrial boiler. Ecolab 15:23–29. https://doi.org/10.20886/jklh.2021.15.1.23-29 US EPA R 1: How does PM affect human health?|Air quality planning unit|Groundlevel ozone|New England|US EPA. https://www3.epa.gov/region1/airquality/pm-human-hea lth.html. Accessed 14 July 2022 Adamkiewicz Ł, Badyda AJ, Gayer A, Mucha D (2014) Disability-adjusted life years in the assessment of health effects of traffic-related air pollution. In: Pokorski M (ed) Environment exposure to pollutants. Springer International Publishing, Cham, pp 15–20 Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, Balakrishnan K, Brunekreef B, Dandona L, Dandona R, Feigin V, Freedman G, Hubbell B, Jobling A, Kan H, Knibbs L, Liu Y, Martin R, Morawska L, Pope CA, Shin H, Straif K, Shaddick G, Thomas M, van Dingenen R, van Donkelaar A, Vos T, Murray CJL, Forouzanfar MH (2017) Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet 389:1907–1918. https://doi.org/10.1016/ S0140-6736(17)30505-6 Savolahti M, Lehtomäki H, Karvosenoja N, Paunu VV, Korhonen A, Kukkonen J, Kupiainen K, Kangas L, Karppinen A, Hänninen O (2019) Residential wood combustion in Finland: PM2.5 emissions and health impacts with and without abatement measures. IJERPH 16:2920. https:// doi.org/10.3390/ijerph16162920

Chapter 30

Assessment of Heavy Metals Uptake by Carrot at Different Contamination Levels of Soil Syed Shabbar Hussain Shah, Tomomi Imura, and Kei Nakagawa

Abstract Heavy metals in different agricultural and industrial products can easily enter the food chain through the contaminated environment. These metals at first cause problems in plant growth and then cause health harm to animals and humans if used in the diet. These metals interfere with normal bodily functions. The soil was collected from an area with thriving agriculture and livestock industry in Shimabara city. Different levels of artificial soil contamination were done to assess the uptake of heavy metals by carrots grown in a controlled weather room. After harvesting, samples were prepared and placed in the sample cups to detect heavy metals using an X-ray fluorescence (XRF) analyzer. It was observed that uptake of Cu, Zn, and Pb were in order of Zn > Cu > Pb while in carrots parts, uptake followed the highest concentrations of metals in the side roots > stem and leaves > main root. Keywords Heavy metals · Livestock waste · Carrots

30.1 Introduction Heavy metals are becoming a serious threat to the ecosystem and living organisms. These metals can cause severe problems for humans and animals and plant growth and production [1]. Many sources of these heavy metals pollution in the environment include mining, untreated sewage sludge, composting, agriculture based products, and industrial production (smelters, pesticide production, chemical industry, oil refineries, and petrochemical plants) [2]. These heavy metals contaminate air, soil, and water. When cultivation is done on these contaminated soils, heavy metals are S. S. H. Shah · T. Imura Graduate School of Fisheries and Environmental Sciences, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki 852-8521, Japan K. Nakagawa (B) Institute of Integrated Science and Technology, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki 852-8521, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_30

393

394

S. S. H. Shah et al.

uptake and transported to plant parts [3]. This way, heavy metals enter the food chain, consuming this contaminated food. Heavy metals cause health-related problems in humans and animals [4]. Some metals are essential for the efficient functions of the body’s systems [5]. Copper, for example, is required by all known living species (including humans and animals) because it functions as a cofactor for enzymes and electron transport proteins involved in energy or antioxidant metabolism [6]. Zinc is essential for the structure and function of over 300 enzymes, as well as for nucleic acid and protein synthesis, insulin secretion, cellular differentiation and replication, and glucose utilization. It also has regulatory roles in several parts of hormone metabolism [7]. Chromium is also a needed component in the human body since it is thought to have a crucial role in the normal functioning of carbohydrate and lipid metabolism [8]. However, exceeding safe suggested levels of these metals can interrupt the organism’s metabolic activities in various ways. These may accumulate in organs such as the liver, heart, kidney, and brain and interfere with proper biological functioning [9]. Once heavy metals enter biological systems, these metals obstruct important bodily functions [10, 11]. Vegetables are considered an important part of the human diet. Many nutrients are provided by vegetables, including potassium, dietary fiber, folate, vitamin A, and vitamin C [12]. Unfortunately, vegetables are considered an easy way for heavy metals to enter the food chain as they can accumulate heavy metals easily from the environment [13, 14]. Therefore, it is becoming necessary to evaluate different sites of contamination and vegetables grown on these sites to control and prevent these heavy metals into the food chain. This study aimed to estimate heavy metal contamination in carrots growing in Shimabara city and to evaluate heavy metals distribution within carrot roots and shoots with different levels of contamination.

30.2 Material and Methods 30.2.1 Sampling Site and Case Study In the northeastern part of the Shimabara Peninsula, Shimabara city has a 22.2% cultivation area. It is known as an agricultural area, and carrots are one of the major crops in that area. Nakagawa et al. [15] found that the concentration of heavy metals will increase and could cause potential risks for humans and animals. Soil samples were taken (Lat/Long: 32.45°N, 130.21°E) to assess heavy metals bioavailability in carrots. The pre-analysis of the physio-chemical characteristics of soil in Shimabara city showed that there was a substantial amount of heavy metal concentrations present in the soil which may affect the growth of plants afterward. A pot study was conducted where the soil was spiked with different levels of Zn, Cu, and Pb in 500g soil using ZnSO4 .7H2 O, CuSO4 .5H2 O, and Pb (NO3 )2, respectively. Heavy metal contamination levels (control, A, B, C) were prepared using each salt and directly applied to

30 Assessment of Heavy Metals Uptake by Carrot at Different …

395

Table 30.1 Heavy metal contamination levels were used in the experiment Contamination levels

Control

A

B

C

Cu, Zn, Pb (mg/kg)

0

30

60

90

the soil (Table 30.1). The study was conducted in a controlled weather room (LPH1P-NCII) with artificial light intensity (20,900–22,600 lx) and time intervals of 12 h of daylight and 12 h of darkness. The temperature was kept at about 20 °C, while the humidity ranged from 65 to 70 percent. Mature carrot plants were harvested after two months (60 days). Carrot leaves and roots were separated by carefully washing, then rinsing with distilled water and storing in a furnace (DOV-300A: AZWAN). The absorption of heavy metals by different sections of carrots was examined using a Niton XL3t X-ray fluorescence (XRF) analyzer (Thermo Fisher, Inc.).

30.2.2 Heavy Metal Analysis Soil and carrot samples were tested following the US EPA 6200 standard. Samples were prepared by grinding homogenized, weighed, and placed in the sample cups (Premier Lab Supply, Inc.). In the cups, samples were compressed between Prolene film (Chemplex Industries, Inc.) and glass fiber filter (Advantec Group). The dumper material was made of polyester fiber wool. Before analysis, reference samples (standard reference material NIST2709a, certified by Rigaku Corporation, and soil certification standard material JSAC 0402, certified by The Japan Society for Analytical Chemistry) were used. For NIST2709a and JSAC 0402, reference sample measurements agreed well with certificated concentrations. The data was then statistically analyzed to check the heavy metal uptake by carrots. The following concentrations (Table 30.2) can be considered as the background concentrations in Shimabara city. Table 30.2 Physio-chemical characteristics of the soil (Mean ± SD (n))

Soil characteristics

(Mean ± SD (3))

pH

5.7 ± 0.35 (3)

EC (mS/cm)

21.7 ± 3.3 (3)

CEC (cmolc/kg)

34 ± 7.1 (3)

Texture

Loamy sand

Cu (mg/kg)

78 ± 6 (3)

Zn (mg/kg)

99.3 ± 7.6 (3)

Pb (mg/kg)

15 3.2 (3)

396

S. S. H. Shah et al.

30.3 Results and Discussions Carrots are an important part of the human diet and play a vital role in human mineral nourishment such as iron (Fe) [16]. The residual time of any heavy metal is great enough to affect the growing plants and ultimately affect human health [17]. In the present experiment following are some heavy metals which were analyzed during the study. The mean concentration of Cu, Zn, and Pb was detected in the main root, stem and leaves, and the side roots of carrots. Soil analysis of Cu, Zn, and Pb before and after the treatments showed that there was a significant relationship between the treatments. There was a significant change in the uptake of Cu, Zn, and Pb after the treatments. The results of the sampled plant showed that there was a significant reduction in the growth of plants due to the uptake of heavy metals present in the soil. The average number of main leaves was limited to 6, the average main root length of the plants was 7.4 cm, the average root diameter of the carrots was 1.21 cm and the average length of stem and leaves was 16.3 cm. The weight of the main root before drying was 6.9 g while after drying it was found 1.53 g while stem and leaves weight before drying was 1.84 g and after drying it was 0.41 g. Carrots that the heavy metals were extracted from the soil by the roots. It was observed that each metal differed considerably in uptake from the other. The concentration of Cu in the main carrot roots in the experiments was in the range of 0 mg/kg to 28 mg/kg. In control and 30 mg/kg, there was no uptake observed however in 60 mg/ kg the average value of Cu recorded was 4.7 mg/kg. The maximum concentration of Cu in carrots was noted at 90 mg/kg where its average concentration was 19.3 mg/kg (Fig. 30.1a). Zn concentration in control conditions was noted as 11.3 mg/kg. The maximum concentration of Zn was recorded at 90 mg/kg where its average concentration was 72 mg/kg (Fig. 30.1b). While in 30 and 60 mg/kg its average value was 64 and 67 mg/kg. The results of the experiment showed that there was no significant influence of Pb on the growth of carrots and its concentration in carrots. The level of Cu in shoots of carrots was in the range of 0 to 17.4 mg/kg. Maximum Cu level was observed at 90 mg/kg where its concentration was 17.4 mg/kg while at

30

(b) 140

25

120

Zn in carrots (mg/kg)

Cu in carrots (mg/kg)

(a)

20 15 10 5 0

100 80 60 40 20 0

Control

A

B

C

Control

A

B

C

Fig. 30.1 Accumulation of a Cu in the main carrot and b Zn in the main carrots with different levels of contamination of soil

30 Assessment of Heavy Metals Uptake by Carrot at Different …

397

60 mg/kg Cu level was 8.7 mg/kg (Fig. 30.2a). While in the side roots, the maximum concentration of Cu was accumulated at 90 mg/kg where its value was 140 mg/kg recorded. The minimum level of Cu was in the control condition which was 61 mg/kg. The results of 30 and 90 mg/kg of contamination depicted the 94.6 and 119.3 mg/kg values of Cu (Fig. 30.2c). Zn content in carrot shoots was measured in the range of 99 to 285 mg/kg in different treatments. The maximum level of Zn content was measured at 90 mg/kg which was 285.66 mg/kg. The minimum level of Zn content in carrots shoot was measured in the control conditions which was 99 mg/kg (Fig. 30.2b). The contamination of 30 and 60 mg/kg showed the value of 144.3 and 243.7 mg/kg of Zn content plant shoot. In the side-root of carrots, the maximum value of Zn content was observed at 90 mg/kg which was 8221.6 mg/kg while at 30 mg/kg Zn value was 1793.3 mg/kg (Fig. 30.2d). In shoots, Pb content in all contamination levels showed a non-significant influence on applied treatments. While the results of the study showed that there was a substantial amount of Pb content accumulated in the side roots of carrots which showed no obvious influence on the growth of carrots. Maximum Pb content was noted at 90 mg/kg which was 73 mg/kg having a greater influence on the roots (Fig. 30.2e). Vegetable species have different cumulative capacities of heavy metals due to different behavior and different enrichment capacities of heavy metals [18]. Many factors affect the intensity of the heavy metal’s uptake by the carrot samples such as the presence of these elements in soil, physio-chemical properties of soil, solubility, plant species, and age, as well as exposure time [19]. Pb concentration was only detected in the side roots whereas in all other samples it was less than the XRF limit of detection (0.3 μg g−1 ). Cd and Pb are non-essential elements, and their presence, even at extremely low concentrations, harms human health [20]. Peeled is good. Cu concentration was also under the limit of dictation in control and 30 mg/kg treatment in main root and stem and leaves samples. Cu concentration increased in carrot parts with the increasing contamination of soil. Zn concentration was high in all samples, and it also increased with soil contamination. Zn uptake in the leaves of carrots was significantly higher than in their roots for every treatment. These results agreed with previous studies that zinc accumulated significantly in carrots [21, 22]. Generally, absorption is higher in plants cultivated in places with more soil pollution. Cd and Zn are two metals that are relatively mobile and easily absorbed by plants [23]. On the other hand, Cu and Pb are heavily adsorbed onto soil particles, decreasing their availability to plants. Metal concentrations (Cd, Cu, Mn, and Zn) in spinach, radish, lettuce, and carrots depend on the total concentration of metals in soils in which plants are grown except for Pb. Although present in all treatments, Pb had lower uptake by most vegetables [24].

398

(b)

25 20

Zn in shoots (mg/kg)

Cu in shoots (mg/kg)

(a)

S. S. H. Shah et al.

15 10 5

Control

A

B

300 200 100

C

Control

(d)

200

Zn in side roots (mg/kg)

Cu in side roots (mg/kg)

400

0

0

(c)

500

150 100 50

A

B

C

12000 10000 8000 6000 4000 2000 0

0 A

B

(e)

80

Pb in side roots (mg/kg)

Control

Control

C

A

B

C

60 40 20 0 Control

A

B

C

Fig. 30.2 Accumulation of a Cu in the shoots b Zn in the shoots c Cu in side roots d Zn in side roots and e Pb in side roots with different levels of contamination

30.4 Conclusion Carrots that the heavy metals were extracted from the soil by the roots. It was observed that each metal differed considerably in uptake from the other. It was noticed that the mean concentration of Cu, Zn, and Pb accumulated in above-ground tissues is less than the mean concentration of metals in below-ground tissues of carrots, however, Zn can easily be uptake by stem and leaves. Heavy metal uptake of Cu, Zn, and Pb

30 Assessment of Heavy Metals Uptake by Carrot at Different …

399

were in order of Zn > Cu > Pb while in carrots parts, uptake followed the highest concentrations of metals in the side roots > stem and leaves > main root. Acknowledgements This research was funded by the Nagasaki University WISE Program.

References 1. Rehman AU et al (2021) Toxicity of heavy metals in plants and animals and their uptake by magnetic iron oxide nanoparticles. J Mol Liq 321:114455 2. Ali J (2004) The impact of industrial waste on human and natural resources: a case study of Khartoum North Industrial Area. Omdurman Ahlia University 3. Singh S et al (2012) Heavy metals accumulation and distribution pattern in different vegetable crops. J Environ Chem Ecotoxicol 4(10):170–177 4. Hembrom S et al (2020) A comprehensive evaluation of heavy metal contamination in foodstuff and associated human health risk: a global perspective. In: Contemporary environmental issues and challenges in era of climate change. Springer, pp 33–63 5. Sadeghi S, Jahani M (2009) New copper (II) ion-selective membrane electrode based on erythromycin ethyl Succinate as a neutral ionophore. Anal Lett 42(13):2026–2040 6. ATSDR T (2000) ATSDR (Agency for toxic substances and disease registry). Prepared by Clement International Corp., under contract, vol 205, pp 88–0608 7. Wang X et al (2019) The zinc transporter Slc39a5 controls glucose sensing and insulin secretion in pancreatic β-cells via Sirt1-and Pgc-1α-mediated regulation of Glut2. Protein Cell 10(6):436–449 8. Panchal SK, Wanyonyi S, Brown L (2017) Selenium, vanadium, and chromium as micronutrients to improve metabolic syndrome. Curr Hypertens Rep 19(3):1–11 9. Anjulo TK, Mersso BT (2015) Assessment of dairy feeds for heavy metals. Am Acad Sci Res J Eng Technol Sciences 11(1):20–31 10. Mansour SA (2014) Heavy metals of special concern to human health and environment. In: Practical food safety: contemporary issues and future directions, pp 213–233 11. Zhang H et al (2015) Research progress on heavy metals detoxification in human body. Agric Sci Technol 16(8) 12. Melse-Boonstra A (2020) Bioavailability of micronutrients from nutrient-dense whole foods: zooming in on dairy, vegetables, and fruits. Front Nutr 7:101 13. Kumar S et al (2019) Hazardous heavy metals contamination of vegetables and food chain: role of sustainable remediation approaches-a review. Environ Res 179:108792 14. Sandeep G, Vijayalatha K, Anitha T (2019) Heavy metals and its impact in vegetable crops. Int J Chem Stud 7(1):1612–1621 15. Nakagawa K, Imura T, Berndtsson R (2022) Distribution of heavy metals and related health risks through soil ingestion in rural areas of western Japan. Chemosphere 290:133316 16. Chevalier W et al (2022) Evaluation of pedoclimatic factors and cultural practices effects on carotenoid and sugar content in carrot root. Eur J Agron 140:126577 17. Oladoye PO, Olowe OM, Asemoloye MD (2022) Phytoremediation technology and food security impacts of heavy metal contaminated soils: A review of literature. Chemosphere 288:132555 18. Huang B et al (2020) Effects of soil particle size on the adsorption, distribution, and migration behaviors of heavy metal (loid) s in soil: a review. Environ Sci Process Impacts 22(8):1596–1615 19. Kumari S, Mishra A (2021) Heavy metal contamination, in soil contamination-threats and sustainable solutions. IntechOpen 20. Halder D, Saha JK, Biswas A (2020) Accumulation of essential and non-essential trace elements in rice grain: possible health impacts on rice consumers in West Bengal, India. Science Total Environ 706:135944

400

S. S. H. Shah et al.

21. Ebbs SD et al (2016) Accumulation of zinc, copper, or cerium in carrot (Daucus carota) exposed to metal oxide nanoparticles and metal ions. Environ Sci Nano 3(1):114–126 22. Intawongse M, Dean JR (2006) Uptake of heavy metals by vegetable plants grown on contaminated soil and their bioavailability in the human gastrointestinal tract. Food Addit Contam 23(1):36–48 23. Sharma R, Agrawal M, Agrawal S (2007) Interactive effects of cadmium and zinc on carrots: growth and biomass accumulation. J Plant Nutr 31(1):19–34 24. Mench M et al (1994) Evaluation of metal mobility, plant availability and immobilization by chemical agents in a limed-silty soil. Environ Pollut 86(3):279–286

Chapter 31

The Economic Impact of California’s Cap and Trade Program: An Interrupted Time Series Analysis with a Matching Approach Tomás Baioni

Abstract In 2012, California presented a Cap and Trade (CaT) initiative to meet its goal of reducing greenhouse gas emissions (GHG) to 1990 levels by 2020 and ultimately, achieving an 80% reduction from 1990 levels by 2050. Although these programs are found to have significant environmental impacts, there have not been many studies that analyze their economic effects. The objective of this paper is to investigate this question using a novel matching framework for 40 U.S. states within an interrupted time series analysis approach (ITSA) for the period 1990–2019. Results show that in the presence of the abovementioned emissions trading system, California’s per capita personal income significantly increases immediately after the intervention in $741.504. My estimations suggest too that there exists a positive difference in trends with respect to control states. Analogously, results indicate that the Californian real GDP index (2012 = 100) pre-post trend is significantly higher than control states. Additionally, it is observed that the renewable electric generation index (2012 = 100) trend for California, significantly outperforms that of the control states. Keywords Cap and trade · California · Carbon pricing · Emissions trading system · Interrupted time series

Abbreviations ITSA DID SCM CaT GHG

Interrupted time series analysis Difference in difference Synthetic control method Cap and Trade Greenhouse gases

T. Baioni (B) National University of La Plata, La Plata, Buenos Aires, Argentina e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Ujikawa et al. (eds.), Environment and Sustainable Development, Environmental Science and Engineering, https://doi.org/10.1007/978-981-99-4101-8_31

401

402

RGGI CO2 MMTCO2

T. Baioni

Regional Greenhouse Gas Initiative Carbon Dioxide Million metric tons of Carbon dioxide

31.1 Introduction In the United States, the first carbon pricing initiative was the Regional Greenhouse Gas Initiative or RGGI developed in 2009, which included the U.S. states of Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, Vermont, and Virginia. Its main objective was to establish an emissions trading system to limit the carbon emissions generated in each of the state members. In the year 2012, California was the second state with the most Carbon Dioxide (CO2 ) emissions from fossil fuels combustion, reaching 365.14 million metric tons (MMTCO2 ).1 Therefore, a CaT program was instrumented with the objective of achieving a reduction in GHG emissions. Although these programs are found to have significant environmental impacts, there have not been many studies that analyze their economic effects, especially focusing on the one initiated by California. Therefore, the objective of this paper is to investigate this question using a novel matching framework for 40 states within an interrupted time series analysis approach for the period 1990–2019. Results show that in the presence of the abovementioned emissions trading system, California’s per capita personal income significantly increases immediately after the intervention in $741.504. My estimations suggest too that there exists a positive difference in trends with respect to control states. Analogously, results indicate that Californian real GDP index (2012 = 100) pre-post trend is significantly higher than control states. Additionally, it is observed that the renewable electric generation index (2012 = 100) trend for California significantly outperforms that of the control states. This paper is structured as follows. In Sect. 31.2, I develop on the existing literature regarding carbon pricing initiatives. In Sect. 31.3, I elaborate on the methodology utilized in this work. In Sect. 31.4, I present the main results. Finally, in Sect. 31.5, I sum up the estimations and elaborate on conclusions to contribute to the environmental debate.

31.2 Literature Review Since the first carbon pricing program is traced back to 2005, the literature related to estimating their effects is new. Murray et al. [1] investigate on the environmental effects of the RGGI initiative using a three-stage regression and they stress that power 1

EIA’s State Energy Consumption, Price, and Expenditure Estimates (SEDS), 2020.

31 The Economic Impact of California’s Cap and Trade Program …

403

sector emissions would be more than 50% higher by 2011 in the states members had not the program been implemented. Yan [2] addresses the environmental performance of the RGGI initiative using a DID framework. He finds that the program directly caused a reduction of coal and natural gas consumption for electricity generation by 73% and 30%, respectively, within regulated states. However, in nearby, unregulated states, an increase in natural gas consumption by 237% and a decrease in coal consumption by 7% were found. Overall, the program reduced carbon dioxide emissions by 4.8 million tons in regulated states, but increased carbon dioxide emissions by 3.5 million tons in unregulated states, i.e., a reduction in total carbon dioxide emissions by 1.3 million tons per year. Despite the articles provided and the scope of their analysis, there is a significant lack of investigation applied to the Californian emissions trading system. Therefore, I believe this paper will significantly contribute to the literature by providing evidence on the economic impact of the mentioned initiative and by incorporating a new tool for policy interventions, the interrupted time series instrument.

31.3 Methodology 31.3.1 Data This paper utilizes data for 40 states and 30 years over the period 1990–2019. The construction of the dataset corresponds to availability of data within the variables used. With respect to the states, I have included every non-RGGI member and excluded RGGI members to avoid mixed effects. As stated before, this paper addresses 3 dependent variables and 4 control variables: ● Real GDP Index (2012 = 100): used as proxy of economic development. This variable is expected to have a positive coefficient due to the ETS. ● Per capita personal income: expressed in US dollars, this variable has been chosen as an alternative proxy of economic performance. Once again, it is expected to have a positive coefficient. ● Index of electric generation from renewable sources (2012 = 100): renewable sources include wind, solar, and geothermal energy sources. This variable is utilized as a proxy of environmental performance. ● Mining, trade and transport, and manufacture employment rate: all three variables are used as control variables. I have chosen these employment rates due to availability of data and environmental relevance: these industries are expected to emit a higher carbon dioxide quantity than other industries such as health, education, etc. ● Unemployment rate: utilized as control variable since its volatility and heterogeneity across states is lower.

404

T. Baioni

31.3.2 Interrupted Time-Series Analysis with a Matching Process The range of tools to evaluate policy interventions based on quasi-experimental designs has increased systematically over the last two decades. Usually, researchers have focused on either DID models [3], synthetic control methods, or a mixture of both [4]. However, the mentioned models are based on certain assumptions that pose significant limitations to the California ETS intervention. Therefore, this paper addresses a novel approach called interrupted time series analysis [5]. The idea is that a time series of a particular outcome of interest is used to establish an underlying trend, which is ‘interrupted’ by an intervention at a known point in time [6]. The novelty arises in the focus of the tool: although it has been widely used in social and epidemiological studies, the ITSA has been ignored in economic or political interventions. Since the objective of this approach is to estimate the change in the outcome variable due to the intervention, a counterfactual scenario is built under which the intervention did not take place. This scenario provides a comparison for the evaluation of the policy by examining any change occurring in the post-intervention period. Our paper is structured under a multi-group framework where one or more units are available for comparison. The key assumption is that the change in the level or trend in the outcome variable is presumed to be the same both for the control group and, counterfactually, for the treatment group, had it not received the intervention. In other words, this paper estimates Yt = β0 + β1 Tt + β2 X t + β3 X t Tt + β4Z + β5Z Tt + β6Z X t + β7Z X t Tt + ∊t

(31.1)

where Z is a dummy variable to denote the cohort assignment (treatment or control), and ZT t , ZX t and ZX t T t are all interaction terms; β 0 represents the starting level of the aggregated outcome variable of the control group; β 1 is the slope or trajectory of the outcome variable for the control group until the introduction of the intervention; β 2 indicates the change in the level of the outcome immediately following the intervention for the control group; β 3 is the change in slope of the outcome in the control group after the intervention until the end of the study; β 4 represents the difference in the level of the outcome variable between the treatment unit and controls prior to the intervention; β 5 entails the difference in the trajectory of the outcome variable between the treatment unit and controls prior to the intervention; β 6 indicates the difference between the treatment unit and controls in the level of the outcome variable immediately following the intervention; and finally, β 7 represents the difference between the treatment unit and controls in the slope of the outcome variable after the intervention compared with pre-intervention (see Fig. 31.1). For a correct representation, we expect the coefficients β 4 and β 5 to not be significant, which means the control states are comparable to California; and β 6 , β 7 to be

31 The Economic Impact of California’s Cap and Trade Program …

405

12 10 8

β7

6

β6 (Z*X)

4

β4 (Z) 2

β0

β3 (X*T)

β5 (Z*T) β2 (X)

β1T

0 0

1

2

3

4

5

Pre-intervention

6

7

8

9

10

Post-intervention

Fig. 31.1 Own elaboration based on Linden and Adams

statistically significant, which means that the difference between the treated unit and the control units for the post-intervention is significant. To select the states used for control, Linden [7] develops a process within the ITSA to create a comparable control group by matching directly on covariates.2 The matching algorithm iterates through each variable, replacing it as the dependent variable Y t , and retaining the control units that exceed a user defined p-value cutoff for the β 4 and β 5 coefficients of all the variables tested. Hence, by definition, the treatment unit and controls will be balanced on pre-intervention level and trend (β 4 and β 5 ) of all the specified covariates. In the second step, these control units are passed on to the ITSA outcome model, where their time-series trajectory is contrasted with that of the treatment unit. In this paper, the matching algorithm will search for states with p-values