Recent Developments in Energy and Environmental Engineering: Select Proceedings of TRACE 2022 981991387X, 9789819913879

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Lecture Notes in Civil Engineering

Rafid Al Khaddar · S. K. Singh · N. D. Kaushika · R. K. Tomar · S. K. Jain   Editors

Recent Developments in Energy and Environmental Engineering Select Proceedings of TRACE 2022

Lecture Notes in Civil Engineering Volume 333

Series Editors Marco di Prisco, Politecnico di Milano, Milano, Italy Sheng-Hong Chen, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, China Ioannis Vayas, Institute of Steel Structures, National Technical University of Athens, Athens, Greece Sanjay Kumar Shukla, School of Engineering, Edith Cowan University, Joondalup, WA, Australia Anuj Sharma, Iowa State University, Ames, IA, USA Nagesh Kumar, Department of Civil Engineering, Indian Institute of Science Bangalore, Bengaluru, Karnataka, India Chien Ming Wang, School of Civil Engineering, The University of Queensland, Brisbane, QLD, Australia

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Rafid Al Khaddar · S. K. Singh · N. D. Kaushika · R. K. Tomar · S. K. Jain Editors

Recent Developments in Energy and Environmental Engineering Select Proceedings of TRACE 2022

Editors Rafid Al Khaddar Oryx Universal College Academic Affairs Oryx Universal College Doha, Qatar N. D. Kaushika Indian Institute of Technology Delhi, India S. K. Jain Amity School of Engineering and Technology Amity University Noida, Uttar Pradesh, India

S. K. Singh Department Civil and Environmental Engineering Delhi Technological University Delhi, India R. K. Tomar Amity School of Engineering and Technology Amity University Noida, Uttar Pradesh, India

ISSN 2366-2557 ISSN 2366-2565 (electronic) Lecture Notes in Civil Engineering ISBN 978-981-99-1387-9 ISBN 978-981-99-1388-6 (eBook) https://doi.org/10.1007/978-981-99-1388-6 © 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

Contents

Trend Analysis of Air Quality of Greater Noida Using Mann–Kendall and Sen’s Slope Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deepak Kumar Soni, Yug Pratap Singh, Vishal Singh, and Varun Rawat

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Spatiotemporal Characterization of LST and Analysis of Its Spatial Dependence: A Spatial Autocorrelation Approach . . . . . . . . . . . . . Diksha Rana, Maya Kumari, and Rina Kumari

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Performance Evaluation of Wastewater Treatment Plant Using AAS-Based Quantification of Heavy Metals in Effluents of Industrial and Healthcare Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pranjal Pandey, Akanksha, Madhuri Kumari, and R. K. Tomar

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Recharge Assessment of a Rain Garden Using HYDRUS-1D: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pooja, Shailendra Kumar Jain, and R. K. Tomar

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Drinking Water Quality Evaluation and Its Hydrochemical Aspects in the Kabul Basin, Afghanistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ali Reza Noori and S. K. Singh

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A Bibliometric Analysis of Social Life Cycle Assessment (2008–2022) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soumen Ghosh

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Establishing a Preliminary Understanding of Loss and Damage in India: Case of Floods in Assam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saumya Jain

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Exploring the Role of Hydrogen Energy Towards Sustainable Energy System of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pooja Kumari and Kamal Kumar Murari

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Contents

Climate Risk Assessment and Adaptation in Small and Medium Enterprise Industries: Lessons from Andhra Pradesh, India . . . . . . . . . . . 111 Hrishikesh Mahadev Rayadurgam, Kamal Kumar Murari, Till Sterzel, Thomas Bollwein, Sylvia Maria von Stieglitz, Prakash Rao, and Dieter Brulez An Approach for Measuring Vulnerability to Risk and Climate Change—A Case Study of Maharashtra State . . . . . . . . . . . . . . . . . . . . . . . . 127 Bilal Khan and Unmesh Patnaik Agriculture Risk Management and Resilience Building Through Community-Based Disaster Risk Reduction: A Case Study of Talmala Village in Kalahandi District . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Ramakanta Naik and Kamal Kumar Murari Greening Indian Defence Forces: A Conceptual Framework Towards Accelerating Carbon Neutrality in India . . . . . . . . . . . . . . . . . . . . 159 Chetan Dhawad Comparing the Performance of Artificial Neural Network and Multiple Linear Regression in Prediction of a Groundwater Quality Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Riki Sarma and S. K. Singh Sources and Toxicity Assessment of Cyanide from Iron and Steel Industry Wastewater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Rachna Garg and S. K. Singh Assessing the Sustainability of Jatropha and Rapeseed Biodiesel: An LCA Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Kulvendra Patel and S. K. Singh Potential of Individual Leaf Traits and Leaf-Associated Microorganisms in the Removal of Particulate Matter . . . . . . . . . . . . . . . . 201 Mallika Vashist and S. K. Singh Fluoride Detection in Groundwater and its Correlation with Various Physicochemical Parameters in Gaya Town, Bihar, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Krishna Neeti, Reena Singh, and Shaz Ahmad Smart Air Quality Management System (SAQMS) for Smart Villas . . . . 217 Amrendra Kumar Singh, Anupriya Verma, Ashutosh Kumar Pathak, and Gaurav Saini Impediments in Contextualizing SDGs: Review on India’s City Plan Framework Towards Agenda 2030 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Neeharika Kushwaha and Charu Nangia

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Water Criteria Evaluation for Drinking Purposes in Mahanadi River Basin, Odisha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Abhijeet Das Estimation of Earth Temperature Profiles for Different Soils and Soil Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Shiv Lal A Conspectus on Recent Methodologies and Techniques Used for the Enhancement of Engineered Landfill . . . . . . . . . . . . . . . . . . . . . . . . . 279 Rohit Maurya, Madhuri Kumari, and Sanjay Kumar Shukla Development of an Integrated Assessment Model in the Climate Policy Framework and Its Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Bikash Kumar Sahoo and Kamal Kumar Murari Assessment of Human Health Risk Due to Contaminated Groundwater Nearby Municipal Solid Waste Disposal Site: A Case Study in Kanpur City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Abhishek Dixit, Deepesh Singh, and Sanjay Kumar Shukla Scenario of Air Quality Index in India and Its Effect on Human Health and Policies for Green and Clean India . . . . . . . . . . . . . . . . . . . . . . . 327 Shiv Lal, Kumud Tanwar, Prakash Chandra Dabas, and Ashok Kumar Kakodia Sustainable Manufacturing: Road to Carbon Zero Footprints . . . . . . . . . 341 Ramandeep Singh, Ravinder Kumar, and Ujjwal Bhardwaj Strategies of Installation of a Solar Integrated Carbon Capture and Sequestration (CCS) Plant on a 500-MW Size Coal-Fired Thermal Power Plant in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Vinod Krishna Sethi, Sudesh Kumar Sohani, Ravi Kumar Singh Pippal, and Meenakshi Samartha Adsorption Study of Chromium by Using Ziziphus Jujuba Sp. Seed as a Biochar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 M. G. Prathap and P. Purushothaman Microplastics in River Sediments Nearby to a Sewage Treatment Plant: Extraction, Processing and Characterization Assessment . . . . . . . . 375 Jaswant Singh and Brijesh Kumar Yadav Characterization and Sustainable Utilization of Municipal Solid Waste Incineration Ash: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 Saurabh Kumar, Sneha Gupta, and Neelam Singh Challenges to Implement Artificial Intelligence for Environmental Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Harshita Mogha and Nitasha Hasteer

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Application of Artificial Intelligence, Machine Learning, and Deep Learning in Contaminated Site Remediation . . . . . . . . . . . . . . . . . . . . . . . . . 411 K. V. N. S. Raviteja and Krishna R. Reddy Assessing the Relationship Among Energy, Economy, and Environment with a Special Reference to India . . . . . . . . . . . . . . . . . . . 427 Akanksha Singh and Nand Kumar Blending the Need for Heritage Fabric to Upgrade the Land-Use for Futuristic Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 Charu Middha, Aditya Bharadwaj, and Neeharika Kushwaha Simulation of D-Type (Darrieus) Vertical Axis Wind Turbine Using Q-Blade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453 Abhishek Gandhar, Vansh Panwar, Hena Varma, Sourav Rawat, Piyush Pant, and Shashi Gandhar Fuzzy Air Quality Index for Air Quality Assessment in Gujarat . . . . . . . . 463 S. A. Nihalani Legibility in a City: An Overview of the Factors Affecting Perceptions of Way-Finding in the Built Environment . . . . . . . . . . . . . . . . . 475 Sandeep Kumar, Amit Hajela, and Ekta Singh Evaluation of Property Pricing Structure of Residential Neighborhoods in Correlation with Urban Green Spaces of Noida City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483 Vikas Kumar Nirmal, Priyanka Singh, Vilas Bakde, and Ekta Singh

About the Editors

Dr. Rafid Al Khaddar has extensive experience in Water and Environmental Engineering with special expertise in wastewater treatment methods. He graduated from the University of Basra, Iraq, as Civil Engineer and obtained his master’s and Ph.D. in Civil Engineering Hydraulics from the University of Strathclyde, Glasgow, UK. He is currently Dean of Academic Affairs at Oryx Universal College, Doha, Qatar, and Emeritus Professor of Water and Environmental Engineering at Liverpool John Moores University, UK. He has maintained a very strong link with the UK Water and Environmental Industry in order to stay involved with any new developments in the aforementioned fields. He also has excellent links with professional bodies especially the Chartered Institution of Water and Environmental Management (CIWEM) where he was President of the Institution in 2015 and 2016. He is also Fellow of the Institution and Honorary Vice President of the Institution. He is also Member of the Joint Board of Moderators (JBM). This is the body that accredits all Civil Engineering Degrees in the UK and all around the world. He has a number of international links which culminated of his appointment as Visiting Professor to a number of International Universities in Turkey, Egypt, and Iraq. He has also joined an international delegation to validate civil and environmental engineering programs in the UK and internationally in Lithuanian and Saudi Universities. He has published over 170 publications in refereed journals and international conferences. Dr. S. K. Singh is Professor and Dean at Delhi Technological University (DTU), Delhi. He has obtained his Ph.D. from BITS, Pilani, and M.Tech. from IIT (BHU), Varanasi, and B.E. from Gorakhpur University having first division with distinction throughout. He is engaged in teaching, research, administration, and consultancy for the last 33 years and is presently Professor of Civil and Environmental Engineering for the last 22 years at DTU, Delhi. He is also Independent Director at WAPCOS Limited (A Mini Ratna-I PSU, GOI). He has guided 12 Ph.D., about 75 M.Tech. theses, and more than 180 UG projects. He has participated in various national and international conferences, published more than 238 research papers in national and international journals of repute, and authored 06 books. He has provided technical assistance as Member to groups of experts, set up for determining polluting industries in NCT of Delhi; examining proposals for establishing degree/diploma-level ix

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About the Editors

technical institutions in NCT of Delhi; evaluation of projects for the Department of Science and Technology (DST), Ministry of Environment and Forest, GOI; Member of Board of Governors, CSMRS, Ministry of Water Resources, GOI; Chairman, Departmental Promotion Committee, IASRI (ICAR) New Delhi; Member, University Court, University of Delhi; Expert Member, Equivalence Committee, UPSC, New Delhi; Advisor, Selection Committee for recruitment at UPSC, New Delhi; Technical Expert for various committees of MoEFCC, GOI; Expert Member, DST, GOI; and Member, Expert Committee, CAPART, Ministry of Rural Development, Government of India. He has received felicitations and awards by professional bodies such as APJ Abdul Kalam Award 2016; Rashtriya Shiksha Gaurav Puraskar 2014; International Felicitation and WEC-IIEE-IAEWP Environmental Award; Rashtriya Samman Puraskar 2005; Excellent Services Award; Clean Up The Earth Award; Eminent Personality Award. Dr. N. D. Kaushika is formerly Professor in Centre for Energy Studies, Indian Institute of Technology Delhi, subsequently Director of reputed engineering institutions in Delhi and National Capital Region, and is Specialist in renewable energy and environment. He had been Visiting Scientist to several institutes in Australia, Brazil, Malaysia, UK, and Hong Kong. He has authored over 200 research publications along with patents, five chapters, four scientific reviews, five books, and two edited volumes in the field of renewable energy and power and assorted topics of ICT. His research papers have been widely cited in international journals and have significantly influenced the trend in contemporary research. He has supervised 25 Ph.D. theses. His research in solar and renewable energy has been widely acclaimed in India and overseas. He is Recipient of the Hariom Prerit S. S. Bhatnagar Research Endowment Award for research in energy conservation in 1987. Dr. R. K. Tomar is Professor and Head of Civil Engineering Department at Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India. He obtained his B.E. (Civil) from Pune University, Pune, M.Tech. (Environmental Engineering) from Delhi University, and Ph.D. from the Indian Institute of Technology Delhi. His major areas of research interests include artificial intelligence applications in buildings and sustainable built environment. He has a combined experience of 32 years in industry and academia in various capacities. He has published several research articles in international peer-reviewed journals and conferences. He is also guiding five students for Ph.D. in the field of energy and built environment. Dr. S. K. Jain is currently Assistant Professor at the Department of Civil Engineering, Amity University Uttar Pradesh, Noida, UP. He obtained his B.E. (Civil) from Engineering College Kota, Rajasthan Technical University, Kota, and M.Tech. and Ph.D. from the Indian Institute of Technology Delhi. His major areas of research interests include municipal solid waste management, green buildings, and wastewater management. He has more than 15 years of experience in teaching and research. He has numerous experiences of attending and presenting technical papers in national and international conferences.

Trend Analysis of Air Quality of Greater Noida Using Mann–Kendall and Sen’s Slope Methods Deepak Kumar Soni, Yug Pratap Singh, Vishal Singh, and Varun Rawat

Abstract Today, air pollution is one of the prominent queries to deal with for any country including India. Reducing the amount of pollutants in the air is important for human health and the environment. The present study is, therefore, aimed at analyzing the readings of concentration of pollutants such as PM (10), CO, NO2 , O3 , and PM (2.5) over the span of 5 years (2017–2021) at UPPCB Reading Center Knowledge Park III, Greater Noida, Uttar Pradesh. The Mann–Kendall method of Sen’s Slope is used for predicting the trend of the recorded data and generalizing the magnitude as well as upward or downward regressions of the trend of each pollutant. We have analyzed the data with software embedded within Excel called MAKESENS (Mann–Kendall test regarding trends with Sen’s slope estimates) and estimated the trends as positive (for PM (10) or negative (for NO2 , PM (2.5), and O3 ). Sen’s slope analysis indicates the steepness and the extent of the increment and decrement in the pollutants. The highest amount of increment was shown by PM (10) and the highest decrement was shown by O3 over the span of 5 years. Interestingly, the trend shown by CO is 0 which means that in upcoming years the CO will neither decrease nor increase. These trends will help the government to take action on severely increasing pollutants such as PM (10) and to keep account of the extent of decrement of the pollutants like O3 and NO2 . Keywords Trend analysis · Pollutants · Air quality · Sen’s slope method · Mann–Kendall

D. K. Soni (B) · Y. P. Singh · V. Singh · V. Rawat Department of Civil Engineering, Galgotias University, Greater Noida, India e-mail: [email protected] Y. P. Singh e-mail: [email protected] V. Singh e-mail: [email protected] V. Rawat e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_1

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1 Introduction Greater Noida is a city situated in the G.B. Nagar district of Uttar Pradesh in India. Originally, this city was built as an extended part of Noida. This occurred under the act of Industrial Area Development of UP. 30 minutes is all it takes to travel through the Expressway connecting Noida to Greater Noida. This city consists of a lot of greenery. Air pollutants like PM (10), PM (2.5), O3, and NO2 have been a significant cause of concern all over the world and are gradually increasing over the past years too. To assess and identify this problem, many research are conducted, especially in the last few decades on air quality. The researchers are continuously searching for different methods to accurately analyze the trend and monitor the quality of air pollutants over a region. A study has been conducted in Malaysia to analyze trends over 2010–2015, and they used an AQI dataset of daily data acquired and then further analyzed it by processing time collection transformation that is accessed in Excel with the use of the XLSTAT add-on statistical software program (Rani et al. 2018). As for India, research in Bangalore by Amrita Thakur was conducted from 2006 to 2015 on ambient quality of air by the use of the Central Pollution Control Board (CPCB) approved Exceedance Factor (EF) which is calculated by dividing yearly observed mean of a selected pollutant by Standard annual value suggested for the same pollutant, which in turn led to the conclusion of success of adoption of Bharat Stage IV in 2014 (Thakur 2017). The use of the Mann–Kendall method became famous in the last decade when several studies successfully analyzed the trend for several major cities in India. A trend analysis research conducted in 5 districts of Gujarat on a dataset of rainfall ranging from 1901 to 2015 used Sen’s Slope regression and Mann–Kendall method to show that monsoon and annual rainfall at Surat, Valsad, and Dang shows the growing trends, and at the same time the districts like Bharuch and Navsari shows declined trends (Kumar et al. 2017). Similarly, a research in Varanasi (2013–2016) successfully implied the M-K test and Sen’s slope estimator on the index of air pollution station of their city to conclude that PM (10) represents a growing trend with annual approximate showing an increase of 273 µg/m3 and PM (2.5), NO2 , sulfur dioxide, and CO show a lowering trend with an annual value of 139 µg/m3 , 38 µg/m3 , 17 µg/m3 , and 1.37mg/m3 , respectively (Jaiswal et al. 2018). Furthermore, in the latest flight of events, a study conducted by Das et al. (2021) in Kolkata metropolitan city showed the aftermath of coronavirus disease (COVID-19)induced lockdown upon pollution caused by pollutants in the city and asserted that at some stage of lockdown in the region, the AQI value was declining nearly to 80% about all the stations because of the lockdown which was imposed there, and this all was achieved by applying the Mann–Kendall method over the values of AQI from air first-class index portal of the Central Pollution Control Board under the Ministry of Forest, Environment and Climate Change, India (Das et al. 2021). The present paper analyzes the data of pollutants in order to investigate the trend (increasing or decreasing) regarding each pollutant over the region Knowledge Park III, Greater Noida, using the pre-assured Mann–Kendall (M-K) test method and Sen’s Slope.

Trend Analysis of Air Quality of Greater Noida Using Mann–Kendall …

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Fig. 1 Study area’s location

2 Study Area and Methodology 2.1 Characteristics and Climate of Greater Noida Greater Noida (28.4744° N; 77.5040° E) is a deliberate metropolis situated in the Gautam Budh Nagar district of the Indian state Uttar Pradesh as shown in Fig. 1. Greater Noida has similar weather to Delhi: particularly warm and dry at some point of summertime, warm and humid at some point of monsoons, first-rate and dry all through spring and autumn, and moderate to cold all through winters. In the summer season (March to June), the general temperature change of Greater Noida ranges from 45 to 23 °C. The rainy season stays from mid of June until mid of September with a precipitation rate of 93.2 cm (36.7 inches). The winters in this region are very cold due to the presence of cold winds that emerge from the Himalayan region. General temperature goes as low as 4 °C at peak times.

2.2 Extraction of the Site’s Meteorological Data The everyday meteorological dataset was acquired from UPPCB Center for meteorological values, Knowledge Park III, Greater Noida, Uttar Pradesh (National Air Quality Index 2021), Fig. 2. The data collected includes the data of each of 5 pollutants PM (10), PM (2.5), CO, NO2 , and O3 over the time stamp of years 2017 to 2021. The dataset is then implied for conducting trend analysis with the help of Mann–Kendall test method and further to extract Sen’s slope.

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Fig. 2 UPPCB center, KP III. Source CPCB

2.3 Mann–Kendall Test For the statistical analysis of the collection of data, the non-parametric Mann–Kendall method or test is used. It is a thoroughly appreciated and accepted test in the nonparametric field (Hamed 2008; Soni and Singh 2017; Ramanathan et al. 2001). Therefore, in this research work to extract the trends regarding air pollutants the Mann–Kendall test is used. M–K method is majorly implied to explore the future values of meteorological facts. The M-K statistics S is calculated by S=

n−1  n 

sgn(x j − xi )

(1)

i=1 j=i+1

Here, time stamp series (x i ) is taken as i = 1, 2, … n – 1, whereas the time series (x j ) is marked in the order of j = i + 1, 2, … N. Every factor of x i is taken as a reference factor and that is then matched alongside the relaxation upon the facts factor x j in order which is calculated where n ≥ 8 and E(S) = 0 is the mean along which statistic S is normally distributed. q, the statistical variance is provided by Var (S) =

n(n − 1)(2n + 5) −

m

i=1 ti (i)(i

18

− 1)(2i − 5)

(2)

The number of ties shown by sample i is denoted as t i . The stats of the test are calculated as Z c which follows a normal distribution which is standard. The values of Z signify the trend being upward or downward with a positive and negative sign, respectively. For initial statistical analysis, the “Auto_MK_Sen.exe” program was used to attain the values of Z. Other descriptive information about the M-K test can be acquired from the studies published by Hamed (2008).

Trend Analysis of Air Quality of Greater Noida Using Mann–Kendall …

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2.4 Sen’s Slope Estimator To evaluate the value of slope for a trend which is being extracted with the use of the Mann–Kendall method, the non-parametric Sen’s method is applied (Soni and Singh 2017; Ramanathan et al. 2001). This method helps in the assumption of the trend as linear by using the linear regression method: f (t) = B + Qt

(3)

where t denotes time; B is a constant whereas Q is the slope. To collect the estimated slope Q, we have to underlie the value of slopes of each and every pair of data values: Q=

x j − xki j −k

(4)

On taking n values of x j in the regarding time series, we will get slope estimates Qi N = n(n − 1)/2

(5)

The median of N solutions of Qi is the slope of Sen’s estimator. These values of N are then arranged in an increasing order; later, Sen’s value is computed by Q = Q [(N +1)/2] , for odd value of N

(6)

Q = 1/2 (Q[N /2] + Q((N + 2)/2))

(7)

Or,

for all even values of N. MAKESENS 1.0 (Excel-Embedded Software) was used to apply the M-K test and Sen’s estimate slope for the calculation of variables regarding each pollutant taken in this study.

3 Results and Discussion Trend variations of different pollutants (PM (10), O3 , PM (2.5), NO2 , CO) over the time span of 5 years, 2017–2021, at Greater Noida are expressed in Figs. 3, 4, 5, 6, and 7 respectively, using MAKESENS.

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Fig. 3 Sen’s estimated slope for PM (10) of 2017–2021

Fig. 4 Sen’s estimated slope for O3

D. K. Soni et al.

Trend Analysis of Air Quality of Greater Noida Using Mann–Kendall …

Fig. 5 Sen’s estimated slope for PM (2.5)

Fig. 6 Sen’s estimated slope for NO2

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Fig. 7 Sen’s estimated slope for CO

In Fig. 3, Sen’s slope line shows the monotonously increasing trend with a maximum annual average value of 9426 µg/m3 with Z and S values being 0.24 and 2, respectively, indicating the increase in the amount of pollutant PM (10) in near future, whereas in Figs. 4, 5, and 6 for O3 , PM (2.5), and NO2 , Sen’s slope regression line plotted shows decrement over the last 5 years and their Z and S values are −0.24 and −2 for O3 , −1.22 and −6 for PM (2.5), and −2 and −0.24 for NO2 which in turn confirms the decreasing or negative trend imposing the idea of future decrement as well. In Fig. 4 for the year 2019–2020, a sudden spike of O3 was seen which indicates the sudden and heavy increment of the ozone in the air in the lockdown phase of coronavirus widespread. Figure 5 of PM (2.5) is of greater accountability as the residual values per year obtained are near to x-axis or the baseline, therefore the accountability and the accuracy of the future decrement in the PM (2.5) pollutant in greater Noida is more in the future years. In Fig. 7, the last pollutant CO gives an approximately straight Sen’s slope and 0 value of the S test and 0.00 value of the Z test further implying that the amount of CO in the atmosphere of Greater Noida is constant on average which is neither decreasing nor increasing drastically. In Fig. 7 of CO, as the residual value becomes less in the last consecutive years Sen’s slope is straight and the slope is 0 which in turn means that the trend is neither increasing nor decreasing; it is likely to follow the same data as per the last year approximately.

Trend Analysis of Air Quality of Greater Noida Using Mann–Kendall …

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4 Conclusions After a thorough analysis of this project and all the results obtained, we were able to conclude for the data of AQI parameters like PM (10), PM (2.5), CO, NO2 , and O3 over the span of 2017–2021 that • The positive slope is shown only by PM (10), and the rest PM (2.5), NO2 , and O3 showed a downward slope. Also, the last pollutant of CO showed no slope and was almost parallel to the x-axis showing no increment or decrement as well. • PM (10) is not in a decreasing trend and maxes out generally in the year 2018 and 2019 but with a little elevation in the upcoming year. Moreover, in Sen’s slope of CO, there is a mild downward dip in the year 2018. • After the final analysis and understanding of the result, we can say that the most crucial pollutant to be concerned in the city of Greater Noida is PM (10) and somewhat CO because they are not going to decrease in near future and can be a potential threat to the people and ecosystem of this city and the nearby NCR.

References Das N, Sutradhar S, Ghosh R, Mondal P (2021) Asymmetric nexus between air quality index and nationwide lockdown for COVID-19 pandemic in a part of Kolkata metropolitan India. Urban Clim 36:100789. https://doi.org/10.1016/j.uclim.2021.100789 Hamed KH (2008) Trend detection in hydrologic data: the Mann-Kendall trend test under the scaling hypothesis. J Hydrol 349(3–4):350–363. https://doi.org/10.1016/j.jhydrol.2007.11.009 Jaiswal A, Samuel C, Kadabgaon VM (2018) Statistical trend analysis and forecast modeling of air pollutants. Glob J Environ Sci Manag 4(4):427–438. https://doi.org/10.22034/gjesm.2018. 04.004 Kumar N, Panchal CC, Chandrawanshi SK, Thanki JD (2017) Analysis of rainfall by using MannKendall trend, Sen’s slope and variability at five districts of south Gujarat India. Mausam 68(2):205–222. https://doi.org/10.54302/mausam.v68i2.604 National Air Quality Index, Central Pollution Control Board, Ministry of Environment, Forests and Climate Change. https://app.cpcbccr.com/AQI_India. Ramanathan VCPJ, Crutzen PJ, Kiehl JT, Rosenfeld D (2001). Aerosols, climate, and the hydrological cycle. Science, 294(5549):2119–2124. https://doi.org/10.1126/science.1064034 Rani NL, Azid A, Khalit SI, Juahir H, Samsudin MS (2018) Air Pollution Index Trend Analysis in Malaysia, 2010–15. Pol J Environ Stud 27(2). https://doi.org/10.15244/pjoes/75964. Soni DK, Singh KK (2017). Trend analysis of climatic parameters at Kurukshetra (Haryana), India and its influence on reference evapotranspiration. In Development of water resources in India (pp. 327–337). Springer, Cham. https://doi.org/10.1007/978-3-319-55125-8_28 Thakur A (2017) Study of ambient air quality trends and analysis of contributing factors in Bengaluru, India. Orient J Chem 33(2):1051–1056. https://doi.org/10.13005/ojc/330265

Spatiotemporal Characterization of LST and Analysis of Its Spatial Dependence: A Spatial Autocorrelation Approach Diksha Rana, Maya Kumari, and Rina Kumari

Abstract The temperature of the land surface is an important indicator of climate as it shows the connection between land and the atmosphere. The relationship between natural factors and Land Surface Temperature (LST) must be determined to address environmental problems, including global warming. The study aimed to model LST and compute spatial autocorrelation using ArcGIS software. To estimate LST data for the years 2011 and 2021, temporal Landsat images were used. The results indicated that the mean LST has increased by 5.26 °C over a period of 10 years. According to Moran’s I, the obtained LST values show a high spatial autocorrelation for both the years as well as variation geographically. Studying the surface thermal environment is crucial for understanding and monitoring climate change. Keywords Land Surface Temperature · Urbanization · Spatial autocorrelation · Global Moran’s I

Abbreviations LST TIR LULC GCOS RS GIS NCR TM

Land Surface Temperature. Thermal Infrared. Land Use Land Cover. Global Climate Observing System. Remote Sensing. Geographic Information System. National Capital Region. Thematic Mapper.

D. Rana (B) · M. Kumari Amity School of Natural Resources and Sustainable Development, Amity University Uttar Pradesh, Sector 125, Noida, India e-mail: [email protected] R. Kumari School of Environment and Sustainable Development, Central University of Gujarat, Sector 30, Gandhinagar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 11 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_2

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1 Introduction Urbanization is a dynamic process and urban areas have been expanding to the outskirts for several years (Hamad 2020). The dynamism of cities is the result of inevitable changes caused by a variety of factors (Bhat et al. 2017). The main factors are population growth and urbanization. Development is taking place in urban areas to meet the increasing demands of the population (Anandharaj and Sulaxna 2016). The growing population in recent decades has had an impact on the environment both locally and globally (Weber and Sciubba 2019). Strong growth in developing countries has a direct and indirect impact on most environmental factors (Lambin and Meyfroidt 2011). Due to the good infrastructure and good resources, people migrate from rural areas to urban areas, and due to these factors urban areas and the global climate are affected by anthropogenic activities (Halder and Bandyopadhyay 2021). Vegetation loss, soil compaction, and the conversion of permeable to impervious surfaces as a result of the construction of roads, parking lots, and skyscraper buildings have an impact on the land’s surface (Kumari et al. 2019). One key environmental factor which is accepted by the Global Climate Observing System (GCOS) is Land Surface Temperature (LST). LST is a crucial indicator of the land environment. Nonetheless, Land Use Land Cover (LULC) change increases the LST (Pal and Ziaul 2017). As vegetation cover has been converted to impervious cover in urban areas, the increase in LST over the last few decades is regarded as a major cause for concern (Abir and Saha 2021). In addition, it impacts the regional material and energy cycles, ecological system balance, and human life and production (Trenberth et al. 2009). To understand land features, landforms, and the associated land surface temperature, geospatial technologies are used extensively in the twenty-first century (Bishop et al. 2012). Temperature observations at meteorological stations are the conventional method of studying thermal climate. However, this method is not feasible on a large scale. Now, satellite images of high resolution can be used to estimate LST (Li et al. 2013). Satellite imagery Thermal Infrared (TIR) bands are used to estimate LST and identify thermal features of the landscape. Satellite data sets like MODIES, LANDSAT, etc. (Sekertekin and Bonafoni 2020), with different spatial and temporal resolutions can be used nowadays to retrieve LST. Thus, Remote Sensing (RS) and Geographic Information System (GIS) techniques are widely used to evaluate LST variation. Therefore, the purpose of this research is to analyze the LST change over a decade in the study area and its dependences geographically using the Spatial Autocorrelation technique.

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2 Data and Methods 2.1 Study Area The Sonipat district is located in the National Capital Region (NCR). Sonipat is bordered by Delhi and Uttar Pradesh, and their latitudes and longitudes lie between 28°48, 15,, to 29°17, 10,, N and 76°28, 40,, to 77°12, 45,, E, respectively. Semi-arid climate dominates the study area, which has three seasons. June is the hottest month of the year, reaching 48 °C, which alternates with the coldest month of the year, December (3 °C). The district receives a normal rainfall of 567 mm per year. Most of the annual rainfall is caused by the southwest monsoon, which typically contributes about 76 percent of the total and begins in the final week of June and lasts until September. In this district, there are three types of sediments: Newer Alluvium, Older Alluvium, and Aeolian Sediments. In Fig. 1, a map of the study area is displayed. In the Quaternary Period, this area was part of the Indo-Gangetic plains. Geomorphologically, the area consists of the Ambala Plains, Older and Active Flood Plains, and the Aeolian Surfaces. The soils vary from sandy to clayey. The district covers approximately 2,260 square kilometres of area.

Fig. 1 The geographical location of the study area

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Table 1 Summary of satellite data Data set

Spatial resolution

Month and Year of acquisition

Details

Landsat 5

30 m

February 2011

The sensor on Landsat 5 is called the Thematic Mapper (TM). The sensor ID for Landsat 5 TM is LT5_TM

Landsat 8

30 m

February 2021

The sensor on Landsat 8 is called the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). The sensor IDs for Landsat 8 are L8_OLI and L8_TIRS, respectively

2.2 Data Used Table 1 lists the specifics of the data sets that were utilized for this study.

2.3 Pre-processing of the Satellite Data USGS Landsat 5 Thematic Mapper (TM) images from 2011 and Landsat 8 OLI/TIRS images from 2021 for February were used to derive LST. Radiometric and geometric errors in the image were implemented during pre-processing.

2.4 LST Calculations Thermal bands from Landsat 5 and 8 were used to quantify LST. ArcMap’s raster calculator needs to be used to apply a set of equations. The following equations are utilized in order to estimate LST using images captured by the Landsat 8 satellite: • Calculation of TOA (Top of Atmospheric)

TOA(L) = ML ∗ Q cal + AL where ML = band-specific multiplicative rescaling factor AL = band-specific additive rescaling factor in the image metadata and

(1)

Spatiotemporal Characterization of LST and Analysis of Its Spatial …

15

Qcal = quantized and calibrated standard product pixel values (DN) (Yasir et al. 2020). • TOA to Brightness Temperature conversion

BT + 273.15 = (K 2 /(ln(K 1 /L λ ) + 1))

(2)

where K 1 = calibration constant (w/m2 sr μm) K 2 = calibration constant (K ) and L λ = spectral radiance. • Calculate the Normalized Difference Vegetation Index (NDVI)

NDVI = (Band 5 − Band 4)/(Band 5 + Band 4)

(3)

where Band5 are NIR and Band4 are Red (satellite image bands). • Calculate the proportion of vegetation Pv

Pv = Square((NDVI − NDVImin )/(NDVImax − NDVImin )),

(4)

where NDVImax = NDVI image highest value and NDVImin = NDVI image lowest value (Sresto et al. 2022). • Calculate Emissivity (ε)

ε = 0.986 + 0.004 ∗ Pv

(5)

where Proportional vegetation is the Pv. • Calculate the Land Surface Temperature LST

LST = (BT/(1 + (0.00115 ∗ BT/1.4388) ∗ I n(ε)))

(6)

Finally, apply the LST equation to get the LST results. Equations that are used for estimating LST with Landsat 5 satellite images are

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• The Spectral Radiance of the satellite image was calculated using Eq. 7 using the Digital Number (DN) of the thermal band.

L λ = Gain × DN + of F set

(7)

where L λ is spectral radiance, DN is a digital number, and the gain value was derived from Landsat metadata. • The conversion formula was used to convert spectral radiance to kelvin. The formula is given in Eq. 8 below

(

TB = In

K2 K1 Lλ

) +1

(8)

where TB = surface temperature (K) K 1 , K 2 = calibration constant and L λ = spectral radiance calculated from Eq. 7 (Kumari et al. 2018). • Equation 9 was used to calculate the land surface temperature.

LST =

1+

(

TB λT B ρ

)

(9) lnε

where TB = surface temperature calculated from Eq. 2 λ = emitted radiance wavelength ρ = h × c/σ (1.438 × 10–2 Mk) h = Planck’s constant (6.626 × 10−34 Js) c = velocity of light (2.998 × 108 ms−1 ) and σ = Boltzmann’s constant (1.38 × 10−23 JK−1 ).

2.5 Spatial Autocorrelation Moran’s Index (I), the most widely used measure of spatial autocorrelation, was created in 1948. Based on a feature’s locations and values, this tool determines whether the data set is random, clustered or dispersed. I (Index value), p-value, and z-score additionally validate the index. The formulas below are used to calculate

Spatiotemporal Characterization of LST and Analysis of Its Spatial …

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these values. n I = S0

Σn Σn x=1

y=1 wx,y z x z y 2 x=1 z x

Σn

(10)

where z x = deviation of an attribute from its mean (xi − X) for feature X wx y = spatial weight between features X and Y n = number of features and S0 = sum of all spatial weights (Kumari et al. 2019). S0 =

Σn Σn x=1

y=1

wx,y

(11)

The statistic’s ZJ score is given by I − E[I ] zx = √ ∨[I ]

(12)

E[I ] = −1/(n−1)

(13)

∨[I ] = E[I 2 ] − E[I ]2

(14)

where

Moran’s index ranges from −1 to +1. I = −1 indicates dispersion or scattering, and I = +1 indicates that the pattern is spatially clustered. The absence of autocorrelation is denoted by a value that is either exactly zero or very close to zero.

3 Results and Discussion 3.1 LST Analysis Satellite-based imagery allows us to estimate the LST by using thermal bands (Sekertekin and Bonafoni 2020). As shown in Fig. 2, the LST values in 2011 ranged from 11.40 °C to 31.23 °C with an average temperature of 17.51 °C. But in 2021 the LST was increased, the minimum LST was 19.85 °C in 2021, and the maximum LST was 31.46 °C with an average temperature of 22.77 °C. Sonipat district falls under the NCR region as there is development happening in the district year after year to ease the stress of the capital. New industries, road networks, and buildings develop, as this incoming solar radiation is absorbed by these structures, resulting

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in less evapotranspiration as green cover and agricultural fields decrease and surface temperature increases. The average LST value has increased by 5.26 °C in a decade, as shown in Fig. 3. The highest LST values are often associated with urban and builtup areas along highways and lower LST values with agricultural land and water bodies. In a study conducted in the Pakhtunkhwa Mountainous Region of Pakistan, it was determined that there was a spatial–temporal variation in LST between 1987 and 2017 as a result of the expansion of built-up areas (Rehman et al. 2022).

3.2 Spatial Autocorrelation In both years, the LST distribution of Global Moran’s Index values was greater than zero, indicating either a positive autocorrelation or a pattern that is highly clustered. P-values are used to validate the same. There is less than 0.05 p-value (p ≤ 0.05), ruling out the assumption of randomness and independence. For both years, there is a greater than 2.58 z-score (z-score >2.58), which means that the observed pattern has a less than 1% probability of being stochastic. LST autocorrelation statistics are shown in Table 2. The global Moran’s I of LST in 2011 is lower than in 2021. From Figs. 2 and 3, it can be seen that the spatial aggregation trends in 2011 are less clustered with less LST values, while in 2021 the high clustered LST values can be seen with increased LST patches. The impact of urbanization can be seen in the results with increased LST values. In addition to LST studies, spatial autocorrelation can also be applied to other GIS and RS studies. A spatial autocorrelation analysis of LULC data at Pipestem Creek, North Dakota, was completed to assess the uncertainty of the correlation; it was determined that this technique aids in identifying the transition between forested and non-forested areas (Rozario et al. 2017).

4 Conclusion In the present study, a decadal analysis of the LST for the years 2011 and 2021 was performed. The LST was derived using the temporal Landsat imagery. The LST has increased in the Sonipat district due to urbanization and a reduction in green cover and agricultural land. An increase in anthropogenic activities and a decrease in evapotranspiration are the influencing factors for increased LST in the study area. The LST decadal analysis shows the average temperature increased by 5.26 °C from 2011 to 2021 in the study area. The obtained LST values showed moderate spatial dependence, and spatial patterns are consistent with the spatial clusters. 2011’s global LST Moran’s I is lower than 2021s. In 2011, spatial aggregation trends are less clustered with lower LST values, while in 2021 they are high with more LST patches. Moran I Index has been used to look into the spatial distribution of ecological and environmental variables in urban ecosystems.

Spatiotemporal Characterization of LST and Analysis of Its Spatial …

Fig. 2 Sonipat district LST for 2011 and 2021

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Land Surface Temperature Statistics Average Maximum LST

Minimum LST 0

5

10

15

20

LST in

0C

25

30

35

2021

2011

Fig. 3 LST statistics for 2011 and 2021

Table 2 LST autocorrelation statistics

Moran’s Index

z-score

2011

0.413117

28.19545

2021

0.496888

33.66412

Year

References Abir FA, Saha R (2021) Assessment of land surface temperature and land cover variability during winter: a spatio-temporal analysis of Pabna municipality in Bangladesh. Environ Chall 4:100167. https://doi.org/10.1016/J.ENVC.2021.100167 Anandharaj S, Sulaxna SC (2016) Urbanization in India by using Remote sensing and GIS techniques. Int J Geoinformatics Geol Sci 3(2). https://doi.org/10.14445/23939206/IJGGS-V3I 4P101 Bhat PA, Ul Shafiq M, Mir AA, Ahmed P (2017) Urban sprawl and its impact on landuse/land cover dynamics of Dehradun City, India. Int J Sustain Built Environ 6(2):513–521. https://doi.org/10. 1016/J.IJSBE.2017.10.003 Bishop MP, James LA, Shroder JF, Walsh SJ (2012) Geospatial technologies and digital geomorphological mapping: concepts, issues and research. Geomorphology 137(1):5–26. https://doi. org/10.1016/J.GEOMORPH.2011.06.027 Halder B, Bandyopadhyay J (2021) Evaluating the impact of climate change on urban environment using geospatial technologies in the planning area of Bilaspur, India. Environ Chall 5:100286. https://doi.org/10.1016/J.ENVC.2021.100286 Hamad R (2020) A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan. SN Appl Sci 2(1):1–9. https://doi.org/10.1007/S42452-019-1806-4/FIGURES/6 Kumari B et al (2018) Satellite-driven land surface temperature (LST) using Landsat 5, 7 (TM/ETM+ SLC) and Landsat 8 (OLI/TIRS) data and its association with built-up and green cover over Urban Delhi, India. Remote Sens Earth Syst Sci 1(3–4):63–78. https://doi.org/10.1007/S41976-0180004-2 Kumari M, Sarma K, Sharma R (2019) Using Moran’s I and GIS to study the spatial pattern of land surface temperature in relation to land use/cover around a thermal power plant in Singrauli district, Madhya Pradesh, India. Remote Sens Appl 15. https://doi.org/10.1016/j.rsase.2019. 100239

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Lambin EF, Meyfroidt P (2011) Global land use change, economic globalization, and the looming land scarcity. Proc Natl Acad Sci U S A 108(9):3465–3472. https://doi.org/10.1073/PNAS.110 0480108/SUPPL_FILE/PNAS.201100480SI.PDF Li ZL et al (2013) Satellite-derived land surface temperature: current status and perspectives. Remote Sens Environ 131:14–37. https://doi.org/10.1016/J.RSE.2012.12.008 Pal S, Ziaul S (2017) Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt J Remote Sens Space Sci 20(1):125–145. https://doi.org/10. 1016/J.EJRS.2016.11.003 Rehman A et al (2022) Modelling of land use/cover and LST variations by using GIS and remote sensing: a case study of the Northern Pakhtunkhwa Mountainous Region, Pakistan. Sensors 22(13):4965. https://doi.org/10.3390/s22134965 Rozario PF, Oduor PG, Kotchman L, Kangas M (2017) Uncertainty analysis of spatial autocorrelation of land-use and land-cover data within Pipestem Creek in North Dakota. J Geosci Environ Prot 05(08):71–88. https://doi.org/10.4236/gep.2017.58008 Sekertekin A, Bonafoni S (2020) Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sens 12(2):294. https://doi.org/10.3390/RS12020294 Sekertekin A, Bonafoni S (2020) Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sens (basel) 12(2):294. https://doi.org/10.3390/rs12020294 Sresto MA, Siddika S, Fattah MdA, Morshed SR, Morshed MdM (2022) A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh. Heliyon 8(8):e10309. https://doi.org/10.1016/j.hel iyon.2022.e10309 Trenberth KE, Fasullo JT, Kiehl J (2009) Earth’s global energy budget. Bull Am Meteorol Soc 90(3):311–323. https://doi.org/10.1175/2008BAMS2634.1 Weber H, Sciubba JD (2019) The effect of population growth on the environment: evidence from European regions. Eur J Popul 35(2):379. https://doi.org/10.1007/S10680-018-9486-0 Yasir M, Hui S, Ur S, Ilyas RM, Zafar A, Mehmood A (2020) Estimation of land surface temperature using LANDSAT-8 Data-A case study of district Malakand, Khyber Pakhtunkhwa, Pakistan. J Lib Arts HumIties (JLAH) Issue 1(4):140–148

Performance Evaluation of Wastewater Treatment Plant Using AAS-Based Quantification of Heavy Metals in Effluents of Industrial and Healthcare Sector Pranjal Pandey , Akanksha , Madhuri Kumari , and R. K. Tomar Abstract Almost 80–90% of raw water used for any industrial process gets transmuted into wastewater and only about 10% is recycled. Hence, provision of efficient wastewater treatment plants (WWTP) becomes a cardinal demand for every industry, may it be any sector. The objective of this study was to carry out performance evaluation of WWTP based on the presence of hazardous heavy metals in the effluents. The sample of effluents was collected from the industries from three different sectors, namely, Packaging (Electroplating), Dyeing and Healthcare (Hospital). Flame Atomic Absorption Spectrometer (FAAS) was used for quantification of heavy metals in the collected samples of effluent. Totally 10 samples were collected from the inlet and outlet WWTPs installed at the selected sites. From the results, it was found the inlet effluent sample from packaging industry had the highest quantity of heavy metals (Cr, Cu, Ni, and Pb) present in it. The inlet samples of healthcare sector had trace amount of heavy metals such as Pb, Ni, and Cr. The heavy metals were not detected in the samples collected from the outlet of the WWTPs of electroplating industry and healthcare sectors, thus proving acceptable efficiency of the WWTP in terms of heavy metal treatment. However, untreated trace of chromium metal was detected in the outlet sample of dyeing industry. This provides an indication of possibility for improving the heavy metal treatment scheme of WWTP. Keywords Wastewater characterization · Wastewater treatment · WWTP · Heavy metals · FAAS · Water pollution

P. Pandey (B) · Akanksha · M. Kumari · R. K. Tomar Department of Civil Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India e-mail: [email protected]; [email protected] M. Kumari e-mail: [email protected] R. K. Tomar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_3

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1 Introduction Industrial wastewater discharged into the environment (such as discharge in freshwater sources, on land disposal, etc.) consists of diverse organic and inorganic pollutants. Approximately 6.2 billion liters of untreated industrial wastewater is generated every day across India (Velusamy et al. 2021). The wastewater constitutes of pollutants including heavy metals that can be toxic and/or carcinogenic in nature and is harmful to humans and other living species (Mohammed et al. 2011). Heavy metals are that category of metals whose density lies within the range of 3.5 g/cm3 to above 7 g/cm3 or exceeds 5 g cm3 (cubic meter) (Heavy metals - Wikipedia 2023). Almost all the metal elements present in this density category are highly water soluble, toxic, and hazardous in nature. Lead (Pb), zinc (Zn), copper (Cu), arsenic (As), cadmium (Cd), chromium (Cr), nickel (Ni), and mercury (Hg) are among the most toxic heavy metals generated through various industrial processes (Sun et al. 2019). Metal complex dyes, insecticides, fertilizers, fixing agents (adds to fiber adsorption boosters), mordants, pigments, and bleaching agents are the most prevalent industrial processes in which such harmful heavy metals are used (Velusamy et al. 2021). Not just industrial wastewater, but also liquid waste discharged from medical facilities has become a key concern since it contains dangerous contaminants such as mercury (Hg), chlorinated plastics, and cleaning agents, and a variety of harmful toxins that are not found in municipal waste (Anju et al. 2010). As per the report by WHO (2009), 80% of medical waste has somewhat same characteristics of domestic waste while the remaining 20% waste is considered hazardous. Most of the hazardous waste generated by healthcare facilities is infectious waste, with sharp and genotoxic waste, heavy metals, chemicals, and pharmaceutical goods accounting for the remainder (Ghasemi and Yusuff 2016; World Health Organization Guidelines (WHO) 2008). In emerging countries, legislation regulating heavy metal levels in wastewater is becoming increasingly stringent. The current maximum contamination levels for heavy metals (ppm–mg/mL) in India are 0.05, 0.01, 0.25, 0.20, 0.80, 0.006, 0.00003, 0.050 for chromium, cadmium, copper, nickel, zinc, lead, mercury, and arsenic, respectively (Raju et al. 2021). The discharge of untreated or poorly treated effluent in water body causes eutrophication and health and safety risks to all the living beings, it also contributes significantly to Greenhouse Gas (GHG) emissions in the form of nitrous oxide and methane. Despite being made mandatory by various government authorities such as Central Pollution Control Board (CPCB) and National Green Tribunal (NGT), still many industries don’t have adequate pollution control measures. The wastewater by these industries is generally discharged into the freshwater water bodies, without adequate treatment, and thus becomes a large source of environmental pollution and health hazard. Chromium is widely used in the tanning of leather, electroplating, nuclear power plants, and textile making. Chromium (VI) is a carcinogenic oxidizing substance that is also toxic to plants and animals (Choppala et al. 2013). Exposure to chromium

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(VI) has been associated to gastrointestinal and lung cancers, as well as epigastria pain, nausea, severe diarrhea, vomiting, and bleeding (Indhumathi et al. 2014). Although chromium may exist in a range of oxidation states, the most prevalent species discovered in industrial effluents are chromium (VI) and chromium (III) (Blázquez et al. 2009). Chromium (VI) is more dangerous than chromium (III) and provides a larger risk (Abbasi-Garravand and Mulligan 2014). The United States Environmental Protection Agency (USEPA) has established the maximum chromium level in drinking water at 0.1 ppm (Kinuthia et al. 2020). Cadmium has been recognized as a human carcinogen by the USEPA, and it is thought to cause bone de-mineralization by direct bone deterioration or renal failure (Saini and Dhania 2020). Cadmium is largely derived from metal refineries, smelting, mining, and the photographic industries, and it is classified as a Category I carcinogen by the International Agency for Cancer Research (IARC) and a Group B-I carcinogen by the United States Environmental Protection Agency (USEPA) (Saini and Dhania 2020). Copper is necessary for enzyme production as well as tissue and bone formation. Copper (II) is hazardous and carcinogenic when ingested in large quantities, causing headache, vomiting, nausea, hepatic and renal failure, respiratory problems, and abdominal discomfort (Bilal et al. 2013). The copper limit in industrial effluents has been set by the USEPA at 1.3 ppm (Zong et al. 2011). Industrial copper sources include melting, mining, electroplating, surface finishing, electrical equipment, electrical components, and electrolysis (Bilal et al. 2013; Lan et al. 2013; Yin et al. 2012). Chemical precipitation, ion exchange, chemical oxidation, reduction, reverse osmosis, ultra-filtration, electrodialysis, and adsorption are some of the heavy metal removal treatment techniques used during the wastewater treatment process (Chaemiso and Nefo 2019). Other technologies have inherent limitations, such as the generation of large volumes of sludge, low efficiency, sensitive operating conditions, and high disposal costs, adsorption is the most efficient of all (Ahmad et al. 2015). Whereas adsorption is a relatively recent technology that is developing as a potentially preferable technique for heavy metal extraction due to its versatility, highquality treated effluent production, reversibility, and ability to recycle adsorbents (Qi et al. 2021). As a result, the current study’s goal was to evaluate the quantitative and qualitative characteristics of heavy metals in wastewater samples collected from five distinct sampling locations, mostly industries and healthcare facilities. Among all the heavy metals only five heavy metals, i.e., Cr, Cd, Cu, Pb, and Ni were shortlisted for the current study based on industrial activities/process, availability of standards in analysis lab, and limitations of the FAAS instrument. The study demonstrates how heavy metal contamination impacts human health and the aquatic ecosystem by degrading the aquatic environment and the surrounding flora and fauna. This study also contains a summary on the efficiency of heavy metal treatment techniques that are commonly used in wastewater treatment plants installed at the sampling locations. It also covers the potential future scope of advanced heavy metal removal methods and treatment techniques.

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Table 1 Sources of heavy metals in wastewater (Sörme and Lagerkvist 2002) S. no. Types of heavy metals Source of heavy metals 1

Arsenic (As)

Geological process, smelting operations, thermal plants, fuel industry

2

Chromium (Cr)

Electroplating and anodizing, milling, mining, chromium salt manufacturing, tannery, industrial coolant

3

Zinc (Zn)

Smelting, electroplating, anodizing-cleaning, milling

4

Lead (Pb)

Battery (lead-acid), paint, E-waste, smelting operations, coal-based thermal power plant, smelting operations, ceramics, bangle industry, gasoline

5

Copper (Cu)

Mining, electroplating, smelting operations

6

Nickel (Ni)

Smelting operations, thermal power plants, battery operations

7

Cadmium

Zinc smelting, waste batteries, E-waste, paint sludge, incineration and fuel combustions

8

Mercury (Hg)

Healthcare sectors, chlor-alkali industries, CFC BULBS and lamps manufacturing

9

Cobalt (Co)

Oil and gas refineries, aerospace industry, pigments

10

Silver (Ag)

Photographic operations, electroplating, anodizing-cleaning, conversion-coating, electrolysis depositions

Besides the analysis the paper emphasizes on the necessity to assess the level of contamination present in the source of sediments and aquatic species to gain a complete picture of the state of the ecosystem in terms of heavy metal poisoning at specific sites so that appropriate mitigation measures can be taken.

1.1 Source of Heavy Metal in Wastewater Heavy metals can enter wastewater through a variety of manufacturing, commercial, and healthcare operations (see Table 1). However, mining, tannery, metal refining, electroplating and galvanizing, dying, lead-acid battery smelting, and other industrial activities/processes are major heavy metal emitters.

1.2 Toxicity and Poisoning of Heavy Metals Heavy metal quantification, identification, and treatment in wastewater streams should be an essential aspect of the wastewater treatment process. Because the treated effluent is directly discharged via drains into surrounding freshwater bodies such as a river, ponds, lakes, and so on, the effluent should be tested for the presence of heavy metals (Rosborg and Kozisek 2016). It is critical to ensure that heavy metals are

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Table 2 Permissible limits of heavy metals according to various water standards (Standard 2012; US EPA 1992; World Health Organization Guidelines (WHO) 2008) S. no.

Heavy metals

National primary drinking water regulation by US EPA

India Standards (IS) 10500:2012—drinking water specification

WHO drinking water standards

Maximum contaminant level goal (MCLG) (mg/l)

Maximum contaminant level (MCL) (mg/l)

Maximum limit (mg/l)

Permissible limit (mg/l)

Limit (mg/l)

1

Arsenic

0

0.010

0.01

0.05

0.05

2

Cadmium

0.005

0.005

0.003

No Relaxation

0.2

3

Chromium

0.1

0.1

0.05

No Relaxation

0.05

4

Copper

1.3

1.3

0.05

1.5

1.0

5

Iron

0.3

0.3

0.3

No Relaxation

0.3

6

Lead

Zero

0.015

0.01

No Relaxation

0.01

7

Mercury

0.002

0.002

0.001

No Relaxation

0.001

8

Manganese

0.05

0.05

0.1

0.3

0.1

10

Nickel

0.02

0.20

0.02

No Relaxation

0.02

discharged within permissible limits and in accordance with other guidelines established by various concerned authorities such as the United States Environmental Protection Agency (US EPA), World Health Organization (WHO), Central Pollution Control Board (CPCB) of India, and others. Table 2 illustrates the maximum allowable concentrations (MCL) of heavy metals in drinking water because effluent (treated or untreated) discharged from industries and other sectors eventually mixes with a nearby river stream or other freshwater source. As a result, it is required to test freshwater for the presence of these heavy metals to treat it properly before human consumption or to discard it entirely.

1.3 Harmful Impact of Heavy Metal on Human Body When heavy metals are consumed or absorbed by the human body, they can have severe health consequences such as cancer, organ failure, and effects on the neurological system, which can be lethal in extreme situations. These heavy metals, even in trace amounts such as ppm (parts per million) or ppb (parts per billion), can

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Table 3 Harmful impacts of heavy metals on human body source (Ghasemi and Yusuff 2016) S. no.

Heavy metals

Impact on human health

1

Arsenic

Skin manifestations, visceral cancers, vesicular diseases

2

Cadmium

Kidney damage, renal disorder, carcinogenic

3

Chromium

Nausea, headache, diarrhea, vomiting, cancer-causing

4

Copper

Wilson diseases, liver damage, dermatitis, insomnia

5

Lead

Fetal brain damage, kidney disorders, impacts the circulatory and nervous systems of the living beings

6

Mercury

Rheumatoid arthritis, kidney disorders, asthma and nervous disorders

7

Manganese

Cardiovascular diseases, liver damage, an intestinal disorder

8

Nickel

Nausea, chronic asthma, sore throat and coughing, carcinogenic nature

9

Zinc

Depression, lethargy, neurological signs and disorders

10

Cobalt

Asthma, pneumonia, wheezing and other lung diseases

increase toxicity in water bodies, contaminating it and rendering it unsafe for human consumption as well as fatal for the flora and fauna that rely on it for survival. The significant effects of heavy metal ingestion on the human body are shown in Table 3.

2 Study Area The Delhi-NCR region is one of India’s fastest growing metropolitan areas, whether it’s due to increased industrialization, increased healthcare spending, or exploding urbanization. Due to this meteoric development, the level of pollution (air, water, noise, etc.) in the capital is dramatically increasing, owing mostly to inadequate pollution control techniques implemented by the key sectors, namely, industrial, health care, domestic, and so on. This research focuses on wastewater treatment with the objective of assessing heavy metals released from two major industrial units in the NCR region’s Sahibabad Industrial area and two healthcare units in South, Northwest Delhi, respectively. The major industrial units are the Packaging (electroplating) Industry and the Dying Industry, from which six samples were obtained from the inlet and outlet of Effluent Treatment Plants (E.T.Ps) and Sewage Treatment Plants (S.T.Ps) installed at both units, whereas four samples were collected from the healthcare sectors’ S.T.P and E.T.P (inlet and outlet) (ref Fig. 1).

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Fig. 1 Sampling locations

3 Materials and Method Figure 2 depicts the course of action which was adapted during the study for the detecting and quantitatively analyzing the heavy metals present in the collected wastewater samples from various sampling locations.

3.1 Instrumentation and Apparatus For this study, a Flame Atomic Absorption Spectrophotometer (FAAS) novAA 350 from Analytic Jena was used to detect and quantify heavy metals in collected wastewater samples. The sample to be studied in a flame atomic absorption spectrophotometer must be turned in its elemental state, vaporized, and thrust into the light source’s beam radiation. The elemental state is attained by aspirating a thin mist of a sample solution into a suitable flame type such as oxidant gas (O2 ) and fuel gas (acetylene). The absorption of the desired element is measured at the specific wavelength, which is distinctive to each element. The absorbance value is directly proportional to the concentration of each element present in the sample. The analysis is performed by comparing the sample’s absorbance to that given under the same conditions by a standard/reference sample of known composition.

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Site Selection

Collection of Samples

Garb Sampling

Blank Solution

Sample Preparation

Sample Filtration

Standard Preparation

Sample Digestion Process

4 standards each of Cu, Cr, Pb, Ni and Cd are introduced. Solution Introduction in FAAS (Nebulization)

Quantification of Heavy Metals (Using FAAS)

Result Analysis and Observation

Fig. 2 FAAS methodology flowchart

The following apparatus were used for the wastewater sampling digestion process: different size beakers, measuring cylinders, micropipette, volumetric flasks, burette, funnel, refrigerator, filter papers (Φ 125 mm), and hot plate.

3.2 Site Selection and Sample Collection Sites selection for this study was determined based on the type of manufacturing processes carried out by the industries and operations performed within the healthcare facilities. A total of ten samples were collected from the selected sites, with six samples (see Table 4) belonging to the packaging (electroplating) and dying industries

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and four samples belonging to the two different healthcare sector units. The samples were taken at the point sources (inlet and outlet) of the sites’ Effluent Treatment Plants (E.T.Ps) and Sewage Treatment Plants (S.T.Ps). Samples were collected in high-quality plastic bottles that had been pre-rinsed with 0.02 M HNO3 to minimize sample loss due to pH imbalance, evaporation, precipitation, and other key physical and chemical factors. On each site where the wastewater samples were to be collected, the sample bottles were first rinsed with the same influent and effluent, respectively. It was ensured that the bottles were filled to the capacity and properly sealed to prevent sample loss due to oxidation. The sampling method chosen was grab sampling, in which samples were collected manually and transferred from the site to the lab. Table 4 Description of wastewater treatment plant (WWTP) units from where samples were obtained Sample no.

Unit type and activity

Type of W.W.T plant

Capacity of the W.W.T Plant (Kilo Liters Per Day—KLD)

1

Healthcare sector—Research institute cum Hospital

Effluent Treatment Plant (E.T.P)—Inlet

150 KLD

2

Healthcare sector—Research institute cum Hospital

Effluent Treatment Plant (E.T.P)—Outlet

3

Healthcare Sector—Super Sewage Treatment Plant Specialist Hospital (S.T.P)—Inlet

4

Healthcare Sector—Super Sewage Treatment Plant Specialist Hospital (S.T.P)—Outlet

5

Packaging Industry—Electroplating Activity

Effluent Treatment Plant (E.T.P)—Inlet

6

Packaging Industry—Electroplating Activity

Effluent Treatment Plant (E.T.P)—Outlet

7

Packaging Industry—Electroplating Activity

Sewage Treatment Plant (S.T.P)—Inlet

8

Packaging Industry—Electroplating Activity

Sewage Treatment Plant (S.T.P)—Outlet

9

Textile Industry—Dying Activity

Effluent Treatment Plant (E.T.P)—Inlet

10

Textile Industry—Dying Activity

Effluent Treatment Plant (E.T.P)—Outlet

35 KLD

10 KLD

50 KLD

300 KLD

32

P. Pandey et al.

Fig. 3 Acid digestion process

Sample Preparation • Sample Filtration: Sample filtration is one of the most basic steps before analyzing any sample with the help of AAS. Filtration is performed to eliminate any suspended solids present in the sample that could interfere with the testing procedure or clog the sampler capillary of the nebulizer. All samples are filtered as soon as they are transferred from the site to the lab, using Whatman filter papers (125 mm.), small sample bottles, and a funnel. • Sample Digestion Sample digestion, also known as acid digestion, is a method of dissolving samples into solution by adding acids such as HNO3 to the wastewater sample and heating it on a hot plate until the matrix is completely disintegrated (ref Fig. 3). It is used when a sample must be disintegrated for the analyte to be released. Acid digestion is highly recommended for the analysis of trace heavy metals. After filtering the ten samples, it was discovered that a few samples had a significant degree of Matrix Interference, necessitating acid digestion in this situation. Matrix interference is one of the types of interferences that occur in FAAS. This type of interference occurs when the sample matrix is so complex that the viscosity, surface tension, and components cannot be accurately matched with standards, negatively affecting the uptake rate by the nebulizer’s capillary tube, affecting the nebulizer’s efficiency, and eventually affecting the entire atomization processes in the flam and causing erroneous results. Samples retrieved from the E.T.P inlet of the Packaging Industry (having electroplating as one of the process operations), i.e., sample number 5, showed a very strong matrix interference due to the presence of a significant quantity of Cr, Cu, and Ni trace heavy metals in it, giving it a very strong blackish-blue color. Furthermore, even after filtration, the sample obtained from the S.T.P inlet (sample ID 7) at the same site was not transparent enough for FAAS analysis, therefore making both influent

Performance Evaluation of Wastewater Treatment Plant Using … Table 5 Standard solution concentrations

Heavy metals

33

Standard concentrations (in ppm)

Cr

0.01, 0.02, 0.03, 0.04

Cd

0.01, 0.02, 0.03, 0.04

Cu

0.01, 0.02, 0.03, 0.04

Pb

0.01, 0.02, 0.03, 0.04

Ni

0.001, 0.002, 0.003, 0.004

samples unsuitable for FAAS analysis. As a result, the samples had to be digested with HNO3 . Similarly, samples 9 and 10 from the textile sector (having dying as the process activity) had to be digested to make the sample more transparent and to break the samples’ high viscosity. However, even after the basic acid digestion process as shown in Fig. 3, the matrix interference of sample number 5 was not influenced and it retained the strong greenish blue color, rendering it unfit for FAAS analysis since it may impact the nebulizer’s efficiency. To overcome the interference, the dilution factor of the sample was adjusted (from 10 ml (sample): 40 ml (distilled water) to 1 ml (sample): 100 ml (distilled water)) using distilled water. Standard Preparation Standard solutions with known concentration for the metals to be detected in wastewater samples were purchased from the market directly (ref Table 5). The standard solution is an important part of the FAAS testing process since it facilitates in determining the concentration of the element to be detected using the calibration curve method. At least three to five different concentrations of standard solutions are prepared for performing the calibration curve method. Standard solutions are element solutions (such as heavy metals) with known concentrations and are used to analyze the same elements with unknown concentration in a wastewater sample using FAAS. The absorbance of these standard solutions is first measured in the FAAS, and the results obtained are shown on a calibration curve. The absorbance of the wastewater sample is then measured and calibrated with the concentration in a quantifiable range. The final concentration of the elements to be tested in the wastewater sample is then calculated by plotting a calibration curve. A calibration curve is a concentration versus absorbance curve that helps in determining an element’s unknown concentration in a sample. The concentration of the analyzed metal is proportional to the number of ground state atoms in the flame. The concentrations of the standard solution of various elements (heavy metals) used for calibration are listed below. After the filtration, digestion, and preparation of the standard solution, the testing for the quantification of trace heavy metals in the wastewater samples using flame atomic absorption spectrophotometer is initiated and the quantitative results were obtained for each heavy metal.

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4 Result and Discussion Table 6 summarizes the findings based on quantitative analysis (amount identified in ppm) of heavy metals in collected wastewater samples using FAAS. Following significant results were obtained after the successful quantitative analysis of trace heavy metals in the samples: • Healthcare facilities: Pb, Ni, Cr, and Cu heavy metals were found in trace amounts in the samples collected from the healthcare sector facilities, i.e., influent wastewater samples (sample ID 1 and 3). Sample ID 2 and 4, which were the effluent samples, indicated that a controlled/allowable or below detection limit (BDL) level of heavy metals were being discharged with the treated wastewater. As a result, the installed wastewater treatment facility at the site was deemed effective in removing heavy metals from the wastewater stream. • Packaging industry (electroplating activity): Among all the samples collected, the influent sample from the effluent treatment plant (Sample ID 5 and 7) contained the highest concentration of Cr, Cu, Ni, and Pb. However, the quantity of heavy metals observed in the effluent/outlet sample (sample ID 6 and 8) was found to be within the permitted limit or BDL. As a result, the wastewater treatment plant’s efficiency for heavy metals treatment was proven to be excellent. • Dying industry: During the quantitative comparison of both inlet and outlet samples collected from the Wastewater Treatment Plant (WWTP) of the dying industry, a significant increase in chromium concentration was detected in the outlet/effluent sample (sample ID 9). This increase in heavy metal concentration in wastewater discharged from the premises indicates that the WWTP installed at the site requires servicing and maintenance, as well as an inspection to scrutinize the heavy metal treatment process implemented.

5 Methods of Removing Heavy Metals from Wastewater Heavy metal removal is a serious problem for any industrial unit due to its extreme toxicity and threat to human health and the environment even when present in trace amounts. There are several conventional methods for removing heavy metals from wastewater. They are divided into four categories: (i) electrochemical treatments (such as electrocoagulation, electroflotation, and electrodeposition), (ii) physicochemical processes (such as chemical precipitation and ion exchange), (iii) absorption processes (such as activated carbon, carbon nanotubes, and wood sawdust absorbents), and (iv) newly developed methods (such as membrane filtration processes, photocatalysis processes, and nanotechnology) (Ghasemi and Yusuff 2016) (ref Fig. 4). Chemical Precipitation is one of the most widely used treatment methods for heavy metals It follows the simple mechanism of removing the dissolved heavy metals by reacting it with a chemical precipitant and making it insoluble in nature.

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Table 6 Observations made after the quantitative analysis of heavy metals in wastewater samples Sample ID

Quantity of analyzed heavy metals (in ppm)

observation

Cr

Cd

Pb

Ni

Cu

1

(BDL)

(BDL)

(BDL)

(BDL)

0.0023

Cu in trace amount was observed in the inlet sewage sample

2

(BDL)

(BDL)

(BDL)

(BDL)

(BDL)

The outlet sample had no heavy metals present in it (observed amount of Cu was treated efficiently)

3

0.0053

(BDL)

0.0020

0.1499

(BDL)

Pb, Ni were observed in inlet effluent which were beyond the permissible limit

4

0.0051

(BDL)

(BDL)

(BDL)

(BDL)

The outlet sample had no heavy metals present in it (observed amount of Pb, Ni was treated efficiently)

5

173.4

(BDL)

0.2

81.18

31.52

The inlet effluent had heavy amount of Cr, Pb, Ni, Cu present in which was not at all within the permissible limit

6

0.0005

(BDL)

0.0000

0.0248

0.0124

The outlet effluent sample had trace amount of heavy metals present in it which was within the permissible limit (observed amount of Cr, Pb, Ni, Cu was treated efficiently)

7

0.0452

(BDL)

(BDL)

(BDL)

0.0133

Cr and Ni in trace amount were observed in the inlet sewage sample

8

0.0423

(BDL)

(BDL)

(BDL)

0.0042

The outlet sample had a permissible amount of heavy metals present in it (observed amount of Cr and Ni was treated efficiently)

9

0.0515

(BDL)

(BDL)

(BDL)

0.0637

Cr and Cu were observed in the inlet effluent which was beyond the permissible limits (continued)

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P. Pandey et al.

Table 6 (continued) Sample ID

Quantity of analyzed heavy metals (in ppm) Cr

Cd

Pb

Ni

Cu

10

0.0620

(BDL)

(BDL)

(BDL)

0.0078

USEPA

0.1

0.005

Zero

0.20

1.3

IS-10500-2012

0.05

0.003

0.01

0.02

0.05

WHO

0.05

0.2

0.001

0.2

1.0

observation The outlet sample still consists of Cr which is beyond the limit and slightly increased which means there is some technical issues within the E.T.P and hence demands for maintenance

Fig. 4 Types of heavy metal treatment techniques in wastewater treatment process (Gunatilake 2015)

Once the metals are converted into insoluble particles, their sizes are increased from very fine particles to large with the help of coagulation and flocculation and the sludge formed is later removed with other physical processes (López-Maldonado et al. 2014). Ion Exchange is one of the cost-effective treatment methods. Cations or anions containing special ion exchangers such as synthetic organic ion exchange resins are used to remove the metal ions from the wastewater. For example, in cationic resins, the positively charged ions such as hydrogen and sodium ions get exchanged with the positively charged nickel, copper, and zinc ions present in the solution. This method can only be used when there is low concentration metal available in the solution and this process is highly pH sensitive (Guruprashanth et al. 2021).

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37

Coagulation and flocculation is based on the electrostatic interaction between the heavy metal particles and coagulant-flocculant agents and is measured based on zeta potential. Few drawbacks of this process are production of sludge, use of chemicals and transformation of toxic compounds present in aqueous solution into solid phase (López-Maldonado et al. 2014). Electro-chemical treatment method consists of both treatment by passing electricity into the wastewater stream and chemical precipitation. In electrochemical treatment, the wastewater is treated for heavy metal by precipitating the heavy metals into either a weak acid or by neutralizing catholytes as hydroxides. This type of treatment method consists of process like electrocoagulation, electrodepositions, electroflotation, and electrooxidation. Electrocoagulation process is one of the types of electrochemical process in which the coagulant is generated in situ by electrolytic oxidation of the anode and removing the charged ionic metal particles from wastewater by allowing it to react with the anion in the wastewater (Das and Poater 2021). It is a slow precipitation process and has a long-term environmental impacts due to the heavy amount of sludge to be disposed (Shim et al. 2014). However, chemical precipitation requires a heavy amount of chemical dosing to reduce the amount of heavy metals for making it acceptable to discharge and also results in huge sludge discharge. The membrane process technique is one of the techniques used for removal of ionic compound from water and wastewater. Electrodialysis/reverse osmosis are the types of membrane process which is based on the principal of transportation of the ions through semi-permeable membrane under the influence of an electronic potential. The removal of heavy metal ions with the help of electrodialysis process can be successfully achieved by placing multiple numbers of membranes by allowing either positively or negatively charged particles to flow through it alternatively, thus due to this variation in the charge of the particles, the ions of heavy metals are separated out (Guruprashanth et al. 2021). Biosorption/bioremediation is a biological method of heavy metal treatment in which biological materials are used to accumulate heavy metals from wastewater via metabolically mediated/using Adenosine Triphosphate (ATP) or spontaneous physicochemical pathways of uptake/not using ATP, or as a property of certain types of inactive, non-living microbial biomass that bind and concentrate heavy metals from even very dilute solutions (Shamim 2018).

5.1 Treatment Method Adopted by the Wastewater Treatment Plant The samples obtained from the inlet of wastewater treatment plant (E.T.P) of the packaging industry, whose primary activity involved electroplating, printing, use of adhesive and lamination process, had the highest concentrations of heavy metals present in it. With a high concentration of Cr (173.4 ppm), Ni (81.18 ppm), Pb (0.2 ppm), and Cu (31.52 ppm), the packing industry was discharging the highest amount of heavy

38

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metals in its wastewater stream among the ten sites chosen for this study. The wastewater treatment plant/effluent treatment plant that was erected at this site has been considered as exceptionally feasible in removing these heavy metals present in such substantial concentrations. The physiochemical treatment method, which encompassed both chemical and physical treatment techniques, was selected to remove the heavy metals present in the effluent. The chemical treatment method includes a chemical precipitation technique involving hydroxide precipitation, followed by a specialized scheme of physical treatments such as Rapid mixing → Sedimentation → Filtration → Sludge treatment The samples collected from the E.T.P’s outlet, when analyzed indicated that all heavy elements, including Cr, Ni, and Cu, were within limits, with concentrations of 0.0005 ppm, 0.24 ppm, and 0.0124 ppm, respectively. The treated wastewater was therefore proven to be safe for any mode of disposal, as all heavy metals were within the acceptable limits set by the government and various authorized agencies.

6 Conclusion Our environment is being polluted by the generation and release of hazardous waste due to an alarming rise in industrialization, industrial production, commercialization, and the rapid expansion of the healthcare industries. The effluent samples from five wastewater treatment units were collected, and it was observed that the trace heavy metals such as Cd, Pb, Cu, Cr, and Ni and their compounds found in the samples are harmful in nature. All of these are used in several industrial activities such as electroplating, textiles, tanneries, metallurgies, petroleum refinery, and various activities performed in healthcare sectors. The presence of these heavy metals beyond the permissible limits in our environment, even in small amounts, has been linked to a variety of health issues as they accumulate in the food chain and become extremely toxic to all living things. Direct release of wastewater comprising of trace heavy metals into natural waterways poses a significant risk to the aquatic ecology, whereas direct discharge into the sewage system may also have a negative impact. For example, Minamata disease was caused by releasing of methyl mercury in industrial wastewater from the Chisso Corporation’s Chemical Factory, which is continued from 1932 to 1968. The highly toxic chemical was accumulated by fishes in Minamata Bay and Shiranui Sea resulting in mercury poisoning when it was consumed by local population (Budnik and Casteleyn 2019). An essential step in safeguarding the health of people and the environment is the analysis of wastewater for trace and heavy metal pollution. Varied nations have different laws governing wastewater, but the objective is to reduce the pollution that enters natural ecosystems. Due to stringent laws, improved treatment technology, and greener industrial operations in recent years, trace heavy metal emissions have reduced in many nations.

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However, continuous efforts are necessary to monitor and control the discharge of heavy metals from various industrial processes such as electroplating, textile processing, tanneries, among others. Not just the industries but even healthcare sectors like hospitals are found to be generating a significant amount of these toxic metals and hence demands for proper analysis. As a result, it is necessary to measure the wastewater emitted by these industries for a spectrum of heavy metals at different concentration, in various wastewater matrices. It was inferred from this study as well that not just the industries but even healthcare sectors like hospitals are found to be generating trace amount of toxic metals such as Pb (0.002 ppm), Ni (0.1499 ppm), Cr (0.0053 ppm), and Cu (0.0023 ppm). Whereas the highest amount of heavy metals, i.e., Cr (173.4 ppm), Pb (0.2 ppm), Ni (81.18 ppm), and Cu (31.52 ppm) were detected in the both the inlet E.T.P of packaging industry. Flame Atomic Absorption Spectrometry (FAAS) was proven to be successful in detecting the heavy metals in all the ten samples successfully which were collected from inlet and outlet of the effluent treatment plants and sewage treatment plants. In recent years, novel methods for removing heavy metals from wastewater, such as biosorption/bioremediation and neutralization, have also been developed. These advanced methods can be used for better treatment of these heavy metals and even by using the conventional treatments methods, these metals can be treated efficiently provided that the wastewater treatment plant installed by the industries and other sectors is up to the par in terms of plant capacity and its operation and maintenance is performed at regular intervals for a prolonged plant life and better efficiency.

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Ghasemi MK, Yusuff RB (2016) Advantages and disadvantages of healthcare waste treatment and disposal alternatives: malaysian scenario. Pol J Environ Stud 25(1) Gunatilake SK (2015) Methods of removing heavy metals from industrial wastewater. J Multidiscip Eng Sci Stud (JMESS) 1(1) Guruprashanth N, Hegde R, Suresh B (2021) A review on organic adsorbents for the removal of toxic metals from waste water. Asian J Adv Res Rep, 75–85 Heavy metals - Wikipedia (2023, April) In: Heavy metals - Wikipedia. https://en.wikipedia.org/ wiki/Heavy_metals. Last modified: April 2023 Indhumathi P, Syed Shabudeen PS, Shoba US, Saraswathy CP (2014) The removal of chromium from aqueous solution by using green micro algae. J Chem Pharm Res 6(6):799–808 Kinuthia GK, Ngure V, Beti D, Lugalia R, Wangila A, Kamau L (2020) Levels of heavy metals in wastewater and soil samples from open drainage channels in Nairobi, Kenya: community health implication. Sci Rep 10(1):1–13 Lan S, Wu X, Li L, Li M, Guo F, Gan S (2013) Synthesis and characterization of hyaluronic acid-supported magnetic microspheres for copper ions removal. Colloids Surf, A 425:42–50 López-Maldonado EA, Oropeza-Guzman MT, Jurado-Baizaval JL, Ochoa-Terán A (2014) Coagulation–flocculation mechanisms in wastewater treatment plants through zeta potential measurements. J Hazard Mater 279:1–10 Mohammed AS, Kapri A, Goel R (2011) Heavy metal pollution: source, impact, and remedies. In Biomanagement of metal-contaminated soils. Springer, Dordrecht, pp 1–28 Qi X, Tong X, Pan W, Zeng Q, You S, Shen J (2021) Recent advances in polysaccharide-based adsorbents for wastewater treatment. J Clean Prod 315:128221 Raju CA, Anitha J, Kalyani RM, Satyanandam K, Jagadeesh P (2021) Sorption of cobalt using marine macro seaweed graciliariacorticatared algae powder. Materials Today: Proceedings 44:1816–1827 Rosborg I, Kozisek F (2016) Drinking water minerals and mineral balance. Springer International Pu Saini S, Dhania G (2020) Cadmium as an environmental pollutant: ecotoxicological effects, health hazards, and bioremediation approaches for its detoxification from contaminated sites. In Bioremediation of industrial waste for environmental safety. Springer, Singapore, pp 357–387 Shamim S (2018) Biosorption of heavy metals. Biosorption 2:21–49 Shim HY, Lee KS, Lee DS, Jeon DS, Park MS, Shin JS, ... & Chung DY (2014) Application of electrocoagulation and electrolysis on the precipitation of heavy metals and particulate solids in washwater from the soil washing. J Agric Chem Environ 3(04):130 Sörme L, Lagerkvist RJ SOTTE (2002) Sources of heavy metals in urban wastewater in Stockholm. Sci Total Environ 298(1–3):131–145 Standard I (2012) Drinking water—specification (Second Revision). IS 10500 Sun L, Guo D, Liu K, Meng H, Zheng Y, Yuan F, Zhu G (2019) Levels, sources, and spatial distribution of heavy metals in soils from a typical coal industrial city of Tangshan, China. Catena 175:101–109 US EPA (1992) EPA National Primary Drinking Water Regulations. Title 40-Protection of Environment Velusamy S, Roy A, Sundaram S, Kumar Mallick T (2021) A review on heavy metal ions and containing dyes removal through graphene oxide-based adsorption strategies for textile wastewater treatment. Chem Rec 21(7):1570–1610 World Health Organization Guidelines (WHO) for Drinking-water Quality in third edition (2008) and fourth edition (2011), sited on 30th June 2019 Yin D, Du X, Liu H, Zhang Q, Ma L (2012) Facile one-step fabrication of polymer microspheres with high magnetism and armored inorganic particles by Pickering emulsion Zong C, Ai K, Zhang G, Li H, Lu L (2011) Dual-emission fluorescent silica nanoparticle-based probe for ultrasensitive detection of Cu2+. Anal Chem 83(8):3126–3132

Recharge Assessment of a Rain Garden Using HYDRUS-1D: A Case Study Pooja , Shailendra Kumar Jain , and R. K. Tomar

Abstract Groundwater is one of the most essential, precious, and renewable resources of our country. It has the potential to operate as a massive subsurface reservoir supplying freshwater during times of drought caused by monsoon failure. Due to the increase in urbanization, population growth, and cultivation, groundwater is constantly being misused further resulting in declining groundwater levels in various sections of our country. Urban development is resulting in impervious surfaces which reduces stormwater infiltration and increases surface runoff, further reducing the groundwater recharge rate. Traditional stormwater management techniques do not mitigate groundwater depletion successfully. Infiltration technique, such as rain garden, can be an effective way to combat groundwater exhaustion. Rain garden can be considered as a designed garden in a steep depression which accumulates rainfall from neighboring impermeable surfaces and concentrates it to recharge the groundwater. It helps in capturing rainwater and channeling it deep into the ground. Rain garden should be tested through carefully designed demonstration projects before being broadly installed. The goal of this study is to build an experimental rain garden that could be used to test the performance of a rain garden in reference to groundwater recharge. For simulations of groundwater recharging based on a year’s worth of daily-resolution meteorological, the well-known program HYDRUS-1D is implemented. Here, we have examined the transient water flow through a 1-meter-deep soil profile that is either bare or covered with crops planted in rain garden. After predicting the rate of groundwater recharge through simulation it has been concluded that when rain falls on a wide-open surface rapid evaporation takes place which allows only a small amount of water to infiltrate deep down into the earth. By concentrating the same amount of volume into a much smaller area, rain garden drives water into the ground quickly before evaporation consumes it. Keywords Groundwater · Stormwater infiltration · Rain garden · HYDRUS-1D Pooja (B) · S. Kumar Jain · R. K. Tomar Department of Civil Engineering, Amity School of Engineering & Technology, Amity University Uttar Pradesh, Noida, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_4

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1 Introduction Precipitation, irrigation, and industrial or municipal spills are all typical ways for water in order to enter the soil. The plant canopy may intercept several of the precipitation or irrigation water. Water would be evacuated either by the surface runoff or would concentrate at top of the soil surface until it evaporates and returned towards atmosphere or infiltrate deep down towards the soil if the rainfall water volume is higher as compared to the soil’s permeate capability. Some water that seeps into the soil profile is redirected to the atmosphere through the evaporation process. Another fraction of water may be absorbed by the roots of plants and later released into atmosphere through the plant by the process of transpiration. Both evaporation, as well as transpiration processes, are frequently integrated into one working cycle called evapotranspiration. Water which ceases to evacuate in the environment through the evapotranspiration process trickles at the bottom of soil area approaching the groundwater. The phenomenon of capillary rise may transfer water from water level by the hydrology process towards root system and then eventually towards soil surface if water table is near enough to the soil surface (Radcliffe and Šim˚unek 2010). Mathematical models are commonly used techniques in the study of groundwater systems. Forecasting simulations must be seen as estimates based on the input data’s quality and ambiguity (Kumar 2015; Baalousha xxxx). The HYDRUS-1D model could be used to predict vertical flow movement. This model helps in predicting recharge rates and also helps in estimating the spatial and temporal changes in soil moisture content (Tonkul et al. xxxx). It is impossible to simply use preliminary weather information, like rainfall, as the atmospheric boundary condition in HYDRUS during a protracted winter. The modeling results demonstrate that the landscape and soil type have a significant impact on seasonal fluctuations in surface and unsaturated or vadose zone water balance (Sergey and Sergey xxxx) Hydraulic conductivity in unsaturated soil, initial water content, porosity all have a significant impact on the absorption and evacuation process. Due to a lack of pore space, the rate of water transport through soil slows as soil particle size increases. Due to particle volume distribution and soil texture sandy soil has the fastest water flow, whereas clay soil has the slowest. For the infiltration and drainage processes, the initial and boundary conditions are critical (Al Mehedi et al. XXX). The diffuse groundwater recharge can be approximated using the numerical vadose zone modeling program HYDRUS-1D by using the ETp and other relevant data. The method aids in the understanding of groundwater recharge features in various areas with varying vegetation kinds, texture of the soil, vegetation cover, and precipitation rate. (Batsukh xxxx). In places where water level is profound and water accessibility on top of outer soil is limited owing to external parameters like temperature, nutrients amount, climatic factors, evaporation, and precipitation, recharge estimation is a challenging procedure to determine (De Silva xxxx). The water amount approaching the groundwater level by trickling down by the unsaturated zone is known as natural recharge of the groundwater. Groundwater recharge has an impact on the amount of water that may be stored in an aquifer over time, hence it’s critical to consider when

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evaluating any groundwater resources. The water amount recovered from the aquifer is mostly determined by groundwater recovery. (Rushton and Ward 1979). The region’s recent decrease in the levels of precipitation and high level of evaporation rates have raised the need for groundwater. Estimating groundwater recharge is done in this study as part of the sustainability of the aquifer.

2 Rain Garden Rain gardens are an innovative method of recharging the water artificially. This facility is something like the small manicured garden in a cursory topographic panhole which collects rainfall from the surrounding impermeable surfaces, such as from roofs or from parking lots. In particular, in saturated conditions, potted plants offer an organically active root zone that helps to maintain soil intrusion and porosity by generating macro pores with strong hydraulic conductivity. Plant transpiration also restores the soil water storage capability between rainstorms during interstorm periods. Rain gardens are distinguished by a carefully built substrate for filtering rainfall which prevents runoff from entering the drainage system and gradually infiltrates water into the earth. Further, upon the installation of rain garden, water discharge from impervious surfaces to the drainage system is limited. This also improves water retention which is critical in the context of climate change adaptation and the reduction of damage caused by heavy rains.

3 Study Area 3.1 Geography and Demography Coordinates: 77°28, 23.677,, E 28°30, 32.542,, N Population: 642,381 (Census 2011) Area: 53,000 ha (204 sq. mile) District: Gautam Buddha Nagar State, Country: Uttar Pradesh, India Elevation: 200 m (656 ft.) The New Okhla Industrial Development Authority (77°22, 46.444,, E, 28°35, 13. 017,, N) was incorporated on April 19, 1976. This city is regarded as one of Delhi’s most modern neighborhoods in the National Capital Region. The city is situated in Uttar Pradesh’s Gautam Buddha Nagar district. Noida is in the northern section of India and it shares land borders with Delhi. Noida is bordered towards the west and

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Fig. 4.1 Geographical location of the study area in India’s Map (Source Gopal et al. 2012)

southwest through the Yamuna River and from the east and southeast through the Hindon River. From the north and northwest side, Delhi has a border with Noida, while, from the northeast, it shares a border with Ghaziabad. Noida is located in the Yamuna River’s catchment area and is built on Yamuna’s historic riverbed (Fig. 4.1).

3.2 Climate The district’s climate is humid subtropical, with a hot summer and a chilly winter. Summer temperatures, i.e., from the month of March–June, weather persists too hot ranging from the maximum of 48 °C to the minimum of 30 °C. From mid-June until mid-September, the monsoon season is in full swing. Winters in Noida are chilly and unpleasant due to the cold waves coming from the Himalayan region. During winter, temperatures can drop to as down as 3–4 °C. Fog and smog are additional issues in Noida. Last year in January, the city is engulfed in dense fog, which reduces visibility on the streets.

3.2.1

Temperature

The monsoon season in Noida is generally hot and oppressive and partially overclouded, whereas the rainless season is pleasing and majorly clear. The temperature generally varies from 47 °F to 103 °F throughout the entire year with temperatures barely falling below 42 °F or rising above the 110 °F. Temperature progressively changes throughout the year, reaching its greatest in May–June (summer) and its lowest in January (winter).

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Table 4.1 Average monthly rainfall history in 2021 in Noida Rainfall

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

0.6,,

0.8,,

0.5,,

0.6,,

1.0,,

2.9,,

6.5,,

7.0,,

4.1,,

0.6,,

0.1,,

0.3,,

Source WeatherSpark.com

From April 15 to July 8, the hot season lasts for around 3 months with a mean daily high temperature of above 96 °F. June is one of the hottest months in Noida with an average high of 101 °F and a low of 83 °F. From December to February, the cool season lasts around for approximately 2.2 months, with average daily elevated temperatures below 74°F. January is one of the coldest months in Noida, with an average low of 47°F and a high of 68°F.

3.2.2

Rainfall

Monthly rainfall in the Noida region changes greatly depending upon the kind of season. From January 8 to October 19, rainy season lasts for 9.4 months of duration, with a 31-day rainfall of at least 0.50 inches. August is the wettest month in Noida, with an average rainfall of around 7.0 inches. From October 19 to January 8, the year’s rainless season lasted 2.6 months November is one of the driest months in Noida, with a mean rainfall of 0.10 inches (Table 4.1).

3.2.3

Humidity

The dew point is used to assess the convenience level of humidity since it impacts weather sweat, which will evaporate from top skin and cool down body. The saturation point determines how humid or dry it is; the lower the dew point, the drier it seems. Since the saturation point fluctuates more sluggishly than the temperature, a humid day is mostly followed by a humid night, even though the temperature may drop at night. Over the course of the year, Noida has a wide range of perceived humidity. From May 20–October 21, the hottest part of the year lasts for around 5.0 months, during this period the range of comfort is muggy and terrible for at least 25% of duration in time. August has the most muggiest days in Noida, with 30.70 days classified as muggy. January has a few muggy days in Noida, with only 0.0 days that are muggy.

3.2.4

Wind Speed and Direction

This section explores the wide-area hourly mean wind, which is 10 m above the ground and includes both speed and direction. Due to local geography and other factors, wind speed and as well as direction fluctuate more noticeably than hourly

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means at any point of spot. This is especially true of immediate wind speed and direction. The average hourly wind speed in Noida changes greatly by season during the course of the entire year. The windiest 6 months of year, lasting from January 25 to July 26, with a mean wind speed of much more than 6.8 miles per hour. The month of May is the windiest of the year in Noida, with average hourly wind speeds of 8.1 miles per hour. The calmer season lasts 6.0 months, from July 26 to January 25. With an average hourly wind speed of 5.2 miles per hour, October is Noida’s calmest month. From July 12 to August 29, the wind is most generally coming from the east, with a maximal frequency of 41% on July 30. With a maximal proportion of 34% on August 30, wind normally blows from the west for around 4.1 weeks, from August 29 to September 27, and for 7.9 months, from November 16 to July 12. From September 27 to November 16, when the wind is most usually approaching from the north, a significant percentage of 40% occurs on November 4.

3.3 Topography Within two miles of Noida region, geography is majorly flat, with about an average height above the sea level of 665 feet and an elevation change of maximum 85 feet. The terrain is majorly flat within the 10 km of distance (384 feet). There are very minor height changes within 50 km (794 feet). Artificial surfaces (89%) cover the area within 2 miles of Noida, artificial surfaces (55%) and cropland (36%) within 10 miles, and cropland (36%) within 50 miles (91%).

3.4 Soil The National Bureau of Soil Survey & Land Use Planning (ICAR) (NBSS &LUP, 2004) developed a soil map of Uttar Pradesh that represents the majority of NCR, Uttar Pradesh, the soils are almost level plain, which are deep down and also welldrained, and have a loamy type soil surface. The soils are often fine and coarse loamy, with dual types coexisting and one or the different being the main component in different areas. Due to minor differences in general characteristics, a variety of varieties of the above soil type occur in the area. Infiltration rates vary widely in Noida, Gautam Budh Nagar district, according to soil infiltration experiments. In Gautam Budh Nagar, the initial infiltration rate ranges from 30.00 to 390.00 mm/h, with the end infiltration rate ranging from 4.00 to 129.00 mm/h. The district’s mean original and final output of infiltration rates are 203.10 mm/h and 24.60 mm/h, respectively. The Gautam Budh Nagar district’s average infiltration rate is predicted to be 113.8 mm/h.

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4 Model Formulation 4.1 Transient Water Flow in Soil Developed mass conservation or continuity equation is ∂ Jω ∂θ = − S(h) ∂t ∂z

(1)

where θ signifies the volumetric water quantity [L3 L−3 ], t denotes time [T], Jw denotes the volumetric flux density [LT−1 ], z denotes spatial information [L], and S(h) denotes a sink function [L3 L−3 T−1 ] To create the water flow governing equation for variably saturated flow, mass conservation equation must be adjoined with one or other more equations characterizing the volumetric flux density, Jw. Buckingham (1907) reformed Darcy’s equation in order to account for unsaturated flow. Consequently, obtained is known as Buckingham–Darcy equation: Jω = −K (h)

( ( ) ) ∂h ∂h ∂(h + z) ∂z ∂H = −K (h) = −K (h) + = −K (h) +1 ∂z ∂z ∂z ∂z ∂z (2)

In the above mass conservation equation, Jw can be replaced with the Buckingham–Darcy equation, and obtained equation is given below: ( ) ∂ ∂h ∂ K (h) ∂θ (h) = K (h) + − S(h) ∂t ∂z ∂z ∂z

(3)

Richards (1931) was the first to formulate this equation, which is recognized as the Richards equation.

4.2 Root Water Uptake The sink word, S, refers to the amount of water released from a unit volume of soil in a given amount of time due to plant absorption of water. S was defined by Feddes et al. (1978) as S(h) = α(h)S p

(4)

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where the root-water absorption moisture stress response function (h) is a dimensionless function of the soil moisture pressure head (0 ≤ α ≤ 1), and Sp is the potential water uptake rate [T−1 ]. Close to the saturation point, water intake is supposed to be nil, i.e., wetter than an arbitrary “anaerobiosis point” h1. Water intake is also approximated to zero for h < h4 which is defined as the wilting point pressure head. Water uptake is regarded as ideal between two pressure heads which are h2 and h3, whereas water uptake falls or develops linearly with h between pressure heads h3 and h4 (or between h1 and h2), respectively. When α(h) = 1, the variable Sp present in Eq. 4 equals to the water uptake efficiency in the course of the duration when there is no moisture stress. Term, Sp becomes Eq. 5, when the potential moisture absorption rate is not uniformly distributed across the plant root zone. Sp = b(z)TP

(5)

where T p is the potential transpiration rate [LT−1 ] and b(z) is the standardized water uptake dissemination [L−1 ]. This function is derived by leveling any arbitrarily measured or predefined root sampling distribution, and it characterizes the spatial variation of the potentially extraction term, S p , over the root zone. Substituting Eq. 5 in Eq. 4, yields the actual water absorption distribution. The obtained function is represented as S(h, z) = α(h, z) b(z)Tp

(6)

whereas the real transpiration rate denoted as T a is calculated by multiplying Eq. 6 by the rooting depth LR as given below: Ta = ∫ S(h, z)dz = T p ∫ a(h, z)b(z)dz f LR

(7)

LR

4.3 Unsaturated Soil Hydraulic Properties HYDRUS-1D supports the usage of five distinct hydraulic analytical models. The hydraulic properties of van Genuchten are chosen for this research because hydraulic properties are commonly characterized using Mualem’s pore size distribution model alongside van Genuchten’s water retention function. The water retention feature created by van Genuchten is one of the most extensively used (1980). 1 Se (h) = ( )m 1 + (−αh)n

(8)

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where α, n, and m are the auxiliary parameters. Se (h) is the effective soil water saturation which is given as Se (h) =

θ (h) − θr θS − θr

(9)

where θ denotes the volumetric soil water content, θ r denotes the residual volumetric water content, and θ s denotes the saturated volumetric water content. Substituting Eq. 9 in Eq. 8 and solving for θ (h), we get (θ (h) − θr ) Se (h) = ( )m + θr 1 + (−αn)n

(10)

5 Result and Discussion 5.1 Meteorological Characteristics The amount of air temperature, wind direction, wind speed, precipitation, and humidity are all computed as part of the meteorological data. The data is then collected from the Climate Data Organization for the year 2021 which were recorded on regular basis for a duration of 1 year. Weather stations at different places in the research area were installed by the organization which used to conduct 1-year long duration meteorological monitoring studies. Using the appropriate method, we computed the potential evapotranspiration values for the study area using the daily temperature data collected from the organization, and then true evapotranspiration and runoff values were produced.

5.2 HYDRUS-1D Model Simulation 5.2.1

Case 1: Water Infiltration into a Single-Layered Bare Soil Profile

This case involves the water infiltration from the top level of soil surface deep down into a 1-m single-layered having loamy soil profile. Having an initial pressure head of −100.00 cm, soil profile is originally unsaturated. Water penetrates through the saturated soil surface from top of soil surface, which is represented by the “Steady Pressure difference” boundary condition, which assumes a pressure difference of 1 cm at soil surface which corresponds to the rainfall depth at the top layer of soil.

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Table 4.2 Soil parameters (Celia et al. 1990) θr

θs

α (1/cm)

n

Ks (cm/day)

l

0.078

0.43

0.036

1.56

24.96

0.5

Fig. 4.2 Pressure difference versus time at the chosen examination points (N1—50 cm, N2—100 cm)

Because the groundwater index is at an indeterminate depth within profile, water drains by gravity from the ground level of the soil profile would be indicated by a “Free Runoff” boundary condition. Since root water intake is not taken into account, only everyday values of evaporation and precipitation are evaluated for the purpose of atmospheric boundary conditions (Table 4.2 and Fig. 4.2). Note that wetting front passes certain depths at approximately 360 days. The loam soil’s hydraulic characteristics determine this timing. The pressure head rises from its initial value of −100.00 cm to −105.00 cm as the wetting front achieves a specific depth. Figure 4.3 illustrates the wetting front’s approach at specific print timings. The wetness front moves forward in time until it, at around 360 days, reaches the bottom of the profile. The whole profile then reaches a 1.00 cm pressure head. Figure 4.4 depicts similar data for the water content, with a starting water information of 0.24 as indicated by the initial pressure difference of -100.00 cm and the loam soil’s soil moisture holding curve. When wetting front reaches a particular point, the water information equalizes to the saturated water capacity (Fig. 4.5). Fluxes towards downward, i.e., against the z-axis are reported as negative, whereas fluxes towards upward, i.e., in the z-axis are reported as positive by HYDRUS-1D. This is because the z-axis of the numerical simulation is often positive and upward. The bottom flux dramatically increases as early as the wetting advance reaches the lower threshold (Fig. 4.6). Figure 4.7 demonstrates how the time sampling frequency changed continuously throughout the simulation. The time step size increases dramatically after roughly

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Fig. 4.3 Pressure difference profiles at chosen print times length (from 30.467 to 365 days)

Fig. 4.4 Water distribution profiles at selected print times length (from 30.467 to 365 days)

of around 355 days. This illustrates how, as the profile reaches steady-state and HYDRUS-1D may hence employ bigger time steps, the solution is subsequently determined considerably more simply (and less non-linearly). Figure 4.8 represents that after about 310 days, the required number of observations to arrive at a solution declines to the minimum permitted number of 2.

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Fig. 4.5 Cumulative surface flux

Fig. 4.6 Soil moisture holding curve (SMHC)

5.2.2

Case 2: Rain Garden Water Infiltration into a Single-Layered Soil Profile

The goal of considering the current case is to mimic water stream flow and root water assimilation in a 100 cm deep homogeneous soil distribution, as well as to assess stream over the profile’s lower border, i.e., groundwater recharge. At first, the soil distribution has a homogeneous pressure difference of −100 cm. An “Atmospheric Boundary Condition with Surface Runoff” is the boundary condition for the soil surface. The downside boundary condition is “Free Drainage”, which assumes that the groundwater index or table is deep enough to prevent water redistribution in

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Fig. 4.7 Time steps versus time

Fig. 4.8 Number of iterations versus time

the soil profile. From the top layer of soil surface to a soil layer of around depth 50.00 cm, roots are evenly dispersed. We can determine daily values of potential evapotranspiration using a 365-day time series of meteorological collected data. Thus, deep discharge below the root surroundings which ultimately recharges the underlying aquifer, and actual crop water uptake or actual transpiration are predicted using the HYDRUS-1D model.

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Fig. 4.9 Pressure head profiles at two selected print times

Designing of Rain Garden At the Amity University Campus in Noida, we created an experimental rain garden. The rain garden functions similarly to a lysimeter in that it is lined to collect and quantify drainage. The rain garden measures 5.4 square meters in size containing 6.5 m3 of soil enclosed within a polyethylene liner. Valves connect one or both roof regions allowing for area ratios of 0.05 and 0.10. This liner distinguishes garden soil from the rest of the environment hydraulically, allowing for direct measurement of the percolation deep down in the soil levels. The ponded depth in the rain garden is monitored by another transducer. Seepage from the rain garden which we considered to be groundwater recharge drains onto the soil below.

Simulate water Flow and Root Water Uptake A consolidated evapotranspiration rate or potential transpiration and evaporation rates will be determined from meteorological data (such as humidity, wind speed, solar radiation, and air temperature) using the Penman–Monteith combination equation, unlike the past example with time-variable boundary conditions, where users directly specified potential rates. Figure 4.9 depicts two pressure difference profiles at 152.50 (blue line) and also 335.50 (red line) days, representing rainy and dry circumstances, respectively. The pressure difference in the upper 50.00 cm of the root zone falls to the wilting or also known as drooping point, −8,000 cm for crop and by the dry period. At 152.5 and 335.5 days, Fig. 4.10 exhibits two equivalent flux profiles. Following the wet season, there is a substantially higher flux (blue line), and following the dry period, there is essentially no flow along most of the root zone (green line).

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Fig. 4.10 Water flux profiles at two selected print times

Fig. 4.11 Potential Root Water Uptake

The predicted propensity root water uptake rates generated from the meteorological data are shown in Fig. 4.11. The seasonal signal which shows reduced intake throughout the winter should be noted is around July. Figure 4.12, depicts real root water uptake as compared to the theoretical uptake in Fig. 4.11 and actual surface and bottom flux owing to precipitation infiltration in Figs. 4.13 and 4.14. It’s worth noting that true root water uptake which is transpiration is much lower during dry spells or right after heavy rains. In Fig. 4.15 black curve shows the sum of the actual root water uptake. On the other hand, the dark blue curve shows the sum of actual surface flux, whereas the light blue curve shows the sum of actual bottom flux which is the recharge.

56 Fig. 4.12 Potential Root Water Uptake versus time versus time

Fig. 4.13 Actual surface flux into soil

Fig. 4.14 Actual bottom flux out soil versus time versus time

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Fig. 4.15 Actual cumulative fluxes over time i.e. surface flux, bottom flux, transpiration flux

6 Summary and Conclusion The study focused on examining the recharge rate in order to understand the role of rain garden which is a stormwater management technique. A comprehensive literature review was conducted first to study the groundwater flow modeling and the effect of water moisture on the roots of plant. The intent of this investigation was to gain insight into the soil–plant–atmosphere relationships that helps manage the moisture content within the soil. The governing equations are formulated by including the water uptake term in the water flow equation for moisture movement and then continuity and mass balance equations for derived. I have presented a mathematical description of processes incorporated in the numerical modeling of groundwater flow in HYDRUS-1D, as well as a simple example of its application to experimental data from Noida. I have considered a transient water stream through a 1.00 m deep down soil profile that was expected to be either bare or covered with crop through rain garden in this simple case. Upon creating a mathematical model, some modeling analyses are done in order to demonstrate the efficiency of rain gardens. I have demonstrated in this report that a soil profile incorporated with rain garden recharges more groundwater than the bare open soil profile. Another conclusion is that, in rain garden, stormwater is collected and focuses on small areas as compared to the case of stormwater collected in a bare open wide field. Stormwater being focused on small space creates maximum impact on the groundwater recharge. Although rain garden can be used not only for plant growing techniques but also for catching the stormwater and allowing it to penetrate deep down into the ground. The infiltration of stormwater penetrates up to a certain depth during 40–350 days of daily average rainfall of the entire year. The quantity of recharge achieved via rain garden rainfall equates to 10% of the amount of rainfall that falls directly on the open surface, according to the HYDRUS-1D

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numerical model results. The HYDRUS-1D model can be used to replicate vertical flow movement, predict recharge rates, and predict spatial and temporal changes in soil water capacity. Improvements to the experimental circumstances are still being focused on to allow for more precise conclusions. The field experiment, however, is regarded as an important contribution towards the study and design of rain garden for stormwater infiltration.

References Aba Khatal S, Ali S, Hasan M, Kumar Singh D, Kumar Mishra A, Asif Iquebal M, Assessment of groundwater recharge in a small ravine watershed in semi-arid region of India. https://doi.org/ 10.20546/ijcmas.2018.702.311 Al Mehedi A, Yazdan S, Tanvir Ahad Md, Variably saturated subsurface flow modelling: sensitivity to the choice of soil hydraulic models. In: HYDRUS 1D Baalousha H, Fundamentals of groundwater modelling, pp 113–130 Batsukh K, The analysis of groundwater recharge in Mongolia using vadose zone modeling. Bowman J, Kirchner N, Personal communication: design plan for rain garden Bredenkamp DB, Quantitative estimation of ground-water recharge in dolomite. In Estimation of natural groundwater recharge. Springer Netherlands, pp 449–460 De Silva C, Simulation of potential groundwater recharge from the Jaffna Peninsula of Sri Lanka using HYDRUS-1D Model. OUSL J 7:43 Dussaillant AR, Wu CH, Potter KW, Richards equation model of a rain garden. J Hydrol Eng:219– 225 Esteves M, Faucher X, Galle S, Vauclin M, Overland flow and infiltration modelling for small plots during unsteady rain: numerical results versus observed values. J Hydrol:228, 265–282 Kumar CP (2015) Modelling of groundwater flow and data requirements, 2(2):18–27 Meteorological Data: Climate Data Organization, https://en.climate-data.org Mualem Y, A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour Res 12(3):513–522 Radcliffe D, Šim˚unek J (2010) Soil physics with HYDRUS: modelling and applications. CRC Press, Boca Raton, FL, pp 183–234 Ries F, Lange J, Schmidt S, Puhlmann H, Sauter M, Recharge estimation and soil moisture dynamics in a Mediterranean, semi-arid karst region https://doi.org/10.5194/hess-19-1439-2015 Rushton K, Ward C (1979) The estimation of groundwater recharge. J Hydrol 41(3):345–361 Sergey G, Sergey P, The use of HYDRUS-1D for groundwater recharge estimation in boreal environments Simunek J, Jirka, Šejna, M., Van Genuchten M (1998) The HYDRUS-2D software package for simulating water flow and solute transport in two dimensional variably saturated media, Version 2.0 Šim˚unek J, Rassam D, Mallants D, Van Genuchten M (2018) The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variablysaturated media: tutorial, version 1.00 Simunek J, Köhne JM, Kodešová R, Šejna M, Simulating non equilibrium movement of water, solutes, and particles using HYDRUS: a review of recent applications. Soil Water Res, (Special Issue 1), pp 42–51 Šim˚unek J, Estimating groundwater recharge using Hydrus-1D. Department of Environmental Sciences, University of California Riverside, pp. 25–36

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Tonkul S, Baba A, Sim¸ ¸ sek C, Durukan S, Can Demirkesen A, Gökmen T, Groundwater recharge estimation using HYDRUS 1D model in Ala¸sehir sub-basin of Gediz Basin in Turkey. https:// doi.org/10.1007/s10661-019-7792-6 van Genuchten MT (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 44(5):892–898 Welcome to PC-Progress (2018) https://www.pc-progress.com

Drinking Water Quality Evaluation and Its Hydrochemical Aspects in the Kabul Basin, Afghanistan Ali Reza Noori

and S. K. Singh

Abstract Almost all water supply systems in Kabul, especially public water supply, use groundwater resources to supply the required domestic water of the city. This study aimed to evaluate the quality of drinking water in Kabul and investigate its hydrochemical characteristics. The study employed descriptive statistics, groundwater classification based on Electrical Conductivity (EC), Total Dissolved Solids (TDS), Total Hardness (TH), major ions chemistry, and correlation matrices. The drinking water quality has been compared with World Health Organization (WHO) guidelines. The outputs indicate that the maximum concentration for parameters exceeds the WHO threshold with different percentages. Based on EC classification, groundwater is mainly classified as permissible (about 90%). The TDS classification indicates that the majority of observed samples are admissible for drinking (about 88%). Also, most of the samples are classified as freshwater types (about 85%). The total hardness indicator of groundwater implies that about 90% of the water sample are very hard. A strong correlation exists between EC with TDS, Ca hardness with Ca, Alkalinity, and HCO3 , total coliform and F. coliform, which indicates that they might have the same origin. The findings of the drinking water quality study demonstrate that the water quality in the Kabul Basin is poor, with several parameters exceeding WHO guidelines. Keywords Drinking water quality · Kabul Basin · Water supply · Statistical evaluation · Groundwater quality

A. R. Noori (B) · S. K. Singh Department of Environmental Engineering, Delhi Technological University, Delhi, India e-mail: [email protected] A. R. Noori Department of Water Supply and Environmental Engineering, Faculty of Water Resources and Environmental Engineering, Kabul Polytechnic University, Kabul, Afghanistan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_5

61

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A. R. Noori and S. K. Singh

1 Introduction Clean and sufficient water is one of the vital necessities of human societies, without which survival is impossible. With the development of human societies and the development of cities, this essential resource faces a severe threat of pollution and a lack of resources. Because of the instability and unequal distribution of precipitation, as well as diminished surface flows, groundwater use as a water supply resource is rapidly increasing, particularly in arid and semiarid regions (Noori and Singh 2021a). In many arid and semiarid settings, groundwater fulfills practically all water demands. Freshwater accounts for only 3% of the world’s total water supply (Noori and Singh 2021b). With a rising reliance on groundwater resources to support the household, agricultural, and industrial demands, clean groundwater is becoming a global issue (Sarma and Singh 2021). Groundwater is the only source of water supply in Kabul City. Still, its quality has deteriorated due to various factors, including the lack of a sewerage system, high fertilizers usage, and direct disposal of household waste into surface water (Zaryab et al. 2021, 2022; Paiman and Noori 2019; Noori and Singh 2021c). Water supply is distributed by public corporations, private enterprises, outreach programs, or households, usually through pumps and pipelines. Public water supply systems are essential for the proper functioning of civilization. People throughout the world get their drinking water via these systems. The water supply system in Kabul is run by a public company named the Afghanistan Urban Water Supply and Sewerage Corporation (AUWSSC), private companies, and individual wells. The current study examines the quality of drinking water in Kabul City, which is sourced from public wells and water reservoirs and is controlled by AUWSSC. The study investigated the hydrochemical characteristics of drinking water resources in Kabul City. Descriptive statistics, groundwater classification based on Electrical Conductivity (EC), Total Dissolved Solids (TDS), Total Hardness (TH), major ions chemistry, and correlation matrices have been employed. The quality of drinking water has been evaluated and compared with World Health Organization (WHO) guidelines, and the percentage of samples that exceeded the guidelines threshold was highlighted. The research’s conclusions will benefit the study area’s sensible expansion, usage, and scientific management.

2 Material and Method 2.1 Study Area The study area is the Kabul Basin (JICA 2011), which encompasses most of Afghanistan’s capital, Kabul. The study location is in the country’s central east, between latitudes 34°36, 30,, and 34°24, 40,, N and longitudes 69°01, 25,, and 69°22, 30,, E. (Fig. 1). The basin is 496 square kilometers in size. The climate in

Drinking Water Quality Evaluation and Its Hydrochemical Aspects …

63

the research region ranges from arid to semiarid. Kabul experiences seasonal rainfall; typically, it snows and showers in the winter months (December, January, and February) and the early spring (March and April). The average yearly precipitation in the research region was estimated to be 330 mm (Noori and Singh 2021b). The basin’s highest and lowest average temperature ranges from 32 ºC in July to −7 ˚C in January. Built-up land is the most frequent land use category in the study area. It is the country’s central business hub, and after 2001, many refugees came back to their country of origin, mainly settling in Kabul. Many citizens from other regions also flocked to Kabul in search of job opportunities. Low but steep mountain ranges surround the basin. The height of the basin varies from 1760 to 2820 m (Fig. 1). Typically, the mountains in the south and southwest limits are greater in elevation than the others.

Fig. 1 Kabul Basin location map

64

A. R. Noori and S. K. Singh

2.2 Data Acquisition The drinking water quality data was obtained from the AUWSSC. They have collected samples and analyzed the quality of drinking water resources in public water supply systems. The sampling start date is May 11, 2020, and the end date is July 22, 2020. The total number of collected samples is 27; out of it, 26 observational points are water supply wells and a water reservoir with a volume of 10,000 cubic meters. Public water supply projects in Kabul City have been divided into separate zones according to the geographical location of water resources. The series of water supply wells along the Logar River route, known as the “Logar Project,” is one of Kabul’s significant water supply sources. The qualitative data from eight wells of the Logar Project has been evaluated in the current study. Alauddin wells, located on the west coast of the Kabul River, are the second largest source of water in Kabul. Meanwhile, eight observation points in this area have been analyzed in groundwater samples. Several water supply wells in different other zones that have drinking water quality figures are included in this study. Zones two, four, and six each have only one observation point. But zones three, five, and seven each have two observation points. All samples have had the following water quality metrics tested and reported on: temperature, EC, pH, TDS, odor, color, turbidity, hardness, calcium hardness, magnesium, total alkalinity, bicarbonate, chloride, fluoride, sulfate, phosphate, nitrite, nitrate, ammonia, iron, copper, aluminum, arsenic, total coliform, and fecal coliform. Out of the above parameters, odor and color provide the requirements for drinking water, and these two parameters are not considered in the analysis of the current study. All other analyzed qualitative variables are considered for the present study.

3 Results and Discussion 3.1 Statistical Evaluations The temperature of the water is calculated based on Celsius degree; EC is calculated in µS/cm. The turbidity unit of measurement is the Nephelometric Turbidity Unit (NTU). Except for pH, all other qualitative water parameters are illustrated in mg/l (Table 1). The inferential analysis of the dataset is also shown in the table. As per WHO regulations, it also shows the proportions of inspections and the quantity of data that surpassed the permitted threshold. Here are the specifics of the groundwater quality measures examined for consumption.

3.80

0.04

(mg/l)

(mg/l)

(mg/l)

Nitrate

Ammonia

Iron

0.01

0.09

0.02

(mg/l)

(mg/l)

5.00

Phosphate

(mg/l)

Sulfate

0.05

25.00

275.00

275.00

Nitrite

(mg/l)

(mg/l)

Chloride

Fluoride

(mg/l)

HCO3

26.82

(mg/l)

(mg/l)

Magnesium

T Alkalinity

24.05

(mg/l)

Calcium

60.05

230.00

as CaCO3 (mg/l)

(mg/l)

T Hardness

0.42

409.20

Ca Hardness

(mg/l)

NTU

TDS

Turbidity

–

pH

7.10

14.30

660.00

°C

µS/cm

EC

Min

Temperature

Unit

Parameters

0.24

0.12

40.60

0.08

0.51

138.67

1.17

135.33

461.48

461.48

76.54

82.68

206.46

520.15

0.57

762.35

7.43

1229.59

15.40

Mean

1.38

0.47

106.20

0.31

2.40

532.00

5.60

564.00

575.00

575.00

168.33

529.06

1321.05

1740.00

0.85

1748.40

7.80

2820.00

18.30

Max

0.05

0.08

40.40

0.07

0.30

109.00

0.50

106.00

475.00

475.00

75.59

64.13

160.13

490.00

0.54

709.90

7.40

1145.00

15.00

Median

0.373

0.105

25.864

0.055

0.489

103.828

1.511

106.536

74.959

74.959

35.633

92.705

231.484

284.678

0.119

264.296

0.238

426.283

1.020

SD

0.3

1.5–3.5

50

3

–

250

1.5

250

600

–

30

75

–

500

5

1000

6.5 – 8.5

1500

–

WHO limit

6

0

9

0

–

2

6

2

0

–

26

9

–

13

0

4

0

4

–

No. of samples

(continued)

22.22

0.00

33.33

0.00

0.00

7.41

22.22

7.41

0.00

0.00

96.30

33.33

0.00

48.15

0.00

14.81

0.00

14.81

–

Percentage

Samples exceed the permissible limit

Table 1 The outputs of descriptive statistics show the minimum, maximum, mean, and standard deviation of all parameters and their acceptable limitations as per WHO regulations

Drinking Water Quality Evaluation and Its Hydrochemical Aspects … 65

CFU/100 ml

CFU/100 ml

Total Coliform

Fecal Coliform

0.00

0.00

0.05

0.00

(mg/l)

(mg/l)

0.06

Aluminum

(mg/l)

Copper

Min

Arsenic

Unit

Parameters

Table 1 (continued)

0.44

0.56

0.00

0.11

0.21

Mean

12.00

15.00

0.00

0.54

0.77

Max

0.00

0.00

0.00

0.09

0.14

Median

2.309

2.887

0.000

0.090

0.195

SD

0

0

0.05

0.2

2

WHO limit

1

1

0

1

0

No. of samples

3.70

3.70

0.00

3.70

0.00

Percentage

Samples exceed the permissible limit

66 A. R. Noori and S. K. Singh

Drinking Water Quality Evaluation and Its Hydrochemical Aspects …

67

Table 2 Categorization of groundwater based on EC EC

Water type

Samples were over the allowable limits

% Of the sample that is over permissible limits

3000

Unsuitable

0

0.00

Total

27

100.00

3.2 Electrical Conductivity (EC) The amount of current that a water environment can transmit depends on this property. The WHO guideline states that 1500 µS/cm is the maximum level of conductance that is permitted. The EC is detectable between 660 and 2820 µS/cm. Todd (Todd 1980) specifies five main EC groundwater quality categories, which are depicted in Table 2. In the research area, no sample exhibited an excellent water type. This research shows that only 4% of samples have a dubious water, whereas only 7% have a good water type and 89% are within acceptable water type. Based on EC analysis, no unsuitable water samples have EC greater than 3000 µS/cm in the study area. This study indicates that just 4% of groundwater should not be used for residential purposes.

3.3 pH The pH characteristic in water samples is the most crucial and decisive aspect in determining its level of corrosivity since water interacts with CO2 in the ground to generate carbonic acid. The pH of natural water is determined by the interplay of CO2 , CO3 , and HCO3 and their balance. The pH levels measured in this study varied from 7.1 to 7.8, which is within the WHO’s recommended range. The results show that the pH value in the research region is not acidic. Equations (1) and (2) depict the pH variations that occur naturally in groundwater before it reaches the aquifer system: 1. When rainfall combines with the atmosphere, carbonic acid is created. H2 O + CO2 = H2 CO3

(1)

2. When carbonic acid splits into bicarbonate, it releases hydrogen ions and becomes acidic.

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A. R. Noori and S. K. Singh

H2 CO3 → (HCO3 )− + H+

(2)

3.4 Total Dissolved Solids (TDS) Total dissolved solids are the minute quantities of minerals and organic molecules that are present in diluted environments. TDS is principally regulated by the degradation, porosity, and penetration of natural rock formations. Anthropogenic causes include things like fertilizer runoff and the discharge of sewage. The WHO standard recommends a maximum TDS value of 1000 mg/l, whereas the greatest and lowest TDS values were 409 and −1748 mg/l. Approximately 7% of the data are desired for consumption having TDS below 500 mg/l, 78% are permitted for drinking with TDS 500–1000 mg/l, followed by 15% suitable for irrigation, and there are no samples that are inappropriate for drinking and irrigation, as shown in Table 3. As shown in Table 4, approximately 85% of the findings are freshwater, 15% are brackish water, and none are salty or brine water, which is in accordance with Freeze and Cherry (Freeze and Cherry 1979). Table 3 Classification of groundwater based on TDS TDS

Water type

Samples were over the allowable limits

% Of sample that is over threshold limits

3000

Unfit for drinking and irrigation

0

0.0

27

100.0

Total

Table 4 TDS concentration determines the categorization of groundwater Water type

Samples were over the allowable limits

100,000

Brine water type

0

0.00

27

100.00

Total

Drinking Water Quality Evaluation and Its Hydrochemical Aspects …

69

Table 5 Groundwater is categorized according to its overall hardness TH as CaCO3

Water type

Sample with more than permissible limits

% Of the sample that exceeds acceptable levels

300

Very hard

24

88.89

Total

27

100.00

0.00

3.5 Total Hardness (TH) Total hardness is crucial in figuring out drinking water’s qualities. It displays the amount of Ca and Mg ions present in the water. TH values in the current research range from 230 to 1740 mg/l. According to WHO recommendations, the criterion for overall hardness has been set at 500 mg/l. Depending on how hard the water is, it is split into four classes (i.e., soft, moderate, hard, and very hard). No sample has ever shown TH levels between 75 and 150 mg/l or less in the moderate and soft water categories. As shown in Table 5, 11% of findings fall into the hard category (TH; 150–300 mg/l), and 89% fall into the extremely hard category (TH > 300 mg/l).

3.6 Major Ions Chemistry In the current study, the following anion concentrations were found, in descending order: F < NO3 < Cl < SO4 < HCO3 with percentages of 0.028% < 0.98% < 3.28% < 3.36% < 11.18%, respectively. The most frequent and stable ion found in water is bicarbonate (HCO3-). The amount of bicarbonate in water depends on several factors, including temperature, pH, dissolved CO2, cations, and other salts. Bicarbonate levels in the groundwater of Kabul City range from 275 to 575 mg/l. All samples meet the WHO’s acceptable level. With a range of 5 to 532 mg/l, sulfate has a mean concentration of 139 mg/l. Two out of 27 samples exceed WHO limits (>250 mg/l). As a result of sulfide minerals like pyrite (FeS2 ) being oxidized, sulfate is frequently discovered in groundwater (Yadav et al. 2012). The third most common component is chloride. Because of erosion, silt, soil drainage, and urbanization, chloride is present in groundwater (Karanth 1987). The tested water samples had Cl− ion levels that varied from 25 to 564 mg/l, except for two samples; all others are within the WHO-permitted limit. The nitrate concentration in the basin ranged from 4 to 106 mg/l. About 33% of tested samples have nitrate concentrations greater than WHO guidelines (greater than 50 mg/l). Fluoride levels in the area are typically 1.17 mg/l, with the lowest and highest rates ranging from 0.05 to 5.6 mg/l. About 22% of investigated samples concentrated greater than WHO limits. “The fluoride source in groundwater is mainly of orogenic processes due to weathering of granitic.

70

A. R. Noori and S. K. Singh

These rocks have F− rich minerals as an accessory like amphiboles, apatite, fluorite, and mica” (Reddy et al. 2010). High amounts of fluoride in water result in fluorosis of the teeth and bones. Ascending numbers represent the number of cations: Mg+2 < Ca+2 contributing 1.8% < 2%, respectively. Calcium is the most common element of the observation, with a mean content of 83 mg/l. 529 mg/l of calcium is the greatest amount, while 24 mg/l is the lowest. A concentration of WHO recommendations is present in around 33% of the analyzed sample. Igneous and sedimentary rocks are the principal calcium sources in groundwater. Calcium and magnesium concentrations are related to water hardness and are readily accessible as carbonates on surface and subsurface water, with sources including limestone, gypsum, and dolomite (Domenico and Schwartz 1998). Magnesium has an average concentration of 77 mg/l in the study area. The highest observed magnesium concentration is 168 mg/l, while the lowest is 27 mg/l. Only 4% of the examined wells contain concentrations that are within the permitted range. The remaining 96% of the wells are outside the WHO recommendations. All three kinds of rocks—sedimentary (amphibolite, talc, and tremolite-schists), metamorphic (basalt, dunites, and pyroxenites), and igneous—contain magnesium (dolomite, gypsum) (Karanth 1987). Total alkalinity, nitrite, phosphate, ammonia, copper, and arsenic are the parameters that have been analyzed, and the results indicate that these parameters are within the permissible limits. About 22% of the study area’s iron concentration exceeds WHO limits. The minimum concentration of iron is 0.01, and the maximum is 1.38 mg/l. Aluminum concentration has a mean value of 0.11 mg/l, with the highest value of 0.54 and the lowest value of 0.05 mg/l. About 4% of the tested sample in the study region has a concentration greater than WHO guidelines. Besides, the chemical characteristics of groundwater bacteriological contamination have also been evaluated in the collected data. The fecal coliform and total coliform of analyzed samples indicate that one sample in the study area has values greater than WHO limits for fecal and total coliform. Figure 2 illustrates the distribution of the proportions of various parameter concentrations in groundwater samples. EC is first with 29.8 percent, TDS is second with 18.48 percent, TH is third with 12.61 percent, and total alkalinity and HCO3 are fourth and fifth with 11.18 percent for each of them.

3.7 Correlation Matrix The coefficient of correlation evaluation approach effectively determines the relationship across numeric values. The correlation coefficient might be computed by comparing the concentration of various water quality metrics. It may be used to figure out how groundwater geochemical processes work. The proximity and point of the linear relationship between independent and dependent variables are measured using correlation analysis. Pearson correlation may be used to determine the degree of linear connection across independent and dependent variables (Rehman et al.

Drinking Water Quality Evaluation and Its Hydrochemical Aspects …

71

35.00 29.80 30.00

Percentage (%)

25.00

18.48

20.00

15.00

12.61

11.18

11.18

10.00 5.00 5.00

2.00

3.28

1.85

3.36

1.25

0.00 EC

TDS

TH

CaH

Ca

Mg

TA

HCO3

Cl

SO4

Others

Parameters

Fig. 2 Scatter plot illustrating percentage distribution of all measured parameters

2018). The coefficient of correlation (r) is an indicator of dependency between two or more variables that are commonly used to measure and identify a relationship between them (Zheng et al. 2017). The relationship between two variables will be deemed good and perfect when the value of r is +1 or −1. A positive sign denotes a positive connection, whereas a negative sign implies a negative correlation. There is no connection between variables if the correlation coefficient is 0. As a result, positive correlations indicate that the variables have a shared source, whereas negative correlations suggest that the variables have distinct sources. If the value of r is >0.7 or −0.7, the variables are strongly correlated, and when the values are 0.5 to 0.7 or −0.5 to −0.7, the variables are moderately correlated (Rehman et al. 2018). These connections suggest that the key factors contribute to groundwater salinity and that their trends are comparable. The impacts of long-term interactions between groundwater and geological formations would be predicted to cause groundwater salinization. Correlation analysis was done using Pearson correlation coefficients (r) among 21 water quality measures. The conclusion of correlation analysis indicates a rapid way of water quality assessment. The values of r were given in a correlation matrix after a correlation study of the characteristics of the groundwater samples in Kabul City (Fig. 3). From the calculated Pearson correlation coefficient analysis, it is clear that EC with TDS; Ca hardness with Ca, Alk, and HCO3 ; total coliform; and F. coliform show the highest correlation with a correlation coefficient of + 1. As well as EC, Ca and Ca hardness, TDS and SO4 , TH and Cl, TH and SO4 , Ca hardness, Cl and F, Cl and SO4 , EC and Al, TDS and Al, Cl and Al, and SO4 and Al are strongly correlated (r > + 0.7). On the other hand, pH and NO3 , Fe, Alk, and HCO3 exhibit low and

72 EC pH TDS TH Ca-H Ca Mg Alk HCO3 Cl F SO4 PO4 NO2 NO3 NH3 Fe Cu Al T.Coli F.Coli

A. R. Noori and S. K. Singh EC 1.0000 0.3948 1.0000 0.9116 0.7539 0.7539 0.5819 0.2557 0.2557 0.9054 0.1810 0.8499 -0.1097 -0.4537 -0.1547 -0.3083 -0.2661 -0.0763 0.7523 0.0344 0.0344

pH

TDS

TH

Ca-H

Ca

Mg

Alk

HCO3

1 0.3948 0.3704 0.2114 0.2114 0.3869 0.2871 0.2871 0.1400 0.7084 0.5935 -0.1447 -0.3404 -0.6900 -0.3520 -0.2162 0.0707 0.1513 0.2268 0.2268

1 0.9116 0.7539 0.7539 0.5819 0.2557 0.2557 0.9054 0.1810 0.8499 -0.1097 -0.4537 -0.1547 -0.3083 -0.2661 -0.0763 0.7523 0.0344 0.0344

1 0.8597 0.8597 0.5867 0.0758 0.0758 0.8581 0.1575 0.8678 -0.1303 -0.3833 -0.0570 -0.2070 -0.2051 -0.1152 0.8808 -0.0843 -0.0843

1 1.0000 0.0907 -0.2811 -0.2811 0.7969 0.0601 0.7664 -0.0589 -0.2610 0.0854 -0.1899 -0.0393 -0.1569 0.9290 0.0550 0.0550

1 0.0907 -0.2811 -0.2811 0.7969 0.0601 0.7664 -0.0589 -0.2610 0.0854 -0.1899 -0.0393 -0.1569 0.9290 0.0550 0.0550

1 0.5933 0.5933 0.4096 0.2118 0.4767 -0.1606 -0.3335 -0.2465 -0.1024 -0.3375 0.0240 0.2445 -0.2517 -0.2517

1 1.0000 -0.0509 0.3107 -0.0026 -0.3641 -0.1912 -0.2393 -0.0920 -0.5835 -0.1831 -0.2470 0.1027 0.1027

1 -0.0509 0.3107 -0.0026 -0.3641 -0.1912 -0.2393 -0.0920 -0.5835 -0.1831 -0.2470 0.1027 0.1027

Cl

1 -0.0247 0.7780 0.1458 -0.3686 0.0127 -0.2394 -0.0739 -0.0355 0.7906 -0.0456 -0.0456

F

SO4

PO4

NO2

NO3

NH3

1 0.2335 -0.0735 -0.1676 -0.4852 -0.2028 -0.1312 -0.0893 -0.0799 0.0036 0.0036

1 0.0072 -0.3670 -0.3312 -0.3039 -0.1035 0.1154 0.7868 0.0391 0.0391

1 -0.1028 -0.1103 -0.2191 0.6915 0.1797 -0.1555 -0.1285 -0.1285

1 0.2316 0.0716 0.0459 -0.0253 -0.1234 -0.0563 -0.0563

1 0.3456 0.0766 -0.2173 0.1039 -0.2658 -0.2658

1 -0.1471 -0.0190 -0.1629 -0.1159 -0.1159

Fe

Cu

Al

T.Coli F.Coli

1 0.3305 1 -0.1246 -0.0906 1 -0.1031 0.1133 -0.0148 1 -0.1031 0.1133 -0.0148 1.0000

1

Fig. 3 Matrix of correlation coefficients of different water quality indicators

weak correlation with correlation coefficient (r < −0.5). Also, NO2 and EC, TDS, F, and NO3 are weakly correlated with the correlation coefficient (r < −0.4). SO4 , Alk, and HCO3 , SO4 and PO4 , CL, and NO3 are not associated with a correlation coefficient of almost zero. There are weak positive or negative correlations between all other parameters.

4 Conclusion Almost all water supply systems in Kabul, especially public water supplies, use groundwater resources to supply the required domestic water of the city. The current study aimed to evaluate drinking water quality in Kabul and investigate its hydrochemical characteristics. The study employed descriptive statistics, groundwater classification based on EC, TDS, TH, major ions chemistry, and correlation matrices. The quality of drinking water has been compared with WHO guidelines. The outputs indicate that maximum concentration in many parameters like EC, TDS, TH, Ca, Mg, Cl, F, SO4 , NO3 , Fe, Al, and total and fecal coliforms exceed the WHO threshold with different percentages. Based on EC classification, the groundwater is mainly (about 90%) classified as a permissible class with less amount of good and doubtful. The TDS classification indicates that most (about 88%) water samples are acceptable for drinking, with less desirable and valuable for irrigation. Also, the majority (about 85%) of the groundwater is classified as fresh water, and only 15% of the tested samples are brackish water types. The total hardness indicator of groundwater implies that the groundwater in the Kabul Basin is classified as hard and very hard water type. About 90% of the water sample are very hard, and only around 10% is classified as hard water type. The strong correlation between EC with TDS, Ca hardness with Ca, Alk and HCO3 , total coliform, and F. coliform exists and indicates that they might have the same origin. The findings of the drinking water quality study

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demonstrate that the water quality in the Kabul Basin is poor, with several parameters exceeding WHO guidelines. Acknowledgements The authors would like to appreciate from Afghanistan Urban Water Supply and Sewerage Corporation (AUWSSC) for providing drinking water quality data of the Kabul zone for the study.

References Davis SN, DeWiest RJM (1966) Hydrogeology. John Wiley and Sons, New York Domenico P, Schwartz F (1998) Physical and chemical hydrogeology. Wiley, New York Freeze RA, Cherry JA (1979) Groundwater. Prentice-Hall, Englewood Cliffs JICA (2011) The study on groundwater resources potential in Kabul basin in the Islamic Republic of Afghanistan final report. JICA Karanth K (1987) Ground water assessment: development and management. Tata McGraw-Hill Education Noori AR, Singh SK (2021a) Spatial and temporal trend analysis of groundwater levels and regional groundwater drought assessment of Kabul, Afghanistan. Environ Earth Sci. https://doi.org/10. 1007/s12665-021-10005-0 Noori AR, Singh SK (2021c) Assessment and modeling of sewer network development utilizing Arc GIS and SewerGEMS in Kabul city of Afghanistan. Journal of Engineering Research 22–31. https://doi.org/10.36909/jer.ICARI.15287 Noori AR, Singh SK (2021b) Status of groundwater resource potential and its quality at Kabul, Afghanistan: a review. Environ Earth Sci 80:1–13. https://doi.org/10.1007/s12665-021-09954-3 Paiman Z, Noori AR (2019) Evaluation of Wastewater Collection and Disposal in Kabul City and Its Environmental Impacts. Modern Environmental Science and Engineering 5:451–458. https:// doi.org/10.15341/mese(2333-2581)/05.05.2019/012 Reddy AGS, Reddy DV, Rao PN, Prasad KM (2010) Hydrogeochemical characterization of fluoride rich groundwater of Wailpalli watershed, Nalgonda District, Andhra Pradesh, India. Environ Monit Assess 171:561–577. https://doi.org/10.1007/s10661-009-1300-3 Rehman F, Chemma T, Lisa M, et al (2018) Statistical analysis tools for the assessment of groundwater chemical variations in Wadi Bani Malik area Sarma R, Singh SK (2021) Simulating contaminant transport in unsaturated and saturated groundwater zones. Water Environ Res 93:1496–1509. https://doi.org/10.1002/wer.1555 Todd DK (1980) Groundwater Hydrology. Wiley, New York Yadav KK, Gupta N, Kumar V et al (2012) Physico-chemical analysis of selected ground water samples of Agra city, India. Recent Research in Science and Technology 4:51–54 Zaryab A, Noori AR, Wegerich K, Kløve B (2017) Assessment of water quality and quantity trends in Kabul aquifers with an outline for future drinking water supplies. Central Asian Journal of Water Research 3:3–11 Zaryab A, Nassery HR, Alijani F (2021) Identifying sources of groundwater salinity and major hydrogeochemical processes in the Lower Kabul Basin aquifer, Afghanistan. Environ Sci Process Impacts 23:1589–1599. https://doi.org/10.1039/d1em00262g Zaryab A, Nassery HR, Knoeller K et al (2022) Determining nitrate pollution sources in the Kabul Plain aquifer (Afghanistan) using stable isotopes and Bayesian stable isotope mixing model. Sci Total Environ 823:153749. https://doi.org/10.1016/j.scitotenv.2022.153749 Zheng Q, Ma T, Wang Y, et al (2017) Hydrochemical Characteristics and Quality Assessment of Shallow Groundwater in Xincai River Basin, Northern China. In: Procedia Earth and Planetary Science. Elsevier BV, pp 368–371

A Bibliometric Analysis of Social Life Cycle Assessment (2008–2022) Soumen Ghosh

Abstract Following the emergence of sustainable development two decades ago, social scientists and policymakers realised that overall sustainability is difficult to achieve without a social dimension. In the last decade, many researchers have tried to measure social impacts, although measuring the social impacts is not as easy as environmental impacts. In 2009, the ‘United Nations Environment Programme’ (UNEP) and the ‘Society of Environmental Toxicology and Chemistry’ (SETAC) drafted the ‘Social Life Cycle Assessment’ (SLCA) method to measure social and socio-economic impacts. Although this method is new and still developing, it has gained widespread acceptance among academics and practitioners due to its inclusive nature. This study aims to address the direction and trend of social life cycle assessment research by using bibliometric information. This study identified that this new methodology has a clear dominance of the European region in terms of both authors and affiliations. We feel that more case studies in different regions could enhance the acceptance of this method. Keywords Social life cycle assessment · Bibliometric analysis · Social sustainability · Sustainability · Sustainable development

1 Introduction After two decades of the Brundtland Commission report (WCED 1987), researchers and practitioners finally recognised the relevance of the social pillar of sustainability. In the last decade, Social Sustainability (SS) has gotten a lot of attention from policymakers and researchers. However, measuring social performance is not an easy task. Various measures and approaches have been created to assess social performance. Finally, the United Nations Environment Programme (UNEP) and the Society of Environmental Toxicology and Chemistry (SETAC) have developed an LCA-like S. Ghosh (B) Tata Institute of Social Sciences, Mumbai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_6

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framework to address this issue. This new methodology is the social complement of the Environmental Life Cycle Assessment (ELCA). It encompasses the social impacts of a product’s whole life cycle, from raw material extraction to disposal. Although this methodology is new and still in its early stages of development, it has gained widespread acceptance among academics and practitioners due to its inclusive nature. This area has received significant attention from various disciplines over the previous decade. We discovered two types of research in the extant literature. The first type mainly addresses the methodological discussion and some new aspects. Some recent articles attempted to combine social and environmental life cycle assessments in a single framework. Martínez-Blanco and colleagues (2015) also talked about how to examine the impact of a product’s life cycle within an organisation and suggested a whole new technique called Social Organisational Life Cycle Assessment (SOLCA). This second type of study focuses on case studies that follow UNEP/SETAC guidelines and aim to analyse the positive and negative consequences of a given product. This study examines the existing literature with the goal of presenting a comprehensive picture of the Social Life Cycle Assessment based on bibliometric data. This research seeks to illustrate the trajectory of this comparatively new field as well as potential research gaps in the existing literature. We have the following questions based on the aforementioned objective: RQ1: What is the direction of research in the Social Life Cycle Assessment field? RQ2: Who are the eminent contributors in this field? Majorly with whom they are collaborating? RQ3: Which are major contributing journals in the social life cycle assessment domain? RQ4: Which are the most consequential scholarly works in this realm? Answering the research questions above will provide us with an overall picture of the Social Life Cycle Assessment. Section 2 describes the Data and methodology followed by the analysis of the study. The conclusion is presented in the last section. This research will provide a comprehensive picture of the social life cycle assessment.

2 Data and Methodology We have systematically chosen relevant social life cycle assessment literature based on the study’s aforementioned aims. We have applied several criteria to discover the pertinent literature. The method for choosing the literature is described in this section. We will first go through the inclusion and exclusion criteria for our search, and then we’ll talk about the kind of bibliometric analysis we conducted.

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2.1 Inclusion and Exclusion Criteria of the Study To detect the most appropriate papers, we have selected only journal articles for the analysis. To fulfil the research objectives, we have set some prerequisite criteria to get the optimum result and reduce the likelihood of bias for the bibliometric analysis. We have primarily ignored grey literature in our criteria. For bibliometric analysis, we have selected articles using some specific keywords (Table 1). We have broadly used the ‘Web of Science Core Collection’ database to get the most pertinent studies. In the first stage, we only used keywords. In the next step, we have selected the language (English) and type of publication (Original Article) for screening. In the last phase, we did a manual screening for our article selection. After all these filters, we finally have 135 journal articles for our bibliometric analysis (Fig. 1). Table 1 Top 5 contributing journals for SLCA Name of the journal

Publication house

TPub

H-Index

TCit

ACA

International Journal of Life Cycle Assessment

Springer

71

29

2636

37.127

Sustainability

MDPI

19

8

215

11.316

Journal of Cleaner Production

Elsevier

13

10

405

31.154

Journal of Industrial Ecology

Wiley

5

5

103

20.6

Science of the Total Environment

Elsevier

4

2

48

TPub: Total Publications; TCit: Total Citations; ACA: Average Citations per Article The calculation is based on an article search done in Web of Science on April 20, 2022

Fig. 1 Selection criteria and process for bibliometric analysis

12

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2.2 Descriptive Analysis In the previous section, we have already delineated the selection criteria for the current study. This descriptive analysis gives us an overall picture of the existing literature on SLCA. To examine the first question (RQ 1), we have analysed the publication trend from the current list of articles. We have presented an overview of publications across the country, regions, affiliations, and institutes. Year-Wise trend of Scholarly output The SLCA is a whole new concept in the arena of research. In the last decade, a task force was formed by UNEP/SETAC to develop operational guidelines for SLCA. Soon afterward, a rising trend was noticed in that area. The SLCA guidelines are broadly an LCA-like framework that covers the entire product’s life cycle. The available literature was published from 2008 to 2022 (Fig. 2). Out of 135 articles available, the average number of publications was between 9 and 10 per year. In the year 2018, the highest number of articles (a total of 31) has been published. Although we have selected 2022 for our analysis, we considered the publications until April. Top contributing journals The top five SLCA contributing journals are described in Fig. 3. A significant publication source in that domain is the International Journal of Life Cycle Assessment. The International Journal of Life Cycle Assessment has produced 71 papers overall, followed by Sustainability and the Journal of Cleaner Production with 19 and 13, respectively. Of the total number of papers available, the top five journals account for 83% (112 out of 135). Apart from this, many journals have made a tiny proportion of contributions to this particular domain. Fig. 2 Number of articles published on Social Life Cycle Assessment per year. Note The diagram is based on an article search done in ‘Web of Science’ on April 20, 2022

A Bibliometric Analysis of Social Life Cycle Assessment (2008–2022)

79

INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT

71 19

SUSTAINABILITY

13

JOURNAL OF CLEANER PRODUCTION

5

JOURNAL OF INDUSTRIAL ECOLOGY

4

SCIENCE OF THE TOTAL ENVIRONMENT 0

20

40

60

80

Fig. 3 Top 5 contributing journals. Note The diagram is based on an article search done in ‘Web of Science’ on April 20, 2022

3 Bibliometric Analysis This section has portrayed a macro picture of available literature using Bibliometric Analysis of SLCA. The bibliometric analysis method creates structural depictions of research areas based on bibliographic data from publication databases (Zupic and ˇ Cater 2015; Kumar et al. 2020). Bibliometric analysis is a technique for describing, evaluating, and analysing scholarly articles published in prestigious journals. So, it helps us to understand the in-depth aspects of a particular topic or direction of research. Previous research suggested two methods for this analysis. First, the measures of the influence of the research article, i.e., impact factors, and second, the connections between diverse fields of study as well as authors. The conjugation of these two methods makes a bibliometric analysis more holistic. In our study, we used citation and co-citation analysis to obtain these results.

3.1 Affiliation Analysis of the SLCA To understand the author’s affiliation of that particular SLCA research, we have undertaken an affiliation analysis for this section (Table 2). Along with the author’s affiliation, geographical regions are also a crucial indicator for that aspect. The highest number of publications in terms of affiliations is held by the Technical University of Berlin, with 13 out of 135 publications. The second highest institute on this list is the Delft University of Technology (TU Delft), which is also very close to the earlier one with 12 publications. The Technical University of Denmark, the University of Calgary, and the University of Coimbra hold nine publications each in that group. Unlike the University of Calgary in Canada, the rest of the universities in the top 5 contributing institutions are situated in Europe. So this picture shows a clear dominance of different European institutes and their affiliations in this field.

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Table 2 Top 5 Contributing institutes/universities for SLCA SL No.

Affiliation

No. of articles

1

Technical University of Berlin

13

2

Delft University of Technology (TU Delft)

12

3

Technical University of Denmark

9

4

University of Calgary

9

5

University of Coimbra

9

The calculation is based on an article search done in Web of Science on April 20, 2022

Table 3 Top 5 contributing countries and continents for SLCA SL No.

Country

No. of articles (%)

Continents

No. of articles (%)

1

Germany

17 (13%)

Europe

87 (64%)

2

Italy

16 (12%)

Asia

22 (16%)

3

Netherlands

11 (8%)

North America

17 (13%)

4

Sweden

9 (7%)

South America

7 (5%)

5

Spain

8 (6%)

Oceania

2 (1%)

The calculation is based on an article search done in Web of Science on April 20, 2022

Similarly, the top 5 contributing countries in the SLCA area are Germany (13), Italy (12), the Netherlands (9), Sweden (9), and Spain (9). Table 3 shows the country of residence of the authors’ affiliation. Like earlier, this picture also shows the clear dominance of European countries. If we see more aggregate level in terms of the top 5 continents, the distribution of Europe (64%), Asia (16%), North America (13%), South America (5%), and Oceania (2%), respectively. The majority of the studies are affiliated with the European and North American regions. Asia is in a moderate position in terms of SLCA research. There is a significantly low representation from South America and Oceania. Interestingly, both European and North American countries fall into the developed nations, so more studies are needed in developing countries. To strengthen this analysis, Fig. 4 portrays the interrelation of the co-authorship network in terms of countries. Here we can see most of the collaborations held with different institutions in Germany, followed by Italy, the Netherlands, Sweden, and Spain.

3.2 Author Influence Analysis of the SLCA This section has tried to answer research question 2 (RQ 2). Here, we have analysed the top contributing authors and their affiliations with countries based on the total number of publications. Here we found that Marzia Traverso and Luigia Petti are the

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Fig. 4 Co-authorship network of countries. The figure is based on an article search done in Web of Science on April 20, 2022

most influential authors and contributed significantly to SLCA, with 8 and 6 articles, respectively. Also, all these articles have been published in journals with high impact factors. Along with this, Andreas Ciroth (GreenDelta TC), Andreas Jørgensen (Technical University of Denmark), Annekatrin Lehmann (Technical University Berlin), and Georgios Archimidis Tsalidis (Delft University of Technology) have significant influence in that area with 4 publications each (Table 4). The top six contributors (the last four authors have contributed four articles in this category) are also from Europe. From the analysis of the existing data, it is evident that a majority of the research in this domain is concentrated in European countries. Our point is that though the area is very much concentrated in the scenario of developed countries, there is a need to examine the SLCA methodology in the context of developing countries as well. Table 4 The most influential Authors in terms of total publications SL No. Author

Affiliation

Country Germany

1

Marzia Traverso

RWTH Aachen University

2

Luigia Petti

‘Gabriele d’Annunzio’ University Italy Pescara

TP 8 6

3

Andreas Ciroth

GreenDelta TC

Germany

4

4

Andreas Jørgensen

Technical Universuty of Denmark Denmark

4

5

Annekatrin Lehmann

Technical University Berlin

4

6

Georgios Archimidis Tsalidis Delft University of Technology

Germany

Netherlands 4

The calculation is based on an article search done in Web of Science on April 20, 2022

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3.3 Journal Influence Analysis of the SLCA We analysed the Average Citation per Article (ACA) of the leading journals in this domain. Hence, this section will answer our next research question (RQ 3). Journal productivity is measured by the number of papers published, but journal influence is determined by the number of citations (Svensson 2010). Therefore, we determined the average number of citations per article of the top 5 contributing journals in this field in order to analyse the most significant publications (Table 1). It’s significant to observe that often journals with more articles do not also have more citations. As a result, a journal’s productivity does not indicate its influence. Both Journals of Cleaner Production and Journal of Industrial Ecology have the second and third highest average citations per document (31.154 & 20.6, respectively) with only 13 and 5 articles, respectively, and Sustainability has only 11.316 ACA score, whereas a total of 19 articles have been published here. As earlier, the international journal of life cycle assessment is the most influential journal (ACA score: 37.127) in the area of SLCA.

3.4 Citation Analysis of the SLCA To answer the question of the most influential work (RQ 4), we have considered the citation of the article as the indicator. Citation analysis explains the connection between the publications being cited and those being referenced. As a result, citation analysis measures a publication’s effect and popularity in the scientific community by counting the total number of times that piece has been referenced in other works. (Ding and Cronin 2011). We have analysed the total global citation of 135 papers based on the ‘total times cited count’ downloaded by the Web of Science core collection. In Table 5, we can see that the article written by Jorgensen (2008) is the globally most cited article among all the articles on the SLCA area, followed by Benoit (2010), Martinez-Blanco (2014), Hosseinijou (2014), and Manik (2013). We have considered a standardised indicator—‘total citation per year’ another indicator to analyse the influential work in that area. As per total citations per year, they are also following the earlier trend.

3.5 Co-citation Analysis of the SLCA Co-citation occurs when two authors or two sources appear side by side in the reference list of a single publication. (Tunger and Eulerich 2018). Two works that are frequently quoted in tandem are probably about the same topic (Hjørland 2013) or have similar content (Small 1973). Thus, co-citation analysis is a technique for

A Bibliometric Analysis of Social Life Cycle Assessment (2008–2022) Table 5 Top 5 most influential Articles in terms of Global total citations

Sr. No

Author (year)

TC (global)

83 TC per year

1

Jorgensen A, 2008

295

19.667

2

Benoit C, 2010

242

18.615

3

Martinez-Blanco J, 2014

143

15.889

4

Hosseinijou Sa, 2014

112

12.444

5

Manik Y, 2013

109

10.9

The calculation is based on an article search done in Web of Science on April 20, 2022

assessing the contextual similarity of numerous publications that are connected to the same theory, method, or empirical field (Gmür 2003; Small and Greenlee 1980). Co-citation analysis signifies when two authors or sources appear simultaneously in a reference list of a single publication and acknowledge the work. Basically, two scholarly articles appearing together in an article means they are probably from the same area of study. To analyse this, we have analysed the co-citation network for major authors, those who have a minimum of 10 citations out of 3595 total authors (Fig. 5). From the analysis, we can see that A. Jorgensen and IC Dreyer are the most cited authors in this field, with more than 100 co-cited authors. This network is showing some bias because the time span is very low and there are a limited number of papers in this field, so there are very few papers that are cited in a repeated manner. Apart from this, Benoit-Norris, Macombe, and Ekener-Petersen are also very popular in this field.

Fig. 5 Co-citation analysis of authors

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Fig. 6 Word clouds of author’s keywords

Author Keywords Analysis of the SLCA The most frequently used words among all the selected articles give us a picture of the direction of the research. Represented by a word cloud, we have seen a very interesting picture of the most used keywords in SLCA research. Here we have selected the top 50 most frequently used keywords by authors (Fig. 6). From the respective figures, a very interesting picture emerges. The top 10 widely used keywords are ‘social life cycle assessment’, ‘sustainability’, ‘s-lca’, ‘life cycle assessment’, ‘social’, ‘social lca’, ‘social life cycle’, ‘slca’, ‘assessment’, and ‘indicators’. Although a few words like Social Life cycle assessment, slca, s-lca, and Social lca are similar words, different authors used these keywords in their articles.

4 Conclusion Social life cycle assessment is a very new method in the discourse of research. Many earlier studies have already suggested that this method is in the incubation stage. A lot of development is still required, and it is improving every day. In that scenario, our study has presented an overall picture of this field till date. Above all, despite its early stage of methodology, SLCA has received widespread acceptance across disciplines. The methodology considers the complete life cycle of a particular product, capturing multiple stakeholders in the entire process, which makes it more holistic. If we see the year-wise trend, then in 2018 we have the highest number of publications and an average publication of 9–10 studies each year. Again, the international journal of life cycle assessment is the major source for this area. A total of 71 articles have been published till date. There is a clear dominance of European countries in terms of the affiliation of the authors. A total of 64% of the studies are affiliated with various institutes in Europe. All four of the top five contributing institutes are European. If we see the co-authorship network of countries, then most of the collaboration was done in Germany, Italy, the Netherlands, Sweden, and Spain. One

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major reason for that dominance is that the method is very new and it actually originated from Europe (members of the task force are primarily from Europe). The same trend is applicable to the author’s influence as well. The top five influential authors are located at various European institutes. To measure the journal influence of that particular field, we have used average citations per article (ACA) as an indicator. Here we have noticed an interesting result. Despite a low number of publications, the ACA score is much higher for the Journal of Cleaner Energy and the Journal of Industrial Ecology. The international journal of life cycle assessment is holding the highest number of ACA scores, as earlier. Although this ACA score depends on the total citations and the number of publications, along with this, the years of publication can influence the ACA score. But still, ACA scores are an appropriate indicator to identify the influence of a journal in a particular field. We also identified the most influential studies in SLCA by using citation analysis. We have considered a standardised indicator—‘total citation per year’—to analyse the influential work in that area. The results show us the article written by Jorgensen (2008) is the globally most cited article among all the articles in the SLCA area, followed by Benoit (2010), Martinez-Blanco (2014), SA Hosseinijou (2014), and Manik (2013). In the co-citation analysis, it has been observed that A. Jorgensen and IC Dreyer are the most concentrated authors in this field, with more than 100 co-cited authors. Although the field is very new and the time span is very low, few papers are cited very often. Again, we have presented a word cloud for the top 50 most used keywords by the authors. We can see that the top 10 widely used keywords are ‘social life cycle assessment’, ‘sustainability’, ‘s-lca’, ‘life cycle assessment’, ‘social’, ‘social lca’, ‘social life cycle’, ‘slca’, ‘assessment’, and ‘indicators’. Although a few words like Social Life cycle assessment, slca, s-lca, and Social lca are similar words, different authors used these keywords in their articles. So, from this bibliometric analysis, we suggest that apart from Europe and North America, more study is required for other continents as well. Although there is an upsurge in Asia, Asia is a very big continent and more studies will improve the acceptability of this study.

References Ding Y, Cronin B (2011) Popular and/or prestigious? Measures of scholarly esteem. Inf Process Manag 47:80–96. https://doi.org/10.1016/j.ipm.2010.01.002 Gmür M (2003) Co-citation analysis and the search for invisible colleges: a methodological evaluation. Scientometrics 57:27–57. https://doi.org/10.1023/A:1023619503005 Hjørland B (2013) Citation analysis: a social and dynamic approach to knowledge organization. Inf Process Manag 49:1313–1325. https://doi.org/10.1016/j.ipm.2013.07.001 Kumar S, Sureka R, Colombage S (2020) Capital structure of SMEs: a systematic literature review and bibliometric analysis. Manag Rev Q 70:535–565. https://doi.org/10.1007/s11301-019-001 75-4 Martínez-Blanco J, Lehmann A, Chang YJ, Finkbeiner M (2015) Social organizational LCA (SOLCA)—a new approach for implementing social LCA. Int J Life Cycle Assess 20:1586–1599

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Small H (1973) Co-citation in the scientific literature: a new measure of the relationship between two documents. J Am Soc Inf Sci 24:265–269. https://doi.org/10.1002/asi.4630240406 Small H, Greenlee E (1980) Citation context analysis of a co-citation cluster: recombinant-DNA. Scientometrics 2:277–301. https://doi.org/10.1007/BF02016349 Svensson G (2010) SSCI and its impact factors: a “prisoner’s dilemma”? Eur J Mark 44:23–33 Tunger D, Eulerich M (2018) Bibliometric analysis of corporate governance research in Germanspeaking countries: applying bibliometrics to business research using a custom-made database. Scientometrics 117:2041–2059 WCED: Our Common Future (‘The Brundtland Report’): World Commission on Environment and Development (1987) ˇ Zupic I, Cater T (2015) Bibliometric methods in management and organization. Organ Res Methods 18:429–472

Establishing a Preliminary Understanding of Loss and Damage in India: Case of Floods in Assam Saumya Jain

Abstract Extreme events caused by climate change have increased dramatically in recent decades. Climate change appears as a clear exacerbating factor in the rising frequency and intensity of these extreme events as indicated in the evolving attribution sciences. The UNFCCC’s (United Nations Framework Convention on Climate Change) institutionalization of Loss and Damage in Article 8 of the Paris Agreement has led to minimal policy action around the world, and as proof of irreversible loss and damage caused by climate change emerges, substantial debate on loss and damage is needed. The global south is particularly vulnerable, as we currently struggle to provide basic necessities for our inhabitants and have only rudimentary coping mechanisms in the event of extreme events. As extreme events such as floods become more common and intense in India, with Assam being particularly vulnerable, loss and damage research is becoming increasingly important. The following study examines the economic loss and damage caused by floods in Assam to determine the impact of extreme events. To judge the adequacy of the extent to which loss and damage are addressed, a detailed assessment of Assam’s SAPCC (State Action Plan on Climate Change) and risk reduction, retention, and transfer mechanisms are conducted, aiding in getting an initial scope of the Loss and Damage policy discourse on a state level in India. Keywords Loss and damage · Extreme events · Attribution sciences · SAPCC

1 Introduction Loss and Damage refer to adverse climate-related impacts and risks from suddenonset events, such as floods and cyclones, and slower-onset processes, including droughts, sea-level rise, glacial retreat, and desertification and the same has garnered S. Jain (B) Centre for Climate Change and Sustainability Studies, School of Habitat Studies, Tata Institute of Social Sciences, Mumbai, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_7

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attention in international climate policy and advocacy over the last three decades (Mechler et al. 2020). However, the topic of loss and damage dates back far before the UNFCCC (United Nations Framework Convention on Climate Change) was established. On behalf of the Alliance of Small Island States (AOSIS), the Republic of Vanuatu submitted a proposal to the Intergovernmental Negotiating Committee (INC) in 1991, which discussed the creation of an International Climate Fund to address the impacts of climate change, as well as an insurance pool to provide a safety net against sealevel rise (Intergovernmental Negotiating Committee for a Framework Convention on Climate Change Working Group II 1991). Post this, loss and damage resurfaced in the climate change debate after a long period but was discussed in length at multiple COP (Conference of Parties) meetings finally culminating in the WIM (Warsaw International Mechanism) on Loss and Damage associated with Climate Change Impacts at COP 19 in Warsaw, Poland. However, WIM was not very effective as it simply requested developed countries to help and assist developing countries with financial, technological, and capacitybuilding support without binding them to do so (UNFCCC 2012). The inclusion of Loss and Damage as a complete article (Article 8) of the Paris Agreement confirmed the importance of the issue and acknowledged the limitations of mitigation and adaptation initiatives but again had little room for action with no mention of liability or compensation in the article due to the vehement opposition of the developed countries (UNFCCC 2015). The significance of anthropogenic emissions in fueling the frequency and intensity of extreme events can no longer be ignored, thanks to substantial advancements in the field of attribution science in recent decades. It is also important to note that, although not contributing equally to past emissions, the global south continues to suffer enormous losses and damages as a result of these catastrophic events. Without any international compensation mechanisms or funding facilities for loss and damage that account for the global north’s historical role in driving the climate crisis, the least developed and developing countries are left to deal with the consequences of these extreme events on their own. Extreme events like floods can force people into perilous levels of vulnerability in developing countries like India, where a vast majority of the population still struggles to afford basic utilities. Given the financial constraints of least developed and developing countries, an increase in the cost of loss and damage, if not compensated, can lead to unsustainable debt levels and the deprivation of funds for important economic activities. As a result, it is critical for India and other developing countries to focus on loss and damage prevention, mitigation, and response. Even though the effects of climate change-induced extreme events are already being felt in India, there is a general dearth of research on loss and damage in public policy in Developing countries with Bangladesh being an exception. The following study aims to contribute to the research of loss and damage in developing countries by studying floods in Assam, the economic loss, and damage incurred and measuring the degree of readiness to deal with it. This will be achieved by studying the loss and damage element in the Assam State Action Plan on Climate Change (ASAPCC) which is a dedicated plan for climate action on a state level. The

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study will also analyze the existing risk retention, reduction, and transfer mechanisms to understand their adequacy and limitations to avert, mitigate, and address loss and damage in the event of floods.

2 Methodology Economic losses are defined as the loss of resources, goods, and services that are commonly transacted in markets, such as lost income from business operations, agricultural production, and tourism, as well as damage to physical assets such as infrastructure and property. Non-economic losses include losses of values not commonly traded in markets but bearing high relevance for those affected, for example, loss of life, biodiversity, and cultural heritage (Institute of Disaster Management and Insurance Institute of India 2021). In the present study, only reported economic losses from floods (extreme events) have been studied and have been compiled from the Central Water Commission (CWC) database for the period of 60 years from 1960 to 2020 (Serdeczny et al. 2018). Figures are presented in US$(Conversion rate-1 Dollar = Rs.74.64), and considering the change in the inflation rate over the years, the current value of reported loss and damage has been calculated by taking the inflation rate of 2018 as the base (World Data n.d.). Three variables have been discussed in the study that are loss and damage to crops, houses, and public utilities given their material significance in the lives of the citizens of Assam.

3 Results The trend analysis of the loss and damage to crops, houses, and public utilities is depicted in Figs. 1, 2, and 3. Agriculture is crucial for Assam as it still has an agrarian economy, with 86% of its rural population depending on it for their livelihoods (Department of Environment and Forest, Government of Assam 2015). Thus, this clear upward trend of loss and damage to crops in Fig. 1 over the years is a cause of concern as it might lead to the shrinking of the GSDP (Gross State Domestic Product) as agriculture contributed about 25% of the total GSDP for the year 2021–22 (Assam Budget Analysis 2022). The Fig. 2 shows a prolonged increase in housing loss and damage, with a considerable spike after 1986, which could be due to the shortage of Pucca and Semi-Pucca houses prior to it. Assam has made significant improvements in housing over the years, but the situation remains dire. In 2014, 33.6% and 22.7% of Assam households, respectively, lived in Semi-Pucca and Pucca houses, which is significantly lower than the Indian average; the exponential rise in loss and damage as seen in Fig. 2 depicts the critical need for more resilient housing infrastructure given the already precarious situation (Assam 2014).

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Fig. 1 Loss and damage to crops due to floods for the period 1960–2020

Fig. 2 Loss and damage to houses due to floods for the period 1960–2020

Fig. 3 Loss and damage to public utilities due to floods for the period 1960 to 2020

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Flood losses and damages are increasing slowly and steadily for public utilities, followed by a sharp increase in the last decade as seen in Fig. 3. The loss and damages initially seem almost invisible due to the massive scale of increase in the last decade rendering the initial loss and damage figures insignificant in the graph. All three figures exhibited a clear increasing trend in the trend analysis of the loss and damage caused due to floods. Following 2016, all three sectors also experienced a sharp increase, with the highest peaks reported between 2016 and 2020. The study investigated the underlying reasons for the emerging trends observed, one of which was the higher intensity and frequency of floods. The Brahmaputra River, one of India’s largest, runs through Assam and has been a major source of flooding. As the Brahmaputra originates in the Himalayas, faster glacier melting because of global warming, combined with erratic and intensified monsoon downpours as a result of climate change, has contributed to increased flood intensity and frequency. Faulty dam management, weak embankments, and political issues have also acted as contributing factors for the same (Floods in Assam 2016). The second reason identified was increased exposure as Assam’s population density increased to 398 people in 2011 from 340 people in the 2001 Census, or 58 more people inhabit every square kilometer in the State than a decade ago (Directorate and statistics Assam. Assam at a glance 2019). Assam’s flood-prone land area accounts for approximately 39.58% of the total land area. While the flood-prone area of India accounts for about 10.2% of the total area, representing the extreme exposure that Assam faces, which is four times the national mark (Government Of Assam India n.d.). The third reason identified was increased vulnerability. The vulnerability of the people of Assam is clear, with 31.98% of the state’s population still living below the poverty line, compared to the Indian average of 21.92% in 2011–2012. An individual’s coping capacity deteriorates significantly when resources are scarce. With a large chunk of this population living in rural areas often remote with few basic services and low levels of development renders them precariously vulnerable to disasters like floods, which devastate people’s lives and livelihoods (Directorate and statistics Assam. Assam at a glance 2019). Although the abrupt increase in flood losses and damages in Assam cannot be attributed only to climate change, our findings are consistent with previous literature on climate change’s function as an exacerbating factor in the frequency and intensity of extreme events, such as floods. It is clear from the statistical and descriptive analysis of the loss and damage data of Assam that the state requires substantial preparedness to tackle the increasing loss and damage due to climate change-induced extreme events; hence, the following study undertakes a critical analysis of the Assam SAPCC and the different approaches undertaken by the state to address loss and damage.

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4 Discussion 4.1 State Action Plan on Climate Change As discussed above with an exponential increase in the loss and damage suffered due to floods, it is important to study its inclusion in the State Action Plan on Climate Change, formulated to deal with the effects of climate change. Through a descriptive assessment of Assam’s SAPCC, this study attempts to understand the steps taken by Assam to avert, mitigate, and address loss and damage in the event of floods. A thorough literature review was conducted to arrive at the key aspects to assess the inclusivity of loss and damage in the SAPCC. 1. Vulnerability assessment A state’s vulnerability assessment is critical for developing an effective climate change action plan to prevent, limit, and address loss and damage from disasters such as floods. Given the dynamic nature of vulnerability and how it varies spatially and temporally, it is critical to comprehend its many facets to design effective loss and damage strategies and policies. In the case of Assam, it lacks a vulnerability assessment that is localized and issue-specific. It also borrows the study of expected economic vulnerability from other South Asian countries, which would undoubtedly differ from Assam in many ways due to its distinct physiography and socioeconomic vulnerability. 2. Targets or goals that aim to avert, minimize, or address loss and damage Assam currently lacks any targets or goals in the area of loss and damage because it is still a relatively uncharted territory but going forward the SAPCC could include goals and targets, preferably quantifiable, that can be monitored and reported to bring accountability and transparency to the process while ensuring that loss and damage are adequately addressed. 3. Decision-making body for loss and damage Assam has established the Assam Climate Change Management Society (ACCMS), a Special Purpose Vehicle (SPV), to coordinate all SAPCC-related and other climate change-related activities although it lacks any distinct decisionmaking working group/steering committee/expert group to avert, minimize, or address loss and damage (Department of Environment and Forest, Government of Assam. 2015). 4. Proactive steps taken to avert, minimize, or address extreme events (in this case, floods) induced loss and damage Assam’s SAPCC mentioned numerous strategies for dealing with flood loss and damage which involved conducting relevant in-depth scientific research, use of best practices, co-production of knowledge with indigenous communities, and a number of other ambitious strategies. But it needs to be noted that these strategies do not have a clear timeline and the question of financial resources looms over their effective implementation.

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5. Governance Structures to support Loss and Damage Assam currently lacks a governance system to support loss and damage. As of today, current climate change governance frameworks, such as the newly founded Assam Climate Change Management Society (ACCMS), and a Special Purpose Vehicle (SPV), do not recognize loss and damage as a separate thematic area or workstream. Given its growing importance in the climate change discourse, a new thematic area for loss and damage should be created within the existing governance structure to provide it with the legitimacy and resources it requires. A comprehensive examination of Assam’s SAPCC found the absence of any policy, legislative, or institutional frameworks addressing loss and damage. However, several proactive loss and damage preventive strategies are suggested, which could be useful if implemented. Despite the lack of formal direction in existing policies, it is vital that scientific targets relating to loss and damage be incorporated into our revised future plans, given their growing importance, as demonstrated in this study.

4.2 Analysis of Approaches to Address Loss and Damage—Assam The following section critically analyzes the existing policies in the state that aid in loss and damage mitigation by understanding their adequacy and limitations and suggesting policy interventions for the same. Risk reduction, risk retention, and risk transfer are three approaches to deal with extreme events. Further, there are two types of risk reduction measures: structural and non-structural. The installation of physical infrastructure such as dykes to control floods is a common structural measure that can be costly yet helpful. Non-structural risk reduction measures use knowledge, practice, or agreement rather than physical construction to decrease risks and impacts through policies and regulations, public awareness, training, and education. Risk-retention strategies emphasize on increasing resilience and providing a buffer when extreme weather-related climate change impacts occur. The final strategy, risk transfer, functions largely as an insurance mechanism in which economic risks from an individual or organization are transferred to an Insurer (Nishat et al. 2013).

4.2.1

Risk Reduction Measures

Structural risk reduction measures Over the years, the Assam government has worked on several fronts to reduce structural risk. The following table shows the progress of Assam’s physical infrastructure to date (Fig. 4). However, there are numerous objections to this approach, for example, embankments as a risk reduction strategy have several flaws, including land loss for building

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Fig. 4 Physical infrastructure development in Assam. Source ISRO (2016)

and relocation, dangers and consequences of unexpected embankment failure, disruption of fish breeding cycles, increase in flooding of unprotected areas, and many more. The Assam government appears to place an overabundance of emphasis on the techno-engineering approaches outlined above for flood control, which should instead be viewed from a political–ecological perspective. To some extent, the increasing loss and damage are also indicators of the ineffectiveness of these technoengineering approaches. Apart from these challenges, Assam has not taken any real moves in the direction of long-term solutions. Surveys, investigations, and full project reports have all been merely suggested. In actuality, efforts have focused solely on speeding up the construction of eight large dams to aid in long-term flood management which can also be a source of flooding if mismanaged. Non-structural risk reduction measures The Assam government has proposed and conducted a few non-structural flood mitigation measures, such as undertaking a flood modeling study of the Brahmaputra River in collaboration with Friedrich Schiller University in Jena, Germany, to better understand future forecasts and dangers. In 2008, the government built a Flood Early Warning System (FLEWS), which is an integrated flood warning system that uses modeling to combine real data and forecasts of rainfall and river water discharge with the physical parameters of the river system (ISRO 2016). The FLEWS structure has a clear division of roles and responsibilities, dissemination of information on a local level, and assures the safety and early preparedness of Assamese citizens, and it can prove to be incredibly effective. However, current measures appear to be insufficient to address the severity of the problem.

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Risk Retention Measures

Risk-retention policies try to help people cope with the effects of extreme events, but the Assam government has taken very few steps in this direction. During floods, the government gives Gratuitous Relief (GR) and financial support through the SDRF (State Disaster Relief Fund), which the State Government uses to provide support to the flood-affected people. The government also guarantees that there is a sufficient supply of food grains, cattle feed, and other supplies for flood victims, as well as a transportation plan in place (Revenue and Disaster Management Department 2015). However, the Assam government has barely any measures in place to ensure sufficient recovery following a disaster like a flood. They can include more initiatives that target vulnerable groups such as rural residents, women, the elderly, and children. Microfinance for persons who have lost their jobs could be one tool that can help with recovery after a disaster (Nishat et al. 2013).

4.2.3

Risk Transfer Measures

Assam’s government does not have any targeted risk transfer mechanisms in place, such as flood insurance. However, there are a few Central government crop insurance programs, such as the Pradhan Mantri Fasal Bima Yojana and the Weather Based Crop Insurance Scheme (WBCIS), that can help with losses and damage caused by catastrophic disasters like floods. The SDRF has also been largely insufficient to offset the economic losses incurred in case of disasters in Assam with economic losses being 944.8% of the allocated SDRF amount. While low-income and other disadvantaged people frequently require rapid assistance in the aftermath of natural disasters such as floods. The speed with which natural catastrophe victims are compensated can have a substantial impact on their rehabilitation. And it’s critical to develop policy instruments that support both protection and restoration that require minimum on-ground presence, as well as an implementation that is completed quickly and efficiently. Several studies have found that making finances more readily available helps speed disaster response and reduce losses. A parametric insurance product is recommended in this case, which is defined as an insurance contract in which the final payment or contract settlement is determined by weather or geological observation or index, such as average temperature or rainfall over a given period or the intensity of an earthquake, flood, or a windstorm. Parametric insurance payouts are based on the measurement of event intensities that are substantially connected to the ‘to be expected loss’ due to the occurrence, rather than on individual losses. This results in faster compensation and assistance to the victims than other types of insurance. As a result, a Parametric Index-Based solution is offered to cover the risks of low-income households in a transparent and efficient insurance-backed manner that can prove to be extremely effective in the case of Assam (National Institute of Disaster Management and Insurance Institute of India 2021). The investigation of the Assam government’s measures to address the impact of floods indicates that they are explicitly promoting techno-engineering approaches,

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have inadequate risk retention measures, and fundamentally lack any risk transfer mechanisms; thus, there is a need to rethink for Assam to effectively address loss and damage.

5 Conclusion Our analysis clearly indicates rising loss and damage due to floods in Assam and the inadequacy of the existing policies in place to avert, minimize, and reduce loss and damage in the state due to floods. And, while climate change cannot be singled out as the sole cause of this increase in loss and damage, there is a large body of evidence that supports its involvement as an exacerbating factor in extreme events. This relationship will become more apparent as the science of attribution progresses. The following research can be viewed as a scoping study to better understand existing institutional mechanisms at the state level, as well as a foundation for future research on climate change-related extreme events. The study’s in-depth analysis of several approaches to addressing flood loss and damage in Assam can be beneficial to both development organizations and policymakers to better understand the impacts of extreme events and the existing capacity to address them.

References Assam HDR 2014: Only 22.7 per cent households in the state and 6.3 per cent households in the two hill districts have pucca houses. (n.d.). NEZINE. https://www.nezine.com/info/R1JVcm Rhd3JKbmFVMXNxMDlsM3lydz09/assam-hdr-2014:-only-22.7-per-cent-households-in-thestate-and-6.3-per-cent-households-in-the-two-hill-districts-have-pucca-houses.html. Accessed 17 May 2022 Assam Budget Analysis 2022–2023. (n.d.). PRS India. https://prsindia.org/budgets/states/assambudget-analysis-2022-23. Accessed 17 May 2022 Department of Environment and Forest, Government of Assam (2015) Assam State Action Plan on Climate Change. https://moef.gov.in/wp-content/uploads/2017/08/ASSAM-SAPCC.pdf Development of inflation rates in India (n.d.) WorldData. https://www.worlddata.info/asia/india/inf lation-rates.php. Accessed 16 May 2022 Directorate of Economics and Statistics Assam (2019) ASSAM AT A GLANCE 2019. https://des. assam.gov.in/information-services/state-profile-of-assam. Flood Management|Water Resources|Government Of Assam, India. (n.d.). Assam Water Resources. https://waterresources.assam.gov.in/portlets/flood-management. Accessed 17 May 2022 Floods Damage Statistics (2022) CWC. https://cwc.gov.in/flood-damage-statistics-statewise-andcountry-whole-during-1953-2020. Accessed 16 May 16 2022 Floods in Assam—Causes, Effects and Solutions—Cities (2016) India Map. https://www.map sofindia.com/my-india/government/why-india-cant-afford-to-ignore-assam-flood-situation. Accessed 16 May 2022 Herring D (2020) What is an “extreme event”? Is there evidence that global warming has caused or contributed to any particular extreme event? NOAA Climate.gov.Climate.gov. https://www.cli mate.gov/news-features/climate-qa/what-extreme-event-there-evidence-global-warming-hascaused-or-contributed Accessed 16 May 2022

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Intergovernmental Negotiating Committee for a Framework Convention on Climate Change Working Group II (1991) Negotiation of a Framework Convention on Climate Change. https:// unfccc.int/sites/default/files/resource/docs/a/wg2crp08.pdf Mechler R, Singh C, Ebi K, Djalante R, Thomas A, James R, Tschakert P, Wewerinke-Singh M, Schinko T, Ley D, Nalau J, Bouwer LM, Huggel C, Huq S, Linnerooth-Bayer J, Surminski S, Pinho P, Jones R, Boyd E, Revi A (2020) Loss and Damage and limits to adaptation: recent IPCC insights and implications for climate science and policy. Sustain Sci Mohanty A, Wadhawan S (2021) Mapping India’s climate vulnerability—a district level assessment. Council on Energy, Environment and Water National Institute of Disaster Management and Insurance Institute of India (2021) Disaster risk financing, insurance and risk transfer. National Institute of Disaster Management, New Delhi National Remote Sensing Center (ISRO) (2016) Flood Hazard Atlas for Assam State (1998–2015) http://sdmassam.nic.in/pdf/publication/Flood_Hazard_Atlas2016.pdf Nishat A, Mukherjee N, Roberts E, Hasemann A (2013) A range of approaches to address loss and damage from climate change impacts in Bangladesh. Centre for Climate Change and Environmental Research (C3ER) Revenue and Disaster Management Department, Government of Assam (2015) Assam disaster management manual. http://www.asdma.gov.in/download/assam_disaster_manage ment_manual_2015.pdf Serdeczny OM, Bauer S, Huq S (2018) Non-economic losses from climate change: opportunities for policy-oriented research. Climate Dev 10(2):97–101. https://doi.org/10.1080/17565529.2017. 1372268 UNFCCC (2012) A literature review on the topics in the context of thematic area 2 of the work programme on loss and damage: a range of approaches to address loss and damage associated with the adverse effects of climate change [FCCC/SBI/2012/INF.14] UNFCCC (2015) Paris Agreement to the United Nations Framework Convention on Climate Change

Exploring the Role of Hydrogen Energy Towards Sustainable Energy System of India Pooja Kumari and Kamal Kumar Murari

Abstract The most significant issue we are facing is natural or human-induced climate change and its unbreakable connection to the present and future energy needs of our global society. Hydrogen is nowadays widely acknowledged as a key component of a potential twenty-first-century energy answer, capable of addressing issues such as greenhouse gas emissions, sustainability, and energy safety. This research paper presents a systematic literature review on the potential of a hydrogen energy system. The study analyses the different methods on the basis of reactions, scale, cost, environmental impacts, etc. India is targeting 2030 as the hydrogen mission but we are still at a nascent stage in terms of technology, skill, infrastructure, etc. This paper is an attempt just to understand the key challenges and opportunities for finding the road to a hydrogen economy in India. Keywords Energy · Green hydrogen · Blue hydrogen · Turquoise hydrogen · Production · Storage · Utilization · Fuel cell

1 Introduction Hydrogen is a powerful alternative energy source. As opposed to coal, or other fossil fuels, hydrogen is not a major energy source. Just like with electricity, it acts like a “secondary energy carrier”, which must be created using energy from other sources before moving to a spot where its latent chemical energy may be completely realized. Several renewable and non-renewable energy sources, such as hydro, wind, and solar, can be used to produce hydrogen. Water is the sole by-product, and it may also be used as a fuel to power mobile and dispersed heating and cooling systems (Edwards et al. 1853). One of the biggest environmental issues facing the globe might be resolved with the use of hydrogen in place of fossil fuels in transportation. P. Kumari (B) · K. K. Murari Centre for Climate Change and Sustainability Studies, School of Habitat Studies. Tata Institute of Social Sciences, Mumbai, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_8

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It can also be used as a “storage medium for electricity” created from infrequent, renewable sources thereby addressing one of the primary challenges of sustainable power, namely irregular supply. However, the hydrogen economy refers to using hydrogen, as both a fuel and in fuel cells, to decarbonize economic sectors which are hard to exhilarate or switch to other sources of power (Sastrit 1987). Automobile emissions, aviation, shipping, utility, and heating are some of the sectors where hydrogen can have the best benefits (Medisetty et al. 2020). When hydrogen is used as a fuel instead of harmful greenhouse gases, the only by-product is water vapour. Hydrogen for this reason is considered a great substitute source of energy in an economy that uses low to no carbon (Sastrit 1987). Although hydrogen is a colourless gas, it can be identified using one of nine colour codes (www.H2bulletin.com). The colour codes for hydrogen refer to the source or manufacturing process. Green hydrogen is produced by renewable electricity and a water electrolysis process (Germscheidt et al. 2021). The fact that no carbon dioxide is emitted throughout the production process is referred to as being “green”. Fossil fuels provide the blue hydrogen. Furthermore, carbon dioxide is captured and stored underground (carbon sequestration). No carbon dioxide is emitted during “blue hydrogen synthesis”. The process of producing grey hydrogen from fossil fuels is known as steam methane reforming (SMR) (Newborough et al. 2020). Carbon dioxide is produced and ultimately released into the atmosphere during this process. Carbon dioxide is used to make black or brown hydrogen. Coal gasification is a method for producing hydrogen. However, the method is very polluting, emitting carbon dioxide and carbon monoxide into the environment as by-products. Methane pyrolysis can be used to extract turquoise hydrogen. Purple hydrogen is produced by combining nuclear power and heat to split water using chemo thermal electrolysis (Germscheidt et al. 2021). Pink hydrogen is produced by electrolyzing water with nuclear power plant electricity (www.H2bulletin.com). And lastly, Natural hydrogen is referred to as white hydrogen (Giovannini 2020). Due to fast advancement in fuel cell equipment, the value of hydrogen as a “potential energy carrier” has significantly increased over the recent times. A lot of countries are already creating roadmaps for the development of hydrogen and fuel cell technologies, many of which have specific numerical goals. The majority of the top automakers in the world have made significant investments in fuel cell vehicle research and development programmes, making mobility the main focus of hydrogen research (Ramachandran 1978).

1.1 Relevance of the Current Study The study is an attempt to understand the hydrogen energy system both from conventional and non-conventional source perspectives. It is by no means a broad or extensive study of the hydrogen energy because of the various process, impacts, technology, and other factors. The main objective of the study is to explore the hydrogen

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energy system with its different production methods both conventional and nonconventional. The other objective is to analyse the different processes on the basis of environment impacts, scale, and cost. Also, it’s an attempt to identify the feasibility or challenges of Hydrogen energy.

2 Methodology The methodology for this paper is based on a systematic literature review that analyses the hydrogen energy system and its possible challenges and opportunity related to the energy transition. The focus of this paper is to deliver a clear understanding of hydrogen energy and all related concepts so that we are able to analyse the current gaps leading to achieve the 2030 hydrogen energy target. The stages were divided into four phases in this study: (i) Determining the research question, (ii) Locating pertinent studies or articles, (iii) Research selection, and (iv) Collection and summary of findings.

2.1 Hydrogen “A Versatile Energy Carrier” The essential function of hydrogen as an “energy carrier” links various hydrogen generation techniques with a variety of uses. One of the most appealing aspects of hydrogen is the wide range of production methods available from various sources. Hydrogen can be produced in a variety of ways, including electrolysis, steam reforming, pyrolysis, and high-temperature thermochemical cycles, as well as biomass and waste materials (Nikolaidis and Poullikkas 2017). A wide range of production sources helps to ensure energy supply security. Hydrogen production, distribution, and delivery, also storage and utilization, are all part of its energy chain. This energy chain would involve the collection of different fuel sources to create hydrogen as “energy carrier”, its storage and delivery, and its conversion to power at an end-tool using either fuel cells or combustion (Srivastava et al. 2015). The major obstacles to acknowledging the hydrogen economy vision are to develop and commercialize fuel cell and hydrogen storage systems that are costeffective, long-lasting, safe, and environmentally friendly (Ramachandran 1978). Also, to create the infrastructure for supply chain to end-users. Another challenge is to lower the costs of production cost from clean sources as well as store the Carbon dioxide by-product from coal and natural gas during production.

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2.2 Production of Hydrogen Energy Hydrogen is one of the “most abundant gases on the planet” and it can be found in water, hydrocarbons, and alcoholic beverages. At the same time, natural biomass from plants, animals, and food waste contributes to hydrogen formation. It is also referred to as an energy carrier rather than an energy source (Bakhtyari et al. 2018). The diverse processes are used for hydrogen production through different resources.

2.2.1

Steam Reforming

Steam reforming is a process of forming hydrogen from natural gas, which is primarily methane (CH4 ). It is the cheapest source of industrial hydrogen at the present time. This method is used to produce nearly half of the world’s hydrogen. A temperature between 700 and 1,100 °C must be reached when the gas is heated in the presence of steam and a nickel catalyst (Levalley et al. 2014). Following the breakdown of methane molecules into carbon monoxide and molecular hydrogen, this endothermic reaction occurs (H2 ) (Kothari et al. 2008). A water–gas shift reaction involving carbon monoxide gas might result in the production of additional hydrogen after steam is applied to iron oxide or other oxides. This process drawback is that it contributes significantly to the atmosphere’s concentration of CO2 , CO, and other greenhouse gases. Because a considerable amount of created steam isn’t needed in the system, it’s either exported or converted to electricity using special equipment. There are two stages to the basic reaction:

2.2.2

CxHy + xH2 O → xCO + (x + y/2) H2

(1)

CO + H2 O → CO2 + H2

(2)

Partial Oxidation

It’s an exothermic reaction in which the reformer’s hydrocarbons, natural gas, and oxygen are partially combusted, yielding a syngas which contains a high amount of hydrogen. It is one of the most significant processes and absorbs all liquid and gaseous fuels, including high-sulphur raw materials (Sengodan et al. 2016). This process may or may not require the use of a catalyst, depending on the kind of procedure employed and the feedstock available. The partial oxidation of methanol has gotten a lot of interest in the petrochemical sector (Kothari et al. 2008). It produces over 30% of all hydrogen on the planet. The fundamental chemical equation for the partial oxidation of heavy hydrocarbons is as follows:

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2CxHy + H2 O + 23/2 O2 → xCO + xCO2 + (y + 1)H2 India is concerned about the status of this process, which is currently limited to the laboratory scale.

2.2.3

Coal Gasification

The synthane method and the CO2 acceptor method are both used to generate hydrogen from coal. CO, CO2 , and H2 are the gaseous products of coal when it reacted with steam at 450 psi and 800–900 °C. Methane is produced in a small amount. Methane emerges as a significant product as the pressure is raised to 1000 psi. The resulting gas is then filtered by using monoethanolamine or potassium hydroxide to remove CO2 . In the end, only 97–98 percent of the gas is pure, and CO2 is the by-product (Kothari et al. 2008).

2.2.4

Electrolysis of Water

Water electrolysis is the breakdown of water into two biological compounds, oxygen and hydrogen, by passing an electric current through it (Ministry of new and renewable energy and Government of India 2006). However, because the input energy is electricity, electrolysis is considered an expensive process. The foundational reaction is H2 O (Liquid) + Energy = H2 (Gas) + 1/2 O2 (Gas) The process is popular because it is the only one that does not require the use of fossil fuels, has high quality, and can be done on both small and large scales.

2.2.5

Pyrolysis

Methane pyrolysis is a pollution-free commercial process that removes solid carbon from natural gas to produce turquoise hydrogen. This one-step procedure generates non-polluting hydrogen in large quantities at a minimal cost. The process of producing hydrogen from natural gas at a temperature of 1065 degree Celsius allows for easy carbon removal. The industrial-grade solid carbon can then be disposed to landfill without being released into the atmosphere, preventing greenhouse gas (GHG) emissions and groundwater pollution from landfills (Bakhtyari et al. 2018). This process is run on renewable energy. It is commonly acknowledged that the catalytic decay of methane takes place by the basic reactions given as follows (Olsvik et al. 1995):

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1. Chemical adsorptions of methane on the catalyst surface. 2. The breakdown of chemisorbed methane into a hydrogen atom and a methyl radical. 3. CH4 ∗ → CH3 ∗ + H∗

(3)

4. Reactions involving stepwise dissociation that produce elemental carbon and hydrogen. 5. CH3 ∗ → CH2 ∗ + H∗

(4)

6. CH2 ∗ → CH ∗ + H∗

(5)

7. CH∗ → C ∗ + H∗

(6)

8.

The formation of molecules from atomic hydrogen. 9. 2H∗ → H2

(7)

10. The development and expansion of carbon deposits after carbon nucleation. 2.2.6

Biomass Gasification

The main method for producing hydrogen from biomass is through various thermochemical routes, which include steam gasification, supercritical water gasification, and fast pyrolysis. Because of its greater efficiency and H2 yield, gasification is seen as more compelling in industrial production than pyrolysis (Cao et al. 2020). Lignocellulosic biomass is one type of biomass that could be used as a feedstock for hydrogen production via gasification.

2.3 Environmental Impact Analysis by Diverse Methods of Hydrogen Production Whereas every method has its particular advantages and disadvantages. The same are tabulated in Table 1. However, increased emission levels of molecular hydrogen to the atmosphere could have unknown environmental consequences as a result of widespread hydrogen use. It is well understood that hydrogen plays a role in the chemical cycles of water and various greenhouse gases in the stratosphere, and that a significant increase in its concentration could cause changes in the equilibrium concentration of stratosphere constituent component.

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Table 1 Overall analysis of hydrogen production method S. No

Production techniques

Advantages

Disadvantage

1

Partial oxidation

Purely exothermic process. Economically advantageous

Slow output rate. Controlling the reaction is bit challenging

2

Steam reforming

High-quality technology, Thermal efficiency is more than 80% Enable the production of 99.9% of pure hydrogen

High energy consumption and high operating cost

3

Biomass gasification

Efficiency rate is high Eco-friendly and economically feasible

High reactor cost

4

Coal gasification

Thermally efficient Clean method

Due to groundwater contamination, problem occurs

5

Pyrolysis

Low operating costs

In order to get rid of the flue gases, more purification is necessary

6

Electrolysis of water

Clean method

Poorer efficiency than hydrocarbon reforming and high production costs. High electricity use

2.4 Scale and Cost of Hydrogen Production For Green hydrogen production, the electrolysis method is pollution free but the cost is a bit high (Table 2). The input amount of water is four times bigger than hydrogen which is an average as compared to others but the electricity demand (renewable) is much higher. For Grey hydrogen production, it needs 5 times more water input of water and two times more hydrocarbon amount to produce hydrogen (Table 2). Here, the cost is almost half of the electrolysis process but it generates a lot of environmental pollution. For blue hydrogen production, it requires three times more water and three times more hydrocarbon to produce hydrogen. This method is clean as compared to grey hydrogen but the cost is a bit high. Apart from that the carbon sequestration system technology is a bit challenging due to its infrastructure and cost-related issues. And for turquoise hydrogen production, four times methane needs to produce hydrogen with no GHG emission. The technology cost is a bit high but less than electrolysis.

2.5 Hydrogen Storage The availability of viable hydrogen storage is one of the most significant and technically difficult impediments to hydrogen’s widespread usage as a sustainable energy

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Table 2 Cost of production method by different processes Input amount

Hydrogen production method

Output amount

Production cost per ton (in dollar)

Refs.

1

Electricity (39.4 MWh) + Water (4.4 ton)

Electrolysis

Green hydrogen (1.1 ton) + Oxygen (8 ton)

4500–9000

IEA (2021)

2

Methane (2.2 ton) + Water (4.9 ton) + Heat (5.7 MWh)

Steam methane reforming and water gas shift method

Grey hydrogen 1000–2500 (1.1 ton) + Carbon dioxide (6.6 ton) Typically released GHG pollution

IEA (2021)

3

Methane (2.9 ton) + Water (H2 O) (3.3 ton) + Heat(Δ) = 5.7 MWh

Steam methane reforming and water gas shift method

Blue Hydrogen 1500–3000 (1.1 ton) + Carbon monoxide (5.1 ton) After ~70% CO capture and sequestrat ion system CO2 (1.5 ton), released GHG pollution + CO2 (3.6 ton) typically CCS deep underground injection well storage

IEA (2021)

4

Methane (CH4 ) 4.4 ton + Heat (Δ) 5.2 MWh

Methane pyrolysis

Turquoise Hydrogen (1.1 ton) + Solid carbon (C) (3.3 ton) Industrial use or to landfill (no pollution)

Germscheidt et al. (2021)

1200–2200

service. It contains the most energy per unit weight compared to all other materials. Its low energy density per unit volume results from its status as the periodic table’s lightest chemical element (Edwards et al. 1853). In the hydrogen economy, there will be a need for two different categories of storage systems, first for stationary uses and second for mobility. Now, both are having unique prerequisites as well as constraints. In the emerging hydrogen economy, transportation is expected to be the first big user of hydrogen. In comparison to stationary usage, transportation applications have tougher hydrogen storage requirements. The

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ideal needs for the “ideal hydrogen storage system” for mobility applications are as follows: (a) (b) (c) (d) (e)

A temperature maintenance of K50 to 1508C for operation. Fast hydrogen uptake/release kinetics. High hydrogen densities in both gravimetric and volumetric terms. Public acceptance and safety while operating setting. The cost of a hydrogen storage system is reasonable.

Because hydrogen is a gas, so it is difficult to use in portable applications. There are essentially three methods for reducing the mass of hydrogen “to conservative and transportable structures”. These include metal hydrides, liquefied hydrogen, and compressed hydrogen (Sastrit 1987).

2.6 Hydrogen Utilization The availability of practical, affordable, and efficient processes for converting hydrogen to power or heat will determine how widely hydrogen is used as a fuel. One of the most remarkable routes to a clean energy future is the complementary synergy of hydrogen and electricity, and fuel cells are without a doubt the most efficient technology for generating hydrogen and other hydrocarbon fuels into power (Ministry of Power GoI 2022). Compared to today’s gasoline-powered automobiles, engines using hydrogen fuel may attain efficiency levels as much as 65% in the transportation sector. Combined heat and power (CHP) systems may employ heat produced by fuel cells to achieve an overall efficiency of more than 85% (Edwards et al. 1853). In contrast, turbines or internal combustion engines, as well as fuel cells, exhibit excellent efficiency throughout a broad range of output power. Due to their scalability, fuel cells are suitable for a variety of applications, including centralized or decentralized largescale stationary power generation, automotive applications, mobile phone batteries, and automotive applications.

3 Key Challenges and Policy Support 3.1 Safety In terms of safety risks, hydrogen is comparable to other fuels like natural gas or gasoline. The extremely low temperatures and high pressures of the current cryogenic and high-pressure hydrogen storage systems create safety concerns in addition to hydrogen’s flammability (Srivastava et al. 2015).

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3.2 Social Challenges Customers will undoubtedly be concerned about the dependability and safety of devices driven by fuel cells. To ensure that hydrogen is introduced and accepted as a fuel in an effective manner, marketing, education programmes, and product exposure strategies should be developed (Kumar et al. 2010).

3.3 Environmental Impacts It can be shown that using fossil fuels to produce hydrogen forms 1.1 tons of hydrogen and five times the amount of carbon dioxide in the atmosphere. To analyse the possible negative consequences of the hydrogen economy, more detailed modelling of atmospheric phenomena, as well as a good knowledge of various other elements like hydrogen absorption in soil system and its impact on microbial ecosystems, are necessary. Before hydrogen is extensively employed as a source of energy, we have 20–30 years to study and mitigate its potential environmental effect.

3.4 Supply Chain Management Green hydrogen energy relies on the growth of a supply chain that begins with the fabrication of electrolysers and ends with the generation of green hydrogen utilizing renewable energy sources and transmission to end-users. Improving back-to-back initiatives may be required for a seamless supply chain execution (Priya 2021).

3.5 High Technology Cost Hydrogen has extremely high production costs. Other renewable technologies such as solar PV and wind have had an impact on the financial assistance needed to develop them (Solomon and Banerjee 2006).

3.6 Water Availability The electrolyzer takes water and green electricity as inputs to produce green hydrogen. 8.92 L of demineralized water is used for every kilogram of hydrogen. To prevent potential water consumption problems, desalination facilities can be set

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up to treat wastewater or saltwater for electrolysis. If such desalination facilities are built in water-scarce areas, freshwater can also be delivered to the local people. Hydrogen can be viewed as a supplement to its options rather than being viewed as a stand-alone solution because it has its own set of limitations. By 2030, current storage and transportation technologies should be mature and cost-effective. As a result, hydrogen production and near real-time use at the same place might be encouraged to protect investments from undesired sunk costs (Ministry of Power GoI 2022).

4 Conclusion Hydrogen is a promising technology for meeting net-zero emission objectives since it does not generate greenhouse gases when burned. Apart from electrification and battery storage systems, its intrinsic chemical properties, varied end-uses, and compatibility with other fuel and energy carriers make it a viable candidate in the clean energy transition. Nations across the world are rapidly commercializing fuel cell technology, and India is still in the initial stages, with hydrogen’s “net-zeroness” reliant on renewable energy sources. The government’s desire and will, together with solid and sustainable policies rather than stop–go policies, guarantee a fast and easy transition to a green hydrogen system. In the midst of the continuous buzz and anticipation around hydrogen, India’s immediate policy initiatives will set the stage for its involvement in the energy transition.

References Bakhtyari A, Makarem MA, Rahimpour MR (2018) Hydrogen production through pyrolysis. Encycl Sustain Sci Technol 1–28. https://doi.org/10.1007/978-1-4939-2493-6_956-1 Cao L et al (2020) Biorenewable hydrogen production through biomass gasification: a review and future prospects. Environ Res 186:109547. https://doi.org/10.1016/j.envres.2020.109547 Edwards PP, Kuznetsov VL, David WIF (2007) Hydrogen energy. Philos Trans R Soc A Math Phys Eng Sci 365(1853):1043–1056. https://doi.org/10.1098/rsta.2006.1965 Germscheidt RL et al (2021) Hydrogen environmental benefits depend on the way of production: an overview of the main processes production and challenges by 2050. Adv Energy Sustain Res 2(10):2100093. https://doi.org/10.1002/aesr.202100093 Giovannini S (2020) 50 shades of (grey and blue and green) hydrogen. Energy Cities,. [Online]. https://energy-cities.eu/50-shades-of-grey-and-blue-and-green-hydrogen/ H2 Bulletin Hydrogen Colour Codes [Online]. www.H2bulletin.com IEA (2021) Global hydrogen review. Glob Hydrog Rev 93. https://doi.org/10.1787/39351842-en Kothari R, Buddhi D, Sawhney RL (2008) Comparison of environmental and economic aspects of various hydrogen production methods. Renew Sustain Energy Rev 12(2):553–563. https://doi. org/10.1016/j.rser.2006.07.012

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Kumar A, Kumar K, Kaushik N, Sharma S, Mishra S (2010) Renewable energy in India: current status and future potentials. Renew Sustain Energy Rev 14(8):2434–2442. https://doi.org/10. 1016/j.rser.2010.04.003 Levalley TL, Richard AR, Fan M (2014) The progress in water gas shift and steam reforming hydrogen production technologies—a review. Int J Hydrogen Energy 39(30):16983–17000. https://doi.org/10.1016/j.ijhydene.2014.08.041 Medisetty VM, Kumar R, Ahmadi MH, Vo DVN, Ochoa AAV, Solanki R (2020) Overview on the current status of hydrogen energy research and development in India. Chem Eng Technol 43(4):613–624 (Wiley-VCH Verlag). https://doi.org/10.1002/ceat.201900496 Ministry of Power GoI (2022) Green Hydrogen Policy. Policy Doc 1–3. [Online]. https://powermin. gov.in/sites/default/files/Green_Hydrogen_Policy.pdf Ministry of new and renewable energy and Government of India (2007) Natl Hydrog Energy Road Map 2006:70 Newborough BM, Cooley G, Plc ITMP (2020) 2020_Spectrum_of_hydrogen_colours Nikolaidis P, Poullikkas A (2017) A comparative overview of hydrogen production processes. Renew Sustain Energy Rev 67:597–611. https://doi.org/10.1016/j.rser.2016.09.044 Olsvik O, Rokstad OA, Holmen A (1995) Pyrolysis of methane in the presence of hydrogen. Chem Eng Technol 18(5):349–358. https://doi.org/10.1002/ceat.270180510 Priya L (2021) India’s National Hydrogen Mission and Prospects for Cooperation with GCC. Manohar Parrikar Inst Def Stud Anal [Online]. https://www.idsa.in/system/files/issuebrief/indianational-hydrogen-mission-n-gcc-lpriya.pdf Ramachandran A (1978) Department of science and technology. Trans Indian Ceram Soc 37(3):96– 101. https://doi.org/10.1080/0371750X.1978.10840680 Sastrit MVC (1987) Hydrogen energy research and development in India-an overview. Int J Hvdrogen Energy 12(3):137–145 Sengodan S, et al (2018) Advances in reforming and partial oxidation of hydrocarbons for hydrogen production and fuel cell applications. Renew Sustain Energy Rev 82(2016):761–780. https:// doi.org/10.1016/j.rser.2017.09.071 Solomon BD, Banerjee A (2006) A global survey of hydrogen energy research, development and policy. Energy Policy 34(7):781–792. https://doi.org/10.1016/j.enpol.2004.08.007 Srivastava ON, Yadav TP, Shahi RR, Pandey SK, Shaz MA, Bhatnagar A (2015) Hydrogen energy in India: storage to application. Proc Indian Natl Sci Acad 81(4):915–937. https://doi.org/10. 16943/ptinsa/2015/v81i4/48303

Climate Risk Assessment and Adaptation in Small and Medium Enterprise Industries: Lessons from Andhra Pradesh, India Hrishikesh Mahadev Rayadurgam , Kamal Kumar Murari , Till Sterzel, Thomas Bollwein, Sylvia Maria von Stieglitz, Prakash Rao , and Dieter Brulez Abstract The frequency and magnitude of extreme climatic events are increasing, which is impacting the industrial sector and, hence, the economy and growth. Climate risk assessment (CRA) is a tool to understand the impact of climate change on the industry and other sectors. The proposed study addresses CRA for seven small and medium enterprises (SMEs) in Visakhapatnam through a qualitative study addressing building, location, processes, logistics, employees and communities, markets and financial aspects impacting industries. It was found that SMEs are extremely vulnerable to extreme climatic events, which hamper their growth, and recovery takes a long time. These SMEs are not prepared for such extreme events, and the awareness level among SMEs is low. The proposed research has suggested generic and specific adaptation measures to be implemented in these industries to cope with extreme climatic events, thereby minimizing the losses. Keywords CCA · Climate change adaptation · SME · Small and medium enterprises · CRA · Climate risk assessment · Extreme Climate Events (ECEs)

H. M. Rayadurgam (B) · D. Brulez Integration Consulting Group, Basheerbagh, Hyderabad, India e-mail: [email protected] K. K. Murari School of Habitat Studies, Tata Institute of Social Sciences, Mumbai, India T. Sterzel · T. Bollwein Adelphi Research Gemeinnützige GmbH, Alt-Moabit 91, Berlin, Germany S. M. von Stieglitz Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, Eschborn, Germany P. Rao Symbiosis Institute of International Business, Symbiosis International (Deemed University), Pune, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_9

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1 Introduction There is substantial evidence that the global temperature is showing an increasing trend, and the Earth’s climate is expected to be warmer in the future (Stocker et al. 2013) and evidence shows that, in spite of mitigation measures, a level of climatic change will be experienced in the global climatic system (Hoegh-Guldberg et al. 2018). One of the serious consequences of global warming is the occurrence of climatic extremes, which are expected to be higher in magnitude, of longer duration, and more frequent in the future (Easterling et al. 2000). Climate extremes will have a serious impact on various sectors, such as agriculture, water resources, and infrastructure. The impact on these sectors has both short- and long-term consequences for business and industrial activities in many places. Industrial and business sectors are particularly vulnerable to exposure to climate extremes and their likely exposure in the future. Keeping in mind their preparedness with respect to both the current and likely future exposure to climatic extremes due to global warming, the industrial sector is vulnerable. Industrial and business sectors are considered to be sensitive not only to extreme climatic hazards such as cyclones, floods, droughts, and heat waves, but other factors such as lightning and cloudbursts that have an impact on the activities of these sectors. Therefore, an understanding of exposure to climatic extremes and their impacts on industrial activities is a prerequisite to identifying the nature of impacts and ways to avoid them. Climate Risk Assessment (CRA) is one approach to understanding how vulnerable is the sector or the region. Climate hazard assessment, sector or region exposure to climate hazards, impacts, vulnerability, and resilience are among the steps (IPCC 2014; Jahn 2015). The recent trends analyzed by Kirbyshire et al. (2017), suggest that resilience in businesses is focusing on ways to improve disaster recovery, the role of the private sector in strengthening resilience and development aid. Climatic hazards already have various degrees of impact on industries and business sectors globally (Romilly 2007). Josephson et al. (2017) have studied how hurricanes have impacted small-scale businesses in the US. The effect of floods on SMEs (Small and Medium Scale Enterprises) in Thailand was carried out by Marks and Thomalla (2017). In India, exposure to heatwaves has been found to impact multiple engineering units (Balakrishnan et al. 2010). Wang et al. (2017) carried out the overall economic impacts of typhoons in China. Ranger et al. (2011) have carried out the impact of floods on SMEs in Mumbai. Heat stress or heat waves are prevalent in tropical countries like India. Heatwaves are a severe concern for the operations of industries because tropical countries like India will be exposed to increased severity, longer duration, and more frequent heatwaves in the future (Murari et al. 2015). Adaptation is defined as the process of adjustment to reduce the impact of climatic events (IPCC 2014; Eisenack and Stecker 2012; Lavell et al. 2012). Adaptation can be categorized into natural and adaptation involving humans (Eisenack and Stecker 2012) and stated different types of adaptation, such as direct, indirect, facilitating, implicit, explicit, and incidental (Eisenack and Stecker 2012). However, IPCC (2014) introduces the concept of adaptation in terms of planned, autonomous, anticipatory,

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and reactive. At the moment, industries are not ready to deal with the problems that come with changing weather patterns and climate. The present study aims to examine climate risk assessment and proposes adaptation measures across MSMEs, which are generally ignored in climate change adaptation research (Islam and Mohd-Nor 2017), despite their economic and social importance, near Visakhapatnam city in Andhra Pradesh in the south-east coast of India. Many MSMEs in this region are ignorant of the extreme events and do not consider adaptation measures to cope with them. Eventually, during extreme events, these MSMEs are prone to disasters damaging their processes, value chains, and physical infrastructure. In this paper, we understand how well industries are aware of adaptation and prepared for ECE. Also this study addresses various climate threats exposed to the industry and proposes adaptation measures to cope with extreme climatic events. The Department of Industrial Policy and Promotion (DIPP), Ministry of Commerce and Industries (Mo C &I), Government of India (GoI), (DIPP 2008), defines industrial parks as “projects in which quality infrastructure facilities in the form of plots of developed land or built-up space or a combination with common facilities are developed and made available to all the allottees for the purpose of industrial activity”. Industrial parks (IPs) are also referred to as industrial estates (IE), industrial areas (IA), or industrial development areas (IDA), based on the area mainly for SMEs. Industrial parks are also categorized into specialized theme parks like biotechnology parks and leather parks, based on export processing zones like Special Economic Zones (SEZs), Export Processing Zones (EPZs), Free Trade Zones (FTZs), and Free Zones (FZs). In the past decade, clustering of industries was introduced like National Investment and Manufacturing Zones (NIMZ), Growth Centres, Special Investment Regions (SIR), Petroleum, chemical & petrochemical investment regions (PCPIR), Industrial Corridors like Delhi-Mumbai Industrial Corridor (DMIC), Visakhapatnam-Chennai Industrial Corridor (VCIC). For the purpose of this paper, the terminology “industrial parks” will be used throughout the paper. There are more than 1850 industrial parks in India (Siddharta 2017) under central and state governments. Micro, Small and Medium Enterprises (MSMEs) constitute about 95% of the total industries in India (Singh et al. 2002) and supply the largest workforce next only to agriculture. This amounts to 63.38 million units, including registered and unregistered sectors (Ministry of MSME I 2018), which forms a key driver for the Indian economy. Microscale contributes to about 99% of total MSMEs, followed by 0.52% of small-scale and 0.01% of medium-scale enterprises. Background of the Study Area. Visakhapatnam (also known as Vizag) is the third largest city in India after Kolkata and Chennai, on the eastern coast of India. The city contributes about USD 26 billion to the GDP and is considered to be one of the top ten cities in the country in terms of economic contribution. Visakhapatnam is now also an important part of the Visakhapatnam-Chennai Industrial Corridor (VCIC). Autonagar (AN) Gajuwaka is one of the oldest and biggest industrial parks near Viskahapatnam. AN Gajuwaka was established in the 1970s and is considered to be a large-sized park in the Visakhapatnam zone with about 532 hectares (Ha). There are a total of 919 industries, and most

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of them are micro, small, and medium-scale enterprises (MSME) scale industries. The majority of industries in Gajuwaka are engineering industries such as fabrication units, transformers, auto repairs, roof manufacturing, etc. Visakhapatnam and nearby areas experience different kinds of water related disasters like depressions, cyclones, sea storms and storm surges with an average of one cyclone per annum (Govindarajulu 2020). Visakhapatnam is the main hub for industries in the state of Andhra Pradesh, and most of the industries in Visakhapatnam were affected by cyclone Hudhud that hit the region in October 2014. A study conducted by Andhra Pradesh Human Resources Development Institute APHRDi (2014) reveals that cyclone Hud-Hud caused a total loss to industries of around Rs. 74.11 billion, with losses to Andhra Pradesh Industrial Infrastructure Corporation (APIIC) parks amounting to approximately Rs. 12.75 billion. Heat waves are common in the coastal districts of Andhra Pradesh. Heat waves along with humidity pose serious problems in Visakhapatnam. More than 800 people died due to severe heat waves in Andhra Pradesh and Telangana between April and May 2015 (Kumar 2015) while 112 people died in the Visakhapatnam district. Andhra Pradesh (before bifurcation in 2014) also experienced severe heat waves in 2002–2003, 1998 and 1996. Heat waves during 2002–2003 are considered the deadliest, where over 3000 people died in one week, which is thought to be the highest in the country (Suchitra 2015). Visakhapatnam is also prone to future rainfall changes (Rayadurgam and Rao 2021a).

2 Study Design The authors have selected the qualitative case study method based on (Yin 2018; Gerring 2004). The study is explanatory in nature, covering the questions why and how. Seven cases have been selected from Autonagar Gajuwaka industrial park, covering different sectors and sub-sectors. The industrial park has more engineering units. Certain sub-sectors were also selected. The MSME industries were selected based on the apparent size of operation, turnover, age, and accessibility of researchers. For ensuring a better outcome of the results, multiple cases from different contexts enable us to compare findings (Yin 2018) as a single case may lead to a high risk of being considered as subjective or non-scientific (Burns 2000). Table 1 provides the details of industries where in-depth interviews were carried out. In this research, three different techniques have been used for data collection: (1) semi-structured interviews, (2) non-participant observation, and (3) review of company documents and websites. The semi-structured interview was based on the climate expert tool (CET), which has been designed by Mangin et al. (2017); Rayadurgam and Rao (2021b) and modified by the authors for carrying out climate risk assessment in industries or private businesses. The processes involve one-toone in-depth interviews covering qualitative analysis with individual industries on the aspects of hazard, impact, sensitivity, resilience, and risk. The discussions were

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Table 1 Selected Industries for the qualitative analysis Name

Line of activity

U1

Fabrication units (electrical poles and roof truss)

Employment 10

U2

Cast iron unit (pump and valves)

12

U3

Rubber re-treading

17

U4

Electrical Transformers and repairs

16

U5

General fabrication, both wood and steel

12

U6

Electrical hardware and engineering works

U7

Food grain milling

14 150

a combination of English, Hindi, and Telugu. The researchers recorded the nonparticipant observations through site visits, experiences from the past, etc., through field notes and photographs. Secondary data collected from literature and government reports states the impact of the extreme climatic event. Based on the data and hazards in the region, the questionnaire was further revised. The two major hazards identified in the industrial parks are cyclones and heat waves. The questionnaire was broadly divided into seven categories, which included the following: 1. Building and Location: addresses information related to sensitivity of individual building and location, past experience, damages, sensitivity to ECE, impact on or due to neighbouring industries. 2. Processes: the questions were asked related to past events in terms of impact on processes, how climate has affected the industries, how much the industries are sensitive to energy and water, and whether they are affected by extreme climatic events. 3. Logistics and Stocks: questions were asked related to the supply chain, stock availability, sources of raw materials, transport of final products and its sensitivity towards extreme climatic events. 4. Employees and Communities: Influence of extreme events on working & living conditions, productivity of employees and impact on nearby communities. An employee’s responses during such incidents on attrition and absenteeism. 5. Government and Regulation: The questions related to support from the government and changes in regulation to cope up with extreme climatic events to understand the response and preparedness of the government during such events. Any changes in regulation due to such impacts. 6. Markets: understanding market responses to changes in demand and products during extreme climatic events was explored. 7. Finance: the availability of credit, insurance coverage, and support from the government was researched.

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3 Results and Discussions The results varied across the industry, considering the aspects mentioned in the previous section. The response of the industry towards cyclone events and regular heat waves has been analyzed based on an in-depth interview with seven MSMEs in An Gajuwaka near Visakhapatnam on the south-eastern coast of India. All the industries experienced cyclone Hud-Hud as one of both a life and business threatening extreme climatic event. Further, most MSMEs believe that heat waves and high temperatures are more hazardous than cyclones because of their frequency and intensity of occurrence. The following sections indicate the responses from the seven industries. A comparison of the seven industries from U1 to U7 will be useful to draw some inferences about the relative climate risk of these industries and similar sectors.

3.1 Building and Location The entire topography of the area is almost flat, and hence there is no major stagnation of water or flooding during heavy rainfall. However, in terms of location, all the industries agreed to have been exposed to cyclones and high temperatures, along with high humidity. In terms of buildings, each industry responded in a different manner. Industry U1, an electrical fabrication unit, has an old asbestos roof. The hygiene within the industry is poor, and most of the welding work is carried out in the open space, which makes them extremely vulnerable to heavy rainfall, high temperatures, and cyclones. Similarly, industrial units U2, U3, U4, U5 and U7 are exposed to both cyclones and high temperatures. These industries are old, and their roofs are made of asbestos sheets, which are vulnerable to heavy winds as they are not anchored properly and absorb heat during summers. Roofs of all these industries were blown away during cyclone Hudhud, with varying degree of intensity. Also, the walls of the industries (U1, U4 and U5) were damaged by the cyclone. Industry U6 has constructed high walls, which lower the impact of heat and maximize the flow of air within the industry. The U6 industry was able to withstand cyclones as well as high temperatures during summers.

3.2 Processes For U1, most of the electrical appliances are placed outside, which are exposed to rainfall. Any rainfall of medium intensity hampers the function of the industry. Similarly, during peak summers, laborers stop working in the afternoon. Further, due to frequent power cuts, the industrial output for U1 and U5 is reduced. The remaining industries have a generator as an alternative source of energy. U2, the cast iron unit, is sensitive to high humidity as their processes require sand as the raw material and

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the sand should be dry. Due to high humidity in the region, sand absorbs moisture content, which industry U2 spends up on drying the sand in an oven, which is further used for molding iron. U3, being a re-treading unit, uses an oven to mold and fixes the tyres, and their industry is also not well ventilated, making it difficult for laborers to work during the peak of summer. All seven industrial processes were affected by cyclone Hudhud due to damage to electrical poles and power cuts. It took nearly 3 weeks to restore power to the industrial estate.

3.3 Logistics and Stocks Industrial unit U7, faced maximum damage to their raw materials due to cyclone Hudhud. Most of the raw wheat stored in their warehouses were damaged. They used to be stored in the industrial shed, where the roofs were blown away during the cyclone, which caused damage to the raw materials. Other units (U1, U2, U3, U4, U5) were damaged with varying degrees of intensity as they were storing their materials either in the open or under sheds with roofs but no walls. The industrial units U6 and U7 were not affected as they do not store any of their raw materials in an industrial shed, which is open on four sides but with a roof. Assessing the supply chain, all the industries, except U7, get their raw materials from local markets. As the local markets were also affected due to cyclone Hudhud, the sourcing of raw materials was delayed. During peak summers and due to high temperatures, handling of materials such as steel (U1, U5) is done during the early morning or late evening hours because steel poles remain hot during the daytime and it becomes difficult to handle or transport the material. Both industrial units U2 and U7 are sensitive to moisture or humidity. Industry U7 also imports raw materials from central and northern India and from Australia, Russia, and Uzbekistan. During peak summers and due to high temperatures, handling of materials such as steel (U1, U5) is done during the early morning or late evening hours because steel poles remain hot during the daytime and it becomes difficult to handle or transport the material. Both industrial units U2 and U7 are sensitive to moisture or humidity. Industry U7 also imports raw materials from central and northern India and from Australia, Russia, and Uzbekistan.

3.4 Employees and Community Employees are vulnerable to extreme climatic events. During peak summers and rainy seasons, there is a high absentee rate among employees. All the units, except U6 and U7, have reported the need to provide rest to the workers during peak summer hours (11:00 AM–3:00 PM). U7 being a larger industry, they work in shifts and indoors, so the impact of summer on workers is comparatively less. In addition, the industrial

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unit U7 provides accommodation to their workers and family within their premises, which ensures industry support to the family during extreme weather events.

3.5 Government and Regulation The government of Andhra Pradesh has come up with a comprehensive heat wave action plan for coping with heat waves for the general public but has not covered any specific sector. In the aftermath of Hudhud, there has not been much change in policy or regulation, but the government has undertaken some initiatives to build climate resilient structures, such as underground cable networks, which are being piloted in Visakhapatnam city. There was limited support from the government to all MSME sector industries and it provided minimum funds for small scale industries. No regulatory changes in the buildings.

3.6 Market The impact of heat waves on market demand is limited to some sectors, such as transformers (U4), as there is a sudden tripping of transformers due to power demand during peak summers. Remaining industries are affected by peak summers due to heat stress and power outages, but there is no considerable change in the demand for products or services during peak summer months. Only U5 has reported an increase in demand for furniture because of the new academic year in educational institutes, which begins after the summer. As informed by U1, the demand for strong industrial roof trusses has increased, which has increased the cost of a roof by 25%.

3.7 Finance All the industries surveyed were satisfied with the support from the government immediately after Cyclone Hudhud. The government’s efforts in restoring power and water supply in the city were appreciated. The government provided compensation to the extent of Rs. 25,000 and Rs. 50,000 to micro and small-scale industries. Table 2 summarizes the climate risk responses from surveyed industries due to cyclones, and Table 3 summarizes the responses from surveyed industries due to heat waves.

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Table 2 Climate risk due to cyclone

U1

Building and logistics

Processes

Logistics and stocks

Employees and communities

Government and regulation

Markets

Finance

High

High

High

High

Medium

High and positive

High

U2

High

High

High

High

Low

Low

High

U3

Medium

High

High

High

Low

Low

Medium

U4

High

High

Low

High

Low

High and positive

High

U5

High

High

High

High

Low

High and positive

High

U6

Low

High

High

High

Low

Low

Low

U7

High

High

High

High

Low

Low

High

Table 3 Climate risk due to heat waves Building and Processes Logistics and Employees Government Markets Finance logistics stocks and and communities regulation U1 High

High

Medium

Medium

Low

High

Medium

U2 High

High

Medium

Medium

Low

Low

Medium

U3 High

Medium

Low

Medium

Low

Low

Low

U4 High

Low

Low

Medium

Low

High

Low

U5 High

High

Medium

Medium

Low

Low

Medium

U6 Low

Low

Low

Low

Low

Low

Low

U7 Medium

Medium

Medium

Low

Low

Low

Low

4 Adaptation Measures The proposed adaptation measures were based on interaction with industries, CRA, ability to implement, their priorities, and short-term and long-term measures. The proposed adaptation measures have considered the ability of MSMEs to implement adaptation measures. The following sub-sections provide the details of the proposed adaptation measures for the industries/MSMEs.

4.1 Strengthen Roof Structures Damage to roof structures has occurred due to severe cyclones, where edges, corners, overhangs, and connections of roof structures are very sensitive. To reduce the damage level, strong roof to wall connections in the continuous load path are proposed

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to provide resistance to high winds. All the industries except U6, were suggested to strengthen their existing roof through anchoring of roof.

4.2 Improving Cross Ventilation Most MSMEs lack basic working conditions for workers and employees. These industries already stated during the visits that high temperatures are deteriorating the working conditions of their workers inside the industries. These small-scale industries lack ventilation in their industries, which aggravates the working conditions during summers and hot days. The cross-ventilation was proposed for U1, U3, U4, and U5, where the ventilation is very low, and it is hot even during the winter seasons. Further, modifications were proposed for U5 and U7 industries.

4.3 Roof Insulation Most of these industries are old and have constructed their roofs using asbestos and galvanized iron (GI) sheets. These roofs absorb heat from industry, which further increases the inside temperature. We have suggested they replace the asbestos roofs with GI or other materials, as asbestos is a hazardous substance and will have serious health implications. The insulation of the roof can be achieved through the use of thermocol or glass wool that can be mounted below the roof structure. Reflective paints have been proposed for the roofs other than asbestos, which can reflect heat radiation from the roofs. This reduces the internal temperature. All industries were proposed to implement roof insulation and reflective roofs along with phasing out asbestos roofs.

4.4 Insurance Most of the MSMEs has not taken insurance (U2, U3, U4, U5, and U6) and those companies which has taken insurance (U1 and U7), did not take comprehensive insurance, which has not included the business discontinuity or disruptions due to the cyclone. We have proposed to include comprehensive insurance to cover property, machinery, liability, financials, and business disruption.

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4.5 Heat and Tropical Cyclone Resilient Planning of Shed One of the main challenges for this region is that the solutions cannot be isolated and need to be integrated, considering various climatic hazards and risks. MSMEs such as U1, U3, and U5 were planning to expand their business by adding a new shed or adding a new floor to the existing buildings. In consideration of both high winds and heat waves, it was suggested to have sturdy foundations, proper orientation of the new building, raising of the floor, and roof insulation. The recommendation includes anchoring of the roof, light-colored roofing material, cross-ventilation including the construction of ridge ventilation.

4.6 Dry Storage There are many units that are prone to humidity. The unit U2 is one of them. These units are prone to extreme humidity. The casting industry requires sand as a binding material for shaping the molds. U2 does not have a storage facility for sand, which is used in casting. The moisture content in the sand increases during the rainy season, which in turn causes high power usage to dry these sands. Storage capacity needs to be built to control the steep increase in the cost of production due to moist sand. Storage units like Vertical Silos can maintain the temperature and keep the sand dry during humid conditions. Energy consumption to maintain the temperature is very low compared to ovens. This proposed measure has also been suggested to U7 for storing wheat, which otherwise absorbs moisture and invites insects.

4.7 Dehumidification Industries such as U7 need to keep their stock free of humidity to avoid any kind of infection or insects in wheat flour. The industry may cover the wheat stock to protect it from high moisture content, especially in the packaging and loading area of the main building, which is closed and has high moisture conditions. High humidity causes difficult working conditions. Also, U7 cannot provide more open ventilation as there is a high chance of the finished product being exposed to insects and worms. Under such conditions, it was proposed to introduce a dehumidifier for the loading– unloading areas, which reduces the moisture condition within the rooms and improves working efficiency.

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Table 4 Summary of proposed adaptation measures for seven industries Strengthen roof structures

U1 U2 U3

U4 U5 U6 U7

ST

ST

ST

ST

ST

ST

ST

Improving cross ventilation

ST

ST

ST

ST

ST

LT

Roof insulation

ST

ST

ST

ST

ST

ST

ST

Insurance

IE

ST

ST

ST

ST

ST

IE

Heat and tropical cyclone resilient planning of new shed LT (future expansion)

LT

LT

ST

Dry storage

ST

Dehumidification Renewable energy Rainwater harvesting

ST LT

ST

MT LT

ST

LT

ST

ST

ST

IE

ST

IE

ST—Short Term MT—Medium Term LT—Long Term IE—Improvement from Existing Blank—Not applicable or not much space to implement

4.8 Renewable Energy and Rainwater Harvesting In addition to the above measures, renewable energy was proposed to counter frequent power cuts during extreme summers and heavy rainfall. The renewables may not help in processes but assists in improving the ventilation and working condition. As the industrial park is located near the coast and the quality of groundwater is deteriorating due to regular extraction of groundwater for domestic and industrial purposes, rainwater harvesting (RWH) was proposed for all the industries. Industries U5 and U7 were already using RWH but were not maintained properly, leading to water stagnation near the RWH structure. It was proposed to repair and maintain RWH. Table 4 summarizes the proposed adaptation measure and its priority of each surveyed industry in AN Gajuwaka.

5 Conclusion The research was carried out in Visakhapatnam city on the south-east coast of India to understand the impact of extreme climatic events on MSMEs and the measures required to cope with these climatic events. A structured questionnaire was developed, and interviews were carried out for 3–4 h in each industry. The discussions covered various hazards in the area: exposure, impact, susceptibility, risk, resilience, and vulnerability for each industry. Each industry was assessed considering the building and location, processes, supply chain, impact on workers, support from government, changes in regulation, market variability and support from financial

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institutions. In-depth interviews were conducted to understand the impact of ECE on industries. Two major events were observed: heat waves, which occur frequently every year in this region, and cyclones, whose intensity and frequency have been increasing. Flooding has not yet impacted industries, but they are aware of the situation and storm water drains have been constructed. The study reveals that, in addition to various other issues related to financial and external markets, MSMEs are vulnerable to extreme climatic events. The present research is limited to seven industries in Gajuwaka, Visakhapatnam. A detailed questionnaire was carried out for each industry. Further studies should be carried out for the entire region in terms of understanding the impact of climate change on industries and industrial sectors. The region of Visakhapatnam-Chennai Industrial Corridor (VCIC) is one of the high economic zones coming up in the near future, and the entire region is prone to heavy rainfall, heat waves, high moisture, storm surge, drought, floods, and cyclones. A statistical analysis can be carried out considering variables such as those mentioned in Sects. 3.1, 3.2, 3.3, 3.4, 3.5, 3.6 and 3.7 to understand which variables impact the most and what measures can be adopted to cope and ensure business continuity during extreme climate events. Furthermore, the study reveals that adaptation measures need to be incorporated during the planning of new industrial parks and mandatory measures need to be proposed during plot allotment guidelines of new industrial parks. Acknowledgements The research described in the paper was financially supported by the German Federal Ministry of Economic Cooperation and Development (BMZ), through Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH and implemented by the consortium of Integration Energy and Environment, Adelphi, and Ifanos Concept and Planning, Germany under the project Climate Change Adaptation for Industrial Areas (CCA-IA). The authors are thankful for the support extended by BMZ, GIZ, and the consortium. We want to thank the officials of the Andhra Pradesh Industrial Infrastructure Corporation and members of seven industries for their cooperation and responding to our questionnaire.

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An Approach for Measuring Vulnerability to Risk and Climate Change—A Case Study of Maharashtra State Bilal Khan and Unmesh Patnaik

Abstract The quantification of vulnerability is required to understand the impacts of an extreme event on a region, with the vulnerability being relative and specific to the particular region as access to resources among the population differs. To capture this, Theil Index is used which when used as a measure of redundancy can act as a measure of lack of diversity and inequality. The vulnerability index designed with the Theil index for the districts in the Maharashtra state is relative to that district and it illustrates the inequality and disparities in the accessibility of resources only for that particular district. It is formed based on the parameters which act as the proxies for exposure and sensitivity which are intrinsically related to the vulnerability of the region. These parameters provide the notion to act up in case of extreme events or disasters arising due to climate change or undue risks. Keywords Vulnerability · Risk · Climate change · Theil index · Inequality · Maharashtra · Consumer expenditure · Education · Health · Assets

1 Introduction Extreme event impacts any region on a different scale based on the vulnerability of that particular region. For quantifying the impacts of extreme events, quantification of vulnerability is essential. For this reason, a vulnerability index is formulated which acts as a measure of the vulnerability of different regions and it can also act as a way of comparison between those regions. For the formation of the vulnerability index two points need to be considered: (1) There exists differential vulnerability even in the same region. (2) The element of uncertainty in the risk parameters needs to be included with the quantification of the vulnerability for the formation of the Vulnerability Index. Quantifying uncertainties requires entropy which is a measure of uncertainty about a system and the Theil index is used to capture it. Theil Index is a measurement of B. Khan (B) · U. Patnaik Tata Institute of Social Sciences, Mumbai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_10

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entropic ‘distance’ where the population is away from the ‘ideal’ egalitarian state and people have equal access to resources and have the same income. It is taken as a measure of redundancy which acts as the negative entropy and can be viewed as the measure for lack of diversity, inequality, segregation, etc. Redundancy is the difference between maximum entropy and actual entropy. When the index is formulated to represent negative entropy, it allows for the measurement of inequality rather than equality. The notion of vulnerability differs in different streams. It is conceptualized as an outcome, given the responses that the system takes with respect to the risks. The impact of the risks is different for different systems and people. It could so happen that after a disaster, the difference between the living conditions of the people can shift for the worse. This has led to the usage of measures of variability to be conceptualized as the measure of vulnerability (Alwang et al. 2001). The vulnerability of the poor is different from the vulnerability of the rich because in a society different groups of people are affected differently. The rich may have more assets that are susceptible to damage than the poor but the rich also have the capability to bounce back to their original position soon. Although they are more vulnerable to the damages caused by the disaster, they are also more resilient to the damages, unlike the poor. There exists a connection between the concept of resilience and vulnerability. The link between vulnerability and resilience is such that the loss of resilience paves way for vulnerability. It can be said resilience is the inverse of vulnerability (Franklin and Downing 2004). Likewise, the risks can be differentiated from the uncertainties, in a way that, risks can be quantified in terms of probabilities taking into account the frequencies of the extreme event happening, while when the quantification is not possible it leads to uncertainty (Dupuy and Grinbaum 2005). There is uncertainty associated with the disaster and hence risk. It is also difficult to ascribe some statistical value to the risk when considering climate change risk. The risk has two components—the consequences of the activity and the uncertainty associated with it (Aven 2019). According to the Fourth Assessment Report, risk is understood as the product of the likelihood of the event happening and the consequences associated with it. Although in recent years this understanding has changed to take impacts and probabilities both into account, still they have a common reference to risk description. For vulnerability research, three principles can be considered, which are—quantifying exposure in a model i.e. identifying people and places that are vulnerable to extreme events and also the conditions that make them vulnerable; taking the assumption that vulnerability is a social construct and is a measure of the societal resilience to extreme events and integrating the exposure and societal resilience for a particular region (Cutter et al. 2003). Similarly, climate change impacts can also be looked at from the characteristics of demographics, infrastructure, and agriculture, with the analysis taken for the district level, with vulnerability depending on the frequency of the disasters happening (Patnaik and Narayanan 2009).

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Vulnerability to climate change differs with respect to the regions since climatic patterns are not even and are subjected to variation even within the same region. In the literature, various methods have been used for capturing vulnerability, which includes: (1) Quantifying exposure in a model i.e. identifying people and places that are vulnerable to extreme events and also the conditions that make them vulnerable (Cutter et al. 2003); (2) Mapping and ranking vulnerability of the region by taking a comprehensive scale of vulnerability into account which includes taking indicators to gather different components of vulnerability that can come from different factors (Patnaik and Narayanan 2009); (3) Using Pareto ranking instead of assigning weights to the parameters (Rygel et al. 2006); and (4) Drawing a Security Diagram with state susceptibility on the x-axis and environmental stress on the y-axis with a crises probability curve (CPC) drawn to represent the probability of the extreme event or crisis occurring paper (Acosta-Michlik et al. 2008). This study captures the socioeconomic vulnerability at the district level of Maharashtra state. The vulnerability index that is created is based on the Thiel index, which is a measure of redundancy i.e. a lack of information transfer to make the situation more egalitarian. It functions as the notion of relative inequality in the region. The advantage of using the Theil index over other methods is that Theil Index does not compare its parameters between other regions but rather it compares it within its own region. It means that the inequalities measured belong to that particular region itself. It is a major advantage because the population differs with the region and Theil Index does not distinguish between the populations. The vulnerability index formed in this study is based on the parameters which act as the proxies for exposure and sensitivity which are intrinsically related to the vulnerability of the region. These parameters provide the notion to act up in case of extreme events or disasters arising due to climate change or undue risks. Each parameter taken like consumer expenditure, education, health, housing, durable asset, financial asset, and informal loans, acts as a proxy to account for certain vulnerabilities in the region. The accumulated proxies define a significant part of the overall vulnerabilities of the region. From the vulnerability index created, the most vulnerable cities are Mumbai Suburban, Nagpur, Chandrapur, and Gadchiroli and the least vulnerable cities are Sindhudurg, Sangli, Washim, and Ratnagiri. There is a high correlation between financial assets value and consumer expenditure and also with housing value. It is to be noted that there is significance in the data points of education expenditure and consumer expenditure. It means that higher-income households may invest more in education.

2 Data and Methods The vulnerability index in this study is created using the Theil Index, which is a measure of redundancy i.e. a lack of information transfer to make the situation more egalitarian. It functions as the notion of relative inequality in the region. Redundancy

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is the fractional difference between the entropy of an ensemble and the maximum possible value associated with it. It can be said that it is the wasted space that is used to transmit the data. The generalized entropy index is a measure of redundancy and hence it is associated with data compression and non-randomness. The Theil Index TT is a special case of the generalized entropy index and it is the same as redundancy which is the observed entropy subtracted from the maximum possible entropy of the data. It is a measure of redundancy as is used to measure inequality and segregation among other things. Theil Index T is given as: TT =

( ) N 1 Σ xi xi ln N i=1 µ µ

ΣN where µ = N1 i=1 xi N = number of individuals i = a particular individual xi = income of i th individual. Theil index is zero when all the agents are similar. For example, considering income, the Theil index will be zero if everyone has the same income. It measures the entropic distance where the population is away from the state of egalitarianism where people have equal access to resources and have the same income. If the index is formulated to represent negative entropy, it allows for the measurement of inequality rather than equality. A higher Theil index indicates that the total resources are not evenly distributed among the population. For the formation of the vulnerability index, these are the steps that are taken: 1. The data that is used, is extracted from the National Sample Survey (NSS) dataset of the 68th, 70th, and 71st Round and is taken for specific parameters, which account for consumer expenditure, education, health, housing, durable assets, financial assets, and informal loans, respectively. 2. The selected parameters are then extracted from each district and Theil Index is calculated using them. The total number of districts is 34, which gives us 34 variables under each parameter. It is to be noted that the data is available for Mumbai Suburban and not Mumbai in these NSS datasets. 3. The Theil index is then normalized. The formula for that is 1 – e(−T) , where e = Euler’s number and T = given Theil’s index. This gives the value of the Theil index between 0 to 1. 4. This forms a table of 34 rows and 7 columns, where 34 rows designate the districts and 7 columns designate the parameters. The vulnerability index is taken as the average of each row, which gives us the vulnerability index for each city. The index formed is also between 0 and 1.

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5. The index shows the level of inequality or the lack of information transfer or the level of redundancy in the city, with 0 being the most equal society and 1 being the most unequal society. The variables that are used in the formation of the index are extracted from the National Sample Survey dataset of the 68th, 70th, and 71st Rounds which are the Household Consumer Expenditure Survey, Debt and Investment Survey, and Education and Health survey, respectively. The parameters that are taken are Monthly per capita expenditure, Educational expenditure, Total Health expenditure, Household owned house value, Household owned transport value, Household owned financial asset value and Household owned kind loans value. These parameters when taken for the Vulnerability Index represent consumer expenditure, education, health, housing, durable assets, financial assets, and informal loans, respectively. These parameters and their definition are given in Table 1. Parameters of durable assets and informal loans are selected because they indicate the increase in the capabilities of the household given proper access to them. It acts as a means to increase the potential of the household to achieve economic well-being. The cities that are taken as variables belong to the state of Maharashtra. The parameters taken are extracted for each city. The total number of cities is 34, which gives us 34 variables under each parameter. Table 1 Parameters, source and their definition Parameter

Source

Definition

Consumer expenditure

68th round NSSO dataset (July 2011–June 2012)

Monthly per capita expenditure (MPCE) is the household consumer expenditure over a period of 30 days divided by household size

Education

71st round NSSO dataset (January 2014–June 2014)

Total expenditure on education and educational services by the household

Health

71st round NSSO dataset (January 2014–June 2014)

Total expenditure on healthcare by the household

Housing

70th round NSSO dataset (January 2013–December 2013)

The value of the house owned by the household

Durable asset

70th round NSSO dataset (January 2013–December 2013)

The value of the transport owned by the household

Financial asset

70th round NSSO dataset (January 2013–December 2013)

The value of the financial assets owned by the household

Informal loans

70th Round NSSO dataset (January 2013–December 2013)

The value of the kind loans undertaken by the household

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3 Results and Discussion From the vulnerability index created, the most vulnerable cities are Mumbai Suburban, Nagpur, Chandrapur, and Gadchiroli and the least vulnerable cities are Sindhudurg, Sangli, Washim, and Ratnagiri. Map illustrations in Fig. 1 are formed using the Maharashtra state map data taken from the Database of Global Administrative Areas (GADM). The vulnerability index for the districts for the state of Maharashtra is given in the range in Fig. 1. A cluster plot is also created to provide a graphical representation illustrated in Fig. 2. The districts in the blue zone have relatively high vulnerability compared with the districts in red which have relatively low vulnerability. Since there were more than 2 variables, principal component analysis is done on the data and is plotted according to the first two principal components. Dimension1 and dimension 2 are principal components 1 and 2, respectively. There are a total of 22 districts in the blue cluster and 12 in the red cluster. The districts of Wardha, Nashik, Satara, Nandurbar, Buldhana, Bid, Bhandara, Kolhapur, Ratnagiri, Washim, Sangli, and Sindhudurg are less vulnerable than the districts in another cluster represented by the color blue. The districts which are more vulnerable and represented by the color blue are Mumbai Suburban, Nagpur, Chandrapur, Gadchiroli, Dhule, Parbhani, Raigarh, Akola, Jalna, Aurangabad, Ahmadnagar, Nanded, Amravati, Osmanabad, Gondiya, Jalgaon, Yavatmal, Pune, Latur, Thane, Hingoli, and Solapur. The vulnerability arises due to the inequality in the

Fig. 1 Vulnerability index of the state of Maharashtra given in a range

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Fig. 2 Cluster plot of the districts indicated with point number

accessibility of resources. The different parameters represent different accessibility and the inequality in the accessibility of these parameters leads to vulnerability in the region. One of the most vulnerable districts in terms of consumer expenditure is Raigargh followed by Gondiya and Mumbai Suburban. This does not mean that the consumer expenditure is more for Raigarh than the district of Gondiya or Mumbai Suburban, it simply means that within those regions there exists more inequality in terms of monthly expenditure and this makes the region more vulnerable. The disparities that exist in the region are illustrated in the value by keeping in mind the limited accessibility of the resources that the people have and their limited expenditure towards it. The value is in the context of relativeness within a region and is specific only to that region. It shows not the monthly expenditure but the disparities in the expenditure leading to the vulnerability of that region. Likewise, the least vulnerable districts are Sindhudurg followed by Kolhapur and Osmanabad. In the education parameter, the least vulnerable districts are Pune followed by Yavatmal and Thane. This means that for the district of Pune, the educational expenditure and access to educational resources are far more equal relatively compared with the districts of Yavatmal or Thane. This does not mean that the literacy rate of Pune is near to the literacy rate of Yavatmal, it simply shows the regional accessibility and expenditure on the education sector, which is also relative within its population. The most vulnerable cities in the

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education parameter are Jalna followed by Nandurbar and Aurangabad. This shows that the relative expenditure for educational purposes and the accessibility of it in these districts is very unequal i.e. there lie huge disparities in it. The districts of Chandrapur followed by Wardha and Bhandara are the most vulnerable in the Health parameter, whereas Jalgaon followed by Ratnagiri and Hingoli are the least vulnerable. This is understood as the most vulnerable districts have huge inequality in their medical expenditure due to disparities in the accessibility of the health infrastructure. For the parameter of housing, the most vulnerable districts are Mumbai Suburban, Dhule, and Gadchiroli and the least vulnerable districts are Ratnagiri, Sinhudurg, and Satara. This entails that the most vulnerable districts have inequalities in their expenditure on their housing infrastructure which may be due to inaccessibility of the basic needs of water, sanitation, and space. The vulnerability here is due to differences in the housing infrastructure and household property worth which tells us that the inequality in these districts is due to some people having high infrastructure value due to accessibility of resources whereas some have very little infrastructure value due to lack of very basic needs. The housing value is high for some due to their accessibility of the road, water, and sanitation infrastructure whereas when this is not the case for others, it leads to the creation of differences in living standards and thus gives rise to inequality. Access to transportation is an important factor in household connectivity within the region and among the different regions. It improves the livelihood of the household and it is captured in the parameter of the durable asset. The most vulnerable districts in the durable assets are Gondiya, Latur, and Hingoli and the least vulnerable districts are Washim, Bhandara, and Sindhudurg. The financial assets that the household owns represent the stability that they might have in case of a vulnerable situation like that of a natural hazard. The parameter of a financial asset represents the differences and the inequality in the region concerning the household capacity to own assets other than their housing infrastructure. This represents the inequality in the region which accounts for the differences in the household to bounce back to the original stable position after an adverse effect of the hazard. The difference in their capacity represents the difference in their standard of livelihood and wellbeing. The most vulnerable districts in this parameter are Jalna, Gadchiroli, and Nagpur whereas the least vulnerable districts are Ratnagiri, Sangli, and Ahmednagar. This is taken as relative to that district which says that the households in the districts have a similar investment in their financial assets for the least vulnerable districts whereas for the most vulnerable districts it says that there are huge differences in the investment capacity of the households in the region. Informal loans play a major part in the market and regions which heavily rely on the informal sector. For this parameter, the most vulnerable cities are Jalgaon, Parbhani, and Nagpur whereas the least vulnerable cities are Sangli, Washim, and Bhandara. It represents that for the most vulnerable districts there are huge differences in the amount of informal loans that are taken

An Approach for Measuring Vulnerability to Risk and Climate …

135

by the households whereas for the least vulnerable districts the differences in the amount undertaken by the households for the informal loans are not so much. It also represents the accessibility and the credit capacity of the household to have access to such loans. The least vulnerable cities are represented by similar accessibility whereas it is not so for the most vulnerable cities where some may have access to it while others may not. For the data, the correlation and the p values are taken. It can be seen that there is a high correlation between financial assets value and consumer expenditure and also with housing value. There is significance in the data points of the education expenditure and the consumer expenditure. It means that higher-income households may invest more in education. The level of correlation and significance of the p-value is illustrated in Table 2 which tells us that there is a significant correlation between (i) housing and consumer expenditure, (ii) financial assets and consumer expenditure, and (iii) financial assets and housing when correlation and p-values of correlation are taken into account. It shows that these parameters are intrinsically involved with each other and a change in one parameter will affect another. There is a need to understand the factors that determine the vulnerability of a certain region. Take the example of Gadchiroli. It is one of the most vulnerable districts but it is in the upper half in terms of the equality in consumer expenditure in the region. It is the inequality in financial asset ownership that makes it so vulnerable. For Chandrapur, it’s the inequality in the educational expenditure. Even Ratnagiri has huge inequality in the educational expenditure but it fares well in other parameters. For consumer expenditure, the districts of Sindhudurg, Sangli, and Ratnagiri are in the top 5 in terms of equality but it may so happen that this is due to low employability in the region. The equality in this term need not be considered as being closer to an egalitarian society but rather the lack of employment and people being equally poor. Chandrapur is the most unequal district in terms of health expenditure. It may be the reason for it being so high on the vulnerability index. The reason for such inequality could be that only a selected few have access to better healthcare whereas the majority have very little or limited access. For financial asset ownership, the least vulnerable districts are the most equal in terms of financial ownership, whereas the most vulnerable districts are the most unequal. This may be interpreted as some people in the society having most of the property and financial assets. Pune and Yavatmal are the least vulnerable and most equal in educational expenditure. It could be interpreted as people being equally literate or equally illiterate. It is important to figure out which parameter is being affected and the causes for the same. For the population of a region, the inability to access one of the parameters may lead it to become more vulnerable and this could affect the entire vulnerability score of the region.

Significant p-value

Significant p-value

Durable asset

Moderate positive correlation

Low positive correlation

Financial asset

Very significant p-value

Informal loans

Low positive correlation

Vulnerability index

Vulnerability index

Informal loans

Very significant p-value

Very significant p-value

Very significant p-value

Moderate positive correlation

Moderate positive correlation

Financial asset

Significant p-value

Low positive correlation

Durable asset

High positive correlation

Very significant p-value

Low positive correlation

Housing

Housing

Significant p-value

Health

Low positive correlation

Education

Health

Education

Consumer expenditure

Consumer expenditure

Table 2 Level of correlation and significance of p-value

136 B. Khan and U. Patnaik

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137

4 Conclusion The vulnerability is context specific and these parameters indicate the inequality and disparities in the accessibility of the resources only for that particular region. The index in this case will show disparities for that particular district which may not be comparable with other districts as the accessibility of the resources and relative accessibility of the resources differs. Relativeness is important because it encompasses the specificity of a particular region. Take, for example, a basket of goods that is created to figure out the threshold of a minimum required consumption level in a monetary sense. And this basket includes milk and eggs. Now the price of milk and eggs differs for each region and also differs in western and eastern Maharashtra. A person in eastern Maharashtra may be able to consume them at a relatively lesser price than a person in western Maharashtra, say Mumbai. That may not be the case if both the people are buying from a mall i.e. a formal market but most people don’t have access to it. This led us to consider that there are differences in regions and even within the same region. A person may be having less consumer expenditure and income in eastern Maharashtra but may have better living standards and may be less vulnerable than a person in western Maharashtra. Vulnerability is relative to that particular region. From the analysis and the vulnerability index, the most vulnerable cities are Mumbai Suburban, Nagpur, Chandrapur, and Gadchiroli and the least vulnerable cities are Sindhudurg, Sangli, Washim, and Ratnagiri. There is a high correlation between financial assets value and consumer expenditure and also with housing value. There is also a significance in the data points of education expenditure and consumer expenditure. It means that higher-income households may invest more in education. When correlation and p-values of correlation are taken into account, a significant correlation is seen between (i) housing and consumer expenditure, (ii) financial assets and consumer expenditure, and (iii) financial assets and housing. These parameters are intrinsically involved with each other and a change in one parameter will affect another. For policy implications, there is a need to understand the factors behind a certain place being vulnerable and to identify those factors which play a role in intensifying the aspect of vulnerability in the context of risk and climate change. Climate change is responsible for causing undue extreme events and intensifying natural hazards. The inability to access certain resources or services in the region is due to existing inequality in the households of the region. This inaccessibility plays a major role when a disaster strikes, as the access to resources and services of certain households, are limited in comparison to others and this makes those households more vulnerable to risk and, already intensified natural calamities due to climate change.

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The vulnerability is relative and differs from region to region and even within the region. There is a need to understand the reason behind the vulnerability of a certain region as the inequality and exposure of a certain parameter differs with the region. For the population of a region, the inability to access one of the parameters may lead it to become more vulnerable and this could affect the entire vulnerability score of that region. For the future scope, if more parameters were taken that align with the characteristics of a State it will give a much clearer and broader picture of the vulnerability in terms of accessibility and inequality in the population. This will help in a dire time of need in case of disaster-struck situations where the accessibility of the resources like healthcare, housing, and financial assets plays an important role. From a policy perspective, more parameters corresponding to the characteristics of the region need to be taken into formulation and analysis has to be drawn with due consideration to these multiple variables that characterize vulnerability. Such a vulnerability assessment could be instrumental in designing disaster risk reduction programs.

Appendix See Fig. 3 and Tables 3, 4 and 5.

Fig. 3 Visual representation of the districts based on the cluster plot

Akola

5

0.1519

0.2623

Nashik

Thane

20

21

0.2285

0.1592

Jalna

Aurangabad

18

19

0.1743

Parbhani

17

0.2254

0.1199

Nanded

Hingoli

15

0.1580

Yavatmal

14

16

0.1521

0.2479

Gadchiroli

Chandrapur

12

0.3090

0.1332

13

Gondiya

11

0.1832

Nagpur

Bhandara

9

10

0.1316

Wardha

8

0.2102

0.1143

Washim

Amravati

6

7

0.2263

0.1787

0.1188

Jalgaon

Buldana

3

4

0.1719

0.1112

Nandurbar

Dhule

1

2

Consumer expenditure

District

District code

0.5169

0.6413

0.6749

0.7186

0.5833

0.5463

0.5894

0.5116

0.6644

0.5923

0.5826

0.6347

0.5675

0.5702

0.5684

0.5844

0.6276

0.5716

0.5452

0.6446

0.6907

Education

Table 3 Vulnerability index for the state of Maharashtra

0.5256

0.6362

0.6180

0.4905

0.4394

0.4160

0.4997

0.6416

0.7450

0.6005

0.6521

0.7113

0.6705

0.7174

0.6416

0.6561

0.7028

0.4852

0.3696

0.4752

0.4879

Health

0.5548

0.4946

0.5476

0.6149

0.6161

0.6036

0.5405

0.6176

0.6804

0.7209

0.5351

0.4946

0.6042

0.5785

0.6244

0.4861

0.5663

0.5262

0.4629

0.8049

0.5529

Housing

0.7034

0.6509

0.7548

0.6680

0.7818

0.8221

0.7480

0.7647

0.7851

0.8102

0.8894

0.5641

0.7159

0.6264

0.7469

0.5168

0.7931

0.6541

0.6966

0.7103

0.6667

Durable asset

0.7606

0.6277

0.7848

0.9619

0.7411

0.7191

0.7005

0.6968

0.8380

0.9172

0.7430

0.7283

0.8638

0.6825

0.7711

0.6747

0.7893

0.7305

0.7620

0.7385

0.7966

Financial asset

0.4969

0.7133

0.5908

0.5082

0.9230

0.5519

0.8159

0.5117

0.4864

0.5033

0.2662

0.2656

0.8870

0.6935

0.6119

0.2145

0.4914

0.5288

0.9230

0.8050

0.3491

Informal loans

(continued)

0.5458

0.5594

0.5900

0.5987

0.6084

0.5398

0.5885

0.5574

0.6353

0.6138

0.5682

0.5045

0.6417

0.5714

0.5827

0.4775

0.5995

0.5165

0.5626

0.6128

0.5308

Vulnerability index

An Approach for Measuring Vulnerability to Risk and Climate … 139

Bid

27

0.1708

0.1669

Solapur

30

0.1173

0.0739

0.0834

Sindhudurg

Kolhapur

Sangli

33

34

35

0.0627

0.1097

Satara

Ratnagiri

31

32

0.0769

Latur

Osmanabad

28

29

0.2201

0.2723

0.1312

Pune

Ahmadnagar

25

26

0.2918

0.3793

Mumbai Suburban

Raigarh

22

24

Consumer expenditure

District

District code

Table 3 (continued)

0.6302

0.5813

0.5700

0.6632

0.6286

0.5861

0.5533

0.6135

0.6183

0.5825

0.4506

0.5685

0.5566

Education

0.5171

0.4975

0.4675

0.3809

0.5724

0.5427

0.6132

0.5327

0.4161

0.6402

0.5690

0.5346

0.6011

Health

0.4520

0.5115

0.2734

0.2723

0.4505

0.5498

0.5869

0.5714

0.4665

0.5304

0.5768

0.6685

0.9398

Housing

0.7497

0.7897

0.6225

0.7139

0.6763

0.7894

0.7784

0.8246

0.6300

0.7899

0.6894

0.7006

0.6786

Durable asset

0.6190

0.7503

0.6506

0.5888

0.6663

0.7835

0.6982

0.8176

0.7159

0.6194

0.7283

0.8151

0.7961

Financial asset

0.1351

0.2879

0.4891

0.6399

0.7261

0.3496

0.6967

0.3566

0.4847

0.8305

0.6124

0.5513

0.8727

Informal loans

0.4552

0.4989

0.4480

0.4812

0.5553

0.5388

0.5719

0.5477

0.5074

0.5892

0.5570

0.6026

0.6767

Vulnerability index

140 B. Khan and U. Patnaik

Rank

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Districts Mumbai Suburban Nagpur Chandrapur Gadchiroli Dhule Parbhani Raigarh Akola Jalna Aurangabad Ahmadnagar Nanded Amravati Osmanabad Wardha Gondiya Jalgaon

Vulnerability Index 0.6767 0.6417 0.6353 0.6138 0.6128 0.6084 0.6026 0.5995 0.5987 0.5900 0.5892 0.5885 0.5827 0.5719 0.5714 0.5682 0.5626

Point Number 22 9 13 12 2 17 23 5 18 19 25 15 7 28 8 11 3

Color

Table 4 Districts with their point number and color based on the cluster plot Rank 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Districts Nashik Yavatmal Pune Satara Latur Thane Hingoli Solapur Nandurbar Buldana Bid Bhandara Kolhapur Ratnagiri Washim Sangli Sindhudurg

Vulnerability Index 0.5594 0.5574 0.5570 0.5553 0.5477 0.5458 0.5398 0.5388 0.5308 0.5165 0.5074 0.5045 0.4989 0.4812 0.4775 0.4552 0.4480

Point Number 20 14 24 30 27 21 16 29 1 4 26 10 33 31 6 34 32

Color

An Approach for Measuring Vulnerability to Risk and Climate … 141

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B. Khan and U. Patnaik

Table 5 Correlation matrix of the parameters along with their respective p-value Row

Column

Correlation P-value Row

Column

Correlation P-value

Consumer Education −0.1411 expenditure

0.4262

Durable asset

Financial asset

0.2118

0.2293

Consumer Health expenditure

0.168

0.3423

Consumer Informal expenditure loans

0.0555

0.7551

−0.0155

0.9305

Education

Informal loans

−0.1957

0.2674

0.377

0.028

Health

Informal loans

−0.131

0.4602

Education

Health

Consumer Housing expenditure Education

Housing

−0.1164

0.5121

Housing

Informal loans

0.2786

0.1106

Health

Housing

0.2862

0.1009

Durable asset

Informal loans

0.0237

0.8941

Informal loans

−0.0282

0.8743

0.4636

0.0058

Consumer Durable expenditure asset

−0.01

0.9553

Financial asset

Education

Durable asset

−0.0891

0.6162

Consumer Vulnerability expenditure index

Health

Durable asset

0.0018

0.9921

Education

Vulnerability index

-0.0171

0.9235

Housing

Durable asset

0.2333

0.1842

Health

Vulnerability index

0.3497

0.0426

Consumer Financial expenditure asset

0.3865

0.024

Housing

Vulnerability index

0.8036

0

Education

Financial asset

0.1702

0.3359

Durable asset

Vulnerability index

0.3379

0.0506

Health

Financial Asset

0.1162

0.5127

Financial asset

Vulnerability index

0.5736

0.0004

Housing

Financial asset

0.568

0.0005

Informal loans

Vulnerability index

0.6095

0.0001

References Acosta-Michlik LA, Kavi Kumar KS, Klein RJT, Campe S (2008) Application of fuzzy models to assess susceptibility to droughts from a socio-economic perspective. Reg Environ Change 8(4):151–160. https://doi.org/10.1007/s10113-008-0058-4 Alwang J, Siegel PB, Jorgensen SL (2001) Vulnerability: a view from different disciplines. Social protection discussion papers and notes 23304. The World Bank. http://documents1.worldbank. org/curated/en/636921468765021121/pdf/multi0page.pdf Aven T (2019) Climate change risk—what is it and how should it be expressed? J Risk Res 1–18. https://doi.org/10.1080/13669877.2019.1687578 Cutter SL, Boruff BJ, Shirley WL (2003) Social vulnerability to environmental hazards. Soc Sci Q 84(2):242–261. https://doi.org/10.1111/1540-6237.8402002 Dupuy J-P, Grinbaum A (2005) Living with uncertainty: from the precautionary principle to the methodology of ongoing normative assessment. CR Geosci 337(4):457–474. https://doi.org/10. 1016/j.crte.2005.01.004

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Franklin S, Downing T (2004) Resilience and vulnerability. Stockholm Environment Institute, pp 1–5. http://www.jstor.com/stable/resrep00448 Patnaik U, Narayanan K (2009) Vulnerability and climate change: an analysis of the eastern coastal districts of India. 20. https://mpra.ub.uni-muenchen.de/id/eprint/22062 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. https://doi.org/10.1007/s11027-006-0265-6

Agriculture Risk Management and Resilience Building Through Community-Based Disaster Risk Reduction: A Case Study of Talmala Village in Kalahandi District Ramakanta Naik

and Kamal Kumar Murari

Abstract Kalahandi district is one of the disaster-prone districts in Odisha. Most parts of the districts are frequently exposed to cyclones, floods, hailstorms, and drought disaster-like conditions. The community is highly vulnerable due to prevailing poverty, disadvantaged caste, low education, and frequent exposure to climatic hazards. This article explores the roots of vulnerability and risk factors in physical and natural contexts at the community and household levels. The study focuses on the assessment of risks and vulnerabilities and on developing a resilience strategy among farmers’ communities for which 200 households from 20 registered self-help groups from Talmala village in Kalahandi district were surveyed. It is seen that most of the households belong to either landless, tenants, marginal, or smallholder farmers who experience drought and flood situations almost every year. The River Tel and Hati are the primary cause of flood situations. Crop cultivation depends on monsoon rain and if erratic rainfall happens, they face a drought situation. The study revealed that the risk management tools, such as government policies and insurance products are not working properly and community intervention is required to make the facilities accessible and applicable to all. Community-based disaster risk reduction approach is a significant tool that has the capability of gathering community people to work together to make the disaster risk management plan. The community will be able to access all the facilities, risk management tools, and support from the government, civil societies, and private sectors to develop a resilience strategy. Keywords Agriculture · Disaster · Vulnerability · Risk · CBDRR · Resilience

R. Naik (B) Jamshetji Tata School of Disaster Studies, Tata Institute of Social Sciences, Mumbai, India e-mail: [email protected] K. K. Murari School of Habitat Studies, Tata Institute of Social Sciences, Mumbai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_11

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1 Introduction Disaster impact on agriculture causes loss and damage to agriculture and farmers all over the world (FAO 2021). Agriculture risks such as production risk, climate risk, market risk, disaster risk, and institutional risk are continuously increasing (Komarek et al. 2020). Flood, drought, and pest attacks are continuously affecting agriculture production, but existing risk management systems and resources are not capable enough to build resilience among farmer communities. Agricultural risk management in the changing environment and uncertain disaster occurrence demands holistic strategic planning (FAO 2022). In this context, Food and Agriculture Organization (FAO) has recommended disaster risk assessment, vulnerability, and hazard mapping. However, agriculture insurance is happened to be a potential tool for developing resilience strategies among various developed countries (FAO 2022). In the present context, community-based disaster risk reduction is a highly recommended approach to developing a resilience strategy for various disaster risks. Developing resilience strategies in the agriculture sector is a necessity to achieve global food security and agriculture sustainability (Tedesco 2019). The farmer community is the most vulnerable to disasters like floods, drought, and cyclones. However, the community can cope and adapt to disaster-like situations to some extent. Although the farmer community has different levels of vulnerability and resilience capacities, they share the same environment and are exposed to the same hazards. Therefore, they share common problems, interests, hopes, and behaviors which helps to design a disaster risk reduction plan (Lao Red Cross 2011). Community-based disaster risk reduction (CBDRR) is a tool that study, understand, and implement the disaster risk management approach incorporated with community participation. This approach follows seven major factors to function the CBDRR. These factors are participatory and inclusive, responsive, integrated, proactive, comprehensive, empowering, and development, which are designed to achieve the CBDRR goals (Lao Red Cross 2011). Kalahandi district comes under the Kalahandi-Balangir-Koraput (KBK) region which is one of the poorest regions in India. Agriculture is the primary source of livelihood and food security for more than 80% of the people in Kalahandi (Pradhan Mantri Krishi Sinchayi Yojana (PMKSY) 2016). Flood and drought-like situations occur almost every alternative year in Kalahandi (OSDMA 2021). In addition to that disease and pest attacks on paddy crops have increased these days (Sahu 2017). Loss and damage recovery is essential for the small and marginal farmer community in the Kalahandi region who rely on agriculture for their livelihood and food security. Despite enough investment in agriculture inputs and effective agricultural practices, the amount of return they are getting is not enough for the survival of their family. The increasing inflation pushes them toward prolonged vulnerability. The agricultural sector is highly vulnerable to extreme weather events, as it is highly climate-sensitive. Natural hazards, as well as biological hazards including animal and plant pests and diseases, can adversely impact the sector and lead to extensive damage and loss of crops, livestock, forestry, fisheries, and aquaculture (FAO 2022).

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Disasters such as floods, drought, cyclones, and pest attacks are the major causes of crop loss in agriculture which affect more during the pre-harvest phase of the crops. The agricultural sector faces multiple risks during different phases of crop cultivation such as production risks including drought and flood during the pre-harvest phase and the market and institutional risk during post-harvest (Komarek et al. 2020). Assessment of agricultural risk through a systematic method helps different stakeholders like farmers, extension officials, and policymakers to take necessary steps for controlling crop and economic loss to the country (Kannan 2015). The agriculture sector is vulnerable to multiple risk factors and cascading effects. To design a multiple risk management tool in developing resilience strategies among the farmer community, the farmers’ risk perception and community-based disaster risk reduction approach are taken into consideration. Natural hazards affect the vulnerable people living in hazard-prone areas. Agriculture risk mitigation is a critical task, and credit and insurance play a key role in risk management and resilience building. However, the lack of effective mitigation measures exacerbated agriculture risks (National Agriculture Disaster Management Plan (NADMP) 2020). Hazards also damage houses, irrigation systems, bridges, roads, agricultural areas, etc. In this context, CBDRR can create opportunities for local people to bring local social and economic conditions back to normalcy and reduce the impacts of such hazards (Lao Red Cross 2011). A study (Haque et al. 2022) has argued that the risk of climate change and disaster lessons from grassroots responses in South Asia will be accepted as the source of knowledge and potential solutions for other developing countries in the world. Community-based disaster risk reduction approach focuses on engaging communities in building their resiliency to disasters. This approach aims to increase community awareness and ownership of Disaster Risk Reduction Initiatives. Importantly these initiatives need to be incorporated in the planning stages, at the local level, and focus on strengthening local capacity to mitigate, prepare and respond to disasters (Lao Red Cross 2011). Community-based approaches existed even before the state and its formal governance structure came into existence. During a disastrous situation, people and communities used to work together and help each other. The evolution of state governance brings new terminology for community-based disaster risk reduction to help communities in an organized manner (Shaw 2012). It is seen that building resilience in communities against disasters has become a priority in many South Asian countries. Resilience building is the pathway to achieving sustainable development goals (SDGs) by 2030. Community-based disaster risk management offers a significant solution for both of these ends. Local communities can gather local and traditional knowledge, be better known to the existing network, stimulate local capacity, and will not rely on external support for a longer-term period. Therefore, it is necessary to engage with people at the grassroots level, empowering the local institution and building resilience among the communities to navigate the journey toward an uncertain climatic future (Haque et al. 2022). It has been discussed by (Pariyar 2020) that the process of emerging community resilient characteristics in which he has drawn the 9 minimum characteristics for a disaster resilient community. These characteristics are based on the Hyogo framework for action, the national strategy

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for disaster risk, Nepal risk reduction, and the flagship program of community-based integrated disaster risk management framework respectively.

2 Methodology This study is based on a quantitative research approach that adopts an explanatory case study design. Secondary literature and data were used to formulate the theoretical framework of CBDRR as a case study. The study area was selected based on a pilot survey conducted with the assistant agriculture officer of Junagarh and Bhawanipatna block, secondary data on disaster occurrence, and agriculture production. There are a total of 524 households in Talmala Gram Panchayat, out of which 204 households were taken as a sample of the study. Tamala G.P. is the most disasterprone region of Junagarh block. Flood affects crops almost every year due to their geographical location. The village is located in the triangular shape land of two rivers Tel and Hati which is the cause of flood-like situations every year. The data collection was undertaken during the April–June months of 2022 which gathered household information based 2021 financial year. Primary data was collected from the field with the help of a household survey questionnaire tool. In the initial phase of data collection, self-help groups, and other community groups were identified. Community group members were surveyed as household and all the necessary information about the households were collected. Household information such as personal information of the respondents including name, age, gender, education status, occupation, and family details (Tables 1 and 2). Other information such as land, economy, and resources of the household were also collected. Agriculture farm production status-related data was also collected. The study also focuses on land tenancy and the credit status of the households. Information related to village infrastructure and facilities, involvement in insurance, machines used on the farm, assets owned by the households, use of agriculture inputs, and awareness and benefits of government schemes were also collected. The collected primary data were analyzed in JASP data analysis software using simple descriptive statistical methods to identify the factors contributing to agricultural risk and resilience strategy among farmer communities. The CBDRR approach is a significant tool that has the capability of gathering community people to work together to make a disaster risk management plan. It will help the community to access all the Table 1 Demographic profile of the respondents Sl Gender Frequency % No 1 2

Female 170

Marital status

Frequency %

82.5 Married 197

Male

35

17

Total

205

100

Single

Family type

95.63 Joint

Frequency % 65

8

3.4

Nuclear 140

205

100

205

31.55 67.96 100

Agriculture Risk Management and Resilience Building Through …

149

Table 2 Educational qualification and occupation of the respondents Sl No

Educational qualification

Frequency

%

Occupation

Frequency

%

120

58.25

1

Graduation

4

2.43

Farmer

2

High school

50

24.27

Daily wage labor

3

Illiterate

94

45.63

Unemployed

17

8.25

4

1.94

4

Plus two

10

4.85

Teacher/lecturer

2

0.97

5

Primary school

47

22.82

Unskilled labor

13

6.31

100

Skilled labor

49

23.79

Total

205

facilities, risk management tools, and support from the government, civil societies, and private sectors to develop a resilience strategy.

3 Results and Discussion Kalahandi district is one of the poorest regions in India and the most vulnerable to drought and flood which caused continuous losses of food grain and livelihoods. Coping and adaptation strategies play a minimum role in enhancing the resilience of the farmers. The demands for risk management through risk transfer and resilience strategy development are high in the region. Policy implementation in this case is not significant enough. The community-based disaster risk management approach has the potential to deal with the dynamic structure of multiple agriculture risks. A community group can better plan for their resilience building in comparison to the outsiders. It has been seen that institutional risk and uncertainties are the hidden obstacles in the path of developing resilience strategies. Crop insurance is a tool that has the capacity for risk transfer in agriculture. The household survey data analysis report is discussed by using tables in the cross-tabulation methods. The village has been historically experiencing floods and drought for a long back. The geographical location of the village is the major reason for its vulnerability. The village is situated within the triangular landscape of two rivers called Tel and Hati which create flood-like situations almost every year. The continuous crop loss due to the floods and drought situation has become regular in the village. Nevertheless, farmers are engaged in agricultural practices at high risk. The concerned authorities are not paying enough attention to the massive crop loss in the region, due to which the farmers are unable to get financial assistance, support, or help. It has been seen that even the farmers with large land holdings, having 10–15 acres of land are also facing difficulties in their livelihood and sustainability. It is seen that after several claims for compensation under the Pradhan Mantri Crop Insurance scheme, the farmers are unable to receive any amount. After applying and paying the premium several times, the crop loss compensation has been denied to them.

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The study found that the majority of households are living in a nuclear family where the average number of members in a household is 4 in the village. On average, two adult men and two adult women are seen in a household. Out of which two men are earning and one female is earning on average. Despite being in poverty and marginalization, few of them are working away from home but the majority of people are sustaining their lives in the village. This fact has come up due to the pandemic situation otherwise the majority of people from marginalized sections are working away from home as migrant workers in different states of India such as Tamilnadu, Bangalore, Kerala, Goa, and Gujarat. Nuclear families are easy to maintain sustainability in comparison to joint families (Table 1). The average earning members of the household are 3 and other family members rely on them (Tables 3 and 4). The study revealed that the majority of people are either landless or marginal and smallholders in the village. A large farmer has more than 10 acres of land minimum in number (Table 5). It is seen that the last year’s cultivated land is less than that of the total land holding of the household. It is because the lands are not suitable for cultivation due to upland and unirrigated areas as it can be seen the irrigated lands are less than 10% in the village. Road access to cropland for mechanization is not a major problem in the village; however, 32.84% of households have claimed that they have no proper road access for farm mechanization. More than 53% of households have road access Table 3 Categories of farmer-wise number of men earning in the households Sl No

How many men are earning? Type of farmer

1

1

2

3

4

Total 62

1

Landless (Tenant farmer)

4

22

27

7

2

2

Large Farmer (10 acres and above)

0

1

2

0

0

3

3

Marginal Farmer (Less than 1 acre)

0

7

15

7

1

30

4

Medium farmer (3–10 acres)

0

12

23

4

1

40

5

Small Farmer (1–2 acre)

0

25

39

5

0

69

Total

4

67

106

23

4

204

4

Total

3

62

Table 4 Categories of farmer-wise number of women earning in the households Sl No

How many women are earning?

1

Landless (tenant farmer)

Type of farmer

0 4

1

2

32

18

3 5

2

Large farmer (10 acres and above)

1

1

1

0

0

3

3

Marginal farmer (less than 1 acre)

1

14

10

1

4

30

4

Medium farmer (3–10 acres)

1

15

21

3

0

40

5

Small farmer (1–2 acre) Total

3

46

16

4

0

69

10

108

66

13

7

204

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151

Table 5 Land ownership and land cultivated last year Sl No

Land own

Frequency

(%)

Land cultivated

Frequency

(%)

Last year 1

Landless

68

33.33

Landless

65

31.86

2

Less than 1 acre

49

24.01

Less than 1 acre

48

23.52

3

1–2 acres

46

22.54

1–2 acres

45

22.05

4

2–10 acres

38

45

22.05

5

More than 10 acres

1

0.49

204

100

Total

18.62

2–10 acres

3

1.4

More than 10 acres

204

100

Total

to their cropland, whereas 13% of them have only 50% access to the road to the cropland (Table 6). It has been reported that the flood situation happens every year due to which they are not able to cultivate paddy. If they transplant paddy or show the seed and a flood situation occurs then all the crops would wash away. Some of the farmers are not willing to take the risk and leave their land barren. Most of the farmers have said that the production of their farm is not sufficient enough to meet their family needs in a year (Table 7). Table 6 Irrigation and road access to the farm Sl No % of irrigated land out Frequency % of total land own 1

0

2

50

3

137 5

100

62

Total

204

% of road access to the Frequency % farm

67.15 0

67

32.84

2.45

28

13.72

50

30.39 100

109

53.43

100

204

100

Table 7 Food and financial requirements of the households Sl No How sufficient was the Frequency % farm production to meet family food requirements last year?

How sufficient is the f annual income of HH to meet the family requirements?

1

0–3 months

2

0.98

Deficit

2

3–6 months

30

14.7

Sufficient

3

6–9 months

54

26.47 Surplus

4

9–12 months

59

28.92 Missing

5

More than 12 months

6

Missing Total

2

0.98

57

27.94

204

100

Total

%

143 70.09 59 28.92 2 0.98 0 0 204 100

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Table 8 How do the household manage their financial requirement and family requirements for deficit months Sl No How do you manage your Frequency % family requirements for deficit months?

How do you manage your f family requirements for deficit months?

%

1

1. Spend from saving

57

27.94 2. Take monetary loan

1 0.49

2

1. Spend from saving, 2. Take monetary loan

81

39.21 2. Take monetary loan, 3. Borrowing food grain, 5. Decrease consumption

1 0.49

3

1. Spend from saving, 2. Take monetary loan, 5. Decrease consumption

3

1.47 2. Take monetary loan, 5. Decrease consumption

2 0.98

4

1. Spend from saving, 5. Decrease consumption

3

1.47 Missing

Total

56 27.45 204 100

More than 26% of the households reported that their farm production helped to meet family food requirements for 6–9 months. However, less than 1% of households are capable enough to sustain for more than 12 months with their farm production. The study says that more than 70% of people’s annual income is not sufficient enough to meet the family requirements and less than 29% of a household’s annual income is sufficient to meet family requirements. The household takes monetary loans from a money lender or bank to meet the family requirements. Nearly 40% of households spend from saving and taking monetary loans to meet the family requirement during deficit months (Table 8). Despite being landless and marginal farmers, people in the village are engaging themselves in manual, daily wage, and skilled laboring activities to earn their livelihood. It is seen that more than 95% of people have any other source of income other than farming but the majority of them are earning less than 5000 per month and that is also not for the entire year. They get earning only for 5 to 6 months in a year and remain unemployed for the rest of the months. The sources of income other than farming are manual, daily wage laboring, migrant laboring, small business such as vegetable vendors, shopkeepers, and private jobs. The annual income of the maximum people in Talmala ranges from 50,000 to 80,000 on average (Table 9). The majority of people rely on MGNREGA for their livelihood and daily wage labor within the area they belong. However, the limited availability of jobs is pushing them into a vulnerable situation. Paddy and pulses are the two major crops cultivated by the farmers in Talmala village. Maximum farmers grow paddy in less than 2 acres of land and the production of paddy is on average 20–30 quintals. The annual sale of farmers is higher in irrigated land which is mainly lifting irrigation. The average unit price for 20–30 quantal is 30–40 thousand rupees. The total crop production on their land is not sufficient to sustain their family; thus, they borrow money or food grains from their neighbor to fulfill the family needs.

10,000–50,000

50,000–100,000

100,000–200,000

200,000–500,000

500,000 and more

1

2

3

4

5

Total

Total annual income of the HH

Sl No

69

204

1

4

24

106

f

Table 9 Annual income and source of income

100

0.49

1.96

11.76

51.96

33.82

(%)

Total

Missing

Yes

No

Other sources of income (other than agriculture) 10

204

0

194

f

100

0

95.09

4.90

%

Total

No

10,000–30,000

5000–10,000

500–5000

204

10

9

76

109

Other sources of income per month f

100

4.9

4.41

37.25

53.43

%

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Out of 204 households, 62 households come under the tenant farmer category. They take land for rent and cultivate the crop. In the village, only eight households are taking rent and cultivating crops, whereas only two farmers are giving away their land for rent. There are two criteria for land tenancy one is a fixed amount that has to be given to the land owner and another is to distribute the production of the crop with a 2/3 50/50 percentage. Out of 3 shares, two shares will remain with the cultivator and one share will be given to the land owner. Farmers who are not capable enough to cultivate their lands give them away to farmers for rent. In this case, the farmer who rented out shares the risk such as production and market risk with another farmer. Most of the rented in and rented out system works with either a fixed amount of money or sharing percentage of production (Table 10). If the farmer takes land rented in then he/she is responsible for holding the production as well as market risk. The land owner can contribute to input cost, management, and decision-making during cultivation if it is sharecropping. Otherwise, if it is a fixed amount-based land tenancy, then the farmer will hold all the risk and the land owner will not be responsible for any risk or any decisive role during pre or post-harvesting. It has been observed that only small and marginal land is being taken as rented in and rented out for sharecropping in the village. Mostly the fixed amount for rent in or rent out is 20–40 thousand rupees per acre. However, the collected samples only show the rent in cost ranging from 20,000 to 40,000 but rented out only shows 10,000–15,000. 70% of people prefer a percentage-based share of production in sharecropping cases and the rest of them prefer a fixed amount of money (Table 10). Decision roles during pre-harvesting, including a selection of seeds, input cost, and mechanization, are mainly taken by the rent in taker in land tenancy. However, risk sharing is also depending on the type of rent. If the rent is a percentage share-based cropping, then both the farmer will share risks such as production and market risk. The study survey revealed that most of the people having agricultural land are facing crop loss due to flood situations. It is reported that the farmers were applying for crop insurance but the crop loss compensation was not surely provided to them. Therefore they are not willing to take crop insurance products as they say, “why should one waste money on paying insurance premiums if the insured amount is not accessible even after a massive crop loss?” When a government official comes to verify the crop loss and damage assessment on the ground, then they behave well and write the correct report but in the end, farmers are not able to receive the compensation amount. Farmers claimed that after several complaints, government authorities are not giving them satisfying assistance to minimize the agricultural risks. When the assistant agriculture officer was asked about the same, he said that it was an issue of top authorization and we couldn’t do anything about it.

4 Conclusion Community-based disaster risk reduction framework has been used widely to minimize the risk and develop resilience strategies in different regions of the world.

Total

204

0

1

1

100

0

0.49

0.49

3.43

95.58

Missing

1.5

1

0

0

1

1

202

f

0

0.49

0.49

99.02

%

40,000

25,000

20,000

0

Cost on rented in

Total

204

100

Total

Missing

3

4

Missing

2

3

7

195

Did land rent out? (in acre)

6

1

2

%

50%

0

1

f

5

Land rented in? (in acre)

Sl No

Table 10 Land rented in and land rented out

4

1

1

3

47

204

147

f

100

72.05

2.45

0.49

0.49

1.47

23.03

%

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However, the limited scope of implementation of CBDDR as a case study has not been utilized in India. The existing study is an attempt to implement CBDRR effectively to assess the risk and vulnerability factors and develop a resilience strategy among the community. In this context, a case study of Talmala village in the Kalahandi district can be a significant contribution to the knowledge system. Kalahandi district is one of the disaster-prone districts in Odisha. Almost every year the community of the district faces floods and drought. Most communities are highly vulnerable due to prolonged poverty, disadvantaged caste, illiteracy, and disasters. This article denotes the roots of vulnerability and factors of risks based on both physical and natural contexts at the community and household levels. The primary focus of the study is to an assessment of risks and vulnerabilities and to develop a resilience strategy among farmers’ communities. The household belongs to either landless, tenants, marginal, or smallholder farmers who experience drought and flood situations almost every year. The village Talmala is located in the triangular shape land of two rivers Tel and Hati which is the cause of flood situations every year. Due to unirrigated areas, farmers are dependent on monsoon rain for crop cultivation and if erratic rainfall happens, they face drought situations. Daily wage and manual laboring are the major sources of their livelihood other than farming. However, the limited availability of jobs is pushing them into vulnerable situations. The risk management tools such as crop insurance, irrigation facilities, disaster loss compensation, seeds, and input facilities are not working properly in the areas. Community intervention is required to make the facilities accessible and applicable to all. The multiple risks and vulnerable situations are pushing the farmer community into poverty. Lack of livelihood sources and continuous crop loss create hardship for the sustainability of the communities in the village. Community groups are willing to work, take risks, and develop resilience strategies but they have no proper idea, training, or any support system that can help them to learn and take action. The community-based disaster risk reduction approach is a significant tool that has the capability of gathering community people to work for themselves and includes all the stakeholders in making a disaster risk management plan. Implementation of CBDRR properly and effectively can help the community to access all the facilities, risk management tools, and support from the government, civil societies, and private sectors to develop resilience strategies. Acknowledgments This project is funded by centrally administered doctoral fellowship, Indian Council of Social Science Research (ICSSR), 2021–2022.

References FAO (2021) The impact of disasters and crises on agriculture and food security, Rome. https://doi. org/10.4060/cb3673en FAO (2022) Comprehensive analysis of the disaster risk reduction system for the agricultural sector in Azerbaijan. Food and Agriculture Organization of the United Nations Budapest. https://doi. org/10.4060/cb8486en

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Haque AKE, Pranab M, Mani N, Md Rumi S (2022) Climate change and community resilience: insights from South Asia. SANDEE and ICIMOD. Springer Nature Singapore Pte Ltd. http://www.indiaenvironmentportal.org.in/files/file/Climate%20change%20and%20comm unity%20resilience.pdf. https://doi.org/10.1007/978-981-16-0680-9 Kannan E (2015) Trends in agricultural incomes: an analysis at the select crop and state levels in India. 15(2):201–219. https://doi.org/10.1111/joac.12068 Komarek AM, De Pinto A, Smith VH (2020) A review of types of risks in agriculture: what we know and what we need to know. Agric Syst 178:102738. https://doi.org/10.1016/j.agsy.2019. 102738 Lao Red Cross (2011) Community based disaster risk reduction. Volunteer Manual, Version 2.0. Lao Red cross. https://www.rcrc-resilience-southeastasia.org/wp-content/uploads/2016/01/ LRC-CBDRR-Curriculum-Volunteer-Manual-Final.pdf National Agriculture Disaster Management Plan (NADMP) (2020) Department of agriculture, cooperation and farmers welfare. Ministry of Agriculture and Farmers Welfare, Government of India, New Delhi OSDMA—Odisha State Disaster Management Authority (2021) A new horizon a ray of hope towards “Disaster Management”. Odisha State Disaster Management Authority. https://kalaha ndi.nic.in/disaster-management/ Pariyar RK (2020) Community based disaster risk management: a case study of Mahakali River Basin, Kanchanpur. https://doi.org/10.3126/tgb.v6i0.26169 Pradhan Mantri Krishi Sinchayi Yojana (PMKSY) (2016) (DIP) District irrigation plan of Kalahandi district. District Level Implementation Committee (DLIC), Kalahandi, Odisha. https://www.dow rodisha.gov.in/DIP/2015-20/kalahandi.pdf Sahu PR (2017) Drought and now pest attacks: double danger stalks Odisha’s rice fields, its farming mainstay. Scroll.in. https://scroll.in/article/1007814/these-books-tell-us-why-we-ignore-plantsand-what-we-are-missing-out-on-as-a-result Shaw R (2012) Community-based disaster risk reduction. Emerald Publishing Limited. ISBN9780857248688. https://www.perlego.com/book/387116/community-based-disaster-risk-red uction-pdf Tedesco I (2019) A holistic approach to agricultural risk management for improving resilience. Platform for Agricultural Risk Management (PARM)

Greening Indian Defence Forces: A Conceptual Framework Towards Accelerating Carbon Neutrality in India Chetan Dhawad

Abstract With an aim to avoid the climate catastrophe, the world is witnessing rapid transition phase in almost all spheres of life, which is primarily being driven by non-fossil resources. India being one of the most vulnerable countries has been playing the lead role in various international forums about range of climate negotiations. India’s increasingly ambitious Nationally Determined Contributions (NDC) targets are assisting it in fulfilling aspirations to emerge as ‘Climate Power’. Every major organization in India has defined its environment commitment in synchronization with national ambitions. One and half million strong Indian Defence Forces with impeccable professional record would certainly like to play a greater role in fulfilling country’s NDC targets. With significant national resources under its command and known for delivering results in time bound manner, the defence forces need to consider reducing its own carbon emissions wherever possible without affecting operational capability and preparedness. North Atlantic Treaty Organisation (NATO) militaries adopted ‘Green Defence Framework’ in 2014 which includes reduction of fossil fuel consumption, diversification of energy supplies within general defence practices in addition to numerous ‘Green Defence’ measures (North Atlantic Treaty Organisation NATO, Green defence framework, 2014). Advancing a step ahead, United Kingdom (UK) militaries are disclosing energy and carbon emission data in annual Ministry of Defence (MoD) report (United Kingdom, Ministry of Defence (MoD), Annual-report 2019–20, Annex D, 2019–2020). The paper aims to analyse the subject holistically without touching any sensitive security related issues. The paper briefly touches upon vulnerabilities of Climate Change to military and its infrastructure, potential of armed forces towards larger contributions of country’s climate targets and elaborates on various recommended options with can be immediately incorporated in its functioning which will reduce the carbon footprint of defence forces. Keywords Green defence · Indian military · Climate security · Reducing carbon emission · Sustainable defence C. Dhawad (B) Veermata Jijabai Technical Institute (VJTI), Mumbai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_12

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1 Introduction The Glasgow conference concluded in Nov 2021 also called as ‘Conference of Parties’ (COP-26) witnessed all world leaders coming together in desperation and setting up ambitious climate targets to restrict global warming temperature rise to 1.5 °C. Global warming is the most certain and immediate threat that the human kind faces in next several decades. The Intergovernmental Panel on Climate Change (IPCC) Annual Report (AR-6) released in Mar 2022 has given a clarion call to the world about reaching the ‘Tipping point’ (a critical threshold beyond which a system reorganizes often abruptly and/or irreversibly) which will trigger climate catastrophe including existential crisis in many parts of world (Panel Climate Change. Assessment report 6, Working Group I, Summary of policy makers, p 41). The woes of climate change are evident with climate hazards such as extreme weather, higher temperatures, severe draughts, frequent floods, unpredictable storm, sea level rise, soil degradation and acidification of oceans that are occurring regularly with severe intensity defying all scientific predications. The extremely disturbing trend in these events is the inaccurate ability to establish (predict) any pattern by the world scientific community. Climate change is a ‘Threat Multiplier’ increasingly influencing security scenarios including military strategy. Militaries of developed countries are accepting threats due to climate change situation as critical to their strategic operation and survival. Future wars would be the fallout of climate change crisis where militaries will be protecting borders from climate change affected refugees, policing food shortage from changed weather pattern and geo-political instability, economic distress and social discontent. The world is going through unprecedented ‘Transition Phase’ where ‘Green Industry’, ‘Green Economy’, ‘Green Technology’, ‘Green Bonds’, ‘Green Strategic Partnership’, ‘Green Fuel’ and ‘Green Transport’ are replacing traditional ‘Industry’, ‘Economy’, ‘Technology’, ‘Bonds’, ‘Strategic Partnership’, ‘Fossil Fuel’ and ‘Transport’ respectively. All major developed economies, international financial institutions like World Bank, International monetary Fund etc. are accelerating transition by a series of transformative mandates. Keeping pace with its transition, many militaries in world are adopting ‘Green Defence Framework’. India remains one of the most vulnerable countries to the effects of climate crisis with its huge coastline which is not only a threat to strategic vital harbours and islands but to numerous coastal cities. The deployment of Indian forces in ecological sensitive Himalayas is being subjected to frequent cloud bursts, landslides, effects of glacier melting where many defence personnel have lost their lives and would continue to lose their life. In this rapid transition of the world, ‘Green defence’ is an inescapable transformation in armed forces. Many developed countries are not only disclosing their Green House Gas (GHG) emissions in the Annual MoD Report but also are adopting ‘Green Doctrines’. Since last two decades India is spearheading numerous green initiatives world over and projecting itself as a major successful ‘Climate Power’ by declaring one of the most ambitious Nationally Determined Contributions (NDC) in COP-26

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(https://pib.gov.in/PressReleseDetail.aspx?PRID=1847812). In this unprecedented transitions and crisis, the Indian Armed Forces would certainly like to be a part of the solution without comprising on its operational capabilities.

2 Defence Forces: Assuming Role as ‘Climate Warriors’ For countries to function, military is the nature of reality in the world but ethical duties also rest with the military to ensure the climate remains healthy and obligations towards nature and earth are fulfilled even after wars are won. For militaries world over, ‘Energy is the lifeblood of war fighting capabilities’ which involves manufacturing and operating combat, warships, running military bases, procuring resource intensive military hardware and carrying out military operation related activities. All these activities generate very high GHG emissions at every stage which remains mostly unaccountable by militaries as Kyoto Protocol (1997) exempted militaries from disclosing emissions (The Kyoto Protocol 1997), however the same was removed by Paris Agreement (2015) (https://unfccc.int/process-and-meetings/ the-paris-agreement/the-paris-agreement). Studies indicate that military of USA contribute close to 50% of GHG emissions of the country and had the Department of Defence (DOD) of USA been a nation, it would have been among the first 50 most polluting nations (Crawford 2019). The Paris agreement allows the countries to voluntarily disclose military emissions. However leaving out military emissions is no small ‘Omissions of Emissions’. Environmental activists allege military as the single largest institutional risk to climate movement as Paris agreement exempts nations from disclosing military emissions thus earning the status of ‘Protected Polluters’. When ‘Climate Concerns’ have become a major agenda in electing governments in developed countries, 189 countries are obliged to submit yearly reports on the national emissions under United Nations Framework Convention on Climate Change (UNFCCC) (https://www.un.org/en/climatechange/report; https://library. wmo.int/index.php?lvl=notice_display&id=22128#.YyNhtFxBxdh). When there is clamour for ‘Net zero Emissions’ world over, most of the organizations would disclose emissions sooner or later. To bolster the role of military in environmental and climate security, ‘International Military Council For Climate and Security’ (IMCCS) was launched in 2019 at ‘Planetary Security Conference’ in Hague (https://imccs.org/2019/02/19/release-int ernational-military-council-on-climate-and-security-announced-at-the-hague). The climate change acceptance as a global security challenge as well as a threat multiplier exacerbating security risk is specially gaining traction. Militaries of developed countries have agreed to believe, ‘No nation can have stable security situation without focusing on climate change crisis’. Military being dynamic, self sufficient and multi faceted is expected as a force that would lead environment stewardship in its cantonments as well as in coordination with civil agencies in nearby domain thereby becoming a part of ‘Climate Solution’.

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The ‘Joint Doctrine of Indian Armed Forces’ (JDIAF) released in 2017 has made sketchy reference of climate change (https://www.ids.nic.in/IDSAdmin/upload_ima ges/doctrine/JointDoctrineIndianArmedForces). It briefly mentions climate change as a non-traditional security and focuses on Humanitarian Assistance and Disaster Relief (HADR) leaving huge scope for contribution in climate action by the Indian Armed Forces. The ‘Ministry of Defence of India Annual Report’ also does not mention anything about the impact of climate change nor any strong road map for contributing to the overall fight against climate change crisis (https://www.mod.gov. in-annual).

3 Effect of Climate Change on Military Campaigns Climate Change is revamping strategic, operational and tactical narratives with significant implications for Indian security and defence. To keep the country safe, we must tackle this impending threat. The unparallel and frequent scale of floods, wildfires, cyclones and other weather extreme events have damaged many military installations and bases repetitively. While the impact of climate change are global, selected impact, hazard and associated risks will defer region by region. Due to mountainous topography in North and North-East of India, the army troops remain deployed in isolated far flung areas in high altitude terrain and are often subjected to cloud bursts, avalanches, extreme rainfall events leading to landslides. The survival in the desert terrain would be further challenging with repetitive heat waves with extreme warm temperatures. The rise of the sea level in the Indian ocean coupled with extreme rainfall events have already threatened vital naval bases along the Indian sea coast as well as isolated strategic Islands. The extreme climate change related events are also likely to affect the functioning of military hardware and equipment. Following are some of the examples of climate risk to be incorporated into war games, operational discussions, modeling and simulation:(a) Sensor Operations. Inconsistent functioning of sensors in military operations due to extreme heat or rainfall. (b) Aircraft performance. Loss of range, payload capacity due to increased temperature. (c) Wildfires. Lack of accuracy due to warm weather conditions. (d) Ground mobility. Heavy rainfall over prolonged monsoon season affects trafficability, medical evacuation and logistics supply on ground. (e) Naval operations and ports. Due to consequence of sea level rise frequent flooding of shores, replenishment difficulty in altered sea conditions. (f) Information flow. To understand how changes in normal climate cycle affect operations. (g) Increased demand of operation in Humanitarian Assistance and Disaster Relief (HADR).

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4 India’s Aspirational Journey as a Climate Power India remains the third largest national emitter of GHG (after China and USA) since long time (https://data.worldbank.org/indicator/SP.POP.TOTL?end=2018& start=2018). However, it has been playing a key role in International dialogue on climate change as a major developing country. The commitment of India on reducing GHG emissions has been evident in letter and spirit since the adoption of ‘National Action Plan on Climate Change’ (NAPCC) in 2008 which was aimed at accelerating India’s development objectives while simultaneously addressing climate change concern. The objective was implemented through eight national missions covering both mitigations and adaptations efforts with the energy sector being instrumental (Rattani 2018). The nature of the first three missions were of mitigations and the balance five were of adaptations. Realizing the shortcomings of missions which were too broad and lacked specific quantified targets, the Government of India (GoI) commenced a new era of ‘Quantified Climate Targets’. In 2015, India announced its first Nationally Determined Contributions (NDCs) targets with primary focus on reduction in emissions intensity of its GDP by 33–35% among other targets (https:// pib.gov.in/newsite/printrelease.aspx?relid=128403). India’s NDC centred around its policies and programmes on clean energy which gave a major thrust to renewability in India since then. While setting up the example by declaring ambitious targets, India has been spearheading climate negotiations as the leader of developing and poor countries for ‘Climate Fund’, ‘Climate Justice’, ‘Green Technologies’ and ‘Technology Transfer’. India is also pursuing developed countries to recognize fundamental equality and their right to economic growth based on the principle of ‘Equality and Climate Justice’. India’s protest against developed countries for drifting away from principles ‘Common But Differentiated Responsibility’ (CBDR) and compelling to amend draft in COP-26 from ‘Phasing Out’ coal to ‘Phasing Down’ are notable achievements. To strengthen its leadership stand among developing countries the initiatives like Lifestyle For Environment (LIFE), ‘Infrastructure Resilient Island States’ (IRIS) to help group Small Island Developing Countries (SIDS), launch of ‘One Sun One World One Grid’ (OSOWOG), ‘Coalition for Disaster Resilient Infrastructure’ (CDRI) are internationally acclaimed initiatives (https://pib.gov.in/PressR eleasePage.aspx?PRID=1768712). Finally in the Glasgow conference (COP-26) in 2022 India declared its road map for achieving ‘Carbon Neutrality’ by declaring five nector elements as NDCs (https://pib.gov.in/PressReleasePage.aspx?PRID=179 5071). Post declaration of these targets the entire government, financial, business and corporate machinery are switching over from fossil fuel to green economy which implies fundamental structural changes in each domain. All giant organization like railways, shipping etc. are quantifying the targets. India’s NDC is ambitious and to achieve the same, all major sectors of country including the military is expected to contribute to the best of its capability.

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The present contribution of defence forces is limited (based on minor mitigation targets by other ministries like Renewable ministry and Forest ministry etc.) and restricted to contribution in terms of setting up of Solar Plants. The Ecological Task Force (ETF) which is the first ecological unit of the Territorial Army is primarily contributing by tree plantation (https://indianarmy.nic.in/Site/FormTemplete/frm TempSimple.aspx?MnId=3e/JwsdND+nE/FFMZOvWiQ==&ParentID=M1vaRI/ 6r2aarvyR6E67LA==). The organization presently has not declared any vision, roadmap in reducing emissions, carbon sequestration or assessing present carbon budget. However few positive steps are taken intermittently. The defence forces would be the leading organization facing the wrath of adverse climate change effects and hence would certainly like to contribute in a much more proactive manner in accelerating India’s environment targets.

5 Case Study: What Other Militaries Are Doing? Western countries, in particular militaries of NATO members, have taken a range of initiatives with shared aim to support the transition to lower carbon energy use in the military. Some of the major initiatives by these leading militaries are summarized as follows.

5.1 Defence Framework In 2014, NATO adopted ‘Green Defence Framework’ which provided broad basis for co-operation within the alliance by developing ‘Green Solutions’, ‘Green Technologies’ in tackling security challenges. This was the first major collective effort in which militaries of 30 countries collectively agreed to reduce GHG emissions. (1) Since then, NATO periodically reviews and promotes ‘Green Energy Scientific Research’ for military developments. In a major development in 2021, NATO adopted ambitious ‘Climate Change and Security Action Plan’ to mainstream climate change considerations into NATOs political and military agenda (https://www.nato.int/nato_static_ fl2014/assets/pdf/2022/6/pdf/280622-climate-impact-assessment.pdf).

5.2 Disclosing Military Emissions A recent study commissioned in European Union (EU) parliament estimated that carbon footprint of EU military expenditure in 2019 was approximately 24.8 million tons of Carbon Dioxide equivalent which is equal to emissions of 14 million cars

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(www.left.eu; https://www.europarl.europa.eu/doceo/document/E-9-2021-002448_ EN.html). The military of United Kingdom in its annual Ministry of Defence (MoD) report dedicates a chapter titled ‘Strategic Objective: Transform the way we do business’ in which it declared 45% GHG emission reduction from estate energy and business travel as its achievement (United Kingdom, Ministry of Defence (MoD) 2019–2020). The same report further under the chapter ‘Sustainable MoD’ highlights the militaries significant contribution to UK’s Sustainable Development Goals (SDG). The ‘Greening Government Commitments’ which are operational targets and commitment to reduce the impact on environment are also summarized as annexure under heading ‘Energy and Carbon emission Data’ in the same report. The other countries like Australia, Canada, Japan, Italy and France report extensively about energy security, military-environment strategy and sustainability in their annual report.

6 How Indian Defence Forces Can Contribute? 6.1 Climatization and Transformation (Establishing Climate Change Division in MoD) Climatization is the gradual movement towards mainstreaming climate change into military strategy based on the militaries perception of climate vulnerabilities’ (Jayaram 2021). Whether it is impact of climate change on the military or the militaries contribution to national climate goals, adopting climatization is the need of the hour. The framework of climatization could be of transformative nature where ‘Mitigations and Adaptations’ actions are foundation pillars. Climatization is just in the nascent stage in Indian contest thereby giving ample room for research for preparing ‘Inductive Approach in Integrating Climate Change into Military Strategy’. Countries like USA have ‘Climate Change Division’ in the military establishments which works on numerous climate agendas. Establishing ‘Climate Change Division/Cell’ would be a committed beginning in understanding and addressing military emissions.

6.2 Climate Performance as Strategic Objective The Indian Defence forces have natural advantage in setting up enhanced clean energy targets as the vast western border of India offers near ideal conditions for generating clean energy during most of the year. The mountainous terrain in North and North-East of India offers adequate opportunity to tap the potential of solar energy in addition to assisting other organizations in tapping the hydroelectricity potential. Based on policies and programmes which can be designed to centre around these advantages, forces can set various annual ‘Greening Commitments’ of strategic nature. Some of the recommended initiatives among these targets could be as follows.

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Estate Emissions

Calculate Emissions. Ministry of Defence is the largest owner and custodian of land in India with 17.99 Lakh Acres of land which is spread over 62 notified cantonments, 237 military stations all over the country (http://www.dgde.gov.in). This huge quantum of land which is in approximately 4900 pockets in diverse terrain and weather conditions all over the country has potential to play a significant role in achieving India’s NDC. We do not manage, what we do not measure. Professional attempts need to be made to estimate emissions from these estate and feasibility of managing the same. The annual targeted emissions could be defined through targeted energy efficient investment on the defence estate and overall reduction in all carbon emissions generated during various activities in cantonments. The UK-MoD ‘Annual Report and Accounts’ discloses energy and carbon emissions data annually (United Kingdom, Ministry of Defence (MoD) 2019–2020).

6.2.2

Green Transport

Prepare Road Map. The defence forces can incorporate e-vehicles wherever operationally possible particularly in peace stations. Conservative estimates mention 3–4% consumption of the country’s fuel by the Ministry of Defence. The phased implementation will be a major breakthrough in contribution by forces. Simulation based training during the induction phase could be explored as it is certainly likely to lead to emission reductions.

6.2.3

Incorporating Renewables

Defining Targets. A pilot project on ‘solar park based locality modules’ in deserts for catering the needs of the forces would go a long way in planning on the strategic scale along the western border. Targeted quantified incorporation of various renewable and low carbon technologies needs to be implemented. The number of modern technologies like biomass, boiler plants, heat pumps, stationary and mobile solar panels can be effectively implemented along the vast areas under military control in peace and field locations.

6.2.4

Best Practices

Paper reduction. With rapid technological advancement, forces implementing 10% annual paper reduction target is an easy way to commence climate commitment. The cumulative effect will be significant.

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Quantification of Expected Reduction

For the defence forces the responsibility of defending the nation is supreme and all mitigation and adaptation measures of climate change would be in sync with its primary duties. A policy directive from MoD, initially identifying the peace locations where the above mentioned recommendations can be implemented in a phased manner would facilitate quantifying the expected reduction. Once emission reductions targets are defined, the defence forces would always deliver the result in a time bound manner.

6.3 Green Doctrines The joint doctrine of the Indian defence forces was adopted in 2017 (https://www.ids. nic.in/IDSAdmin/upload_images/doctrine/JointDoctrineIndianArmedForces). The commitment of forces in protecting nature can be incorporated as ‘Green Doctrines’. When the world is going through green transition, voluntarily disclosing emissions by the military would weaken it’s image as ‘Protected Polluters’. In consonance with India’s climate power ambition, the Indian military needs to consider adopting some part of ‘Green Doctrine’ (Green Defence). It will also exemplify advocacy of use of military resources for the purpose of environmental protection and climate action.

6.4 Sustainable Defence Design a Model. The Indian government is signatory to UN-SDGs towards which Indian military makes significant contribution in addition to India’s own sustainable development objectives. For Indian defence forces, sustainability should be ensuring that we have equipment, and military war machinery resources to deliver against our strategic objectives successfully while respecting the environment and climate security at home and abroad, now and in future. Sustainable defence strategy is deeply incorporating changes for future operating environment, judicious use of natural resources in the design and delivery of defence machinery. So far, there has not been any attempt by MoD to design ‘Climate Change and Sustainability’ (CC&S) defence operating model which will define governance framework and clarify the responsibility of all agencies in forces. The diagram highlighting linkage between UN SDGs and defence activities is shown in (Fig. 1).

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Fig. 1 The original diagram is of United Nations Sustainable Development Goals (SDG). The defence related SDGs have been explained above

6.5 Setting Example for Other Defence Forces Green defence may appear to be rhetoric, particularly against the background of the heavy dependence of military on fossil fuel and Environment protection is not included even in the secondary duties of militaries. Every effort by the Indian Armed Forces would strengthen India’s aspirational image as climate power and would

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encourage other defence forces particularly in Asian countries to adopt the green defence model.

7 Conclusion To keep the nation secure, we must tackle the existential threat of climate change which is increasingly shaping various security narratives. The responses to the same demand integrated the approach of developing adaptive capacity and mitigations measures. In Indian Defence Forces there is less clarity on specificity of such approach which demands a major policy shift requirement towards the comprehensive approach. Climate Change Risk Assessment Methodologies (CCRASM) must become a norm to develop climate resilience. India has been projecting itself ‘Climate Power’ and has successfully implemented numerous environment related initiatives on the global platform. India’s sincere Endeavour to achieve carbon neutrality before the intended timeline of 2070 is praiseworthy. Complying to the Indian objective, the Indian military must remain inclined to contribute towards reducing emissions and addressing climate change concerns by both necessity and demand for stewardship by joining India’s efforts at the national level.

References Crawford NC (2019) Pentagon fuel use, climate change, and the costs of war. Watson Institute, Brown University Director General Defence Estate. www.dgde.gov.in Director General Defence Estate. http://www.dgde.gov.in Indian Army. https://indianarmy.nic.in/Site/FormTemplete/frmTempSimple.aspx?MnId=3e/Jws dND+nE/FFMZOvWiQ==&ParentID=M1vaRI/6r2aarvyR6E67LA== Indian Defence Services. https://www.ids.nic.in/IDSAdmin/upload_images/doctrine/JointDoctrin eIndianArmedForces Intergovernmental Panel Climate Change. Assessment report 6, Working Group I, Summary of policy makers, p 41 International Military Council on Climate and Security. https://imccs.org/2019/02/19/release-intern ational-military-council-on-climate-and-security-announced-at-the-hague Jayaram D (2021) ‘Climatizing’ military strategy? A case study of the Indian armed forces. Int Polit 58(4):619–639 Ministry of Defence (MoD), Government of India, Report (2018–19). https://www.mod.gov.inannual North Atlantic Treaty Organisation NATO (2014) Green defence framework. https://natolibguides. info/ld.php?content_id=25285072 North Atlantic Treaty Organisation NATO. https://www.nato.int/nato_static_fl2014/assets/pdf/ 2022/6/pdf/280622-climate-impact-assessment.pdf Press Information Bureau. https://pib.gov.in/PressReleseDetail.aspx?PRID=1847812. Posted 03 Aug 2022 2:33 pm by PIB, Delhi Press Information Bureau. https://pib.gov.in/PressReleasePage.aspx?PRID=1768712 Press Information Bureau. https://pib.gov.in/PressReleasePage.aspx?PRID=1795071

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Press Information Bureau. https://pib.gov.in/newsite/printrelease.aspx?relid=128403 Rattani V (2018) Coping with climate change: an analysis of India’s national action plan on climate change. Centre for Science and Environment, New Delhi The Kyoto Protocol (1997) The Kyoto Protocol to the United Nations framework convention on climate change, United Nations Under the radar: the carbon footprint of Europe’s military sectors study by ‘Scientists for Global Responsibility (SGR), UK and The Conflict and Environment Observatory (CEOBS), UK’. www.left.eu. https://www.europarl.europa.eu/doceo/document/E-9-2021-002 448_EN.html. Emissions equal to 14 million cars United Kingdom, Ministry of Defence (MoD) (2019–2020) Annual-report 2019–20, annex D. Ministry of Defence (MoD), United Kingdom, p 213 United Nations framework convention on climate change. https://unfccc.int/process-and-meetings/ the-paris-agreement/the-paris-agreement United Nations. https://www.un.org/en/climatechange/report; https://library.wmo.int/index.php? lvl=notice_display&id=22128#.YyNhtFxBxdh United Nations. UNWPP Data based on Global Carbon Project, Carbon Dioxide Information Analysis Centre, BP, Maddison, UNWPP Population data from The World Bank. https://data.worldb ank.org/indicator/SP.POP.TOTL?end=2018&start=2018

Comparing the Performance of Artificial Neural Network and Multiple Linear Regression in Prediction of a Groundwater Quality Parameter Riki Sarma and S. K. Singh

Abstract Various techniques have been developed in recent decades to evaluate the suitability of groundwater for different purposes. In arid and semi-arid regions, groundwater is a vital resource that meets all irrigation needs. An important irrigation quality parameter is electrical conductivity (EC) which is an indicator of salinity. This study compares the performance of two models—artificial neural network (ANN) and multiple linear regression (MLR) in predicting the EC of groundwater in Delhi, India. The models were developed based on 64 data records obtained from the Central Ground Water Board. Principal component analysis (PCA) revealed that EC showed significant positive correlations with Ca2+ , Mg2+ , Na+ , Cl− and SO4 2− , which were used as inputs for the models. The dataset was divided into training (80%) and testing (20%) subsets. The performance of the models was compared using coefficient of determination (R2 ) and root mean squared error (RMSE). The modeling results showed that MLR performed better than ANN in predicting the EC, with higher R2 and lower RMSE values for both the training and testing stages. MLR can thus be used as a reliable model for making future predictions of the EC of groundwater in the study area. Keywords Artificial neural network · Multiple linear regression · Prediction models · Groundwater quality · Coefficient of determination · Root mean squared error

1 Introduction The dependence on groundwater resources to meet domestic, agricultural and industrial demands is rising rapidly with increasing global population (Singh 2014). The need of the hour is not only to monitor excessive abstraction of groundwater that R. Sarma · S. K. Singh (B) Department of Environmental Engineering, Delhi Technological University, New Delhi 110042, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_13

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reduces subsurface water levels but also to study its quality. Anthropogenic activities release toxic chemicals in the environment that adversely affect groundwater quality (Sarma and Singh 2021a). Scientists and researchers worldwide have developed novel approaches to evaluate the existing groundwater quality scenarios that can protect this resource from contamination (Jenifer et al. 2021). Various methods such as graphical plots, water quality indices, geospatial technology, numerical modeling and soft computing techniques have been established and implemented for assessing the groundwater quality in different aquifers (Machiwal et al. 2018). Artificial neural networks (ANN) have seen widespread applications in recent years. ANNs replicate biological neurons and consist of input, hidden and output layers (Sarma and Singh 2022). Numerous studies have reported the application of ANNs for predicting hydrological parameters (Elbeltagi et al. 2022; Khudair et al. 2018; Singha et al. 2021). For example, Kulisz et al. (2021) used ANN with Levenberg–Marquardt algorithm, sigmoid activation function and pH, electrical conductivity (EC), Ca2+ , Mg2+ , K+ , PO4 –P and SO4 2− as inputs to simulate the water quality index. Bilali et al. (2021) applied ANN to forecast irrigation water quality parameters like total dissolved solids (TDS), potential salinity, sodium absorption ratio, exchangeable sodium percentage, magnesium absorption ratio and residual sodium carbonate using EC, temperature and pH as inputs. They reported that ANN is less sensitive to input variables when compared to other models such as Random Forest (RF) and Adaptive Boosting. Jafari et al. (2019) used Na+ , HCO3 − , Ca2+ , Mg2+ and SO4 2− as inputs to estimate the TDS using four soft computing methods—multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and gene expression programming (GEP). Their study concluded that GEP outperformed MLP, SVM and ANFIS models in terms of root mean squared error (RMSE), coefficient of determination (R2 ) and mean absolute error (MAE). Sakizadeh (2016) predicted the WQI using 16 groundwater quality variables as inputs in ANN and reported that phosphate and iron were the most influential parameters in prediction. The multiple linear regression (MLR) model is a data-driven technique that predicts the dependent variable by predicting the linear relationships between the independent variables (Egbueri and Agbasi 2022). MLR is a statistical model and owing to its easy application, can be used to predict numerous variables. For example, Poursaeid et al. (2022) compared the performance of MLR with three AI-based models to predict the groundwater level in Iran and concluded that extreme learning machine model performed better. Hosseinzadeh et al. (2020) compared MLR with ANN in predicting nutrient recovery from solid waste under different vermicompost treatments. Kadam et al. (2019) used ANN and MLR to predict the WQI with pH, EC, TDS, total hardness (TH), Ca2+ , Mg2+ , Na+ , K+ , Cl− , HCO3 − , SO4 2− , NO3 − and PO4 3− as input variables. Kouadri et al. (2022) applied ANN, MLR and Long ShortTerm Memory (LSTM) models to predict irrigation water quality parameters and concluded that ANN and MLR were highly accurate in predicting the residual sodium carbonate values. Egbueri and Agbasi (2022) used ANN and MLR to predict groundwater quality parameters and reported that MLR performed better than ANN in predicting EC, TH and TDS while ANN performed better than MLR in predicting pH.

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Electrical conductivity is an important parameter that helps in evaluating the suitability of water for irrigation. The EC is an indicator of the salinity and high EC can reduce the osmotic activity of crops and decrease their ability to absorb water and nutrients from the soil (Snousy et al. 2022). According to the Wilcox classification (Wilcox 1955), EC > 2250 µS/cm has very high salinity and is unsuitable for irrigation. The EC measures the ionic strength and is dependent on the presence of ions such as Cl− , Na+ , Ca2+ , Mg2+ and SO4 2− . Thus, high levels of EC imply high concentrations of these ions which also affect drinking water quality and cause gastrointestinal problems and heart disease (Snousy et al. 2022; Ullah et al. 2022). This study was undertaken to predict the EC of groundwater in Delhi, India. Sixty-four groundwater records containing concentrations of major ions, obtained from the Central Ground Water Board, India were fitted in ANN and MLR modeling environments and their efficiency was compared in predicting the EC.

2 Materials and Methods 2.1 Study Area The National Capital Territory (NCT) of Delhi occupies an area of 1483 sq. km in North India (CGWB 2016). Delhi receives a normal annual rainfall of 611 mm. This region is characterized by very hot summers and cold winters. Delhi has a distinct monsoon season between the months of July–September which receives about 81% of the annual rainfall (CGWB 2021). Geology of Delhi is complex and ranges from Quartzite to Older and Younger Alluvium (Sarma and Singh 2021b). There are seven drainage basins in the NCT of Delhi which discharge in the Yamuna River (CGWB 2016).

2.2 Data Used Groundwater data of samples collected and analyzed by the Central Ground Water Board in May 2019 were obtained from the CGWB State Unit Office, Delhi (CGWB 2021). The data contained coordinates of 64 sampling locations and their pH, EC (µS/cm at 25 °C), CO3 2− , HCO3 − , Cl− , SO4 2− , NO3 − , F− , PO4 3− , Ca2+ , Mg2+ , Na+ and K+ values (in mg/L). There are no missing values in the dataset. The sampling locations are depicted in Fig. 1. Pearson correlation matrix was first applied to the dataset in SPSS statistical software (version 26) to understand the relationships between EC (dependent variable) and the other parameters (independent variables). The selected independent variables were then fitted in ANN and MLR algorithms in R software (version 4.1.0) as per their respective methodology given below. The dataset was normalized between 0 and 1 to prevent larger values from dominating the smaller ones (Sarma and Singh 2022) according to the following equation:

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Fig. 1 Location of study area

xn =

(xi − xmin ) (xmax − xmin )

(1)

where, x n is normalized data, x i is actual value, x min is minimum value and x max is maximum value (Shirmohammadi et al. 2013). The dataset was divided into a training set (80% of the samples, i.e., 51 samples) and a testing set (20% of the samples, i.e., 13 samples). The models were first trained using the training set and the predicted EC values (calculated back according to Eq. (1)) were compared against the testing set. The efficiency of the models was compared using accuracy measures, R2 and RMSE which were calculated according to the following equations: ⎡

⎤2 ) )( O P − O − P i i ⎦ R 2 = ⎣ /Σ ( )2 Σn ( )2 n i=1 Pi − P i=1 Oi − O [ | n |1 Σ RMSE = | (Oi − Pi )2 n i=1 Σn ( i=1

(2)

(3)

where, n is number of data points, Oi are observed variables with mean O and Pi are predicted variables with mean P (Kouadri et al. 2022).

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Fig. 2 Simple feed-forward ANN

2.3 Artificial Neural Network (ANN) ANNs replicate biological neuron processing and can simulate linear, non-linear and complex relationships between independent variables. ANN contains an input layer (consisting of input variables), one or more hidden layers and one output layer. The input values are multiplied by their corresponding weights in the hidden layer to give the outputs. These are then multiplied by their corresponding weights in the output layer to give the predicted value of the dependent variable (Othman et al. 2020). In the current study, the applied ANN used the feed-forward backpropagation algorithm during the training stage and sigmoid (or logistic) activation function to calculate weight and bias. A typical feed-forward ANN is depicted in Fig. 2. The inputs are denoted by i1 and i2, weights are w1–6, h1 and h2 are nodes (or neurons) in the hidden layer, and b1 and b2 are biased.

2.4 Multiple Linear Regression (MLR) MLR is a statistical model that is an advanced form of the simple regression model. It is based on linear relationships between inputs and outputs by incorporating a regression formula. Mathematically, MLR is expressed as: y = b0 + b1 x1 + b1 x1 + . . . + bi xi

(4)

where, y is the dependent variable, x is the independent variable and b is the regression constant (Kouadri et al. 2022).

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3 Results 3.1 Pearson Correlation Matrix The dataset obtained from the CGWB contained observations of pH, EC, CO3 2− , HCO3 − , Cl− , SO4 2− , NO3 − , F− , PO4 3− , Ca2+ , Mg2+ , Na+ and K+ for 64 locations in Delhi, India. The descriptive statistics of these variables are presented in Table 1. Pearson correlation matrix was applied in SPSS software to reveal the variables that contribute to the EC of groundwater in Delhi. The matrix is presented in Table 2. From the correlation analysis, it is clear that EC is strongly correlated with Cl− , Na+ , Ca2+ , Mg2+ and SO4 2− at 99% confidence interval. EC shows positive correlation with NO3 − , F− and HCO3 − also but the correlation coefficient is small ( Concentration of Indoor air pollutants(CIAP): A special advisory will be given to inhabitants to keep all doors and windows closed. If the concentration of ambient air pollutants is very high, then a recommendation to start water sprayers installed on the boundary and roof of the smart villas will be given to inhabitants. These sprays will settle down the pollutants (especially dust and particulate matter) near the smart villa and will be operated on the wastewater released from the water purifying system as well as air conditioners’ duct water and stored in a separate tank for this purpose. Ventilation system and

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other air quality control equipment will be switched off when the concentration of pollutants again falls below the threshold levels. Concentration of Indoor Air Pollutants (CIAP) is within the threshold value: Primary concern is IAQ inside the smart villa, so if the concentration of indoor air pollutants is within the threshold value whether from the start of the day or due to positive action taken to improve indoor air quality, air purifiers, as well as the ventilation system, will be stopped by the central hub. The decision about when to switch off the air quality control equipment will depend upon the concentration of pollutants in individual rooms as well as on the health condition/s of individuals in that room. Special Conditions: Here special conditions refer to situations when an increase in the concentration of air quality parameters happens very rapidly due to any kind of routine activities such as cooking or abnormal situations such as a fire in the house or climate conditions. During Dust Storms. Several parts of India experience dust storms as seasonal weather patterns during the summer months when dry weather causes dust to be picked up by the passing winds. These dust storms cause an increase in particulate matter of all sizes which leads to an increase in health problems and hospitalization in people, especially for those who are already suffering from comorbidities such as asthma and cardiovascular diseases. During dust storms, central hub will close all the windows and doors as well as give recommendations to inhabitants to take caution while going outside. It will also start a water sprayer system so that most of the dust near the smart villa will settle down quickly. When the concentration of particulate matter reduces on the outside, water spraying systems will be switched off. During Kitchen Working Hours. As soon as cooking starts in the kitchen, sensors installed in the kitchen will sense various pollutants inside the environment and provide data to the central hub. The central hub will start the chimney and open windows and if the pollutants do start reaching out of the kitchen to other rooms, sensors in individual rooms will sense that too and central hub will start the ventilation systems and air purifiers of that area according to the health condition of the individual present in those rooms/regions and pollutant concentrations. During Hazardous Levels of Pollutants. Here hazardous conditions refer to a rapid or sudden and large increase of pollutants concentration in the smart villa that may be due to fire or sudden aerosol release due to the bursting of deodorant bottles and other pressurized gas containers. Central hub will cut the electricity supply to reduce the damages while transitioning all the air quality management support equipment to backup external power support. It will start all the ventilation systems and chimneys as well as all the air purifiers inside the smart villa. It will also automatically open all the windows and doors. Central hub will immediately inform the emergency services and all the inhabitants of the house who are not present in the house. It will also start a loud alarm so that neighbours can be informed about the imminent danger. During the hazardous levels of the pollutants, all the action will be taken by central hub without taking the consent of the inhabitants while giving the option of aborting the action physically.

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During hazardous situations, the chances of inhabitants falling unconscious is very high due to the inhalation of pollutants in large quantity. Actions taken by smart air quality management systems will provide valuable time to inhabitants for escaping out of these situations without falling unconscious and thus saving lives.

3 Results Although air pollution has been identified as a major problem for human health, the development of smart air quality management system capable of not only effectively managing indoor air quality but also capable of modifying air quality to suit people with pre-existing health issues has not been discussed. Our smart air quality management system has been developed with the aim of tackling the problem of air quality management while also taking into consideration the medical condition of people suffering from any kind of comorbidities adversely affected by air pollutants. The proposed SAQMS will provide solutions to problems such as follows: • Identification of Indoor and outdoor air quality. • Interactive ways to decimate information regarding AAQ and IAQ. • Determining the best course of action for the safety of inhabitants while considering all the variables. Our air quality management system will provide smart and dedicated care to the inhabitants of the villa by managing various air quality control equipment in an efficient way.

4 Conclusion Air pollution is turning out to be a major problem for human health as well as the global economy. Smart cities and villas have been a focus of many governments throughout the world for driving sustainable economic growth, while enhancing the comfort and promoting the health and well-being of the inhabitants. The purpose of these cities cannot be complete without an efficient air quality management system. The smart air quality management system provides a means to assess indoor air quality and take necessary measures to improve it by utilizing additional information on ambient air quality and meteorological parameters. It utilizes various air quality control equipment such as ventilation systems, air purifiers, and chimneys as well as automated windows and doors and water spraying systems to improve the indoor air quality by using these types of equipment in the most efficient way possible to save energy and maintain the healthy living atmosphere inside the smart villa. These systems will lead to the growth of the global economy by reducing losses due to air pollution in terms of life as well as in terms of manpower hours lost.

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References Ajala JA, Saini G, Pooja (2020) CLOUD-IOT based smart villa intrusion alert system. In: 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE Explore, pp. 1326−1329. https://doi.org/10.1109/ICRITO 48877.2020.9198022. Central Pollution Control Board: National Ambient Air Quality Status (2009) https://cpcb.nic. in/openpdffile.php?id=UHVibGljYXRpb25GaWxlLzYzMF8xNDU3NTA2Mjk1X1B1Ymxp Y2F0aW9uXzUxNF9haXJxdWFsaXR5c3RhdHVzMjAwOS5wZGY= Central Pollution Control Board. https://app.cpcbccr.com/AQI_India/?c2l0ZV81NTg1 Delhi pollution control committee: real time ambient air quality data page. https://www.dpccairdata. com/dpccairdata/display/index.php India meteorological department homepage. https://mausam.imd.gov.in/ International Energy Agency (IEA) (2016) Energy and Air Pollution. World Energy Outlook Special Report. Kankaria A, Nongkynrih B, Gupta SK (2014) Indoor air pollution in India: implications on health and its control. Indian J Community Med 39(4):203–207. https://doi.org/10.4103/0970-0218. 143019 Kennedy O, Noma-Osaghae E, Modupe O, John S, Oluwatosin O (2018) A smart air pollution monitoring system. Int J Civ Eng Technol 9:799–809 Manisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E (2020) Environmental and health impacts of air pollution: a review. Front Public Health 8. https://doi.org/10.3389/fpubh.2020. 00014 Mitra A, Pooja, Saini G (2019) Automated smart irrigation system (ASIS). In: International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 327−330. https:// doi.org/10.1109/ICCCIS48478.2019.8974466 Omidvarborna H, Kumar P, Hayward J, Gupta M, Nascimento EGS (2021) Low-cost air quality sensing towards smart homes. Atmosphere 12(4):453. https://doi.org/10.3390/atmos12040453 Pandey A, Brauer M, Cropper ML (2021) Health and economic impact of air pollution in the states of India: the global burden of disease Study 2019. Lancet Planet Health 5(1):e25–e38. https:// doi.org/10.1016/S2542-5196(20)30298-9 Pathak AK, Sharma M, Katiyar SK, Katiyar S, Nagar PK (2020) Logistic regression analysis of environmental and other variables and incidences of tuberculosis in respiratory patients. Sci Rep 10(1). https://doi.org/10.1038/s41598-020-79023-5 Pathak AK, Sharma M, Nagar PK (2020) A framework for PM2.5 constituents-based (including PAHs) emission inventory and source toxicity for priority controls: a case study of Delhi, India. Chemosphere 255:126971. https://doi.org/10.1016/j.chemosphere.2020.126971 Pathak AK, Sharma M, Nagar PK (2022) An approach for cancer risk-based apportionment of PM2.5 constituents and sources. Hum Ecol Risk Assess: Int J 28(2):205−221. https://doi.org/ 10.1080/10807039.2022.2033612 Smart cities mission homepage. https://smartcities.gov.in/ The World Bank and Institute for Health Metrics and Evaluation University of Washington, Seattle (2016) The Cost of Air Pollution Strengthening the Economic Case for Action. https://openknowl edge.worldbank.org/bitstream/handle/10986/25013/108141.pdf?sequence=4&isAllowed=y Tran VV, Park D, Lee Y-C (2020) Indoor air pollution, related human diseases, and recent trends in the control and improvement of indoor air quality. Int J Environ Res Public Health 17:2927. https://doi.org/10.3390/ijerph17082927 USEPA Indoor Air Quality Home Page. https://www.epa.gov/report-environment/indoor-air-qua lity World Health Organization: Household air pollution and health. https://www.who.int/news-room/ fact-sheets/detail/household-air-pollution-and-health

Impediments in Contextualizing SDGs: Review on India’s City Plan Framework Towards Agenda 2030 Neeharika Kushwaha

and Charu Nangia

Abstract A key lever for a pragmatic adoption of Agenda 2030, lies in harnessing the synergies between city planning actions and different SDGs. Yet, in a quest to attain the objectives of the 17 goals, the major challenge pertaining to India remains in transferring the broader sustainability targets onto the contextual level. The paper in this relation intends to examine the road map of India towards sustainable urban development and the associated challenges barring the holistic inclusion of SDGs in the city planning process. It presents a semantic literature perusal on the competency of existing sustainability assessment (SA) indices and interlinking of their targets in city development plan (CDP) preparation. An aggregated analysis indicates negligible translation and contextual adoption of sustainable development goals pertaining to clean energy, responsible consumption of resources, climate action (in and above Water) in the city planning process. Along with the ecological vulnerability, economic inclusivity of goals remains a critical challenge for stimulating sustainable project strategies in India, with inefficient mobilization of land and fiscal resources. The paper contributes in providing useful insights for urban planners and policy makers, to take initiatives in rethinking the role of SDG indicators as context-driven targets by ensuring their presence in city plans as the key drivers of implementation. Keywords City development planning · URDPFI · Sustainable development goals · Indicators · Ease of living index · Municipal performance index · Sustainability index

1 Introduction The concept of Sustainability has evolved since the inception of Agenda 2030, by placing sustainable urbanization of cities at the center stage (Koch and Krellenberg 2018). Different goals supported by relevant targets and indicators cater to the N. Kushwaha (B) · C. Nangia Amity School of Architecture and Planning, Amity University, Noida, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_19

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performance of nations at the global level (Guijarro and Poyatos 2018). Though this arrangement can be seen as an indivisible system to assess the sustainability of growth areas, questions remain on their inception, definitive nature and adaptivity in the contextual elements of the place, colliding with the local objectives. Hence, there remains a gap in their implementation due to the qualitative nature and vague applicability by nations (Biermann et al. 2017). India in this current scenario of the system, intends to achieve the SDG targets by adapting 300 globally standardized indicators, measured through different indices (Aayog 2020). These performance measurements portray the sustainability of states and cities in India, but still showcase the lagging nature of sustainable urban development at the same time (Nangia et al. 2019; Nangia et al. 2019; Kushwaha and Nangia 2030; Kushwaha et al. 2022). “It is realized that there remains a long-standing commitment towards achieving the sustainable vision and a deterministic relationship between its 3 pillars, measured solely by indicator performance” (Patel et al. 2019). As a result, a growing body of research pushes the role of spatial planning and management at the local level as important anchor tenants or process leaders for sustainability interventions (Gustafsson et al. 2019). The study thus propels us to understand the translation of Agenda 2030 in India’s response to SUD and impediments in contextualizing SDGs in Indian City Development Plan (CDPs). Understanding how elements of these plans are linked to the application of sustainable goals and objectives, can help to identify current gaps in promoting SUD for cities and provide an additional overview of how efficiently the concept of sustainability evolved at the city-level planning.

2 India’s Approach to Agenda 2030 India plays a central role in the exponential urbanization happening on a global level. The universal application of SDGs reckons on India’s development trajectory towards sustainability for its 7000 plus cities and towns. The elevated issues in the field of urban planning such as responsible consumption of resources, traffic and pollution, urban sprawl, etc. highlight the urgency of necessary actions for SUD before the prevailing issues worsen (Randhawa and Kumar 2017). Given the importance of these vulnerable urban sectors for sustainable development, India catalyzes the achievement of SDGs with the proposed framework of sustainability indices (as shown in Fig. 1) adopted by NITI Aayog (MoUHA 2018). The sustainability index (SI) at the top of the hierarchy, takes care of seventeen global sustainability goals at the state level along with a defined set of indicators. Quality of life in cities is measured by the “Ease of Living (EoL) Index” acting as a supporting framework to SI and aims at assessing the quality of life along with the economic ability for participant 111 cities under Smart cities. The Municipal Performance Index (MPI) is enacted as the accompaniment to the EoL Index seeking to examine local government practice of Indian municipalities. Research showcases that for the cities in Global South, data availability and their reliable nature comes across as major setback in adopting urban SDG dimensions.

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Fig. 1 Sustainability indices adopted in India to measure SDG indicators performance of states and smart cities defined along with their respective categories covered for measurement. (Source by author)

A study carried out by researcher David Simon, tested the existing set of indicators proposed under the above-mentioned indexed in the city of Bangalore. The results found out that due to the shortcomings of adequate data accessibility for the proposed index measuring SDGs and an “incompatibility existed between what is useful at the practical level of city planning, and what was useful for the scientific goal of better characterization and understanding the complexity of city” (Klopp and Petretta 2017). This usually results in a lack of data comparison and its association with the city development plans. According to the SDGII report (Ayog-UN 2020), it was observed that due to non-availability of indicator data for several cities, null is computed for a SDG target rather than a proxy score achieved from local information of other data sources. Along with this issue, cities performing best in the group, usually metropolitan cities, are treated as benchmark scores for non-available data information of other cities, hampering the chance for them to appropriately weigh their importance on the national level towards SUD.

2.1 City Development Plans (CDPs) and the SDGs Any city’s performance is highly influenced by the quality of spatial and social planning sectors related to transport, energy, design of public spaces, and as well as the interactions of people in these sectors (Lützkendorf and Balouktsi 2017). City development planning in India is no longer seen as predictive and decisive but more provisional and inclusive in the face of an uncertain city future. As city development plans (CDPs) represent the link between regional plans and local area plans, over the years different ministries have advocated the preparation of specific sector plans to support CDPs such as Slum Redevelopment Plan, Comprehensive Mobility

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Plan, City Sanitation Plan, Environmental Conservation Plan, Heritage Conservation Plan, etc. (G. of India and M. of U. Development 2020). Yet, these plans not only struggle to integrate among each other but also remain inherently disconnected from the objectives of SUD required for Indian cities (Patel et al. 2019). In his study, Patel analyses that other South Asian cities have endeavored towards sustainable development by not laying their focus only on execution capabilities but by striding their attention on “industrial restructuring, designing sensible transit systems and green space, pushing improvements through standards” (Patel et al. 2019). These cities have incorporated a long-standing commitment to sustainable growth vision in the spatial planning framework whereas Indian cities are still struggling to define a relationship between sustainable urban growth and their performance on the proposed indices (UN-HABITAT 2012). Among some important planning deficits, the issue of disintegration of national policies from any CDP appears to be the most pressing concern. For instance, a major drawback of the national policies towards SDGs is the absence of a requirement to include and work around its indicators as the project targets. The CDPs made under the current urban planning framework, represent paucity in the provision of sustainability factors concerning SDGs related to equity, gender, socio-economic factors (Haque et al. 2019). Adapting globalized business as usual approach by standardizing indicators for comparative weightage among different indexes (Krellenberg et al. 2019), makes it difficult to realize relative and continuous developments taking place at the local level towards SUD (Lützkendorf and Balouktsi 2017). It is recognized that due to the different yet interconnected nature of SDGs, individual schemes or proposals for new indices may not act as a prolonged strategy for India but rather a convergent set of interventions on a sustained basis through city-level planning can be beneficial in long run (Bhamra et al. 2015).

2.2 Synergizing CDP Elements with SDG Targets Under Different Sustainability Indices Ministry of Statistics and Programme Implementation (MoSPI) works closely with NITI Aayog to endorse over 300 indicators under National Indicator Framework (NIF) across all the three sustainability indices. The study is built on the analysis of the individual report prepared for all three official indices, acting as sustainability assessment (SA) tools, by the Ministry of Housing and Urban Affairs (MoHUA) in alignment with NIF (Ayog-UN 2020). The results laid down utilize Urban and regional development plans formulation and implementation (URDPFI) guidelines, Vol I − 2015 prepared by the Town and Country Planning organization (G. of India and M. of U. Development 2020), to exhibit the relationship of CDP elements and sustainability targets across the SA tools (Sustainability index; EoL; MPI). 16 comprehensive elements of CDP (refer Fig. 2) have been grouped across 4 categories (urban planning approach; resource mobilization; sustainability guidelines; infrastructure planning) to analyze the inclusion of sustainability indicators in city-level

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Fig. 2 Elements of city development plans as per URDPFI guidelines in India (Source by author)

plans. The relevance of this comparison is based on literature perusal and author’s understanding of the process and may differ slightly in their contextual adoption for the planning of a specific city. Thus, the results discussed further should not be interpreted as a precise mapping of everything that the URDPFI guidelines and MoHUA reports have to offer. The evaluation of the interdependency between sustainability targets of 3 indices mentioned in Fig. 1 and Indian CDP components mentioned in Fig. 2, reveal low synergies of sustainability indicators (displayed in shades of red) outweighing their involvement (displayed in shades of green) in urban planning components of the cities. For sustainable city development strategies in India, the mapping corroborates that urban planning approach of CDPs hardly synergize the indicators of SDG 7(Affordable and clean energy); SDG 12(Responsible Consumption and Production); SDG 13(Climate Action); SDG 14(Life below Water); SDG 15(Life on Land) under sustainability index. Aspects of CDP under this category like identification of project needs, evolving innovative planning concepts for cities, and developing parameters for plan evaluation, which measure the balance of present growth of the city v/s planned future growth, should be necessitated for an inclusive adherence of sustainability targets at city level. Looking at the inefficient linkages of city plan enforcement, its effectiveness, amount of public participation with CDP elements (refer Fig. 3), the potential for a conspicuous involvement of these indicators seems to lie once again in the urban planning approach of plan preparation. An undeviating link that is to be deemed most important is the identification of the projects with the appropriate amount of participation, for its effective implementation in future. Then and only then, the localization of sustainability targets can be moved forward in a conceivable way for the city planning. Another major category of CDP falling back in localizing sustainability targets is the resource mobilization. The results in Fig. 3

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Fig. 3 Localization of sustainability targets through CDP components*: Dark green cells indicate more than 75 − 100% of synergies between CDP elements and sustainability indicators and can be represented as highly inclusive of promoting SUD; light green cells indicate 50 − 75% synergies; light red indicates 25 − 50% synergies and dark red indicates 0 − 25% synergies and can be represented as highly exclusive of SUD strategies. * For 16 elements under 4 major components of CDP, refer Fig. 2. *SDGs 2,3,4,5,10,16,17 are excluded from list due to qualitative nature of indicators covered in welfare schemes of GoI. On similar lines, education category from EoL index and MPI have not been included in the table

indicate that the CDP elements (such as efficient mobilization of fiscal resources, proper land development, utilizing PPP), are neglected in enhancing the city potential of promoting identity & culture, public open spaces, safety & security in the assessment of EoL index. A similar disintegration can be observed along the SDG 7(Affordable and clean energy); SDG 8 (Decent work and economic growth); SDG 12 (Responsible Consumption and Production); SDG 14(Life below Water); and SDG 15(Life on Land), promoting insufficient coverage of indicators in resource mobilization. Therefore, this component of CDP remains a critical concern for SUD of cities in either elements of land development or fiscal resources, with its distraught inclination towards SDGs. The sustainability guidelines for city plan preparation propose climate change and mitigation; energy efficiency and environmental planning strategies as retrospection to disaster management strategies in India. Efforts are seen in the localization of SDG 12, SDG 7 and physical infrastructure indicators of sustainability.

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However, these guidelines fail to synergize with the effective usage of digital technology and fiscal management in climate adaptation, to prepare future-ready cities. These visible underutilizations of synergies restrict the probable achievement of climate change strategies incorporated in CDPs. Infrastructure planning component across CDP as well requires dormant localization of sustainability indicators, for a secured and sustainable environment of a city. The dominant synergies in analyzing the sustainability move of CDP components can be highlighted in the indicator categories of transportation and mobility, SWM, water management. Their fair incorporation in preparing sustainable plans for cities, emphasizes the importance of interrelated elements of sustainability guidelines and resource mobilization. For instance, compact city or retrofitting enables energy-efficient planning but is highly dependent upon the project life cycles, and guided and inclusive land development laid under URDPFI guidelines for CDP preparation. Thus, more such interdependent synergies can also be explored with mapping of these linkages and establishing clear denominators for achieving sustainability at the city level. The dominant synergies in analyzing sustainability move of CDP components can be highlighted in the indicator categories of transportation and mobility, SWM, water management. Their fair incorporation in preparing sustainable plans for cities, emphasizes the importance of interrelated elements of sustainability guidelines and resource mobilization. For instance, compact city or retrofitting enables energy-efficient planning but is highly dependent upon the project life cycles, guided and inclusive land development laid under URDPFI guidelines for CDP preparation. Thus, more such interdependent synergies can also be explored with mapping of these linkages and establish clear denominators for achieving sustainability at the city level.

3 Conclusion All SDGs are to be seen as “interacting cogwheels” (Pradhan et al. 2017) collaborating the social, economic, and environmental growth of the urban areas, to achieve Agenda 2030. As Indian cities take a step forward in their city planning, a balanced approach to achieve sustainability can only come across from an inclusive and informed handling of critical issues of cities (Nilsson et al. 2018). Promotion of city planning elements as key drivers of SDG implementation in any CDP preparation, may further enable a fair assessment of the cities through existing SA indices. Both the approaches can be thus seen as valuable and feasible: centralized thresholds for measurement of city performance, adopted with a more context-sensitive approach (Koch and Krellenberg 2018). The study indicates that weak synergies of sustainability indicators and local planning elements, make the SDG translation at the local level ineffective. Without incorporating SDG 14(life below water) and SDG 15(life on land) in the urban planning approach of CDP preparation, Indian cities are unlikely to achieve climate action targets. This further weakens the environmental pillar of sustainability to a greater extent. Such distortion between CDP components and SDG targets are required to be enhanced promptly. Environmental policies

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concerning these goals should become an inevitable part of the plans for each small and big Indian city. The ecological dimension balanced along with economic front, remains a key to sensitize the dynamics of urbanization. The understanding of local challenges and mitigating negative results in different urban morphologies account for SDG accomplishment once contextual objectives are clear to us. The study ensues that envisaging the goals with the inclusion of physical planning aspects of CDP, may effectively help in contextual adoption of sustainability indicators and lead to more healthy, livable cities. A defined quantitative approach to measure SDGs can only be achieved once the cites understand the rules of entering this competition by leveraging synergies of sustainability indicators and city planning components (Costanza et al. 2016). Sustainable city planning is an experiment and transitioning of goals at local level is a tangible way to contribute to this process in long run. Therefore, suitable policy measures and considering the indicators not as definitve benchmarks for city performance, can help the cities to adopt their own definition of sustainability and achieve the Agenda beyond 2030.

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Water Criteria Evaluation for Drinking Purposes in Mahanadi River Basin, Odisha Abhijeet Das

Abstract Water is a vital, finite resource whose quantity and quality are deteriorating as the world population increases. For agriculture, industry, and the needs of people and wildlife, rivers are essential. Weighted Arithmetic (WA) Water Quality Index (WQI), Canadian Council of Ministers of the Environment (CCME) WQI, and multivariate approaches are used in this work to examine the quality of the surface water in the Mahanadi Basin, Odisha, for the 2019–2022 timeframe. All physicalchemical parameter findings were compared to the ranges or values specified by WHO guidelines. The geographic distribution maps were created in ArcGIS 10.5 using the Inverted Distance Weighted (IDW) interpolation method to understand the changing behavior of parameters in surface water. For effective management of surface water quality, multivariate techniques like Principal Component Analysis (PCA) and Cluster Analysis (CA) were used to evaluate the water conditions. The findings show that 10.53 % (CCME WQI) and 15.8 % (WA WQI) of the samples depict extremely poor and poor water quality. Twenty surface water metrics and 19 sampling locations consist of three major clusters with related surface water properties, were investigated using dendrogram plots, for identification of elemental associations. The PCA creates the five primary components, which strongly influence the hydro-chemistry of river water by 93.915%. The results of the combined analysis of the two–water quality (WQ) indices with GIS, CA, and PCA indicate that the majority of the samples come from the areas ST–8 (Cuttack D/s), ST–9 (Paradeep), and ST–19 (Choudwar D/s) and are categorized as poor to extremely poor. Numerous human-made activities, organic waste, as well as the outflow of sewage wastewater, were shown to be the principal reasons for the deterioration in water quality. According to the current study, surface water in the region with poor water quality needs to be treated before being consumed. This study provides insight into fundamental processes that are significant for the sustainable management of surface water resources. A. Das (B) Research Scholar, Department of Civil Engineering, C.V. Raman Global University (C.V.R.G.U), Bhubaneswar, Odisha 752054, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_20

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Keywords Mahanadi basin · WQI · Interpolation method · Principal component · Cluster · Sewage · Human-made

1 Introduction Rivers being one of the primary freshwater supplies used by people for irrigation, industry, and daily necessities (Sunil et al. 2010; Meshram et al. 2020a). Water bodies are considerably contaminated by the uncontrolled dumping of toxic mining and industrial wastes and agricultural runoff operations (Adomako et al. 2007; Ram et al. 2021). However, the influence including both natural and anthropogenic elements has caused the quality of the world’s water to drastically degrade for decades (Vadde et al. 2018). Urban planners and administrators now have severe concerns about the quality of the surface water (Das 2022). Overuse, agribusiness, and poor illegal dumping are all factors that could endanger the town’s waterways (Bam et al. 2011). The origin of fertilizers as well as other pollutants in surface water has been investigated using statistical techniques including descriptive statistics and visualization (Hounslow 1995). The simple and scientific Water Quality Index (WQI) gives broad assessments of water quality and prospective trends (Kaurish and Younos 2007). The most often used indexing method for evaluating the quality of surface water for diverse uses is the Weighted Arithmetic Water Quality Index (WA WQI), which integrates all the factors and compares them to the guidelines set by the government to protect public health (Nong et al. 2020). Horton (1965) created this, in which multiplicative variables were employed with arithmetic weighting (Lkr et al. 2020; Islam and Mostafa 2021f). The water quality index was evaluated using the widely used and widely acknowledged computer model known as the Canadian Council of Ministers of the Environment-Water Quality Index (CCME WQI) (CCME 2001). Even when the same objectives and variables are employed across sites, the index can indicate relative disparities in water quality (Bharti and Katyal 2011). Even though these indices show the quality of the water, the general people cannot visualize the situation due to the numbers. As a result, using a geographic information system (GIS) to integrate the data set can aid in visualizing the current state of the water quality. GIS is frequently used to gather a variety of spatial data, perform overlay analysis in the spatial register domain, and describe phenomena that vary in space (Srivastava et al. 2011; Saha and Paul 2019a). A method called Inverted Distance Weighted (IDW) is used to calculate values within measurements or spatially interpolate datasets. It is utilized for spatial modelling, nevertheless, and each value calculated an average that is weighted of the nearby sampling locations (Burrough and McDonnell 1998). The government and policymakers can better regulate water pollution by identifying the sources of the contamination through various statistical techniques, such as Cluster analysis (CA) and Principal component analysis (PCA) (Bhatti et al. 2019; de Andrade Costa et al. 2020). Data are grouped using the CA approach into clusters or classes. The most popular method is hierarchical agglomerative clustering (HCA), which shows how any sample and the full dataset are intuitively comparable

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using a dendrogram or tree diagram as an illustration (McKenna 2003). A prominent multivariate statistical technique frequently employed in studies, i.e., PCA, which combines a variety of associated and independent variables (Singh et al. 2004; Le et al. 2017). The principal component method, which reduces correlating a vast amount of data into a smaller subset of correlated values, is how the environ metrics produce the most insightful information (Shrestha and Kazama 2007). There are only a few studies on the Mahanadi River in Odisha that have aided the local government in the management of the water supply (Chakrapani and Subramanian 1993; Das 2022). A substantial data matrix gathered over a three-year (2019–2022) surveillance period at 19 separate sites for 20 water quality factors. In light of these considerations, WA WQI and CCME WQI are only occasionally used in conjunction with GIS to assess water quality. The goal of this current work is applied using integrated methodologies to examine the drinking quality in the aforementioned river stretch while keeping the importance and necessity in mind. The delineation of the pollution potential zones has also been attempted by utilizing multivariate statistical analysis techniques like PCA and CA.

2 Study Area The Mahanadi River system is the largest river in the state of Odisha and the third largest river system in all of India’s peninsula. The watershed lies in between 19°21 and 23°35’N latitude and longitude value varied from 80°30 to 86°50’E. It carries an area of 141600 km2 , or about 4%, of total land area of the country. It begins in Chhattisgarh’s Dhamtari area at a height of around 442 m, presents above the mean sea level, and finally drains into Bay of Bengal. The river has a length of 851 km accompanying some of the tributaries namely Seonath, Bhadar, Jonk, Hasdeo, Mand, Ib, Ong, and Tel. It generally collects effluents from many industrial and urban areas along its course. At Sambalpur, Cuttack, and Paradip, where industrial expansion and sewage discharges are widespread, human influences are noticeable (Das 2022). The average annual rainfall is about 142 cm, with 90% of that falling during the southwest monsoon, primarily from June till September, with sporadic high rainfall thunderstorms with cyclones (Panda et al. 2013). The basin is divided into three sub-basins namely the Upper (21.34%), Middle (37.16%) and Lower Mahanadi (41.5%). One of the most prevalent types of soil is mixed red and black soils and red and yellow soils (laterite soils). The principal land use practices in the basin include farming, forestry, quarrying, and urban areas. The river is a significant source of fisheries and agricultural irrigation in the Indian state of Odisha. Average annual temperature in the basin varied between 15.8 and 28°C, the catchment supports a tropical monsoon climate, which predominates throughout the river basin. The study region and the sites where the Mahanadi River’s water quality was being monitored are depicted in Fig. 1.

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Fig. 1 Description of the study area

3 Methodology 3.1 Materials The chemicals applied in this study seem to be from Merck in India and were of analytical grade. Water that has been twice distilled was used for the entire investigation. In order to develop a network of sample stations with enough historical data to build a solid statistical database of the researched characteristics, considering a proper spatial distribution on the river, the sampling locations were overlaid with the river’s map in ArcGIS (Das 2022). The study has taken into account the quality assurance and quality control (QA/QC) process of the data. About 500 cc of the volume of samples were specifically isolated and examined in the lab to assure QA/QC procedures. Charge balance errors and samples with an error of less than 5% have been used to validate the accuracy of the chemical analysis.

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3.2 Dataset Preparation Twenty water samples (Table 1) were collected from 19 sites along the Mahanadi River system from the Hirakud reservoir to the estuary points, i.e., at Paradip, from 2019 to 2022. These samples were provided by CWC and used in the current study. From 10:00 am to 12:00 pm, samples were collected. All of the water samples were taken in around three days in 500 ml containers that had been preconditioned by being first washed with 5% nitric acid and then repeatedly rinsed with distilled water. Due to their susceptibility to contamination, water samples were chosen, collected in polyethylene bottles, sent to the lab, and examined within 48 hours. Through GPS, a global reference was obtained for each place. The ionic balance error was calculated, taking the link between total cations (Hardness, B+ , Fe++ ) and the total ions (Cl– , SO4 2– , F– , NO3 – ) with each set of comprehensive analysis of water samples. At every place along the river, the water quality was analysed using standardized techniques for examining water and wastewater (APHA 2017). With the aid of XLSTAT (2015), Table 1 Materials and methods adopted for present research work (All units are in mg/l except pH (unitless) and EC in µs/cm

Parameters

Materials and methods

pH

pH metre (Systronics MK VI)

Dissolved Oxygen (DO)

Winkler Azide phenomenon

Biochemical Oxygen Demand (BOD)

Non seeded dilution

Coliform

MPN Coliform test/Incubator

Total Suspended Solids (TSS)

TSS Conductivity metre

Total Alkalinity

H2 SO4 titrimetric

Chemical Oxygen Demand (TA/Al)

Potassium Dichromate process

Ammoniacal Nitrogen (NH3 –N)

Spectrophotometric

Free Ammonia (Free NH3)

Spectrophotometric

Total Kjeldahl Nitrogen (TKN) Spectrophotometric Electrical Conductivity (EC)

EC-metre

Total Dissolved Solids (TDS)

TDS-metre

Boron (B)

Flame photometer

Hardness

EDTA Titrimetric

Chloride (Cl– )

Titrimetric

Sulphate (SO4 2– )

Spectrophotometric

Fluoride

(F– )

Spectrophotometric

Nitrate (NO3– )

Spectrophotometric (Dionex ICS–90)

Iron (Fe2+ )

Flame photometer

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MATLAB (2016), and Microsoft Excel (2013), statistical and computational studies were carried out. In the present work, WQI is taken into consideration, which signifies a unique and helpful mechanism for monitoring how effectively water is treated (Kansal 2011). In this study, we took into account the WQI calculation guidelines suggested by WHO (2011). This was calculated by weighted arithmetic (WA) index method. This determination aids in determining whether waterways are suitable for its drinking were selected needs (Chauhan et al. 2010; Tiwari and Mishra 1985). All parametersΣ n W i ∗ Qi, for the computation. WQI was determined using the formula: WQI = i=1 where Wi is referred to as the ith parameter’s weighting factor, while Qi is referred to as the parameter’s quality rating. The following equation can be used to calculate the Wi , i.e., Wi = K/ Si with K, the proportionality constant. Here Si represents standard values of World Health Organization (2011). Computed weightage scores can be found in Table 2. Quality rating (Qi ) was computed by using the equation: Qi = {(Vactual –Videal )/(Vstandard –Videal )} * 100, where Qi –Quality rating of ith variable for a sum of i; Vactual = value generated from laboratory analysis; Videal = Value Table 2 Water quality parameters, their standard values, their ideal values and the assigned weighting factors Parameter

Standard value (Vstandard )

Ideal value (Videal )

1/Si

Assigned Weighting factor, (Wi )

pH

8.50

7

0.117647059

0.022573488

DO

6.00

14.6

0.166666667

0.031979108

BOD

5.00

0

0.2

0.03837493

TC

5000.00

0

0.0002

3.83749E–05

TSS

1000.00

0

0.001

0.000191875

200.00

0

0.005

0.000959373

20.00

0

0.05

0.009593732

NH3 –N

2.00

0

0.5

0.095937324

Free NH3

2.00

0

0.5

0.095937324

TKN

5.00

0

0.2

0.03837493

1500.00

0

0.000666667

0.000127916

10.00

0

0.1

0.019187465

0.50

0

2

0.383749296

TDS

500.00

0

0.002

0.000383749

TH

200.00

0

0.005

0.000959373

Cl–

250.00

0

0.004

0.000767499

SO4 2–

250.00

0

0.004

0.000767499

Alkalinity COD

EC SAR B

F

1.50

0

0.666666667

0.127916432

NO3 –

45.00

0

0.022222222

0.004263881

Fe2+

1.50

0

0.666666667

0.127916432

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taken from standard tables, Videal for pH = 7, DO = 14.6 and for other variables, it is taken as zero. Vstandard = The required drinking water standard (WHO 2011). The grade of water is computed from WQI values, which is categorized under excellent (0–25), good (26–50), poor (51–75), extremely poor (76–100), or unsuitable (>100) as per Tiwari and Mishra (1985). At first, the Canadian Water Quality Index (CCME WQI) was developed (CWQI). It contains three components and plenty of supporting evidence (CCME 2001). The following expression can be used to calculate index values: CCME WQI = 100 – [({F12 + F22 + F32 }0.5 )/1.732] where scope (F1) = There are numerous elements whose targets are not fulfilled. , F1 = (Quantity including failed variables/ Sum of variables)*100, Frequency (F2) = The frequency with which the expectations are not realized, F2 = (Number of failed tests/ Sum total of all tests) *100, Amplitude (F3) = By how much the objectives fall short of being met, ExcursioniΣ= (Failed test valuei / Objectivej ) – 1, sum of Normalized n excur sioni/ Number of tests. F3 = (NSE/0.01NSE + Excursions (NSE) = i=1 0.01). The index has been standardized using a range of 0 to 100 and a value of 1.732. Be advised that a score of zero (zero) indicates very poor water quality, whereas values from one (1) to one hundred (100) indicate high water reliability. The five categories used to classify the water quality are Poor (numbers 0−44), Marginal (values 45–59), Fair (values 60–79), Good (values 80–94), and Excellent (values 95– 100). Powerful and effective statistical techniques for determining how significantly associated variables are correlated, including correlation analysis (Nath et al. 2021). Using MATLAB (2016), the coefficient between measured water quality parameters calculated to establish the possible source of river water contamination at various places (Hameed et al., 2010). Values between −1 and +1 show the strength of the linear relationship between the two variables. There are some positive coefficients, signifying a resemblance going in the same direction, while a few are unfavourable, signifying inconsistency. A coefficient with a value of +1 indicates a perfect positive link, whereas a value of 0 indicates no relationship at all between correlated variables. When creating a cluster, Cluster Analysis (CA) is used to identify objects that are related to one another but distinct from those in other clusters (McKenna 2003). Using linkages and differences as the basis for cluster evaluation, hierarchical cluster analysis (HCA) is used to create the most cost-effective possible spatial surveillance network in the future (Ragno et al. 2007). It described the connection mechanism and the range of similarity quantities among clusters (Bratchell 1989). When comparing groups, the Euclidean distance is the primary factor to consider (Brereton 2007). The dominating ions present in the research area were used to identify three unique clusters or groups from the data shown as dendrograms (Shrestha and Kazama, 2007). In order to determine the natural correlations and to observe their characteristics, Principal Component Analysis (PCA), which is dependent on the correlation matrix, has been implemented (Barakat et al. 2015). The weightages were categorized as strong (1−0.75), moderate (0.75−0.50), and weak (0.50−0.30) as per Azid et al. (2015). Substantial positively and negatively loadings highlight to key parameters for the given source of contamination (Burstyn 2004). Several PCs were used to analyse the underlying data structure (Helena et al. 2000). To categorize the number

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of PCs necessary to comprehend the underlying data structure, the scree plot is employed (Liu et al. 2003).

4 Results and Discussion The World Health Organization’s (WHO 2011) guidelines for drinking water have been used as a guide for this investigation. Table 3 displays the summary analysis for every parameter taken into account during the study. The quantity of organic molecules, water temperature, and aquatic plants’ photosynthetic activity all have a significant impact on pH (Nienie et al. 2017). According to WHO (2011), the pH range is from 6.5 to 8.5. However, pH in the study, showed value from 7.74−7.92, indicating all the samples showed a minor alkalinity, which may have been characterized by increased in warmth and photosynthetic capacity (Rostom et al. 2017). DO must be more than 6 mg/l which offers a quite healthy water body that guarantees better water quality (WHO 2011). The readings varied between 7.26 and 7.83 mg/l, and it consistently reported the ideal DO level. The amount of oxygen needed to stabilize household and commercial waste is determined by BOD. According to Table 3 Range of physicochemical results during the study period

Parameters

Range

Standard Deviation (SD)

pH

7.74−7.92

0.05

DO

7.26−7.83

0.14

BOD

1.05−2.4

0.34

TC

1212.4−42529.20

TSS

28.63−74.90

9193.97 11.56

Alkalinity

70.40−100.90

8.24

COD

6.76−21.88

3.96

NH3 −N

0.51−1.93

0.31

Free NH3

0.02−0.06

0.01

TKN

3.28−11.80

EC

138.10−7779.35

SAR

0.41−16.59

2.07 1743.33 3.69

B

0.03−0.55

TDS

82.30−13230.60

3007.19

TH

0.12

51.20−2195.20

486.60

Cl−

9.65−4904.91

1122.58

SO4 2−

4.97−376.07

F−

0.26−1

NO3 Fe2+

−

84.68 0.17

1.29−2.70

0.41

0.60−2.61

0.46

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Patel et al. (1983), a greater level denotes a higher level of organic contamination in a water sample. This study’s observations of BOD ranged between 1.05 and 2.4 mg/l. The permitted thresholds established by the WHO (5 mg/l) are met by all samples. TC is currently employed as a sign of bacterial contamination. In rural places, poor sanitation increases health risks, and persistently tainted water might result in catastrophic illnesses that will particularly harm new born babies (Suthar et al. 2009). The TC count during the research time frame varied from 1212.4−42529.3 per 100 ml. According to (WHO, 2011) guidelines, the value of TC (permissible for drinking) should be less than 5000. Except ST–8, ST−9, and ST−19, the results showed that all stations are within the permitted limits. Due to the proximity of industrial or sewage discharges, ST−8 and ST−9 have higher coliform levels than the rest of the sites (Kumar and Puri 2012). A higher TSS implies that tiny particles, particularly those from city sewage and urban runoff, are present in the river water (Rehman et al. 2013). In this investigation, the results varied from 28.63 to 74.90 mg/l. 100 mg/l is the permitted limit (WHO 2011). However, the drinking water criterion was met at every location. Due to the numerous hydroxides, bicarbonates, and the ability of an aqueous solution to neutralize an acid, water contains alkalinity (TA) (Sundar and Saseetharan 2008). All locations in the current investigation met the WHO requirements (200 mg/l), with TA values ranging from 70.40 to 100.90 mg/l. With the use of a powerful chemical oxidant, COD calculates the O2 amount, which is necessary for the chemical oxidation of organic matter and oxidizable inorganic compounds (Sawyer and McCarty 2003). The findings in the current study range from 6.76 to 21.88 mg/l, which is significantly below the 30 mg/l limit. The occurrence of NH3– N in water often denotes an incomplete process for the degradation of organic material and is considered to be an excellent sign of contamination from sewage from cities (Abdulwahid et al. 2013). The range of the readings was 0.51 to 1.93 mg/l. The preferred level is 2 mg/l. Biodegradation of organic waste, comprised of contributions from home, industrial, agricultural, as well as faeces contamination or sources, is the results of Free NH3 origin in water. The value in the current study ranged from 0.02−0.06 mg/l, which is less than the WHO-recommended limit of 2 mg/l. Due to the widespread use of NPK (Nitrogen-Phosphorous-Potassium) fertilizers in agricultural areas, surface runoff from those farms contributes nitrogen to the surface water. Between 3.28 and 11.80 mg/l, TKN was found. The findings clearly reveal that majority of the areas had high concentrations over the recommended WHO limit of 5 mg/l. Increasing scores are likely related to direct industrial and urban river discharges as well as intensive agricultural activity (Capone et al. 2004). High EC water reduces crop output by creating physiological circumstances that are similar to drought (Joshi et al. 2017). With the exception of ST–9, EC values varying from 138.10 to 7779.35 µS/cm, which is well under the WHO guideline (2250 µS/cm). High ST–9 levels may be connected to the amount of organic matter present and are most likely the result of waste discharges, residential wastewater, and soil leaching (Hossain et al. 2012). A method called SAR is used to measure the fraction of Na+ divided by Ca2+ along with Mg2+ ions and it helps in the propensity of water used for irrigation to trigger a reaction of cation exchange in the soil (Ravikumar et al. 2010). SAR

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A. Das

values were calculated in the current investigation and ranged from 0.41 to 16.59 mg/l. The preferred SAR limit is 10 mg/l. Highest value observed in ST–9 causes soil permeability to decrease, soil alkalinity to rise, and plant water availability to decrease (Singh et al. 2016). Entering of wastewater in contact with washing chemicals including borate compounds or leaching from underlying sediment or rocks also can be blamed for the presence of boron (B) in surface water (WHO 2011). The readings were in the ideal range defined by the WHO (2 mg/l) and varied from 0.03−0.55. TDS is used to assess dissolved particles directly in water samples (Kamboj et al. 2017). The range of results was 82.30−13230.60 mg/l, while the WHO (2017) acceptable limit is 100 mg/l. The water column in ST–9 has a high TDS load due to increased water mixing with the underlying sediment as a result of faster river flow and more freshwater intake. The quantity of Ca2+ and Mg2+ compounds in the water is indicated by TH. The WHO’s standard for drinking water allows for a maximum hardness of 300 mg/l. In the current investigation, the results ranged from 51.20−2195.20 mg/l. The elevated value of hardness documented in the ST–9 identifies the existence of cations like Ca2+ and Mg2+ that can readily reach into the river and lead to a rise in hardness, may end up causing kidney problems and the formation of stones, and also demonstrates its importance in heart diseases (Mitra, Pal, and Das 2018). During the study period, a relatively small change in Cl– concentration was discovered, starting with 9.65 to 4904.91. Substantial leaching through upper soil layers by industrial activities, Cl– was significant in ST–9 (> 250 mg/l) (Cheong et al. 2012). Gypsum and other common minerals leak sulphate (SO4 2– ) into nearby bodies of water, which may also be added artificially or by rampage use of fertilizers (Hem 1970). 200 mg/l is the typical SO4 2– threshold for drinking water (WHO, 2011). The result in the current study ranged from 4.97−376.07 mg/l. The amount of sulphate in water grows in ST–9 as an outcome of home and commercial waste (Kumar and Puri 2012). As many other ions do, F− migrates with flowing water and is ultimately enhanced by evaporation. In children under the age of 8, it lowers tooth cavities and encourages the development of enamel (Sunitha et al. 2014). During the period of study, the concentration varied from 0.26 to 1 mg/l. The WHO suggests using 1 mg/l. The boundaries encompass all sites. By oxidizing ammonium ions, faeces can also produce NO3 – ions. Despite extensive human activity and severe land development in upstream vicinities (Muhaya et al. 2017b), loads of nitrate in the river water are relatively lower. During the observation period, the range varied from 1.29 to 2.70 mg/l, which fulfils the WHO definition (45 mg/l). The most pressing concern with rural drinking water is Fe2+ (Islam and Mostafa 2021f). The range of the reading, however, was from 0.60−2.61, which falls under the limit of drinking criteria of 3 mg/l. Nearly all samples exceed the (BIS 2003) limit of 1 mg/l. Movement of rainwater that made contact with the soil also raised the Fe2+ value in the surface water (Kumar and Puri 2012). Figure 2a–t displays the spatial distribution of each parameter. The results of these metrics cannot individually decide the level of variation in water quality from the standard drinking criterion between all samples examined above. As a result, this survey was conducted to judge the effectiveness of river water conditions using the WA WQI and CCME WQI. The origins of pollution and

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247

Fig. 2 Spatial variation diagram of a pH b DO c BOD d TC e TSS f TA g COD h NH3 –N i Free NH3 j TKN k EC and l SAR m B n TDS o TH p Cl− q SO4 2− r F− s NO3 − and t Fe2+

248

Fig. 2 (continued)

A. Das

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249

Fig. 2 (continued)

contamination were also recognized using multivariate statistical methods, which led to the suggestion of the necessary remedial steps. A well-known tool to support the drinking water quality is the WA WQI. For easier interpretation, the WQI findings were interpolated using IDW Geo statistics and ArcGIS. The WA WQI varied from 23.78−96.09, indicating categories ranging from good to extremely poor. Three samples belong to excellent category, 13 comes under good category, 2 within the poor class, and finally, 1 sample fell in very poor criteria, as per the results. Figure 3a shows the sample distribution broken down by percentage. The research area’s WA WQI map interpolation is shown in Fig. 4a. The location of ST–9 is under the very poor category, as shown by the map. Similar results were found for the CCME WQI, which indicated an excellent to fair category and ranged from 34.71 to 85.52. The findings show that 3 samples fit into the “excellent” category, 14 samples fit into “good,” 3 samples fit into “fair,” and 2 samples fit into the “bad” category. Figure 3b shows the sample distribution broken down by %. The research area’s CCME WQI map interpolation is shown in Fig. 4b. The map clearly shows that ST–8 and ST–9 are in the poor category. This is because the levels of EC, SAR, TDS, TH, Cl– , SO4 2– ,

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A. Das Water Quality based on WA WQI (%) 10.5

Water Quality based on CCME WQI (%) 10.53

5.3 15.8

15.79 73.68 68.4

Excellent

Good

Poor

Very poor

Good

Fair

Poor

Fig. 3 Water quality expressed in % based on a WA WQI and b CCME WQI

TKN, TC, and DO were greater than WHO requirements and consequently had a negative impact on the CCME WQI values. In comparison to the current research region, the surface water had a high salinity and EC range. According to two indexing methods, massive overflows of river sediments, farming runoff, and the convergence of domestic sewage from multiple point sources are the main causes of contamination (Srivastava et al. 2011). Identical pollution status findings from several analyses were also seen during the observation period (Chakrapani and Subramanian 1993). The Pearson correlation matrix for various parameters is shown in Table 4. The amount of oxygen consumed by microorganisms to break down organic matter was confirmed by the highest positive correlation (0.77) between BOD and TC of water samples, while the highest negative correlation (–0.77) between TC and DO was discovered, indicating the level of pollution in water bodies. The correlation matrix revealed that TC and TKN were more important than other characteristics in all sites for influencing the water quality of the Mahanadi River. The quality of the water at position ST–9 is additionally impacted by additional variables such as TH, Cl− , SO4 2− , EC, TDS, and SAR. Both WA WQI and CCME WQI rated the water quality of the river as excellent to good and good to very bad, respectively, indicating that it has to be treated before consumption. However, unless the sources of the contamination are found, it is not possible to apply the treatment methods. Thus, multivariate statistics were used to identify the level of pollutants. CA was also employed to elucidate the link just between observed variables in the research region as part of an investigation into the impact of natural or human contributions which plays a crucial role for the enrichment of dissolved ions and associated mobility in the river water (Rath et al. 2009). CA, which was carried out using wards linkage and Euclidean distance measure, supports the outcomes of indexing methodologies. The dendrogram (Fig. 5a) shows close linkage between TSS, Alkalinity, pH, DO, BOD, COD, Free NH3 , TKN, NH3 −N, SAR, SO4 2− , F− , B, NO3 − and Fe2+ . These indicators come within first cluster, which signifies geogenic and anthropogenic origins. Contrarily, the TC found in the second group may be caused by high levels of bacterial contamination and nonpoint sources, such as bathing, washing, and animal husbandry (Suthar et al. 2009). The third linkage, which includes anthropogenic sources, percolation, surface runoff,

Water Criteria Evaluation for Drinking Purposes in Mahanadi River …

251

(a)

(b)

Fig. 4 a. Spatial distribution map based on IDW for WA WQI for Mahanadi region, Odisha. b. Spatial distribution map based on IDW for CCME WQI for Mahanadi region, Odisha

0.340

0.660

−0.230 −0.540

0.450 −0.150

TSS

0.530

Free NH3

Fe2+

NO3 −

0.030

0.060 −0.420

0.280

SO4 2−

F

1.000

TSS

0.620

0.060

0.080

0.080

0.080

0.030

0.110

0.080

0.080

0.150

0.310

0.120

0.140

0.170 0.500

0.750 0.020

0.080 0.520

0.300 0.360

0.740 0.450

0.750 0.450

0.740 0.460

0.140 0.120

0.710 0.480

0.740 0.450

0.740 0.460

0.730 0.230

0.040 0.020

0.070 0.060 0.880

1.000

0.970

0.100

0.170

0.250

0.740 0.160

0.090

0.880

0.210 −0.050

0.200 −0.060

0.220 −0.050

0.670

0.330

0.200 −0.070

0.210 −0.060

EC

SAR

B

0.670 0.980 0.970 1.000

0.710 1.000 1.000

0.710 1.000

1.000

TKN

TDS

0.240

−0.050

0.700

−0.150

−0.160

−0.140

TH

Cl−

SO4 2–

0.540 0.680 0.680 0.690 0.170 0.680 0.680 0.680

0.470 0.420 0.410 0.470 0.180 0.420 0.410 0.410

0.140 0.370 0.360 0.510 0.930 0.380 0.370 0.380

0.710 1.000 1.000 0.980 0.040 1.000 1.000 1.000

0.710 1.000 1.000 0.980 0.040 1.000 1.000

0.700 1.000 1.000 0.980 0.050 1.000

0.780 −0.080 0.040 0.030 0.200 1.000

−0.010

−0.160

−0.150

−0.100

1.000

NH3− N Free NH3

0.160 −0.140

0.750

0.720

1.000

Alkalinity COD

0.140 −0.040 1.000

0.250

1.000

TC

0.150 −0.080

0.190 −0.200 −0.050

−0.100 −0.260

0.120

0.020

0.040 −0.420

Cl−

0.010

0.020

0.030

0.140

0.160

0.060 −0.420

TDS

TH

0.020

0.070

0.050 −0.420

0.100 −0.410

0.030

0.050 −0.420

EC

SAR

0.130

−0.180 −0.540

TKN

B

0.390

0.080

0.130

0.570 −0.390

0.290

COD

NH3 −N

0.150

0.040

0.360 −0.770

TC

Alkalinity

1.000

0.770

0.490 −0.590

BOD

1.000

1.000

BOD

0.050

DO

pH

pH

DO

Variables

Table 4 Matrix of pearson’s correlation between the variables NO3 −

0.360 0.010

0.330 1.000

1.000

F

1.000

Fe2+

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industrial and household activities, and mineralization, derives EC, TDS, TH, and Cl− from weathering processes. A dendrogram view of the sampling site cluster analysis is shown in Fig. 5b. Three clusters are produced by CA using 19 sampling sites. 17 sampling sites make up Cluster 1, which is aggregated and depicted in the dendrogram. Since all 17 sites have high-quality water, the area is thought to be minimal in pollution. Cluster 2 is made up of 1 station i.e., Cuttack D/s. It represents poor quality water due to the presence of high TC, TKN, and Fe2+ , so it is termed as moderately polluted zone. Cluster 3 is comprised of 1 sampling station i.e., ST–9. This cluster is known as high pollution zone due to the presence of higher value parameters like EC, SAR, TDS, TH, Cl, SO4 2– , TKN, Fe2+ , and TC. The overall CA results show that anthropogenic activities, leaching, and dissolution together control the chemistry of the research area’s surface waters. To further limit the contribution of factors with weaker importance, PCA of the normalized variables was also carried out in the current study in order to extract significant principal components (PCs). The scree plot used to display the original average data set’s variability, which depicts first 5 PCs account for 93.915 % of the total variance. Table 5 shows the observed factors’ weight values in relation to each major component. Five principal components (PCs) were identified as being significant based on the eigen value criteria since their eigen values are larger than 1. Figure 6 displays the factor weightages for all PCs, eigenvalues (Ev), and cumulative variance. A diminish in slope is seen after the fifth eigen component, according to the scree plot. The importance of loadings is clearly defined by a high eigen value. It was determined that the variables TH, SO4 2− , EC, TDS, Cl− , SAR, B, TSS, and TKN had a positive factor weight, that enables the development of PC−1, which is associated with 47.68% of the variability. These loadings defined minerals and nutrients play a huge role which arises on account of anthropogenic impact, particularly from biological debris, fertilizers, and agrochemicals. On account of large proportion in variance, natural processes like breakdown of soil components are the main factors that propel the migration of the dissolved ions. The second component PC−2 is attributed to COD, Free NH3 , TC, and NH3 −N, with a variance of 20.4%. This component is associated with increased human pressure on rivers, an indication of flooded organic matter. The PC−3 was associated with a positive weight with DO, TC, F− and negative loaded with BOD and TSS, with variance of 11.186%. The higher concentration may have been caused by photosynthesis, turbulence of water bodies, and a decline in temperature (Bouslah et al. 2017). The fourth PC (PC−4) had a substantial positive score for TA and a moderate loading accompanying NO3 − , accounting for 8.67 % of the total variance. This results from autotrophic bacteria oxidizing ammonia in sewage, agricultural runoff, and industrial effluents (Indirani 2010). Finally, 5th PC shows moderately positive (pH) and negative (NO3 − ) loading, showing anthropogenic input to surface water via leaching of fertilizer from agricultural land, accounting for 5.96% of the overall variance (Mahlknecht et al. 2004). The factor scores have also been evaluated to determine the zone of the factor’s representation and the spatial variation of the factor depiction (Srinivasamoorthy et al. 2008). The factor scores in the experiment are computed using the regression approach. ArcGIS 10.5 used the factor score values for every sample location to

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Fig. 5 Cluster analysis of a 20 physicochemical parameters and b 19 sampling locations

illustrate the spatial variance (Fig. 7). All five components together identified the additional sources that contributed to the variation, including mineral components, anthropogenically produced household and industrial waste, natural processes (such as runoff and soil erosion), home and municipal sewage, polluted wastewater, and waste from industrial, mining, and fishing operations.

5 Conclusion The current study used an integrated method of various water quality indices, including WA WQI and CCME with GIS, CA, and PCA, to provide an overview regarding the state of the water quality of River Mahanadi, Odisha. Because of its minor alkaline composition, the river’s water promotes the growth of phytoplankton. All locations with higher TC and TKN levels were found to have altered water quality. DO has been identified as a factor that positively affects water quality. The results of various indexing techniques, including WA WQI, demonstrates that 84.21 % of locations comes under excellent to good category and CCME WQI also suggests 73.68% of samples are of good quality. With the exception of a few areas like ST−8, ST−9 and ST−19 that require treatment, it is evident from the results of both indexing approaches, which suggests, most of the water comes under good class and, however, fit for drinking. It was discovered that ST–8 (TC), ST–9 (EC, SAR, TDS, TKN, TH, Cl, SO4 2– , TKN, Fe2+ , TC), and ST–19 (TC), which are the main monitoring stations having more influence on water quality than other stations, have exhibits of bad or very poor status. Therefore, it appears that abrupt changes in water quality in ST−9,

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Table 5 Results of multivariate principal component loadings for the physicochemical parameters Parameters

PC 1

PC 2

PC 3

PC 4

PC 5

pH

0.097

0.626

0.022

0.301

0.654

DO

−0.515

−0.192

0.748

0.164

0.238

BOD

0.157

0.669

−0.615

0.147

0.025

TC

0.212

0.580

−0.706

−0.194

0.102

TSS

0.782

−0.097

−0.078

−0.560

−0.084

Alkalinity

0.478

0.279

−0.005

0.761

0.072

COD

0.367

0.914

−0.024

0.067

−0.060

NH3 −N

0.066

0.774

0.549

−0.218

−0.172

Free NH3

−0.025

0.877

0.226

−0.317

−0.137

TKN

0.763

−0.150

−0.202

−0.139

−0.300

EC

0.978

−0.166

0.045

0.053

0.074

SAR

0.977

−0.176

0.043

0.039

0.069

B

0.977

−0.031

0.130

0.051

0.021

TDS

0.978

−0.169

0.047

0.046

0.074

TH

0.979

−0.154

0.051

0.058

0.074

Cl−

0.977

−0.172

0.046

0.044

0.072

−0.162

0.048

0.047

0.079

0.470

0.622

0.581

−0.042

−0.158

NO3 −

0.466

0.051

0.060

0.522

−0.614

Fe2+

0.709

−0.006

0.174

−0.450

0.330

Eigenvalue

9.538

4.081

2.237

1.734

1.192

Variability (%)

47.689

20.407

11.186

8.670

5.962

Cumulative %

47.689

68.096

79.283

87.953

93.915

Eigenvalue

12

100

10

80

8 60

6 40

4 20

2 0

0

PC PC PC PC PC PC PC PC PC PC PC 1 2 3 4 5 6 7 8 9 10 11

Components

Fig. 6 Scree plot and loadings of various PCs

Cumulative variability (%)

0.978

F

SO4

2−

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Fig. 7 The spatial distribution map of all factor scores

ST−10, and ST−19 have a cascade effect on both the ecosystem’s health and the lives of people whose survival depends directly on the river. Association analysis within the study region demonstrates a high and positive correlation between BOD and TC. TKN is strongly associated with TH, Cl– , SO4 2– , EC, and SAR. Additionally, a combined strategy of several WQIs utilizing GIS that identifies the most typical sites that are suitable for or unsuitable for human intake in accordance with various indexing occurrences. The results show that samples from stations ST−8, ST−9, and ST−19 fall into the category of low/very poor. CA backs up the outcomes of indexing and correlation matrix techniques. PCA identified five elements that could be involved in biological pollution, livestock manure, runoff from rains, and deterioration of riverside. With the integration of biological entities, the WQI values derived in this study could be closely monitored across greater spatial and temporal scales. The WQI values could then be modelled using deep learning techniques, including artificial intelligence, to reduce water pollution. From all these, it can be concluded that the study is very much useful for planning and management of the available resources in this river basin. Acknowledgements The Department of Civil Engineering at C.V. Raman Global University (C.G.U), Bhubaneswar, Odisha, India, is gratefully acknowledged by the author for providing all the necessary resources for the completion of this work. Moreover, the author also wish to thank State Pollution Control Board (SPCB), Odisha for providing necessary data during research. The author would like to thank the Chief Editor and the anonymous reviewers for their insightful criticism and helpful ideas.

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Declaration of Competing interest The author affirms that they have no known financial conflicts of interest or close personal ties that would have appeared to have an impact on the work disclosed in this publication.

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Estimation of Earth Temperature Profiles for Different Soils and Soil Conditions Shiv Lal

Abstract The thermal conductivity of the soil is responsible for the surface temperature penetration inside the earth. At a certain depth from the earth surface, a constant temperature of the earth is reported by various researchers. So in this communication, thermal conductivity of various soils like dry soil, ordinary soil, dry loamy soil, dry river base sand, organic soil, moist river base sand have been measured in the laboratory. It is generally found above 3 m depth. Based on thermal conductivity and diffusivity, a MATLAB program has been prepared and simulated to assess the depth of undisturbed earth temperature. This temperature is found approximately constant for whole year, and it can be used in the Earth air tunnel heat exchanger (EATHE). The EATHE is an effective energy-saving technology for heating and cooling of buildings in different weather conditions. The results are innovative, and the study is very fruitful for the designers and researchers of EATHE, sustainable energy, zero energy emission and energy conservation field. Keywords Earth temperature profile · EATHE · Soil thermal conductivity etc.

1 Introduction The highest energy-demanding sector is the building sector in the present scenario due to the improvement in the living standard and comfort. The energy demand in building sector is increasing in developing countries like India in present time. The buildings sector’s energy consumption is observed around 40% approximately of global energy use. The indoor thermal comforts are mainly provided by the fossil fuels, which account in the order of 28% of the total global energy consumption (Lal et al. 2012; Lal 2022a). This demand is realizing indoor thermal comfort which being increased significantly in recent scenario due to economic and infrastructure growth for developing new living standards. This demand may increase the greenhouse gas S. Lal (B) Department of Mechanical Engineering, Rajasthan Technical University, Kota, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_21

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(GHG) emissions and environmental pollution. The fourth IPCC was reported that the GHG emissions in the environment increased by an average increment of 1.6% per year and CO2 emissions at a rate of 1.9% per year (Fourth Assessment Report of the Intergovernmental Panel on Climate Change: Climate Change 2007). The passive thermal system concepts can provide indoor thermal comfort and it reduces the energy consumption in the buildings. By using this option, GHG emissions can also be reduced (Labs 1989). The passive concepts for cooling or heating are used to provide indoor thermal comfort by using natural sources of thermal energy. Direct or indirect coupling of buildings with the earth is one of the passive concepts for achieving indoor thermal comfort and one of the ways of reducing energy consumption in HVAC (Heating, Ventilation and Air Conditioning) (Sodha et al. 1994). The EATHE can be adopted as one option for thermal comfort in building. It is working on the basis of earth to air or air to earth heat exchange process. Sharan and Jadhav studied the subsoil temperature fluctuation with the variation of depth of soil from the earth surface and found the constant or undisturbed earth soil temperature at 3–4 m or more. This undisturbed earth temperature (UET) is unique and observed higher than the winter seasonal ambient average temperature (SAAT) and lower than the summer SAAT. The UET can be used to providing the indoor thermal comfort by applying the EATHE system. The performance of an EATHE system depends on the UET, ground moisture condition, ground surface condition as well as the thermal conductivity of the earth soil where EATHE system would be established (Sharan et al. 2001). The application of EATHE system is a highly potential option for thermal conditioning of buildings, and it will help to reduce energy demand in buildings and adds a root of sustainable development which has added a significant energy saver during the last three decades (Kumar et al. 2003; Singh 1994; Santamouris et al. 1996; Mihalakakou et al. 1994; Sodha et al. 1990; Kaushik et al. 2014). Kaushik et al. (Mihalakakou et al. 1994) experimentally studied the EATHE situated at TERI India and presented the energy saving in heating and cooling of building and CO2 mitigation. Kaushik et al. (2013) reviewed the various theoretical and experimental studies on EATHE and compare the performance of EATHE and Borehole heat exchanger by Lal and Kaushik (2019). The earth air tunnel heat exchanger is a good option for space conditioning of building. It can reduce the energy demand by air conditioners and electrical heaters applied for building space cooling and heating. The undisturbed soil temperature is utilized for the net heat exchange in EATHE and it depends upon the thermal conductivity of the soil. The authors have experimentally analyzed the thermal conductivity of various soils and after that the undisturbed soil temperature depth will be estimated by the MALAB simulation. Its work will be highly beneficial for design and development of ETHE in any area of the world and energy researchers of this area.

Estimation of Earth Temperature Profiles for Different Soils …

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2 Material and Methods 2.1 Earth Temperature Modelling 2.1.1

One-Dimensional Transient Heat Flow Model in Ground Soil

The transient boundary condition at earth surface is a regular harmonic or periodic type of heat flow, which may be sinusoidal or cosinusoidal (Nag 2002). A sinusoidal temperature variation with time is shown in Fig. 1, where T represents the temperature and P represents the time period (wavelength) and the amplitude is represented by Theta (θo ). The amplitude (at x = 0) is given by, θo =

Tmax − Tmin 2

(1)

Time period of oscillation = P. ω , where ω is the angular velocity in simple harmonic Frequency f = P1 = 2π motion (S.H.M.), ω = 2π f =

2π , at any time θ = θo sin ωt P

At t = 0, θ = 0 mean T = Tm .

Fig. 1 Temperature variation about a mean temperature with time (Nag 2002)

(2)

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S. Lal

[1]

Fig. 2 Element of ground assumed as plane body

Z [2]

[3]

Let us consider a semi-infinite solid as shown in Fig. 2. Here z represents the depth of the ground at Z = 0 surface temperature is varying sinusoidally. One-dimensional transient heat conduction equation is given by, ∂2T 1 ∂T = ∂z 2 χ ∂t

(3)

T is replaced by θ and above equation can be written as, ∂ 2θ 1 ∂θ = ∂z 2 χ ∂t

(4)

Let us again assume that the Theta (θ ) is a function of depth and time then θ = Z (z) .Y(t)

(5)

From separation variable theorem ∂ 2θ ∂2 Z =Y 2 2 ∂z χz

(6)

∂Y ∂θ =Z ∂t ∂t

(7)

and

Estimation of Earth Temperature Profiles for Different Soils …

265

From Eqs. (4), (6) and (7) Y

Z ∂Y ∂2 Z = 2 ∂z χ ∂t

(8)

The solution of above equation is given by 1 ∂2 Z 1 ∂Y = = ±i λ2 = separation constant Y χ ∂t Z ∂z 2

(9)

where λ is real First take the positive sign and part one of Eq. (9) can be written as, 1 ∂Y = i λ2 Y χ ∂t

(10)

∂Y = i χ λ2 δt Y Integrating above equation and we get: lnY = iχ λ2 t + lnC, and Y(t) = B1 ei λ

2

χt

(11)

And take the positive sign and part two of Eq. (9) can be written as, 1 ∂2 Z = i λ2 Z ∂ z2

(12)

It can be written as, ∂2 Z − i λ2 Z = 0 ∂z 2 The solution of above second degree differential equation can be given as, Z (z) = B2 e And

√ i=

1+i √ 2

√ iλZ

√ iλZ

+ B3 e−

(13)

{from, (1 + i )2 = 2i} Z (z) = B2 e

(1+i √ ) λZ 2

+ B3 e

√ λZ − (1+i) 2

(14)

By substituting value of Z (z) and Y(t) from Eqs. (14) and (11) in Eq. (5) and we get,

266

S. Lal

) ( (1+i 2 √ ) λZ √ ) λZ − (1+i .(B1 eiλ χ t ) θ+ = B2 e 2 + B3 e 2 θ+ = e

λZ −√ 2

( ) )) ( ( √ λZ λZ 2λZ +i λ2 χ t+ √ +i λ2 χ t− √ 2 2 C1 e + C2 e

(15)

Similarly, solution for negative sign can be find out as, θ− = e

λZ −√ 2

( ) )) ( ( √ λZ λZ 2λZ +i λ2 χ t+ √ −i λ2 χ t− √ 2 2 C3 e + C4 e

(16)

And, θ = θ+ + θ− from Eqs. (15) and (16), ( ) )) ( ( √ λZ λZ +i λ2 χ t− √ 2λZ +i λ2 χ t+ √ 2 2 C1 e + C2 e ( ) )) ( ( √ λZ λZ λZ −i λ2 χ t− √ 2λZ +i λ2 χt+ √ −√ 2 2 + e 2 C3 e + C4 e

θ =e

λZ −√ 2

Applying boundary conditions in above equation as, Z → ∞, θ = 0, than C1 = C3 = 0 And we get θ =e

λZ −√ 2

( ) )) ( ( λZ λZ −i λ2 χ t− √ +i λ2 χ t− √ 2 2 + C4 e C2 e

(17)

Above equation can be simplified as, θ =e θ =e

λZ −√ 2

λZ ( −√ 2

C2 e+iu + C4 e−iu

)

(C2 (Cosu + Sinu) + C4 (Cosu − Sinu))

And θ =e θ =e

λZ −√ 2

(

λZ −√ 2

(

(A1 Cosu + A2 Sinu)

λZ A1 Cos λ χ t − √ 2 2

)

(

λZ + A2 Sin λ χ t − √ 2

))

2

At Z = 0, ( ) ( ) θz=0 = A1 Cos λ2 χ t + A2 Sin λ2 χ t But

(18)

Estimation of Earth Temperature Profiles for Different Soils …

267

θz=0 = θo Sinωt, from sine wave equation at ground surface

(19)

Comparing Eqs. (18) and (19) and we find, A1 = 0, A2 = θo And ωt = λ2 αt, λ2 = χω , and / λ=

ω = χ

/ 2π Pχ

(20)

where P is time period, either 365 days or 8760 h, therefore the temperature variation at depth z from earth surface is given by, )) ( ( λZ θo Sin λ2 χ t − √ 2 ( ( ( )1/ 2 )) ( ) 1/ 2 ω ω .Z − ω .χ t − θ = e 2χ .Z θo Sin χ 2χ ( ( ( )1/ 2 )) ( ) 1/ 2 ω ω .Z − 2χ θ = θo .e .Z Sin ωt − 2χ θ =e

λZ −√ 2

(21)

Putting the value of θ = T − Tm in above equation T = Tm + θo .e

( ) 1/ 2 ω .Z − 2χ

Gain putting the value of ω = T = Tm + θo .e

2π P

(

( Sin ωt −

(

ω 2χ

)1/ 2

)) .Z

(22)

and P = 365

( )1/ 2 π − 365χ .Z

(

(

2π t − Sin 365

(

π 365χ

)1/ 2

)) .Z

(23)

The above Eq. (23) expresses the temperature at any time t at any distance z from the surface. The solution can be found out for cosine wave by applying same solution technique.

2.1.2

Heat Transfer from Surface to a Particular Depth

The heat flow rate can be calculated by using Fourier equation as,

268

S. Lal

Q = −k A( ( From Eq. (18), let

)1/ 2

ω 2χ

dθ ) dz z=0

(24)

= μ, then

dθ = θo .e−μ.Z (Cos(ωt − μ.Z ))(−μ) + θo (Sin(ωt − μ.Z ))(−μ).e−μ.Z dz

(25)

And (

dθ dz

) = θo .(−μ).(Cosωt + Sinωt)

(26)

z=0

( π π π) = SinωtCos + Cosωt Sin Sin ωt + 4 4 4 ( Sinωt + Cosωt π) = Sin ωt + √ 4 2 √ π Cosωt + Sinωt = 2Sin(ωt + ) 4

(27)

From Eqs. (26) and (27), we get, (

dθ dz

) z=0

√ π = θo .(−μ). 2Sin(ωt + ) 4

Putting the value of μ in above equation and simplify as, (

dθ dz

) z=0

( )1/ 2 ω π = −θo . .Sin(ωt + ) χ 4

(28)

From Eqs. (24) and (28) ( )1/ 2 Q ω π = kθo . .Sin(ωt + ) A χ 4 It is seen that Q is positive within the limit and, ωt + the limit. Then, ωt + π4 = 0 π ωt = π4 , and t = 4ω And, ωt + π4 = π , Then t = 3π 4ω Integrating Q between these limits in half cycle is,

π 4

(29) = 0or π , and negative is

Estimation of Earth Temperature Profiles for Different Soils …

269

( )1/ 2 π 4ω ω π Q = k Aθo . . ∫ Sin(ωt + )dt 3π χ 4 4ω

( )1/ 2 [ ω π ] 4ωπ Q = k Aθo . . Cos(ωt + ) 3π χ 4 4ω Q=

2.1.3

2 (ωχ )1/ 2

k Aθo

(30)

The Amplitude at Any Depth

The amplitude at any depth of ground is, θz = θo .e

( )1/ 2 ω .Z − 2χ

(31) ( )1/ 2 ω .Z − 2χ

, and the range (Rz ) is And the amplitude gets diminished by the term, e double of the amplitude at particular depth of ground, it is given by, θz = 2.θo .e 2.1.4

( )1/ 2 ω .Z − 2χ

(32)

The Velocity of Thermal Wave Propagation

From Eq. (21), the temperature at zero ground depth is given by, θz=0 = θo .Sinωt

(33)

θz=0 = Tm − Tm = 0 Sinωt = Sinπ ωt = π t=

π ω

at any depth T = Tm , θ = 0 ) ω 1/ 2 .Z = π 2χ ( )1/ 2 ω .Z ωt2 = π + 2χ (

When, ωt2 −

(34)

270

S. Lal

t2 =

π + ω

t2 =

π + ω

( (

ω 2χ

)1/ 2

1 2ωχ

.

)1/ 2

Z ω .Z

Δt = t2 − t1 Δt =

π + ω

( (

Δt = But ω =

2π P

1 2ωχ 1 2ωχ

)1/ 2 )1/ 2

.Z −

π ω

.Z

(35)

( ) 1 P 1/ 2 .Z 2 πχ

(36)

and we get, Δt =

The velocity of thermal wave propagation in to the ground soil is given by, ϑ=

Z =( Δt

1 2ωχ

Z )1/ 2

.Z

= (2ωχ )1/ 2

(37)

And wavelength of the thermal wave is given by, 1/ 2

λ = ϑ.P = (2ωχ )

( ) 2ω P 1/ 2 2π = 2π . ω 2π

λ = 2(π χ P)1/ 2

2.1.5

(38)

Phase Difference at Any Depth

Sinωt = 1, for maximum temperature variation. ωt = π2 , 3π , 5π . . . . . . . . . . . . . . . . . . . . . .. where, n = 0, 1, 2, 3−−−− − 2 2 and, t=

(n + 1)π 2ω

(n+1)π 2

Estimation of Earth Temperature Profiles for Different Soils …

(

(

Sin ωt −

ω 2χ

)1/ 2

271

) .Z

=1

(39)

Putting the value of t in above equation and we get, (

ω 2χ

)1/ 2

(n + 1)π 2ω ( )1/ 2 ω Z (n + 1)π + . t= 2ω 2χ ω

ωt −

.Z =

(40)

)1/ 2 ( 1 Δt = 2ωχ .Z , The phase difference decreases with increasing thermal diffusivity and angular frequency and increases with increasing depth of ground.

2.1.6

The Maximum Temperature at Any Depth

The maximum temperature at any depth can be obtained from Eqs. (21) and (39). And we get, (θz )max = θo .e

( )1/ 2 ω − 2χ .Z

(41)

It is seen from Eq. (41) that higher the frequency (ω), the less the penetration of thermal wave. Thus high frequency thermal oscillations are rapidly damped out. The thermal diffusivity also affects the penetration depth. The lower the thermal diffusivity, the smaller will be the depth at which (θz )max is negligible.

2.2 Thermal Conductivity of Soil The thermal conductivity of the soil takes an important role in heat transfer from soil to the air (flow in the pipe) through the buried pipe wall thickness. So the measurement of the thermal conductivity is important for which thermal conductivity measuring equipment is used in the laboratory of Rajasthan Technical University Kota. The collected sand from various regions of Rajasthan and India is used as base to estimate the thermal conductivity in that region at the depth of undisturbed earth temperature.

272

S. Lal

Fig. 3 Thermal conductivity measuring equipment

Dimmer 7

6

Thermocouples (1 to 10) Soil Outer Sphere Inner Sphere 8 10

Nichrome wire heater 9 1-5-inner sphere thermocouples 6-10 outer sphere thermocouples

2.3 Measurement and Instrumentation Thermal conductivity measuring equipment make of K.C. Engineering and assortment type, was used. The equipment is constructed with two thin-walled spheres of diameters 200 mm and 400 mm having air gap between them and supported with some internal arrangements. The soil can be filled in the gap of spheres to measure the soil thermal conductivity. A Nichrome wire heater is fixed inside the inner sphere. A total of 10 thermocouples are fixed at the particular distances on the surfaces of the inner and outer spheres. It is assumed that the heat is radially flowing in the sphere. The voltage and ampere are measured with the digital volt meter (0–250 V) and ammeter (0–5 A) equipments and the temperatures are measured by the help of digital indicators. All the temperature sensors and whole equipment is shown in Fig. 3.

2.4 Uncertainty Analysis There is no such thing as a perfect measurement. All measurements have errors and uncertainties, no matter how hard we might try to minimize them. The errors and uncertainties may occur during the experimental due to type measurement, climatic conditions, calibration methods; method of observation and testing method of evaluation of ventilation and space conditioning (ambient temperature, wind speed and airflow velocity) and global solar radiation at the regular interval of time. The uncertainty during temperature measurement through thermocouples at various locations on inside and outside spheres is given evaluated by the method suggested by Holman (2012), and it is estimated by 0.21.

Estimation of Earth Temperature Profiles for Different Soils …

273

Table 1 Thermal properties of various soil Type of soil

Thermal conductivity, Experimental thermal Thermal diffusivity Wm−1 K−1 conductivity, *107 m2 s−1 m2 /day Wm−1 K−1

Dry soil*

0.485

0.4846

0.033

0.000285

Ordinary#

1.818

1.8175

1.377

0.0118981

Dry loamy soil*

0.211

0.2086

1.85

0.015984

Dry river base sand*

0.238

0.2379

2.22

0.0191808

Organic#

0.904

0.9039

10

0.0864

Moist river base sand 2.119 (saturated)*

2.1179

10.23

0.0883872

*Singh and Chaudhary (1992) and # Kumar and Kaushik (1995)

3 Results and Discussion 3.1 Experimental Analysis The thermal conductivity of various types of soils is measured in the laboratory of Rajasthan Technical University Kota and found that the results are similar to the Singh and Chaudhary (1992) and Kumar and Kaushik (1995). The experimental results and data taken from researchers are shown in Table 1. It is observed that the thermal conductivity evaluated higher for moist river base sand and lowest for dry loamy soil, it means the moisture content in the soil increases the thermal conductivity of soil. It is also found that the organic contents’ availability in the soil will increase the thermal conductivity. As concern to the EATHE location, the moist soil will be the best location, which is found near the underground water tank or septic tank in the buildings.

3.2 Theoretical Earth Temperature Profiles for Soils The thermal properties of diverse soil are listed in Table 1. From Eq. (23), it is seen that the penetration temperature at particular depth of ground depends upon the thermal diffusivity of the soil. The different type of soil has different thermal diffusivity. The thermal diffusivity can be calculated by help of thermal conductivity only; that’s why the experimental evaluation of thermal conductivity is needed. Therefore, six types of soil samples were collected from the various regions, these are dry soil, ordinary soil, dry loamy soil, dry river base sand, organic soil and moist river base sand. By the help of measured thermal conductivity values, the thermal diffusivity can be estimated. The simulation in the MATLAB is carried out based on these evaluated values of thermal diffusivities of various soils.

274

S. Lal

The earth temperature profile for six different soils is presented in Fig. 4. It is observed that, the penetration depth increases with increase in thermal diffusivity of soil. In desert (dry soil), the annual ambient average soil temperature is observed at lower depth than the river basin soil. The annual earth temperature variations for different depths (1 to 5 m) at different thermal diffusivities 0.0118981, 0.0191808 and 0.0883872 are shown in Fig. 5a–c respectively. It is seen that the annual mean air temperature found at lower depth for lower thermal diffusivity soil. The annual temperature swings at a specific depth (above 3 m) for different soils and is presented in Fig. 6 for various soils like dry soil, ordinary soil, dry Loamy soil, dry river base sand, organic soil, moist river base sand. It is observed that the slight variation at the depth of 3 m and it is insignificant variation and after that at 4 m and 5 m there the temperature observed likely to be straight. This depth is called undisturbed earth temperature depth and a pipe can be buried at this depth access the thermal exchange between earth and air (inside the pipe). The buried pipe depth is estimated lower in dry soil or ordinary soil than the organic and moist soil. This study takes help to design the depth of buried pipe for economic consideration. The specific depth for EATHE has been given as 3 m from the economic considerations. The maximum seasonal variation is observed for moist river basin sand. So the EATHE project cost will be less for ordinary soil than other type of soil because of extra earth excavation.

4 Conclusions The thermal conductivity of six soils like dry soil, ordinary soil, dry loamy soil, dry river base sand, organic soil, moist river base sand has experimentally investigated and after that the thermal diffusivity is evaluated. The MATLAB codes are generated to observe the temperature variation at various depths and thermal diffusivities under the earth surface. It is observed that the undisturbed temperature depth is lowest for dry soil and highest for moist river base sand. The variation of depth is observed as dry soil < ordinary soil < dry loamy soil < dry river base sand < organic soil < moist river base sand. This undisturbed temperature depth may be used for installation of EATHE pipe to generate the thermal comfort in buildings. The depth will also be decided the money involved in the project of EATHE. The study will be benefitted the researchers of earth air tunnel heat exchanger (EATHE and its integrated approaches with solar chimney and other possible Hex’s (Lal and Kaushik 2017; Lal 2018, 2022b).

Estimation of Earth Temperature Profiles for Different Soils …

(a). Dry Soil

(b). Ordinary soil

(c). Dry Loamy soil

(d). Dry river base sand

(e). Organic soil Fig. 4 Earth temperature profiles for different soils

(f). Moist river base sand

275

276

S. Lal

(a). For thermal diffusivity=0.0118981

(b).For thermal diffusivity=0.0191808

(c).For thermal diffusivity=0.0883872 Fig. 5 Annual earth temperature variation for different depths

Estimation of Earth Temperature Profiles for Different Soils …

277

Fig. 6 Annual temperature swings at a specific depth (3 m) for different soils

References Fourth Assessment Report of the Intergovernmental Panel on Climate Change: Climate Change (2007) Mitigation, chapter 6. Cambridge University Press, https://www.ipcc.ch/site/assets/upl oads/2018/03/ar4_wg2_full_report.pdf. Assessed 10 August 2022 Holman JP (2012) Experimental methods for engineers. McGraw-Hill/Connect Learn Succeed, 8th ed. Kaushik SC, Lal S, Bhargava PK (2013) Earth air tunnel heat exchanger for building space conditioning: a critical Review. Int J Nano-Mater Energy (ICE) 2(4):216–227. https://doi.org/10.1680/ nme.13.00007 Kaushik SC, Tarun G and Lal S (2014) Thermal performance prediction and energy conservation potential studies on earth air tunnel heat exchanger for thermal comfort in building. J Renew Sustain Energy (JRSE-AIP), 6:013107(1–12), https://doi.org/10.1063/1.4861782 Kumar GS, Kaushik SC (1995) Theoretical earth temperature profiles for different soils and soil conditions. Int J Solar Energy 17:199–209. https://doi.org/10.1080/01425919508914298 Kumar R, Ramesh S, Kaushik SC (2003) Performance evaluation and energy conservation potential of earth air tunnel system coupled with non-air-conditioned building. Build Environ 38(6):807– 813. https://doi.org/10.1016/S0360-1323(03)00024-6 Labs K (1989) Earth coupling, passive cooling (Cook J (ed)). Cambridge: The MIT Press Shiv L (2018) CFD Simulation studies on integrated approach of solar chimney and borehole heat exchanger for building space conditioning. Periodica Polytechnica Mech Eng 62(4):255–260, https://doi.org/10.3311/PPme.11023 Lal S (2022a) Green building design concept: a sustainable approach. J Mech Const Eng, ISSN2583–0619, 2(001), pp 1–10, https://doi.org/10.54060/jmce/002.01.003 Shiv L (2022b) Significance of energy efficient component in the buildings design towards the green footprint. Submitted J Mech Constr Eng 2(1:005):1–13, ISSN: 2583:0619. https://doi.org/10. 54060/jmce.v2i1.17 Lal S and Kaushik SC (2019) Comparative study of earth air tunnel and borehole heat exchanger applied for building space conditioning. Adv Energy Built Environ ,36:1–12, Lecture Notes in Civil Engineering 36,https://doi.org/10.1007/978-981-13-7557-6_1 Lal S, Kaushik SC, Bhargava PK (2012) A study on stack ventilation system and integrated approaches. Conference on emerging trends of energy conservation in buildings, CBRI, Roorkee India, November 1–3: 255–263

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Lal S, Kaushik SC (2017) CFD simulation studies on integrated approach of solar chimney and earth air tunnel heat exchanger for building space conditioning. Int J Econ Energy Environ 2(3):32–39. https://doi.org/10.11648/j.ijeee.20170203.11 Mihalakakou G, Santamouris M, Asimakopoulos D (1994) Modelling the thermal performance of earth-to-air heat exchangers. Sol Energy 53(3):301–305. https://doi.org/10.1016/0038-092 X(94)90636-X Nag PK (2002) Heat transfer. Tata McGraw-Hill publishing company ltd., Pp 171–179 Santamouris M, Mihalakakou G, Balaras CA, Lewis JO, Vallindras M, Argiriou A (1996) Energy conservation in greenhouses with buried pipes. Energy 21:353–360. https://doi.org/10.1016/ 0360-5442(95)00121-2 Sharan G, Sahu RK, Jadhav R (2001) Earth-tube heat exchanger based air-conditioning for tiger dwellings. Zoos Print 16(5):1–4, May (RNI2:8) Singh SP (1994) Optimization of earth air tunnel system for space cooling. Energy Conver Manag 35(8):721–725. https://doi.org/10.1016/0196-8904(94)90057-4 Singh AK, Chaudhary DR (1992) Experimental investigation on the thermophysical properties of moist porous materials. Heat Recovery Syst CHP 12(2):113–121. https://doi.org/10.1016/08904332(92)90038-J Sodha MS, Sawhney RL, Jayashankar BC, Sharma AK (1990) Effect of different earth surface treatments on the thermal performance of a room coupled to an earth-air tunnel. Int J Energy Res 14:337–354. https://doi.org/10.1002/er.4440140309 Sodha MS, Usha Mahajan, Sawhney RL (1994) Thermal performance of a parallel earth air pipes system. Int J Energy Res 18:437–477,https://doi.org/10.1002/er.4440180404

A Conspectus on Recent Methodologies and Techniques Used for the Enhancement of Engineered Landfill Rohit Maurya , Madhuri Kumari , and Sanjay Kumar Shukla

Abstract In recent years, it’s been observed that the necessity of engineered landfill around the world has increased. The development and enhancement took place to design the engineered landfill much more efficiently and effectively. Rapidly growing global industrialization is a concerning issue that has made an increment in solid waste generation, due to which we require more number of landfills through which we can contain and control the landfill leachate more effectively. The main problem faced by the engineers and planners in landfill is controlling and monitoring the generated leachate. Investigate such developments is a concerning issue for engineered landfill, in this paper, we have compiled various recently published research papers to put forth a meaningful conclusion regarding the new technologies/methodologies. In this paper, we also have compared the journals and respective publishers, which are highly used for paper publication based on engineered landfill, and analysis shows which journal published more technologically enhanced research paper. Keywords Landfill liner · Solid waste · Engineered landfill · Methodologies · Leachate

1 Introduction Municipal Solid Waste (MSW) and its management around the world are utmost and prime to looking for sustainable solutions. It is becoming a concerning matter R. Maurya (B) · M. Kumari Amity University Uttar Pradesh, Noida, India e-mail: [email protected] M. Kumari e-mail: [email protected] S. K. Shukla Edith Cowan University, Joondalup, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_22

279

280

R. Maurya et al.

as day-by-day solid waste generation has grown rapidly due to global industrialization and consumption of products. That’s why landfills are becoming an essential part of Solid Waste Management (SWM). This difficulty faced by the engineered landfill (EL) designer/planners is due to the leachate generation and controlling of it. The leachate generation is generally based on the proportions of the biodegradable components present in the MSW; such food waste are rotten foods, green waste, wood, cardboard, paper, edible liquids, oils, and dairy products. The characteristic of this leachate from mentioned waste is normally toxic in nature, which makes it more important to resolve it. Since an engineered landfill is an improved landfilling type for leachate control, leachate containment is becoming an essential design aspect of landfill. If we investigate leachate, it consists of heavy metals, dissolved organics compounds (DOC), inorganic macro components (IMC), and xenobiotic organic compounds (XOC). The unpredictable nature of leachate generation makes it much more problematic. There are many advancements that have been done to the engineered landfill design to protect the environment and living beings. It has been noted that based on the quality and characteristics of the solid waste, specific landfills were selected to construct. Sanitary landfill, MSW landfill, C&D landfill, Industrial landfill, and Hazardous waste landfill are a few of the basic engineered landfill types. These landfills function differently as the solid waste is required to dump it properly. Regarding the maximum amount of municipal solid waste (MSW), which generally dumps in engineered landfills, is the cause of higher leachate generation. This waste is non-inert waste and will get biodegraded/decomposed with time. The biodegradation process starts within the landfill and generates the blackish contaminated liquid called leachate. This leachate goes downwards at the bottom of the landfill floor and the damage took place in the liners. The damages are done in the form of a chemical reaction mainly with a barrier system (BS), liner system (LS), geosynthetic liner system (GLS), geosynthetic clay liner (GCL), leachate collection system (LCS), etc. The main reason behind landfill leachate extraction at the bottom of the landfill is to reduce the damage that occurred on the landfill liner system. In general, this leachate permeated through the landfill to the groundwater and makes the water contaminated. Also, the soil beneath the landfill is also toxified and will not be utilized further. So, it is recommended that the geolocation of the landfill construction should be decided based on the permanence of its use.

2 Literature Review Studying the numerous developments and advancements in landfill leachate containment is quite a challenge for researchers around the world. To compile such research work, we have included the attributes like publishers, journals, and publication years to showcase the consistency and traffic of paper publication in respective journals. We have basically collected research papers from three consecutive years of data and

A Conspectus on Recent Methodologies and Techniques …

281

a few earlier research data in the compilation process. This is due to the fundamental research related to the topic that was conducted in earlier time. The temporal data comparison between the journal and publisher with publication year has been depicted in Fig. 1. This shows that the majority of the publication was done in the year 2020 and the highest number of papers have been published by Elsevier publisher. Also, it has been noticed that the maximum number of research papers is published under the Geotextile and Geomembrane Journal and then in Computers and Geotechnics. In this paper, we have compiled all engineered landfill-related research papers into five fundamental categories to express the whole extent of the research work. In Table 1, we have shown the general comparison of five parameters as ‘Base’, ‘Specification’, ‘Material’, ‘Work’, and ‘Methodology’. These parameters summarize the main techniques/methodology used in the process of research or understanding of the Landfill under leachate generation. In the table below, we used the term ‘Base’ to explain the main domain of the study that took place. In our case, we have categorized (landfill) as a base into which we have many subcomponents to work on. Also, few studies have been conducted on the whole landfill. So, we have used landfill as a Material also in categorization. Later, we have used the term ‘Specification’ to specify the Base of the research work. In a few of the cases, the specification has been missing or unavailable.

Fig. 1 Journals and Publisher versus Publication Year

Landfill

Landfill

Jacome et al. (2021)

Iskander et al. (2018)

Gautam and Kumar (2021)

Liu et al. (2013) Aquifer

1

2

3

4

Leachate

Landfill

Leachate

Landfill

Leachate

Feng et al. (2017)

Zha et al. (2021) Leachate

Leachate

Suzuki et al. (2008)

Saxena et al. (2021)

Feng (2021)

Akter et al. (2021)

He et al. (2021)

7

8

9

10

11

12

13

2013

2021

2021

2021

2021

2021

2021

2021

2021

2017

2008

1996

Seepage effect

Recirculation

–

–

Layered

Intermediate

–

Deformed

System

Hazardous

–

Sanitary

Publication Specification Year

Aquitards 2019

Landfill

Li et al. (2019)

Nadarajah and Rowe (1996)

5

6

Landfill

Base

Sr. Papers No.

Table 1 Methodologies conducted on engineered landfill

Leachate

Leachate

Landfill

Domestic Sewage

Pb-Contaminated Silt

Landfill

Cover Soil Barrier

Landfill

Contaminant

Contaminant

Leachate

Leachate

Landfill

Material

Mechanical Properties

Characteristics

Slope Stability Analysis

Treatment

Characteristics

Gap Flow Model

Performance Evaluation

Multilayered Flow

Transportation

Leachate Transportation

Characteristics

Treatment

Characteristics

Work

Microstructure

Photodegradation

(continued)

Bio Hydro Coupled Process

Moving Bed Hybrid

Microbial-Induced Carbonate Precipitation (MICP)

Vertical Extraction Well

Heavy Metal Removal

Mathematical Modelling

Delayed Drainage

Mathematical Modeling

Statistical Analysis ANOVA

UV Quenching Substance

Numerical Approach

Methodology

282 R. Maurya et al.

2021

2021

Warmadewanthi MSW et al. (2021)

Feng et al. (2021a, b)

Ray et al. (2021) MSW

Ray et al. (2022) Landfill

16

17

18

19

Devarangadi Liner and Uma (2019)

22

MSW

MSW

Ke et al. (2021)

Ke et al. (2021)

20

21

Landfill

Landfill

Slimani et al. (2021)

15

2021

2021

2021

2021

2021

2021

2021

Landfill

Aryampa et al. (2021)

14

–

–

–

–

–

Sanitary

Degraded

–

Uganda

Publication Specification Year

Base

Sr. Papers No.

Table 1 (continued)

Real and Simulated MSW Leachate

Stability Analysis and Control Measures

Methane Gas Generation

Mechanical Behavior

Environmental Impact Assessment

Work

Ground Granulated Blast Furnace Slag, Bentonite, Cement

Landfill

Waste

Retain Diesel Oil Contamination

Evaluation of Leachate Production

Anisotropy of leachate

Bentonite, Fly Ash, Hydraulic Performance, Sewage Sludge, Consolidation Paper Mill Leachate Characteristics, Shear Strength Analysis

Leachate

Landfill

Waste

MSW

Landfill

Material

(continued)

Consistency Limits, Free Swell Index (FSI), Proctor Compaction, Hydraulic Conductivity (HC), Leachate Characteristics (LC), and Unconfined Compressive Strength (UCS)

Secondary Compression

Hydraulic Conductivity Study

Experimentation on two Indian Bentonites Along with Fly Ash

Hydraulic and Swelling Behavior

High Leachate Level

Agreed Used as Daily Cover

Visco-plastic Constitutive Model

EVIAVE* Methodology

Methodology

A Conspectus on Recent Methodologies and Techniques … 283

2021

2019

Dominijanni et al. (2021)

Ambat (2020)

Feng et al. (2021a, b)

Zhan et al. (2021)

Yohanna et al. (2021)

Yalçuk and Ugurlu (2020)

Emmanuel et al. Landfill (2020)

Khoury et al. (2019)

Wang et al. (2019)

Li et al. (2021)

25

26

27

28

29

30

31

32

33

34

Landfill

GCL

Landfill

Landfill

MSW

Landfill

Landfill

Landfill

Landfill

GCL

Yesiller et al. (2021)

24

2021

2019

2020

2021

2021

2021

2020

2021

2021

2021

Landfill

Mello et al. (2022)

23

–

–

–

–

–

–

Unsaturated

Bioreactor

Sanitary

–

Exhumed

UAV

Publication Specification Year

Base

Sr. Papers No.

Table 1 (continued)

MSW

Leachate

Landfill

Leachate

Leachate

Contamination

Cover

Landfill

Landfill

Landfill

Liner

Landfill

Material

Hydro-Mechanical Biodegradation Process

Disposal Design

Performance-Based Design

Multi-Stage Membrane Behavior Tests

Topographic Measures

Methodology

Coupled Modeling

Inorganic Waste

Expansive Stability

Suitability of Bottom Liner

Treatment

Diffusion Study

(continued)

Thermo-Hydro-Mechanical Biochemical

Hydraulic Conductivity Study

Geotechnical Modeling

Interaction With Olivine Treated Marine Clay

Laboratory Scale Vertical Flow Constructed Wetlands

Lateritic Soil Treated with Bacillus Coayalans

Modeling of Water Vapor and Moisture And Gas Transfer Temperature Gradient

Slope Stability Analysis

End Of Waste

Risk Assessment Procedure

Membrane Behavior

Operation Monitoring

Work

284 R. Maurya et al.

GCL

Landfill

Zainab et al. (2021)

Kumar and Reddy (2021)

Yan et al. (2021) Landfill

Kumar et al. (2020)

Chen et al. (2020)

Rowe and Barakat (2021)

Touze-Foltz et al. (2021)

Okurowska et al. (2021)

Yan et al. (2021) Landfill

Kareem et al. (2021)

Gopikumar et al. (2021)

35

36

37

38

39

40

41

42

43

44

45

Landfill

Landfill

Landfill

Landfill

Landfill

Landfill

Landfill

Base

Sr. Papers No.

Table 1 (continued)

2021

2021

2021

2021

2021

2021

2020

2020

2021

2021

2021

–

–

–

Domestic

–

–

–

–

System

–

Bentonite polymer

Publication Specification Year

Leachate

Landfill

Leachate

Leachate

Barrier System

MSW

MSW

MSW

Liner

MSW

Leachate

Material

Thermo-Hydro-Bio-Mechanical Model

Hydraulic Conductivity

Methodology

Sustainable Smart City

Optimum Site Selection

Treatment

Adapting Growth of Algal Microbiome

Performance Issues

Transport Modeling

Stabilization Behavior

Biochemical And Thermal Behavior

(continued)

Management Using IoT

GIS Techniques

Membrane Distillation Evaluation

Taxonomic and Predicted Metagenome Analysis

Numerical modeling, Lab Testing

Single Lined PFOS**

Degradation-Consolidation Model

Coupled Numerical Modeling

Transient Coupled Analytical Model Consolidation-Contamination Transport

Performance Evaluation

Coal Combustion Product

Work

A Conspectus on Recent Methodologies and Techniques … 285

Runoff

MSW

Landfill

Landfill

Verma et al. (2017)

Bonaparte et al. (2020)

Gaudie Ley et al. (2021)

Pandey and Shukla (2020)

Mukherjee and Mishra (2019)

Yesiller et al. (2019)

Shaji et al. (2021)

Breitmeyer et al. (2019)

Robey et al. (2019)

Zheng et al. (2019)

Ospanbayeva and Wang (2020)

46

47

48

49

50

51

52

53

54

55

56

Landfill

Landfill

MSW

GCL

Landfill

Landfill

Landfill

Base

Sr. Papers No.

Table 1 (continued)

2020

2019

2019

2019

2021

2019

2019

2019

2021

2020

2017

Old

Liner Defects

Model Comparison

Geotechnical Stability

NRCS-CN*** Inspired Model

Work

MSW

MSW

Subsoil

Liner

Landfill

Avoid Turbidity Contamination

Saturated Hydraulic Conductivity

Groundwater Contamination Hazard Rating System

Moisture Suction Response

Evaluation For Landfill Application

Leakage Detection

Predicting Model and Monitored Data

Cone Penetration Test, Simulation

Coupled With RS And GIS Estimation

Methodology

Rehabilitation

(continued)

Cost Benefit Analysis

Design Of Horizontal Landfill Gas Collection Wells

Practical Method for Drainage Media

Compression And Decomposition

Subsoil Condition Beneath MSW

Hydration Fluid and Field Exposure

Sand-Bentonite-Tire Hydrological Strength Fiber Characteristics

Leachate

Leachate

Waste Fills

Water

Material

Non-homogenous Landfill

–

Bioreactor

–

–

–

MSW

Sanitary

–

–

Publication Specification Year

286 R. Maurya et al.

Landfill

Landfill

Rezaeisabzevar et al. (2020)

Salihoglu (2018)

Scheutz and Landfill Kjeldsen (2019)

Sujetovien˙e et al. (2019)

Su et al. (2019)

Spigolon et al. (2018)

Tahmoorian and Landfill Khabbaz (2020)

Chabok et al. (2020)

Feng et al. (2020)

58

59

60

61

62

63

64

65

66

Landfill

Landfill

Landfill

Landfill

Landfill

Pan et al. (2017) Landfill

Base

57

Sr. Papers No.

Table 1 (continued)

2020

2020

2020

2018

2019

2019

2019

2018

2020

2017

Gas

Landfill

Landfill

Leachate

Material

–

Ahvaz, Iran

Tehran

–

System

Landfill and Leachate

Landfill

MSW

Landfill

Landfill

Central Lithuania Lichens

–

–

–

–

Publication Specification Year

Effect Of LCRS# Clogging and Slope Stability

Site Selection

Performance Comparison

Landfill Siting

Fate With Landfill Age

Metal Accumulation and Physiological Response

Gas Emission Monitoring

Electricity Generation

Site Selection

Assessment Of Leakage Hazard

Work

(continued)

3-D Landfill Slope Model with Vertical Recirculation Wells

Fuzzy-AHP**** Approach Combined with GIS

MSW Settlement Prediction Model

Optimization, Multiple Decision Analysis, And Geographic Information System Analyses

Occurrence of Microplastics in Landfill

Heavy Metal Analysis, Physiological ANOVA Study

Tracer Gas Dispersion Method

Landfill Gas-To-Energy Plants Method

Multi-Criteria Decision Making

3-D Excitation-Emission Fluorescence and Parallel Factor Analysis Method

Methodology

A Conspectus on Recent Methodologies and Techniques … 287

Landfill

Di Maria and El-Hoz (2020)

Jovanov et al. (2018)

Hölzle (2019)

Al-Ruzouq et al. Landfill (2018)

Canopoli et al. (2018)

Bian et al. (2020)

69

70

71

72

73

74

Landfill

Landfill

Landfill

Landfill

Landfill

Mora et al. (2020)

68

Landfill

Özkan et al. (2020)

Base

67

Sr. Papers No.

Table 1 (continued)

2020

2018

2018

2019

2018

2020

2020

2020

–

–

Sharjah, UAE

–

Closed Methanogenic

Bioreactor

Sanitary

–

Publication Specification Year

Cover Soil Barrier

Excavated Plastic

Landfill

Leachate and Gas

Residual Waste

Landfill

Material

Analytical Model for Plant Radial Oxygen Loss on Methane Oxidation

Physico-Chemical Properties

Site Selection by Micro/Macro Geo-Spatial Parameter

Mathematical Analysis

Optimization Of the Monitoring

Management of the Biodegradable Fraction

Site Selection

Evaluation

Work

(continued)

Proposed A Theoretical Model Verified by Simulation Model

Laboratory Testing and Experimenting

Fuzzy Membership/Analytical Hierarchy Process (AHP)

Correlation Of Contaminants Patterns in Landfill Soils

Physical, Chemical, And Biological Processes

Mechanical Biological Treatment with Leachate Recirculation

Multi-Criteria Decision Analysis and Analytical Hierarchy Process

GIS-Based MCDA## With Hesitant Fuzzy Linguistic Term Set

Methodology

288 R. Maurya et al.

He et al. (2019)

Rout and Singh (2020)

Xu et al. (2022)

Jinlong et al. (2022)

Khasawneh et al. (2022)

76

77

78

79

80

Landfill

Landfill

Landfill

Landfill

Landfill

Landfill

Base

2022

2022

2020

2019

2021

2021

Engineered

–

–

–

MSW

Aerobic

Publication Specification Year

Evaluaci´on del Impacto Ambiental en Vertederos ** Perfluoro octane sulfonate *** Natural Resource Conservation Service—Curve Number *** * Analytic Hierarchy Process # Leachate collection and removal system ## Multi-criteria decision analysis & Landfill Barrier System

*

Feng et al. (2021a, b)

75

Sr. Papers No.

Table 1 (continued)

Leachate

Leachate

Landfill

Liner Material

Microplastic

Intermediate Layer

Material Mathematical Model Analytically Solved by Homotropy and Perturbation Method in 3 Type Barrier System

Methodology

Geotechnical Testing on Pond Ash and Sodium Bentonite

Leachate Generation, Collection, Treatment

Prediction Max. Leachate Depth

Theoretical Study of Leachate Phenomenon

Analytical Analysis and Orthogonal Design Method

Translational Failure Analysis Wedge Method for Slope Stability

Multi Linear Regression Model on Experimental Data

Microplastic Extraction From Experimental Studies On 12 12 Leachate Samples Leachate Samples

Transport Through LBS, and Simulation Verification by COSMOL Software

Work

A Conspectus on Recent Methodologies and Techniques … 289

290

R. Maurya et al.

3 Remarks and Discussion In the above table, we can see the various methodologies that have been proposed and executed for the enhancement/betterment of the landfill design. It is noted that we have not only come far from just experimental analysis of scenarios but also jumped on the new levels of virtual analysis and simulation techniques. These techniques are widely used for analyzing any modification exerted in the engineered landfill design. It is obvious that the enhancement of engineered landfill is required new sustainable technologies by executing approaches like mathematical modeling, process analysis, scenario simulation, and novel devices. To do the summarization of Table 1, we have done the analysis of it in the form of comparative graphs. These graphs show a quite significant extent of all 80 research papers. Also, at the end of the paper, we have also shown a particular output of the whole work. Firstly, the terminology that has been used in the above table consists of some common words. These words lie in different categories of gradings to understand the concept of the research that has been done. To do that, we have some of the words such as ‘Landfill’, ‘Leachate’, ‘Landfill liner’, ‘MSW’, and ‘Waste’. These words can work as a domain of the study and the main object of the study as well under which, researchers can make changes for the enhancement of landfill. These words are shown in Table 2. We can observe that most of the research has been conducted on the landfill as the main functional body of solid waste. In Fig. 2, we can observe that most of the base (domain of work) and work done with respect to the landfill and leachate is much more than others. In this figure, four major bases of the work such as Landfill, MSW, Leachate, GCL, and liner are depicted. These bases of work (Landfill, Leachate, Liner, MSW) have similar or the same material under them, e.g., a landfill-based research paper consists of the whole landfill as a material for analysis, similarly like landfill has leachate as the material of analysis. As we can see in Fig. 2 that the main area of work is either ‘Site selection’, ‘Treatment’, or ‘Characteristics’ which explains that the majority of the work is for the treatment of leachate and finding the suitability of the site for landfill construction along with analysis of characteristics of leachate. Later, in Fig. 3, we have depicted the base of the research w.r.t. the material. This shows that at the majority level, base ‘Landfill’ have the highest number of Table 2 Basic Terms of Research in different sections of explanation Sr. No.

Words

Base

Material

Yes, used as a domain of research area

Denoted as a working material within the domain area

1

Landfill

2

Leachate

3

Landfill Liner

4

MSW

5

Waste

No

A Conspectus on Recent Methodologies and Techniques …

Fig. 2 Basic categorization of topics and their respective works versus materials

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Fig. 3 Comparative graph of works and materials versus base topics and materials

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Fig. 4 Bar chart of effective word count (150+ abstract) versus terms

‘Landfill’ as a research material, which is about 27.5% of all papers. This is in the case of ‘Leachate’ as a material in landfill is about 16.25% of all papers. Now for the ‘MSW’ as a material, it is about 11.25% of all papers. After that, we can deduce that the landfill as a base has leachate, MSW, and the landfill itself is the main material of the past studies. After that, we have other combinations too like MSW with waste, GCL (Geosynthetic clay liner) with leachate and liner, Landfill with microplastic, etc. In Fig. 4, we have made the word cloud of all the abstracts of directly related and correlated 150+ research papers to analyze their rankings of the important terminologies used for the study of research work. We can easily observe that Landfill is the main primary word for the research, which must be obvious. But what is important is its correlated other words like soil, leachate, waste, municipal, and model, and analysis words come as secondary words for the study, as shown in Table 3. We have also done the numerical ranking of the words and observed all main terms of the research as a bar chart shown in Fig. 4. If we pick the major three concerns of research then we have Soil, Leachate, and waste as a prime interest of work area for enhancement by current researchers. The importance of this word analysis done on the 150+ abstracts of research papers to deduce the frequency domain of the research is conducted through these years. The usability of such analysis is focused on the various terminologies of specific domains of work, which are either directly or indirectly related to the landfill design or enhancement. In the analysis, we can not only easily remove the landfill term but it is also essential to have information regarding the information of landfill-centered research. If we talk about the outcomes of the compilation of all these research papers, we can follow Figs. 5 and 6. In these figures, we can easily deduce that the landfill as the ‘base of the work’ in the research paper related to engineered landfill under all concerned outcomes is higher than the MSW, Leachate, Liner, and runoff as

Words

59

39

35

29

23

20

19

18

17

Solid

15

14

13

11

10

Modeling

Clay

Interaction Site Study Characteristics Case Geosynthetic

16

Method Liner

Landfill Soil Leachate Waste Water Municipal Model System Analysis Effect

Counts 104

Table 3 Effective word count (150+ abstract) versus terms

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295

Fig. 5 Outcome-based comparison with base (domain of work)

Fig. 6 Outcome-based comparison with materials

the base of the research. The outcomes are categorized into four parts as ‘Math’, ‘New Concept’, ‘New Material’, and ‘Process’. Here, ‘Math’ explains the mathematical enhancement in the mathematical model, and ‘Process’ explains the processbased enhancement in the landfill. As we can see the landfill has the highest level of ‘Process’-based research, after that we have ‘New concept’-based research landfill as shown in Fig. 5.

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The same outcomes have been deduced based on various materials. If we follow Fig. 6, it is noted that three prime materials are ‘Landfill’, ‘Leachate’, and ‘Liner’ which show a spike in the shadow-gram method. This spike in landfill shows a 17.5% of the research done on ‘Process’ for ‘Landfill’ as material. This spike for leachate row shows a 10% of the research done on ‘Process’ for ‘Leachate’ as material. Landfill also shows a significant spike of ‘New concept’ of having 8.75% of all research papers.

4 Conclusion After thoroughly studying all the included research papers, we have concluded that the main fundamental insights regarding landfills are based on distinct methodology and techniques, so that an efficient comparison can be done. These insights will give us a better understanding of the related research and its methodological growth of direction, with which we can pinpoint the right path to choose which technique or methodology is much more effective and applicable. To do that, we have collected over 80+ current research papers mainly during 2018–2022 which is directly related to the topic. These are the conclusions based on the meaningful insights from the comparison (Table 1). . Firstly, we can easily deduce that Elsevier publisher has the highest publications among any other publishers which is around approximately 40% of all 80 research paper publications, which is included in this paper. . We have also observed that based on the major publisher, the highest number of research papers is published in the specific journals such as ‘Geotextile and Geomembranes’ which is 10% out of 40% and then we have ‘Computer and Geotechnics’ journal which has about 6% out of 40%, and then we have ‘Waste Management’ journal which is about the 4% out of 40% of Elsevier publications. . After that, we have concluded, along with landfill, ‘Leachate’, ‘Landfill Liner’, and ‘MSW’ are used as major areas of research, which is depicted as ‘Base’. This also shows that major research works that have been done basically are ‘Site Selection’, ‘Leachate Treatment’, and ‘Leachate Characteristics’. Consequently, we observed that fundamental materials on that work that have been done are ‘Landfill’, ‘Leachate’, ‘Landfill Liner’, and ‘MSW’ which is obvious but in the case of ‘landfill’ as material it has the highest number of research papers. . Then we have the word cloud analysis that has been done on the 150+ abstracts, suggesting that major research is also focused on the soil, which has the repetition of 59 times, then we have the word ‘Leachate’ which has the word count of 39 times; after that, we have a word ‘Waste’ and ‘Water’ which have a count of 35 and 29, respectively. . Later, we have done the outcome-based analysis of all 80 research papers based on their base (Domain of work) and their material used in the research. We have found that on landfill, we have done the highest number of research based on their

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297

modification in the process of landfill design. After that, based on material ‘Landfill’ still holds the highest number of research done on their process enhancement and development of a new concept. The materials ‘Leachate’ and ‘MSW’ come later which also have the second highest number of research conducted on their process and new material generation. . New finding shows that Gas collection and control is also a primary part of the advancement containment system for that landfill due to which various methodologies have emerged and require novel approaches to do accurate modeling of the scenario.

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Development of an Integrated Assessment Model in the Climate Policy Framework and Its Challenges Bikash Kumar Sahoo

and Kamal Kumar Murari

Abstract The use of Integrated Assessment Model (IAM) in research has been frequent since 1994 since the work of William Nordhus. In earlier days, it was mostly used in the benefit and cost analysis at the global scale. However, the usage has also included the analysis through a detailed process model to generate different emission scenarios and their impact on different climate policies. Choosing mitigation measures for a climate change impact throughout the IMA modelling process is the primary challenge. Its architecture and characteristics pose different challenges and uncertainties both conceptually and computationally. Equity and fairness concepts are the foundation of climate change negotiation. These two concepts are completely absent in the IMA research. Nevertheless, the importance of IAM is very relevant to assess different policy decisions both at global and regional scales. So, this paper tries to discuss usages and the challenges of Integrated Assessment Model from the existing literature. Keywords Integrated assessment model · Equity and fairness · Benefit–cost IAMs · Detailed process IAMs

1 Introduction The impact of human activity on the earth has long been known. This transition, often known as “Global Warming” or “climate change,” causes a change in the atmosphere’s average surface temperature due to the increase in cumulative greenhouse gases (GHG). Carbon dioxide (CO2 ) is a major factor in the increase in atmospheric temperature. Methane, like other GHG gases, plays an important role (IPCC 2014b). B. K. Sahoo (B) School of Habitat Studies, Tata Institute of Social Sciences, Mumbai, India e-mail: [email protected] K. K. Murari Centre for Environment, Climate Change and Sustainability Studies, School of Habitat Studies, Tata Institute of Social Sciences, Mumbai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_23

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The major CO2 emission sources are the burning of fossil fuel as a result of different developmental activities which add up in the atmospheric GHG concentration. The contribution of developed countries to the global CO2 stock is very significant from the pre-industrial level. The impact of climate change is very visible nowadays, and it is felt by both developing and developed countries (Campagna and Fiorito 2022; Habibullah et al. 2022; Tarekegn et al. 2022; Wang et al. 2022). Climate is a very complex phenomenon that requires an understanding of various components such as the atmospheric component, biogeochemical component, and socioeconomic component of the earth to understand its impact on human society. So, to understand this complex phenomenon, models have been developed, known as integrated assessment model (IAM). These IAMs combine inputs from various modules of the Earth’s System to produce a plausible climate change impact on human society. For example, the Global Change Analysis Model (GCAM) is an IAM that connects 5 sectors, such as economy and fuel, land usage, wetness, and soil systems, to provide optimal climate mitigation policies. One of the several IAMs’ broad goals is to cast forthcoming alternative atmospheres with and without diverse sorts of mitigation and adaptation guidelines in position, allowing policymakers to choose the best approach to mitigate the impact of climate change (Weyant 2017). IAM first appeared in the late 1980s. It was initially used to understand the various phenomena of long-term environmental concerns. Although it has been used in the context of acid rain (Hordijk and Kroeze 1997; Weyant et al. 1996) and air quality (Hordijk 1995), among other things, over time, IAM scope has expanded in terms of scale and interdependence across natural and human systems. So, based on the existing literature, this article will discuss what drove the development of new types of IAMs, as well as their scope and applications in the climate change context. Further, the quest is how this article is organized. In the subsequent section, we will discuss how scholars have defined IAMs and how different IAMs specific to the climate change field have evolved. Then we discuss how each IAM can be used or implemented in the context of changing the atmosphere. An assessment of the difficulties IAMs encounter in getting the best climate change mitigation plans heeds this. The conclusion is discussed in the final section.

2 What is Integrated Assessment Model and Its Evolution Integrated Assessment (IA) has had a remarkable evolution during the past few decades. Still, this field is somewhat cluttered because sometimes IA is ill-defined. IA as a process is not new today. For example, Egyptian farmers used this concept thousands of years ago to use land management techniques more efficiently, specifically through the combination of crop farming and irrigation techniques (Rotmans 1997). In the modern age, IA as an idea was initially used to explore complex environment-related problems. Since the 1970s, the IA has been adopted universally, notably in Europe and North America. Although in Europe, IA emerged from

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population-environment, ecological, and acidification research, in North America the importance was predominantly on the economic implications of anthropogenic GHG impacts (ibid.). There are several definitions of IA. Modelling is an interdisciplinary and participatory process that communicates acquaintances from diverse scientific domains to improve our understanding of convoluted interactive systems. Parson (Parson and Fisher-Vanden 1997) claim that IA is a tool that aims to incorporate data from various fields. It aims to provide policymakers with structural knowledge, significant uncertainties, and a deeper comprehension of the extensive strategic interconnections and feedback, particularly between socioeconomic and biophysical processes. Furthermore, according to Weyant (Weyant et al. 1996) Integrated Assessment Model (IAM) is a tool that combines a number of elements from several models from other disciplines that have been taken into account mathematically. Perhaps the emphasis on interaction at many sizes, such as pertinent sectors, geographical, and temporal, has changed the focus of IA modelling. Fisher-Vanden and Weyant (Fisher-Vanden and Weyant 2020) define IAM as a tool that aids in understanding the exchange and interdependencies between real and human techniques, as well as across spatial and temporal scales, for comprehensive coverage of users, such as advancing the science of fine-scale effect estimation, multi-stakeholder policy making, and the development of transformation processes. One of the common findings from the aforementioned two definitions is that they encompass a wide range of potential cause–effect phenomena and attempt to provide the greatest knowledge from the interrelationship of many occurrences. IAMs were created with the goal of better understanding and solving environmental problems. As a result, the early stages of international climate negotiations relied heavily on IA modelling. The goal of the Nordhaus Dynamic Integrated Climate and Economy (DICE) measure is to select the best environmental relief guideline trade-off between reduced costs and climate change harm (Nordhaus 1994). This type of IAM is known as a benefit–cost IAM because it includes both the economic implications of global warming and the socioeconomic component. Despite the fact that the benefit–cost (BC) IAMs were global in scope, the regional outcomes revealed more uncertainties. As a result, the new IAMs included improved spatial, temporal, and sectoral scales along with a comprehensive illustration of the biophysical and economic processes that have an in-depth representation of the fundamental socioeconomic processes that generate carbon emissions. Weyant (Weyant 2017) referred to it as detailed process (DP) IAMs. These models focus more on the physical implications of climate change. When developing countries started to experience the effects of climate change, the emphasis of international environmental negotiations switched from mitigation targets to estimating the financial effects of atmospheric change and its adaptation strategies (Stern 2006). The economic implication of the deterioration of the atmosphere is explained by Social Cost of Carbon (SCC). The economic cost of a single unit change in emission is another way to define it. This type of study was first started in the United State by using different benefit–cost IAMs (Metcalf and Stock 2017). In order to do this, the US Interagency Working Group (IWG) was established in 1998.

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The IWG used three BC IAMs to estimate the SCC, including DICE, Policy Analysis of the Greenhouse Effect (PAGE) (Hope 2006), and FUND (Climate Framework for Uncertainty, Negotiation, and Distribution) (Tol 2002). The SCC value, on the other hand, is solely dependent on the consideration of extreme event and adaptation strategies, and discounting rate which has been heavily criticized by various scholars (Botzen et al. 2019; Pindyck 2013, 2017). Pindyck (Pindyck 2013) stated that the IAMs have substantial limitations that render them nearly useless as a tool for policy analysis. Prior to 2006, the majority of IAM research focused on long-term scenarios of anthropogenic greenhouse gas effect over a century timeline. Such scenarios were created with the help of a single model or a group of models. However, the nature of questions has significantly changed in the last few years. These inquiries range from providing data on shorter-run distinct socioeconomic futures for assessing pertinent policy and regulatory alternatives to providing information on long-run patterns of the energy and land systems under various socioeconomic and policy scenarios (FisherVanden and Weyant 2020). There could be several reasons for asking such questions. The IPCC’s AR5 report (IPCC, 2014a) highlighted the increasing risk that weather extremes pose to human systems and came to the conclusion that climate change exacerbates the material and financial ramifications in disadvantaged regions and industries. The Paris Agreement, which was adopted at the Conference of Parties-21 in Paris, established a global framework to limit global earth heat to a sufficient level below 2° and to take steps to limit it to 1.5° relative to pre-industrial echelon to greatly minimize its risk (UNFCCC 2015). Each nation must create a nationally determined contribution (NDC) to reduce voluntary GHG discharge in order for it to be possible. The Paris Agreement also required that nations routinely exchange actual emissions, a requirement known as affirmation and assessment under Articles 13 and 14 (ibid.). In the event that the entire discharge decline mark is not reached, this enables pledge progress to be monitored and commitments to be counted. These demands have increased the demand for modelling estimations of nation-centric ease programmes. Furthermore, there has been a parallel increase in demands on the IA community to provide associated information on the non-climate change-related metrics highlighted in the UN Sustainable Development Goals, particularly those related to air quality, water availability and quality, food security, and income distribution (Fisher-Vanden and Weyant 2020). This requirement of the IA community pushes the emphasis even more on the detailed addition of different components to both DP IAMs and BC IAMs. This results in a new type of IAM known as Impact, Adaptation, and Vulnerability Integrated Assessment Models (IAV IAMs). IAV IAMs operate as a framework of coupled detailed system models in which detailed structural relationships, uncertainties, and damage function limitations are covered in the estimation of SCC. It tried to address the critiques of Pindyck for DP and BC IAMs (Fisher-Vanden and Weyant 2020; Pindyck 2017). In their articles, Moss (Moss et al. 2016) and Kling (Kling et al. 2017) discuss the significance of IAV IAMs. Kling (Kling et al. 2017) used three different models, including a crop model, an economic model of land use, a water quality model, and a bioenergy model, to

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highlight the importance of integrating different models in the food, energy, and water nexus. However, there are still difficulties in interpreting the results of various independent models.

3 Contribution of Integrated Assessment Model to Climate Policy As we discussed in the previous section, the IAMs are broadly divided into two groups such as BC IAMs and DP IAMs. These IAMs have been heavily used in mitigation policy to answer different questions so as to get an optimal mitigation policy. To understand the physical implications of global warming and the economic cost of mitigation policies, DP IAMs play an important role. However, DP IAMs are more complex in nature which add to the simple aggregated BC IAMs to improvise it.

3.1 The Use of Disaggregated DP IAMs The assessment of the energy and economic impact of mitigation policies in long term is done through the Disaggregated DP IAMs. To be more specific, climate change impact analysis, mitigation policy analysis, and integration of both mitigation and impact analysis are done by these IAMs. Chapter 6, “Assessing Transformation Pathways” of the IPCC 5th assessment report, summarizes a thousand scenarios produced by many DP IAMs in detail (Clarke et al. 2014). Though this report is one of the important reports in climate change research, some of the highlights of this report are as follows: (1) all major emission contributing regions include significant reduction from their baseline CO2 equivalent (CO2 eq) emission over the years that brings atmospheric concentration to about 550 ppm CO2 eq or below by 2100, (2) bring the atmospheric concentration to about 550 CO2 or below will be required significant modifications in energy system both globally and nationally. (3) carbon dioxide removal (CDR) and Several solar radiation modification techniques have been proposed, but none of these can effectively replace measures at adaptation or mitigation. So, a significant amount of studies consider biomass energy with carbon dioxide capture and storage (BECCS) and increase of forest area and its quality through afforestation and reforestation as CDR technique in low-GHG concentration scenarios. Riahi (Riahi et al. 2015) used the assessment of Climate Change Mitigation Pathways and Evaluation of the Robustness of Mitigation Cost Estimates (AMPERE) model to examine the implications of short-term policies for the cost-effectiveness and feasibility of long-run climate goals. It also suggests a climate mitigation effort equivalent to the commitment for 2030 will outcome in an additional exhibiting of the energy budget to fossil fuels, hindering the necessary energy transition to attain low greenhouse-gas stability levels (450 ppm CO2 eq). With respect to technology,

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large-scale bioenergy implementation, carbon capture and storage (CCS) would be some important policy options to limit the temperature by 2030. The cost of mitigation policy differs depending on the model. Clarke (Clarke et al. 2014) concluded that the carbon price in 2020 is around $50/tCO2 or less for both 650 ppmv CO2 eq and 550 ppmv CO2 eq cases. Further he stated that the pricing of carbon is higher for more aggressive climate action. According to another study, the cost of mitigation ranges from $5 to $50 per tCO2 eq for 550 ppm CO2 eq and $12 to $92 per tCO2 eq for 450 CO2 eq (Kriegler et al. 2014). It went on to say that the model’s variation is due to model structure, assumptions, and the options considered for decarbonizing the emission and mitigating requirements for fossil fuel and industry sectors. So, the degree of uncertainty in mitigation cost projection is not unexpected. It could be due to the various assumptions used to tune the model, discount rate, and mitigation options considered, productive growth, fuel price, technology diffusion, and new technology development. In order to execute mitigation policies effectively and lessen the global warming effect, it is crucial to know the economic efficiencies of the mitigation policies. One of the general principles emerged that narrowly focused, inflexible policies increase costs while flexible ones reduce them (Weyant 2008). In another recent work from Riahi (Riahi et al. 2015), delay in implementing action can be caused by paying a higher price in future to meet a specific temperature target. Clarke (Clarke et al. 2014) summarizes the most recent literature in this area by taking into account the consequences of key technology availability as well as alternative assumptions about when and who participates in a global GHG mitigation regime. The manner in which climate change mitigation or adaptation policies are implemented can change the climate only if the efficiency of mitigation and adaptation strategies changes (Weyant 2017). So, to do this, both the mitigation and impact analysis module is integrated into disaggregated DP IAMs. Such models are employed to understand the climate and air pollution policy, policy for the necessity of water between agriculture and the power plant, global policies on land emission and adaptation, and mitigation policies across the globe. Reilly (Reilly et al. 2007) looks at how global warming, CO2 increases, and changes in tropospheric ozone affect crop, pasture, and forest productivity, as well as the implications for global and regional economies. The study’s main findings are that climate and CO2 effects on crop, livestock, and forest yields are generally positive across most of the world, and resource allocation among sectors can have a significant impact on a country’s economic estimation. Another study, conducted by Daioglou (Daioglou et al. 2015), found that the overall emission reductions that may be accomplished in one sector at a time are constrained by cross-sector leakage and emissions from biomass conversion. There are also many studies done to analyze mitigation and adaptation policies together (Calvin et al. 2012; Herrero et al. 2013; Hinkel et al. 2013; Mosnier et al. 2014).

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3.2 Application of Aggregate BC IAMs Since many years ago, the BC IAMs have been used to determine the optimal trajectory for global CO2 emissions and the cost to charge for such emissions (Weyant 2014). Such an ideal policy equates marginal benefits with a marginal cost of the global warming measures. The social cost of carbon as determined by the BC IAMs, has been regularly cited in recent US climate policy debates. The understanding of optimal carbon policy comes from the economic concept which defines that marginal costs are equal to marginal benefits. There are numerous ways to implement such policies. Utilizing a carbon tax or emission tax, which results in a marginal cost of mitigation, is one method. In general, there are three types of BC IAMs utilized in these studies such as DICE, RICE, and Fund models developed over a different period of time (Nordhaus 2014). These three aggregate BC IAMs used to estimate the optimal carbon price for an incremental tonne of carbon emissions in 2015. Three values such as $10 per tonne CO2, $18 per tonne CO2, and $71 per tonne CO2 were estimated by FUND, DICE, and PAGE models, respectively. Each model uses different damage functions which result in a difference in carbon price estimation values. Additionally, each model has its own assumptions of climate impact which also have a potential difference in carbon price (Weyant 2017). In addition, the Monte Carlo simulation technique is used by FUND and PAGE models which produce probability distribution over key model variables and produce the probability distribution over the model result (ibid.). Further, the FUND model’s assumptions to adopting to climate change also have a significant contribution to a lower carbon price. So, the choice of the model plays a vital role in projecting the optimum carbon tax. In fact, the combination of the model and its uncertainties can increase the project’s optimal carbon tax (Rose et al. 2014). In spite of the uncertainties, the use of BC IAMs in global warming policies helps us to have a better understanding of the driver that plays an important role in projecting the cost and benefit of climate policy. Simultaneously, it also enhances the capability to compute uncertainties about the model output. Another important usage of BC IAMs is the estimation of “social cost of carbon” (SCC). Initially, IWG did estimate the SCC using these three models (DICE, PAGE, and FUND). According to Rose et al. (2014), a wide range of SCC was estimated with varying results depending on model and input assumptions. SCC was estimated by IWG for discount rates of 5%, 4%, 3%, and 2.5%. A similar pattern was observed, with FUND producing the lowest average SCC estimation and PAGE producing the highest average SCC estimation while accounting for more uncertainties about climate damage.

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4 Challenges and Uncertainty in IAMs IAMs have been constructively criticized for being employed in the study of mitigation policies and guiding the establishment of earth systems research priorities (Pindyck 2013, 2017; Stern 2013). It is also argued that economic models contribute to a gross underestimation of risk because the hypotheses built into economic modelling on growth, damages, and risks come extremely close to considering directly that the impacts and costs will be modest, thus further excluding the possibility of catastrophic outcomes (Stern 2013). So, in this section, we summarize some of the criticism and uncertainties of IAMs used in climate change policy framework. One of the most important problems is the selection of types of global warming impact and which mitigation options need to be considered and how to measure them, and, further, which climate change impacts to consider, which mitigation options to consider, and how to measure them. The complications arise when deciding whether to quantity the impact in physical or economic terms. This issue occurs in both DP and BC IAMs. It is possible to take some values in terms of economic impact and others in terms of physical impact in DP IAMs, whereas BC IAMs work very differently (Weyant 2017). So, which climate change impact to choose depends on the type of decision you’re making and the potential affected sectors. Another crucial concept is equity and fairness which have been very debatable in climate change negotiations. The equity aspect has only occasionally been directly addressed in the IAM study across numerous socioeconomic sections within and between countries with certain exceptions. One more challenge that arises while aggregating the climate policy across countries: should market exchange rate or purchasing power parity weights be used, for example, to value economic damage in developing countries? However, the difficulty does not end there: giving weight to losses due to climate change for both wealthy versus poor countries is not straightforward (Adler 2012). Poor countries are well aware that because they create less economic output, their impacts are given less weight, which can result in recommendations for climate policies that are unfair to them. In addition to equity and fairness, intergenerational equity is also an important concept to consider in IAMs. Therefore, those making decisions today on behalf of those who are not yet alive must come to a collective ethical and moral decision concerning the opportunities they wish to provide for upcoming inhabitants of Planet Earth (Arrow et al. 2013). Apart from the benefit–cost analysis, BC IAMs are also used for interaction and feedback to the climate system. Additional challenges that IAMs face is the association between affected system and its feedback to the climate system. According to the IPCC, most severe climate change impacts will be region-specific as well as the interaction between impacted sectors (IPCC 2014a). Climate change, for example, might make a certain location hotter and dryer, increasing competition for the scarcity of water among agricultural, power plant cooling, and home uses (Taheripour et al. 2013). Such shortage can cause dramatic economic transformations, with food and energy in the region becoming more costly, potentially affecting other economic

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sectors (Baldos and Hertel 2014). So, these are some of the challenges which need to be addressed in the tune-up of IAMs to develop optimal global warming policies (both mitigation and adaptation) to reduce the impact of global warming on environment.

5 Conclusion This article has summarized the types of different IAMs and their contributions to the optimal climate policy with some of the key challenges of IAMs. With some uncertainties, IAMs develop estimations for the probabilities and potential consequences of catastrophic outcomes. However, the high uncertainties of IAMs caused high criticism over a period of time. Due to the uncertainties, Pindyck (Pindyck 2013) recommends using an expert-based review rather than estimating SCC values using IAMs. Weyant (Weyant 2017) responded by claiming that BC IAMs provided the theoretical backgrounds for developing a highly complex and nonlinear dynamic system. It is, however, a useful place to begin with developing short-term policies and research objectives, but may not be helpful for longer-term global policies. Nonetheless, when the model is updated, the results will offer policymakers more relevant information.

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Assessment of Human Health Risk Due to Contaminated Groundwater Nearby Municipal Solid Waste Disposal Site: A Case Study in Kanpur City Abhishek Dixit , Deepesh Singh , and Sanjay Kumar Shukla

Abstract The rapid expansion of the urban population and industrialization have led to significant contamination in the soil, surface water, and groundwater. Leachate is a complex organic waste extracted from waste dump sites, and it is one of the causes of groundwater contamination. This study investigated the effect of leachate extracted from the municipal solid waste dumpsite, Kanpur, on the groundwater and the health of human receptors. This location is a non-engineered open dumpsite, which handles about 1200 metric tons/day of domestic waste generated and collected from Kanpur city. This site is being used since the year 2010 and has completed 11 years of operation tenure till today. Different researchers have mentioned that any dumpsite having more than 10 operational years is categorized as matured landfill/dumpsite. The leachate samples were collected from the study area and tested for their physicochemical parameters. The effect of this leachate was also assessed on the groundwater quality parameters with the dilution factor of 1:100 in the three different seasons: Pre-monsoon (April), Monsoon (July), and Post Monsoon (October) in the year 2021. The test results were analyzed, and it was found that some hazardous metals were present in leachate samples. This may give rise to carcinogenic/non-carcinogenic health risks. To assess human health risks, the USEPA guidelines were adopted in the analysis. There is a residential area nearby the dumping site. In this study, adults and children are considered as receptors and pathways as oral and dermal. In all A. Dixit (B) · D. Singh Harcourt Butler Technical University, Kanpur 208002, UP, India e-mail: [email protected] D. Singh e-mail: [email protected] A. Dixit Pranveer Singh Institute of Technology, Kanpur 209305, UP, India S. K. Shukla Founding Research Group Leader, School of Engineering, Edith Cowan University, Joondalup, Perth 6027, WA, Australia e-mail: [email protected] Indian Institute of Technology Madras, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_24

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three seasons, the health risk was observed as non-carcinogenic, but children are more prone to health risks below the carcinogenic level. The chronic daily intake (CDI) during monsoon season was highest in the case of chromium (1.92 for children) and minimum for zinc (6.86 × 10–6 for adults). This study highlights that a child is a critical receptor during the monsoon season due to the highest value of the total hazard index, HItotal (= 0.68) among all values. Keywords Groundwater contamination · Heavy metals · Human health risk · Leachate

1 Introduction The generation of municipal solid waste (MSW) depends upon the lifestyle, urbanization, and income level of the population. Rapid population growth forces the habitants to use the contaminated land, water, and air which poses a severe health risk. It is expected that the total quantity of waste generated in low-income countries would increase by more than three times by 2050 (Kaza et al. 2018). Dumping hazardous mixed wastes in municipal solid waste (MSW) landfills can have catastrophic consequences. However, improper landfill construction can pose a serious hazard to both the environment and human health (Chaudhary et al. 2021; Dixit et al. 2023a). Inhalation of air pollutants, intake of contaminated drinking water, exposure to contaminated soils, and consumption of contaminated food are some of the pathways that generate human health risks. It might not always be possible to identify the precise characteristics of sources of groundwater contamination (Datta and Singh 2014). Groundwater is particularly vulnerable in areas with large population densities and extensive human usage of the land (Singh and Datta 2021; Zahra et al. 2021). Groundwater may also get contaminated by several common practices that degrade the environment, like the unrestrained and unlawful injection of chemicals into aquifers (Datta and Singh 2014). The hazard identification process involves identifying possible contaminants of significance that might endanger human health and the environment (Singh et al. 2022; Rajasekhar et al. 2018). Landfill leachate is produced by the release of waste moisture, decomposition of organic waste, and percolation of surface water into the landfill (Dixit et al. 2023b; Aldaeef and Rayhani 2014). Leachate is a toxic byproduct generated from landfills, which is a significant threat to public health because it may seep into groundwater and then move into surface water (Dixit et al. 2022; Parvin and Tareq 2021).

1.1 Study Area and Sampling Location Kanpur is one of the Indian cities with a population greater than 4 million with critical environmental and socio-economic issues of municipal solid waste management

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Fig. 1 Location of the study area (Municipal solid waste dumpsite, Kanpur): Google Earth as on 20.11.2021

(Dixit et al. 2022; Kanpur smart city 2021). The Ganga River flows across Kanpur on the north, while the Pandu River flows over it on the south (Yamuna). The city is situated between 25°26, and 26°58, north latitude and 79°31, and 80°34, east longitude (Sanitation and Plan(2013)). The sample collection of leachate was carried out from a municipal solid waste dumping site (26°27, 12,, N, 80°14, 19,, E) as presented in Fig. 1. Kanpur has a mild, semi-humid monsoon climate with an annual average temperature of 25.7 °C and 939 cm of annual rainfall (Climate data 2021, Bhadauria et al. 2022). Kanpur city has one MSW processing plant to handle the city’s per day generated municipal solid waste. The leachate samples were collected from municipal solid waste dumping sites in April 2021 (S-1), July 2021 (S-2), and October 2021 (S-3) to assess leachate properties and potential health risks. After cleaning the sample containers with distilled water and treating those with a 1 M solution of the preservative acid, representative samples were taken as described by different researchers (Rajasekhar et al. 2018; Baird et al. 2018). The samples were collected in 5-L plastic containers from a municipal solid waste disposal site in Kanpur, then transferred to the laboratory and kept at 5 °C temperature until the study was completed. The samples were maintained for heavy metal analysis by adding a few ml of concentrated HNO3 to the glass bottles. The heavy metal analysis is carried out

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Table 1 Chemical parameters of leachate S. No.

Parameters

S-1 (Pre-monsoon)

S-2 (Monsoon)

S-3 (Post Monsoon)

1

pH

8.4

7,67

8.14

2

Electrical conductivity (EC)

6720

8940

6875

3

Total dissolved solids (TDS) (mg/l)

3350

4540

3994

4

Biochemical oxygen demand as BOD (mg/l)

540

680

498

5

Chemical oxygen demand as COD (mg/l)

1250

2185

1185

6

Alkalinity as CaCO3 (mg/l)

612

712

687

7

Calcium (Ca) (mg/l)

208.5

252 2

196.8

8

Sulphate as SO4 (mg/l)

97.5

112.8

92.8

9

Chloride as Cl (mg/l)

105

108

126.9

10

Sodium as Na (mg/l)

108

115

112

11

Potassium as K (mg/l)

8.35

8.85

11.4

12

Iron as Fe (mg/l)

2.25

2.92

1.97

13

Copper as Cu (mg/l)

BDL

BDL

BDL

14

Cadmium as Cd (mg/l)

BDL

BDL

BDL

15

Chromium as Cr (mg/l)

4.1

4.8

3.8

16

Lead as Pb (mg/l)

BDL

BDL

BDL

17

Nickel as Ni (mg/l)

1.12

1.25

1.32

18

Zinc as Zn (mg/l)

0.45

0,48

0.46

BDL

19

Silver (Ag) (mg/l)

BDL

BDL

20

Total Kjeldahl Nitrogen 8.8 (TKN) (mg/l)

9.5

9.2

21

Ammonical Nitrogen (NH3-N)(mg/l)

12.5

14.7

10.5

BDL—Below Detection Level

following the criteria of Water Environment Federation (1999). The 21 parameters (listed in Table 1) were examined on the leachate samples.

2 Assessment of Health Risk The presence of heavy and trace metals in groundwater has hazardous impacts on the aquatic ecosystem and human health when these concentrations exceed the maximum permissible limits or accumulate over time (Moldovan et al. 2020). When

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319

leachate percolates from the soil into subsurface water, its contents are diluted in a 1:100 ratio (Chaudhary et al. 2021; Christensen et al. 2001; Guleria and Chakma 2021). This dilution factor was adopted to estimate the concentration of chemicals in groundwater.

2.1 Hazard Identification The first stage in assessing human health risks caused by metal leaking from a dumping site is to identify the hazards (USEPA 1989). Some researchers highlighted that the most common metals having health hazards even present at a low level in water were cadmium (Cd), lead (Pb), chromium (Cr), copper (Cu), iron (Fe), nickel (Ni), and zinc (Zn) (Giri and Singh 2015; Moldovan et al. 2020). The major health effects due to these study metals are lung cancer, gastrointestinal damage, brain edema, and respiratory failure (Dixit and Roy 2016; Dutta et al. 2006; Manoj et al. 2021). The variations in the concentration of Fe, Cr, Ni, and Zn in leachate in three seasons are presented in Fig. 2a–d. (a) 3.5

(b) 6

3

5 Cr (mg/l)

Fe (mg/l)

2.5 2

1.5 1

3 2 1

0.5 0

4

Pre-Monsoon

Monsoon

Post-Monsoon

0

Pre-Monsoon

Season (d)

1.4 1.2 1

Zn (mg/l)

Ni (mg/l)

Post-Monsoon

Season

(c) 1.6

0.8 0.6 0.4 0.2 0

Monsoon

Pre-Monsoon

Monsoon Season

Post-Monsoon

0.5 0.49 0.48 0.47 0.46 0.45 0.44 0.43 0.42 0.41

Pre-Monsoon

Monsoon Season

Post-Monsoon

Fig. 2 a Seasonal variation in concentration of Fe in leachate. b Seasonal variation in concentration of Cr in leachate. c Seasonal variation in concentration of Ni in leachate. d Seasonal variation in concentration of Zn in leachate

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2.2 Exposure Assessment Exposure assessment is the process of measuring or estimating the magnitude, frequency, and duration of human exposure to an agent in the environment or estimating future exposures for an agent that has not yet been released. Exposure assessment can be done by using three different parameters: Selection of contaminant, selection of exposure pathways, and selection of receptor.

2.2.1

Selection of Contaminant

Based on the presence of metal concentrations in leachate Iron (Fe), Total chromium (Cr), Nickel (Ni), and Zinc (Zn) were considered in the study (Table 1).

2.2.2

Selection of Exposure Pathways

The study area is surrounded by an urban population and consumes underground water through different ways of exposure. Direct groundwater intake and skin contact through daily activities are the major exposure pathways. So direct ingestion and dermal contact were considered exposure pathways for health risk assessment. A similar type of exposure pathway was also considered by different researchers (Chaudhary et al. 2021; Guleria and Chakma 2021).

2.2.3

Selection of Receptor

In this study, a receptor is considered as human from the age of 0–70 years and divided into two classes child (0–21 years) and adult (21–70 years). An adult of working age at the dumpsite is more likely to be the critical receptor in most cases, and a child can be considered the most sensitive receptor nearby the dumpsite (Agency 2009).

2.3 Risk Assessment and Characterization Most of the researchers (Ghosh et al. 2020; Giri and Singh 2015; Guleria and Chakma 2021) adopted the USEPA guidelines for health risk assessment. According to Moldovan (2020), the chronic daily intake (CDI) can be calculated by using Eqs. (1) and (2) for direct ingestion and dermal pathways, respectively (Agency 2009): C D I oral =

C ∗ I R ∗ EF ∗ ED BW ∗ AT

(1)

Assessment of Human Health Risk Due to Contaminated Groundwater …

C D I der mal =

321

C ∗ SA ∗ K p ∗ ET ∗ E F ∗ E D ∗ C F BW ∗ AT

(2)

where C is the concentration of contaminant (µg/ L), IR is the ingestion rate of contaminant (L/day), EF is the exposure frequency (days/year), ED is the exposure duration (years), BW is the average body weight (kg), AT is the average exposure duration (days), SA is the skin surface area (cm2 ), Kp is the permeability coefficient of permeability (cm/h), ET is the exposure time (h/event), and CF is the conversion factor (L/cm3 ). The values of different parameters required for the calculation of CDI are presented in Table 2 (Moldovan et al. 2020; Saleh et al. 2021). The Hazard Quotient (HQ) was estimated for the oral and dermal paths to assess non-carcinogenic health hazards and calculated by comparing chronic daily intake of pollutants from various exposure routes to the associated reference dosage (RfD) (Eq. 3) (Giri and Singh 2015; Moldovan et al. 2020). Reference dosages for different contaminants are presented in Table 3. A Hazard Index (HI) was used to measure the risk of several metals in groundwater by adding HQ values of all of the contaminants (Eq. 4). If the calculated value of HI > 1 then the risk is characterized as non-carcinogenic adverse health risk with non-carcinogenic health effect and HI < 1 shows an acceptable level of non-carcinogenic risk (Boateng et al. 2018; Negi et al. 2020). The total health risk is the summation of the hazard index of both oral and dermal pathways which can be presented in Eq. 5 (Moldovan et al. 2020), and this value of HI total indicates the level of non-carcinogenic potential risk due to contaminated groundwater. Table 2 Various parameters for the oral and dermal pathways (Moldovan et al. 2020; Saleh et al. 2021) Parameter

Adult

Child

Ingestion rate (IR) (L/day)

2.2

1

Exposure frequency (EF) (days/year)

365

365

Exposure duration (ED) (years)

70

10

Average body weight (BW) (kg)

70

25

Average exposure duration (AT) (days)

25,500

3650

Skin surface area (SA) (cm2 )

18,000

6600

Exposure time (ET) (h/event)

0.58

1.00

Exposure frequency (EF) (days/year)

350

350

Exposure duration (ED) (years)

30

6

Conversion factor (CF) (L/cm3 )

1/1000

1/1000

Average body weight (BW) (kg)

70

25

Average exposure duration (AT) (days)

10,950

2190

Permeability coefficient (Kp) (cm/h) for Fe, Cr, Ni

1 × 10−3

1 × 10−3

10−4

6 × 10−4

For oral pathway

For dermal pathway

Permeability coefficient (Kp) (cm/h) for Zn

6×

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Table 3 Reference dose for different contaminants and pathways (Moldovan et al. 2020)

Contaminant

RfD (Oral) (µg/kg-day)

RfD (Dermal) (µg/kg-day)

Fe

300

45

Cr

3

Ni

20

5.4

Zn

300

60

H azar d Quotient(H Q) =

0.015

C DI Rf D

(3)

H azar d I ndex(H I ) = H Q Fe + H Q Cr + H Q N i + H Q Z n

(4)

T otal H azar ds I ndex H I total = H I oral + H I der mal

(5)

3 Results and Discussion The non-carcinogenic potential health risk due to considered heavy metals (Fe, Cr, Ni, Zn) depends on the CDI values obtained in different seasons. Based on the CDI value, the hazard quotient, hazard index, and total hazard index for different metals are shown in Table 4. From Table 4, it is obvious that HItotal was higher in the monsoon season (0.53 for adults and 0.64 for children) than in another season. The obtained values of HI for all the seasons were less than unity, indicating that the health risk was at noncarcinogenic level. HQ for all the contaminants less than unity and similar results were also reported by Negi et al. (2020). In the pre- and post-monsoon periods, HItotal values were determined to be 0.45 and 0.44 for adults, and 0.58 for children indicating that the dumpsite has a comparatively low impact on the health of the people who live nearby the site, while the HItotal values of monsoon season for adults and children were 0.53 and 0.68 respectively showed a comparatively high risk to the population.

4 Conclusions This study was performed for a municipal solid waste dumpsite which is surrounded by an urbanized area. The main aim was to determine the health risk due to migrated contaminants from the dumped site in groundwater. In the case of children

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Table 4 Values of HQ, HI, and HI total for different metals and seasons S-1 (Pre-monsoon)

S-2 (Monsoon)

S-3 (Post Monsoon)

Oral

Oral

Oral

Dermal

Dermal

Dermal

Adults Fe

2.36E-03

7.15E-07

3.07E-03

9.28E-07

2.07E-03

3.76E-06

Cr

4.30E-01

3.91E-03

5.04E-01

4.58E-03

3.99E-01

2.17E-02

Ni

1.76E-02

2.97E-06

1.97E-02

3.31E-06

2.08E-02

2.10E-05

Zn

4.72E-04

1.07E-07

5.04E-04

1.14E-07

4.83E-04

6.58E-07

HI

4.50E-01

3.91E-03

5.27E-01

4.58E-03

4.22E-01

2.17E-02

HItotal

0.45

0.53

0.44

Children Fe

3.00E-03

1.27E-06

3.89E-03

1.64E-06

2.63E-03

6.65E-06

Cr

5.47E-01

6.92E-03

6.40E-01

8.10E-03

5.07E-01

3.85E-02

Ni

2.24E-02

5.25E-06

2.50E-02

5.86E-06

2.64E-02

3.71E-05

Zn

6.00E-04

1.90E-07

6.40E-04

2.03E-07

6.13E-04

1.16E-06

HI

5.73E-01

6.93E-03

6.70E-01

8.11E-03

5.37E-01

3.85E-02

HItotal

0.58

0.68

0.58

as a receptor, the values of HItotal are higher than that of the adults. The study area is situated adjacent to a school where the children (3–17 years) are the specific receptor using the groundwater for their drinking and other activities. Among the values of HItotal for children, maximum values are found as 0.68 during the monsoon season. Thus, school authorities should take care of this fact and avoid or minimize the use of underground water during this season. It is also suggested to the urban local body (Kanpur Municipal Corporation) to take necessary precautionary steps to minimize health hazards toward children being the most sensitive receptor near the dumpsite, although, from this study, it is clear that both types of receptors have no severe health impacts (HI < 1 for all cases), i.e. non-carcinogenic, due to leachate-contaminated underground water for oral and dermal pathways.

References Aldaeef AA, Rayhani MT (2014) Hydraulic performance of Compacted Clay Liners (CCLs) under combined temperature and leachate exposures. Waste Manag 34:2548–2560. https://doi.org/10. 1016/j.wasman.2014.08.007 Baird RB, Andrew D, Eaton EWR (2018) Standard methods for the examination of water and wastewater. Am Public Heal Assoc Bhadauria S, Dixit A, Singh D (2022) Estimation of air pollution tolerance and anticipated performance index of roadside plants along the national highway in a tropical urban city. Environ Monit Assess 194(11). https://doi.org/10.1007/s10661-022-10483-0

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Boateng TK, Opoku F, Akoto O (2018) Quality of leachate from the Oti Landfill Site and its effects on groundwater: a case history. Environ Earth Sci 77:0. https://doi.org/10.1007/s12665-0187626-9 Chaudhary R, Nain P, Kumar A (2021) Temporal variation of leachate pollution index of Indian landfill sites and associated human health risk. Environ Sci Pollut Res 28:28391–28406 Christensen TH, Kjeldsen P, Bjerg PL, Jensen DL, Christensen JB, Baun A, Albrechtsen HJ, Heron G (2001) Biogeochemistry of landfill leachate plumes. Appl Geochem 16:659–718. https://doi. org/10.1016/S0883-2927(00)00082-2 City Sanitation Plan (2013) http://kmc.up.nic.in/smartcity/Kanpur%20CSP_%20Final_Report_% 202013_RS_ASCI.pdf. Accessed 17 Decemeber 2021 Datta B, Singh D (2014) Optimal grondwater monitoring network design for pollution plume estimation with active sources. Int J GEOMATE 6:864–869. https://doi.org/10.21660/2014. 12.3258a Dixit A, Roy S (2016) Assessment of health risk due to contaminated soil and remediation techniques—a case study. Resour Environ 6:148–153. https://doi.org/10.5923/j.re.20160606.08 Dixit A, Singh D (2022) Significance of landfills on climate change: challenges and opportunities. Water Energy Int 65(9):15–21. https://www.indianjournals.com/ijor.aspx?target=ijor:wei&vo Dixit A, Singh D, Kumar S (2022) Changing scenario of municipal solid waste management in Kanpur city. J Mater Cycles Waste Manag, India. https://doi.org/10.1007/s10163-022-01427-4 Dixit A, Singh D, Shukla SK (2023a) Assessment of human health risk due to leachate contaminated soil at solid waste dumpsite, Kanpur (India). Int J Environ Sci Technol. https://doi.org/10.1007/ s13762-023-04868-y Dixit A, Singh D, Kumar SK (2023b) Effect of expansive soils on swelling behavior of encapsulated sodium bentonite of geosynthetic clay liner (GCL). Mater Today: Proc. https://doi.org/10.1016/ j.matpr.2023.02.220 Dutta S, Upadhyay VP, Sridharan U (2006) Environmental management of industrial hazardous wastes in India. J Environ Sci Eng 48:143–150 Environment Agency (2009) Updated technical background to the CLEA model—Science report SC050021/SR3 Federation WE (1999) Standard methods for the examination of water and wastewater standard methods for the examination of water and wastewater Giri S, Singh AK (2015) Human health risk assessment via drinking water pathway due to metal contamination in the groundwater of Subarnarekha River Basin, India. Environ Monit Assess 187. https://doi.org/10.1007/s10661-015-4265-4 Ghosh GC, Khan MJH, Chakraborty TK, Zaman S, Kabir AHME, Tanaka H (2020) Human health risk assessment of elevated and variable iron and manganese intake with arsenic-safe groundwater in Jashore, Bangladesh. Sci Rep 10:1–9. https://doi.org/10.1038/s41598-020-621 87-5 Guleria A, Chakma S (2021) Probabilistic human health risk assessment of groundwater contamination due to metal leaching: a case study of Indian dumping sites. Hum Ecol Risk Assess 27:101–133. https://doi.org/10.1080/10807039.2019.1695193 Kanpur climate data (2022) https://en.climate-data.org/ Accessed 12 January 2022 Kanpur smart city (2021) https://kanpursmartcity.org/about-us. Accessed 21 Apr 2021 Kaza S, Yao LTBP, WF V (2018) ‘What a Waste 2.0’ a global snapshot of solid waste management to 2050 Manoj S, RamyaPriya R, Elango L (2021) Long-term exposure to chromium contaminated waters and the associated human health risk in a highly contaminated industrialised region. Environ Sci Pollut Res 28:4276–4288. https://doi.org/10.1007/s11356-020-10762-8 Moldovan A, Hoaghia MA, Kovacs E, Mirea IC, Kenesz M, Arghir RA, Petculescu A, Levei EA, Moldovan OT (2020) Quality and health risk assessment associated with water consumption—a case study on karstic springs. Water (Switzerland) 12. https://doi.org/10.3390/w12123510

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Negi P, Mor S, Ravindra K (2020) Impact of landfill leachate on the groundwater quality in three cities of North India and health risk assessment. Environ Dev Sustain 22:1455–1474. https:// doi.org/10.1007/s10668-018-0257-1 Parvin F, Tareq SM (2021) Impact of landfill leachate contamination on surface and groundwater of Bangladesh: a systematic review and possible public health risks assessment. Appl Water Sci 11:1–17. https://doi.org/10.1007/s13201-021-01431-3 Rajasekhar B, Nambi IM, Govindarajan SK (2018) Human health risk assessment of ground water contaminated with petroleum PAHs using Monte Carlo simulations: a case study of an Indian metropolitan city. J Environ Manage 205:183–191. https://doi.org/10.1016/j.jenvman. 2017.09.078 Saleh HN, Valipoor S, Zarei A, Yousefi M, Asghari FB, Mohammadi AA, Amiri F, Ghalehaskar S, Khaneghah AM (2021) Correction to: assessment of groundwater quality around municipal solid waste landfill by using Water Quality Index for groundwater resources and multivariate statistical technique: a case study of the landfill site, Qaem Shahr City, Iran (Environmental G. Environ Geochem Health 43:2205. https://doi.org/10.1007/s10653-020-00765-2 Singh D, Datta B (2021) Sequential characterization of contaminant plumes using feedback information. Springer, Cham, pp 21–25 Singh D, Shende V, Agrahari SK, Devnani GL, Jaiswal Y, Pal SL (2022) Modeling and optimization of electrocoagulation process for removal of Cr(VI) and total suspended solids from tannery effluent. Desalin Water Treat 278:93–101. https://doi.org/10.5004/dwt.2022.29010 USEPA (1989) Risk assessment guidance for superfund. Volume I Human Health Evaluation Manual (Part A). I:289. doi: EPA/540/1–89/002 Zahra T, Tiwari AK, Chauhan MS, Singh D (2021) Evaluation of Groundwater Quality Using Multivariate Analysis: Rae Bareli District, Ganga Basin, Uttar Pradesh. Soc Earth Sci Ser 37–52. https://doi.org/10.1007/978-3-030-60869-9_3

Scenario of Air Quality Index in India and Its Effect on Human Health and Policies for Green and Clean India Shiv Lal, Kumud Tanwar, Prakash Chandra Dabas, and Ashok Kumar Kakodia

Abstract Air pollution is the main source of bronchitis disease and lung infections. The air quality index is the measuring number of total air pollution in the air which includes PM2.5, PM10, CO2 , CO, NOx, and HC. The AQI should be maintained in limit and its higher value indicated higher pollution. Twenty cities in India come into the most polluted fifty cities in the world which have the highest AQI of more than 266. It is an alarming time to think about the AQI of air and implement the policies by which AQI can be reduced. This manuscript is presenting the Indian AQI scenario and its effect on human. A small study of Rajasthan is also carried out to rethink about the AQI scenario of this state. The policies for reducing the AQI and increasing the green and cleanliness of India are also presented. Keywords Air pollution · PM2.5 · PM10 · Air quality index

1 Introduction Air pollution is defined by the Oxford dictionary as “the presence in or introduction into the air of a substance which has harmful or poisonous effect”. It refers to the release of pollutants into the air which is injurious to human health and the planet as a whole. As per the WHO report, air pollution is responsible for approximately 7 million deaths in the world. The whole globe air is likely to be polluted because nine S. Lal (B) Department of Mechanical Engineering, Rajasthan Technical University, Kota 324010, India e-mail: [email protected] K. Tanwar Department of Chemistry, Maharani Girls College, Jaipur, India P. C. Dabas Department of Chemistry, Government Girls College, Shahpura, Jaipur, India A. K. Kakodia Department of Chemistry, Government College Rajgarh, Alwar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_25

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out of 10 human beings currently breathe polluted air or air which exceeds the limit of pollutant as per WHO guidelines. The first clean air act was established in 1970 in the U.S. which authorised the U.S. environmental protection agency to regulate the emission of other pollutants. John Walke (Director of NRDC clean air project) says that most of the air pollution comes from energy use and production (https:// www.nrdc.org/stories/air-pollution-everything-you-need-know#whatis). As per the Indian pollution scenario, at least 140 million people breathe air that has 10 times more pollutants than the WHO safe limit. Air pollution is mainly caused by power plants and industries which contribute 51% of total air pollution apart from 27% by vehicle, 17% from crop burning, and 5% by other sources. The air (prevention and control of pollution) act was passed in 1981 in India to regulate the air pollution but it failed to reduce the air pollution after that government of India launched the national Air Quality Index (AQI) in 2015 and “the clean air program in 2019”. Its aim is to reduce at least 20–30% reduction in PM2.5 and PM10 concentration by 2024 (Ai and Tan 2021; https://en.wikipedia.org/wiki/Air_pollution_in_India). On daily basis, the air quality can be reported by AQI. It is measured how the air pollution affects the human health within a short time. The purpose of the AQI is to help the people how the local air quality impact their health (https://www.bus iness-standard.com/about/what-is-air-quality-index). AQI is an integral part of the environmental quality index (EQI) which was used by the national wildlife federation (NWF) of U.S. in late 1960 (Inhaber 1975). Chelani et al. (2002) presented the AQI model for the evaluation of air pollution indexing and compared the AQI of five fast-growing major cities in India like Delhi, Calcutta, Mumbai, Chennai, and Nagpur. It was observed that Delhi was the most polluted city from 1991–1996, and it is the most polluted even in 2022 also; Nagpur was found less polluted as compared to the other four cities. Suman (https://en. wikipedia.org/wiki/Air_pollution_in_India) reviewed the air quality Index methods to interpret air status. It used National Ambient Air Quality Standards (NAAQS) dependent on the AQI method and air quality depreciation index to estimate the air quality status in four categories as Low, moderate, high, and very high polluted. Bishoi et al. (Suman 2020) calculated the AQI based on factor analysis of the National air quality index (NAQI) which incorporated the deficiencies of USEPA methods. It was stated that the higher value of AQI indicates more pollution in relative terms. The AQI and air pollution affect the human health and so many researchers have worked and presented them in many ways. Mostly air pollutants badly affect the human body and these affect in various ways as follows: Eye irritation occurs due to NOx, O3, PAN, and particulates; Nose and throat irritation due to SO2 , NOx, etc.; Odour nuisance by H2S, SO2, NO2, and hydrocarbons; irritation of respiratory tract caused by SOx, NOx, CO, and O3 ; Bronchitis and Asthma caused by the high concentration of SO2, NO2 , and SPM; Reducing the O2 -carrying capacity of blood due to the high concentration of CO and NO; and Higher concentrations of heavy metals like Lead and Mercury cause poisoning and higher concentration is responsible for damage to liver and Kidney (Bishoi et al. 2009; Gorai and Goyal 2015; Ashikin et al. 2014; Zahran et al. 2018; Nowak et al. 2014; Krzyzanowski 2021;

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Maji et al. 2017; Martuzzi et al. 2003; Manisalidis et al. 2020; Anenberg et al. 2020; Malmqvist et al. 2018). The overall climate has changed now due to increasing the AQI day by day. The AQI measurement started in the major cities of the globe and presented the work or observations accordingly, in the same line of action. Hong et al. (Hong et al. 2019) presented the Impacts of climate change on future air quality and human health in China. The pandemic situation is increased due to the high AQI that’s why the lockdown was implemented. Due to the lockdown during COVID-19, the AQI was reduced as confirmed by the research work of Gope (2021) and Kaur (2021). The paper deals with the Global AQI scenario as well as the Indian AQI scenario after that the effect of various AQI ingredients on human health is to be described. The Indian government’s new plans and schemes for reducing AQI are also elaborated in this communication. The small discussions on the Rajasthan state are also carried out in this manuscript.

2 Material and Methods 2.1 Scenario of Air Quality Index (AQI) The EPA as well as Indian standards calculate the AQI using five measured air pollutants; these are given as Particle pollution/Particulate matters (PM2.5/PM10), Carbon dioxide (CO2 ), Sulphur dioxide (SO2 ), Nitrogen dioxide (NO2 ), and ground-level ozone (O3 ). As concerns with AQI, a higher value of it indicates greater pollution. The AQI index is categorised into six major groups as shown in Table 1 with causes of measure health effects. The annual mean and 24 h mean limitations of PM2.5 under WHO guidelines and Indian standards are shown in Table 2. Table 1 AQI level and measure health effect (https://www.lung.org/clean-air/outdoors/air-qualityindex) S. No. 1

AQI Level 0-50

Category of Level Good

Colour Coding Green

Minimum Impact

2

50-100

Satisfactory

Yellow

It may cause minor breathing difficulties for sensitive people

3

101-200

moderately polluted

Orange

4

201-300

Poor

5

301-400

Very poor

Purple

6

401-500

Severe

Maroon

It may cause breathing difficulties for sensitive people or children / old persons with lung disease like asthma, and discomfort with heart disease It may cause prolonged exposure and discomfort to the people with heart disease It may cause respiratory illness on prolonged exposure with lung and heart disease. It may cause respiratory issues in healthy peoples and serious health issue in already sick people and difficulty may be experienced during physical activities.

Red

Measure Health Impact

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Table 2 Air quality limitation guideline (https://www.who.int/news-room/fact-sheets/detail/amb ient-(outdoor)-air-quality-and-health) S. no

Duration

WHO Guidelines For PM10 µg/m3

O3 , µg/m3

NO2 , µg/m3

SO2 , µg/m3

5

15

60 (8 h mean Peak Season)

10

40

15

45

100 (8 h daily 25 Maximum)

For M2.5 µg/m3 1

Annual Mean

2

24-h Mean

As per the AQI standard, the world’s most populated 50 cities according to the rank are shown in Table 3. It is observed that 21 Indian cities contribute their name as the most polluted cities in the world. The first most polluted city of India is Kalyan (AQI 718) with the rank of 5th and next to it is Bhiwandi (AQI 452) with 15th rank; the other 18 cities of India ranked under 50 are also presented. Aksu, China, is the most polluted city on the earth with 1042 AQI and scored First rank. It is also observed that the first four most polluted cities are situated in China; it means the pollution is proportionately increasing with Industrialization, mines expansion, forest reduction, and conventional energy production. The annual mean AQI is represented by colours in the map of India for the years 2001, 2005, 2010, 2015, and 2020 in Fig. 1. It shows that the maroon region is observed in a few cities after red colour indicates the poor environment and observed majorly in the area of Delhi, Haryana, Uttar Pradesh, Rajasthan, and in few cities of other states of India. Most of the states are in the range of moderate or satisfactory. It is found that India is a highly polluted country and for reducing it clean air program is already started in 2019. Rajasthan is 6th in the rank of polluted states over the other 36 states of India. The yearly rank is shown in Fig. 2. It is also observed that the AQI is varying between 45 and 65 from the year of 1998 to 2020; it comes into the satisfactory range throughout but some small cities of Rajasthan also come into the moderate and poor range of AQI. The clean and green city or clean and green campus may help to reduce the AQI.

2.2 PM2.5 Pollution Bins from 2001 to 2020 in India PM2.5 plays an important role in increasing AQI which mostly affects the human health in so many manners like reducing the function of the lungs. Figure 3 shows the percentage area of India under various pollution bins like PM2.5. It is observed that the highest area of 30.3% was polluted with 29–30 µg/m3 in the year 2001 which was caused by PM2.5 concentration. The higher pollution due to PM2.5 is offering more than 100 µg/m3 in the years 2010, 2018, and 2020. As per the record of EPA, the lowest PM2.5 concentration was observed in the lockdown period in the globe

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Table 3 Most polluted 50 cities of the world (https://www.aqi.in/real-time-most-polluted-city-ran king) S.No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

City with Country Aksu, China Wuwei, China Xibeijie, China Jiayuguan, China Kalyan, India Jinchang, China Afula, Israel Qiryat Ata, Israel Yoqneam Illit, Israel Shuangqiao, China Khartoum, Sudan Fuxin, China Tromso, Norway

AQI 1042 806 791 745 718 662 648 542 541 494 492 474 468

S.No. 26 27 28 29 30 31 32 33 34 35 36 37 38

Bandar-e Bushehr, Iran Bhiwandi, India Ulhasnagar, India Bhayandar, India Junnar, India Ahmadnagar, India Al Jahra, Kuwait Baghdad, Iraq Daman, India Silvassa, India Diu, India Nesher, Israel

459 452 426 421 418 417 413 407 385 385 384 383

39 40 41 42 43 44 45 46 47 48 49 50

City with Country Nasik, India Bandar E Mahshahr, Iran Tongliao, China Navsari, India Et Tira, Israel Kilis, Turkey Dongta, China Ahvaz, Iran Saharsa, India Begusarai, India Mardin, Turkey Dubai, United Arab Emirates Faizabad, India

AQI 382 380 364 360 355 348 345 343 340 338 329 326 326

Shahjanpur, India Sitalpur, India Bahraich, India Iranshahr, Iran Koratagere, India Manama, Bahrain Tieling, China Fatehpur, India Patna, India Muzaffarpur, India Yakacik, Turkey Dezhou, China

320 320 305 304 300 298 296 295 290 282 275 266

(Earth). Therefore, when the PM2.5 concentration is higher in the region, it means a highly polluted area. The percentage of the population of India that comes under the various pollution bins of PM2.5 is shown in Fig. 4. It is observed that no area is found pollution free but it is observed that less than 5% population lives in very low pollution area. Mostly the population of India is living under the limit of PM2.5 (20–70 µg/m3). But approximately 10 percentage of the population is living in the most polluted area where PM2.5 is observed to be more than 100 µg/m3. Table 3 represents the most polluted cities of the world and so many cities are listed from India.

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(a) Pollution Map 2001

(b) Pollution Map 2005

(c) Pollution Map 2010

(d) Pollution Map 2015

(e) Pollution Map 2020

Fig. 1 Air quality in India -Reanalysed PM2.5 Pollution for years 2001,05,10,15, and 20 (https:// urbanemissions.info/india-air-quality/india-satpm25/)

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Average annual AQI of Rajasthan

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WHO Guideline

Indian Standard

Average annual AQI

70.00 60.00 50.00 40.00 30.00 20.00 10.00 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

0.00

Year Fig. 2 Average Pollution rank of Rajasthan among the other 36 states in India

% Area of India under various pollution bins

2001

2005

2010

2015

2020

35 30 25 20 15

10 5 0 100 PM2.5 Concentration bins (µg/m3)

Fig. 3 Percentage area of India under various pollution bins

2.3 AQI and Health The higher AQI will affect the human health and it is a cause of various infections and sickness syndromes. Figure 5 represents the various reasons for concerns to AQI. The lifespan of human is decreasing due to increasing infections like skin infection, eye infection, TB & lung infection, decreasing breathiness, and cancer. Higher AQI will also be increased by the global warming. The effect of pollution like PM0.1, PM2.5, and PM10 is shown in Fig. 6. It is observed that the fine particles of pollution insert into the lungs with breathing and

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% Population of India under various pollution bins

30 2001

2005

2010

2015

2020

25 20 15 10 5 0 100 PM2.5 Concentration bins (μg/m3)

Fig. 4 Percentage population of India under various pollution bins

Reasons for Concern Decrease in lifespan No response Global warming Impact on weather Cancer Eye infection Skin infection General health problems TB and Lung infection

Aasthama & Breathlessness 0

10

20

30

40

50

60

Fig. 5 Reasons for concern of AQI

are deposited inside the alveolar, lungs, and mouth. They infected the lungs and reduced their working which generate asthmatic problem.

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Fig. 6 Various micro-particles come into the Human body via breathing

2.4 Various Plans of the Indian Government to Reduce AQI 2.4.1

Greener and Cleaner India Program

The world environment day 5 June, 2021 was celebrated by the Indian government with the launching of seven schemes towards Greener and Cleaner India for “Ecosystem Restoration”. This is a mark of the beginning of the United Nations decade on “Ecosystem restoration” from 2021to 2030, in which the government included the following seven programs as follows (https://www.jagranjosh.com/general-knowledge/world-environment-dayimportant-government-schemes-for-a-greener-and-cleaner-india-1622825398-1): 1.

Namami Gange Programme which was already launched in 2014 and is renewed with the integration of a conservation mission with a budget of Rs. 20,000 Crores. The objective of the program is to reduce the pollution, and the conservation and rejuvenation of the national river Ganga. The Key achievements of the programs are Collection of solid floating waste from the surface of ghats and rivers. Creation of sewage treatment with capacity, construction of toilets in village panchayats of five Ganga Basin States, Industrial effluent monitoring, biodiversity conservation, and Ganga rejuvenation. This program has been implemented jointly by the state program management groups (SPMG) and the national mission of clean Ganga (NMCG). 2. Green Skill Development Program was launched in 2018 on May 15, its aim is to initiate the training of over 5.5 lakh workers in the environment and forest sectors. It helps to attain sustainable development goals, national biodiversity target, waste management rules (2016), and the nationally determined contribution. In the next phase, the course will train the workers for the advance stage which contributes to the training for para-taxonomists. 3. Swachh Bharat Abhiyan was launched in 2014 on October 2 (Mahatma Gandhi Jayanti). The mission was divided into two groups such as rural and urban.

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4.

5.

6.

7.

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Phase-1 of this program was completed in 2019 and at now its second phase is running (until 2024–25). Its aim is to aware the people for cleanliness and collect the garbage into three different containers (solid, green (Wastage of fruits and vegetables), and recyclable polythene, glass, etc.). Nagar Van Scheme was launched in 2020 on June 5, and its aim is to develop the 200 urban forests in pan India in the next five years. In this program, the Forest department is distributing the plants to the people for plantation nearby their houses, nagar palika/UIT/nagar nigams are working together to develop the green parks/green belt development in the cities, and DFO organises the van mahotsav at variable places to develop dense forest in the school/college or barren lands. Warje urban forest situated in Pune, Maharashtra, is the role model for the scheme and the scheme is funded by the compensatory Afforestation fund management and planning authority (CAMPA). Atal Bhujal Yojna was launched in 2019 on December 25 on the occasion of the birth anniversary of the former prime minister of India Sh. Atal Bihari Vajpeyee. It is implemented by Jal sakti mission (Earlier known as Ministry of water resources, River development and Ganga Rejuvenation). Jal Jeevan Mission was launched in August 2019 with the aim of supply of 55 L water per person per day to every rural person. Initially, the budget is Rs 3,50,000 crore for 1592 stress blocks in 256 districts. National Clean Air Program (NCAP) was launched in January 2019 with the aim to reduce 20–30% of air pollution in the next five years till 2024. The key achievement of the program is to shift the petrol and diesel vehicles to CNG, Smart city, implementation of the city-specific program, and AQI measurement in 122 cities. The effective implementation of NCAP means it can be followed into four sections such that the area should be designated as non-attainment; setting permissible pollution load targets based on the ambient air quality measurements; setting tracking procedures to ensure effective and timely implementation of the controls by sector; and outline the role of implementing agencies (Ganguly et al. 2020).

2.4.2

Green Campus Program

The green campus program enables Colleges, universities, and schools to conserve natural resources like water and biodiversity, energy efficiency, waste management, and educate students about climate change and sustainable development. The plantation program must be organised each year by the institutions and organised the awareness camp regarding the green and clean campus. The institutions can get PV systems at a discount rate or at billing agreement proposals (at very low/nominal rate like 3.20 Rs/unit) in India. AICTE, UGC, Governor offices, and state/national education departments are regularly issuing the orders regarding the organising of activities related to this program.

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Table 4 Transmission Line status of India as on 31 October 2021 (https://mnre.gov.in/green-ene rgy-corridor) State

Lines Target (ckm)

Line constructed (ckm)

Substations Target (MVA)

Substations Charged (MVA)

Tamil Nadu

1068

1058

2250

1850

Rajasthan

1054

984

1915

1915

Andhra Pradesh

1073

696

2157

635

502

456

937

353

1908

1320

7980

3660

618

565

2702

2490

2773

2714

4748

4365

Himachal Pradesh Gujarat Karnataka Madhya Pradesh Maharashtra Total

2.4.3

771

612

9767

8405

–

–

22,689

15,268

Green Energy Program

As per the Renewable energy country attractiveness index (RECAI), India is the third largest consumer as well as the third largest renewable energy producer country in the world. India had the 150GW RES capacity, whereas 48.55GW solar, 40.03GW wind, 4.83GW small hydro, 10.62GW bio-mass, 46.51 Large hydro, and 6.78GW nuclear energy. Among this, India has committed to the goal of 450GW RES capacity by 2030. It is also published by RECAI that India is number one in the energy production from the solar PV (https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/ power-and-utilities/power-and-utilities-pdf/ey-recai-57-top-40-ladder.pdf). The green energy corridor project was started by the Ministry of New and Renewable Energy (MNRE) in 2015–16 with the aim of synchronising electricity produced from renewable energy sources with conventional power in the grid. The Infra state transmission system (InSTS) project was started under the GECP in the rich RES states like Rajasthan, Maharashtra, Tamil Nadu, Karnataka, Himachal Pradesh, Madhya Pradesh, and Gujarat. The target or the transmission was revised with the new target of 97,000 ckm lines and substations of a total capacity of approx. 22,600 MVA and the target was achieved before the date of December 2020. The total project cost was 10,141 Crore Indian Rupees. The Indian Transmission line status as on 31 October 2021 is given in Table 4 as follows:

2.4.4

Green Building Program

The green building means a building that uses less water and energy and generated less waste along with which provides a healthier environment/space for the

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occupants. The IGBC (Indian Green Building Council) was formed in the year 2001 and it is a part of CII (Confederation of Indian Industries). The vision of the council is, “To enable a sustainable built environment for all and facilitate India to be one of the global leaders in the sustainable built environment by 2025”. There are three major green building rating and certification systems available in India and these are LEED, IGBC, Green Rating for Integrated Habitat Assessment (GRIHA), and Bureau of Energy Efficiency (BEE). The LEED (Leadership in Energy and Environmental Design) was developed by USGBC. The LEED rating system is used in mostly buildings by its silver, gold, platinum, etc. rating systems. India has achieved 7.17 billion square feet which is 75% of its green building target in 2020 (https://www.timesnownews.com/business-economy/real-estate/art icle/india-has-achieved-75-of-the-green-building-footprint-target-in/633584).

3 Conclusions The Indian AQI scenario is presented in this manuscript and it is observed that the air pollution index may be categorised into six colours or levels whereas green indicates good and the level of AQI lie between 0 to 50, Yellow indicates satisfactory and AQI level of 50–100, orange indicates satisfactory and AQI level of 101–200, Red indicates satisfactory and AQI level of 201–300, purple indicates satisfactory and AQI level of 301–400, and maroon above 400. The Effect of higher AQR on human health has also been presented. The AQI level of the whole India has been shown in the map. The human health’s most affecting parameter is PM2.5 and its concentration in the Indian environment has been presented for 20 years from 2001 to 2020. Higher AQI affects the human health and it is observed that the fine particles of pollution insert into the lungs with breathing and are deposited inside the alveolar, lungs, and mouth. They infected the lungs and reduced their working which generate asthmatic problem. Some policies related to green and clean India have been discussed these are Namami Gange program, green skill development program, swachh bharat abhiyan, nagar van scheme, Atal bhujal yojna, jal jeevan mission, green campus program, green energy program, and national clean air program.

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Sustainable Manufacturing: Road to Carbon Zero Footprints Ramandeep Singh , Ravinder Kumar , and Ujjwal Bhardwaj

Abstract Global warming, climate change and carbon footprints are very buzzy words these days. Reducing carbon footprint with economized utilization of resources and practices depends on how sustainability concepts are embraced in the value chain. Manufacturing sector being the largest contributor in GDP of major economies all over the globe, have a major share in carbon emissions too. Carbons neutral, carbon zero or net zero are few terms coined for carbon footprints. In current research, the paper authors have discussed the concept of sustainable manufacturing in the direction of reducing carbon footprints and adopting zero carbon manufacturing practices. Authors have discussed sustainable manufacturing in automobile sector and identified barriers on the path of adopting carbon zero concept in this sector. A case study was performed to discuss the sustainability practices and judge the importance of the identified barriers in the context of an Indian SME. Authors have also discussed the future directions to carbon zero footprints. Keywords Carbon zero · Carbon footprints · Net zero emission · Carbon zero manufacturing · Carbon neutral · Industry 4.0 · Sustainable manufacturing · SDGs

1 Introduction 1.1 A Subsection Sample Global warming and climate change are two terms, which don’t require any introduction. They have been etched in the minds of today’s generation as crucial aspects whose effects need to be evaluated at every step of the way. This is more than just an indicator and serves as a reminder of the time we live in, where environmental pollution has risen to such an extent that the concept of sustainability is not just R. Singh · R. Kumar (B) · U. Bhardwaj Amity School of Engineering and Technology, Noida, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_26

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a conscious choice but rather the need of the hour. Sustainable development can be defined as satisfying the needs and demands of the current generation in such a way that it does not affect the ability of the upcoming generations to meet their own needs (Brundtland 1987). Sustainable development was first believed to be a measure to be taken against the harmful degradation of the environment but since then has evolved to include the social and economic ideas of development and in today’s date is more commonly known as the sustainable development goals (SDGs) (Hajian and Kashani 2021). Sustainable development goals are a group of 17 goals that replace the millennium development goals (MDGs) and were introduced by the United Nations in 2015 as a part of the 2030 agenda for sustainable development (Palmer 2015). In current study, the authors will be discussing the manufacturing practices of automobile industries and how we can directly or indirectly achieve any of the SDGs, particularly targeting SDG 12 (Responsible consumption and production) and SDG 13 (Climate action), by adapting our industries to become more sustainable. Along these lines, the authors pose the following research questions (RQ): RQ-1: What is carbon zero and how can the carbon emissions of any organization be classified? To answer the above RQ, the authors have used the GHG protocol guidelines to classify the carbon emission into three categories and subsequently used the classification to define zero carbon. RQ-2: What is carbon zero manufacturing and what are some steps that can be taken to implement it? For answering the above RQ, we have adopted the example of automobile industry and discussed the history of steps taken in the industry that has directly or indirectly helped reduce carbon emissions. RQ-3: What are the barriers to carbon zero manufacturing? The authors have combed through existing literature to identify the critical barriers to the adoption and implementation of carbon zero manufacturing. Researchers all over the world are looking for solutions to combat the climate change problems as carbon emissions are rising rapidly with an annual rate of 2.7% as reported in the last decade. Policy makers are focusing on reducing carbon dioxide levels to be cut by half in order to stop the earth’s surface temperature to increase by 2 °C by 2050 (Olabi et al. 2022). Vehicular emissions are one of the major contributors in carbon emissions, however, the manufacturing of vehicles also leads to lots of carbon emissions. It is estimated that in the year 2016, 1.82 million vehicles were produced across the UK with 4.673 TWh of energy consumed resulting in the emission of 1.344 million tons of carbon dioxide (Giampieri et al. 2019). In 2010, 1.7 tons of CO2 emissions were produced per ton of hot rolled coil from basic blast furnace system companies just for manufacturing the steel that was to be used for passenger car production (Rootzén and Johnsson 2016). Countries all over the world have recognized the emission levels from automobile manufacturing industries and thus there has been a push for making the manufacturing practices more sustainable and shifting from traditional practices to zero carbon manufacturing.

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This paper will discuss the concept of carbon neutrality and carbon zero in Sect. 2. Section 3 will discuss the concept of carbon zero manufacturing in automobile manufacturing sector. Section 4 will discuss the barriers to zero carbon manufacturing implementation. Section 5 presents a case study in the context of an Indian SME and Sect. 6 will summarize and conclude the discussion.

2 Carbon Neutral Versus Carbon Zero Very often the climate change and global warming solutions, leads the discussion to on reducing carbon footprints. Carbon neutral and carbon zero are two common terms used in reference to carbon footprints. Both these terms are at times used alternatively with the main idea being that the end goal is to reduce carbon emissions to nil. However, the method or path adopted to achieve our goal is somewhat different. Carbon neutral can be defined as managing the negative effects of CO2 emissions by means of other activities that positively affect the environment in equal amounts or by purchasing carbon offsets so that the harmful effect on the environment gets cancelled out (Murray and Dey 2009). Carbon zero, on the other hand, has not been clearly defined in the literature. To clearly define zero carbon operations, we must first develop a clear understanding of our goals and associated problems. The greenhouse gases (GHG) protocol provides a clear guideline for industries to calculate their GHG emissions and is also accepted in the international community as the standard reporting measure (Versteijlen et al. 2017). Authors take inspiration from the GHG protocol and divide the carbon emission into similar three scopes in order to better define the goal of achieving carbon zero and the means to achieve it. Table 1 consists of the descriptions of the three scopes along with examples of the emissions with respect to the automobile industries. The first step for reduction of carbon emissions is calculation of the carbon emissions. The above classification will not only help in developing a better understanding of the CO2 emissions and its causes but also allow for calculation of the percentage contribution of each scope, thus helping industries concentrate their efforts towards Table 1 Classification of CO2 emissions Scopes

Descriptions

Scope 1 Direct emissions–generated from operations of an organization’s facilities and vehicles

Examples Manufacturing emissions, HVAC systems emissions

Scope 2 Indirect emissions–generated from the use Electricity purchased to run the plants and of purchased electricity, steam etc offices Scope 3 Tertiary emissions–including emissions Emissions from exhausts of vehicles sold due to use of the product, waste generated in operations, employee commutes, end of life of product, purchased goods etc

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the biggest contributors in order to lower their overall carbon emissions. The classification also allows us to understand that while it may be easy for many industries such as IT, banking and other service industries to buy offsets and brand themselves as carbon neutral, the manufacturing firms cannot adopt such initiatives as it will be very costly for them and thus is born the need for carbon zero. In this context, authors define carbon zero as the reduction of carbon emissions resulting from running operations throughout the value chain to a near zero level by use of more eco-friendly materials, relying on renewable sources of energy and adopting innovative processes. In this way, carbon zero pushes back against all three scopes of carbon emissions and looks to reduce the organization’s carbon footprint to the least possible value. One should also keep in mind that while reducing carbon footprint to zero emission levels has certain technological barriers associated with it in today’s date, carbon zero actually looks to drive innovation as well as the use of offsets rather than solely relying on buying offsets and continue to deplete the environment.

3 Carbon Zero in Automobile Industries Even though developing countries such as South Korea and India have presented their targets of becoming carbon free by 2050. Manufacturing and other key sectors are highly dependent on natural resources, so reduction of carbon emissions from manufacturing is a big challenge for the policymakers (Kannan et al. 2022). Moreover, the adoption of new technologies, process changes or material changes varies from product to product where in some cases the changes made do not affect the product characteristics while in others the product gets altered to such an extent that the market might respond in a negative manner, thus serving as a barrier or impediment to the technological revolution (Stefano et al. 2016). The same case was once considered to be with the automobile sector where the major issue with adopting any technological change was the worry of maintaining company’s profitability while ensuring market hold and customer satisfaction. However, this issue has since died down with the rise in production and sales of electric vehicles (EVs). Today EVs are not only available as normal daily commuting cars, rather there are EV hypercars such as the Rimac Nevera and electric trucks like Ford’s F-150 Lightning and GMC’s Hummer EV, proving that technological changes if planned correctly can create and sustain their own market. The roads to technology changes, however, are long and iterative requiring risky initiatives as evidenced in the work of (Taub and Luo 2015) in which they lead us through the history of manufacturing innovations for lightweight materials for automobiles. Their work presents the outlook of innovative material use for vehicle weight reduction following the global oil crisis of the 1970s as vehicle fuel efficiency is highly influenced by vehicle weight. Some of the manufacturing processes mentioned are:

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. Use of all wrought-aluminum cradle which was 10 kg lighter than usual with less number of parts. It was introduced by GM in 1999 Chevy Impala and the complex extrusions were welded using pulsed-gas metal arc welding performed by 40 robotic welders in 4 welding stations. . Change of car body structures with developments in aluminum vacuum die casting and introduction of aluminum alloys like Aural-2 etc. that allowed for the production of cars such as the Acura NSX. . The use of fiber reinforced polymers (FRP) in the 1953 Corvette made using the open mold/hand lay-up process was the first application of such light materials in a production line. The reduction in vehicle weight means not only a decrease in fuel consumption but also a decrease in CO2 emissions, thereby reducing the tertiary emissions (scope 3). However, working on tertiary emissions is not enough and one must adopt a holistic approach where a systematic methodology should exist to reduce emissions of all three scopes. The work of (Ball et al. 2009) discusses and guides the design of zero carbon manufacturing facilities. They talk about the solutions available in the market and how wind turbines and solar energy are the most obvious choices but rather suggest combining the technologies with the approaches. They also suggest the use of lean activities to reduce the waste output making the company processes in accordance with green manufacturing. Another suggestion by the authors is using green supply chain practices or green supply chain operations reference as well as the use of community waste by converting it into biomass as a relatively cheaper source of energy. They also talk about an integrated view where the use of value stream mapping (VSM), not in its primary current form, can provide the needed support for moving towards zero carbon manufacturing. Designing the plant layout and operations for a new company might be a lot easier than converting an existing organization into zero carbon firm. It can be a lengthy, time taking and iterative process. We recommend the use of the latest technologies available to mankind such as digital twins, mixed and extended reality, smart materials, complex adaptive systems, artificial intelligence etc. which all come under the umbrella of industry 5.0 (I5.0). Modeling and simulation are two of the biggest hard hitters in today’s world and by using them, one can see and feel virtually what their processes will look like and the reduction in their carbon levels with maybe even some recommendations from an AI system developed over time. Moreover, the processes should not be looked at independently but rather a complete top-down view of the organization should be the line of focus, where the use of pictorial and flowchart representation can help divide the organizational processes into various departments along with their inputs, outputs and carbon emissions. Concentration of efforts to reuse and process self-waste as input to other departments can help reduce the carbon footprint connected with the disposal of waste and sourcing of raw materials by quite some margins. Another major issue is the emission due to facility running. Proper steps need to be taken to evaluate the energy consumption and how much of it can

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be met using renewable sources. The remaining energy consumption should also be shifted from coal reserves to a low carbon producing source.

4 Barriers to Zero Carbon Manufacturing Implementation There always exists certain barriers towards the implementation of any concept. This section discusses the possible barriers that might impede the implementation of zero carbon manufacturing and divides these barriers into three basic categories: Social and Environmental, Technological, and Economic barriers. Economic barriers . High operational costs (Bataille 2020; Kannan et al. 2022; Zhang et al. 2022)— Various known and hidden costs ranging from shifting to new types of materials, need of latest machines for design and process changes. . Low profit margins (Bataille 2020)—Many sectors such as steel and cement industries are already working at low profit margins due to increased competition and cannot invest in new process technologies. . Inconsistent market demand—The ever-changing market demand for sustainable and eco-friendly products makes industries hesitate in implementing new processes. . Lack of funding (Kannan et al. 2022)—Lack of funds required to buy latest technologies, train the workforce and set up new business strategies. Technological barriers . Non availability of mature technologies (Ohene et al. 2022)—The technologies available for zero carbon manufacturing practices are in their developing stages and not mature enough for companies to implement. . Lack of skilled workforce (Kannan et al. 2022; Ohene et al. 2022; Zhang et al. 2022)—Unskilled labor will slow down and impede the implementation of new technologies for zero carbon manufacturing. . Lack of awareness in informal sector (Kannan et al. 2022)—A lot of manufacturing and servicing activities occur in the informal sector where the lack of awareness about technologies and zero carbon concept makes the implementation more difficult. Social and Environmental barriers . Lack of consumer awareness (Ohene et al. 2022; Zhang et al. 2022)—Aware consumers can make a conscious choice and even guide other consumers towards the use of more sustainable products, thereby growing the market demand. . Lack of government policies, regulations and incentives (Ohene et al. 2022; Zhang et al. 2022) – An underdeveloped policy means low pressure on organizations to adopt zero carbon manufacturing and companies are less likely to abide by the policies if there is no regulatory authority. Incentives such as tax exemptions and

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Table 2 Barriers of zero carbon operations S. no

Criteria

Barriers

References

1

Economic barriers

B1–High operational costs

Bataille (2020); Kannan et al. (2022); Zhang et al. (2022)

B2–Low profit margins

Bataille (2020)

B3–Inconsistent market demand

Own contribution

B4–Lack of funding

Kannan et al. (2022)

B5–Non availability of mature technologies

Ohene et al. (2022)

B6–Lack of skilled workforce

Kannan et al. (2022); Ohene et al. (2022); Zhang et al. (2022)

B7–Lack of awareness in informal sector

Kannan et al. (2022)

B8–Lack of consumer awareness

Ohene et al. (2022); Zhang et al. (2022)

B9–Lack of government policies, regulations and incentives

Ohene et al. (2022); Zhang et al. (2022)

B10–Lack of in-house reverse logistics

Kannan et al. (2022)

2

3

Technological barriers

Social and environmental barriers

government recognition can help increase the company’s brand value and make more companies adopt zero carbon manufacturing. . Lack of in-house reverse logistics (Kannan et al. 2022)—Recycling, remanufacturing and reusing are key sustainable practices and can also help companies achieve better control over the product lifecycle, thus helping reduce their carbon footprint. The barriers are also presented in Table 2 along with the relevant references.

5 Case Study in Indian SME Context It is very important to give regard to the opinions of the industries when taking decisions and making policies that will affect the way they carry out their processes. Thus, the authors approached ‘Devsons Engineering Works’ and provided them with a questionnaire survey to record their input. The organization is a manufacturing firm dealing in AC alternators and classifies itself as a small enterprise. They run a B2M (business to many) types of firm where they cater to businesses as well as individual customers. Some of their regular key customers are ‘Ammi Redy Arc Welding’ situated in Andhra Pradesh and ‘Crown Machinary’ situated in Delhi. The process

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Fig. 1 Process steps for manufacturing of AC alternators

steps followed by the organization for manufacturing the AC alternators are shown in Fig. 1. The organization was chosen for the case study due to their awareness on sustainability and carbon zero concepts. They also follow sustainable manufacturing practices and rate themselves very high with respect to their current practices. The organization was asked to rate the identified barriers from Table 2 on a scale of one to five with five being the highest and one being the lowest. Five barriers were rated as being of utmost importance and very crucial to the adoption and implementation of carbon zero manufacturing. The five barriers are B1—High operational costs, B2—Low profit margins, B3—Inconsistent market demand, B8—Lack of consumer awareness and B9—Lack of government policies, regulations and incentives. A crucial takeaway from the answers can be observed that 3 out of the 5 barriers with the highest importance come under economic criteria and the other two come under social and environmental criteria, while none of the barriers under technological criteria were considered a priority. This means that industries feel that the technological barriers are not affecting their ability to reach carbon zero manufacturing, but rather it is the economic and social and environmental barriers that pose the major impediment.

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Barriers B4—Lack of funding and B6—Lack of skilled workforce received a score of three and four respectively, while barrier B5—Non availability of mature technologies received the least score (two) out of all the barriers. It is also worth noting that the organization did not feel barriers B7—Lack of awareness in informal sector and B10—Lack of in-house reverse logistics were representative of the problems that they faced in adopting carbon zero manufacturing. Furthermore, the organization felt that a major barrier for them is “Power and Water supply shortage” meaning that there is insufficient supply of low carbon intensive energy and water, which are very essential for any manufacturing process. When asked if they see themselves adopting any steps to become a carbon zero manufacturing firm, they replied that adopting it is doable but implementing carbon zero completely would not be possible as being a small enterprise they do not have the much-needed resources that the medium and large enterprises have access to.

6 Summary and Conclusions The lack of sustainable practices has become a big issue for people and governments worldwide, with more and more people becoming aware of the need to shift to environmentally friendly practices and consumers selflessly opting for green products. Thus, a change has been prompted in the production and consumption patterns with the aim of reducing the carbon footprint to a near zero level. However, some companies and certain sectors might be perceived as treating this carbon free mission as a chance to enhance their brand image. Hence the need to differentiate from the path to carbon neutral. The current work successfully establishes that there is a difference between the concepts of carbon neutral and carbon zero. While both look to reduce carbon emissions in their own ways, relying on carbon credits and offsets is not viable as an option for the sustainable development of the earth. Companies need to first look at possible options for carbon reduction in their operations and value chain by working on reducing emissions from all the three scopes as discussed. . The work presents a novel idea for defining carbon zero and the associated three scopes of emissions based on which organizations can calculate their overall carbon footprint in a classified way. . The identification of possible barriers that impede the implementation of zero carbon manufacturing will help industries move towards reducing their carbon emissions. . The barriers have been studied and evaluated in the context of an Indian SME concerned with manufacturing of AC alternators. . The economic and the social and environmental barriers are the most influential while the technological barriers are relatively less important. Finally, it is the author’s opinion that sustainability should not be a brand concept to improve self-image in the market but rather steps taken to help the society and the environment, therefore, firms should first look to decrease their carbon footprint by

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striving to become carbon zero firms and only after exhausting all the options should move towards buying offsets to become carbon neutral and maybe even tie in the idea of becoming carbon negative. The work also presents a new research area that is not holistically explored. By identifying and evaluating the enablers, barriers and critical factors for a firm to adopt carbon zero, it would become much easier for organizations to implement zero carbon manufacturing practices and hence reduce their carbon footprint.

References Ball PD, Evans S, Levers A, Ellison D (2009) Zero carbon manufacturing facility—towards integrating material, energy, and waste process flows. Proc Instit Mech Eng Pt b J Eng Manuf 223(9):1085–1096 Bataille C (2020) Low and zero emissions in the steel and cement industries: barriers, technologies and policies Brundtland GH (1987) Our common future—call for action. Environ Conserv 14(4):291–294 De Stefano MC, Montes-Sancho MJ, Busch T (2016) A natural resource-based view of climate change: innovation challenges in the automobile industry. J Clean Prod 139:1436–1448 Giampieri A, Ling-Chin J, Taylor W, Smallbone A, Roskilly AP (2019) Moving towards low-carbon manufacturing in the UK automotive industry. Energy Procedia 158:3381–3386 Hajian M, Kashani SJ (2021) Evolution of the concept of sustainability. From Brundtland Report to sustainable development goals. In: Sustainable resource management. Elsevier, pp 1–24 Kannan D, Solanki R, Kaul A, Jha PC (2022) Barrier analysis for carbon regulatory environmental policies implementation in manufacturing supply chains to achieve zero carbon. J Clean Prod 358:131910 Murray J, Dey C (2009) The carbon neutral free for all. Int J Greenhouse Gas Control 3(2):237–248 Ohene E, Chan AP, Darko A (2022) Prioritizing barriers and developing mitigation strategies toward net-zero carbon building sector. Build Environ 109437 Olabi AG, Obaideen K, Elsaid K, Wilberforce T, Sayed ET, Maghrabie HM, Abdelkareem MA (2022) Assessment of the pre-combustion carbon capture contribution into sustainable development goals SDGs using novel indicators. Renew Sustain Energy Rev 153:111710 Palmer E (2015) Introduction: the 2030 agenda. J Glob Ethics 11(3):262–269 Rootzén J, Johnsson F (2016) Paying the full price of steel–perspectives on the cost of reducing carbon dioxide emissions from the steel industry. Energy Policy 98:459–469 Taub AI, Luo AA (2015) Advanced lightweight materials and manufacturing processes for automotive applications. MRS Bull 40(12):1045–1054 Versteijlen M, Salgado FP, Groesbeek MJ, Counotte A (2017) Pros and cons of online education as a measure to reduce carbon emissions in higher education in the Netherlands. Curr Opin Environ Sustain 28:80–89 Zhang A, Alvi MF, Gong Y, Wang JX (2022) Overcoming barriers to supply chain decarbonization: case studies of first movers. Resour Conserv Recycl 186:106536

Strategies of Installation of a Solar Integrated Carbon Capture and Sequestration (CCS) Plant on a 500-MW Size Coal-Fired Thermal Power Plant in India Vinod Krishna Sethi , Sudesh Kumar Sohani , Ravi Kumar Singh Pippal , and Meenakshi Samartha Abstract While coal contributes to over 58% of the entire commercial energy demand in India, it also accounts for more than 50% of gross pollutants. About 1500 MTPA of CO2 is emitted by coal-based thermal power plants with capacities greater than 209 GW, out of total annual emissions of about 3000 MTPA. India has pledged to reduce its carbon intensity by 33% at COP-21: Paris, which would result in a decrease in CO2 emissions intensity at our coal plants from an average level of 0.9–0.58 kg/kWh by 2030. This is achievable through the induction of postcombustion Carbon Capture and Sequestration (CCS) plants in our coal-fired thermal power plants. Out of a total Renewable Energy target of 175 GW by 2022, we have already achieved over 114 GW as of date and the current focus is on photovoltaic (PV) solar plant installation at a rapid pace. At COP-26, India pledged to meet its goal of generating 50% of its energy needs from renewable sources by 2030, as well as to build 500 GW of green power capacity by that time. Solar thermal technology with Thermal Storage (TES) is also picking up at a faster pace through R&D efforts (Rudra V. Kapila, R. Stuart H: Opportunities in India for Carbon Capture and Storage as a form of climate change mitigation. GHGT-9, l; Washington DC, USA (2008).). If solar thermal devices are used to generate steam for solvent regeneration and CO2 stripping in post-combustion CCS plants on coal-fired units, the CO2 capture by an amine system of 30% CO2 capture, it would result in an energy penalty reduction by about 50%. This has been demonstrated at RKDF University, Bhopal, India, which has established a pilot plant of post-combustion CCS integrated with Concentrated Solar Power (CSP) for solvent regeneration (Sethi V K,: An Innovative Approach in Post Combustion Carbon Capture and Sequestration towards Reduction of energy V. K. Sethi · M. Samartha RKDF University, Bhopal, India S. K. Sohani IES University, Bhopal, India R. K. S. Pippal (B) Vedica Institute of Technology, Bhopal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_27

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penalty in Regeneration of Solvent. Intec open Book. Print ISBN 978–1-78,923–7641. Online ISBN 978–1-78,923–765-8 (2019).). This paper presents the results of a CCS Pilot Plant as well as a feasibility study of scaling up CCS Plant and strategies for the installation of CCS on a 500-MW coal-fired thermal power plant. Keywords CCS · CPRI · CSP · MTPA · PCCC · TES

1 Introduction Global warming and erratic climate changes have created an urgency to seriously examine the possible routes of green energy. The Indian Power sector, which mainly relies on Coal-based Conventional Power generation emits more than 50% of our total CO2 emission which is today at a level of about 3000 Million Tons Per Annum (MTPA), while the world total emission is at the level of above 36,300 MTPA. Increased use of coal has been recognized as a prime factor driving up global energyrelated CO2 emissions by over 2 billion tons last year, the largest ever annual rise in absolute terms. Combating these challenges is the key to shaping the future of our sustainable Power scenario and the technology options of Green Mega Power are having a strong transforming effect on the power industry and on the well-being of the people as a sustainable society in the coming decades. The contribution of renewable energy in the installed capacity which is at about 30% today has a major positive social and economic impact on rural and remote area population. Out of the total RE target of 175 GW and we have already achieved a level of 115 GW on date. The Energy sector in the country is heading towards “a perfect blend” of advanced fossil fuel technologies and affordable RE technologies. An impact of energy efficiency measures has resulted in energy savings of 203.5 billion units and 25.77 million tons of oil equivalent (TOE). Further, the technological innovations in CCS with solar thermal integration will pave way for deployment of CCS on Mega scale Power Projects in a cost effective manner. As a part of India’s commitment towards reduction of greenhouse gases in the Paris Agreement INDC, the primary focus shall be on Climate Change Adaptation and Mitigation in Energy generation and use in industry, agriculture, and livelihood. The strategic focus of Knowledge Mission in the arena of climate change research and innovation shall be targeted towards 24 × 7 Thermal storage in the solar thermal plants and deployment of post combustion CCS on mega-scale coal-based projects. Also, Government of India is committed to sequester 2.5–3.0 billon tons of CO2 emission through increased forest cover; one of the many steps put forward by our Hon’ble Prime Minister to the world community in UNFCCs COPs aimed at mitigation of climate change and providing a carbon space for the developing world, still dependent on coal.

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In the realm of Green Energy, Carbon Capture and Sequestration (CCS) is considered to be one of the most advanced technologies (Sethi and Vyas 2017; Mokhtar et al. 2012). It is important that India does not fall behind in this area, as CCS technology is still in the demonstration phase. Research collaborations and knowledge transfer may still be necessary, despite the fact that there is already a significant amount of work being done at home. Adsorbent development, process integration with renewable energy sources, and conversion of CO2 to valuable multi-purpose fuels and products are all being experimented on pilot scale in these fields (Sethi and Vyas 2017; Phadke and Chandel 2016).

2 Genesis of the Study Post Combustion Carbon Capture (PCCC) is largely a technology-driven success story. Only technologies such as amine-based and ammonia absorption in thermal power plants currently have large-scale demonstration capabilities. For PCCC technologies based on amines, there is a good deal of confidence that the cost of CO2 capture and sequestration will continue to fall over the next few years. Commercially viable costs are yet to be reached in all other cases. There are a number of issues that need to be addressed before CCS can be commercialized in India (Sethi 2019, Sethi et al. 2011, Sethi 2017, Vyas et al. 2016). . Potential areas of R&D: Estimation of CO2 conversion into multi-purpose fuels or its geo-sequestration to depleted oil and coal fields is an important part of the research phase, as is the estimation of its location. India is still in the process of conducting a comprehensive geological assessment of the country’s CO2 storage capacity. . Lack of financing: Local and central government incentives and good governance policies are needed to attract foreign direct investment for the implementation of cost-intensive CCS technologies in India, but these are not currently in place on a date. . Environmental and legal concerns: Land acquisition, groundwater contamination, etc. are some of the important concerns that are to be addressed. . Energy penalty: Due to India’s high power demand and economic constraints, CCS is not yet a compulsory requirement for the country’s power generation. In fact, the energy penalty in India acts as a barrier (Sethi 2019). An enormous amount of research and development has gone into energy storage through MNRE sponsored solar thermal Project at RKDF University. Therefore, the Ministry of Power (MOP); (Government of India) financed a pilot project at RKDF University, which has been successfully completed under the sponsorship of CPRI, deploying this thermal storage material for CCS Plant operation after sunset. RGPV, the stateowned technological university of MP state was also CO-PI in this Project, where early trials were conducted between 2008 and 2012 under a DST-sponsored pilot study by the same team (Sethi et al. 2011; Sethi 2017).

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India’s commercial application of carbon capture and storage (CCS) faces a wide range of hurdles, including the need to reduce the energy penalty through solar thermal integration due to the need of developing Thermal Energy Storage (TES) for the solar steam availability 24 × 7. CCS Pilot Project sponsored by CPRI points towards the “Genesis” of taking up this project. This project on Solar Integrated CCS has been augmented by parallel work on solar thermal storage at the RKDF University, financed by the Ministry of New and Renewable Energy (MNRE). It is being done since 2015 under the collaborative agreement with RPI, NY, USA, for TES development through technology transfer (Sethi 2019; Sethi et al. 2018).

3 The Solar Integrated CCS Pilot Plant of Capacity 45 kg/hr CO2 at RKDF University In the pilot plant at RKDF University, the flue gas is being tapped-off @ 6 tons per day or 250 kg/hr (i.e., CO2 @ 18% about 45 kg per hour) running in 8 h shift and regeneration in 2 shifts with solar steam having over 8 h additional thermal storage capacity using Halide salt (Energy density in excess of 300 kWh/m3). Steam production is achieved @ 50 kg/hr by a set of 10 Scheffler solar thermal reflectors of capacity 5 kg/hr each. (Refer Fig. 1). The project has met its goal during intense testing. The CCS plant has achieved the following:-

Fig. 1 A view of pilot plant integrated with solar thermal steam generator and a coal-fired boiler for production of CO2

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. Potential areas of R&D: Estimation of CO2 conversion into multi-purpose fuels or its geo-sequestration (potential site estimation) is an important part of the research phase, as is the estimation of its location. India is still in the process of conducting a comprehensive geological assessment of the country’s CO2 storage capacity. . CO2 Capture efficiency of 87–90% has been achieved. . CO2 release from Reactor is of the order of 18–23% with steam from solar plant for solvent regeneration. . The energy penalty in re-generation of Solvent has come down to a level of 2.18 GJ/ ton of CO2 from standard value achieved elsewhere of 4.2 GJ/ Ton of CO2 using Steam from Solar thermal units. . The study conducted for CO2 capture from flue gases & release has revalidated the amine absorption system for the CO2 application for conversion to fuel molecules through pilot studies for algal Bio-diesel & Hydrogen production. The Energy penalty has been found to become almost half as may be summarized as under:

. Energy penalty by taking steam from the power cycle (extraction steam from the turbine)—4.6 MJ/ kg CO2 or 4.6 GJ per ton of CO2 captured. . Energy penalty by taking steam from the associated solar thermal plant. The steam generation system consisting of 10 nos. Scheffler Units of 5 kg/hr steam output each, along with Solid Halide thermal Energy Storage device developed under collaborative agreement with RPI, NY, USA and produced by ENELYS Energy, Hampton, USA is found to be around 2.18 MJ/ kg CO2 or 2.18 GJ per ton of CO2 captured.

4 The Strategies for a Scaled-Up CCS Plant on a 500 MW Unit An emission of CO2 per day from a 500 MW coal fired unit is about 330 tons per hour, accordingly a 30% capacity scaled-up carbon capture and sequestration plant will be of capacity 100 tons per hour. The strategy of scaling-up of the pilot plant proposal is diagrammatically represented at Fig. 2, which is based on CPRI, Government of India sponsored Project entitled “Post Combustion Carbon Capture & Sequestration (CCS) Plant on a Coal Fired Power Plant—Feasibility Study”, and briefly summarized below. The issues to be resolved are briefly; the scale of Carbon Capture, sequestration options like CO recycling to boiler furnace, stage of algae production from beginning or later and sequestration to depleted coal mines. The salient requirements as discussed at ref.10 are as under: . Solar thermal plant of capacity 70 tons per hour is required to be integrated for steam production. . Make-up water requirement @ 3 tons/hr for a scaled-up plant . Algal strain capable of surviving in Ash decantation water.

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Fig. 2 Strategies for Scaling up of CCS Plant on a 500 MW Unit

Our analysis shows that best solution would be to initially produce Algae based Bio-diesel and later use the depleted coal mine for sequestration.

5 The Scaled-Up CC Plant Layout Using the ASPEN Plus modeling tool the layout of the Scaled-up pilot plant of CCS of capacity 100 ton per hour of captured CO2 , at Anpara TPS Unit no. 4 of 500 MW is as per Fig. 3, given further. Corresponding to the above the estimated quantities of in-puts required is: . Land area between stack-area slab (now being used for De-sulfurization) and fuel oil corridor in front of the stack: … 70 m x 125 m, with reactors at 2 levels. . Additional water requirement … 4–5 tons per hour as make-up and gasification. . Additional power requirement … 4 MW that increases auxiliary power consumption by 0.8%.

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Fig. 3 Design of CCS plant at Anpara 500 MW Unit

6 Conclusion Over the next few decades, renewable energy and other low-carbon technologies will play an increasingly important role in the power generation mix, but coal remains our primary source of energy. Capturing, using, and storing carbon will be an essential part of a portfolio of lowcarbon energy technologies that will help us to meet our commitment to combating Climate change as we increase our use of fossil fuels. The deployment of CCS is critical given the current trends of rising global carbon dioxide emissions from the energy sector and coal’s continued dominance in primary energy consumption. Solar thermal plant with Thermal Energy Storage for steam generation in offsolar hours and Strategies for scaling-up plant are also covered in this paper and a feasibility study of deployment of PCCC Plant on a 500-MW unit has been presented based on results of CCS pilot plant installed at RKDF University with CPRI, MOP, and the Government of India funding. Unless a RE source of steam like solar thermal is integrated the energy penalty cannot be reduced to a moderate level of below 15%. ASPEN Plus software was used to estimate the reduction in energy penalty from a level from 4.6 GJ/ ton CO2 to 2.187 GJ/ ton CO2 . The ASPEN Plus has also been used to provide a layout plan

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and overall design of a scaled-up CCS Plant integrated with a solar thermal plant on coal-fired unit in Singrauli pit head region on the border of UP and MP states of India.

References Mokhtar M, Ali MT, Khalilpour R, Abbas A, Shah N, Al Hajaj A, Armstrong P, Chiesa M, Sgouridis S (2012) Solar-assisted post-combustion carbon capture feasibility study. Appl Energy 92:668– 676 Phadke PC, Chandel MK, Rao AB (2016) Feasibility Study of CO2 mitigation from coal power plants via CCS with auxiliary solar thermal and biogas. In: Global energy technology summit, New Delhi Rudra V, Kapila R, Stuart H (2008) Opportunities in India for carbon capture and storage as a form of climate change mitigation. GHGT-9, l. Washington DC, USA Sethi VK (2017) Low carbon technologies (LCT) and carbon capture & sequestration (CCS), key to green power mission for energy security and environmental sustainability, carbon utilization. Springer Nature Sethi VK (2019) An innovative approach in post combustion carbon capture and sequestration towards reduction of energy penalty in regeneration of solvent. Intec open Book. Print ISBN 978–1–78923–764–1. Online ISBN 978–1–78923–765–8 Sethi VK, Vyas S (2017) An innovative approach for carbon capture & sequestration on a thermal power plant through conversion to multi-purpose fuels—a feasibility study in indian context— GHGT-13. Energy Procedia 114:1288–1296. Lausanne (2017) Sethi VK, Vyas S, Dutta PS (2019) A pilot study of post combustion carbon capture & sequestration pilot plant aimed at feasibility analysis of installation of a carbon capture, utilization and sequestration (CCUS) plant on a large thermal unit in India. In: GHGT 14, 21st–25th October, 2018. Elsevier Sethi VK, Vyas S, Jain P, Gour A (2011) A novel approach for CO2 sequestration and conversion in to useful multipurpose fuel. J Environ Res Dev IJSERT 5:732–736 Vyas S, Sethi VK, Chouhan JS, Sood A (2016) Prospects of integrated collaborative technology of carbon dioxide capture. In: 32nd national convention of environmental engineers: challenges in environment management of growing urbanization, IEI

Adsorption Study of Chromium by Using Ziziphus Jujuba Sp. Seed as a Biochar M. G. Prathap and P. Purushothaman

Abstract Present work focusses on the effective removal of chromium (Cr) using Ziziphus Jujuba seed biochar (ZJSB) as an adsorbent. Ziziphus Jujube seed was modified as biochar at temperatures 400 °C and 500 °C (ZJSB400 & ZJSB500). Batch tests were conducted to evaluate the effects of pH (2, 3, 4, 5, 6, 7), Adsorbent Dosage (0.1, 0.2, 0.4, 0.6, 0.8, 1 g/l), Contact Time (0 to 180 min), and Initial Concentration of Chromium (0, 20, 40, 60, 80, 100 ppm). The characterisation study such as proximate analysis, Elemental analysis, Functional Groups, Surface area analysis and Thermogravimetric analysis (TGA) was investigated for ZJSB400 & ZJSB500. Thermogravimetric Analysis (TGA) showed ZJSB500 exhibit good thermal stability and ZJSB400 a lesser stability. Study reveals that the characteristics of ZJSB500 were better than ZJSB400 and also efficient in the removal of chromium of more than 89% at pH 6 for an adsorbent dosage of 0.6 g/l and 120 min contact time for initial concentration of 60 ppm, on the other hand, ZJSB400 demonstrated 77% efficiency. The Langmuir isotherm and Pseudo-Second-order equation provided the best fitted isotherm and kinetic study for the ZJSB500 adsorption experiment. The study concludes that biochar of ZJS at 500 °C (ZJSB500) is better in removing maximum amount of chromium from the aqueous solutions. The modification of ZJSB500 like activated, magnetic biochar can be used for better removal of chromium from the aqueous solutions. Keywords Ziziphus Jujuba seeds · Tannery wastewater · Kinetics · Proximate analysis · Ultimate analysis · pH

1 Introduction Hexavalent Chromium is the most harmful heavy metal due to its carcinogenic, toxic and mutagenic properties (Deveci and Kar 2013). Chromium (Cr) is a metallic M. G. Prathap · P. Purushothaman (B) Department of Civil Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamilnadu 603203, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_28

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element generated from various sectors including metallurgy, tanneries, and power plants (Mokrzycki et al. 2021; Wang et al. 2019). The World Health Organization (WHO) and the United States Environmental Protection Agency (US-EPA) both recommend that the maximum contamination level for Cr6 + in drinking water be less than 0.1 and 0.05 mg/l, respectively (Kahraman and Pehlivan 2017; Yusuff et al. 2022). Hexavalent chromium (Cr(VI)) is the most prevalent oxidised form of Chromium which is more soluble in water and generates soluble and mobile oxyanions that are known to be harmful to biological organisms (Rajapaksha et al. 2018). Because of its strong crystalline structure, corrosion resistance, and yellow colour, chromium is widely used in industry (Ghorbani-Khosrowshahi and Behnajady 2016). Prior to the safe disposal, It is required to remove Cr(VI) ions from the acidic industrial wastewater (Choudhary et al. 2017). Adsorption, Membrane Separation, Ion-Exchange, Coagulation and chemical precipitation were the most common methods for removing the hexavalent chromium (Mokrzycki et al. 2021). Adsorption has been shown to be the most efficient, cost-effective, and effective method of removing chromium from wastewater/water (Yusuff et al. 2022; Mohadi et al. 2021; Patra et al. 2017). Biochar (BC) is a low-priced, carbon-rich material which is made from thermochemical decomposition of lignocellulosic biomass in aerobic or anaerobic conditions (Elnour et al. 2019). Because oxygen-containing functional groups, mineral fractions, and aromatic carbon all play important roles in chemical adsorption processes, the surface features of biochar can have a large influence on its adsorption capabilities (Zhou et al. 2017). Biochar has been used to remove trivalent and hexavalent chromium because of its larger specific surface area, high porosity, and abundance of functional groups on the surface (Mohan et al. 2014; Chen et al. 2018). The biomass produced from diverse agricultural wastes is a valuable, affordable, and renewable resource, and its pyrolysis into biochar looks very promising because biochar has a higher potential for sorption than basic biomass (Tariq et al. 2020). Different Biochar such as Pomelo Peel (Wu et al. 2017), Rice husk (Mohadi et al. 2021), Sugar beet (Dong et al. 2011), Tea Waste (Khalil et al. 2020), Maize straw (Wang et al. 2019), Tomato Green (Mokrzycki et al. 2021), Eichhornia Crassipes (Zhang et al. 2015), Cotton stalk (Tariq et al. 2020), Indian Grassies (Karunakaran et al. 2021), Corn Cob (Hoang et al. 2019), Oleaster Seed (Kahraman and Pehlivan 2017), Cherry stones (Kahraman and Pehlivan 2017), Phoneix Reclinata Seeds (Katenta et al. 2020) and Water hyacinth (Hashem et al. 2020) have been used to remove the chromium from waste water. Jujube (Ziziphus jujuba) is mostly grown in China, Asia, Australia and Europe, particularly in the inner region of northern China (Aykaç et al. 2018; Gao et al. 2020). It is a member of the Rhamnaceae family, with the botanical name Z. mauritiana (Ahmad et al. 2012) with the species named as Ziziphus Jujuba Sp. Ziziphus Jujuba Seed is a hard material derived from the ziziphus jujuba pulp discarded by the people. It is acidic in nature as the pH is less than 6.5 and possess anionic character (Gao et al. 2020). Sivarama Krishna et al. (2014) found that Indian jujuba seed powder (IJSP) was investigated as a low-cost and environmentally acceptable biosorbent for the removal of Acid Blue 25 (AB25) from aqueous solution, with an adsorption capacity of 54.95 mg/g (Ahmad et al. 2012). Reddy et al. (2012) discovered that Jujuba Seeds

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were an efficient and possibly low-cost adsorbent for the removal of Congo Red dye from synthetic wastewater at an adsorbent concentration of 150 mg (Reddy et al. 2012). The Ziziphus Jujuba Seed biochar (ZJSB) prepared from the Ziziphus Jujuba Seed are not extensively investigated for the removal of chromium from wastewater. The main purpose of this study is to investigate the Ziziphus Jujuba seed pyrolyzed at a temperature of 400 & 500°C as biochar for its adsorption capacity and chromium removal efficiency from the wastewater. The adsorption isotherm, Kinetics studies were conducted to understand the chromium removal efficiency of Ziziphus Jujuba Seed biochar.

2 Materials and Methodology 2.1 Preparation of ZJSB The dried Ziziphus Jujube Seeds (ZJS) were heated at 400 and 500 °C separately for 30 min in a muffle furnace and then washed with deionized water to remove dust particles. The heating rate is 20 °C/minute for the preparation of biochar at different temperatures. For one hour, the washed biochar was heated in a hot air oven at 100 °C to remove the moisture content. In this study, the biochar samples were grounded and the sample’s diameter of less than 153 µm was utilised for further analysis (Ahmad et al. 2012).

2.2 Materials According to IS: 3025 (Part 52) and the APHA manual for water and wastewater, a 500 mg/l chromium stock solution was made in distilled water using potassium dichromate. In a flask, combine 0.283 g of potassium dichromate with 100 ml of distilled water to make a 1000 mg/l concentration of hexavalent chromium (Reddy et al. 2012; APHA 1984). The amount of hexavalent chromium in aqueous solution was evaluated using a colorimetric approach, and the samples were studied by UV-Vis spectrophotometry at 540 nm following complexation with 1,5-diphenylcarbazide (Zhang et al. 2017).

2.3 Batch Experiments: The batch tests were performed in a 100 ml conical flask with an orbital shaker for mixing. Experiments were carried out for a variety of parameters, including adsorbent doses of 0.1, 0.2, 0.4, 0.6, 0.8, and 1.0 g/l (Liang et al. 2020), Various

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initial concentrations such as 20, 40, 60, 80 and 100 mg/l at different time intervals such as 30, 60, 90, 120, 150 and 180 min and various pH 2, 3, 4, 5, 6, 7 for ZJSB400 and ZJSB500. For changing the standard solution into different initial chromium concentrations, a dilution process is done to the standard chromium solution. pH was initially adjusted using 0.1 N HCl or 0.1 N NaOH, and the stirring speed was set to 200 rpm. The sample which was taken from the batch experiments were being centrifuged for few minutes and sample is tested for finding out the concentration of chromium. Hexavalent chromium (Cr (VI)) absorbance was measured with a UVVis Spectrophotometer (Environmental Engineering Lab, Dept. Civil Engineering, SRMIST) at 540 nm using 1–5 diphenyl carbazide as an indicator (Deveci and Kar 2013; Saravanan et al. 2021).

2.4 Instruments Used for Characterisation of the Biochar The moisture content was calculated after heating the ZJS for 1 h 30 min at 105 + 5 °C until the mass was constant. The ash content was calculated after heating the JS for 1 h 30 min at 815 ± 10 °C. The volatile matter of the ZJS was estimated by heating it for 7 min in the absence of oxygen at 850 ± 10 °C (Mokrzycki et al. 2021; Aller et al. 2017). The biochar was weighed and its yield (Y) was calculated from the below equation (Michalak et al. 2019). Y = (Mass after pyrolysis/Mass before pyrolysis) ∗ 100%

(1)

The concentration of elements such as carbon, hydrogen, nitrogen and sulphur were by the ELEMENTAR Vario EL III at Cochin University of Science and Technology’s Sophisticated Test and Instrumentation Centre. The surface area analysis was done at SRM Central Instrumentation Facility in SRMIST using nitrogen absorption/desorption equipment (Quantachrome Instruments, Autosorb IQ series). The crystalline phase study was determined by means of an X-Ray Diffractometer (Panalytical X’ pert pro) (XRD) in the Department of Physics and Nanotechnology at SRMIST for all the samples. The functional group of each sample is done by using Fourier Transform Infrared Spectroscopy (SHIMADZU, IRTRACER 100) at the wavelength variety between 4000 and 400 cm−1 at Nanotechnology Research Centre, SRMIST. Thermogravimetric analysis (SDT Q600) was used for determining the stability of the ZJS which was done at Vellore Institute of Technology, Vellore.

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3 Results and Discussion 3.1 Characterisation Study 3.1.1

Proximate Analysis

Biochar yield was defined as the percentage of biochar recovered at the end of pyrolysis (Saravanan et al. 2021). The moisture content in ZJS was 4.71% whereas the moisture content in ZJSB400 and ZJSB500 is 4.06 and 3.63% respectively (Table 1). This shows that pyrolysis results in carbonization and reduced water content. The biochar yield in the ZJSB400 and ZJSB500 was 50.70 and 47.35% respectively. The volatile matter content in biochar significantly decreased as the pyrolysis temperature increased, whereas the fixed carbon amounts increased (Sun et al. 2017). From this, the biochar yield in ZJSB400 was more than the ZJSB500 because the volatile matter in ZJSB400 is more than ZJSB500.

3.1.2

Elemental Analysis

The carbon content present in ZJS, ZJSB400 and ZJSB500 was 57.08, 62.33 and 67.26% respectively (Table 1). The increased carbon content in ZJSB500 may indicate that there is more carbonization in biochar with increase in pyrolysis temperature. As well the amount of Nitrogen, Hydrogen and Oxygen was higher in the ZJS than in ZJSB400 and ZJSB500. The elemental analysis results demonstrate that when the pyrolysis temperature rises, the amount of nitrogen, hydrogen, and oxygen decreases. It was observed that the C/N ratio was 22.70 in ZJS, whereas in ZJSB400 and ZJSB500 was 37.89 and 41.99 respectively due to the presence of high carbon content in ZJS.

3.1.3

Surface Area Analysis

The surface area of the ZJSB400 and ZJSB500 was determined. It was observed that ZJSB400 and ZJSB500 have 97.368 and 113.682 m2 /g for BJH analysis and for BET analysis it has 190.968 and 212.815 m2 /g (Table 1). In comparison, ZJSB500 has more surface area than ZJSB400 observed from this analysis. The surface area, pore volume and pore diameter increases with the increase in pyrolysis temperature (Shakya and Agarwal 2019).

3.1.4

FTIR

Fourier Transmission Infrared Spectroscopy (FTIR) was applied to determine the functional groups found in the ZJS, ZJSB400, and ZJSB500 (Kahraman and Pehlivan

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Table 1 Characteristics of Ziziphus Jujube Seed Biochar (ZJSB) S. no

Name of the test

Ziziphus Jujuba seed (ZJS)

Ziziphus Jujuba seed Biochar @400 °C (ZJSB400)

Ziziphus Jujuba seed Biochar @500 °C (ZJSB500)

Proximate analysis 1

Moisture content (%)

4.71

4.06

3.663

2

Biochar yield (%)

NA

50.70

47.35

3

Volatile matter (%)

84.63

50

22.40

4

Ash content (%)

92.57

53

28

Elemental analysis 1

Carbon

57.08

62.33

67.26

2

Nitrogen

2.51

1.64

1.60

3

Hydrogen

4.73

3.02

2.45

4

Oxygen

35.68

33.00

28.69

5

C/N ratio

22.70

37.89

41.99

6

N/C ratio

0.044

0.026

0.024

Pore size analysis 1

BJH surface area (m2 /g)

–

97.638

113.682

2

Pore volume (cc/g)

–

0.155

0.183

3

Pore radius (Å) –

14.874

19.477

4

BET surface area (m2 /g)

–

190.968

212.815

5

average pore diameter (nm)

–

0.0634

0.00644

2017; Aminu et al. 2020). The methoxyl C-H stretching bond is represented by the peak with a range of 2855–2925 cm−1 , the peak in the range of 1630.80–1742 cm−1 confirms to the presence of C = O stretching frequency, 1572 cm−1 may be due to the presence of C = C stretching bond, 1029–1378 cm−1 corresponds to C-O bond in the alcohol group (Fig. 1a, b). As a result of Chromium (6+ ) adsorption/Chemisorption, the energy decreases to a lower level. FTIR graph suggests that the necessity functional groups which are needed for adsorption of hexavalent chromium from water are present.

Adsorption Study of Chromium by Using Ziziphus Jujuba Sp. Seed …

(a)

365

(b)

Fig. 1 a Comparison of FTIR of ZJS, ZJSB400 and ZJSB500, b Comparison of FTIR of ZJSB500 before and after treatment

3.1.5

TGA

Thermogravimetric analysis is used to analyse material and product changes by looking for patterns in weight loss variations as a function of temperature and time (Elkhalifa et al. 2022). The heating rate was 20 °C/minute in this full experiment. In this test, two stages of weight loss have been identified. First weight loss of around 52% of ZJS occurred in the range of room temperature to 500 °C and it may be due to the carbonised and also due to decomposition of volatile components. Second weight loss of 5.161% of ZJS occurred from 500 to 800 °C may be due to forming of ash (Fig. 2). The remaining 42.84% of the total weight of the ZJS is residue. In this analysis, ZJSB500 shows more stability than ZJSB400. The ZJSB500 yield value is determined by TGA and the calculated value shows that it is a similar value.

3.2 Adsorption Studies 3.2.1

Effect of Initial Concentration

Initial concentration is an important adsorption characteristic because it affects metal ion mass transfer barriers between the aqueous and solid phases (Deveci and Kar 2013; Ertugay and Bayhan 2008). The effect of initial chromium concentration was studied by using a batch experiment involving various chromium concentrations such as 20, 40, 60, 80 and 100 mg/l for ZJSB400 and ZJSB500 samples. The results of both batch experiments for ZJSB400 and ZJSB500 are plotted in the graph by using origin (Fig. 3a). As per the batch experiment results, the ZJSB500 showed more chromium removal efficiency for 77.24% whereas ZJSB400 showed only 70%. The study indicates that with the increase in initial concentration up to 60 mg/l removal

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Fig. 2 TGA result

(a)

(c)

(b)

(d)

Fig. 3 a Comparison of initial concentration versus removal efficiency, b Comparison of contact time versus removal efficiency, c Comparison of adsorbent dosage versus removal efficiency, d Comparison of pH versus removal efficiency

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of chromium increases and decreases thereof. Efficiency of ZJSB was observed to be better than the Jujube Seed (ZJS) in the removal of chromium from aqueous solutions. This is evident from the study of Labied et al. (2018) who observed an efficiency of 64% chromium removal for an initial concentration of 100 mg/l. Higher efficiency for ZJSB than ZJS is due to availability of more adsorption sites in ZJSB (Labied et al. 2018).

3.2.2

Effect of Contact Time

Chromium ion is more likely to adsorb as chromic hydroxide the longer it is in contact with the adsorbent (Duan et al. 2017). Batch studies were carried out for various contact time intervals of 30, 60, 90, 120, 150, and 180 min for both ZJSB400 and ZJSB500. ZJSB500 gave better removal efficiency of up to 77.24% in 120 min of contact time and the removal efficiency of ZJSB400 is 62.5% for same contact time after which the efficiency decreases (Fig. 3b). Present study result is lower than already reported values of Katenta et al. (2020) who found that the removal efficiency to be 96% at 120 min for a dosage of 2 g (Katenta et al. 2020). The lower removal efficiency of chromium may be due to less time available for interaction with the active sites of the biochar.

3.2.3

Effect of Dosage

Adsorbent dosage is a very important factor that governs the extent of chromium removal from aqueous medium (Choudhary and Paul 2018). In ZJSB500, the removal efficiency increases up to 77.24% for a dosage of 0.6 g/l (Fig. 3c) and then decreases with increasing dosage. Whereas, for ZJSB400 chromium removal efficiency is lesser (29.49%) for a dosage of 0.6 g/l. Similar study by Labied et al. found that the removal of hexavalent chromium was 31.6–64% for an adsorbent dosage of 0.5–3 g/l (Ma et al. 2020). Higher efficiency can be attributed to increase in active sites of biochar.

3.2.4

Effect of pH

The aqueous solution includes a high concentration of hydroxyl ions. These ions provide a barrier between negatively charged ions and negatively charged charcoal at high pH values (Kahraman and Pehlivan 2017). Removal of chromium was found to be lowest at pH 2 for both ZJSB 400 and ZJSB 500. On the other hand, it increased up to pH 6 for a maximum of 80.36 and 89.71% for ZJSB 400 and ZJSB 500 respectively (Fig. 3d). Although ZJS possess an acidic nature it shows low adsorption at pH 2 and with the pyrolysis of ZJS helps in opening up of the pore space which acts as a possible site of adsorption of chromium. It is observed that ZJSB500 acts as a better adsorbent of chromium at low to medium/ neutral pH when compared with ZJSB400. Similar study on chromium removal using Eichhornia Crassipes was found to show

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better efficiency of 90% for pH 8.3 (Hashem et al. 2020). Better removal of Cr at low pH by ZJSB shows its potential nature when compared with another adsorbent.

3.3 Adsorption Isotherm Adsorption isotherms that can be effectively applied to preliminary adsorption data for any adsorbent material are fundamental prerequisites for constructing adsorption systems (Deveci and Kar 2013; Vinodhini and Das 2010). In this work, adsorption isotherm models such as the Freundlich and Langmuir were utilised.

3.3.1

Langmuir Isotherm Model

The most well studied isotherm model is the Langmuir isotherm model, which depicts the connection between the quantity of solute adsorption on adsorbent (mg/g) and the chemical content in solution (mg/L) at equilibrium (Choudhary and Paul 2018). This model is a monolayer on homogenous surface. The following is the formula used to determine the Langmuir Isotherm model (Tariq et al. 2020). Ce Ce 1 + = qe (K L qmax ) qmax

(2)

where Ce represents the equilibrium concentration of Cr (VI), qe represents the amount of Cr (VI) attached per mass of adsorbent material (mg/g), KL represents the Langmuir coefficient (L/mg), and qmax represents the maximum adsorption of Cr (VI) at equilibrium (mg/g). The R2 value for the ZJSB400 was 0.915 and for the ZJSB500 was 0.928 (Fig. 4a, b).

3.3.2

Freundlich Isotherm Model

The Freundlich isotherm model is used to illustrate the adsorption of a multimolecular layer on an uneven surface (Ma et al. 2020). 1 L n qe = l n K f + l n C e n

(3)

where Ce is the equilibrium concentration of Cr (VI), qe is the quantity of Cr (VI) attached per mass of adsorbent material (mg/g), Kf is the Freundlich Coefficient (L/mg), and 1/n is the Freundlich constants. The correlation coefficient (R2 ) value for ZJSB400 and ZJSB500 was 0.672 and 0.716 respectively (Fig. 4c, d). The observed R2 is lower than 0.798 to 0.998 as reported by Kahraman and Pehlivan (2017).

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(a)

(b)

(c)

(d)

Fig. 4 a Langmuir isothermal model for ZJSB400, b Langmuir isothermal model for ZJSB500, c Freundlich isothermal model for ZJSB400, d Freundlich isothermal model for ZJSB500

Langmuir isotherm shows better R2 value than the R2 value of Freundlich isotherm for both ZJSB 400 and ZJSB500 0.915 and 0.928 respectively, and suitable for ZJSB.

3.4 Adsorption Kinetics Adsorption kinetics were analysed in order to understand the dynamics of chromium ion adsorption into the adsorbent (Labied et al. 2018). Adsorption kinetics causes the adsorption process rate (Hashem et al. 2020). The kinetics models such as pseudofirst-order (PFO) and pseudo-second-order (PSO) were utilised to analyse the kinetics of Cr(VI) adsorption (Duan et al. 2017; Thangagiri et al. 2022). The linearized forms of both the pseudo-first-order and pseudo-second-order kinetic analytical model are provided in Eqs. (4) and (5), respectively (Liang et al. 2020). ln(qe − qt ) = ln qe − K 1 t

(4)

t 1 t = + 2 qt K 2 qe qe

(5)

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(a)

(b)

(c)

(d)

Fig. 5 a Pesudo First Order for ZJSB400, b Pesudo First order for ZJSB500 c Pesudo Second order for ZJSB400 d Pesudo Second order for ZJSB500

where qt (mg/g) represents Cr (VI) uptake at time t and K1 (1/min) and K2 (g/mg.min) represent the pseudo-first-order and pseudo-second-order model rate constants, respectively. The correlation value (Fig. 5) obtained from the PFO for ZJSB400 is 0.791 and for ZJSB500 is 0.652 whereas the correlation coefficient value obtained from PSO for ZJSB400 and ZJSB500 was 0.873 and 0.911 respectively. Study shows hexavalent chromium fits well with the Pseudo-Second-Order model for ZJSB500 (R2 = 0.911) with qe and K2 values 3.543 mg/g and 0.0094 min−1 respectively. Similar study by Premlatha et al. found Cr(VI) adsorption by water hyacinth biochar had correlation coefficient R2 (0.99) and adsorbent capacity qe (10.40 mg/g) of the pseudo-secondorder model (Premalatha et al. 2018). It is found that the amount of adsorption cavities available on the ZJSB500 influences the rate of Cr (VI) adsorption, with the best fit at PSO.

4 Conclusion In this study, the Ziziphus Jujuba Seed was pyrolyzed into biochar at a temperature of 400 and 500 °C. The study shows that ZJSB500 is an effective adsorbent for the removal of chromium from aqueous solution with removal efficiency of 88.94% whereas ZJSB400 shows only 79.55%. Langmuir Isotherm model and

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Pseudo-Second-order were found as the best fit for ZJSB500 in the effective removal of chromium. The efficiency of Ziziphus Jujube Seed Biochar for chromium removal can be improved by modification of biochar Activated Biochar, Magnetic Biochar. Acknowledgements The authors would like to express our heartfelt gratitude to the Management of SRMIST and Head of Department, Department of Civil Engineering, SRMIST KTR campus, for permitting us to carry out the study. Prathap MG would like to express his gratitude to the Dean, College of Engineering and Technology, SRMIST, KTR campus, and the Directorate of Research for providing financial support in the form of a Doctoral Research Fellowship. Authors would like to thank SRMIST’s Environmental Engineering Laboratory for providing experimental and analytical facilities. We acknowledge the Material Characterisation Facility, Department of Physics and Nanotechnology, College of Engineering and Technology, SRMIST for XRD analysis. We acknowledge Nanotechnology Research Centre (NRC), and SRM Central Instrumentation Facility (SCIF), SRMIST for providing analytical facilities in the form of FTIR, Surface area analysis. We acknowledge "DST-SAIF Cochin" for providing research facilities for doing CHNS analyser. We would also like to acknowledge Thermo-Gravimetric Analysis, School of Advanced sciences, VIT, Vellore for providing research facilities for doing TGA test.

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Microplastics in River Sediments Nearby to a Sewage Treatment Plant: Extraction, Processing and Characterization Assessment Jaswant Singh and Brijesh Kumar Yadav

Abstract Microplastic pollution is an emerging concern for the aquatic environments as it has been reported in many rivers, lakes, reservoirs and aquatic organisms around the globe. Rivers behave as a temporary sink for microplastics and also serve as a medium through which these particles reach the ocean. This study shows the river transport of microplastics originating from sewage effluents. Six different locations were sampled, including upstream, downstream, and at the sewage disposal point. H2 O2 was used to clean the raw samples and then separation was done using NaCl solution. FTIR technique was used for the polymeric characterization of suspected microplastics. The highest concentration of microplastics was reported near the sewage disposal point as 477 MPs/kg. White and transparent were the most predominant microplastics found in all the samples with 0.3 to 1 mm in size. PE and PP were the most common microplastic polymers found in each sample. This work provides significant information about the distribution of microplastics in freshwater sources near the sewage treatment plant. Furthermore, this study highlights the need for more research related to the microplastics found in rivers. Keywords Microplastics · River sediment · STP effluents · FTIR

1 Introduction Due to numerous uses for domestic, industrial and commercial purposes, global plastic production has increased from 2 million metric tons to 368 million metric tons from 1950 to 2019 (Europe 2020). It is estimated that the production volume will increase double in the next 20 years (Lebreton and Andrady 2019). The high durability of plastics, along with their propensity to disintegrate into microplastics, J. Singh · B. K. Yadav (B) Department of Hydrology, Indian Institute of Technology, Roorkee, India e-mail: [email protected] J. Singh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_29

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has led to an increasing concern about their impacts on different environmental compartments (Koelmans et al. 2019). Microplastics are the emerging contaminant found ubiquitously in the environment. Primary microplastics originate directly from industrial production, whereas secondary microplastics are formed due to the degradation and decomposition of larger plastic debris (Browne et al. 2011; Cole et al. 2011). These tiny particles can be found in a wide variety of shapes (pellets, fragments, films, fibres, microbeads, and foams), sizes (1 µm to 5 mm) and colours. In spite of the fact that microplastics are found everywhere, their origin has not been fully comprehended. Land-based activities are thought to be the primary source of microplastic pollution in the environment. Still, terrestrial and freshwater systems have received a notably lower priority in terms of research (Fahrenfeld et al. 2019). Microplastics can enter the aquatic environment through a variety of routes, either from primary or secondary sources. According to a recent study, wastewater treatment plants (WWTPs) may play a significant role in microplastic pollution in the aquatic environment (Browne et al. 2011). Microplastics added in the toothpaste and the facial cleaner can directly release microbeads into wastewater (Cheung and Fok 2017; Fendall and Sewell 2009). Besides the skincare items, the laundry machine also produces microplastics. From a study, it has been found that a single load of laundry can produce up to 1,900 fibre microplastics that enter into wastewater (Dris et al. 2015). India is one of the top countries in the world that consumes plastics and makes about 5.6 MMT of waste every year (Mahesh 2014). But as of yet, very few studies have been conducted concentrating on microplastic pollution in aquatic environments. In this work, microplastic concentration and their spatial distribution have been investigated in the Solani River adjacent to a sewage treatment plant (STP). Two sediment samples within a distance of 50 m were collected from each of the three locations and properly analysed in the laboratory to identify the microplastic types and morphological features. The present study will provide evidence of the seepage and disposal of microplastics from the STP plants into nearby water resources.

2 Materials and Method 2.1 Study Area and Sample Collection The study area is located in Roorkee, a city of Haridwar district, Uttrakhand. The sewage collected from the city will traverse over 3 km to reach a 33 MLD sewage treatment plant situated at the outskirt of the Roorkee. The STP releases its treated sewage directly into the adjacent Solani River. We took sediment samples from the Solani River at three distinct locations along its banks (Fig. 1). The riverbank deposits were scooped up with a stainless steel spoon from a depth of 2–3 cm, stored in individual plastic bags with zippers and taken to the laboratory. Cotton lab coats were worn throughout the experiments to prevent cross contamination, and

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Fig. 1 Location of six different sampling points at upstream (prior to the disposal point; A, B), midstream (near to the disposal point; C, D) and downstream (after the disposal point; E, F)

each stage (filtration, digestion, and measurement) was carefully monitored. Glass made laboratory materials (Glass bottles, Petri dishes, Erlenmeyers, and a filtration system) were used during the experiments and everything was immediately covered after use. Filtered ethanol (70 percent) and MilliQ water were used to pre-rinse all the equipment before use.

2.2 Extraction of Microplastics from Sediments The collected samples were processed for density separation using sodium chloride (NaCl) solution. This solution was selected due to its high efficiency and easy availability. After mixing with the NaCl solution, the samples were allowed to settle for 24 h. The sediment samples have a higher density than microplastics, which causes the particles to settle into the bottom of the vessel (cylinder). The supernatant was then filtered through filter paper with a pore size of 25 µm in a vacuum filtration unit. The extracts in the filter paper were kept in a 30% hydrogen peroxide (H2 O2 ) overnight for the digestion of the organic matter present. Following this, the mixture was poured into a conical flask containing 500 mL of distilled water, filtered three times through filters with a pore size of 25 µm, and finally rinsed three times with distilled water. For further examination and characterization, the filter paper was desiccated for three days.

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2.3 Characterization and Quantification The particles that were retained in the filter paper were visually inspected using a 40 × magnification stereo zoom microscope. Microplastic particles that could be seen through the microscope were counted, categorised (film, foam, fragment, pellet, and fibre), and recorded according to colour and size. To reduce the possibility of human error, the filter papers were divided into quadrants. Each sheet of filter paper was counted three to four times for accuracy. Suspected MP particles were first isolated using stainless steel tweezers and identified based on their morphological characteristics, as established by prior research (Ding et al. 2020). ATR-FTIR (Attenuated total reflectance Fourier transform infrared spectroscopy: Agilent Cary 630 with diamond ATR) was used for polymeric identification of the suspected microplastics.

3 Results and Discussion 3.1 Spatial Distribution of Microplastics Microplastics were found in each sample with higher concentration at locations C (456 MPs/kg) and D (476 MPs/kg) near the sewage outflow point (Fig. 2). The lowest concentration of microplastic was obtained prior to the sewage disposal point at the upstream side A (89 MPs/kg) and B (102 MPs/kg). On the downstream side, at points E and F, the concentration slightly decreased to 268 and 244 MPs/kg, which may be due to the continuous flow of water and meandering of the river.

Fig. 2 Concentration of microplastic at different sampling points

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3.2 Morphological Characteristics of Microplastics The microplastic particles were separated into their respective categories based on their polymer types, shapes, colours and sizes (Fig. 3). Polyethylene (PE: 33.34% to 40.5%) was the dominant microplastics found in all the samples, followed by some other major polymers like Polypropylene (PP:15–29.5%) and Polystyrene (PS: 50.26% to 21.5%). Polyamide (PA), polyethylene tetraphthalate (PET) and some other minor polymers were found in very small concentrations, as shown in Fig. 3a. Based on the shape and appearance, the observed microplastics are categorised as fibres, films, fragments, pellets, foam and beads (Fig. 3b). Those particles that did not fit into any of these categories were kept in the class of others. Fibres and fragments were the dominant microplastics in all the samples, covering around 60% of the total microplastics identified, followed by other shapes. Microplastics come in a rainbow of colours, but white and transparent ones are the most common in every sample (Fig. 3c). Microplastic’s colour may have been associated with the colours of the original plastic trash, however, this colour may shift as a result of weathering. There is a possibility that the colour of the microplastics is related to the colours of the original plastic waste; however, this colour may change as a result of weathering. (Natesan et al. 2021). Commonly used plastic products such as plastic bags, packaging, and disposable containers are usually transparent. Previous studies show that transparent and white microplastics are often more abundant than microplastics of other colours

Fig. 3 Morphological characteristics a Polymer types, b Shape c colour and d sizes of microplastics reported in the sediment samples

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in soil, inland water, and oceans (Horton et al. 2017, Sarkar et al. 2019). The majority of microplastics were between 0.3 to 1 mm in size (Fig. 3d). Microplastic size is a crucial factor in microplastic-based contamination studies (Dris, et al. 2015). Numerous aquatic creatures are known to be seriously threatened by the increased abundance of small MPs that they consume (Mak et al. 2019).

4 Conclusions This study characterized and quantified the microplastic pollution in sediments along the riverine shore of the Solani River adjacent to an STP. According to the findings, all the sediment samples were found to have microplastic pollution. Secondary microplastics (fibres and fragments) were more in all the samples with white and transparent colour predominantly. The upstream side before the sewage disposal points have less microplastic pollution than after the sewage disposal point. A higher concentration of microplastics near the disposal points shows the incapability of the sewage treatment unit to filter microplastics properly. Smaller microplastics of size range 0.3-1 mm were high in concentration. The findings of this study provide important baseline information for future risks caused by microplastics in soil-water systems.

References Browne MA et al (2011) Accumulation of microplastic on shorelines woldwide: sources and sinks. Environ Sci Technol 45(21):9175–9179. https://doi.org/10.1021/es201811s Cheung PK, Fok L (2017) Characterisation of plastic microbeads in facial scrubs and their estimated emissions in Mainland China. Water Res 122:53–61. https://doi.org/10.1016/j.watres. 2017.05.053 Cole M, Lindeque P, Halsband C, Galloway TS (2011) Microplastics as contaminants in the marine environment: a review. Mar Pollut Bull 62(12):2588–2597. https://doi.org/10.1016/j.marpolbul. 2011.09.025 Ding L et al (2020) The occurrence and distribution characteristics of microplastics in the agricultural soils of Shaanxi Province, in north-western China. Sci Total Environ 720:137525. https://doi. org/10.1016/j.scitotenv.2020.137525 Dris R, et al (2015) Beyond the ocean : contamination of freshwater ecosystems with ( micro-) plastic particles To cite this version : HAL Id : hal-01136690. Environ Chem CSIRO Publ 32 Europe P (2020) Plastics–the facts 2020. PlasticEurope 1:1–64 Fahrenfeld NL, Arbuckle-Keil G, Naderi Beni N, Bartelt-Hunt SL (2019) Source tracking microplastics in the freshwater environment. TrAC–Trends Anal Chem 112:248–254. https://doi.org/10. 1016/j.trac.2018.11.030 Fendall LS, Sewell MA (2009) Contributing to marine pollution by washing your face: microplastics in facial cleansers. Mar Pollut Bull 58(8):1225–1228

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Horton AA, Walton A, Spurgeon DJ, Lahive E, Svendsen C (2017) Microplastics in freshwater and terrestrial environments: evaluating the current understanding to identify the knowledge gaps and future research priorities. Sci Total Environ 586:127–141. https://doi.org/10.1016/j.scitot env.2017.01.190 Koelmans B, et al (2019) A scientific perspective on microplastics in nature and society. SAPEA Lebreton L, Andrady A (2019) Future scenarios of global plastic waste generation and disposal. Palgrave Commun 5(1):1–11. https://doi.org/10.1057/s41599-018-0212-7 Mahesh P (2014) Plastics and the Environment Assessing the Impact of the Complete Ban on Plastic Carry Bag. pp 1–70 Mak CW, Yeung KC-F, Chan KM (2019) Acute toxic effects of polyethylene microplastic on adult zebrafish. Ecotoxicol Environ Saf 182:109442 Mani T, Primpke S, Lorenz C, Gerdts G, Burkhardt-Holm P (2019) Microplastic pollution in benthic midstream sediments of the rhine river. Environ Sci Technol 53(10):6053–6062. https://doi.org/ 10.1021/acs.est.9b01363 Martí E, Martin C, Galli M, Echevarría F, Duarte CM, Cózar A (2020) The colors of the ocean plastics. Environ Sci Technol 54(11):6594–6601. https://doi.org/10.1021/acs.est.9b06400 Natesan U, Vaikunth R, Kumar P, Ruthra R, Srinivasalu S (2021) Spatial distribution of microplastic concentration around landfill sites and its potential risk on groundwater. Chemosphere 277:130263. https://doi.org/10.1016/j.chemosphere.2021.130263 Sarkar DJ, Das Sarkar S, Das BK, Manna RK, Behera BK, Samanta S (2019) Spatial distribution of meso and microplastics in the sediments of river Ganga at eastern India. Sci Total Environ https://doi.org/10.1016/j.scitotenv.2019.133712

Characterization and Sustainable Utilization of Municipal Solid Waste Incineration Ash: A Review Saurabh Kumar , Sneha Gupta , and Neelam Singh

Abstract The exponential growth in urbanization and industrialization has resulted in a rapid increase in the amount of municipal solid waste (MSW). Among the many methods of disposal of MSW, incineration is the most common method that produces two types of ash; fly ash (FA) and bottom ash (BA). These ashes can be used in different areas. The current research looks at current efforts for repurposing municipal solid waste incineration ash (MSWI ash) and offers new uses. Construction materials (cement, alternative fine aggregate in concrete, glass–ceramics), Geotechnical applications (subgrade in road pavement, embankments), “agricultural” (soil modification), and other uses were identified and classified into major categories. Each application is extensively investigated, including the technical qualities of the end product as well as the impact on the environment. In this paper a comparative examination of the various possibilities is carried out, emphasizing the benefits as well as the drawbacks of each option. The widespread application of such green technology will help to reduce the need for civil building materials, hence minimizing greenhouse gas emissions. Utilizing MSWI ash has emerged as an appealing disposal alternative as a result of rising environmental consciousness and its possibly dangerous impacts. This article has been prepared to learn the optimal techniques and use of the final products in different areas, resulting in significant waste reduction and resource conservation benefits. Keywords Waste management · MSW incineration · MSWI ash · Characterization · Chemical composition · MSWI application

S. Kumar (B) · S. Gupta Madan Mohan Malaviya University of Technology, Gorakhpur, India e-mail: [email protected] N. Singh Central University of Haryana, Haryana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_30

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1 Introduction In today’s society, waste management is a critical environmental concern. MSWs (municipal solid wastes) are thrown in large amounts on a regular basis and must be managed carefully. Although incineration is a standard strategy for lowering the volume of these wastes, it creates ashes that must be evaluated further. The bulk byproduct of the incineration process, MSWI ash has been categorized into two ashes; fly ash (MSWI-FA) and bottom ash (MSWI-BA) and MSWI-BA has the potential to be utilized in the enhancement of weak soil. The usage of MSWI-BA as a stabilizer in weak soil is discussed in this research. MSWI-BA is being reused as a partial or full substitute to the stabilizer in weak soil due to the scarcity of land space in urban areas. Although MSWI-BA has different physical and chemical qualities than soil in terms of OMC, dry density, percentage of water absorption capacity, and fineness, they may be treated in a variety of ways to guarantee that they are appropriate for landfilling soil. Thermal treatment, solidification and stabilization, and separation procedures are the three types of treatment methods. This study focuses on the strategies that eliminate defects that restrict the use of MSWI-BA as landfilling soil. There are three primary steps in the MSW incineration process: incineration, power generation, and air pollution management (Damgaard et al. 2010). For incineration, the MSW is continuously fed into the burner. A minimum of 850 ° C should be used for incineration, with a residence duration of at least two seconds. Here Fig. 1 shows the flow diagram for the process of incineration of MSW. Incineration was chosen as a method to manage with the massive quantity of waste since it decreases waste volume by 60–70%, depending on the type of waste and incinerator device installed (Williams 2005). MSW incineration is a traditional solution to reduce on the quantity of the garbage that has to be disposed of in lowlying areas. (Show et al. 2003). Organic waste is converted to inorganic residues when it is incinerated, and ferrous and non-ferrous components are produced into silicates (Forteza et al. 2004).

2 Solid Waste Management Practice in India India is the second-most populous country in the world and has the second-fastest expanding economy. According to the United Nations “State of World Population Report (2020–21)”, India’s urban population was 17.92% of the total population in 1960, and it increased to 35.92% in 2021, and the total population is also increasing continuously. According to the CPCB (Central Pollution Control Board), 2020–21, India’s urban population of 377 million creates a massive 1, 43,449 metric tons of MSW every day, and these figures are rising every day as the population grows. To compound the matter, the country’s total number of towns (statutory and census) climbed from 5,161

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Steam

Exhaust Gas

Boiler

Combustion Chamber

Chimney

Fly Ash MSW Transport ation

Mixed MSW

Bottom Ash

Fly Ash Processing

Landfilling Materials recovery and Reuse as a resources

Fig. 1 Schematic diagram for the process of incineration of MSW

in 2001 to 7,936 in 2011, resulting in a 2,775 rise in municipal garbage generation in a decade. As per CPCB 2020–2021 report, the total amount of solid waste generated in our country (India) is 160038.9 TPD (ton/day), out of which 152,749.5 TPD (95.4%) is collected, 79,956.3 TPD (50%) is treated, while 29,427.2 (18.4%) is landfilled and 50,655.4 TPD, or 31.7% of the total waste, is still unaccounted. The Okhala landfill has been in use since 1994. Okhala landfill was supposed to last until 1997–1998, according to the plan. It’s in Delhi’s south, next to a densely populated residential neighborhood and one of the city’s largest industrial regions shown in Fig. 2 and Fig. 3. Domestic garbage, such as kitchen waste, paper, plastic, glass, cardboard, and textiles, as well as construction and unlicensed industrial waste, is among the wastes thrown in landfills (Gupta and Rajamani 2015).

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This conceivable use for MSWI ash has three major benefits: 1. The utilization of a zero-cost raw material 2. Natural resource conservation 3. Waste minimization.

Fig. 2 Three major landfill sites in Delhi (Source Gupta and Rajamani 2015)

Fig. 3 Location of three landfill sites in Delhi (Source Srivastava and Chakma 2020)

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3 Characterization and Composition of MSWI Ash The wastes produced by the municipal incineration method are BA and FA, respectively. BA is a by-product of the incineration process, consisting primarily of oxides of Si, Fe, Ca, Al, Na, and K. Typically, garbage from incinerators is disposed of in landfills or recycled as a secondary raw material. According to Forteza et al. (2004), and Mohamedzein et al. (2006), if the BA is held for at least 1 month, the metal concentration of the BA and its leachates meets environmental criteria, according to the findings. Because the engineering qualities of BA are similar to that of natural aggregates, its application in the subgrade of highway construction appears to be viable. Bottom ash might be utilized in civil engineering projects including slope embankments, subgrade of highways , and landfills. MSW combustion produces approximately 80% BA and 20% FA, which are typically dumped in landfill (Dontriros et al. 2020). Furthermore, according to Kaniraj and Gayathri (2003), mixing MSWI ash with various stabilizers can increase the bearing capacity of weak soil. MSWI ash can be used in a variety of geotechnical applications to help solve the problem of disposal. (Shah and Ahmad 2008; Singh and Kumar 2017a). If toxic metals like Pb, Zn, and Cd are not properly managed, they might precipitate on the surface or leak into the groundwater. This usually results in soil fertility and groundwater being polluted, resulting in negative impacts on our ecosystem. (Wang et al. 2015). MSW ash is commonly disposed of by landfilling. Landfill takes up a lot of space, causing precious land to be lost. As a result, landfill sites can be used for reconstruction, recreation, and other purposes following the dumping of MSW Ash. Recreational facilities include sports clubs, golf courses, natural parks, and public walking and cycling paths. Because these waste sites have a limited bearing capacity and are very compressible, ground renovation procedures are required to alleviate the problem. As a result, these waste sites require special attention in order to be used in civil engineering projects. Physical characteristics and chemical properties are the two types of ash properties. We can decide the most appropriate way for ash utilization by understanding the characteristics of ashes, especially their chemical properties. The kind and quality of waste, as well as the technique of burning, influence the ignition of MSWI ash. Table 1 shows the chemical composition of MSWI ash as examined by various studies. Due to the change in the lifestyle of people, changes are made in the waste recycling methods. The ignition of MSW varies in different seasons and varies from country to country, due to which the ash content also varies. The raw MSW components, operational circumstances, burner type, and air pollution control device design all influence the chemical and physical characteristics of ash. The primary components in the chemical composition are Si, Al, Fe, Mg, Ca, K, Na, and Cl. In addition, SiO2 , CaO, Al2 O3 , Fe2 O3 , K2 O, and Na2 O, are frequent oxides found in ash. In MSWI fly ash, CaO is the most prevalent component, indicated that

9.2

9.1–15.2

Zhang et al. (2016)

6.98–14.4

Weng et al. (2009)

43.1–56.5

0.83–9.48

Nedkvitne et al. 52.54–89.73 (2021)

13.4–21.4

1

Almalkawi et al. 3.48 (2019)

55.37

Singh and Kumar (2019)

11.8–21.6

4.47–27.64

38.1–42.8

48.16

19.39

36.60%

Aluminum oxide Calcium oxide (Al2O3) (CaO)

13.30%

Silicon dioxide (SiO2)

Varaprasad et al. 22.80% (2020)

Authors name

Compositions

Table 1 Chemical composition of MSWI ash in percentage

5.6–19.1

1.20–18.99

3.4–6.1

–

Ferric oxide (Fe2O3)

1.35–1.8

–

–

–

0.41

2.26%

Magnesium oxide (MgO)

5.79

–

–

0.9

0.24

1.43%

Sodium oxide (Na2O)

–

–

–

21.81

0.44

–

Chloride as Cl

–

–

9.7–12.5

16.10%

8.67

14.09%

Loss on ignition

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up to 46% of the total, whereas SiO2 is the most important compound in MSWI bottom ash, occurring in up to 49% (Gines et al. 2009; Pan et al. 2008). The use of incineration to handle MSW is becoming more and more common, appropriate MSWI ash management has become a critical responsibility for reducing the negative health and environmental repercussions. The majority of previous MSWI ash research was done to reduce the chemical risks of the ashes so that they may be safely disposed of in landfills. Many ways have been evolved over the past to achieve this goal. Typically, these include. 1. Washing, Leaching, and Electrochemical treatment are used to remove hazardous compounds from the ashes. 2. Multiple approaches are used to preserve hazardous components in the ashes, including solidification, hydrothermal treatment, chemically stabilization, and mechanical operations. 3. Disposal of the finished product in landfills.

4 Application of MSWI Ashes (as a Resources) The treatment of MSWI ash requires the use of additional resources and elements. Still, we can say that MSWI ash can be safely disposed of in landfill. With the growing economy of every country, experts agree that MSW and MSWI ash should be viewed as a resource rather than a waste. It is clear from the above-mentioned solution that MSWI ash can be seen as a useful and value-added product. Various researchers have done many researches for this explanation as mentioned in the sub-sections below as shown in Fig. 4.

5 Construction Materials Zhang et al. (2021) added BA to dry-cast concrete having a slump value of zero in which BA was found as a cementing additive. The final strength of BA concrete is 18% higher than concrete made from ordinary cement after 90 days and Concrete made from BA has improved strength as well as resistance to freeze–thaw damage. Finally, the study discovered that MSWI-BA improves the performance of dry-cast concrete and might be employed as a supplemental cementitious material. Since MSWI-FA and BA have comparable cementitious characteristics but MSWI-FA have higher toxic substances therefore MSWI-BA can often be used as a partial alternative to OPC, (Biswal et al. 2019). Gholeh and Shao (2018) suggested setting up the cement manufacturing process in a waste incineration plant to fully utilize the MSWI-FA, waste heat, and carbon dioxide (CO2 ) produced. Because cement made from MSWI ash contains 51.2 vol percent chloro-alstadite and clinker which contains FA (42.8 wt%), waste lime (42.8 wt%), hydrated lime (9.1 wt%), and silica sand (5.3 wt%), And also the reactivity of CO2 in this cement is high.

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Construction Industries

Landfill covers

Stabilisation of soil

Application of MSWI ashes

Construction of Embankment

Land reclamation

Construction of road

Fig. 4 Applications of MSWI ash as a resources

6 Road Construction In addition to the previously listed effective recycling approaches, the MSWI-BA may be used in road pavement applications as unbound, hydraulically bound, or bitumen-bound materials (Lynn et al. 2017). These programs have been extensively adopted in countries like Denmark, the Netherlands, and Belgium since the corporate sustainability theory calls for diverting MSWI ashes out of landfills. Similarly, research groups in other countries are considering whether to use such a strategy. For example, Xie et al. (2016) demonstrated that as long as the problem of toxic substances leaching is solved, MSWI-BA may be used as well-graded gravel or sand in buildings and road construction in China. The road base materials created with the MSWI-BA replacing 20% of the aggregates must, however, satisfy the strength requirements for a heavy traffic roadway in China. However, if such a solution is used as an MSWI ashes management option, further research is needed to determine whether acid rain might potentially leach metals from the road surface (Yang et al. 2018). The recycling of MSWI ashes into significant materials, as described in “Utilization of MSWI ashes (as a resource”), is an important technique for reducing the economic burden of landfill construction. When contemplating such an alternative, however, the potential leakage of pollutants from the value-added goods is a major worry. Furthermore, it is said that sulfates encourage the growth of crystalline structures, which cause severe cement cracking, whereas chlorides are thought to

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inhibit the formation of calcium-silicate-hydrate gels in cement. Additionally, chlorides corrode the reinforcing steel in RCC concrete, which accounts for more than 40% of structural failures (wong et al. 2020).

7 Soil Stabilization Stabilization using additives is the most popular method for enhancing the qualities of problematic soil. Many waste products, such as jute, palm fiber, nylon fiber, aluminum residue, iron residue, fly ash, and coal, can be utilized to stabilize expansive clayey soils. These compounds improve the soil’s strength and durability (Hejazi et al. 2012; Fatahi et al. 2012). To enhance mechanical properties, MSWI ash can be combined with weak soil, lime, cement, and some other non-traditional stabilizers. MSWI ash may be used in a variety of geotechnical applications to effectively solve the problem of disposal. Heavy metals may be reduced if cement is included in MSWI bottom ash. (Shah and Ahmad 2008; Zekkos et al. 2013; Zhang et al. 2016; Singh and Kumar 2017b). Geotechnical features demonstrated MSWI Ash by Gupta and Paulraj (2016) paper. As a result, FA and BA should be kept separate. While MSWI-BA often exists in a sand and gravel zone, MSWI-FA usually occurs in sandy silt. Both of them lack plasticity, and they have a lower specific gravity than soil. MSWI ash has a lower compacted unit weight than traditional fill materials, making it acceptable for reuse. They have shear strength values that are equivalent to thick sand and gravel. More than half of the MSWI-BA is classified as medium-coarse sand (0–4 mm) and nearly a third of the MSWI-BA falls into the fine gravel range, according to on-site GSD (grain size distribution) of BA from two WtE (waste to energy) facilities in India (4–16 mm). Organics that have not been burned, metals, glass, and ceramics make up less than 10% of the overall material. The organic composition of the 0–4 mm fraction ranges from 2 to 6%.

8 Embankment Embankments are constructed with the help of mud (clay) or stone and they are used to hold back water. In the form of embankments, we use retaining walls, land reclamation, etc. It is standard practice to stabilize soils using lime or cement when they do not exhibit the necessary geotechnical qualities. This enhances engineering characteristics by lowering soil compressibility and raising shear strength. Goh and Tay (1993) looked at the viability of MSW fly ash being used in place of filler in geotechnical applications. Findings in research by researchers suggest that bottom ash possesses essential properties similar to those of coarse aggregates used in the manufacture of embankments and that such coarse aggregates have higher strength, free-draining characteristics, and greater strength than conventional earth

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fills. They also contain low compressive densities. Additionally, they looked into the viability of utilizing fly ash in place of lime or cement to stabilize soil, and they discovered that these combinations of soil and fly ash had higher shear strengths and lower compressibilities than other non-treated soils.

9 Land Reclamation The process of regenerating abandoned land or generating new land in the ocean, lake, and river is known as land reclamation. Land reclamation is used to avoid construction work on fertile land. Yin et al. (2018) conducted a series of large-scale column trial tests to assess the leaching tendency of heavy metals from MSWI-BA into saltwater as a result of land reclamation. During initial contact, heavy metals were observed to leach into the seawater instantly, having a uniform distribution of heavy metals throughout the horizontal layer at all ocean depths. However, lower depths yielded higher concentrations (where BA settled). According to Yin et al. 2019, the use of a chute to deposit heavy metals minimizes the total quantities of heavy metal leaching. However, greater concentration gradients were reported over the ocean depth. The amount of heavy metals released by BA is influenced by the depth of the salt water. As a result, the duration of BA exposure to water and the degree of seawater disturbance caused by BA settlement and re-suspension is related to this parameter.

10 Conclusions In this article, the use of MSWI ash is demonstrated in many ways. It has been concluded that, with the help of current technology, MSWI ash can be treated and utilized in a wide range of applications. These applications are discussed from an engineering and environmental perspective and both the advantages and drawbacks of each approach are highlighted. After MSWI ash is used in different areas and from experiments, it has been found that most of the environmental constraints are caused by the leaching process in the final products. Furthermore, most applications either require or strongly recommend pre-treatment. While pre-treatment increases cost, sometimes pre-treatment is necessary to increase ash quality and reduce toxic metals. MSWI ash can also be used as a non-traditional material for the construction and improving any soft soil or weak soil. This paper covers different MSWI fly ash applications and compares the relative benefits of these applications based on current understanding. These applications are examined from an environmental/engineering standpoint, highlighting the flaws of each technology while also emphasizing its benefits. The findings are encouraging because they demonstrate new real-world potential for the repurposing of this waste in a variety of industries, ranging from ceramics to construction materials to road

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bottoms, in the short term. The successful application of this trash will have significant waste reduction and resource conservation benefits, and this fact appears to be driving both the research community and legislators to find viable disposal alternatives. Investigations are still ongoing regarding several uses for MSWI ash. Reusing MSWI ash has been discouraged due to technical and environmental issues. The pre-treatment technique raises the overall cost, but it also makes it possible to reuse the ashes. Any of the approaches would make a significant contribution to waste reduction and landfill alternatives.

11 Recommendations and Future Scope A smaller number of works have been done on the enhancement of the shear behavior of MSW Ash. There is a deficiency of study and experimental investigation found associated with the footings constructed on MSW Ash and the settlement of that footing. Less number of works found on the leaching behavior of MSW ash dumped on landfill sites. More studies and experimental investigations are needed about the behavior of MSW Ash and the enhancement of its geotechnical property with different admixtures and a comparative study between them.

References Almalkawi AT, Balchandra A, Soroushian P (2019) Potential of using industrial wastes for production of geopolymer binder as green construction materials. Constr Build Mater 220:516–524. https://doi.org/10.1016/j.conbuildmat.2019.06.054 Annual Report on Solid Waste Management (2020–21), CPCB, Delhi Biswal BK, Chen ZT, Yang EH (2019) Hydrothermal process reduced Pseudomonas aeruginosa PAO1-driven bioleaching of heavy metals in a novel aerated concrete synthesized using municipal solid waste incineration bottom ash. Chem Eng J 360:1082–1091. https://doi.org/10.1016/ j.cej.2018.10.155 Damgaard A, Riber C, Fruergaard T, Hulgaard T, Christensen TH (2010) Life-cycle-assessment of the historical development of air pollution control and energy recovery in waste incineration. Waste Manage 30(7):1244–1250. https://doi.org/10.1016/j.wasman.2010.03.025 Dontriros S, Likitlersuang S, Janjaroen D (2020) Mechanisms of chloride and sulfate removal from municipal-solid-waste-incineration fly ash (MSWI FA): effect of acid-base solutions. Waste Managment 101:44–53. https://doi.org/10.1016/j.wasman.2019.09.033 Fatahi B, Khabbaz H, Fatahi B (2012) Mechanical characteristics of soft clay treated with fiber and cement. Geosynth Int 19:252–261. https://doi.org/10.1680/gein.12.00012 Forteza R, Far M, Segui C, Cerda V (2004) Characterization of bottom ash in municipal solid waste incinerators for its use in road base. Waste Manage 24(9):899–909. https://doi.org/10.1016/j. wasman.2004.07.004 Ghouleh Z, Shao YX (2018) Turning municipal solid waste incineration into a cleaner cement production. J Clean Prod 195:268–279. https://doi.org/10.1016/j.jclepro.2018.05.209

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Challenges to Implement Artificial Intelligence for Environmental Sustainability Harshita Mogha

and Nitasha Hasteer

Abstract In this study, we conducted a comprehensive literature assessment of highly influential works that focused on artificial intelligence and its advantages for reaching the Sustainable Development Goals (SDGs) and environmental sustainability. The systematic literature review method employed was PRISMA 2020. Environmental challenges, such as pollution control, the extinction of species, climate change, transportation, waste management, and others, are among the most pressing concerns of the modern day. IoT along with Artificial Intelligence technologies can play a significant part in addressing these problems. This work brings forth the challenges to implement Artificial Intelligence for Environmental Sustainability. The findings reveal that Artificial Intelligence is dependent on complex data which is at risk due to security threats. Keywords Environmental sustainability · Systematic review · Applications · Challenges · Artificial intelligence · Sustainable development goals (SDGs)

1 Introduction Environmental protection is one of the most important and frequently debated topics in contemporary times. According to a report, Carbon emissions will be 80% greater in 2050 than they are now, necessitating absolute reductions in carbon footprints between 50 and 85 percent to reach 450 ppm stability objective of the Intergovernmental Panel on Climate Change (Xia and Niu 2020). The International Energy Agency reports that the world’s energy-related carbon footprints increased by 1.7% in 2018, hitting a record high of 33.1 gigatons (Xia and Niu 2020). Most environmental problems can be solved with Artificial Intelligence (AI) (Taddeo et al. 2021). We can track and anticipate local and global environmental issues using AI and machine learning, and we can support policy initiatives to reduce emissions (Taddeo H. Mogha (B) · N. Hasteer Amity University, Uttar Pradesh, Noida, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_31

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et al. 2021). According to a collaborative research conducted by Microsoft and PwC, applying AI to environmental applications may reduce global greenhouse gas emissions by 1.5–4% while increasing GDP by 3.1–4.4% (Taddeo et al. 2021). The environmental crisis has become a global issue, and every nation is in a catastrophic situation, whether we talk about climate change, pollution that leads to damage of the ozone layer, acid rain, the greenhouse effect, extinction of species or decline of biodiversity (https://news.climate.columbia.edu/2019/09/23/beyond-climate-crisesenvironmental-sustainability/). Changes in the atmosphere can have an impact on forests, biodiversity, freshwater and marine ecosystems, and economic activities such as agriculture. Climate change, pollution, global warming, increased carbon footprint, deforestation, loss of biodiversity, ozone layer depletion, species extinction and natural resource depletion are few of the major concerns that are widely acknowledged to be a part of environmental catastrophe. Since the Sustainable Development Goals (SDGs) were announced in 2015, the 2030 agenda has been a topic of discussion (Vallez et al. 2030). Sustainable development can be achieved and the environment can be protected with good technology applications. The goal of modern ecological advancement is to employ green technologies to monitor, protect, and minimize the negative effects of technology on the environment while addressing a variety of environmental problems, such as global warming, carbon emission and energy conservation. The use of green technology may have significant advantages over the long term. Progress in terms of technologies has made the SDGs’ implementation considerably easier and more attainable (Shen et al. 2021). Novel technologies such as Artificial Intelligence and IoT Systems plays a very important role in accomplishing not only SDGs related to environment, but all other SDGs, such as eradicating hunger and poverty, obtaining sustainable energy, achieving gender equality, and conserving and preserving biodiversity. To detect pollution levels, reduce energy consumption, and better understand the ramifications of climate change, Artificial Intelligence (AI) technologies and algorithms are being created (https://earth.org/data_visualization/ai-can-it-help-achieve-environme ntal-sustainable/). Local and national governments all around the world are adopting AI into their programme and policy roadmaps for environmental preservation. Integration of AI and IoT technologies has the ability to expedite global efforts to safeguard the environment and conserve resources. AI and environmental sustainability are categorized into four important areas: sustainable agriculture, environmental asset conservation, waste and pollution management, pollution monitoring, and pollutant treatment (Nti et al. 2022). In addition to exploring the obstacles that prevent the use of artificial intelligence for environmental sustainability, this study assesses the current state of the art surrounding these applications. Considering the relevance of the topic, there is a need to analyze in depth the role of AI in environmental sustainability. Sections of the remaining text are organized as follows: Section 2 begins by describing the research that have been found to support the idea that AI is good for environmental sustainability. The difficulties that prevent the use of AI for the wellbeing of our environment are then discussed in Sect. 3. Section 4 addresses the

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possibilities AI offers for environmental sustainability while Sect. 5 wraps up the article.

2 Literature Review A systematic overview of the reported issues and uses of AI for environmental sustainability has been obtained using the Systematic Literature Review (SLR) methodology. This is so that SLR may aid in the exploration and assessment of any particular research field by identifying and filling in research gaps and encouraging more study. This article employs the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) technique (Page et al. 2021). To get an accurate and thorough analysis and synthesis of the results, it comprises of a checklist with 32 items. The flow of the review carried out is represented in Fig. 1. A study in literature explored the significance of AI in environmental sustainability, focusing on biodiversity, energy, water, and transportation (Nti et al. 2022). It covered several levels and disciplines of AI, such as Machine Learning (ML), Artificial Neural Networks (ANN), Robotics, Natural Learning Processing (NLP), Fuzzy Logics (FL), and Expert Systems (ES). The study also discusses the applications and issues that arise while deploying AI. Another work in literature addressed the environmental sustainability of products enabled by AI (Frank 2021). The study also discusses how marketing businesses might gain from creating and deploying AI-enabled solutions and classified

Fig. 1 PRISMA 2020 used for systematic literature review

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a product’s environmental benefits into two categories: static environmental benefits and autonomous impact of the two groups using the notion of signaling theory. Another group of authors reported on the use of AI in dentistry for improving environmental sustainability (Huang and Chang 2022). The study also demonstrated how using AI in dentistry may minimize carbon emissions, waste, and the use of consumables such as plastic teeth which is helpful in saving water and energy as well. A qualitative study reviewed literature examining the difficulties associated with processing environmental data and overcoming them through the use of data science, where they examined machine learning and deep learning (Shukla et al. 2020). The authors also covered how AI helps society manage high impact of environmental threats that have a variety of effects on companies and human health, as well as how data analytics is of great importance for the natural environment. A work in literature covers the function of AI in Agriculture; the authors discuss the application of Artificial Neural Networks (ANN), Expert Systems (ES), Machine Learning (ML), and Fuzzy Logic System for solving farmer problems ranging from seed sowing to crop harvesting (Jha et al. 2019). There is an urgent need to comprehend on concerns such as the use of dangerous pesticides, regulated irrigation, pollution management, and environmental repercussions in agricultural practices, which may be handled by AI. Another work reported in the literature discusses a project aimed at assisting the Regional Agency for the Environment of the Regione Veneto (ARPAV) and some theoretical models that the authors developed during that time period to model a general architecture of a multi-agent system used for environmental monitoring, identify the difficulties in its installation and upkeep, concentrate on some difficulties associated with the management of the cloud architecture in this context, and finally deploy it (Cristani et al. 2020). A group of authors discussed several academic fields related to climate change and environmental monitoring, agriculture, forestry, and the extraction of marine resources that employ different types of AI (Galaz et al. 2021). The authors also thoroughly discussed about the systematic risks that can be faced while deploying AI in research fields. A work in literature focuses on the dangers of transportation fuels and their impact on the environment (Kasim et al. 2019). It also discusses how technologies like AI, block chain, the Internet of things (IoT), and cloud computing have enabled the construction of intelligent transportation systems and traffic networks to combat climate change and minimize carbon footprints. Another study focuses on the use of artificial intelligence to further certain goals for sustainable development, particularly in those that have a big impact, like agriculture (Goh and Vinuesa 2021). Additionally, it discussed a number of concerns and difficulties, including fairness, equality, automated judgement, and the morality of profiling. The impact of AI on human civilization and moral standards was also covered. To put the Technology Code of Conduct into practice, the authors suggested creating a progression matrix and choosing model papers (Myers and Nejkov

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2020). The authors also spoke about the benefits of adopting the Technology Code of Conduct, including how it would increase consumer trust, guarantee that AI technologies are human-centric, and benefit people, society, and the environment. An author studied, evaluated, reviewed, and investigated how the adoption of the 2030 agenda for sustainable development, which contains 17 SDGs and associated 169 objectives, might impact the usage of renewable energy, including AI applications, favorably or unfavorably (Hannan et al. 2021). The 17 SDGs were categorized into three sections for this assessment: environment, society, and economics. These three categories are seen by the authors as the main pillars of sustainable development, and a consensus-based expert elicitation approach is employed to meet the study’s goals. Another literary work contrasts the two types of AI while concentrating on the sociological and environmental pillars of sustainability from a legal and consumer protection perspective (Kindylidi et al. 2021). All throughout the study, it was emphasized that any effective policy action on sustainable AI needs the cooperation and support of the AI ecosystem. The subject of how corporate culture affects the usage of artificial intelligence in terms of sustainable development was explored in a literary work (Isensee et al. 2021). By offering a normative definition of Sustainable Artificial Intelligence (SAI) as a means and end to sustainability with specific usage and application guidelines derived from well-known methods towards sustainable development, including the SDGs, this study contributes to the discussion of AI for social good. It further highlights how the chance to apply AI for SAI depends on a company’s and its corporate culture’s capacity to create AI for sustainable growth through its human capital. An additional review is a survey of the literature on current advancements in sustainability and digital transformation (Madureira et al. 2022). The most recent studies demonstrate the potential societal effects of the digital revolution on economic and environmental sustainability. The digital transition in environmental sustainability involves the use of technologies like artificial intelligence, big data analytics, the Internet of Things, social media, and mobile technologies to create and put into practice sustainability solutions in fields like sustainable urban development, waste management, sustainable production, and pollution control. Using deep learning algorithms, a literature work, focuses on predicting proenvironmental consumption (Lee et al. 2017). To forecast the consumption index that is more environment friendly, the authors applied the Recurrent Neural Networks (RNN) model. The accuracy of the predictions was then confirmed by contrasting RNN with Ordinary Least Squares regression and ANN models. Another article provides an overview and elaborates on potential systemic risks for sustainability brought on by the adoption of AI and related technologies (Frescativägen n.d.). Early applications of AI and related technologies in fields crucial for what some have called “biosphere-based sustainability” are the sole focus of the authors’ empirical analysis and discussion. The necessity of farmers’ digital and environmental abilities for greenhouse farming is the subject of a study (Kavga et al. 2021). The authors promoted the acceptance and dissemination of greenhouse technology training in Greece. They also

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Year wise distribution of studies

15

12

2

2

1 2017

2019

Year wise distribution of studies 2020

2021

2022

Fig. 2 Year wise distribution of studies

emphasized how enhancing greenhouse training may cut production costs, increase farm profitability, and expose farmers to new technologies. Digital transitions have been extremely important in creating and implementing sustainability solutions for environmental issues, as noted in a work in literature. In this study, different strategies are discussed, such as innovation through experimentation and dynamic and durable advantages attained through momentary gains (Isensee et al. 2021).

3 Challenges to Deploy Artificial Intelligence for Environmental Sustainability AI can significantly contribute to environmental sustainability by reducing carbon emissions, deforestation, energy consumption and extinction of species. However, its application is hampered by various challenges. Some of the issues are listed in Table 1.

Challenges

Data dependent

Data at risk

Inadequate measurements

Data complexity

Algorithmic bias

S. no

1

2

3

4

5

Nti et al. (2022), Frank (2021), Eluwole et al. (2022)

Sources

Nti et al. (2022), Eluwole et al. (2022)

Applying AI systems can be negatively impacted by biases and inconsistencies in training data and security flaws that impact data gathering and decision-making systems

(continued)

Lee et al. (2017), Eluwole et al. (2022)

The environment data is very diverse in nature and Shukla et al. (2020) the nature of its storage and retrieval is very different. Also, the data may be distributed across a variety of geographic regions. Thus, making it a key challenge in finding insights from the data

Measuring and monitoring of interference in Environmental Sustainability is very difficult and mostly complex to perform

Increased risk of cybersecurity in AI, makes the Nti et al. (2022), Eluwole et al. 2022) sensitive and critical data in risk of getting in hands of a third person via hacking

Model training in AI uses past data, and with the evolving nature of AI due to variability of human behavior, this dependency poses challenges. Past data reflects ages of information which makes it hard to predict the future

Description

Table 1 Challenges to deploy Ai for environmental sustainability

Challenges to Implement Artificial Intelligence for Environmental … 403

Challenges

Unequal access and benefits

Cascading failures and external disruptions

Trade-offs between efficiency and resilience

S. no

6

7

8

Table 1 (continued)

AI based energy and traffic systems may be progressively optimized and efficient, however become more vulnerable and subject to “regime shifts,” which are characterized by quick, unwanted, and occasionally irreversible changes in a particular environment

AI systems are vulnerable to endogenous cascades and unanticipated shocks that arise. This suggests that internal failures might appear unexpectedly, have ripple effects and magnify across network linkages (for example, a regional food supply chain), and produce problems in the system as a whole, especially if the system’s components are not optimized and controlled efficiently

In the agriculture sector, even if some farmers are able to cost-effectively improve their own businesses, mass use of AI in farming might still lead to increased inequality and capital concentration

Description

Galaz et al. (2021)

Lee et al. (2017)

Lee et al. (2017), Eluwole et al. (2022)

Sources

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4 Application of Artificial Intelligence in Environmental Sustainability Applications of AI have considerably advanced over the last few years, and they are now being used to support environmental sustainability. To anticipate, monitor, and protect ecological services, AI may be employed in predicting energy production, distribution, operations, maintenance and biodiversity (Shen et al. 2021; Eluwole et al. 2022). AI may lead to increased environmental monitoring capabilities, improved supply chain transparency, and more effective use of land and water resources (Isensee et al. 2021). Additionally, AI may be very useful in a variety of fields, including Environment Water Management (EWM), Hydrology and Water Resources, Environmental Remote Sensing, Soil Science, and Agriculture, it may be used to analyze water quality parameters and forecast stream flows (Shen et al. 2021; Frank 2021). AI aids in traffic management, safety enforcement, and road marking (Shen et al. 2021). Environmental sustainability powered by AI can help companies create new products, modest profits and promote their products to customers more effectively (Nti et al. 2022). Because fewer consumables like disposables are used, carbon emissions are reduced, potentially slowing the pace of global warming (Page et al. 2021). AI also aids in removing harmful chemical pollutants and minimizing radiation damage (Page et al. 2021). In a similar way, AI has had a significant positive impact on urban sustainability, waste management, sustainable production, and pollution control (Isensee et al. n.d.). AI can facilitate the adoption of the circular economy (CE), the digital sharing economy, and environmentally friendly industrial and infrastructure design (Isensee et al. 2021). Robotics and AI have been employed in bionics to enhance and complement human abilities (Galaz et al. 2021). Numerous academicians and researchers have concentrated on optimizing industrial robotic models using AI techniques to increase efficiency and speed up the manufacturing process (Kasim et al. 2019). Several apps have also been developed to leverage artificial intelligence to enhance environmental sustainability. Table 2 describes the features of few of the apps that have been analyzed in this study.

5 Conclusion The purpose of this study was to evaluate influential literature for identifying the challenges that hinder AI implementation for environmental sustainability and also to see the substantial contributions of AI to achieve environmental sustainability. It is found that there are many studies which are focused on sustainability in the areas such as Biodiversity, Species Extinction, Transportation, Climate Change, Pollution Control, and so on which concludes that the major issues we face in implementing AI in environmental sustainability are the challenges such as data dependency, complex nature

Wedonthavetime climate change

2 With the help of this platform, people may share one’s environmental efforts with other climate activists and learn about new environmental actions

Description This platform tries to improve public awareness of how one person may negatively impact climate change, global warming, and other related topics

App

Oroeco

S. no

1

Table 2 Apps to leverage ai to enhance environmental sustainability Features

Source

(continued)

This app helps you to always stay up https://ecobnb.com/blog/2020/02/ to date with the most recent facts on eco-friendly-apps/, https://app.wed global climate change onthavetime.org/ It can let businesses know about their climate. Users will learn how to work together to solve the climate crisis and preserve the globe with the help of this platform

It keeps tabs on one’s carbon https://ecobnb.com/blog/2020/02/ footprints and environmental effects. eco-friendly-apps/, https://www.oro It examines every aspect of one’s life, eco.org/ including their eating habits and travel patterns It also provides useful advice for the users, aids in cost savings, and reduces pollution The application is a highly precise calculator that considers all factors and shows signs of positive self-improvement

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This platform functions as a task that aids in protecting the environment

iRecycle

Carbon footprint and CO2 tracker

4 The software makes predictions about CO2 emissions and offers information

Description

App

S. no

3

Table 2 (continued) https://ecobnb.com/blog/2020/02/ eco-friendly-apps/, https://earth911. com/irecycle/

Source

(continued)

This platform uses a GPS-based https://apps.apple.com/us/app/car technology to automatically measure bon-footprint-co2-tracker/id1491 transportation emissions as well as 455903 users to choose projected travel times and modes of transportation. It encourages the user’s to choose meals and travel options that are low in carbon

With the use of this platform, users are able to locate nearby recycling facilities for any products they need to recycle, including glass, chemicals, papers, metal, old technology, and batteries With the help of this app, users can keep their homes organized and get rid of items that are no longer required

Features

Challenges to Implement Artificial Intelligence for Environmental … 407

Description With the help of this smartphone app, one can monitor, cut, and eliminate carbon emissions

App

Capture

S. no

5

Table 2 (continued) Source

The CO2 emissions that result from https://www.finder.com/environme one’s eating habits and transportation ntal-impact-apps, https://www.the may be tracked, reduced, and capture.club/ eliminated using Capture. The software uses the GPS in user’s phone to track their carbon emissions whether they drive, cycle or take a bus or plane. The user may monitor their meals with the mobile app. The app will provide them daily tips on how to reduce their carbon footprint. Additionally, it will inform the user if their monthly CO2 target has surpassed

Features

408 H. Mogha and N. Hasteer

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of data, risk of hacking, inadequate measure and many more. Monitoring is critical in harnessing AI to contribute to the environment. AI advantages include improved environmental governance, industrial environmental performance, risk reduction, and safety. It is reported that AI and IoT can aid in environmental sustainability. They need more implementation to address the majority of the difficulties. The adoption and adaption of these technologies are required to observe a significant shift in environment for sustainability. There is much more effort to be done in this area to produce meaningful outcomes, so that our current and future generations may live in a safe environment. In the actual world, new tactics and AI-based advances can be deployed. For further study, ranking of the challenges identified can be done using AHP and we can identify which challenge is hindering the implementation of AI for environmental sustainability the most to make any major progress toward environmental sustainability, actions must be measured and monitored in a timely, precise, and accurate manner.

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Application of Artificial Intelligence, Machine Learning, and Deep Learning in Contaminated Site Remediation K. V. N. S. Raviteja

and Krishna R. Reddy

Abstract Soil and groundwater contamination is caused by improper waste disposal practices and accidental spills, posing threat to public health and the environment. It is imperative to assess and remediate these contaminated sites to protect public health and the environment as well as to assure sustainable development. Site remediation is inherently complex due to the many variables involved, such as contamination chemistry, fate and transport, geology, and hydrogeology. The selection of remediation method also depends on the contaminant type and distribution and subsurface soil and groundwater conditions. Depending on the type of remediation method, many systems and operating variables can affect the remedial efficiency. The design and implementation of site remediation can be expensive, time-consuming, and may require much human effort. Emerging technologies such as Artificial Intelligence, Machine Learning, and Deep Learning have the potential to make site remediation cost-effective with reduced human effort. This study provides a brief overview of these emerging technologies and presents case studies demonstrating how these technologies can help contaminated site remediation decisions. Keywords Site remediation · Artificial intelligence · Machine learning · Deep learning

K. V. N. S. Raviteja (B) · K. R. Reddy Department of Civil, Materials, and Environmental Engineering, University of Illinois, Chicago, IL 60607, USA e-mail: [email protected] K. R. Reddy e-mail: [email protected] K. V. N. S. Raviteja Department of Civil Engineering, SRM University, Amaravati, AP 522240, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_32

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1 Introduction From the inception of environmental awareness during the mid-twentieth century, several regulations have been promulgated to mitigate the further impact on the environment from human activities. The population explosion, rapid urbanization, and increased living standards of people have all contributed to greater pollution. Until 1970, there was minimal awareness about the adverse effects of the wastes, which were disposed of carelessly without any environmental laws and regulations. The derivatives from chemicals and other toxic substances used on an industrial scale were disposed of without considering potential impacts on public health and the environment (Sharma and Reddy 2004). Though pollution control is becoming more mandated to prevent the creation of new contaminated sites, existing contaminated sites must be assessed and remediated. Recently, multiple technologies have been developed for site remediation, which includes soil vapor extraction, soil washing, stabilization and solidification, electrokinetic remediation, bioremediation, phytoremediation, pump-and-treat, in-situ flushing, and permeable reactive barrier (Sharma and Reddy 2004). Environmental site remediation is the process of removing, stabilizing, or degrading contaminants in the soil and groundwater, protecting human health and the environment. Site remediation is performed in multiple phases consisting of site characterization, risk assessment, risk management, and finally, selecting, designing and implementing a remediation technique. Remediation restores the contaminated site for redevelopment or to return it to its natural state (Hamilton 2012). Assessment of site contamination and selection of remediation method requires significant input data, which is obtained by performing site investigations or estimated using existing site information. The advancement in computer-based technological analysis can simplify and improve site assessment and remediation decisions. Emerging technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have the potential to be powerful tools for contaminant site assessment and strategy development for remediation. These technologies can provide a platform to support hierarchical integrated analysis of resources and environmental attributes and integrate the information into comparative theoretical ecosystem analysis. These technologies could overcome enormous challenges and improve the design of efficient site remediation systems. In this study, an overview of AI, ML and DL is provided and then example applications of these technologies for site assessment and remediation are presented.

2 Emerging Technologies Figure 1 depicts the emergence of AI, ML, and DL technologies and the methodologies widely used for engineering systems. A brief description of these technologies is provided below.

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Fig. 1 Schematic representation of AI, ML, and DL technologies

2.1 Artificial Intelligence (AI) Artificial Intelligence (AI) is the simulation of human intelligence in machines and computers. It works on a foundation of specific hardware and software capable of writing and training machine learning algorithms. There are four general types of AI: (1) reactive machines, (2) limited memory, (3) theory of mind, and (4) selfawareness. The broad set of techniques that uses AI as a primary platform includes: Machine Learning, Neural Networks, Robotics, Expert Systems, Fuzzy Logic, and Natural Language Processing. Machine Learning technique is described in the next section, and the other techniques are briefly described below: . Neural Networks: It works similarly to nerves that transfer signals to the brain. A neural network implicates the brain that comprises neurons and replicates the brain-neuron to a system of network functions. Neural Networks comprise a set of algorithms that determine the elemental relations between the various data sets. The data can be actual or artificial models known as perceptrons. The neural network works based on various statistical techniques to form networks. Neural networks have various applications in risk analysis, futuristic analysis, and data predictions. . Robotics: A subset of AI mainly is focused on designing robots. AI is generally employed in robotics for assembly lines in automobile manufacturing, packing, and manufacturing industries, precise moving of heavy and oversized objects related to space technology.

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. Expert Systems: These are one of the initial models of AI. Expert systems refer to a process depicting human expertise’s decision-making intelligence. This technology employs an extensive knowledge database and uses reasoning and insight regulations corresponding to the rising queries. Expert systems are very responsive, used for high executions and reliable. The models’ efficiency depends on the quality and quantity of the data in the knowledge base. . Fuzzy Logic: This technique presents and transforms an uncertain condition by measuring the correctness degree of the hypothesis. Fuzzy logic can be used to implement ML techniques and logically depicts the human thought process. The standard logic can be determined in terms degree of truth that ranges from 0.0 to 1.0, where 0.0 indicates complete false logic, and 1.0 indicates complete true logic. . Natural Language Processing (NLP): NLP is used for searching, analysing, and deriving the data in text form. NLP libraries are used to train machines to interpret the text data logically. NLP has vast applications in consumer-based services, e-commerce websites, and email clients. The above approaches can be used to formulate various models. Some of the widely used AI models are: Linear Regression, Logistic Regression, Deep Neural Networks, Linear Discriminant Analysis Naive, Bayes Learning, Vector Quantization, K-nearest Neighbors, and others. More details on these models can be found in Rusell and Norvig (2022).

2.2 Machine Learning (ML) As a subset of artificial intelligence (AI), Machine Learning (ML) is the process used to build mathematical models that aim to make predictions and take decisions (Zhang 2020). Generally, ML is based on training data (i.e., sample data), and it is highly affected by the completeness of this data. An inadequate or incomplete set of training data is likely to result in poor predictions (Melhem and Nagaraja 1996). Different ML algorithms are currently being used in the engineering field, such as extreme learning machines (ELM), artificial neural networks (ANN), and support vector machines (SVM) (Majumder and Lu 2021). Moreover, the following are different approaches to machine learning: . Supervised Learning: These mathematical models comprise input and output data sets. The training data can have multiple outputs. The training data contains multiple arrays known as future vectors represented in the form of a matrix. Supervised works on an objective function through iterations until convergence to the optimum point. The model can formulate a function that can accurately predict the output. . Unsupervised Learning: The data is latent with only input variables in unsupervised learning. The data gets trained based on various operations like clustering and likelihood. The algorithm identifies the common features in the data

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to form clusters. Unlike supervised, in unsupervised learning, sorting happens after various iterations to identify the data with similarities prior to delivering the output. . Classification: Classifications are useful when the output has constrained boundary conditions. The objective is to classify the population into various sets based on similar quantifiable properties. The data analysis output is based on the classifications and population in each classified group. . Clustering: In clustering, the data is assigned to various subgroups known as clusters. The data variables in the same cluster are closely associated with similar properties. The dissimilarities form the distinctions between clusters. Clusters are formed by various techniques like similarity metrics and internal compactness. Although clustering is similar to classification, the primary assignment of data is different. In classification, the groups are pre-defined, and data is assigned to them. However, in clustering, groups are formed based on similarities in the data. Machine learning approaches can be applied through various developed models. Some of the models that have wide applications in engineering are: Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), etc. A detailed discussion of the ML models is provided by Ng (2018).

2.3 Deep Learning (DL) Generally, DL is applicable to more complex and big-data for which conventional ML models may result in inaccurate assessments. Deep Learning typically employs neural networks with various layers in order to achieve a more precise estimation of output. DL models are very advanced and mimic human brain neural behavior for predicting outputs. DL has two types of algorithms, convolutional and recurrent neural networks. Most common DL model types include: . Discriminative: The discriminative models are applied for statistical classification in supervised algorithms. These models generate new instances using the joint probability density function of the data points. Discriminative models are more robust than generative models in terms of outliers. . Generative: As the name indicates, these models can generate new data instances. The generative models can assess the probabilities, model data points, and classify the data based on the differences in the probabilities. Generative models are mainly based on the Bayes theorem to deal with complex data analysis. Unlike discriminative models, generative models employ unsupervised learning to formulate the data phenomena. . Hybrid Learning: It combines machine learning and deep learning, resulting in a fusion network. In this model, deep learning models extract features from unstructured data and use machine learning approaches to form accurate classifications

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from the same unstructured data. The hybrid learning models are very accurate and yet computationally expensive. Most commonly used DL algorithms include: Convolution Neural Networks (CNN), Long Short Term Memory Networks (LSTM), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), Self-Organizing Maps (SOM), Deep Belief Networks (DBN), and Restricted Boltzmann Machines (RBM). More details on these models are reported by Bengio and Courville (2016).

3 Applications of AI, ML and DL to Site Remediation The application of AI, ML, and DL technologies in site remediation field is still in infancy. Very few professionals working in the site remediation field possess expertise in these emerging technologies. However, few research studies have reported on how these technologies could be potentially applied in site remediation projects. Selected example studies are presented in this section.

3.1 AI-Based Optimization of Pump-Treat-Inject Groundwater Remediation: Case Study Sadeghfam et al. (2019) reported the use of Optimum Control by Artificial Intelligence (OCAI) to regulate the pumping schedule to treat a critical aquifer in the East Azerbaijan region of Iran. The aquifer is an essential source of drinking water and irrigation for the region’s agriculture. However, the aquifer was contaminated by the breaching of banks of wastewater lagoons during a significant storm event and the waste infiltrating into the soil, eventually reaching the aquifer. Groundwater samples from 29 monitoring wells showed high levels of total dissolved solids (TDS). The pump-treat-inject (PTI) technology is proposed to treat the contaminated aquifer. OCAI determined the optimum pumping rate during the treatment process. The OCAI is implemented in three modules. In the first module, information about properties such as hydraulic head and contamination were collected using flow and transport models, respectively. The second module converted the outputs of the first model into two Sugeno Fuzzy Logic (SFL) models. The third module was a user-defined unit that implemented OCAI as well as to run the genetic algorithm. Implementation of optimum pumping schedule through OCAI resulted in a substantial reduction in contaminant concentration to 3500 mg/L. The observations from 9 different pumping wells over a remediation period of 27 years are shown in Fig. 2. The reduction in contaminant concentrations at each well can be noted. The concentrations were not reduced to the allowable maximum contaminant level

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Fig. 2 Variation of hydraulic head and contaminant concentration at different well locations under optimum pumping schedule (Source: Sadeghfam et al. 2019)

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(MCL) of 1500 mg/L. However, a substantial reduction in the concentration is desirable as the PTI remediation process is often limited in its effectiveness due to the rate-limiting desorption of contaminants (Sharma and Reddy 2004). Further, a significant reduction in operating costs for pumping and treatment was achieved. It is perceived that with the implementation of first two modules, the run time was nearly 60 days. In addition to OCAI in module 3, the time period was reduced to two weeks to process and less than 30 min for running. The output from OCAI included: identified optimum pumping schedule, minimized contaminant concentrations, and reduced costs. The results of this study reinforce the idea that AI provides an assortment of applications in the environmental remediation projects.

3.2 ML-Based Assessment of Electrokinetic Remediation of Contaminated Groundwater: Case Study American Geophysical Union (AGU) takes an initiative to solve the limitations of long run times with increasingly complex simulators and problem-solving techniques from machine learning surrogate models trained on the outputs of processbased simulations. The electrokinetic remediation process (EK), one of the remediation technologies, involves using Coulomb interactions between charges of cathode and anode along with geochemical and biological reactions. The two-dimensional process-based modeling of the cathode and anode interacting is considered with electrokinetic distributions reacting with the in-situ biodegradation of the chlorinated compounds via simulation runs (Sprocati and Rolle 2021). This remediation strategy aims for lactate (C3 O5 H3 − ) to be electrokinetically delivered through electromigration, and transport of charged species, from the cathode electrode to support this biodegradation of the chlorinated contaminants found in groundwater. The microbial activity of organohalide-respiring bacteria (OHRB) was simulated by lactate leading to the degradation of tetrachloroethylene (PCE). The PCE is found in groundwater as both dissolved state and segregated non-aqueous phase liquid (NAPL). PCE degrades into trichloroethylene (TCE) and dichloroethylene (DCE). The addition of augmented OHRB (KB-1) supports absolute dehalogenation of the chlorinated solvents, which includes the conversion of DCE to vinyl chloride (VC) and finally into the targeted non-toxic ethene. This process could be accomplished with electroosmotic flow, transport of the non-charged chlorinated compounds and KB-1 from the anode to the cathode. Figure 3 shows a detailed representation of the EK process. It should be noted here that KB-1 is the commercial name for natural non-pathogenic microbial culture that is used to promote the complete dechlorination of chlorinated ethenes to non-toxic ethene. Sprocati and Rolle (2021) simulated the electrokinetic remediation process using COMSOL (multiphysics flow and transport) model and Phreeqc (geochemical code) that calculates equilibrium and kinetic reactions. Different ranges of input parameters were assumed in simulations. Different explanatory variables were examined with

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Fig. 3 Schematic representation of electrokinetic (EK) process (Source: Sprocati and Rolle 2021)

DOE (Design of Experiment), divided into training, validation, and test sets used to obtain a simulation plan with multiple design sets. The number of process-based simulation runs were such that each design sets displays a unique combination of the explanatory variables. Once the process-based simulations were executed with NP-Phreeqc-EK code, the approximation function was trained using training sets and cross-validated by testing model performances with test sets. The training procedure involved using a stochastic gradient optimizer with a set momentum value of 0.9 with a learning rate of 0.46. This approximation function was used as a surrogate of the process-based model to explore similarities between inputs and outputs. Moreover, the approximation function was attained through a neural network that uses a stack of multilayer perceptrons (MLP). An MLP is used with dense layers, representing all neurons within one layer connected to every neuron within a previous layer. In every dense layer, the outputs are calculated using Eq. 1. h W, b (X ) = φ(X W + b)

(1)

where hW,b represents the output value; W is the weight matrix of all connection weights (not including bias neuron); b is the bias vector (weight of connecting bias neurons and artificial neurons); and f is the activation function. A resulting calculation of the dense layer output value makes up the approximation function. MLPs

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Fig. 4 Process-based modeling with surrogate model framework (Source: Sprocati and Rolle 2021)

are typically used for regression analysis within machine learning implementations. The surrogate models were developed for a better understanding of the analysis and performances of the EK remediation process and uncertainty quantifications. To expedite the process-based models, developers performed different approaches to engage ML surrogates in the reaction step. Figure 4 shows the simulation structure that used the process-based model with the surrogate modeling procedure. Results showed a good agreement with predictions of outcomes from processbased and surrogate models. It indicates that the machine learning capabilities combined with the surrogates made from the process-based model work efficiently. The most important finding is identifying the relationship between the process-based and surrogate models. Figure 5 presents eight different scatter plots comparing the outputs of the process-based model with the predicted surrogate model performance for training (TR), validation (VA), and test sets (TE). The red line indicates that the 1:1 ratio between the process-based and surrogate models provides a visual means of their correlations. The training (TR) data obtained R2 values close to 100% as this is only training the outputs and response surrogate. For the validation, R2 values above 90% indicate good agreement with training. The test set results have shown similar correlations, averaging above 96% coefficient of determination. Thus, the results show excellent prediction performances and optimization. It can be noted that the improvement of approximation functions further can achieve greater accuracy.

3.3 DL-Based Simulation of Contaminant Migration: Case Study Li et al. (2021) modeled a study region as a two-dimensional inhomogeneous medium with irregular borders. Steady groundwater flow with hypothetical conservative

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Fig. 5 Comparison of process-based and surrogate-based models (Source: Sprocati and Rolle 2021)

contaminants that would not undergo biological or chemical transformations was considered. Contamination transport was simulated for ten years, with twenty simulation periods. The unknown variables are identified through numerical simulations. The contaminant was released during the six-month simulation period. MODFLOW and MT3DMS toolboxes were used to perform numerical calculations to model groundwater flow and contaminant transport. The simulation model was fed with GCSs information and the corresponding contaminant concentrations were determined. To determine the GCSI, the optimization model was employed which was linked with the generated simulation model. Thousands of repetitive computations were necessary to solve the optimization model. The computation load can be avoided by a surrogate model developed more accurately. In sequence, five hundred groups of input variables were identified for the simulation model. The developed surrogate models were trained by four different methods with the input variables (400 groups) and concentrations at well locations as output variables. Li et al. (2021) used a DL method with a long-short term memory (LSTM) network, which has great potential for characterizing the input–output conversion relationship of complex nonlinear numerical simulations to a surrogate model. Researchers used LSTM network, Radial Basis Function (RBF), Kriging, and Kernel extreme learning machines method. Several models were developed, including numerical simulation, surrogate, and nonlinear optimization models. The four surrogate models were compared and examined. The most accurate surrogate model was chosen and linked to the optimization model. Figure 6a and b show the study area and the locations of point contaminant sources as well as pumping and observation wells, respectively. Figure 7 shows the concentration distribution of contaminants in groundwater at different elapsed times simulated by numerical models. The accuracy of developed surrogate models was tested by: the coefficient of determination (R2 ), relative error (RE), and the root mean square error (RMSE). The surrogate model (LSTM, Kriging, RBF, and KELM) with the highest accuracy was

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(a)

(b)

Fig. 6 a Details of study area b The distribution of hydraulic conductivity in the study area (Source: Li et al. 2021)

Fig. 7 Contaminant concentration distribution in groundwater at different elapsed time phases: a 360 days b 720 days c 1800 days, and d 3600 days (Source: Li et al. 2021)

selected and linked to the optimization model. A non-linear optimization model was developed to capture the precise location and past release sources of groundwater contamination. The objective function, constraints, and the decision variables were the parts of the model. As reported by Guo et al. (2019), a generic algorithm (GA) is employed to optimize the model. The accuracy of the LSTM surrogate model was found to be the best. The optimization model can be linked to the LSTM surrogate model to reduce about 99% of the computational time required.

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Fig. 8 Variation in the contaminant concentration in observation wells at various time periods (Source: Li et al. 2021)

Variations of the contaminant concentrations in the six observation wells (O1 –O6 ) shown in Fig. 6a at different periods are presented in Fig. 8. It is determined that R2 values for the four models vary from 92.89–98.35%, the RMSE ranges from 88.14– 182.75, and the RE ranges from 7.63–18.62%. When the network layers or sequence length exceeds a specific limit, the gradient may disappear during the training. It could result in a decline in surrogate model accuracy. The results suggest that LSTM can create the surrogate model. The variations in the trends of the observation wells can be attributed to their locations (refer to Fig. 6a) from the contaminant source, pumping well, and the transport of the contaminant. The monotonic decrement in the concentration levels in O1- O3 was due to their close vicinity to the pumping well. Similarly, the initial increment in the wells O4 -O6 was due to their location away from the pumping well. Since the contamination was released from the point sources in small slugs, the initial concentration levels were less in observations wells O4 -O6 . Later, with the direction of the flow, the contaminant concentration increased as the contaminant plume migrates and then reduced after a certain time period after the plume further migrates. It can also be noted that the hydraulic conductivity (refer to Fig. 6b) was high in the location of observation wells O4 –O6 .

4 Concluding Remarks The emerging Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have the potential to address several site remediation challenges, such

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as contamination mapping and remedy optimization, leading to cost-effective and efficient remedial strategies. These emerging technologies can speed up the decisionmaking process in a way similar to human thought, just at a faster and more precise pace. They enhance the effectiveness of the remediation strategy design, either by reducing its cost or increasing its input parameters’ reliability. Based on this study, the following conclusions can be drawn: . AI has been used in many different ways in site remediation. Some of these uses are to assist decision-makers by recommending the desired remediation alternatives. AI can also be used to optimize the pumping schedule for groundwater remediation. These applications are just a few examples where AI can supplement site remediation. From the given case study, it is understood that AI techniques can: identify optimum pumping schedule, minimized contaminant concentrations, and reduced treatment costs. The results of this study reinforce the idea that AI provides an assortment of applications in the environmental remediation process. . ML applications are also emerging to work best with groundwater remediation studies. With the help of surrogate modeling, this technique supports active learning to improve further training data. ML significantly improves training accuracy in further data analysis. ML also promotes better computational timing for complex problems and algorithms to gain a more in-depth process understanding and allow quicker model explorations. ML shows promising interaction with AI strategies in hopes of complete use without human communications. . DL can produce specific models better than both AI and ML. The more advanced key factor in deep learning is the application of neural networks. They imply that the technology will work like a human brain and can solve more complex problems than ML. The functionality and structure of a human brain applied to a computer program will result in more sophisticated technology. It proved to be a great emulator for groundwater transport models, making predictions and speeding up the process. From the given case study, it is determined that R2 values of the four models vary from 92.89–98.35%, the RMSE ranges from 88.14–182.75, and the RE ranges from 7.63–18.62% indicating the efficiency of the model. The limitations of DL include complexity in using 3D models, and handling multiple parametric models. It is optimistic that deep learning can be expanded and be an essential technology for site remediation. Many studies use the surrogate-based remediation data for the analysis. However, it is recommended to use real field data for accurate analysis. Surrogate-based data should only be used in cases of non-availability of raw data. Acknowledgements The first author is grateful for the financial support provided by the Science and Engineering Research Board (SERB), Govt. of India, through the SERB International Research Experience Fellowship (SIRE) (Award # SIR/2022/000374), which allowed performing this research at the University of Illinois Chicago (UIC), USA.

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References Bengio Y, Courville A (2016) Deep learning (Adaptive computation and machine learning). MIT Press, Cambridge (USA). ISBN 978-0262035613 Guo JY, Lu WX, Yang QC, Miao TS (2019) The application of 0–1 mixed integer nonlinear programming optimization model based on a surrogate model to identify the groundwater pollution source. J Contam Hydrol 220:18–25. https://doi.org/10.1016/j.jconhyd.2018.11.005 Hamilton J (2012) Careers in environmental remediation. Office of Occupational Statistics and Employment Projections, US Bureau, Washington, DC Li J, Lu W, Luo J (2021) Groundwater contamination sources identification based on the long-short term memory network. J Hydrol 601:126670. ISSN 0022–1694. https://doi.org/10.1016/j.jhy drol.2021.126670 Majumder P, Lu C (2021) A novel two-step approach for optimal groundwater remediation by coupling extreme learning machine with evolutionary hunting strategy based metaheuristics. J Contam Hydrol 243:103864. https://doi.org/10.1016/j.jconhyd.2021.103864 Melhem HG, Nagaraja S (1996) Machine learning and its application to civil engineering systems. Civ Eng Syst 13(4):259–279. https://doi.org/10.1080/02630259608970203 Ng A (2018) Machine learning yearning, deeplearning.ai Russell S, Norvig P (2022) Artificial Intelligence: a modern approach, 4th US ed Sadeghfam S, Hassanzadeh Y, Khatibi R (2019) Groundwater remediation through pump-treatinject technology using optimum control by artificial intelligence (OCAI). Water Resour Manage 33:1123–1145. https://doi.org/10.1007/s11269-018-2171-6 Sharma HD, Reddy KR (2004) Geoenvironmental engineering: site remediation, waste containment, and emerging waste management technologies. Wiley, Hoboken, NJ Sprocati R, Rolle M (2021) Integrating process-based reactive transport modeling and machine learning for electrokinetic remediation of contaminated groundwater. Water Resour Res 57:e2021WR029959. https://doi.org/10.1029/2021WR029959 Zhang X-D (2020) A matrix algebra approach to artificial intelligence, Chapter 6, pp 223–440. https://doi.org/10.1007/978-981-15-2770-8

Assessing the Relationship Among Energy, Economy, and Environment with a Special Reference to India Akanksha Singh and Nand Kumar

Abstract There exists a complex relationship between energy, economy, and environment. The coupling and coordination development of energy, economy and environment would help to analyze their relationship and it would further provide the basis of rational use. In this paper coupling and coordination are developed for India’s energy, economy, and environment from 2006–2019. The results revealed four patterns such as: (1) The coordination between energy and economy improved with the passing year, it started with mild disorder category in 2006 and reached intermediate coordination in 2018; (2) The coordination between economy and environment showed a frequently unbalanced category only once, in 2016 it improved and reached to primary coordinated category; (3) The coupling and coordination development between energy and economy started with a mild disorder in 2006 and improved in 2008 to barely coordinated, it was stable until 2016 when it further improved to primary coordinated development; (4) The coordination between energy, economy, and environment repeated barely coordinated category most often, although there was an improvement in 2016 to primary coordinated but again in 2018 it deteriorated to barely coordinated. There exists a complex relation between the energy, economy, and environment in which they interact, promote and limit each other. The world at present is in the energy transition phase hence it’s very important to know the relationship between the 3Es for the policy makers so that they can take decision in the right direction. Keywords Energy · Economy · And environment (3Es); Coupling coordination and development

A. Singh (B) · N. Kumar Department of Humanities, Delhi Technological University, New Delhi 110042, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_33

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1 Introduction Energy, economy and environment (3Es) are the wheels to reach the goal for development of any country. Achieving a sustainable development agenda while maintaining the right balance between environmental preservation, energy security, and economic growth has proven to be one of the greatest challenges of the modern period. We all need energy and its consumption increments to ensure a better quality of social life and to support the economic development, especially in developing countries. However, we need to generate more energy to fulfill the modern energy demand and ensure that the environment remains protected at the same time; hence the 3Es analysis is important. Therefore, 3Es have an important role in all past, present and future era, and also plays an important role in sustainable development. It is important to know the relationship of the 3Es to reach the desired equilibrium. The SDG7 affordable and clean energy, SDG12 responsible consumption and production, and SDG13 climate action can only be reached when all the 3Es are balanced. (Zhang et al. 2020) presented the assessment of the 3Es based on LCA and remanufacturing. Kılkı¸s and Kılkı¸s (2017) studied the New exergy metrics for the 3Es for nearlyzero exergy airport (nZEXAP) systems. It applied the second law of thermodynamics to analyze the relationship between energy, environment, and economy. Pao et al. (2015) Used Lotkae Volterra model to examine the competitive interactions among the 3E in the U.S. The study was an emphasis on the existence of the environment Kuznets curve. Raza et al. (2019) applied wavelet analysis to analyze the impact of energy and economics on environmental degradation in the USA by Wang et al. (2019) analyzed the relationship between the 3Es economy by using the coupling coordinates and panel data approach and observed that the status has improved. Li et al. (2015) evaluated the decoupling of economic growth and CO2 emissions in China. Cao et al. (2020) developed a Ternary coupling model to show the relationship between the 3Es. The observations were drawn based on the panel data of Hebei province. Yan et al. (2019) analyzed the rational use of the 3Es for the protection of ecological environment. Zile (2019) analyzed the coupling relationship by establishing a coupling model of energy-economy-environment. Shen and Mo (2014), and Sun and Sun (2016) calculated the average coupling coordination degree of the 3Es in China and claimed that it is lower as compared to the international standard. China (n.d.) studied and rationally contributed to the situation and development trend of the 3Es in China. Jin et al. (2016), Wang et al. (2020) drew a coupling and coordination model analysis of the complex relationship between urbanization, economy and environment. Mederly et al. (2003) considered the development of an integrated environmental sustainability index in Czech Republic. Singh et al. (2022) examined the effect of environment and economic variables on logistic performance in India using ARDL model on a time series data from 2007 to 2018. It showed a complex relation between the energy, economy and environment where they interacted, promoted and restricted each other. Yan et al. (2019) studied the primary issues faced by the world today major which is climate change mainly because of increasing population, energy consumption and

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economic development. The author examines the relationship between economic growth, food, biomass, and greenhouse gas emission. The results revealed the existence of Kuznets curve hypothesis. Wang et al. (2019) studied clean energy and renewable energy remains a major concern for most of the countries, the author suggests how biogas can be purified through combined UV irradiations and thermal annealing treatments. As there is an increase in the pace of developmental work around the world increased the petroleum consumption majorly for transportation, industrial application and electrification that has increased the air pollution, which in turn degraded the environment. Kusworo et al. (2020), the author suggests ways to reduce NOX emission by adding hydrogen-rich synthesis gas generated by plasmaassisted fuel reformer. By using the technique, it was observed that approximately 50% of harmful pollution was reduced. Alharbi et al. (2020), even renewable energy has some disadvantages like voltage instability in wind and solar energy. The author reviews the importance of optimal location and sizing distributed generation units for voltage stability. Rawat and Vadhera (2019), the challenges in renewable energies of instability in solar and wind energy were addressed by designing a new hybrid power plant. The energy obtained from solar and wind was stored in accumulators and as soon the accumulators were full the energies were transferred to plateau houses or to the network for production. Alhaddad et al. (2015), the AERMOD program and the Industrial Source Complex Short-Term Model are used by the author to analyze the air pollution emission patterns from Kuwaiti oil refineries. The studies also indicated that oilfields and oil refineries were the biggest contributors, with all pollutants being higher than anticipated. This paper contributes to the literature in several ways firstly to the best of my knowledge no literature has stated the relationship among the 3Es using coupling coordination in the Indian context secondly the world at present is in the energy transition phase hence it’s very important to know the relationship between the 3Es for the policy makers so that they can take decision in the right direction. The author examines the coupling and coordination of development between the 3Es of India between the years 2006 to 2019. The level of coupling and coordination development of the three E’s exhibited an increasing trend.

2 Material and Methods 2.1 Index Selection The selection of indicators was such that they can mostly represent and reflect the development degree of energy, economy, and environment.

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2.1.1

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Energy

The energy use per $1000 GDP is the commercial energy use measured in units of oil equivalent per $1000 GDP converted from national currencies using the purchasing power parity. Energy use refers to using primary energy before transformation to other end-use fuels, equal to native manufacture plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport. The energy data have been taken from World Bank data as shown in Table 1. Energy production: It refers to the value of how much primary energy the country obtains from nature. It does not include any exports or imports and is just the total of what is extracted from nature. Total primary energy supply: The total amount of primary energy that a country has at its disposal. The total primary energy supply includes the imported energy minus the exported energy. It also consists of the energy obtained from natural resources. Electricity final consumption: Electricity consumption serves as an essential measure for a country’s electric power development. Electric consumption usually grows faster during the development and industrialization phase. Table 1 Energy data

Unit

Million tons of oil equivalent

Million tons of oil equivalent

Terawatt-hour

Year

Energy production

Total primary energy supply

Electricity final consumption

2006

405.77

532.99

584.45

2007

424.73

568.1

632.18

2008

445.26

603.83

670.79

2009

483.6

663.17

720.47

2010

503.78

700.76

785.81

2011

521.46

733.66

856.44

2012

524.18

766.26

907.19

2013

522.04

778.81

965.45

2014

532.76

822.05

1059.21

2015

537.3

834.53

1116.86

2016

550.84

852.22

1199

2017

551.62

882.88

1258.19

2018

573.56

919.44

1309.44

2019

593.5

973.7

1345.41

Source World Bank Database

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2.1.2

431

Economy

Table 2 represents the economic data referenced from World Bank database. Gross Domestic Product It is a standard measure for measuring the value-added of the total goods and services produced in a nation during a specific period. Although GDP is the single most crucial indicator of economic growth, it does not indicate anything about the material well-being in a country. The GDP growth rate: It tells us about the percentage growth of a country’s GDP. It is calculated by finding the percentage increase in the GDP year on year. GDP per capita is the value obtained by dividing the country’s population from its total GDP. The GDP output approach: It is occasionally referred to as GDP (O), is the amount of output or production in the economy. It covers the entire economy and uses the same data that make up the index of production, output in the construction industry, retail sales index, and services index. The output approach to calculate GDP sums the gross value added of various sectors, plus taxes and less subsidies on products. The final consumption expenditure: It is the residential units’ expenditure, including households, enterprises, and other consumers whose economic interests lie in the particular economic region. The goods and services used are for the direct satisfaction of individual wants and needs and the community. Table 2 Economy data Unit

US $ trillion

US $ trillion

Percentage

Percentage

INR billion

Year

GDP

Final consumption expenditure

GDP growth rate

GDP per capita growth

GDP output approach

2006

0.94

0.619

8.061

6.403

40,486

2007

1.217

0.798

7.661

6.048

47,142

2008

1.199

0.805

3.087

1.588

54,562

2009

1.342

0.904

7.862

6.351

59,895

2010

1.676

1.101

8.498

7.042

72,938

2011

1.823

1.227

5.241

3.894

84,871

2012

1.828

1.227

5.456

4.165

96,370

2013

1.857

1.261

6.386

5.135

108,981

2014

2.039

1.398

7.41

6.187

122,142

2015

2.104

1.461

7.996

6.797

133,907

2016

2.295

1.597

8.256

7.083

149,452

2017

2.653

1.851

7.044

5.912

166,226

2018

2.713

1.91

6.12

5.024

185,792

2019

2.901

1.97

6.34

5.891

189,275

Source World Bank Database

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2.1.3

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Environment

The environment plays a fundamental role in healthy living and existence of life. Over the years we have been witnessing an increased number of abnormal situations related to the environment. The weather is becoming extreme, increase in the number of tropical storms, the sea level rising and deterioration in air and water quality. The environmental factors that are important to track and the critical indicators are Air quality, waste management, country’s green cover, and deforestation rate. The environmental performance Index (EPI) uses 32 performance indicators in 11 different categories which help countries to identify problems and set targets for best policy practices. The overall EPI rankings indicate which countries are the best in addressing environmental issues in various categories like waste management, sanitization and drinking water, air quality, and biodiversity.

2.2 Model Construction and Index Normalization Indicators can be positive or negative indicators. A positive indicator correlates with increasing values, while a negative indicator correlates with decreasing values. A typical example of an indicator used to measure economic direction is the GDP per capita or nominal growth rate. Indicators have units associated with them and are thus prone to all the biases that come with non-standardized variable calculus and are therefore difficult to group with other indicators, to effectively compound different indicators, these variables need to be converted into a normalized standard index. The given indicators are used to construct indices between 0 and 1 as lower and upper boundary values respectively, but in some cases, these bounds can be increased (or decreased) with the original minimum (or maximum) values in order to diminish the influence of extreme outliers. The natural logarithmic values of these bounds are used in some cases this takes care of probable deviations brought on by non-linear and exceptionally skewed indicator values’ distribution. We first convert an indicator value (V) into an index score (I) by: I =

V − min max − min

(1)

To avoid linearization, we can use the lognormal transform and standardize the transform / V − min (2) I = max − min Standardization to check for outliers I, =

I −μ σ

(3)

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2.3 Coupling Development Coupling is a concept in which interaction between two or more elements affects each other where Coordination and development are the two critical levels. Coupling is a probabilistic tool used in the joint construction of two or more random variables usually to deduce the individual elements’ properties. Coordination is the degree of close interaction between systems, and development is the process of continuous improvement of the level of interaction between systems.

2.3.1

Binary Coupling (Coordination Coefficient)

For a statistic X & Y, the coupling or coordination coefficient C is given by xy C=( x+y )2

(4)

2

The corresponding development is given by D = CT

(5)

where C stands for coordination and T is a weighted index of X & T = α X + βY

(6)

With α, β being the appropriate weights.

2.3.2

Tertiary Coupling (Coordination)

Here coordination C’ for statistics X, Y & Z is equal to C, =

3(X Y + Y Z + Z X ) (X + Y + Z )2

(7)

The development D’ is equal to D, = C , T ,

(8)

T , = α X + βY + γ Z

(9)

where

And α = β = γ = 13 .

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3 Results and Discussions Table 3 represents the comprehensive index of the 3Es where the value of energy and economy was formulated by using Eqs. 1, 2 and 3 whereby environment index was obtained from epi.yale.edu. Table 4 represents the judgment criteria table based on which the results were formulated for the 3Es. Table 5 represents the results of the 3Es using Eqs. 4–9, where D is the development index and T is a weighted index of X & Y. Figure 1 shows the trends of comprehensive index of the 3Es from 2006 to 2018 whereby the environment index shows a downward trend until 2014 and after that there exists an improvement in the trend between energy economy and environment. Figure 2 represents the coupling coordination between energy and economy (XY) and shows the rising trend. Similarly, Fig. 3 shows the coupling coordination between energy and environment (XZ), it is observed that in 2014 it reached the level of 0.83 value and put to the barely coordinated. In 2016 the relationship was improved. Figure 4 represents the trends of coupling coordination between economy and environment (YZ) and observed that a dip was observed in 2014. Figure 5 shows the trends of coupling coordination among energy, economy and environment (XYZ) and it is presented that after 2014 the riding trends were observed in relation and after 2016 it started to decline the coordination. Figure 6 explained the combined trend of coupling coordination between energy, economy (XY), energy environment (XZ), economy environment (YZ) and energy, economy environment (XYZ). Figure 7 represents the trend of 3Es development in India from 2006 to 2018. Table 3 Comprehensive index of the 3Es

Year

Energy (X)

Economy (Y)

Environment (Z)

2006

0.277189136

0.468615693

0.477

2007

0.367030754

0.508199605

0.54

2008

0.437007993

0.348110576

0.603

2009

0.532282842

0.54625564

0.543

2010

0.591973325

0.608634013

0.483

2011

0.643224312

0.543528903

0.42265

2012

0.672915897

0.560308965

0.3623

2013

0.690717768

0.601823314

0.3373

2014

0.737801927

0.655672615

0.3123

2015

0.758341222

0.684423193

0.42405

2016

0.791876136

0.715503978

0.5358

2017

0.814440162

0.721088501

0.42075

2018

0.852549672

0.709972969

0.3057

2019

0.885482712

0.719756213

0.4057

Source Authors ‘estimation energy and economy index’, environment index from (epi.yale.edu)

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Table 4 Comprehensive index of the 3Es Coordination level

Criterion

Types

1

0–0.09

Extremely imbalanced

2

0.10–0.19

Sever disorders

3

0.20–0.29

Moderate disorder

4

0.30–0.39

Mild disorder

5

0.40–0.49

Frequent imbalanced

6

0.50–0.59

Barely coordinated

7

0.60–0.69

Primary coordinated

8

0.70–0.79

Intermediate coordinated

9

0.80–0.89

Good coordinated

10

0.90–1.00

Highly coordinated

Source Authors’ estimation-energy and economy index, environment index from (epi.yale.edu)

Table 5 Coupling and coordination development degree of the 3Es of India Year

XY

YZ

ZX

XYZ

T

D

Types

2006

0.9337

0.999928

0.92964

0.974284

0.407

0.397

Mild disordered

2007

0.974

0.99906

0.96361

0.987306

0.471

0.465

Frequent imbalanced

2008

0.9871

0.928102

0.97452

0.973917

0.462

0.45

Frequent imbalanced

2009

0.9998

0.999992

0.99989

0.999938

0.539

0.54

Barely coordinated

2010

0.9998

0.986686

0.98971

0.995055

0.56

0.558

Barely coordinated

2011

0.993

0.984343

0.9574

0.985949

0.536

0.529

Barely coordinated

2012

0.9916

0.953882

0.90971

0.970776

0.531

0.516

Barely coordinated

2013

0.9952

0.920355

0.88141

0.961711

0.542

0.522

Barely coordinated

2014

0.9965

0.87371

0.83539

0.947338

0.568

0.538

Barely coordinated

2015

0.9973

0.944936

0.92015

0.973487

0.621

0.605

Primary coordinated

2016

0.9974

0.97933

0.96283

0.987588

0.68

0.672

Primary coordinated

2017

0.9963

0.93099

0.89873

0.966923

0.651

0.63

Primary coordinated

2018

0.9916

0.84188

0.77725

0.930883

0.622

0.579

Barely coordinated

2019

0.9934

0.85623

0.77564

0.948543

0.634

0.586

Barely coordinated

Source Authors’ estimation coupling coordination of (XY), (XZ), (YZ), and (XYZ)

There was a mild disorder in the coordination of economy and energy in the year 2006, India has a huge population and to provide energy to all citizens demanded huge investments in the energy sector is required. The National Grid system was yet not established in 2006 that could facilitate electricity to all. As the National Grid or Central Grid system was established in 2013 and since then India’s energy supply has increased unfold by 18% from 2013 to 2018. Whereas, the overall energy supply from 2006 to 2018 has increased by 55%. The coordination between energy economies

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Fig. 1 The trend of the comprehensive index of 3Es in India

Fig. 2 Trends of coupling of energy-economy (XY)

also improved from barely coordinated to primarily coordinated in 2012. Whereas, it moved to intermediately coordinated in 2015 and showed stability by maintaining the coordination degree. The coordination between economy and environment has been very unstable and has majorly had frequent imbalanced coordination only once in 2016 it improved to primary coordinated. The reason for this was India’s strong average economic growth rate and the ever-rising population of the country, created pressure on the biosphere, which has led to environmental degradation. The Indian energy sector has steadily increased its production. The domestic energy production is dominated by coal and bio-energy. If we go back to 1973 the domestic energy production was largely dominated by bio-energy and waste around three fourth share of the total energy production. But the exploration of coal mines in the last three decades has made coal a prime material for the production of energy and its share has increased ever since. Therefore, the coordination between energy and environment was in a mild disorder in 2006. In 2008 at the conference of the parties15,

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Fig. 3 Trends of coupling of energy-environment (XZ)

Fig. 4 Trends of coupling of economy-environment (YZ)

India announced to reduce the emission intensity by 20–25% of its GDP against 2005 levels by 2020. Therefore we can see an improvement in the coordination between energy and environment from mild disordered to barely coordinate and for seven consisting years it maintained the same coordination. In 2016 after Paris Agreement, India focused on achieving the NDC’s built target of reducing emissions by 33–35% of GDP against 2005 emissions by 2030, to increase the cumulative electric power by 40% from non-fossil fuel based energy and to increase the forest and tree cover to increase carbon sink by 2030 (IEA 2020). Therefore, there is an improvement in the coordination between energy and environment from barely coordinated to primary coordinated. In 2018 as electrification work in the country was going on due to which there was a huge gap between demand and supply of energy and the coordination again deteriorated to barely coordinate.

438

Fig. 5 Trends of coupling of energy-economy-environment (XYZ)

Fig. 6 Combine trends of coupling of (XY), (XZ), (YZ), and (XYZ)

Fig. 7 The trends of development of India from 2006–2018

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4 Conclusion Energy, Economy and Environment are the components of a complex system that interact, promote and restrict one another. The three Es’ were systematically evaluated by constructing a coupling coordination and development model of India from 2006 to 2018. In the years from 2006 to 2018, the development trends between the 3Es have been in recession or mild disorder category in 2006 but in 2009 it improved and moved up to barely coordinated and maintained the same for six years. This was mainly due to the Indian government being driven to reduce the energy’s intensity, also to provide electricity to all the citizens while the growth rate of the economy was also maintained. Hence, there was further improvement in the coordination with 0.6049 to primary coordinated in 2015 and was stable till 2017. Again in 2018, it dipped back to barely coordinated with 0.579 score. This was due to the humongous pressure on the environment due to the ambitious targets of the government to provide electricity to all, which was mostly met by coal that was the reason for the increase in the emission of greenhouse gases. The coupling coordination between economy and environment has mostly been unstable. It has shown a declining trend since 2010 till 2014, mainly due to the increase in demand of energy that was majorly met by coal. The use of unclean energy degraded the environment. In 2015, we see an improvement in the coordination but it was short lived and again declined after 2016. It was due to an increase in the pace of developmental work. Barely coordinated category has been observed in case of energy and environment. It started with mild disorder in 2006 and in 2018 it was still in frequent imbalanced category only once in 2016 it was in primary coordinated category due to growing environmental concerns internationally. The EPI was at its worse in 2018 with score of 0.306. The energy, economy, and environment are interlinked with each other to an extent that changes in one E affect both the Es’. In India, the coupling coordination and development between the 3Es have been very unstable. There is a need for efficient policies and research & development and deployment for positive coupling coordination between the 3Es in India.

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Blending the Need for Heritage Fabric to Upgrade the Land-Use for Futuristic Growth Charu Middha , Aditya Bharadwaj , and Neeharika Kushwaha

Abstract Deoghar (Devo Ka Ghar) has historically been a destination of pilgrimage and cultural interest for Indians. One of the twelve Jyotirlingas of India is worshipped in the city by devotees from all over the world. The city’s historical significance and tourism provide tremendous potential for its development and planning. Heritage is viewed as a part of a larger living environment as well as a cultural landscape. Hindus revere the Deoghar, India, Baidyanath Dham (temple) as a sacred site. One of India’s Shakti Peeths is in the city of Deoghar. The paper’s main focus was on heritagebased development in the area of Shri Baidyanath Temple, which both addresses the needs of pilgrims and helps the city reclaim its former glory. The study started by identifying the site and defining the aim and objective of the selected study area. Using an existing, comparable project as a case study, the same condition was studied, then, digitising the base map and other thematic maps such as tourist traffic, local activity, and other aspects in order to analyse the site area and identify the major issues and lacking infrastructure that restrict simple movement of tourists in the area. The main problems were determined after investigation and analysis. Integrating the architectural environment and cultural heritage into spatial planning is crucial. The paper recommends the re-development of Baba Baidyanath Temple and its surroundings. Keywords Heritage-based development · Tourism · Heritage corridor · Baidyanath dham deoghar · Pilgrims · Shivganga

1 Introduction Heritage is a resource that is underutilised for sustainable development, which includes promoting the quality of the environment, education, inclusive economic growth, social cohesion, equity, and community well-being. It is currently the focus of growing scholarly and public interest, and its conceptual scope is growing C. Middha · A. Bharadwaj (B) · N. Kushwaha Amity School of Architecture and Planning, Noida, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_34

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(Hosagrahar 2017). There are few principles that guide development based on cultural heritage, and these principles are Collaborate, Find the Fit between the Community and Tourism, Make Sites and Programs Come Alive, Focus on Quality and Authenticity, Preserve, and Protect (Heredia n.d.). One of India’s oldest cities, Deoghar, was formerly known as “Chidabhoomi” and attracts pilgrims from all over the world. It is a well-known wellness destination and a significant Hindu pilgrimage, the Baba Baidyanath (kamana-linga) temple, which houses one of India’s twelve jyotirlingas. On his voyage to Sri Lanka while carrying Lord Shiva, Ravana is said to have halted in Deoghar, violating the terms of a non-stop journey. He was unable to carry the Lord to Lanka as a result, and the Lord remained rooted in this place. Baidyanath Dham is the name given to the location in honour of Lord Shiva, also known as Baba Baidyanath. Numerous followers make the arduous 100 km journey on foot from Ajgaivinath (Sultanganj) to Baba Baidyanath every monsoon (in the month of Shravan). This holy site receives a large number of visitors from throughout the globe. There are no such facilities available to host this vast number of pilgrims (about 10 million). They are not given access to adequate spatial planning, management, or infrastructure. The city’s amenities are far less than expected in practice. In order to serve the crowd, this paper will concentrate on the heritage-based development of Deoghar city around the temple. The city must be able to accommodate visitors from all occupations—those with huge pockets and those on a shoestring budget. There is a proposal for heritage-based development near the holy pond and the temple alleys. Our goal was to strike a balance between heritage and growth requirements.

1.1 Problem Statements See Fig. 1.

Fig. 1 Article in the news describing the issue in the area. Note According to the publication “The Hindu,” crowding during the festive season might result in stampedes in some parts of the city. Source: Tewary, A. (2018, August 19). Pilgrims died in the Deoghar temple stampede. The Hindu

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2 Literature Study 2.1 Kashi Vishwanath Corridor One of the 12 Jyotirlingas, or temples, devoted to Lord Shiva is Kashi Vishwanath Temple, which is located in Varanasi on the western bank of the holy river Ganga. The temple’s current situation is that it is currently encircled by Varanasi’s narrow lanes, making entry extremely difficult, especially during significant events when people increase. Additionally, the Ganga River could not be seen directly from the temple. The current Kashi Vishwanath complex, which includes private homes, vendors, stray animals, etc. is one of the oldest of its sort. The development of Shri Kashi Vishwanath Dham, Manikarnika Ghat, and its beautification are the subjects of the planned project. It is connected by a wellconstructed road network. The temple area has grown by 166 structures being demolished as a result of the renovation from 3,000 square feet to around 5,000 square feet and can now hold 50,000–75,000 people. The project’s construction led to the rediscovery of more than 40 ancient temples. They were repaired while taking care to preserve the historic architecture. A tourist facilitation centre, a Vedic Kendra, a Mumukshu Bhavan, a Bhogshala, a city museum, a viewing gallery, and a food court are among the 23 buildings that make up the project. Smart signs have been put up in Varanasi to tell people about the 84 ghats and the cultural significance of the city’s heritage attractions. Ganga Cruises have been planned for tourists, and the road infrastructure has been upgraded. Tourists can see the famed Ganga aarti and the aarti at the temple on LED screens that have been placed (Patel 2019). The lessons learned from this project will support tourism by offering pilgrims and tourists amenities including larger, cleaner roads and lanes, improved lighting from brilliant streetlights, and pure drinking water.

3 Site Introduction Deoghar (Devo ka ghar) is one of the twenty-four districts of Jharkhand state in eastern India. Deoghar is known to be the cultural capital of Jharkhand state due to its religious and cultural significance. Based on our ancient Indian books and scripts, the age of the city is traced back to the Vedic period. The temple is well-known for its annual Shravan Mela. More than 10 million devotees from all over India participate in the Deoghar Yatra every year between July and August. Deoghar district comprises 10 CD-Blocks and has a total population of 1,665,586 (Census 2011). Further zooming in to only Deoghar city, it has a total area of 119 sq. km. Deoghar city comprises 36 wards under the Deoghar Municipal Corporation (DMC). Further, focusing on only wards 19, 20, and 21, the Site was selected on the basis of roads around the most affected neighbourhood of the temple.

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The place was chosen to be near the main thoroughfares and a temple for pilgrims, drawing travellers from all across the nation. The neighbourhood around the temple is disorganised, with crowded pathways filled with impromptu stores and small approach routes. The area has to be revitalised, and giving pilgrims and tourists amenities like larger, cleaner roads and lanes, improved lighting from brilliant streetlights, and pure drinking water will encourage tourism. The surrounding region demonstrates the mixed-use character of the land. Along with the residences, there are a few small businesses in the region. Those who reside there frequently are priests or otherwise those associated with the temple. The existence of this religious facility has a significant impact on both the locals and the economy of the city. Shivganga Pond, another important city attraction, is located close to the temple. A little sacred pond is called Shivganga. It is situated 200 m away from the Baidyanath temple. Many devotees take a holy dip in the water before visiting the temple. These places attract a large number of Indian religious tourists, who visit them for their spiritual value. Often, these pilgrims get exhausted from the hassle of navigating the narrow roads and byways to get to the shrines and jostling with others in the long queues for a glimpse of the deity only to be pushed aside in an instant.

3.1 Percentage of Tourist Footfall During specific festive seasons, temporary shelters/tents are made to accommodate lakhs of pilgrims. However, most pilgrims stay at the places of their respective priests, whether that be a hotel or the priest’s residence (Fig. 2). The city receives a large influx of floating population which ranges from an average of 20,000 tourists on a regular day to around 4,00,000 to 7,00,000 pilgrims on special occasions. Fig. 2 Tourist footfall percentage chart. Note The maximum footfall is during the month of Shravan. Source Author

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3.2 Activity Analysis Due to high tourist footfalls, it creates more jobs and income opportunities, boosting the local economy. Most of the economic activities of the neighbourhood are based on the temple (Priests, Grocers, Photographers, Restaurateurs, Hoteliers, Florists, Religious Goods Sellers, etc.). Activities there are as follows: . Florists block the pathways; they make the temple premises dirty and wet which leads to several accidents. lt also affects the hygiene of the neighbourhood. . About 47% of the working men in the neighbourhood are priests, so them sitting on the roadsides/walkways hinders pedestrian movement. . Many ceremonies around the ghats and near the temple such as marriage, thread ceremony, and rudra abhishek ceremony affect mobility. . Aartis along the ghats and also even on the smaller scales in each separate street affect the neighbourhood. . Illegal encroachment around the temple by the shopkeepers causes unnecessary crowds and affects the movement. . Photographers ponder around the temple and pond area. They look for potential customers who would want their photographs taken at the most popular tourist attractions. . Congested shops in narrow lanes around the temple cause unnecessary crowds and affect the movement.

4 Existing Scenario . Street—An unbroken line of people in saffron-dyed clothes stretches over the area of Deoghar. After they reach Deoghar, they take a holy bath at Shivganga pond and then follow the kilometres of queue spread all over the neighbourhood. This long queue creates a problematic situation and hampers the daily activities of the locals. . Pedestrian Foot Over Bridge—After they reach Deoghar, they take a holy bath at Shivganga pond and then follow the kilometres of queue spread all over the neighbourhood and then go up to the foot over bridge which starts at one side of Mansarovar pond and then finally the pilgrims reach the Baidyanath Temple. . Baidyanath Temple Precinct—The Temple precinct has an area of total 4460 sq.m (48,000 sq.ft), with a total of 22 temples in the Mandir Parisar (Precinct). The precinct area lacks proper space assigned to the florists and puja goods sellers (Vendors) and priests, hence leading to the encroachment of the temple precinct, affecting the movement. . Shivganga Lane—The narrow 210 m heritage lane directly connects Baidyanath Temple with the holy pond (Shivganga). In the present scenario, this lane has a negative impact on pilgrims due to open drains, broken manholes, poor condition

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of the flooring of the lane, wires hanging all over the lane, and encroachment by the residents and the shopkeepers. Shivganga Pond—There is excessive silting, stinky smell, and contamination due to offerings by the pilgrims. Also, the ghats are encroached on by the priests during festive seasons due to the lack of space provided for certain activities which include Thread Ceremony, Mundan Ceremony, etc. Poor infrastructure at the ghats leads to several accidents due to a large number of uncontrolled pilgrims. The ghat areas are also encroached upon by the florists and photographers. Mansarovar Pond—Without any authorised use, Mansarovar is gradually drying up, and the area around it is utilised for queuing purposes. It’s disappointing that a pond with such historical significance is suddenly drying up. Encroachment by the vendors and florists all over the neighbourhood, be it temple premises or the ghat of Shivganga. Lack of proper drain covers and broken manholes leads to accidents. Both locals and visitors experience mobility and everyday living challenges due to a lack of amenities and infrastructure. Furthermore, encroachment is done from (G + 1) floor leading to the hindrance of natural light on the street. (For someone passing by, it seems that someone may dump anything from the top.) (Fig. 3).

Fig. 3 Existing infrastructure. Note Map showing the precise locations of the temple’s surrounding infrastructure. Source Author

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5 Short-Term Proposals 5.1 Streets and Drop-Off Zones It is very challenging for automobiles to access all the lanes of the neighbourhood as it is encircled by intense urban development and is interwoven with spiralling, narrow tortuous lanes. There are more issues such as . Access to the Mandir, which is hampered by mobility issues caused by rickshaws and other two-wheeled vehicles. . Vendor encroachment is yet another cause of this problem. . Considering these issues, two types of drop-off zones have been proposed, six for motorised vehicles (MV) and the other six for non-motorised vehicles (NMV), in order to minimise such mobility concerns. Further, the lanes are also divided on the basis of vehicles allowed on the roads, with special gate permit for the people residing in that area, which are as follows: . Vehicular Streets—These streets have their existing ROWs ranging 15 m–18 m. . NMV (non-motorised vehicle) and MCWG (motorcycle with gear) Streets— These streets have their existing ROW of 7 m. . Pedestrian Streets—These streets have their existing ROWs ranging 2 m–4 m (Fig. 4).

5.2 Welcome Gate Design For a city with such historical significance as Deoghar, the welcome gate is important and it is also quite crucial as it serves as a symbol from the standpoint of tourism. The style of the temple is reflected in the design of this gate, and the history of the sacred city is portrayed on both sides of the gate. The welcome gate, which can’t be closed by anyone, symbolises that anyone and everyone is invited to visit the temple. Four of these entrances are proposed to surround the Lanes approaching the temple and greet pilgrims to the area close to the temple. On the gates, also there are security cameras mounted for safety purposes.

6 Long-Term Proposals 6.1 Baidyanath–Shivganga Corridor The proposed project is a re-development of Baidyanath Dham Temple, Shivganga Pond, and its beautification, making the area more systematic and organised (Fig. 5).

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Fig. 4 Proposed vehicular zone map. Note In the current condition, all streets are used for vehicles, and during the holidays, these lanes get congested with traffic, which affects mobility. Proposal considers categorising streets according to the requirements of the current situation. Source Author

6.2 Shivganga Ghat The ghat restoration proposal aims to redevelop the Shivganga ghat area in a more systematic and organised manner. The proposal aims to enhance the experience of tourists and make it memorable by beautifying the area. The major objective is to make Shivganga ghat accessible to the public by providing public spaces for sociocultural amenities, and rejuvenating neighbourhoods around the Shivganga pond. The proposed ghat has new infrastructures named after the names of kings who had contributed to religious infrastructure in Deoghar, in commemoration of their contributions and to regain the lost historical glory. The proposed infrastructure is designed to perform various functions assigned to them which includes Yatri Suvidha Kendra-I—A place for pilgrims to leave their footwear and belongings. Public Washrooms—Two separate washrooms are proposed for Males and Females. Shops—10 shops are proposed in the ghat area for puja goods, flowers, prasad, etc. which will prevent encroachment all over the ghat. Raja Bir Bikram Bhawan—A public complex with spaces for cultural and community activities. There are also proposed spaces for priests in the complex.

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Fig. 5 Proposed infrastructure map. Note Infrastructure development and alterations to the surrounding land-use of the temple and around the holy pond. Source Author

Raja Man Singh Swasthya Kendra—A hospital for temporary and emergency treatments for the visitors and the residents. Food Courts and Bir Bikram Food Park—A space for food courts for tourists and the locals of the city, which will increase younger tourist engagement. Aarti Ghat—A properly planned and designed place for evening aarti. Boating Station—Younger tourists are not much inclined towards the temple. So, to increase the engagement of the younger tourist boating facility is introduced at Shivganga Pond (Fig. 6).

Fig. 6 Model of proposed Shivganga Ghat. Note Different views of the model showing the proposed infrastructure. Source Author

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6.3 Mansarovar Complex To resolve the problem caused due to the long unbroken queue of people in saffrondyed clothes stretching over the neighbourhood, a 5-storey colonnade structured complex around the Mansarovar pond is proposed for pilgrims to queue, with washrooms and drinking water facility. This 5-storey complex has queuing capacity of up to 10 kilometres which is connected with the foot over bridge leading towards the temple. On the other end, the entrance is connected to the Bir Bikram Complex at the Shivganga Ghat, from where the Pilgrims can enter the Mansarovar Complex after taking the holy bath from Shivganga Pond. This will also save the Mansarovar pond and water will be refilled in the pond.

6.4 Mandir Parisar The temple precinct has been expanded from 48,000 sq.ft to 1,30,000 sq.ft. The land parcel which is to be included with an area of 82,000 sq.ft was the land of Babadham Temple Trust with two buildings to be demolished, Pathak Dharamshala and Sanskar Bhavan. The proposed Mandir Parisar (Precinct) has new infrastructures named after the names of kings who had contributed to religious infrastructure in Deoghar, in commemoration of their contributions and regaining the lost historical glory. The proposed infrastructure is designed to perform various functions assigned to them which includes Yatri Suvidha Kendra-II—A place for pilgrims to leave their footwear and belongings. Public Washrooms—Two separate washrooms are proposed for male and female pilgrims. Shops—14 shops are proposed in the precinct for puja goods, flowers, prasad, etc. which will prevent encroachment all over the precinct. Raja Puran Mal Singh Bhawan—A public complex with spaces for cultural and community activities. There are also proposed spaces for priests in the complex. Sewadal Sabha—A temple trust office to look over the administration of temples and their activities. VIP Ticket Counter—A space for booking tickets for VIP Darshan. Security Office—A place for temple security members to ensure the security of the temple. Mandir Parikrama—A shaded colonnade that provides the pilgrims with shelter within the Mandir Parisar.

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6.5 Heritage Walk The 210 m Shivganga lane is re-developed into the heritage walk with Singh Dwar at the entrance from the Shivganga ghat to the Mandir Parisar. The lane is covered from the top with a translucent material, which will allow natural light to illuminate the lane during day time as well as at the same time will create a hindrance barrier of the encroached buildings and the hanging overhead wires. This path will have surveillance cameras installed everywhere for safety purposes, and the facades of the shops in these lanes will be re-developed and will create a unique experience for the pilgrims ascending towards the temple (Fig. 7). The proposed project of re-development of Baidyanath Dham temple, Shivganga pond, and its beautification will boost tourist footfall and will create more jobs and income opportunities, and thus will boost the local economy. This proposal will improve and enhance the areas surrounding the temple. The proposal will fulfil the following: . . . . .

Furnish an environment befitting to the Mandir’s importance. Provide tourist amenities required for their comfort, security, and safety. Provide a path for pilgrims to follow as they ascend from Shivganga to the Mandir. Provide venues for social and cultural activities. Ghats will be improved with the purpose to return them to their previous glory.

Increased tourist footfalls will be accommodated by this development, which will assist generate additional employment and income opportunities and strengthen the local economy.

Fig. 7 Model of proposed Shivganga Lane. Note Different views of proposed heritage walk with facade control. Source Author

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References About Deoghar District | Baba Dham Deoghar Jharkhand - DEOGHAR. (n.d.). DEOGHAR BAIDYANATH DHAM. https://deoghar.co/about-deoghar-baidyanathdham-babadham/ Baidyanathdham (Deoghar) (n.d.) Incredible India. https://www.incredibleindia.org/content/incred ibleindia/en/destinations/baidyanathdham-deoghar.html Heredia D (n.d.) What are the five principles for successful and sustainable cultural heritage tourism? ForeSite Consulting, LLC. https://foresiteconsultingllc.com/what-are-the-five-princi ples-for-successful-and-sustainable-cultural-heritage-tourism/ History | District Deoghar, Government of Jharkhand | India (n.d.) Deoghar. https://deoghar.nic.in/ history/ Hosagrahar J (2017) Culture: at the heart of SDGs. UNESCO. https://en.unesco.org/courier/apriljune-2017/culture-heart-sdgs Patel B (2019) Shri Kashi Vishwanath Precinct Development, Varanasi. Signify. https://www.sig nify.com/global/lighting-academy/browser/webinar/vishwanath-dham-varanasi Vishwanath Dham (2018) HCP design. https://hcp.co.in/urbanism/vishwanath-dham-varanasi-3/

Simulation of D-Type (Darrieus) Vertical Axis Wind Turbine Using Q-Blade Abhishek Gandhar, Vansh Panwar, Hena Varma, Sourav Rawat, Piyush Pant, and Shashi Gandhar

Abstract This paper presents a simulation analysis of vertical axis wind turbine (VAWT) rotor blades in open-source software Q blade for a better understanding of different parameters of wind turbines. Though not so popular like horizontal axis wind turbine (HAWT) but have some edge over it by being omni-directional for incoming wind velocity, generator placing being on the ground and having a simpler assembly makes the VAWT a popular area of research in the current scenario. This paper can be a stepping stone in the emergence of a user-friendly D-type VAWT simulation software. The presented paper can also contribute to a growth evolution in the area of development of VAWT. Keywords VAWT simulation · HAWT · NACA foil · Blade geometry · Power generation

1 Introduction There is a considerable increase in the population around the globe and the people are now going for better standards of living. Resource energy can be grouped into two types: renewable and non-renewable sources of energy (Gupta et al. 2016a, b). Nonrenewable energy is the energy extracted from natural sources that will be exhausted very soon and would take a huge amount of time to be replenished. Common nonrenewable energy sources include coal, petroleum, natural gas, etc. Meanwhile, renewable energy is extracted from the energy resources that can be replaced naturally and can therefore be used without the worries of depletion (Gandhar and Ohri 2020; Gandhar and Ohri 2019). Various studies show that the energy mostly comes from the non-renewable energy sources which will be empty in future and need millions or billions year times to replenish it. Hence, energy-sustainable development is important for the future to A. Gandhar (B) · V. Panwar · H. Varma · S. Rawat · P. Pant · S. Gandhar EEE Department, Bharati Vidyapeeth’s College of Engineering, New Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_35

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ensure that humans have sufficient energy to carry out their daily activities especially electricity. Extracting energy from wind reduces the load on conventional fuels and further reduction of pollutant gases. The main equipment of wind systems is wind turbines, which help kinetic gearbox and turbine blades to create the force and produce the torque and thus obtain the electrical energy. Vertical axis wind turbine and horizontal axis wind turbine are two known types of wind turbines and application areas that decide the choice of turbine between them. The VAWTs are more appropriate for the low wind velocity areas while HAWTs are installed in areas experiencing high wind speed. Besides that, there is also a yaw mechanism to align it to the wind direction to obtain higher efficiency. One of the advantages of VAWT is that they are easy to install as compared to HAWT. VAWT are of two types: a Darrieus and a Savonius. The Darrieus turbine rotor is based on lift to produce torque, whereas a Savonius turbine rotor works on the principle of drag (Brusca et al. 2014). Be it a kind of wind turbine, the main focus is to produce as much as possible energy. If a wind turbine is to be installed in urban/rural areas, it needs to be small with low installation costs all the while providing energy at maximum possible efficiency. The VAWTs can be instated on roofs of various buildings. But wind conditions at such low heights can be turbulent and complex. As a result, the design of VAWT should be such that it can work at low wind speeds. VAWT and HAWT, both types of wind turbines, have their own goal of application. The vertical axis wind turbine is appropriate for the low wind speed areas while the horizontal axis wind turbine is installed in areas experiencing high wind speed. Besides that, there is also a yaw mechanism to align it to the wind direction to obtain higher efficiency. One of the advantages of VAWT is that they are easy to install as compared to HAWT. VAWTs are of two types: a Darrieus and a Savonius. The Darrieus turbine rotor is based on lift to produce torque, whereas a Savonius turbine rotor works on the principle of drag (Brusca et al. 2014). Be it a kind of wind turbine, the main focus is to produce as much as possible energy. If a wind turbine is to be installed in urban/rural areas, it needs to be small with low installation costs all the while providing energy at maximum possible efficiency. The VAWTs can be instated on roofs of various buildings. But wind conditions at such low heights can be turbulent and complex. As a result, the design of VAWT should be such that it can work at low wind speed applications. The vertical axis wind turbine is appropriate for the low wind speed areas while the horizontal axis wind turbine is installed in areas experiencing high wind speed. Besides that, there is also a yaw mechanism to align it to the wind direction to obtain higher efficiency. One of the advantages of VAWT is that they are easy to install as compared to HAWT. VAWTs are of two types: a Darrieus and a Savonius. The Darrieus turbine rotor is based on lift to produce torque, whereas a Savonius turbine rotor works on the principle of drag. Be it kind of wind turbine, the main focus is to produce as much as possible energy. If a wind turbine is to be installed in urban/rural areas, it needs to be small with low installation costs all the while providing energy at maximum possible efficiency. The VAWTs can be instated on roofs of various buildings. But wind conditions at such low heights can be turbulent and complex. As a result, the design of VAWT should be such that it can work at low wind speeds.

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2 Darrieus Type Vertical Axis Wind Turbine The generation of electricity from wind can be done using VAWT of Darrieus type. It is having arched aero-foil blades placed on a rotating shaft system with vertical axis. The curvature of the blades permits it to be taught due to tension when rotating at greater speeds. This design was patented and named after a French aeronautical engineer G. J. M. Darrieus in 1931. In the initial design, the aero-foils are placed symmetrically with a zero angle making with the mounted structure. This positioning was impressive and not much affected by direction of the wind. During the spinning of Darrieus rotor, the aero-foils are used to advance through the air in a curvature. Vector addition of the airflow relative to the blade and the wind is done, as result positive angles of attack are generated due to the resultant airflow. This results in a net force pointing in the direction of a certain ‘line-of-action’. A positive torque is generated because of this force that acts upon the shaft, thereby promoting its rotation in the direction it is already traveling in. When the aero-foil rotates and reaches the rear side of the structure, the attacking angle becomes negative, but because of symmetry of wings and the zero-rigging angle, the resultant force still follows the rotation patterns. Here the rotor speed becomes independent of wind speed.

3 Blade Software 3.1 Home Screen In the HAWT mode, following tabs are used to appear at screen–HAWT Rotor Blade Design, Rotor BEM Simulation, Multi-parameter BEM Simulation, Turbine BEM Simulation, Q FEM Structural Design and Analysis, Turbulent Winfield Generator and FAST Simulation and when we choose VAWT mode, we get VAWT Rotor Blade Design, Rotor DMS Simulation and Turbine DMS Simulation.

3.2 Profile Input and Design Clicking the aero-foil design tab and adding the predetermined National Advisory Committee for Aeronautics (NACA) contours, joining of two contours and customized in different colors and dimensions for different investigations. The NACA0018 and NACA0021 are depicted in red and blue colors in Fig. 1. Here users can also customize their profiles by varying the control parameters (Deisadize et al. 2013).

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Fig. 1 NACA foils used in the given diagram

4 Extrapolation I. It is a process of estimation beyond boundaries. It measures the relationship patterns beyond the estimation of initial range of findings depending upon the relationship between operational parameters. It is similar to interpolation, designed the estimation between explored findings. II. The blades of both VAWTs and HAWTs are often operated at high inflow angles. To obtain reasonable results, it is essential that the airfoil performance polar is extrapolated to a 360z inflow angle range. Fig. 2, graph, shows the experiment based on extrapolation to have positive power delivered as it is exponential in nature.

5 Results and Discussion 5.1 Pressure Distribution Along Aero-Foil The graph shows the relation of Coefficient of Power vs TSR (Tip Speed Ratio). The TSR is a decisive parameter for designing a turbine. TSR is defined as the ratio of the wind speed to the turbine blade speed (Fig. 3). Turbine Power Coefficient (Cp) or Power Coefficient (Cp) may be a measure of turbine efficiency often employed by the wind generation industry (Khammas et al. 2015).

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Fig. 2 Extrapolation

Fig. 3 Pressure distribution diagram

Cp is that the ratio of actual wattage produced by a turbine divided by the overall alternative energy flowing into the turbine blades at specific wind speed. The analysis of DMS simulation for various Reynolds number gives best coefficient of power w.r.t to -TSR.

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Fig. 4 Power dissipated w.r.t. inverse of TSR

5.2 Power Dissipated The graph is showing the relation between dissipated or delivered power by turbine blades with respect to the inverse of TSR. The graph is parabolic in nature, which shows that the design used in the simulation is the most efficient, and there was a discrepancy in the first simulated graph which is observed due to less simulation time and starting power and is corrected when the simulation is moved further, there will be an optimum value for which the maximum power is extracted (Fig. 4).

5.3 Structure and Orientation of Blades Figure 5 shows the structure of blades and the angle of orientation of blades for maximizing the efficiency of the power delivered by the turbine. In a result of this, the highest conversion efficiency of wind speed to the rotational motion can be achieved.

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Fig. 5 Orientation of blades

The blade angular position must be proportional to get the maximum wind stream for achieving the optimum quantity of energy using flat-blade wind turbines. The variation of angle with power shows the optimal orientation of blades on the shaft.

5.4 Positive Power Delivery from VAWT Figure 6 depicts a relation of power and velocity of wind. The designed blade as per the simulation shows positive power with reference to the speed of air which rotates the blade. The cumulative power will increase cubically with wind speed between the range of cut-in speed and the rated speed. Therefore, e.g., the output power will be 27 times if the wind speed will be tripled (Shah and Dr. Shivprakash. 2021). The cubic factor of wind speed makes it a prominent driving parameter for factor for designing of wind power stations.

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Fig. 6 The given figure shows positive power deliver by VAWT

5.5 Power and Velocity of Winds Figure 7 shows the simulation, which is commenced at the end of blade parameters designing. The power generated by the blades is more than 1.05 kW as shown above but as the complete simulation stages are more than 500, which takes a considerable amount of time but in 250 stages of simulation, positive power is generated which confirms that the blade designing is perfect and after complete simulation of all stages, it is highly and positively expected that the power is above 2 KW. The results show that the blade parameters taken into consideration are the most optimal to show the positive power delivery from the vertical axis wind turbine. In Conclusion, the parameters that are taken in simulation are most optimal and give the best efficiency of VAWT (Cassini 2016).

6 Conclusion After completing the simulation, some problems when constructing the simulation are overcome. Solution is figured out to overcome the problem to make our simulation run smoothly and successfully. Besides, students can have more understanding about the working principle and formula about the VAWT wind turbine. Furthermore, students also have understood about the advantages and disadvantages of VAWT when compared to HAWT. We can analyze and conduct performance analysis through

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Fig. 7 The graphical relation between power and velocity of wind

Q-BLADE. The presented simulations are supported by the design considerations and suitable material selections for the test system. Hence, the objective of the project has been accomplished similar to Cassini (2016). Wind energy is one of the widely used renewable energy nowadays. People may focus only on wind capacity and energy obtained which led to environment issues that need to be focused on. There are two major impacts that are caused by the wind energy facilities, which directly impact individual organism as well as on habitat structure and functioning. The wind turbine installation had caused the disturbance in the livelihood of birds and bats through accident in the form of collisions. This may lead to unbalancing of ecosystem in long term. Besides that, the building and maintenance of wind turbine equipment disturb ecosystem balance through clearing of greenery, soil erosion and create imbalance in the life of natural habitats. The activities that are mineral extraction are compulsory for wind energy development. Hence, the wind industries and government agencies have the responsibility to overcome these issues.

References Brusca S, Lanzafame R, Messina M (2014) Design of a vertical-axis wind turbine: how the aspect ratio affects the turbine’s performance. Int J Energy Environ Eng 5:333–340. https://doi.org/10. 1007/s40095-014-0129-x Cassini M (2016) Small vertical axis wind turbines for energy efficiency of buildings. J Clean Energy Technol 4(1), (2016) Deisadize L, Digeser D, Dunn C, Shoikat D (2013) Vertical axis wind turbine evaluation and design. Worcester Polytechnic Institute (2013), No: 11–13 Gupta N, Kumar A, Banerjee S, Jha S (2016a) Magnetically levitated VAWT with closed loop wind speed conditioning guide vanes. In: 2016 IEEE 1st international conference on power electronics,

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intelligent control and energy systems (ICPEICES), pp 1–5. https://doi.org/10.1109/ICPEICES. 2016.7853126 Gandhar S, Ohri J, Singh M (2020) A critical review of wind energy based generation systems. Asian J Water Environ Pollut 17(02), 29–36. https://doi.org/10.3233/AJW200016 Gandhar S, Ohri J, Singh M (2019) Improvement of voltage stability of renewable energy sourcesbased microgrid using ANFIS-tuned UPFC. In: Advances in energy and built environment, LNCE, vol 36, pp 133–143 Gupta N, Kumar A, Joshi D, Banerjee S (2016b) Fuzzy inference model synthesis of a novel magnetically levitated VAWT with MPPT via guide flaps, pp 1–6. https://doi.org/10.1109/ICP EICES.2016.7853384 Khammas F, Suffer K, Usubamatov R, Mustaffa MT (2015) Overview of vertical axis wind turbine (VAWT) is one of the wind energy application. Appl Mech Mater 793:388–392. https://doi.org/ 10.4028/www.scientific.net/AMM.793.388 Shah D, Barve S (2021) Design, analysis and simulation of a Darrieus (eggbeater type) wind turbine. Int Res J Eng Technol (IRJET)08:1655–1660

Fuzzy Air Quality Index for Air Quality Assessment in Gujarat S. A. Nihalani

Abstract In the current scenario, the determination of air quality is of utmost importance, as it is the most imperative factor for human health and the environment. Monitoring of air pollutants and their estimation is one of the essential prerequisites for predicting air pollution. The Air Quality Index (AQI) is a reliable indicator of air quality in any location. The chief air pollutants considered for assessment of AQI are particulate matter, SO2 , NO2 , ground-level ozone, and carbon monoxide. The Central Pollution Control Board and State Pollution Control Boards in India monitor the quality of the air. The conventional AQI determination implies a linear interpolation method to calculate the AQI. A discrete score is allocated to each pollutant based on its measured concentration, and the AQI is computed using the pollutant with the highest score. The present investigation is done to compute AQI in the territory of Gujarat from 2015 to 2017 by using a fuzzy inference system. In order to create the fuzzy air quality index in this work, the fuzzy logic system is used, and membership functions are fed into the Mamdani fuzzy inference system (FIS). The projected AQI values using a fuzzy system are compared with the conventionally calculated AQI values. It is observed that the Fuzzy logic-based system is a more consistent method and provides precise prediction. As a result, the article suggests a fuzzy logic-based method for choosing the Air Quality Index that becomes progressively dependable. Keywords Fuzzy logic · Membership function · AQI · Particulate matter

1 Introduction Clean air all around is termed as an unbounded and uninhibitedly accessible normal asset. Be that as it may, right now, clean air can’t be underestimated. Atmospheric pollution has seen an inconvenient set off from personal exercises, evidently from the time, when the flame was first lit by cavemen. The air contamination problem is S. A. Nihalani (B) Civil Engineering Department, PIET, Parul University, Vadodara, Gujarat, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_36

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accelerated to the extent of expanding urbanization. Urbanization, industrialization, and financial development cause significant deterioration of air quality in urban areas. The common pollutants usually include TSP, RSPM, SO2 , NO2 , CO, and particulates. The particulate matter may comprise solid, liquid, or gaseous pollutants like residue, smoke, mist, fly ash, dust, etc. For the most part, these toxins are available in lower concentrations naturally, when they are principally not having any negative effects (Balashanmugan et al. 2012). They become inconvenient just as their grouping becomes considerably higher as contrasted with the background values and harmful impacts begin to trigger (Sadhana et al. 2013). The National Ambient Air Quality Standards, which is used to represent the quality of the surrounding air, is a prerequisite for the AQI tool. AQI as a tool facilitates individuals to distinguish the amount of air pollution in the surrounding atmosphere. AQI can be determined by various tools like mathematical equations, sensors, soft computing tools, etc. Fuzzy logic is one such tool, that is reasonable to tackle genuine issues, that by and large encompass a specific measure of uncertainty like air pollution parameters. Data on air pollution are represented using fuzzy logic instead of discrete numbers by using phrases like high, medium, and low. Defining variables for input and output, membership functions while defining the variables, and inference rules to obtain the defuzzified output are all required when applying a fuzzy logic system. The premise of the fuzzy framework is fuzzy logic, and the essential hypotheses for this are acquired utilizing fuzzy rationale (Zadeh 2010). The two conventionally utilized frameworks in fuzzy systems are the Mamdani framework and the Sugeno framework. The variables used as input are characterized linguistically and membership functions are defined. After the interference rules are defined in the system, each output fuzzy set requires defuzzification. This incorporates changing the resulting crisp values into linguistic fuzzy sets, which are then allocated positioning using membership functions (Pankaj et al. 2010). This study’s most significant input parameters are SO2 , NO2 , and PM10 . As the amount of input variables and the rule base system both expand, the database’s complexity will also climb (Nihalani et al. 2020).

2 Air Quality Index The air quality index (AQI) is a measure used to report air quality on an hourly or daily basis. AQI underscores the well-being or health impacts that will be realized by individuals in the wake of breathing in contaminated air for hours or days. In Indian conditions, AQI ranges from 0 to 500, with higher AQI values representing higher pollution levels and significant health impact (Anand et al. 2011). For example, an AQI value of 50 indicates good air quality, whereas a value greater than 300 indicates hazardous air quality (Nihalani and Kadam 2019). For computing AQI values, breakpoint ranges are defined for different parameters. The breakpoint values and corresponding AQI values are shown in Table 1 (Nagendra et al. 2007).

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Table 1 Breakpoint concentrations and AQI values AQI value

Category

SO2

NO2

CO

O3

PM10

0–100

Good

0–80

0–80

0–2

0–180

0–100

101–200

Moderate

81–367

81–100

2.1–12

180–225

101–150

201–300

Poor

368–786

181–564

12.1–17

225–300

151–350

301–400

Very poor

787–1572

565–1272

17.1–35

301–800

351–420

401–500

Severe

>1572

>1272

>35

>800

>420

2.1 Input Parameters In order to consider the collective effect of industries, traffic movement, and urbanization, the parameters used for constituting AQI are SO2 , NO2 , PM10 and PM2.5 . AQI data for Gujarat for various prominent locations are considered for 3 years from 2015 to 2017 (Anand and Ankita 2011). The AQI dataset for Gujarat state has been procured from the website of GPCB.

2.2 AQI Computation The arrangement of AQI is ought to include air quality information for a more extended length notwithstanding occasional, diurnal, and month-to-month meteorological parameters. Along these lines, air quality information for three years is selected. Air Quality Index is an effective communication tool to recognize the air quality status of surrounding areas for common people. AQI converts intricate air quality data comprising several pollutants into an index value that has associated nomenclature and colour. AQI is computed using the maximum sub-index approach using the number of parameters considered in the study. By genuinely calculating values like SO2 , NO2 , PM10 , and PM2.5 , the sub-index of each individual parameter is obtained. For the purposes of this inquiry, each case’s AQI is calculated using the numerical equation listed below. Ip =

) IHi − ILo ( Cp − BPLo + ILo BPHi − BPLo

where: IP = sub-index CP = measured concentration. BPHi = higher breakpoint concentration. BPLo = lower breakpoint concentration. IHi = AQI corresponding to BPHi . ILo = AQI corresponding to BPLo .

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Fig. 1 SO2 concentration in the study area

2.3 Air Quality in Study Area The SO2 , NO2 , PM10 , and PM2.5 concentrations for prominent locations in Gujarat state for the years 2015 to 2017 are represented in Figs. 1, 2, 3 and 4. The computed sub-index values and AQI for prominent locations are given in Table 2.

3 Fuzzy Logic Approach for Air Quality The extreme weakness in employing scientific calculation for AQI is that it does not take into consideration the technical know-how of domain experts. The evaluation of AQI using fuzzy logic shall comprise the below-mentioned steps.

3.1 Defining Variables In this study, the output variable is FAQI, and the four pollutants SO2 , NO2 , PM10 , and PM2.5 were taken into consideration when determining the AQI. Instead of having

Fuzzy Air Quality Index for Air Quality Assessment in Gujarat

Fig. 2 NO2 concentration in the study area

Fig. 3 PM10 concentration in the study area

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Fig. 4 PM2.5 concentration in the study area

numerical values, the input and output variables are defined to have linguistic values and words for the syntax (Daniel et al. 2011).

3.2 Membership Functions Fuzzification and defuzzification are done through membership functions (Gopal and Nilesh 2010). They relate the numerical information to fuzzy linguistic terms and the other way around. The crisp values are converted into linguistic variables using the membership functions defined in the system. The membership functions available in the fuzzy system are trapezoidal, triangular, Gaussian, etc. (Mckone and Deshpande 2010). The input–output variables along with membership functions and a few sample inference rules used in our study are shown in Fig. 5.

3.3 Fuzzy Inference Rules Fuzzy logic control endeavours to catch instinct as IF–THEN standards and conclusions are drawn from these guidelines (Nihalani and Kadam 2019). In light of both

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Table 2 Values for sub-indices and AQI via equation City

Location

AQI 2017

Ahmedabad

2016

2015

Naroda, G.I.D.C., Ahmadabad

113.12

111.04

104.46

Cadilla Bridge Narol

120.04

110.35

107.92

Bhagavathi Estate, Keval Kanta Road, Rakhiyal

112.77

109.31

105.15 104.81

Dyno Wash,27 Ilaben Estate

122.81

109.31

L.D. Engg. College

113.81

111.04

99.62

Shardaben Hospital, Saraspur

111.73

111.04

102.04

R.C. Technical High School, Mirzapur

112.08

110.35

102.73

AZL Behrampura, Ahmadabad

112.77

110.35

103.77

Ankleshwar

Rallis India Ltd.

110.69

108.96

103.08

Durga Traders, Bhavanafarm Society

109.65

108.96

103.77

Jamnagar

Fisheries Office

108.96

104.81

102.04

Rajkot

Nr. Sardhara Industrial Corporation

112.77

106.19

103.08

GPCB Regional Office

106.88

103.77

100.65

Surat

S.V.R. Engg. College

105.85

102.73

101.69

B.R.C. High School, Udhna

114.50

106.19

105.50 103.42

Vadodara

Vapi

Near Air India Office

108.96

105.50

GPCB Office, Geri Vasahat

106.19

103.77

99.62

Dandia Bazaar

113.46

104.12

103.42

CETP Nandesari

114.50

107.23

104.81

GEB, IIIrd Phase, GIDC

113.12

109.31

104.12

Vapi Nagar Palika, Vapi

111.73

108.96

102.38

intuitive and expert knowledge, framework parameters can be displayed as etymological factors and their related participation capacities can be planned. To produce a yield set, the fuzzy inference engine applies the aggregation component to the layout of the fuzzy rule base’s guidelines (Lokeshappa and Kamath 2016). This entails coordinating the fuzzy input set with the assumptions of the rules, enacting the rules to complete each terminated rule’s end, and blending each actuation end using fuzzy set association to produce fuzzy output. A few fuzzy inference rules used in this study are depicted in Fig. 6.

3.4 Defuzzification A fuzzy set output is converted to a crisp output via an output mapping known as the defuzzifier (Kumaravel and Vallinayagam 2012; Saddek et al. 2014). The defuzzified FAQI along with the surface viewer is presented in Figs. 7 and 8.

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Fig. 5 Input, output variables, and membership function for PM10

Fig. 6 Fuzzy inference rules

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Fuzzy Air Quality Index for Air Quality Assessment in Gujarat

Fig. 7 Rule viewer for FAQI

Fig. 8 Surface viewer for FAQI

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4 Conclusion The current paper exhibits the benefit of the fuzzy system for air quality grouping over the conventional AQI technique. Utilising various data is one of the most effective and trustworthy ways for decision-makers and the general public to predict or take into account ecological phenomena. A few countries are establishing their unique methods for the AQI count. The fuzzy system technique used in the current study can be an unmatched representation of an efficient framework that gives a completely novel approach to air quality observations from the perspective of vulnerabilities covering air pollution and its quality. The acceptance of any fuzzy framework is, however, subject to the judgement of subject-matter experts and their perceptions. Acknowledgements The study is part of the Industry Defined Research Project titled “AI, IoT and Digital Technologies for Future Sustainable Cities” funded by Royal Academy of Engineering under Newton Bhabha Fund in collaboration with L&T S&L, Nirveda Technologies and NASSCOM with Parul University.

References Anand A, Ankita B (2011) Revised Air Quality Standards for Particle Pollution and Updates to the Air Quality Index 25:24–29 Anand K, Ashish G, Upendu P (2011) A study of ambient air quality status in Jaipur city (Rajasthan, India), using air quality index. Nat Sci 9(6):38–43 Balashanmugan P, Ramanathan A, Elango E, Nehru Kumar V (2012) Assessment of ambient air quality Chidambaram a South Indian Town. J Eng Sci Technol 7(3) Daniel D, Alexandra A, Emil L (2011) Fuzzy inference systems for estimation of air quality index. Rom Int J 7(2):63–70 Gopal U, Nilesh D (2010) Monitoring of air pollution by using fuzzy-logic. Int J Comput Sci Eng IJCSE 2(7):2282–2286 Kumaravel R, Vallinayagam V (2012) Fuzzy inference system for air quality in using Matlab, Chennai, India. J Environ Res Dev 7(1):181–184 Lokeshappa B, Kamath G (2016) Feasibility analysis of air quality indices using fuzzy logic. Int J Eng Res Technol 5(8) Mckone TE, Deshpande AW (2010) Can fuzzy logic bring complex environmental problems into focus? IEEE 15:364–368 Nagendra SM, Venugopal K, Jones SL (2007) Assessment of air quality near traffic intersections in Bangalore city using air quality indices. J Elsevier 1361–9209 Nihalani S, Kadam S (2019) Ambient air quality assessment for Vadodara City using AQI and exceedence factor. In: Proceedings of ınternational conference on advancements in computing and management (ICACM) Nihalani SA et al (2020) Air quality assessment using fuzzy inference systems. In: Advanced engineering optimization through intelligent techniques. Springer, Singapore, pp 313–322 Pankaj D, Suresh J, Nilesh D (2010) Fuzzy rule-based meta graph model of air quality index to suggest outdoor activities. Int J Comput Sci Eng Technol (IJCSET) 2(1):2229–3345 Saddek B, Chahra B, Chaouch Wafa B, Souad B (2014) Air quality index and public health: modelling using fuzzy inference system. Am J Environ Eng Sci 1(4):85–89

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Sadhana C, Pragya D, Ravindra S, Anand D (2013) Assessment of ambient air quality status and air quality index of Bhopal city (Madhya Pradesh), India. Int J Curr Sci 9:96–101 Zadeh LA (2010) A summary and update of “fuzzy logic”. In: 2010 IEEE ınternational conference on granular computing. IEEE

Legibility in a City: An Overview of the Factors Affecting Perceptions of Way-Finding in the Built Environment Sandeep Kumar , Amit Hajela , and Ekta Singh

Abstract The cities are complex urban systems that keep on surpassing their own contours as a resultant of bearing the burden of abundant population and unprecedented growth. The urban areas keep on expanding and intensifying in size and volume of hybrid urban forms which develop as the sub-systems in itself within the system of cities. These urban forms are the mix of buildings and open space elements, the complex configurations of built and open spaces, typologies and functions of these buildings and spaces in a built environment and the connections between these elements and spaces. This paper is an attempt to give an overview of the factors affecting perceptions of legibility and way-finding in the built environment at city/neighbourhood level. It will also study and identify the urban complexities through accessibility and way-finding in a city with spatial configuration and settlement pattern of built environment in a city. This paper is part of an ongoing doctoral research on Role of Urban Legibility in Planning and Designing of Cities. Keywords Legibility · Urban form · Built environment · Way-finding · Urban density

1 Introduction Legibility denotes the apparent clarity of any cityscape that helps in comprehending the city and its various parts. It can also be defined as the ease or comfort by which the various areas of the city become recognizable and arranged into a consistent and connected pattern (Lynch 1960). Lynch also outlined the five city elements, which include edges, nodes, pathways, districts, and landmarks. He also highlighted that S. Kumar (B) · A. Hajela Amity School of Architecture and Planning, Amity University Uttar Pradesh, Noida, India e-mail: [email protected] E. Singh Practicing Architect, Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_37

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these five elements create a mental impact on the people and play a key role in the way-finding experience in a city. The elements of urban design including landmarks or districts or pathways are easily recognizable and arranged in a legible city. In such a city, the urban layout reflects the main forms and qualities, and these contribute to the continuity, visibility, regularity, clarity and then the overall clarity of an urban built environment. Kubat et al. (2012) explained the impacts of spatial arrangement of urban layout and landuse patterns on the way-finding experience in the built environment. The general spatial arrangement of the urban forms—both at the nearby and worldwide level—demonstrates to be a critical variable for the depiction and change of human spatial behaviour in urban built-form scenarios (Kubat et al. 2012).

2 Identifying the Factors Affecting Legibility The aesthetic quality of built form and the spatial clarity may not depend on each other and may not be corresponding to each other all the time (Taylor 2009). As shown in the flow chart below, a modelling approach and computational framework have been developed by Silavi et al. (2017) to review the concepts of spatial clarity and permeability of the built form in a city (Fig. 1).

Fig. 1 Mapping of the urban legibility in a city (Silavi et al. 2017)

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These ideas are identified as two important factors while assessing the arrangement of an urban built form along with the way-finding capabilities in the city. Similarly, the computational system has been formally created and built on the perceived ideas of urban from clarity and way-finding. The experimental discoveries uncover diverse degrees of coordination between the computational analysis of an urban layout and how legibility and permeability are perceived by the people’s observations depending on their general understanding of the considered urban layout. Particularly, the detailed results also show that new users to an urban area are likely to get the clear sense of urban built form based on spatial and morphological characteristics rather than the residents and other users. As for the way-finding in the urban areas, the individuals familiar with their local urban built environment have generally recognized the spatial clarity of built environment with a clear reliance on the basic spatial and built properties.

3 Quality of Urban Design The urban design features are more significant than physical features. Therefore, the need for form-based codes is justified for city development and planning in future (Ewing and Street 2014). As Fig. 2 shown, Ewing et al. (2006) had already established that urban design qualities affect movement and way-finding in a city by reflecting the correlation of physical features of built environment and urban design qualities including legibility, imageability, coherence, etc.

Fig. 2 Urban design qualities affecting movement and way-finding in a city (Ewing et al. 2006)

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4 Elements of Built Environment According to Shokouhi (2003), the arrangement of the pathway into simple and geometric shapes would provide a pattern that might be easier for users to remember. As per him, following are the three main attributes of pathway configuration in legibility of cities: i. Consistency of pathway layouts ii. Pattern of spatial elements iii. Densities of movement and pedestrian flow. Sadrabadi et al. (2012) highlighted that the significance of physical components and mental perspectives is exceptionally critical to progress the clarity in a city/neighbourhood. They have highlighted that in spite of the fact that landmark and node were found as the most identifiable physical components, still these have not been able to compensate the shortcomings or need of the other physical components which means, all these physical components outlined by Lynch (1960), including path, node, edge, landmark and district ought to be contributing to create a mental mapping with the user and helping in way-finding and spatial alignment. Mohammed (2012) also reiterated that spatial arrangement and visual shape are closely connected. The cities ought to be outwardly and fundamentally clear for superior way-finding capacities. Sohrabi (2015) concluded that the vertical elements of the street’s structure including the sidewalls and the frontages of the buildings have considerable effects on the legibility, clarity and also identity of the streets. An urban setting should have an organized composition of different components at various scales and functions. The following elements can be summarized as the possible indicators and/or parameters for urban legibility as described by various experts and researchers: i.

Legibility parameters: singularity, continuity, clarity, simplicity, dominance, directional differentiation, visual scope, motion awareness ii. Regularity of pathway configuration iii. Spatial configuration, spatial cognition iv. Pattern of spatial elements, buildings, etc. v. Densities of movement and pedestrian flow vi. Street connectivity, metric and directional reach vii. Human perception of urban legibility and aesthetics viii. Urban space network, mental mapping ix. Accessibility, movement, etc.

5 Legal Framework for Controlling the Built Environment The supporting legal framework includes the systems that interpret diverse modes of urban clarity in an endeavour to form “urban experience” comprehendible for urban designers and planners. The political adequacy of rules and pre-established bureaucratic boundaries implies that the urban designers and planners can only mediate with

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an arrangement of combinations, mediations and approximations. It sets a frontal area for the “middle layer of urban governance” that is frequently disregarded in city administration (Lee 2014). Ahmad Nia and Atun (2016) explored how to utilize, find and plan the physical urban components in an important, conventional and inborn way that includes a positive effect on expanding the clarity and quality of the Built environment. It was concluded that planning and designing recognized, particular, and interesting urban components initiate a feeling of memory and belongingness and are socially and practically associated with the city. The urban components ought to be found in a way to bring forward a sense of arrangement. An arrangement of buildings and signages can be identified as components that have concordance and speak to a solidarity in the meantime they may be distinctive from each other to extend the legibility of the streets within the city.

6 Responses from Developed Countries London, Bristol and Rio de Janeiro are a few of the cities that have created modern way-finding frameworks. London’s way-finding framework called as “Legible London” that is based on the comprehensive understanding of the individuals and the built environment they move into or explore. The Legible London Project was initially launched on an experimental basis in London’s “Bond Street” and it was executed at the city level just before the London Summer Olympics games in 2012. It was created in reaction to a research that claimed the 32 different pedestrian sign systems in central London produced visual noise instead of trustworthy and wellcoordinated information. Legible London was designed with the goal of facilitating cooperation between city boroughs and neighbourhoods and integrating it with other modes of transportation (Fig. 3). In 2015, the city of Rio de Janeiro also executed a similar programme called as “Walk Rio”, with more than 500 signages and map kiosks installed in the city to serve the city’s 12 million inhabitants and 6 million visitors. Sydney, Bristol and many other cities have also opted for and implemented similar programmes.

7 Conclusion and Way Forward Based upon the several studies and examples discussed above, it can be concluded that the extent of legibility of the built form in a city can be well understood and comprehended by the study and analysis of the urban built form and its configurations in terms of placement and orientation of buildings, layouts of pathways and connections and interrelationship among the buildings and spaces.

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Fig. 3 Legible London Placement Strategy and Base Mapping (Legible London 2010)

It is quite evident that the built-form density and the population density or movement density are also the important parameters in design and planning of cities and neighbourhoods to ease the way-finding in a city with better spatial configuration and settlement pattern of built environment in the city as a whole. Therefore, along with the above, further research is required that would be exploring and establishing the role of the following important components of built environment in making a city or urban area legible: i. ii.

Open space structure/system Alignment/structure of roads, pathways and streets

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iii. iv. v. vi. vii. viii.

481

Dispersal of uses—bulk disposition Volumetric disposition Activity structure Building typologies Urban infrastructure Public spaces/public realm.

A synergy is needed between the top-down planning approach and built form development at ground level. This will be an intended step towards bridging the gaps of planning theories and urban built-form design practices by developing a strategy for integration of urban legibility and development control rules and building bye-laws in the fast-developing urban centres in developing countries like India.

References Ahmad Nia H, Atun RA (2016) Aesthetic design thinking model for urban environments: a survey based on a review of the literature. Urban Des Int 21(3):195–212. https://doi.org/10.1057/udi. 2015.25 Ewing R, Clemente O (n.d.) Urban street design measuring urban design, by urban design qualities. 5686 Ewing R, Street W (2014) William Greene Department of Economics Stern School of Business, New York University 44 West 4 th St., 7–90 P: 212-998-0876. Transportation research board 93rd annual meeting. January 12–16, Washington, D.C. https://doi.org/10.1177/0739456X1559 1585 Ewing R, Handy S, Brownson R, Clemente O, Winston E (2006) Identifying and measuring urban design qualities related to walkability. J Phys Act Health 3:223 Kubat AS, Ozbil A, Ozer O, Ekinoglu H (2012) The effect of build space on wayfinding in urban environments: a study of the historical peninsula in Istanbul. In: Eighth international space syntax symposium, pp 1–20. http://www.sss8.cl/media/upload/paginas/seccion/8029_1.pdf Lee KW (2014) Feeling like a state: design guidelines and the legibility of “urban experience” in Singapore. Int J Urban Reg Res 38(1):138–154. https://doi.org/10.1111/1468-2427.12102 Legible London (2010) Transport for London, UK Lynch K (1960) The image of the city. MIT Press Mohammed A (2012) Evaluating way-finding ability within urban environment. In Eighth international space syntax symposium, pp 1–39. http://www.sss8.cl/media/upload/paginas/seccion/ 8204_2.pdf Sadrabadi MR, Ujang N, Sadrabadi SR (2012) Legibility of Safaieh neighborhood in the city of Yazd, IRAN Department of Landscape Architecture, Faculty of Design and Architecture, pp 8–17 Shokouhi M (2003) The role of visual clues and pathway configuration in legibility of cities. In: Proceedings of the 4th international space syntax symposium London (1990), pp 1–14 Silavi T, Hakimpour F, Claramunt C, Nourian F (2017) The legibility and permeability of cities: examining the role of spatial data and metrics. ISPRS Int J Geo-Inf 6(4). https://doi.org/10.3390/ ijgi6040101 Sohrabi M (2015) Analysis of the Place of Outdoor Architecture in the Legibility of Spaces 3(3):44– 54 Taylor N (2009) Legibility and aesthetics in urban design, vol 4809. https://doi.org/10.1080/135 74800802670929

Evaluation of Property Pricing Structure of Residential Neighborhoods in Correlation with Urban Green Spaces of Noida City Vikas Kumar Nirmal , Priyanka Singh , Vilas Bakde , and Ekta Singh

Abstract Cities are heterogeneous mixtures of many facets such as ethnicity, culture, income groups, lifestyle, and the same diversity is more prominent in metropolitan cities. The bigger the city the more diverse it is and hence more cosmopolitan. The needs and demands of every community depend upon their purchasing power or income strata and the same is reflected in their neighborhood as well. The physical attributes of the neighborhood define the people’s standard of living and their purchasing power to afford that desirable quality of life. These attributes vary from accessibility to public transport, social infrastructure, population density, neighborhood amenities, open green spaces, etc., and all contribute toward driving the property prices of a neighborhood and sector of any city. Though there are other considerable features such as structural features, gated community, crime, and safety but the significance given to these traits by residential consumers is relatively less. Hence, the onus of driving the pricing index falls on the physical characteristics of the neighborhoods rather than non-tangible attributes. This paper assimilates such similarities between residential neighborhoods of Noida city to understand the qualitative facet of physical characters and their effect on the circle rates of different sectors in the city. The analysis is based on the Master Plan of Noida 2021, and current circle rates as defined by Noida authority and Gautam Buddha Nagar district. The paper is a part of Doctoral research on ‘Framework for Assessing the Impact of Neighborhood green spaces on Prices of Residential Properties, Case Study: Noida’.

V. K. Nirmal (B) Amity School of Architecture and Planning, Amity University, Noida, Uttar Pradesh, India e-mail: [email protected] P. Singh Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh, India V. Bakde Department of Architecture, Visvesvaraya National Institute of Technology, Nagpur, India E. Singh Practicing Architect, Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Al Khaddar et al. (eds.), Recent Developments in Energy and Environmental Engineering, Lecture Notes in Civil Engineering 333, https://doi.org/10.1007/978-981-99-1388-6_38

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Keywords Metropolitan cities · Neighborhood · Property price · Community · Quality of life · Noida

1 Introduction Indian cities have seen tremendous growth in terms of urban areas and metropolitan cities in the last two decades. Predominantly property pricing is influenced by multiple factors such as structural features or the development of a particular area as shown in Fig. 1. Whereas sometimes it is also inclined toward the quality of social life due to neighborhood green spaces (WHO Regional Office for Europe 2017), crime ´ et al. rates (Anderson and West 2006), or the security of a gated community (Zróbek 2015; Strippelhoff 2011). The dominance of individual parameters such as proximity to central business district or City Center will always exist but many times the influence is jeopardized due to multiple factors (Chen and Hao 2008; Anderson and West 2006). A detailed literature review was conducted with more than 100 research studies across world with influential factors like schools, hospital, green belts, forests, expressways, CBD, etc. It was found that the single amenity may not always be the driving force toward boosting the demand of property but cluster of amenities reinforce each other to have a comprehensive impact on users’ perception about a locality (Smith 2010; Guagliardo 2004; Staikos and Xue 2017). An individual perception is very important to justify the property selection in a locality sometimes just the existence of facilities but many times experiential inferences in the area (Shi and Yu 2014). The phenomenon of rise in property prices is an indirect impact of multiple factors as listed above in Fig. 1. The classification of these influential factors is important to understand the impact under each category so as to have a clear picture about his research intends to derive an understanding of the pricing geography of residential properties in Noida city where the market rates and circle rates are volatile. Moreover, the circle rates are exceeding the market rates at many places against the general phenomenon of market rates being higher in cities.

Fig. 1 Factors influencing property pricing in cities. Source Literature Review

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2 Study Area: Noida City New Okhla Industrial Development Authority (NOIDA) is a satellite city of Delhi, the National Capital of India. The city has an area of 203 sq. km with an inhabitant population close to 1 million divided into 163 sectors for all land uses such as industrial, residential, commercial, etc. It is one of the planned cities in India and one of the greenest with nearly 50% area under green cover. It is a mixed blend of sectoral development, gated neighborhood societies, industrial SEZs, etc. The city enjoys pretty good connectivity from South Delhi through DND and Kalindi Kunj point in Delhi and is the hub for many multinational companies like HCL, Capgemini, Genpact, etc. Due to large industrial area including IT parks and manufacturing setups around the city, it is able to provide a mixed blend of the gentry in every part of city. Though the establishment of NOIDA was done to facilitate the spillover industrial growth of Delhi but it also contributed to inhabiting the residential consumers of National Capital Region in quite efficient manner. With introduction of the Noida Metro, the connectivity from City Center to Greater Noida via central Noida has reinforced the growth of city. The property prices in the city are relatively affordable in comparison to Delhi NCT or the industrial satellite town of NCR like Gurgaon. As mentioned above, the city is one of the greenest in the country with many largescale green covers including the forest and recreational areas as shown in Fig. 2. The city is having an edge of Yamuna River on one side and Hindon River on the other providing huge agriculture land, which enhances the green cover of the city to the large extent. Further, the botanical garden and Okhla bird sanctuary give the taste of a natural forest to the city. Apart from these, large-scale planned urban green spaces like multiple Golf courses and Noida stadium enhance this attribute of city as well. Fig. 2 Urban green spaces in Noida city. Source Literature Review

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3 Circle Rates of Noida City The circle rates for residential land use in Noida city are defined on the basis of three categories based on the infrastructure availability as evaluated by the multiple government agencies by Uttar Pradesh administration. Therefore, it is imperative to understand the whole mechanism of the property administrative structure of Noida city in order to analyze the evaluation of property pricing of residential neighborhoods. Circle rates are determined by many factors. Some of the parameters that are affecting the circle rates in Noida are: 1. Type of property: Circle rates vary for apartments, flats, independent houses, and plots in the same sector. The government charges higher rates for commercial complexes when compared to residential complexes. 2. Market value and amenities: Amenities and the market value play key roles in determining the circle rate of a locality. The circle rates are high for group housing societies as it provides better amenities. The UP government in Noida has levied additional costs on common facilities provided in the group housing societies such as parking, community center, club, swimming pool, gym, security, etc. 3. The government has allowed a relief of 2% per floor for high-rise apartments. This relief is provided for buildings with four floors and above and is capped at 20% of the minimum value. Three categories of residential circle rates are shown in Fig. 3, which shows the complexity of the evaluation of circle rates of each individual sector of Noida city. These three categories are: 1. Plot land rates

Fig. 3 Noida city map showing classes and their respective circle rates. Source Master Plan Noida 2031

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2. Built-up area rate of independent floors 3. Built-up area rate for group housing.

4 Land Rates The complexity further strengthens due to authority powers existing with multiple agencies in Noida city with land rates being decided by Gautam Buddha Nagar District administration, built-up area circle rates by NOIDA authority whereas the transfer of property is taken care by Yamuna expressway Industrial development authority (YEIDA). As far as land rates are concerned, as shown in Fig. 3, they are categorized into six classes from Class A+ to Class E. Beyond these circle rates by GBN district administration, the NOIDA authority has been given discretionary powers to levy 5% extra on the circle rates if the residential property is having green belt or parks in the vicinity. As of now, only the properties with park facing can be levied extra. The exclusivity of residential properties adjacent or near to neighborhood green spaces is diminished due to uniform circle rates for residential plots. However, the market of property demand revolves around the premium location or exclusivity of properties for which the user is willing to pay extra. On the other hand, if we look at the circle rates of built up area for plotted developed area its only based on the road width approachable to the individual property, whereas for group housing its single circle rate that prevails irrespective of neighborhood green spaces or other social infrastructures present in the housing society (Tables 1 and 2).

5 Comparative Analysis The maps show the circle rates of both the categories in Noida city. The overlapping of circle rates on the map shows the similarity in the terms of location and prices. The mapping of circle rates was done to understand the spatial arrangement of pricing structure of sectors dominated by residential land use. It was found that though there is overlapping of primacy of circle rates near city center of Noida city but still there are significant differences in their rates which may be due to other amenities present. It can be hypothesized that this variation in property pricing is due to social infrastructure present in the individual sectors of which urban green spaces green spaces can also be a major factor. Moreover, the impact of individual factors and finding the influence of urban green spaces and green belts in green city like Noida will give the insight about new town development in India. The urban green spaces not only act as environmental assets but also provide a healthy tax revenue to the administration (Fig. 4). The range of circle rates for independent floors on plotted development varies from Rs. 40,000 to 119,000/m2 , which is up to 3 times from lowest to the highest

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Table 1 Circle rates for independent floors of residential area (based on front road width) Sectors for residential properties in Noida city Sector 66, 102, 138, 139, 140, 140A, 141, 145–150, 158–167, NEPZ (NSEZ), Noida Phase 2

12 m road 12–18 m road 18–24 m road Above 24 m road 40,000

42,000

44,000

46,000

Sector 115

44,000

42,000

44,000

50,600

Sector 54, 57–60, 63, 63A, 64, 65, 67–69, 80, 81, 83–91, 95, 101, 103, 106, 109, 111, 112, 113, 114, 116, 117, 118

44,000

46,200

48,400

50,600

Sector 104

44,000

55,150

57,750

60,400

Sector 168

52,500

52,500

57,750

60,400

Sector 1–12, 22, 42, 43, 45, 70–79, 107, 110, 119–121, 123, 125–137, 142, 143, 143B, 144, 151–157

52,500

55,150

57,750

60,400

Sector 15, 19, 20, 21, 23–25, 25A, 26–29, 31–34, 37, 40, 41, 46–49, 53, 55, 56, 61, 62, 82, 92, 93, 93A, 93B, 96–100, 105, 108, 122

72,000

75,600

79,200

82,800

Sector 14, 14A, 15A, 16, 16A, 103,000 16B, 17, 18, 30, 35, 36, 38, 38A, 39, 44, 50, 51, 52, 94, 124

109,000

114,000

119,000

Source Stamps and Registration Department, UP Table 2 Circle rates for built-up areas in group housing residential areas Sectors for residential properties in Noida city

Circle rates (in INR)

Sector 14, 14A, 15A, 17, 25A, 30, 32, 35, 36, 38A, 39, 44, 50, 51, 52, 92, 55,000 93, 93A, 93B, 96, 97, 98 Sector 15, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 37, 38, 40, 50,000 41, 45, 46, 47, 48, 49, 53, 55, 56, 61, 62, 82, 99, 100, 105, 108, 122, 128, 129, 131, 134, 135, 137 Sector 11, 12, 16, 16A, 16B, 22, 24, 42, 43, 70, 71, 72, 73, 74, 75, 76, 7, 78, 79, 104, 107, 110, 115, 117, 110, 115, 117, 118, 119, 120, 121, 130, 133, 143, 143B, 144, 150, 151, 168

40,000

Sector 63A, 86, 112, 113, 116

35,000

Sector 102, 158, 162

32,000

Source Stamps and Registration Department

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Fig. 4 Circle rates of residential areas (group housing and independent floors). Source Stamps and Registration Department, Uttar Pradesh (INDIA)

circle rates. Similarly, for the group housing, the range is Rs. 32,000–55,000/m2 which is 1.7 times. Further the highest circle rate for built up area has a significant gap from Rs. 55000 to 119,000/m2 , which suggest that the demand of residential property in Noida city in inclined toward independent floors in plotted development. It is worth pondering over that the city known from its group housing development has a demand inclination toward plotted development houses. Hence, the next level of study is to investigate the rational behind this variability in the pricing sector through primary data of users.

6 Summary and Conclusion As we have seen, the circle rates are derived using multiple factors such as infrastructure development and proximity to CBD. On the contrary, it was also found that this phenomenon of influence of individual factors is not directly proportional to the property prices with cases like sectors on Noida-Greater Noida expressways. Moreover, the intangible attributes such as quality of life due to neighborhood green spaces do impact the prices but location of sectors still plays an important role in defining the rates with cases like Sector 2 like 150, 151 where urban green spaces are abundant but still lags behind the sector 137 and sector 142. Apart from domination urban green spaces, the green belts on the expressways and major roads of city play important roles in property demands (Nicholls and Crompton 2005). In order to understand the correlation of individual factors and derive the coefficient of impact

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due to urban green spaces will need to have a detailed study using the following operative framework: 1. Proximity analysis of adjacent sectors and corresponding residential properties 2. Comparative analysis within and across group housing and plotted residential areas 3. Listing of variables and attributes influencing the property demand of area 4. Analyzing the growth pattern and future scope of development in an area 5. Viewability of properties in a neighborhood vicinity 6. Factor analysis of individual indicators.

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