Sustainable Development of Water and Environment: Proceedings of the ICSDWE2020 [1st ed.] 9783030452629, 9783030452636

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Environmental Science

Han-Yong Jeon   Editor

Sustainable Development of Water and Environment Proceedings of the ICSDWE2020

Environmental Science and Engineering Environmental Science

Series Editors Ulrich Förstner, Technical University of Hamburg-Harburg, Hamburg, Germany Wim H. Rulkens, Department of Environmental Technology, Wageningen, The Netherlands Wim Salomons, Institute for Environmental Studies, University of Amsterdam, Haren, The Netherlands

The protection of our environment is one of the most important challenges facing today’s society. At the focus of efforts to solve environmental problems are strategies to determine the actual damage, to manage problems in a viable manner, and to provide technical protection. Similar to the companion subseries Environmental Engineering, Environmental Science reports the newest results of research. The subjects covered include: air pollution; water and soil pollution; renaturation of rivers; lakes and wet areas; biological ecological; and geochemical evaluation of larger regions undergoing rehabilitation; avoidance of environmental damage. The newest research results are presented in concise presentations written in easy to understand language, ready to be put into practice.

More information about this subseries at http://www.springer.com/series/3234

Han-Yong Jeon Editor

Sustainable Development of Water and Environment Proceedings of the ICSDWE2020

123

Editor Han-Yong Jeon GeoSynthetics Research Laboratory (GSRL) Division of Nano-Systems Engineering Inha University Incheon, Korea (Republic of)

ISSN 1863-5520 ISSN 1863-5539 (electronic) Environmental Science and Engineering ISSN 1431-6250 ISSN 2661-8222 (electronic) Environmental Science ISBN 978-3-030-45262-9 ISBN 978-3-030-45263-6 (eBook) https://doi.org/10.1007/978-3-030-45263-6 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Dear Distinguished Authors and Guests, It was a great pleasure to welcome all participants in the 2020 the 3rd International Conference on Sustainable Development of Water and Environment (ICSDWE2020) held in Inha University, South Korea, during January 13–14, 2020. ICSDWE2020 is sponsored by Inha University. The aim of ICSDWE2020 is to present the latest research and results of scientists (professors, students, Ph.D. students, engineers, and postdoc scientists) related to sustainable development of water and environment. The key goal of the conference provides opportunities for academic scientists, engineers, and industry researchers to exchange and share their expertise, experience, new ideas, or research result and discuss the challenges and future in their expertise. ICSDWE2020 also provides a platform for the students, researchers, and engineers to interact with experts and specialists on the technical matters and future direction of their research area. The papers were selected after the peer review process by conference committee members and international reviewers. The submitted papers were selected on the basis of originality, significance, and clarity for the purpose of the conference. The papers should provide the reader an overview of many recent advances in the field related to sustainable development of water and environment. The conference program is extremely rich, featuring high-impact presentation. We hope that the conference results constituted a significant contribution to the knowledge in these up-to-date scientific fields. On behalf of the Organizing Committee, we would like to especially thank all technology committee members, reviewers, conference chairs, keynote speakers, sponsors, and conference participants for their support and contributions to ICSDWE2020. We look forward to your participation in the 4th ICSDWE2021. With our warmest regards Incheon, Korea (Republic of)

Han-Yong Jeon

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Contents

Environmental and Microbiological Assessment of Small-Scale Vegetable Farming Systems Irrigated With Wastewater from Upper Balili River, La Trinidad Benguet, Philippines . . . . . . . . . . Rosemary M. Gutierrez and Venus Montalla

1

The Effects of Sea Level Rise on Salinity and Tidal Flooding Patterns in the Guadiana Estuary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lara Mills, João Janeiro, and Flávio Martins

17

Effects of Climate Change on Runoff in the Heihe River Basin Under Different Sensitivity Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingtao Li and Qianqian Guo

31

Comparison of UVA-LED and UVC-LED for Water Disinfection: Inactivation of Escherichia Coli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhilin Ran, Meng Yao, and Shaofeng Li

39

Application of Integrated Median Ranked Set Sample and Analytic Hierarchy Process to Enhance Decision Making Process in Environmental Issues-Selecting Best Wastewater Collection as a Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad K. Younes Study on Preparation of Selective Nickel Ion Exchange Membrane by Ion-Imprinting Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jih-Hsing Chang, Shan-Yi Shen, Chian-Yu Lin, Lien-Hsuan Chou, Yu-Chun Li, and Hisen-Chin Yen Degradation of Phenol by Three-Dimensional Electrode-UV Photo-Oxidation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fuchen Ban, Qiu Jin, and Meiran Li

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Contents

Material Flow Analysis of CRT Monitor, Electric Fan and Refrigerator Through the Primitive E-waste Dismantling in Buriram Province, Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sangsuree Srisa-ard, Penpato Siriruttanaprasert, Thapanee Piboon, Tassanee Prueksasit, and Narut Sahanavin

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Heavy Metal Contamination of Surface Water and Groundwater from the Waste Electrical and Electronic Equipment (WEEE) Recycling Area in Buriram, Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . Nathida Kongsricharoen, Jayrisa Champa, Navaporn Kanjanasiranont, and Tassanee Prueksasit

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A New Method to Evaluate Tight Oil Reservoir of Chang7 Member in Zhidan Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Li Kang, Dang Hailong, Kang Shengsong, Chang Bin, and Wang Weibo Wastewater Treatment Plant Optimization: Case Study of Benchmark Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Dinh Huan Nguyen, Phuoc Cuong Le, and M. A. Latifi E-waste Dismantling Community Toward Circular Economy with Ineffective Hazardous Waste Management: A Case Study in Buriram Province, Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Mongkolchai Assawadithalerd, Sangsuree Srisa-ard, Pensiri Akkajit, and Tassanee Prueksasit Possible Impact of Future Dams on Suspended Sediment Load Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Zuliziana Suif, Yoshimura Chihiro, Nordila Ahmad, and Maidiana Othman Application of Activated Carbon and PGa21Ca to Remove Methylene Blue from Aqueous Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Le Thi Xuan Thuy, Le Thi Suong, Le Phuoc Cuong, and Tatjana Juzsakova Characterization of Erosion of the Sand Bed Near Wide Piers . . . . . . . 159 Nordila Ahmad, Zuliziana Suif, and Maidiana Othman Hydraulic Ram Pump: A Practical Solution for Green Energy . . . . . . . 171 Maidiana Othman, Nur Fadzatul Huda Abd Halimee, Muhammad Nizam Mohammad Sobri, Zuliziana Suif, and Nordila Ahmad Assessment of the Rudnaya River Geochemical Barriers Water Composition Using Physico-Chemical Modeling Method (Dalnegorsk Ore District, Russia) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Konstantin R. Frolov

Contents

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Spatial and Temporal Variations of PM2.5 in the Vicinity of Expressways in Bangkok, Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Navaporn Kanjanasiranont, Tassanee Prueksasit, Narut Sahanavin, and Songkrit Prapagdee Geographic Information System and Integrated Spatial Analysis on Area Selection for WEEE Collection Site at Buriram Province, Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Komsoon Somprasong, Suthathip Chitwiwat, Mongkolchai Assawadithalerd, and Tassanee Prueksasit Simulation and Optimization of Hoa Cam Wastewater Treatment Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Dinh Huan Nguyen and M. A. Latifi Escherichia coli (E. coli) as an Indicator of Fecal Contamination in Groundwater: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Farhan Mohammad Khan and Rajiv Gupta Sea Ice Variability of the Amur Estuary: Survey Data Analysis . . . . . . 237 Zinaida Verbitskaya, Maxim Medvedev, and Maria Kotelnikova Project Management Affecting the Productivity and Sustainability of a Green Building: A Literature Review . . . . . . . . . . . . . . . . . . . . . . . 251 Yuxin Dong An Automatic Method for Drainage Basin Spatial Range Delineation Using DEMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Xinming Li, Ding Li, Chengzhi Qin, A.-Xing Zhu, and Lin Yang Estimation of Flow from Hunza Watershed Under Possibly Changed Climatic Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Muhammad Zaeem Rana and Rana Muazzam Ali Optimizing Industrial Facility’s Demand for Combined Heat-and-Power (CHP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Stanislav Chicherin, Lyazzat Junussova, Timur Junussov, and Chingiz Junussov An Analytical Approach to Sustainable Beneficial Use of Dredged Materials in Yangon River, Myanmar . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Khin Myat Noe and Kyoungrean Kim Impacts of Silk Garment Production on Water Resources and Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Fangli Chen, Wanwen He, Zejun Tian, and Laili Wang

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Performance and Stability of Algal-Bacterial Aerobic Granular Sludge in Batch Column and Tubular Reactors . . . . . . . . . . . . . . . . . . . 321 Sanha Kaizer Tajamul Basha, Caixing Tian, Zhongfang Lei, Zhenya Zhang, and Kazuya Shimizu Spatial Variation of Heavy Metals Contamination in Soil at E-waste Dismantling Site, Buriram Province, Thailand . . . . . . . . . . . . . . . . . . . . 333 Nisakorn Amphalop, Tassanee Prueksasit, and Mongkolchai Assawadithalerd Research on the Influence Factors of Degradation of Pyrimidine with Anaerobic Bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Dexin Lin, Yong Wang, Dexin Wang, Fei Yang, Li-ping Sun, and Xuesong Yi Spatial-Temporal Changes of Wetland Landscape Patterns in the Eastern Shandong Peninsula . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Xinmeng Shan, Luyang Wang, Ning Xu, Yamin Lv, Jin Tang, and Jiahong Wen Spatial Distribution of PM10 and PM2.5 in Ambient Air at E-waste Dismantling Community in Buriram, Thailand . . . . . . . . . . . . . . . . . . . 371 Siriwipha Chanthahong, Tassanee Prueksasit, Narut Sahanavin, and Navaporn Kanjanasiranont Blood Lead and Cadmium Levels of E-waste Dismantling Workers, Buriram Province, Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 Thidarat Sirichai, Tassanee Prueksasit, and Siriporn Sangsuthum Major Microorganisms Involved in Nitrogen Cycle in Plateau Cold Region and Its Relationship with Environmental Factors . . . . . . . . . . . 391 Jianwei Wang, Tianling Qin, Fang Liu, Baisha Weng, Kun Wang, Xiangnan Li, Hanjiang Nie, and Shanshan Liu

Environmental and Microbiological Assessment of Small-Scale Vegetable Farming Systems Irrigated With Wastewater from Upper Balili River, La Trinidad Benguet, Philippines Rosemary M. Gutierrez and Venus Montalla Abstract In developing countries like the Philippines, it is a common practice to use river wastewater for the irrigation of agricultural lands. The determination of coliform bacteria in the Balili River wastewater, agricultural soils irrigated by it, and vegetable, particularly lettuce, Lactuca sativa, grown in these areas were carried out to serve as indicators of their microbiological quality and potential risks. Bacteriological counts of the water, soil, and vegetable samples were enumerated via membrane filter technique and multiple tube fermentation technique. Primers of the wecA gene, which encode for the protein responsible for the enterobacterial common antigen (ECA), were used for the detection of E. coli by the Polymerase Chain Reaction Method. The results revealed that the total coliform and fecal coliform of the samples for the four sampling periods all exceeded the acceptable standards (>6000–13,000 MPN/100 mL). The low Water Quality Index values ranging from 21 to 28, let alone the presence of coliform bacteria such as Enterobacter, Pantoea, Escherichia, and Klebsiella in the samples, confirm the reports about the worsening quality of the river and stress the danger of directly introducing the wastewater to these agricultural fields. Furthermore, lettuce is a very high-risk crop for coliform contamination and as fecal coliforms were isolated from this vegetable, it can be inferred that the continued use of Balili River wastewater for crop irrigation is unsuitable and an unhealthy practice to consumers. Keywords Balili River · Benguet · Philippines · Lactuca sativa · Escherichia · Enterobacteriaceae

R. M. Gutierrez (B) · V. Montalla Department of Biology, College of Science, University of the Philippines Baguio, Governor Pack Road, Baguio City 2600, Benguet, Philippines e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_1

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R. M. Gutierrez and V. Montalla

1 Introduction Water from rivers, soil and agricultural products such as vegetables can easily be contaminated because of agricultural practices such as the use of untreated river water for irrigation. The use of this untreated river wastewater for irrigation of vegetable farmlands is a common practice in developing countries like the Philippines. However, this practice can be of danger to human health because of the spread of disease-causing microorganisms contaminating vegetables especially those that can be eaten raw in the form of fresh salads made of vegetables like lettuce, tomatoes, green pepper, cabbage, spinach, watercress and others. It has been recommended by the World Health Organization (WHO) that crops and vegetables, in order to be eaten raw, should be irrigated only with biologically treated effluent which has undergone a disinfection process in order to achieve a safe coliform level of not greater than 100 most probable number (MPN) of coliforms per 100 mL in 80% of the samples (WHO 1976). A major river found in La Trinidad, Benguet in Northern Philippines is the Balili River, which stretches from Baguio City to La Trinidad, Benguet. It spans approximately 50 km from Baguio City from Benguet province to La Union province. Water coming from the said river aids in the irrigation of many vegetable farmlands and flower gardens in the municipality (Bengao et al. 2015). Under the standards of the Philippine Water Act, the Balili River was classified before as Class A body of water. The standard of Class A water is that its water quality should be potable, i.e., it should only contain 1.1 MPN fecal coliform per 100 milliliters (mL) of sample. However, it was reported by the Environment Management Bureau, Cordillera Administrative Region (EMB-CAR) of the Department of Environment and Natural Resources (DENR) that the Bailli River, in its current state, has failed the Class A standard because the river is highly polluted (Hent 2019). This study therefore aimed to assess the bacteriological quality of the water, soil, and vegetables, particularly lettuce, Lactuca sativa from selected La Trinidad, Benguet agricultural farms that have been identified to be irrigated with wastewater coming from Balili River. Using the physicochemical and bacteriological parameters of the river, the water quality of Balili River was also evaluated using a method of computation for the water quality index using missing parameters.

2 Materials and Methods 2.1 Study Site Description The municipality of La Trinidad, situated in the Province of Benguet is popularly known as the vegetable capital of the Philippines. La Trinidad is approximately located 256 km north of Metro Manila. Barangay Balili, the site of collection in this

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Table 1 Agricultural farms from Barangay Balili, La Trinidad Benguet that were identified to be irrigated with Balili River wastewater based on interviews and reconnaissance trips Site

Coordinates

Location/description

Vegetables and fruits found during sampling periods

Site 1

16° 27 37 N, 120° 35 19 E

Private agricultural farm across the Commission on Higher Education (CHED) Office

Lettuce, strawberries

Site 2

16° 27 18 N, 120° 35 28 E

Experimental University Farm of Benguet State University

Lettuce, broccoli, oranges

Site 3

16° 26 28 N, 120° 35 39 E

Private agricultural farm behind Apple Blossom Bakery

Lettuce, onions, mint

study is located on the southwestern part of the municipality of La Trinidad. According to the Department of Environment and Natural Resources (DENR) cadastral survey, this barangay has an estimated land area of 119.0164 ha or 1.4731% of the municipality’s total land area. The three sampling sites in this present study, which have been verified to be vegetable farmlands irrigated with Balili River wastewater are located in Barangay Balili (Table 1). Different water sources of La Trinidad flow out to the different creeks and tributaries going to Balili River, the main drainage of the municipality. Aside from being the main catchment in the valley, Balili River stretches or spans about 50 km all the way to the Province of La Union where it is known as the Naguilan River, and from there, this river flows out directly into the China Sea. Twelve major creeks have been recorded to drain from three directions in the municipality and are considered tributaries to the river. Balili River is characterized by a relatively steep to gradient, suggesting that the flow pattern of water run-off takes the form of a rapid stream which is a loss of the flow resource (CLUP 2019).

2.2 Sample Source Samples of Balili River water, soil, and vegetables (lettuce, Lactuca sativa) were collected from three agricultural farmlands in Barangay Balili, La Trinidad, Benguet (Table 1). The sampling periods were done on a quarterly basis and were extended for a period of one year, from September 2016 to August 2017 with one type of vegetable specimen collected at each sampling time. Lettuce was the vegetable of choice since it was the vegetable found common to all three sampling sites. Water, soil and vegetable specimens were collected during each sampling time. Sampling period 1 was last September 2016, Sampling period 2 was last January 2017, sampling period 3 was last April 2017 while sampling period 4 was last August 2017.

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Upon collection on field, the samples of vegetables were placed in sterile containers and transported to the laboratory and processed, where leaves and roots from the lettuce vegetable were separated. Two sets from these samples were prepared: the first set was analyzed without previous rinsing, whereas the second set was rinsed in tap water before being subjected to bacteriological analysis. The rinse from the vegetable using tap water was also subjected to analysis. After processing, the samples were then homogenized. Ten (10) g of each soil sample was collected and weighed. Serial dilution was done using a previously prepared sterile phosphate buffer, and mixed for 1 min in a blender. Soil samples were then serially diluted before being plated out in a microbiological media for bacteriological examination and isolation of pure cultures of bacteria.

2.3 Bacteriological Examination Water Fecal (FC) as well as Total Coliform (TC) were enumerated by the membrane filter technique. The diluted samples (1.0, 0.1 and 0.01 µL) were then filtered through sterile membrane filters (with pore size 0.045 µm, with a diameter of 47 mm). The culture medium that was used for enumeration of FC and TC was M-endo broth. Vegetables and Soils The bacteriological examination of vegetables and soils were performed by the three-tube fermentation technique as described in the Standard Methods for the Examination of Water and Wastewater (APHA 2005).

2.4 PCR Amplification of E. coli Genomic DNA The Freeze-Thaw method was used for the isolation of crude DNA samples, and were used as a template for PCR analyses. PCR amplification of bacterial DNA was done using the designed wecA gene primers, these primers are used for the specific detection of E. coli DNA. The set of primers used were the following: forward primer, GGT GTT CGG CAA GCT TTA TCT CAG and reverse primer, GGT TAA ATT GGG GCT GCC ACC ACG. The PCR procedures used in this study were patterned after the methods described by Bayardelle and Zafarulla (2002). This wecA gene is important for enterobacterial common antigen (ECA) and is specific for detecting E. coli (Debroy et al. 2006).

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2.5 Identification of Bacterial Isolates and Phylogenetic Analyses Identification of bacterial isolates was done by submitting pure cultures for 16S ribosomal RNA gene sequencing to Macrogen, South Korea. Using the MEGA X (v. 10.1) (Debroy et al. 2006) software, the 16S rRNA gene sequences were then aligned using the BLAST software v. 2.6.1 (Tamura et al. 2013) of the National Center for Biotechnology Information (NCBI), in comparison with 16S ribosomal RNA sequences (Bacteria and Archaea) Database.

2.6 Physicochemical Properties of Soil and Water from La Trinidad, Benguet Data on the physicochemical and bacteriological characteristics of Balili River water were obtained from the Environment Management Bureau of the Cordillera Administrative Region, (EMB-CAR) Ambient Monitoring and Technical Services Section of the Department of Environment and Natural Resources (DENR), Baguio City, Philippines.

2.7 Water Quality Index Computation Water Quality Index (WQI) was computed using the physicochemical parameters and bacteriological data obtained from the EMB-DENR from the period of 2012– 2017. The formula for computation for Water Quality Index with missing parameters was used (Srivastava and Kumar 2013). WQIMP =



WYQY



WY

(1)

where Y = available parameters QY = q-values of available parameters WY = weighting factors of available parameters. The above Eq. 1, is actually based from a standard equation for computing the Water Quality Index given by the National Science Foundation (Srivastava and Kumar 2013; Gupta et al. 2017) as shown in Eq. 2: WQI =



WXQX

(2)

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WQI = WBOD QBOD + WDO QDO + WpH QpH + WPHOSPHATE QPHOSPHATE + WNITRATE QNITRATE + WFC QFC + WTDS QTDS + WTEMP .QTEMP. + WTURBIDITY QTURBIDITY where WX = weight factors of the water quality parameters QX = q-value of the water quality parameters X = water quality parameters. The average values of the above parameters (BOD, DO, pH, phosphate, nitrate, fecal coliform, total dissolved solids or TDS, temperature and turbidity) were computed per year for the six-year monitoring period (2012–2017) and values for WQIMP were computed for each year using the Microsoft Excel Program.

3 Results and Discussion 3.1 Water Quality Index of the Balili River In the Philippines, there is no formulated and developed guidelines on water quality index. Only water quality standards given by the Department of Environment and Natural Resources (DENR) are used as a basis for acceptable values of physicochemical parameters. This was issued by DENR in an Administrative Order 34 series of 1990, or DAO, 2016 (Ichwana et al. 2016). This administrative order stipulates the water quality criteria or standards based on beneficial usage of the body of water or classification of freshwater and marine waters, which provides the basis for determining the suitability of water bodies for specific use (DAO 2016). Results of WQI computation and interpretation of river water quality for the Balili River for the six-year monitoring period using the mathematical formula in Eq. 2 are shown in Table 2. The data indicate that the water quality of the Balili River is really Table 2 Computed water quality index with missing parameters for Balili River for the period of 2012–2017 using the physicochemical and bacteriological data from DENR-EMB Year

WQIMP

Water quality

2012

28.95

Bad

2013

22.07

Very bad

2014

20.58

Very bad

2015

28.06

Bad

2016

27.26

Bad

2017

23.21

Very bad

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bad to very bad confirming that the said river is polluted and therefore not suited to be used for irrigation of vegetable farmlands. These results are in contrast to the study done in the rivers of Palawan, Philippines wherein the study also computed the WQI of the rivers using the WQI developed by the Canadian Council of Ministers of the Environment (Martinico-Perez et al. 2019). The study done in the rivers of Palawan described the rivers as having good to excellent water quality and thus can be recommended to be utilized for irrigation in agriculture and other purposes (Class D, classification) (Ichwana et al. 2016). After complete treatment, there were five (5) river monitoring stations that were found to have good to excellent ratings and can be considered as sources of drinking water (Class A, classification of DAO 2016; CCME 2001).

3.2 Bacteriological Examination Overall, the results of our bacteriological analyses indicated that the level of fecal coliform and total coliform in the three sampling sites of this study were exceedingly high with values that ranged from >6000 to 13,000 MPN/100 mL for the four sampling periods. On average, the highest number of both total coliform and fecal coliform were observed during the fourth sampling period in May 2017 (Figs. 2 and 3). It was also observed that the water samples from the rinse of the vegetables harbored a large number of coliform bacteria, having as much as 1500 MPN/100 mL for total coliform and 2500 MPN/100 mL for fecal coliform. Accordingly, the highest coliform levels in the samples were also detected during the fourth sampling period. After the three phases of presumptive, confirmatory, and completed tests in the vegetable and soil samples, the presence of coliform bacteria was confirmed. As shown in Fig. 2, most of the total coliform of the soil and unrinsed vegetable samples were above the detection limit of the method used (more than 2400 coliforms per gram) whereas the total coliform of rinsed samples does not appear to be far from these values. In general, the results show that total coliform and fecal coliform were also highest during the fourth sampling period (May 2017). The high coliform values can be attributed to anthropogenic activities since there is a large number of residences along the river. And unfortunately, these residences directly discharge their sewerage wastes to the river. Also, as a means of livelihood, people living in the area engage in backyard piggeries that discharge animal wastes directly to the river (Hent 2019).

3.3 Heavy Metal Content of Soil and Water The computed mean values of the five types of heavy metal studied and monitored for six (6) years by DENR-EMB for Balili River surface water were compared with the

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Fig. 1 Average total coliform counts in Balili River wastewater, soil samples irrigated by the river, and the vegetable samples during the four sampling periods. SW = water samples from the three sites, VW = tap water, S = soil samples from the three sites, V = unrinsed vegetable samples, VW = rinsed vegetable samples

Aquatic Toxicity Reference Value (TRV) (Table 3). The TRV is defined as the dose above which significant ecological effects may occur to wildlife species after prolonged dietary exposure and below this value, it is expected that relevant effects will not occur (Cornell and Schwertmann 2003). The mean values of copper, cadmium, lead, and mercury exceeded the TRV, while the mean value of zinc (117.75 µg/L) is very close to the TRV value (120 µg/L). The computed mean values of the metal content follow this decreasing order: Zn > Cd > Cu > Pb > Hg. This means that the Balili River wastewater has high content for all metals analyzed (except for zinc) since the values obtained by EMB-CAR greatly exceeded the TRVs for cadmium, copper, lead and mercury. The high heavy metal content may be attributed to domestic sewage disposal and agricultural runoff from the vegetable and flower gardens of the area (Burris et al. 2001).

3.4 Isolation and Identification of Bacteria and PCR Analyses In this study, a total of eight morphologically distinct bacteria were successfully isolated from the river wastewater, agricultural soils, and vegetable samples. Results

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Fig. 2 Average fecal coliform counts in Balili River wastewater, soil samples irrigated by the river, and the vegetable samples during the four sampling periods. SW = water samples from the three sites, VW = tap water, S = soil samples from the three sites, V = unrinsed vegetable samples, VW = rinsed vegetable samples

Fig. 3 Phylogenetic tree constructed using the MEGA software version X. At branched points, bootstrap values of >50% are indicated. Isolates in the present study were shown in bold, whereas the gene sequence from Acidoplasma aeolicum was used as the outgroup. The tree was constructed using MEGA software version X

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Table 3 Comparison of the means of the heavy metal content of Balili River wastewater analyzed by DENR, EMB-CAR for the period of 2012–2017 with the aquatic toxicity reference values Heavy metal

Mean value for 6 years (µg/L)

Copper (Cu)

20.67

6.54

Cadmium (Cd)

111.55

0.66

Lead (PB)

13.00

1.32

Zinc (Zn)

117.75

120

Mercury ( Hg)

0.778

0.12

* Source:

Aquatic toxicity reference value (µg/L)*

Burris et al. (2001)

of Polymerase Chain Reaction (PCR) revealed that the wecA gene primers used were able to detect enterobacterial DNA in the isolates. All of the isolates analyzed after PCR showed the presence of bands, with the products of the amplification at 763 bp (data not shown). Enterobacteriaceae is a group of Gram-negative bacteria which are rod-shaped and are usually found in the gastrointestinal tract of most animals. Furthermore, comparison to sequence databases (Table 3) shows that the bacterial isolates exhibited up to 97–99% similarity with Enterobacter hormaechei, Pantoea dispersa, E. coli, Enterobacter ludwigii, and Klebsiella variicola, all of which belongs to the family Enterobacteriaceae. However, among the isolates of this present study, no strain of E. coli O157:H7 was detected. Instead, a different strain of the bacterium that was not further characterized was found. Water, Soil, and Vegetable Two types of bacteria, isolated from Balili River wastewater were found to be closely related to the bacteria Enterobacter hormaechei and Enterobacter ludwigii. Members of the genus Enterobacter are known to cause hospital-acquired infections, such as lung, urinary tract, intra-abdominal, meningeal, and surgical site infections. On one hand, Enterobacter hormaechei is a species of bacterium that belongs to the Enterobacter cloacae complex. Bacteria under this group are mostly found in clinical specimens and are known to cause bloodstream infections like sepsis and potent producers of a wide spectrum of beta-lactamases (Islam et al. 2015). On the other hand, one study reported one case of Enterobacter ludwigii infection that was acquired through surgery (Townsend et al. 2008). Disturbingly, this study also found that this species is resistant to the drug carbapenem, indicating that it could possibly be a multiple drug-resistant bacterium. With this said, the researchers of the present study speculate about the possibility of the presence of hospital wastes in Balili River wastewater. It has been suggested that soils in tropical environments are efficient in supporting the growth of coliform bacteria (Khajuria et al. 2013). A study showed a higher number of isolated E. coli in organic farms as compared to their conventional counterparts (Byappanahally and Fujioka 1998). This is possible enough because some of the sampling sites in the present study were once conventional farms that have been converted into organic farms.

Environmental and Microbiological Assessment of Small …

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Table 4 Comparison of isolated coliform bacteria from Balili river wastewater, soil samples irrigated by the river, and the vegetable samples to sequence databases Sample code

Closest relative

GenBank accession number

Similarity (%)

SWP2a

Enterobacter hormaechei

CP017179.1

99

SWG1a

Pantoea dispersa

AB907780.1

99

VWPRNb

Enterobacter ludwigii

CP017279.1

99

SPP4c

Enterobacter hormaechei

CP017179.1

99

SG4c

Escherichia coli

KC985144.1

97

VP1d

Klebsiella variicola

CP010523.2

99

VG3d

Pantoea dispersa

AB907780.1

99

VRG2e

Pantoea dispersa

AB907780.1

99

a Isolated

from Balili River Wastewater from the tap water from the rinse of vegetable samples c Isolated from the soils d Isolated from the unrinsed vegetable samples e Isolated from the rinsed vegetable samples b Isolated

This is because organic fertilizers contain manure that may have pathogenic bacteria (Mukherjee et al. 2004) hence, readily explaining the detection of higher coliform levels in these areas. While the genera Enterobacter and Escherichia, comprise most of the known soilderived coliforms (Johannesen et al. 2004), their presence in the agricultural soils irrigated with Balili River wastewater should be considered significant. Although no Shiga-toxin producing strain of E. coli (E. coli O157:H7) was isolated in the present study, the presence of E. coli alone stresses the danger of directly introducing these pathogens from wastewater to these agricultural fields. Often misidentified as Klebsiella pneumoniae, the bacterium Klebsiella variicola, which has been obtained from the vegetable samples in the present study, is an enteric pathogen that is frequently isolated from clinical specimens and various species of plants (Heaton and Jones 2007). Members of the genus Klebsiella exhibit a wide array of virulence factors such as the presence of bacterial capsule and metabolic versatility, allowing them to thrive in large spectra of environments and demonstrate infective potential. Like K. pneumoniae, K. variicola is known to cause bloodstream infections in humans and cattle. Alarmingly though, Klebsiella variicola was found to cause higher mortality rates in patients as compared to the former (Rosenblatt et al. 2004). Another bacterium isolated in the present study is Pantoea dispersa, one of the very few species of the genus Pantoea that is capable of growing up to 41 °C. While members of the genus Pantoea have been rarely reported to cause infections in humans, they are known to be plant pathogens, specifically causing leaf spots in okra Abelmoschus esculentum (Maatallah et al. 2014; Mehar et al. 2013). For instance, the bacterium Pantoea agglomerans has been found to colonize and aggregate with the bacterium Salmonella enterica on the phylloplane (leaf surface) of cilantro (Falkow

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R. M. Gutierrez and V. Montalla

et al. 2006). Because of their ability to degrade the leaf surface and increase the release of plant nutrients, the presence of plant pathogens like Pantoea per se increases the likelihood of the entry of enteric pathogens like Salmonella (Brandl and Mandrell 2002). Several scientific studies have suggested that bacterial contamination of vegetables can happen at any time during production but the irrigation using wastewater, with the edible parts of the plants being directly applied, has the greatest potential to cause contamination (Wells and Butterfield 1999; Pescod 1992). The general morphology of the lettuce leaves, having much of their surface exposed to the irrigation water and soil, as well as the method of irrigation both account for the high total coliform and fecal coliform in the samples. According to a study, the contamination of soil and plants grown in substrates irrigated with coliform-contaminated water is also highly dependent on the survival capabilities of the pathogens (Buck et al. 2003). Normally, bacterial pathogens survive for two months but this can extend up to five months given the favorable moist condition and protection against the heat from the sun (Gerba and Smith 2005). In this study, vegetable samples, as well as the water and soils, were collected on the same day or few days after wastewater irrigation. Hence, this account could readily explain the detection of fecal coliforms in the samples. However, based on the results of the DNA analysis shown in this present study, the researchers were intrigued by the presence of Enterobacter hormaechei on both river wastewater samples and soil samples and as well as the occurrence of Pantoea dispersa on both river wastewater samples and vegetable samples. As alluded to earlier, soil and vegetable samples in this study were collected on the same day or probably only a day or two after irrigation. Interestingly, Pantoea dispersa was also detected in the rinsed vegetable samples. With this, the researchers suspect that the bacteria isolated from the soil samples and vegetable samples could be a combination of recently established bacteria and naturally occurring soil as well as plant bacteria. Furthermore, the researchers of this present study were also taking into account the possibility of internalization of bacteria. Studies about the adherence of pathogens to plant surfaces and modes of internalization are not new to the scientific field. A number of studies have already reported that there are some enteric pathogens that have the ability to survive on the phylloplane, and may have the possibility of being taken up the plant root systems and gain entry to the edible portions of plants like lettuce, apples, and tomatoes (Johannesen et al. 2004; Islam et al. 2002; Wachete et al. 2004; Seo and Frank 1999; Solomon et al. 2002; Burnett et al. 2000). Aside from that, these pathogens may also enter through plant structures like lenticels and wounds (Guo et al. 2001; Janisiewicz et al. 1999). The phylogenetic tree that was constructed to establish the evolutionary similarity on the 16s rDNA sequences of the isolates and those in Genbank databases is shown in Fig. 3. Using the MEGA Software, Isolates VG3, SWG1, and VRG2 were shown to be very similar to Pantoea dispersa while isolate VWPRN was closely grouped with both Pantoea agglomerans and Pantoea dispersa. Isolates SWP2, and SPP4 were closely associated with Samonella enterica while isolate S64 had similar sequences with Shigella dysenteriae. Isolate VP1 was the only isolate with similar sequences

Environmental and Microbiological Assessment of Small …

13

with Kleibsiella aerogenes (Fig. 3). This clustering confirmed the alignments that was obtained using the BLAST algorithm.

4 Conclusion Because of the increasing awareness of the health benefits of natural and organic products, the consumption of fresh vegetables on the agricultural market is on the rise. The findings of this study stress the danger of vegetable crops and soil contamination with pathogenic bacteria because of the use of the untreated Balili River wastewater for irrigation. Being consumed raw, lettuce, Lactuca sativa is a very high-risk crop for coliform contamination. Bacteria, such as Enterobacter, Pantoea, Escherichia, and Klebsiella, all of which have been isolated in this study are just a few examples of opportunistic pathogens in the mammalian gut, and they were confirmed to be present in the river wastewater, vegetable samples, and agricultural soils through isolation techniques and PCR analyses. Moreover, high numbers of fecal coliforms were determined and these fecal coliforms were isolated in this study from river water, vegetables, and soil. Computation of the Water Quality Index of Balili River done in this study using the physicochemical and bacteriological characteristics of the river for six years gave index values or WQI values ranging from 21 to 28. These WQI values mean that the Balili River is actually in very bad condition and therefore very much polluted. This study therefore, confirms the report that the water quality of Balili River has worsened through the years. Acknowledgements This research project was funded by the Enhanced Creative Work and Research Grants (ECWRG 2015-2-030) of the University of the Philippines System. Special thank you goes to the owners of the vegetable farms in La Trinidad Benguet. The authors are also grateful to the Benguet State University (BSU), and the Local Government Unit in the area of study. The authors would also like to acknowledge the Environmental Management Bureau of the Department of Environment and Natural Resources (EMB-DENR), Cordillera Administrative Region (CAR) for providing the data on the physicochemical properties of Balili River. The University of the Philippines Baguio is hereby acknowledged.

References American Public Health Association (APHA) (2005) Standard methods for the examination of water and waste water, 21st edn. USA Port City Press, Washington DC Bayardelle P, Zafarullah M (2002) Development of oligonucleotide primers for the specific PCRbased detection of the most frequent Enterobacteriaceae species DNA using wec gene templates. Can J Microbiol 48:113–122 Bengao ACA, Cababat RAP, Anacin CG, Azarcon DEJ, Janeo MA, Lubrica NVA (2015) Physicochemical and biological analysis of St. Joseph Waterway. https://www.researchgate.net/ publication/312280265. Last accessed 2019/6/21

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Brandl MT, Mandrell RE (2002) Fitness of Salmonella enterica serovar Thompson in the cilantro phyllosphere. Appl Environ Microbiol 68:3614–3621 Buck JW, Wallcot RR, Beuchat LR (2003) Recent trends in microbiological safety of fruits and vegetables. Plant Health Prog. https://www.plantmanagementnetwork.org/php/2003.asp Burnett SL, Chen SL, Chen J, Beuchat LR (2000) Attachment of Escherichia coli O157:H7 to the surfaces and internal structures of apples as detected by confocal scanning laser microscopy. Appl Environ Microbiol 66:4679–4687 Burris J, Hoff D, Charters W, Russom CL, Ells S, Woodbury L (2001) Development of toxicity reference values for ecological soil screening levels (EC)-SSLS) for terrestrial wildlife. In: Presented at 22nd annual SETAC meeting, Baltimore, MD Byappanahally MN, Fujioka RS (1998) Evidence that tropical soil environment can support the growth of Escherichia coli. Water Sci Technol 38:171–174 Canadian Council of Ministers of the Environment (CCME) (2001) Canadian water quality guidelines for the protection of aquatic life: CCME Water Quality Index 1.0, Technical Report. In: Canadian environmental quality guidelines, 1999, Canadian Council of Ministers of the Environment, Winnipeg CLUP (2019) Comprehensive Land Use Plan (CLUP) of Barangay Balili 2000–2011. Barangay Balili physical and socio economic profile. https://latrinidad.gov.ph/wp-content/uploads/2015/ 08/BaliliProfile.pdf. Last accessed 2019/7/11 Cornell RM, Schwertmann U (2003) The iron oxides, 2nd edn. Wiley-VCH, Weinheim Debroy C, Roy C, Roberts E (2006) Screening petting zoo animals for the presence of potentially pathogenic Escherchia coli. J Vet Diagn Invest 18:597–600 Department of Environment and Natural Resources Administrative Order (DAO) No. 2016-08 (2016) Water quality guidelines and general effluent standards, 2016. Issued by DENR, Republic of the Philippines. https://www.denr.gov.ph. Accessed 2019/6/24 Falkow S, Rosenburg E, Schleifer K, Stackebrandt E (2006) The prokaryotes: vol. 6: proteobacteria: gamma subclass AUB. Springer Science & Business Media, pp 22–24 Gerba CP, Smith JE (2005) Sources of pathogenic microorganisms and their fate during land application of wastes. J Environ Qual 34(1):42–48 Guo X, Chen J, Brackett RE, Beuchat LR (2001) Survival of Salmonellae on and in tomato plants from the time of inoculation at flowering and early stages of fruit development through fruit ripening. Appl Environ Microbiol 67:4760–4764 Gupta N, Pandey P, Hussain J (2017) Effect of physicochemical and biological parameters on the quality of river water of Narmada, Madhya Pradesh, India. Water Sci 31:11–23 Heaton JC, Jones K (2007) Microbial contamination of fruit and vegetables and the behaviour of entero pathogens in the phyllosphere: a review. J Appl Microbiol Hent (2019) Balili River water quality worsens. https://baguioheraldexpressonline.com/balili-riverwater-quality-worsens/. Last accessed 2019/6/4 Ichwana I, Syahrul S, Nelly W (2016) Water quality index by using national sanitation foundationwater quality index (NSF-WQI) method at Krueng Tamiang Aceh. In: Proceeding of the first international conference on technology, innovation and society Islam SM, Doyle MP, Phatak SC, Millner P, Jiang X (2004) Persistence of enterohemorrhagic Escherichia coli O157:H7 in soil and on leaf lettuce and parsley grown in fields treated with contaminated manure composts or irrigation water. J Food Protect 67(7):1365–1370 Islam MS, Ahmed MK, Raknuzzaman M, Habibullah-Al-Mamun M, Islam MK (2015) Heavy metal pollution in surface water and sediment: a preliminary assessment of an urban river in a developing country. Ecol Ind 48:282–291 Janisiewicz WJ, Conway WS, Brown MW, Sapers GM, Fratamicor P, Buchanan RL (1999) Fate of Escherichia coli O157:H7 on fresh-cut apple tissue and its potential for transmission by fruit flies. Appl Environ Microbiol 65:1–5 Johannesen GS, Froseth RB, Solemdal L, Jarp J, Wateson Y, Rorvik LM (2004) Influence of bovine manure as fertilizer on the bacteriological quality of organic iceberg lettuce. J Appl Microbiol 96(4):787–794

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Khajuria A, Praharaj AK, Grover N, Kuma M (2013) First report of an Enterobacter ludwigii isolate coharboring NDM-1 and OXA-48 carbapenemases. Antimicrob Agents Chemother 57(105):189– 519 Martinico-Perez MFG, Jara JP, Madrono P, Cabrestante J (2019) Evaluation of water quality of major rivers in Palawan, Philippines using physico-chemical parameters and water quality index. https://psa.gov.ph. Last accessed 2019/7/25 Maatallah M, Vading M, Kabir MH, Bakhrou A, Kalin M, Naucler P, Brisse S, Giske C (2014) Klebsiella variicola is a frequent cause of bloodstream infection in the stockholm area, and associated with higher mortality compared to K. pneumoniae. PLoS One 26:9 Mehar V, Yadav D, Sanghvi J, Gupta N, Singh K (2013) Pantoea dispersa: an unusual cause of neonatal sepsis. Braz J Infect Dis 17:6 Mukherjee A, Speh D, Dyck E, Diez-Gonzalez F (2004) Preharvest evaluation of coliforms, Escherichia coli, Salmonella, and Escherichia coli O157:H7 in organic and conventional produce grown by Minnesota farmers. J Food Prot 67(5):894–900 Pescod MB (1992) Wastewater treatment and use in agriculture. In: FAO irrigation and drainage paper 47. Rome Rosenblatt M, Martinez L, Silva J, Romer EM (2004) Klebsiella variicola, a novel species with clinical and plant-associated isolates. Syst Appl Microbiol 27:27–35 Seo KH, Frank JF (1999) Attachment of Escherichia coli O157:H7 to lettuce leaf surfaces and bacterial viability in response to chlorine treatment as demonstrated by using confocal scanning laser microscopy. J Food Prot 62:3–9 Solomon EB, Yaron S, Matthews KR (2002) Transmission of Escherichia coli O157:H7 from contaminated manure and irrigation water to lettuce plant tissue and its subsequent internalisation. Appl Environ Microbiol 68:397–400 Srivastava G, Kumar P (2013) Water quality index with missing parameters. Indian J Res Eng Technol 2:609–614 Tamura K, Stetcher G, Peterson D, Filipski A, Kumar S (2013) MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol 30:2725–2729 Townsend S, Hurelli E, Barro JC, Carillo CL, Forsythe SJ (2008) Characterization of an extendedspectrum betalactamase Enterobacter hormaechei nosocomial outbreak, and other Enterobacter hormaechei misidentified as Cronobacter (Enterobacter) sakazakii. Microbiology 154:3659–3667 Wachete MR, Whitehand LC, Mandrell RE (2002) Association of Escherichia coli O157:H7 with preharvest leaf lettuce upon exposure to contaminated irrigation water. J Food Prot 65:18–25 Wells JM, Butterfield JE (1999) Incidence of Salmonella on fresh fruits and vegetables affected by fungal rots or physical injury. Plant Dis 83:722–726 World Health Organization (WHO) (1976) The work of WHO, 1975: annual report of the DirectorGeneral to the world health assembly and to the United Nations

The Effects of Sea Level Rise on Salinity and Tidal Flooding Patterns in the Guadiana Estuary Lara Mills, João Janeiro, and Flávio Martins

Abstract Sea level rise is a worldwide concern as a high percentage of the population accommodate coastal areas. The focus of this study is the impact of sea level rise in the Guadiana Estuary, an estuary in the Iberian Peninsula formed at the interface of the Guadiana River and the Gulf of Cadiz. Estuaries will be impacted by sea level rise as these transitional environments host highly diverse and complex marine ecosystems. Major consequences of sea level rise are the intrusion of salt from the sea into fresh water and an increase in flooding area. As the physical, chemical and biological components of estuaries are sensitive to changes in salinity, the purpose of this study is to further evaluate salt intrusion in the Guadiana Estuary caused by sea level rise. Hydrodynamics of the Guadiana Estuary were simulated in a twodimensional numerical model with the MOHID water modeling system. A previously developed hydrodynamic model was implemented to further examine the evolution of salinity transport in the estuary in response to sea level rise. Varying tidal amplitudes, freshwater discharge from the Guadiana River and bathymetries of the estuary were incorporated in the model to fully evaluate the impacts of sea level rise on salinity transport and flooding areas of the estuary. Results show an overall increase in salinity and land inundation in the estuary in response to sea level rise. Keywords Guadiana Estuary · Numerical model · Sea level rise

L. Mills (B) · J. Janeiro · F. Martins Centro de Investigação Marinha e Ambiental (CIMA), University of Algarve, Faro, Portugal e-mail: [email protected] F. Martins Instituto Superior de Engenharia (ISE), University of Algarve, Faro, Portugal © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_2

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1 Introduction 1.1 Sea Level Change Global mean sea level is rising at a rate of approximately 3.2 mm per year (Church et al. 2013). According to the most recent report from the Intergovernmental Panel on Climate Change (IPCC), the rate of global mean sea level rise has been increasing over the last two centuries and continues to accelerate. Sea level rise will affect coastal areas, which should be a concern considering 10% of the world’s population live within 10 m elevation of the present sea level (Carrasco et al. 2016). Direct consequences of sea level rise on coastal areas include an increase in flooding area, an increase in erosion, an increase in salinity and changes in ecosystems (Nicholls et al. 2011). Of relevance to the present study is the impact of sea level rise on estuaries, where rivers intersect with sea and freshwater mixes with saltwater.

1.2 Effects of Sea Level Rise on Estuaries Salt intrusion is a direct consequence of sea level rise in estuaries (McLean et al. 2001). Estuarine circulation is mainly driven by freshwater flow, tides and density differences (Garel et al. 2009). A study by Chua and Xu (2014) found a stronger longitudinal salinity gradient in estuaries due to sea level rise, which in turn drives a stronger gravitational circulation. The increase in salinity will cause the water to become denser and thus, increase the stratification of the water column. Changes in estuarine stratification and circulation will further cause oxygen depletion (Hong and Shen 2012). These effects are detrimental to ecosystem services and marine habitat as estuaries hold highly diverse and complex ecosystems (Sampath et al. 2015). The objective of this study is to evaluate the evolution of hydrodynamics and salinity transport in response to various sea level rise scenarios in a major estuary in the Iberian Peninsula, the Guadiana Estuary.

1.3 Physical Characteristics of the Guadiana Estuary The Guadiana Estuary is formed at the interface of the Guadiana River and the Gulf of Cadiz. The head of the Guadiana River begins in Spain and extends 810 km south toward the Gulf of Cadiz (Delgado et al. 2012). From its mouth in front of Vila Real de Santo António, the Guadiana Estuary extends 80 km north to its tidal limit near Mértola, Portugal. Because of its narrow width and moderate depth the Guadiana Estuary is considered a rock-bound estuary where the volume of water entering the estuary during the flood tide is larger than the freshwater discharge (Garel 2017). Tidal and riverine processes are the dominant forces in the estuary and the action

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of the waves is considered negligible (Garel et al. 2009). The Guadiana Estuary is characterized by a semidiurnal mesotidal regime (Sampath and Boski 2016) with an average neap tidal range of 1.28 m and an average spring tidal range of 2.56 m (Garel et al. 2009). The western margin of the estuary is characterized by a salt marsh sheltered by a littoral sand spit that drains into the estuarine channel (Garel et al. 2009). The eastern margin is composed of barrier islands and sand spits separated by extensive salt marshes that drain to the Gulf of Cadiz through a tidal inlet (Garel et al. 2009). The flow rate from the Guadiana River varies drastically from less than 10–4660 m3 /s. The construction of over 100 dams for water storage and irrigation since the 1950s has strongly reduced the freshwater flow rate (Garel et al. 2009). Of importance is the Alqueva Dam located 60 km upstream the head of the estuary (Garel and D’Alimonte 2017). This large reservoir was completed in 2002 and since then the freshwater flow into the estuary has been reduced from a yearly average of 143–16 m3 /s (Garel et al. 2009). The reduction in flow rate also has an impact on residence time, the time it takes a particle to exit the system (Oliveira et al. 2006). The residence time of salinity for a river discharge of 8 m3 /s ranges between 6 and 60 days, but when the river flow is high the residence time ranges from a few hours to 9 days (Oliveira et al. 2006). Thus, the discharge of the Guadiana River has a large effect on the horizontal distribution of salinity in the estuary. The Guadiana Estuary is well-mixed when the freshwater discharge from the Guadiana River is low and is partially stratified for higher discharges. When there is a higher tidal amplitude and lower discharge from the Guadiana River, tidal processes control the water circulation of the estuary and the estuary becomes well-mixed (Garel and D’Alimonte 2017). The estuary is weakly stratified when it is neap tide and the river discharge is low (Garel and D’Alimonte 2017). In the latter case, density-driven processes control the mixing of the estuary. The water column is stratified only under extreme conditions (Basos 2013).

2 Methods 2.1 MOHID Hydrodynamics and water properties of the Guadiana Estuary were simulated with the MOHID water modeling system in response to different scenarios of sea level rise. MOHID is programmed in ANSI FORTRAN 95 in order to produce objectoriented models integrating hydrodynamic processes for different marine systems (MARETEC 2017). The present study used MOHID in 2D, as the Guadiana Estuary is classified as a well-mixed estuary (Garel and D’Alimonte 2017). The Guadiana Estuary can become stratified, but only under freshwater flows of 1000 m3 /s (Fortunato et al. 2002). The present study ran simulations for freshwater flows of 10 and 100 m3 /s as these are typical conditions of the system after the closure of the Alqueva Dam. This further justifies the use of a two-dimensional model, allowing for long

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(one to two month) simulations, which would have had prohibitive computational costs with a three-dimensional model. MOHID solves Navier–Stokes equations with the hydrostatic approximation, using the finite volume method over a generic geometric grid. In this work, hydrodynamic and Eulerian transport modules were used. The finite volume method allows for the transport equations to be applied to the entire cell volume at specific points in discrete time (Neves et al. 2000). Of relevance are the results of a study by Mills et al. (2019) as the present study is a continuation of the work and methodologies used to simulate hydrodynamics and evolution of salt transport with respect to sea level rise. These authors used MOHID in 2D to simulate the hydrodynamics of the Guadiana Estuary considering different sea level rise scenarios up to the year 2100 along with varying freshwater flow rates of the Guadiana River (Mills et al. 2019). Results of the model demonstrated an overall increase in salinity in the estuary as well as flooding area around the estuary with respect to sea level rise. Sea level rise led to a reduction in velocity in the main channel, most likely due to an increase in depth and water volume (Mills et al. 2019). The model considered only the present bathymetry as well as a tidal amplitude equivalent to the average tidal amplitudes. The horizontal distribution of salinity in estuaries is dependent on several factors. One major variable is the balance between spring and neap tidal cycles and freshwater flow (Vargas et al. 2017). It is thus the aim of this work to expand upon the methodologies of Mills et al. (2019) to determine how the horizontal distribution of salinity varies between spring and neap tide as well as across a bathymetry that will allow flooding of the surrounding marshes.

2.2 Model Setup The present study implements the same general setup of the model by Mills et al. (2019) and uses the same Cartesian computational grid of 1400 × 350 cells with a resolution of 30 m. This computational mesh was chosen as it provides the most appropriate spatial resolution without incurring excessive calculation time. The calculation time for each simulation is high, especially when the river discharge is low. Two months of simulation time is required due to the high residence time when the freshwater flow rate is low (Oliveira et al. 2006). The hydrodynamic MOHID model of the Guadiana Estuary has been previously validated, calibrated and used in several studies (Morais et al. 2012; Lopes et al. 2003). The present model consists of two separate bathymetries: (1) a bathymetry in which coastal management strategies are implemented to keep the coastline as it is (Fig. 1) and (2) a bathymetry that allows for geomorphological changes caused by sea level rise and thus allows flooding around the estuary (Fig. 2). The first bathymetry is the current bathymetry of the Guadiana Estuary and was computed by triangular interpolation of measured bathymetric data on the Cartesian grid of 1400 × 350 cells (Mills et al. 2019). The second bathymetry was computed by Sampath et al. (2011) in a behavior-oriented model. This bathymetry allows the surrounding saltmarshes and low-lying areas of

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Fig. 1 Bathymetry of the Guadiana Estuary corresponding to a maintained coastline on a mesh of 1400 × 350 cells with a resolution of 30 m (Mills et al. 2019)

Fig. 2 Bathymetry of the Guadiana Estuary corresponding to an unmaintained coastline (1400 × 350 cells with a resolution of 30 m). The grey space is land (Sampath et al. 2011)

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the estuary to be flooded. It was solved based on the rate of sea level change, accommodation space for the deposition of sediments and vertical accretion of sediments dependent on inundation (Sampath et al. 2011). Various sea level rise cases were chosen based on future sea level rise projections from the 5th assessment report by the IPCC. The 5th assessment report includes sea level rise forecasts up to the year 2100 for different Representative Concentration Pathways (RCP), more commonly known as greenhouse gas emissions (Church et al. 2013). The study by Mills et al. (2019) considered values forecasted by the IPCC for a case of low greenhouse gas emissions (RCP 4.5) and a case of very high greenhouse gas emissions (RCP 8.5). Values were chosen based on the average predictions for the years 2045–2065, 2081–2100 and 2100 for both RCP values. The values imposed in the model include a sea level rise of 0.24 m for 2040, 0.48 m for 2070 and 0.79 m for 2100. Two freshwater flow scenarios were chosen based on typical annual conditions of the freshwater flow rate from the Guadiana River and include: (1) a low discharge of 10 m3 /s representing the summer months when there is little rain and (2) a high discharge of 100 m3 /s for the remaining months when there is more rainfall. These two values for river discharge are appropriate as the freshwater flow has been strongly regulated since the closure of the Alqueva Dam, yielding a reduction in the seasonal variability of the river discharge (Quesada et al. 2019). Two tidal amplitudes were imposed in the model to examine how the dynamics of the estuary vary at spring tide and neap tide. Since this study did not aim at representing a specific period, a simple M2 tide was imposed. The amplitudes representing spring and neap tides are 1.28 m and 0.64 m, respectively.

3 Results 3.1 Time Series Locations Time series graphs of velocity, water elevation and salinity were produced along several locations of the estuary. The locations and nomenclature used for the time series analysis are shown in Fig. 3.

3.2 Temporal Evolution of Salinity Time series graphs were produced at each location shown in Fig. 3, allowing for an assessment of the evolution of salinity over two tidal cycles (approximately 24.48 h) for each sea level rise scenario. The average change in salinity every thirty years is summarized in Tables 1 and 2. The simulations for salt transport assume a salinity value of 0 for freshwater and a value of 36 for seawater.

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Fig. 3 Location and nomenclature for points used in the time series along the main channel (left) and saltmarshes (right)

3.3 Horizontal Distribution of Salinity The following section examines the horizontal distribution of salinity along with velocity direction at a time instant one hour before high tide. As can be seen in the time series results, changes in salinity throughout the scenarios of sea level rise vary for the present bathymetry, whereas results from the alternate bathymetry reveal a much more linear relationship between sea level rise and salinity. Thus, all horizontal distribution maps of salinity are shown for the present bathymetry, whereas only the present year compared with 2100 are shown for the bathymetry allowing flooding. High Freshwater Discharge at Neap Tide See Figs. 4 and 5. High Freshwater Discharge at Spring Tide See Figs. 6 and 7. Low Freshwater Discharge at Neap Tide See Figs. 8 and 9. Low Freshwater Discharge at Spring Tide See Figs. 10 and 11.

4 Discussion The results obtained from the MOHID model have shown the dynamics of the Guadiana Estuary to be complex, especially with respect to the tides. For the bathymetry

Marshes

Lower

Middle

Upper

0.003

0.008

−0.005

−0.049

−0.049

−0.010

−0.008

−0.013

−0.124

−0.148

391_96

350_80

304_27

0.046

0.063

446_109

−0.053

−0.051

381_147

313_153

392_23

0.000

0.000

558_103

0.000

0.000

0.000

0.000

1214_61

0.000

903_102

0.000

2040–2070

2070–2100

−0.002

−0.013

0.006

0.042

0.025

0.127

−0.028

0.000

0.000

0.000

0.000

0.176

0.190

0.492

0.255

0.369

1.098

1.030

0.005

0.000

0.000

0.000

Present–2040

0.410

0.437

0.768

0.550

0.718

1.195

1.191

0.014

0.000

0.000

0.000

2040–2070

2070–2100

0.710

0.766

0.989

0.565

0.901

1.437

1.494

0.032

0.056

0.000

0.000

−1.119

−0.970

−0.432

−0.152

−0.151

0.584

0.380

0.050

0.000

0.000

0.000

Present–2040

−1.272

−1.191

−0.609

−0.239

−0.235

0.174

0.083

0.012

0.000

0.000

0.000

2040–2070

Present bathymetry

Present–2040

1377_45

Estuary location

Spring tide

Present bathymetry

Flooding bathymetry

Neap tide

Change in salinity

Table 1 Change in salinity for a discharge of 100 m3 /s at neap tide and spring tide

−0.915

−0.928

−0.416

−0.186

−0.184

0.271

0.121

0.002

0.000

0.000

0.000

2070–2100

0.766

1.321

1.590

1.233

2.041

1.286

1.236

0.710

0.000

0.000

0.000

Present–2040

1.151

1.661

1.709

1.720

1.805

1.341

1.409

0.921

0.000

0.000

0.000

2040–2070

Flooding bathymetry 2070–2100

1.631

1.974

1.995

2.038

2.125

1.432

1.540

1.058

0.000

0.000

0.000

24 L. Mills et al.

Marshes

Lower

Middle

Upper

3.459

3.075

4.257

2.333

1.68

−3.822

−4.557

−3.002

−2.526

391_96

350_80

304_27

3.435

1.885

−3.422

−1.869

381_147

313_153

−3.834

9.093

−6.993

558_103

446_109

0.344

−0.170

392_23

0.003

−0.001

1214_61

903_102

0.000

0.000

2040–2070

2070–2100

3.371

2.535

1.774

2.959

2.773

1.143

1.321

0.136

0.375

0.009

0.001

1.596

1.551

1.503

0.839

1.685

0.649

1.072

1.025

0.081

0.001

0.000

Present–2040

1.875

1.85

1.584

1.821

1.571

0.653

1.174

1.288

0.106

0.002

0.000

2040–2070

2070–2100

1.489

1.456

0.057

−6.112

0.524

0.507

0.966

1.414

0.138

0.002

0.000

−2.228

−2.199

−2.287

−2.287

−2.464

−0.944

−1.311

−2.378

−0.342

−0.008

−0.001

Present–2040

−0.725

−0.682

−0.551

−0.551

−0.460

−0.096

−0.212

0.037

0.114

0.003

0.000

2040–2070

Present bathymetry

Present–2040

1377_45

Estuary location

Spring tide

Present bathymetry

Flooding bathymetry

Neap tide

Change in salinity

Table 2 Change in salinity for a discharge of 10 m3 /s at neap tide

0.618

0.754

1.239

1.239

1.368

0.774

1.114

2.02

0.547

0.017

0.002

2070–2100

1.488

1.352

2.164

5.989

2.201

0.606

1.061

1.739

0.311

0.01

0.001

Present–2040

1.227

1.16

0.83

0.684

0.834

0.482

0.784

1.636

0.36

0.013

0.002

2040–2070

Flooding bathymetry 2070–2100

2.126

1.849

1.273

0.186

1.409

0.777

1.165

2.494

0.527

0.018

0.002

The Effects of Sea Level Rise on Salinity and Tidal Flooding … 25

26

L. Mills et al.

Fig. 4 Velocity direction and salinity distribution for a high freshwater flow at neap tide for the present bathymetry for each scenario of sea level rise one hour before high tide

Fig. 5 Velocity direction and salinity distribution for a high freshwater flow at neap tide for the bathymetry allowing flooding one hour before high tide

Fig. 6 Velocity direction and salinity distribution for a high freshwater flow at spring tide for the present bathymetry one hour before high tide

Fig. 7 Velocity direction and salinity distribution a for a high freshwater flow at spring tide for the bathymetry allowing flooding

The Effects of Sea Level Rise on Salinity and Tidal Flooding …

27

Fig. 8 Velocity direction and salinity distribution for a low freshwater flow at neap tide for the present bathymetry at a time instant of one hour before high tide

Fig. 9 Velocity direction and salinity distribution for a low freshwater flow at neap tide for the bathymetry allowing flooding one hour before high tide

Fig. 10 Velocity direction and salinity distribution at all areas affected by the salinity front for a high freshwater flow at spring tide for the present bathymetry

allowing flooding, salinity increases almost linearly with respect to sea level rise for all cases of freshwater flow and for each tidal scenario. On the other hand, the present bathymetry that does not allow the coastline to be changed results in a complex dynamic where salinity values vary at different locations with respect to sea level rise.

28

L. Mills et al.

Fig. 11 Velocity direction and salinity distribution for a low freshwater flow at spring tide for the present bathymetry

The horizontal distribution of salinity intrusion depends on the freshwater flow and tidal scenario. Salinity extends horizontally and into the marshes when the freshwater flow from the Guadiana River is high. Salinity extends further upstream when the river discharge is low. These results are consistent with those from Mills et al. (2019) who also found larger changes in velocity and salinity in the marshes near Esteiro da Leziria (381_147) on the western margin of the estuary for higher freshwater discharges compared to changes in velocity and salinity in the main channel. Likewise, results from Mills et al. (Mills et al. 2019) found larger changes in velocity and salinity upstream the main channel for low freshwater discharges. The bathymetry allowing flooding allows salinity to extend much further into the marshes, with some areas reaching salinity values close to 36 by 2100. Velocity magnitude and direction also justifies several of the salinity results. Especially for the present bathymetry, decreases in salinity coincide with decreases in velocity. This result is attributed to the deepening of the channel from the increase in water volume, which has been known to occur in other shallow ebb-dominated estuaries (Friedrichs et al. 1990). Salinity increases with respect to mean sea level rise. Although the results from the present bathymetry may not portray an increase in salinity at the points chosen for the time series, salinity does increase on the Spanish side of the estuary as shown in Figs. 4 and 8. The bathymetry allowing flooding reveals a more linear relationship at each of the time series locations between sea level rise and salinity. Results obtained from this model are consistent with results from other numerical models who have found increases in estuarine salinity with respect to sea level rise (Chua and Xu 2014; Hong and Shen 2012; Vargas et al. 2017). The results of this model indicate that bathymetry, freshwater flow and spring-neap tide variability impact the horizontal distribution of salinity intrusion caused by sea level rise.

The Effects of Sea Level Rise on Salinity and Tidal Flooding …

29

5 Conclusion The two-dimensional MOHID water model produced hydrodynamic and salt transport maps to gain further insight into the future state of the Guadiana Estuary as it responds to climate change. The results of the model demonstrate that the estuary responds differently to sea level rise based on freshwater flow, bathymetry and tidal amplitude. All results portray an increase in salinity in response to sea level rise. The spatial distribution of salinity is dependent on the bathymetry as well as freshwater flow and tidal amplitude. When the freshwater flow is low in the spring and summer months, salinity extends upstream. When the freshwater flow is high due to rainfall in the winter, salinity extends westward and eastward into the marshes of Portugal and Spain. A limitation of this work is the use of a two-dimensional model instead of a three-dimensional model. This was justified because the estuary is well-mixed, but there may be some variability in the water column due to the complex dynamics of the system, especially at neap tide. Additionally, a simple M2 tide was used, excluding the effects of overtides identified by Garel and Cai (2018) and Quesada et al. (2019). Future studies should use a three-dimensional model with real tidal signals to allow for a more complete evaluation of the Guadiana Estuary and how it responds to climate change.

References Basos N (2013) GIS as a tool to aid pre- and post-processing of hydrodynamic models. Application to the Guadiana Estuary Faculdade de Ciências e Tecnologia e Instituto Superior de Engenharia GIS as a tool to aid pre- and post-processing of hydrodynamic models. Applica. Faro, Portugal Carrasco AR, Ferreira O, Roelvink D (2016) Coastal lagoons and rising sea level: a review. Earth Sci Rev 154:356–368. https://doi.org/10.1016/j.earscirev.2015.11.007 Chua VP, Xu M (2014) Impacts of sea-level rise on estuarine circulation: an idealized estuary and San Francisco Bay. J Mar Syst 139:58–67. https://doi.org/10.1016/j.jmarsys.2014.05.012 Church JA, Clark PU, Cazenave A, Gregory JM, Jevrejeva S, Levermann A. Merrifield MA, Milne GA, Nerem RS, Nunn PD, Payne AJ, Pfeffer WT, Stammer D, Unnikrishnan AS (2013) 2013: Sea level change. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, pp 1137–1216. https://doi.org/10.1017/CB09781107415315.026 Delgado J, Boski T, Nieto JM, Pereira L, Moura D, Gomes A, Sousa C, García-Tenorio R (2012) Sea-level rise and anthropogenic activities recorded in the late Pleistocene/Holocene sedimentary infill of the Guadiana Estuary (SW Iberia). Quat Sci Rev 33:121–141. https://doi.org/10.1016/j. quascirev.2011.12.002 Fortunato AB, Oliveira A, Alves ET (2002) Circulation and salinity intrusion in the Guadiana Estuary (Portugal/Spain). Thalassas 18(2):43–65. (December 2015) Friedrichs CT, Aubrey DG, Speer PK (1990) Impacts of relative sea-level rise on evolution of shallow estuaries. In: Cheng RT (ed) Residual currents and long-term transport. Coastal and Estuarine Studies, Spring, New York, pp 106–122 Garel E, D’Alimonte D (2017) Continuous river discharge monitoring with bottom-mounted current profilers at narrow tidal estuaries. Cont Shelf Res 133(December 2016):1–12. https://doi.org/10. 1016/j.csr.2016.12.001

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Garel E, Pinto L, Santos A, Ferreira Ó (2009) Tidal and river discharge forcing upon water and sediment circulation at a rock-bound estuary (Guadiana estuary, Portugal). Estuar Coast Shelf Sci 84(2):269–281. https://doi.org/10.1016/j.ecss.2009.07.002 Garel E (2017) Present dynamics of the Guadiana Estuary. In: Investigating the past, present and future. https://hdl.handle.net/10400.1/9887 Garel E, Cai H (2018) Effects of tidal-forcing variations on tidal properties along a narrow convergent estuary. Estuaries Coasts 41(7):1924–1942. https://doi.org/10.1007/s12237-018-0410-y Hong B, Shen J (2012) Responses of estuarine salinity and transport processes to potential future sea-level rise in the Chesapeake Bay. Estuar Coast Shelf Sci 104–105:33–45. https://doi.org/10. 1016/j.ecss.2012.03.014 Lopes J, Neves R, Dias JMA, Martins F (2003) Calibração De Um Sistema De Modelação Para O Estuário Do. In: 4th Symposium on the Iberian Atlantic Margin, 4–6. MARETEC (2017) MOHID Water. Retrieved April 4, 2019, from https://wiki.mohid.com/index. php?title=Mohid_Water McLean RF, Tsyban A, Burkett V, Codignott JO, Forbes DL, Mimura N, Beamish RJ, Ittekkot V (2001) Coastal zones and marine ecosystems. In: Climate change 2001: impacts, adaptation and vulnerability. Cambridge, UK, pp. 343–379 Mills L, Janeiro J, Martins F (2019) The impact of sea level rise in the Guadiana Estuary. Rodrigues J et al (eds) Computational science—ICCS 2019. ICCS 2019. Lecture Notes in Computer Science, vol 11539, pp 287–300. https://doi.org/10.1007/978-3-030-22747-0_23 Morais P, Martins F, Chícharo MA, Lopes J, Chícharo L (2012) Merging anchovy eggs abundance into a hydrodynamic model as an assessment tool for estuarine ecohydrological management. River Res Appl 28(2):160–176. https://doi.org/10.1002/rra.1443 Neves R, Silva A, Delfino J, Leitão P, Leitão J, Pina P, Braunschweig F, Miranda R, Coelho H (2000) Coastal management supported by modelling: optimising the level of treatment of urban discharges into coastal waters. Environ Stud 5:41–49 Nicholls RJ, Marinova N, Lowe JA, Brown S, Vellinga P, De Gusmão D, Hinkel J, Tol RSJ (2011) Sea-level rise and its possible impacts given a “beyond 4 °C world” in the twenty-first century. Philos Trans R Soc A: Math, Phys Eng Sci 369(1934):161–181. https://doi.org/10.1098/rsta.2010. 0291 Oliveira A, Fortunato AB, Pinto L (2006) Modelling the hydrodynamics and the fate of passive and active organisms in the Guadiana estuary. Estuar Coast Shelf Sci 70(1–2):76–84. https://doi.org/ 10.1016/j.ecss.2006.05.033 Quesada MCC, García-Lafuente J, Garel E, Delgado Cabello J, Martins F, Moreno-Navas J (2019) Effects of tidal and river discharge forcings on tidal propagation along the Guadiana Estuary. J Sea Res 146(January):1–13. https://doi.org/10.1016/j.seares.2019.01.006 Sampath DMR, Boski T (2016) Morphological response of the saltmarsh habitats of the Guadiana estuary due to flow regulation and sea-level rise. Estuar Coast Shelf Sci 183:314–326. https://doi. org/10.1016/j.ecss.2016.07.009 Sampath DMR, Boski T, Loureiro C, Sousa C (2015) Modelling of estuarine response to sealevel rise during the Holocene: application to the Guadiana Estuary-SW Iberia. Geomorphology 232:47–64. https://doi.org/10.1016/j.geomorph.2014.12.037 Sampath DMR, Boski T, Silva PL, Martins FA (2011) Morphological evolution of the Guadiana estuary and intertidal zone in response to projected sea-level rise and sediment supply scenarios. J Quat Sci 26(2):156–170. https://doi.org/10.1002/jqs.1434 Vargas CIC, Vaz N, Dias JM (2017) An evaluation of climate change effects in estuarine salinity patterns: application to Ria de Aveiro shallow water system. Estuar Coast Shelf Sci 189:33–45. https://doi.org/10.1016/j.ecss.2017.03.001n

Effects of Climate Change on Runoff in the Heihe River Basin Under Different Sensitivity Scenarios Mingtao Li and Qianqian Guo

Abstract The impact of climate change on the hydrological process has been paid more attention, especially in water shortage regions. The purpose of this study was to explore the sensitivity of climate change to runoff in the Heihe river basin in China. On the basis of long-term meteorological series data, climate change sensitivity scenarios were generated by the method of arbitrary scenario setting, which were used as input data for HSPF model and climate assessment tool (CAT) simulation runoff evaluation. Results show that: (1) The HSPF model has been calibrated and validated to perform well and is considered appropriate for predicting the hydrological effects of climate change in the study area. (2) The simulated hydrological process were sensitive to climate variations, and the effect of precipitation on runoff is greater than that of temperature. The impact of precipitation increase on flow was greater than the precipitation decreases. These results will provide scientific references for watershed water resources management and planning at the basin scale. Keywords Climate change impacts · Model simulation · Runoff · Heihe river basin

1 Introduction Climate change has an extremely significant impact on the global water resources, ecological environment, social economy and other fields. Global warming has become an important global environmental problem. Water is the most important part of the global hydrological cycle and the global atmospheric circulation. Climate change will alter the global water cycle and atmospheric circulation, and redistribute

M. Li (B) College of Geography and Environmental Engineering, Lanzhou City University, 730000 Lanzhou, People’s Republic of China e-mail: [email protected] Q. Guo College of Geography and Environmental Science, Northwest Normal University, 730000 Lanzhou, People’s Republic of China © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_3

31

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M. Li and Q. Guo

the distribution of water resources. In the context of global climate change, the sustainable development and utilization of water resources in our country is faced with various pressures from human society and nature. Water environment deterioration, soil erosion, drought, water shortage and flood disaster are still the main problems in water resources management in China. Therefore, the solution of climate change is also related to the future of national security, and has important significance for the sustainable development of the ecological environment and national economy. Climate change and Land use change lead to the potential change of precipitation, runoff (Beyene et al. 2010; Liew et al. 2012). Global climate change mainly includes changes in precipitation, temperature, relative humidity and other climate variables. The amount and intensity of precipitation can influence the stream flow frequency and volume (Reichwaldt and Ghadouani 2012). Warmer temperatures caused by temperature changes will promote algal blooms, reduce dissolved oxygen and reduce ecological productivity. Many studies have focused on assessing the separate impacts of climate change and land use change on water resources (Choi 2008; Franczyk and Chang 2009). Due to the complex mechanisms and uncertain characteristics, assessment of runoff has become a challenge for decision-makers and planners. Computer modeling is a useful and popular tool for runoff. In the past decades, variety of models such as SWAT and HSPF have been developed for assessing and predicting the change of runoff on watersheds. While previous studies have discussed the impact of climate change and land use change on water resources, most of these studies focused on the effects of climate change on flow (Fricklin et al. 2010), and few studies have addressed the combined effects on runoff and water quality at basin scale. Understanding the hydrological effects of climate change has significant significance for sustainable water resources management. Therefore, it is imperative to carry out a detailed study on quantitative evaluation of runoff by incorporating the possible impacts of climate change into watershed management planning. The Heihe river basin was selected as research area. The sensitivity assessments of climate change impacts on runoff will be helpful to predict future impacts of climate change on agriculture, ecology and the environment. The objectives of this study were: (1) to assess the applicability and predictive capability of HSPF in Heihe river basin; (2) to analyze the variation characteristics of runoff based on the validated HSPF model; (3) to explore the sensitivity of watershed runoff to climate change.

2 Methods 2.1 Study Area Heihe river basin is located in hexi corridor of northwest China, covering an area of 142,900 km2 . The two major tributaries of the Heihe river basin are the Babao

Effects of Climate Change on Runoff …

33

Fig. 1 Location of the Heihe river basin

river (river length of 80 km) and Heihe river (river length of 175 km). The average annual temperature in the basin is between −3.0 and 4.0 °C, and the average annual precipitation is between 200 and 600 mm (Fig. 1).

2.2 Model Description HSPF model was developed by EPA in 1980 on the basis of Stanford model. After many improvements, the latest version is HSPF12.0. In 1998, the EPA integrated ArcView module and HSPF model into basins system through the integration of model and GIS, and developed basins system. Based on the basins system, the HSPF model can use the ArcView module to process the DEM, land use, soil type and other spatial data of the study area, and also use the data management software in the basins system to manage the attribute data and compare and analyze with the simulation results, so as to verify the model and display the simulation results intuitively. On the basis of hydrological simulation, the influence of climate or land use on hydrological processes can be analyzed. HSPF model is necessary to divide the study area into different sub basins according to the basin range controlled by the river. There are two types of input data needed in the model, attribute data, including rainfall, evaporation, air humidity and solar radiation, and spatial data, including digital elevation map (DEM), land use and soil type. HSPF model divides the surface

34

M. Li and Q. Guo

of each sub basin into three parts: permeable surface, impermeable surface and river reservoir, and simulates the hydrological process of these three different surfaces. The three modules are divided into several sub modules, which can continuously simulate the runoff and sediment of each sub basin. Previous studies have examined that HSPF is a comprehensive watershed hydrological model (Patil and Deng 2010) and can be used to predict hydrological responses in future climate change scenarios (Göncü and Albek 2007). Basins climate assessment tool (CAT) is a recognized tool for water management and climate change impact assessment. It is developed by EPA of the United States and is perfectly integrated into the basins system. Basins cat extends the ability of basins to assess the potential impacts of climate change in a watershed and to simulate water and watershed systems through HSPF models. In particular, basins cat can flexibly establish climate change scenarios to facilitate users to quickly assess the various hypothetical problems caused by climate change on the modeling system. Combined with the HSPF model to evaluate the existing land use change and management measures, basins cat can also be used to evaluate the coupling effect of land use change and climate change, and guide the designation of effective management policies. Basins cat allows users to modify the historical meteorological data to generate climate change scenarios, and substitute the generated data as HSPF model meteorological data into the model for simulation.

2.3 Data Preparation Land use maps were represented using Landsat-TM/ETM (2010) satellite imagery and digitized soil maps with a scale of 1:100,000. Daily weather parameters in recent 40 years (1961–2010) were obtained from four meteorological stations located within and near the study area. Stream flow data (2000–2004) were obtained from a hydrological monitoring station located at the watershed outlet (Table 1). Table 1 Data required for the model Data type Spatial data

Scale

Description

DEM

1:25,0000

Basin elevation, slope length, slope, river network and sub basin generation

Land use

1:10,0000

Classification of land use types, such as cultivated land, forest land, grassland, etc.

Soil type

1:100,0000

Distribution of soil types and physical and chemical properties of various types

Meteorological data

20 stations

Air temperature, rainfall

Hydrological data

1 station

Monthly discharge

Effects of Climate Change on Runoff …

35

3 Results and Discussion 3.1 Calibration and Validation of HSPF Model There are many parameters in HSPF model, and the values of some parameters in different research areas are not identical. The model should be calibrated and verified, that is, some key parameter values of HSPF model should be optimized and adjusted. Calibration and validation are the key steps to affect the simulation accuracy in the process of model application. Model calibration refers to the process of adjusting model parameters and improving simulation effect by comparing model simulation value and observation value. Model verification is a verification of model calibration, in order to ensure a better stability of the calibrated model. By using the observed monthly flow data from the year of 2000–2004, the calibration and validation of stream flow was conducted by adjusting the key parameters. Nash efficiency coefficient (Ens) and relative error (RE) were used to evaluate the goodness of fit of the model. Figure 2 showed the simulation results of monthly flow. After adjusting the model parameters, for the calibration and validation periods, The Ens value were 0.78 and 0.70, respectively, and the relative error of observed flow and simulated flow was within 10%. Overall, the simulation results of HSPF model can meet the precision requirement and basically reflect the characteristics of runoff in watershed. Therefore, the calibrated model is considered suitable for runoff prediction and climate sensitivity analysis.

Fig. 2 Comparison of monthly observed and simulated flow discharge

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M. Li and Q. Guo

Table 2 Annual flow and change percentage under different climate sensitivity Change of temperature (T/°C)

Change of precipitation +20%

+10%

0

−10%

−20%

+2

68.23%

30.18%

0%

−26.65%

−53.57%

+1

68.23%

30.18%

0%

−26.65%

−53.57%

0

68.23%

30.18%

0%

−26.65%

−53.57%

−1

68.23%

30.18%

0%

−26.65%

−53.57%

−2

68.23%

30.18%

0%

−26.65%

−53.57%

3.2 Impacts of Climate Sensitivity on Flow By using the CAT model, climate change sensitivity generated by methods of arbitrary scene settings. Modify precipitation data by increasing the historical mean by ±0, 10%, and 20%. The historical values of temperature data were modified with ±0 °C, 1 °C and 2 °C, respectively. Then 25 sensitivity scenarios were generated, which combined the change of precipitation and temperature. The simulation results of Heihe River Basin under different climate scenarios and annual average runoff under different climate scenarios are obtained by substituting climate scenarios into HSPF model simulation. Table 2 shows the annual flow changes under 25 climate change scenarios. It was found that precipitation has greater influence on the flow. The impact of precipitation increase on flow was greater than the precipitation decreases. This mainly because of flow generation was mainly due to the heavy precipitation. Temperature change had no obvious effect on the flow change, which may be due to the influence of temperature on flow is an indirect effect, temperature changes affect other climatic factors (precipitation) lead to variation of flow. Because of the direct influence of precipitation on flow, temperature taken as a constant, five scenarios of precipitation change were selected for the analysis of impacts of precipitation on monthly flow. Table 3 showed changes in monthly flow at five precipitation change scenarios. It can be seen that the flow mainly concentrated in the summer (July and August). There was least influence of precipitation on the flow (December, January and February) in winter whereas the greatest impact in summer (June, July and August). The increased flow by precipitation increase was the largest in the June, a relative increase of 109.38%, while the decreased flow by reducing precipitation is the largest in July with a relative reduction of 70.78%.

4 Conclusions Based on the HSPF model of Heihe river basin, the future climate change scenario of the basin was determined, and then the runoff of the basin under different climate

Effects of Climate Change on Runoff …

37

Table 3 Percentage change in monthly runoff due to climate change scenarios Month

S2

S3

S4

S5

January

S1 43.44

27.18

0

−36.99

−51.33

February

51.35

30.65

0

−38.35

−53.77

March

62.35

31.69

0

−43.81

−60.67

April

77.26

37.78

0

−46.18

−62.81

May

81.66

40.14

0

−49.43

−65.91

June

109.38

50.66

0

−51.68

−69.02

July

91.77

48.33

0

−48.32

−70.78

August

80.53

40.98

0

−40.85

−59.76

September

65.33

34.27

0

−37.73

−54.37

October

59.78

30.04

0

−35.64

−52.36

November

50.46

28.83

0

−34.82

−50.46

December

50.17

26.25

0

−34.55

−51.01

change scenarios was simulated and estimated to reflect the influence of rainfall and temperature changes on the hydrological process. The main conclusions are as follows: The HSPF was used to assess the sensitivity of annual and seasonal flow to possible climate changes. The calibration and validation of HSPF was able to reveal the characteristics of runoff. The simulated hydrology were very sensitive to climatic variations. The effect of precipitation on runoff is greater than that of temperature.. The impact of precipitation increase on flow was greater than the precipitation decreases. These results point to temperature shows a minor importance in the impact of climate change on runoff. Although this paper analyzes the climate change of Heihe River Basin, and simulates and analyzes the hydrological process of Heihe River Basin under different climatic and hydrological conditions by using HSPF model, due to the limited research time and insufficient data, it is inevitable that there will be some inadequacies in the research: the data length of meteorological stations in Heihe River Basin is short, and the analysis results cannot fully represent the whole flow The future climate change trend of the region can only be used as a reference for the climate change trend of the region. During the operation of HSPF model, there are many parameters that affect the simulation. When using the model in a new watershed, it needs to be calibrated. However, some data are monitored late in our country, even without monitoring data. As a result, part of the data can only be generated by software or adopt the model default value when building the model database. Therefore, the HSPF model of Heihe River Basin is still unsatisfactory, and some parameters need to be optimized and adjusted. The selection of climate change scenarios is relatively simple, especially the amount of temperature change may be too small to reflect the substantial impact of temperature change on runoff. The research results would be useful and valuable in evaluating potential runoff. Future climate change studies should also be performed

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with improved land-use data, which facilitates the assessment of both water resource and environmental impacts for current and potential future climate patterns. Acknowledgements This work was supported by Doctoral Scientific Research Foundation of Lanzhou City University (LZCU-BS2015-10; LZCU-BS2018-16).

References Beyene T, Lettenmaier DP, Kabat P (2010) Hydrologic impacts of climate change on the Nile River Basin: implications of the 2007 IPCC scenarios. Clim Change 100(3–4):433–461 Choi W (2008) Catchment-scale hydrological response to climate-land-use combined scenarios: A case study for the Kishwaukee River Basin Illinois. Phys Geogr 29(1):79–99 Franczyk J, Chang H (2009) The effects of climate change and urbanization on the runoff of the Rock Creek basin in the Portland metropolitan area, Oregon, USA. Hydrol Process 23(6):805–815 Fricklin DL, Luo Y, Luedeling E, Gatzke SE, Zhang M (2010) Sensitivity of agricultural runoff loads to rising levels of CO2 and climate change in the San Joaquin Valley of California. Environ Pollut 158(1):223–234 Göncü S, Albek E (2007) Modeling of evapotranspiration from forested watersheds using HSPF. In: Heinonen M (ed) Climate and water. The third international conference on climate and water. Finnish Environment Institute, Helsinki Van Liew MW, Feng S, Pathak TB (2012) Climate change impacts on streamflow, water quality, and best management practices for the shell and logan creek watersheds in Nebraska. Int J Agric Biol Eng 5(1):13–34 Patil A, Deng ZQ (2010) Analysis of uncertainty propagation through model parameters and structure. Water Sci Technol 62(6):1230–1239 Reichwaldt ES, Ghadouani A (2012) Effects of rainfall patterns on toxic cyanobacterial blooms in a changing climate: between simplistic scenarios and complex dynamics. Water Res 46(5):1372– 1393

Comparison of UVA-LED and UVC-LED for Water Disinfection: Inactivation of Escherichia Coli Zhilin Ran, Meng Yao, and Shaofeng Li

Abstract Disinfection is an essential part of the water treatment process, ensuring the destruction of pathogenic microorganisms present in aquatic systems. The inactivation of Escherichia coli (E. coli) in water was investigate after irradiating by UV-LED. When the radiation dose was 24.48 mJ/cm2 , the log inactivation of E. coli was more than 4. The effects of UV-LED parameters (wavelength, UV irradiation intensity), water quality factors (temperature, pH value) were investigated on the E. coli inactivation rate. At shorter UV-LED wavelengths, E. coli inactivation increased as UVC-LED radiation intensity is stronger. Temperature, pH value of E. coli inactivated effect. The mechanism of UVC-LED inactivation of E. coli was studied by scanning electron microscopy and system soluble protein detection, showing that the UVC-LED bactericidal activity involved the destruction of E. coli nucleic acids and to a lesser degree through protein damage. Keywords UV light-emitting diodes · E. coli · Inactivation · Mechanism

1 Introduction In recent years, more and more drinking water sources have been polluted, leading to strong concerns about the safety of drinking water quality (Li et al. 2019). Millions of people globally, lack access to safe drinking water and are exposed to various contaminants and waterborne diseases annually (Liu et al. 2019). Development of novel water treatment technology to inactivate pathogenic microorganisms in drinking water sources, is great significantly for human health and well-being (Kim and Kang 2018). With the rapid development of the semiconductor industry, the UV radiation could be generated by UV light-emitting diodes (UV-LEDs) owing for its Z. Ran · M. Yao Institute of Innovational Education Research, Shenzhen, China e-mail: [email protected] S. Li (B) Department of Building and Environmental Engineering, Shenzhen Polytechnic, Shenzhen, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_4

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effective and economically viable source (Kneissl et al. 2019; Song et al. 2016). UV-LEDs is an adjusted device that is small and generated a mercury-free source (Gora et al. 2019), which can be used without a warm-up period. Additionally, this device enabling a diverse range of potential applications and on-demand operation (Rattanakul and Oguma 2018). Disinfection effects of UV-LEDs at various wavelengths, has been deeply investigated in other literature. Most reported studies have investigated surrogate indicator microorganisms, such as aerobic spore-forming bacteria, bacterium Escherichia coli, or viruses such as the bacteriophage MS2, ϕb, or T7 (Disinfection et al. 2019; Li et al. 2018). The main objective of this study was to study the efficiency of E. coli inactivation and further investigation of the mechanism of action using UV-LEDs at wavelengths of 275 and 365 nm. These results can be used as important supporting information for optimizing UV-LED wavelengths selection during water treatment and for the future development of UV-LED water disinfection systems.

2 Materials and Methods 2.1 Characterization of UV-LEDs and UV Disinfection Procedure The experimental apparatus was shown in Fig. 1. This device had a quartz tube and two UVC-LED arrays. Each array was made up of 40 UVC-LED lamp beads, installed on a circuit board and apart 7 mm. The constant cryostat was connected outside the reactor chamber to guarantee there was a constant temperature system inside the reactor.

Fig. 1 Experimental apparatus for UV-LED experiments

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The bacterial suspensions were added to the inner chamber and disinfected by the immersed UVC-LED array. At the same time, the suspension was also mixing by magnetic stir bar, in order to ensure a consistent UV radiation intensity during every part of this system. At the beginning of experiments the E. coli suspension was inactivated by UVCLED for 600 s. Samples were collected at 30, 60, 120, 180, 240, 300 and 600 s postirradiation, then enumerated the number of E. coli cells in the water samples. The peak UVA-LED emission wavelength applied was 365 nm, with the same apparatus and disinfection procedure applied as described for the UVC-LED experiments.

2.2 Culturing and Enumeration of Microorganisms A pure culture of E. coli (ATCC8099), provided by the CGMCCC (China General Microbiological Culture Collection Center). The process of resurrection, enrichment and purification refer to Oguma’s method and obtain a final concentration of approximately 107 CFU/mL (Oguma et al. 2016). The linear relationship between the applied UV radiation dose and log inactivation (log (N0 /Nt )), is used to describe UV disinfection models and the first-order model of Chick-Watson (Hijnen et al. 2006), the Eq. (1) was described as follows: Log(N0 /N) = Kd × UV dose

(1)

where, N0 and N indicate the number of colony forming units (CFU/mL) before and after UV irradiation, respectively; UV dose is the fluence at the given wavelength; and Kd (cm2 /mJ) is the inactivation rate constant.

2.3 Morphological Observations and Protein Analysis During Disinfection Scanning electron microscopy (SEM) (Model 4700, HITACHI, Japan) was employed to observe the surface changes on E. coli cells with time. The supernatants of samples were collected for protein concentration measurements using an ultra-micro spectrophotometer (Nanodrop 2000, Thermo Fisher Scientific). The method is according to Cho’s literature (Cho et al. 2006).

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Fig. 2 Effect of exposure time on the inactivation of E. coli

3 Results and Discussion 3.1 Inactivation Effectiveness of UV-LEDs at Various Wavelengths UV wavelength is an essential factor for microbial inactivation, although its effectiveness may vary according to microbial species. The bacterial suspension containing E. coli was prepared in pure water at room temperature, pH 7.5 and a turbidity of 1 NTU. The inactivation of E. coli was observed at UV-LED wavelengths of 275 nm (UVC) and 365 nm (UVA), with the results presented in Fig. 1. Results show that the E. coli UV exposure time response at 275 nm for 10 min was 5.35 log, while at 365 nm it was only 0.48 log. When the irradiation time was extended to 60 min, the inactivation response at 365 nm was approximately 2.4 log. This indicates that treatment using UVC-LED (275 nm) arrays is more efficient than UVA-LED (365 nm) arrays. Which is consistent with previously reported results, showing that log reduction of E. coli can be improved by treatment with UVC-LEDs and UVA-LEDs (Song et al. 2016) (Fig. 2).

3.2 UVC-LED Radiation Intensity The degree of damage to cells by UV light and the rate of microbial inactivation depend on the dose of UV light absorbed by microorganisms and their resistance to UV light. Different pairs of UV radiation doses were obtained by controlling the

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Fig. 3 Effect of varying UVC-LED irradiation intensity on the inactivation of E. coli

UVC-LED radiation intensity. At a turbidity of 1 NTU and pH 7, varying irradiation intensities of 0.014, 0.029, 0.034, 0.048, 0.076 and 0.102 mw/cm2 were compared for E. coli irradiation, with the inactivation of E. coli examined (Fig. 3). As shown in Fig. 3, under increasing UVC-LED irradiation intensities and with increased irradiation time, the ultraviolet irradiation dose increases along with the E. coli inactivation logarithm. At an irradiation intensity of 0.014 mw/cm2 and an irradiation time of 10 min, the E. coli inactivation value was only 3.2, while at an irradiation intensity of 0.029 mw/cm2 and irradiation time of 5 min, the E. coli inactivation logarithm reached 3.2. Under irradiation intensity conditions of 0.034, 0.048, 0.076 and 0.102 mw/cm2 , the irradiation time required for E. coli inactivation to reach the same value was 4 min, 3 min, 2 min and 1 min, respectively. At an irradiation intensity of 0.102 mw/cm2 and an irradiation time of 4 min, the logarithm of E. coli inactivation was greater than 4. With irradiation times exceeding 4 min, the inactivation value increased slowly, while after 10 min, the inactivation logarithm value was only 4.6. These results show that 0.102 mw/cm2 is the optimum UVC irradiation intensity.

3.3 The Effect of Temperature During UVC-LED Treatment As shown in Fig. 4, when the irradiation time was 5 min, the E. coli inactivation values at 5 °C, 15 °C and 35 °C were 4.83, 4.45 and 4.65, respectively, while the logarithmic inactivation value was slightly higher at 25 °C, reaching 5.04. When the irradiation time was 10 min, the E. coli inactivation value was slightly increased

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Fig.4 Effect of temperature on the E. coli inactivation

at 25 °C, reaching 5.67. The logarithmic inactivation was slower at 5 °C, 15 °C and 35 °C, at 4.93, 4.75 and 5.16, respectively. Based on these results, 25 °C was selected as the optimum inactivation temperature due to the high inactivation rate exhibited at 25 °C. The temperature change affects the UVC-LED luminescence intensity, as higher temperatures cause an increase in the non-radiative transition of the semiconductor. This causes the internal quantum efficiency to decrease, resulting in a decrease in the luminescence intensity and the E. coli inactivation rate. With a reaction temperature of 35 °C, the probability of this mechanism occurring is low and the bactericidal effect is limited.

3.4 The Effect of pH Value During UVC-LED Treatment The effect of different pH conditions (pH 5, 6, 7, 8, 9 and 10) were assessed on the inactivation of E. coli at a reaction temperature of 25 (±0.1) °C with the UV-LED, reaction time of 10 min and a turbidity of 1 NTU. As shown in Fig. 5, there are no significant differences could be observed in the E. coli inactivation value under different pH conditions, although at pH 7, the inactivation of E. coli was slightly improved.

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Fig. 5 Effect of varying pH value on the inactivation of E. coli by UVC-irradiation

3.5 Morphological Analysis E. coli suspensions were irradiated for 3 min or 5 min with UVC-LED (275 nm) treatment at a reaction temperature of 25 (±0.1) °C, initial pH of 7 (±0.1) and turbidity of 1 NTU. 1 ml of sample was collected and sequentially fixed, washed, dehydrated, replaced and dried. Following this, the processed samples were observed by SEM at ×10,000 and ×45,000 magnifications, with the morphological and structural changes in E. coli analysed (Fig. 6). Based on the SEM results, it can be seen that before UVC-LED irradiation the surface of E. coli was smooth with no observable damage. In contrast, following UVC-LED irradiation for 3 min, wrinkles and depressions appeared on the surface of E. coli cells, although the change was not significant. When the irradiation time was extended to 5 min, cells contracted and the surface was wrinkled, with visible deformation. Although UVC-LED irradiation failed to cause breakage of E. coli cells, the change in surface morphology following UVC-LED irradiation indicates that treatment destroyed the E. coli cell membrane and cell wall to some extent, which may cause cell lysis and bacterial inactivation (Song et al. 2018).

3.6 Protein Assay UVC-LED (275 nm) and UVA-LED treatments were undertaken at a reaction temperature of 25 (±0.1) °C, an initial pH of 7 ± 0.1 and turbidity of 1 NTU. E. coli suspensions were irradiated from 0 to 10 min and each sample was taken for analysis. Samples were subjected to high-speed centrifugation and the supernatant was

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256nm

0min

365nm

256nm

3min

365nm

256nm

5min

365nm

Fig. 6 SEM images of E. coli inactivated by UV-irradiation at 265 nm or 365 nm

collected to establish the protein concentration, with the results shown in Fig. 7. Although the dissolved protein content cannot accurately express the protein content within E. coli cells, it may be inferred that the extracellular protein pool is relative to intracellular protein spillage from E. coli due to UVC-LED irradiation. When UVCLED irradiation was performed for 0.5 min, the protein content rapidly increased from 0.016 to 0.018 mg/L. Following irradiation for 1–5 min, the dissolved protein

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Fig. 7 Changes in soluble protein concentration following UV-LED treatment of E. coli cells at 275 nm or 365 nm

concentration decreased to 0.007 mg/L by 5 min, as UV light can penetrate cells and damage cell membranes, resulting in the instantaneous outflow of some cellular proteins. As the irradiation duration increases, UV light destroys the molecular structure of proteins and affects the activity of protein molecules, resulting in a continuous decrease in the protein content of the system (Oguma et al. 2016). Although the dissolved protein concentration in the system does not accurately represent the total protein content of E. coli, the cell protein efflux can be evaluated based on the change. As shown in Fig. 7, when exposed to UVA-LED irradiation for 0.5 min, the dissolved protein concentration increased from 0.0188 to 0.0227 mg/L. When the reaction duration was 0.5–3 min, the extracellular protein content in the system began to decrease, while at 3 min the protein concentration reduced to 0.012 mg/L. With UVA-LED irradiation exposure for 5 min, the protein concentration increased to 0.0203 mg/L, while exposure for 5–10 min caused the soluble protein concentration to gradually decrease. The reason for this trend, is that in the initial 0.5 min UVA-LED acts rapidly and disrupts E. coli cell membrane permeability, resulting in a large outflow of intracellular proteins. Due to the outflow of proteins in the initial 5 min of treatment, further prolonged irradiation times induce decomposition via the release of a large amount of active oxygen species, causing protein oxidation and a rapid decline in protein concentrations (Xiao et al. 2018).

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4 Conclusion The inactivation of E. coli in water by UVC-LED treatment was assessed in this study. The effects of UVC-LED irradiation dose, temperature, pH conditions, turbidity and humic acid content, were investigated on the inactivation of E. coli. The effectiveness of the treatment system was observed by scanning electron microscopy, with analysis of protein and nucleic acid leakage. The following conclusions were obtained: 1. When the irradiation intensity was 0.102 mw/cm2 and the irradiation duration was 5 min, the E. coli log inactivation was 4.35. 2. When the temperature was 25 °C and the irradiation time was 5 min, the E. coli log inactivation was 5.67. At pH value 7, the inactivation effect of E. coli was slightly better, while turbidity and humic acid concentration had no obvious effect on UVC-LED inactivation of E. coli. 3. UVC-LED irradiation strongly penetrates cells, allowing UV light to affect cell DNA and proteins inside the E. coli cell wall and membrane. The absorption spectrum of ribonucleic acid and deoxyribonucleic acid ranges from 240 to 280 nm, with an absorption peak at 260 nm. 275 nm UVC-LED irradiation has a strong capacity to destroy DNA, changing its molecular structure and disrupting replication and protein synthesis. In addition to inducing E. coli cell death, some proteins (such as phenylalanine, tryptophan and tyrosine) can absorb UV light and play a role in the E. coli inactivation process. Acknowledgements This research was funded by the project of Shenzhen Institute Information Technology (PT201703), the Science and Technology Project of Shenzhen Institute of information technology (SZIIT2019KJ006), Innovation and Enhancing College Project of Guangdong Province, China (2017GKTSCX065).

References Cho M, Kim J, Yoon J (2006) Investigating synergism during sequential inactivation of Bacillus subtilis spores with several disinfectants. Water Res 40:2911–2920 Disinfection B, Matsumoto T, Tatsuno I, HasegawaT (2019) Instantaneous water purification by deep ultraviolet light in water waveguide: Escherichia Coli. Water 11:1–9 Gora SL, Rauch KD, Ontiveros CC, Stoddart AK, Gagnon GA (2019) Inactivation of biofilmbound Pseudomonas aeruginosa bacteria using UVC light emitting diodes (UVC LEDs). Water Res 151:193–202 Hijnen WAMÃ, Beerendonk EF, Medema GJ (2006) Inactivation credit of UV radiation for viruses, bacteria and protozoan (oo)cysts in water: a review. Water Res 40:3–22 Kim D, Kang D (2018) UVC LED irradiation effectively inactivates aerosolized viruses. Appl Environ Microb 84:1–11 Kneissl M, Seong TY, Han J, Amano H (2019) The emergence and prospects of deep-ultraviolet light-emitting diode technologies. Nat Photonics 13:233–244

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Liu L, Xing X, Hu C, Wang H, Lyu L (2019) Effect of sequential UV/free chlorine disinfection on opportunistic pathogens and microbial community structure in simulated drinking water distribution systems. Chemosphere 219:971–980 Li G, Huo Z, Wu Q, Lu Y, Hu H(2018) Synergistic effect of combined UV-LED and chlorine treatment on Bacillus subtilis spore inactivation. Sci Total Environ 639:1233–1240 Li X, Cai M, Wang L, Niu F, Yang D, Zhang G (2019) Evaluation survey of microbial disinfection methods in UV-LED water treatment systems. Sci Total Environ 659:1415–1427 Oguma K, Rattanakul S, Bolton JR (2016) Application of UV light—emitting diodes to adenovirus in water. J Environ Eng 142:1–6 Rattanakul S, Oguma K (2018) Inactivation kinetics and efficiencies of UV-LEDs against Pseudomonas aeruginosa, Legionella pneumophila, and surrogate microorganisms. Water Res 130:31–37 Song K, Mohseni M, Taghipour F (2016) Application of ultraviolet light-emitting diodes (UV-LEDs) for water disinfection: a review. Water Res 94:341–349 Song K, Taghipour F, MohseniM (2018) Microorganisms inactivation by continuous and pulsed irradiation of ultraviolet light-emitting diodes (UV-LEDs). Chem Eng J 343:362–370 Xiao Y, Chu XN, He M (2018) Impact of UVA pre-radiation on UVC disinfection performance: inactivation, repair and mechanism study. Water Res 141:279–288

Application of Integrated Median Ranked Set Sample and Analytic Hierarchy Process to Enhance Decision Making Process in Environmental Issues-Selecting Best Wastewater Collection as a Case Study Mohammad K. Younes Abstract Environmental issues usually have various aspects; criteria and it involves many stakeholders from various backgrounds and level. It is characterized as multicriteria decision-making problem that requires extensive environmental, financial, social, operational and technical evaluations. Furthermore, the various background of involved stakeholders and conflict of interest complicate the decision-making process. The Median Ranked Set sample (MRSS) and Analytic Hierarchy Process (AHP) were integrated in this research. MRSS was used to enhance the weighting process and to minimize inconsistency and conflict of interest during the stakeholder’s involvement. The stakeholders were divided into four groups in which four experts to form 4-by-4 matrix. The environment got the highest importance followed by social and economic criteria, respectively. Furthermore, the most important criteria are outflow emissions (importance weight = 0.27) followed by public acceptance (importance = 0.22). The introduced integrated model may offer a promising tool to improve the decision-making process and help the environmental planners in term of reduction uncertainty and subjectivity of human judgments. Keywords Multi criteria decision making · Analytic hierarchy process · Ranked set sample

1 Introduction Management of environmental issues are Multi Criteria Decision Making (MCDM) process. It is complex and require extensive assessment, comparison efforts, involvement of various stakeholders and analysis of a huge number of relevant factors and criteria that are vary in their importance as well as the need of different processing

M. K. Younes (B) Department of Civil Engineering, Philadelphia University, P.O. Box 19392, Amman, Jordan e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_5

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types. Such evaluation procedures are essential to contain both health and environmental impacts, reduce the costs of construction and operation phases and end-up with more sustainable decision (Afzali et al. 2011). Consequently, they maximize society involvement and acceptance. For instance, proper siting of unwanted facilities such as landfill and/or wastewater treatment plant minimizes the objection phenomenon like BANANA (build absolutely nothing anywhere near anyone) or NIMBY (not in my back yard) (Younes et al. 2015; Siedentop 2010). However, in order to strengthen and widen the popularity of the decision making process, it is recommended to increase the number of participating parties (Holman 2008; Coteur et al. 2016), but homogenizing and analysis of participated stakeholders preferences are sophisticated, especially when conflict of interests are exist among the participated stakeholders. Decision support systems, like AHP, that was introduced by Saaty, has been widely implemented to manage and solve the complex MCDM problems. In order to derive weights and priorities, AHP implements experts and/or stakeholders informed judgments, thus it has the capacity to treat both qualitative and qualitative data. In addition, by implementing a pairwise comparisons AHP simulates human thinking (Sener ¸ et al. 2010). Furthermore, it is relatively easy to use and understand. Although the AHP approach has attracted criticism for certain aspects, such as ambiguity and uncertainty of the expert preferences, but it is widely used in MCA applications due to its capability to handle complex and multi attribute multi stakeholder’s problems hierarchically. Moreover, AHP method allows examining each level of the hierarchy separately. AHP is widely used in environmental and MCDM problems. For example, by implementing AHP technique the suitable landfill location was determined by Demesouka et al. (2013). Furthermore, AHP was used to develop the relation between precipitation and six of climatic factors that affect rainfall (Vaishnavi et al. 2017). An integration between AHP and the strategic SWOT analysis has been introduced by Abdel-Basset et al. AHP was implemented to quantify SWOT factors and to weight the available alternatives (Abdel-Basset et al. 2018). Moreover, the resulted spatial maps that have been developed by conventional and fuzzy AHP were compared to help the strategic city planners to determine the potential lands for residential area in land use plan development. On the other hand, in 1952 Mc Intyre was firstly introduced the Ranked Set Sample (RSS) to estimate the population mean. However, a modifications for RSS has been introduced by various researchers since that time (Ibrahim et al. 2010; Ibrahim 2011). These modifications aimed to enhance the estimation precision of the population mean (Husby et al. 2005). As a results of such development a MRSS has been adopted. In this method, only the median observation is considered from each of randomly selected sets. Due to the capacity of MRSS to reduce the ranking errors and enhance the estimation efficiency, through representing a study population without extensive observations, it exceeds the traditional RSS (Hossain and Muttlak 2006). Consequently, it proposes promising applications in varicose fields including the environmental researches (Deshpande et al. 2006). For instance, to estimate the average spray deposit on apple tree leaves, RSS was used (Murray et al. 2000).

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In order to reduce the cost and enhance the sampling process, RSS method was implemented to collect a representative sample from gasoline stations for further analysis (Wolfe 2012). Moreover, RSS was integrated with prior sample knowledge to minimize the cost of evaluating a stream habitat region for salmon (Mode et al. 2002). In this study, an integrated model of AHP and MRSS is presented to reduce the imprecision and vagueness of human decision-making process. The proposed model aims to strengthen the link between decision making and sustainability assessment. It leverages the power of an expert system to extract knowledge that is subsequently applied to a hybrid MRSS-AHP system to obtain the criteria weights.

2 Methodology AHP is implemented in MCDM process to organize the critical aspects of a problem into a hierarchical structure similar to a family (hierarchy) tree. By building the hierarchy tree, AHP reduces the complicated problems into a series of simple pairwise comparisons, then it ranks and synthesizes the results to determine the weights of the evaluating criteria. AHP helps the planner to hit the best decision, but also it provides a clear rationale for the concluded choice. In addition, pair-wise comparisons are the fundamental building blocks of the AHP. These comparisons reflect expert’s opinion and experience and usually are done independence on expert knowledge that is gained from the observation and continuous learning of the targeted system. The following summarizes the developed method.

2.1 Problem Structuring A literature review is used to construct the problem, that includes building the hierarchy structure in which the main and sub-criteria are shown. However, there is no common rule to select and build the hierarchy tree but the criteria generally are selected based on their usage and reporting by literature and the opinion of model developer. However, each main criterion has a target, i.e., to maximize the nature protection (environmental), acceptance by the public (social), or appropriateness of the site, used technique and ease to deal (technical). Furthermore, the economic criteria aim to control and minimize the anticipated costs. In order to complete the Hierarchy structure, all sub-criteria that have common characteristics were grouped together under every main criteria. Since the removal efficiency, ease to handle and construct and future prospective and expansion all belong to technical issues of the implemented technology and site they grouped under the technical main criteria. Moreover, to protect the environment it is important to develop sustainable solution, control the effluent and emission and reduce the impact. Thus, these criteria were grouped under environmental main criteria. To minimize the overall costs lifespan, construction, and operational costs were considered under

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the economic main criteria. Finally, social criteria aim to increase local cooperation and acceptance for the project, thus it includes the aesthetic issues like appearance, odor, compatibility of the site and the treatment option with local needs.

2.2 Pairwise Comparison and Stakeholders Selecting After building the hierarchy tree, the experts have been selected and clustered in four groups that are government, academic, privet and NGO’s. This classification aims to split the feedbacks into significant groups that are usually related or has a common characteristics. Furthermore, to minimize the uncertainty and risk of reproducing homogenous decision by participating conflicted stakeholders. However, stakeholders’ feedbacks were collected by pair wise comparisons between the criteria and sub criteria.

2.3 Determining the Final Weights This phase includes assigning weights for each of the main and sub criterion to reflect their relative importance in the decision-making process. Then the obtained weights were combined to determine the final scores of each option to end up with the final and overall values for all options. However, Fig. 1 summarizes the presented model. Fig. 1 Model flow chart

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3 Results and Discussion Determining the final Ranks of each option starts from the literature review to determine the main and sub-criteria and build the hierarchy tree. In this study the target is selecting the best decentralized wastewater treatment option. Thus, the evaluation criteria are subsequently arranged and clustered into four main groups based on shared characteristics using cluster analysis. These groups are social, economic, environmental, and technical. Developing groups in dependence on common characteristic is called cluster analysis (Kushwah et al. 2012). Additionally, as shown in Fig. 2 the sub-criteria that share a common characteristics were fall under single main group. However, every main criterion group has an ultimate objective i.e., to maximize the preservation of nature (environmental), acceptance by the public (social), or appropriateness of the site (technical). The economic criteria also aim to minimize costs. After that, the involved stakeholders were grouped into four groups. Each group contains an expert from government, private, academia and non-government organizations interested in environmental issues. A questioner has been developed to obtain the weights of all main and sub-criteria, it asked the respondents to draw pairwise comparisons between the main-criteria together and between every group of sub-criteria with respect to decentralized wastewater treatment. Then, the expert’s pairwise preferences were collected and randomly clustered into four groups to form matrix for every pairwise comparison with a size of four by four as shown in Table 1. The table shows the stakeholders grouped preferences for the comparison between technical versus economic criteria. Each set (group) includes the responses of single

Fig. 2 Hierarchy tree structure

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Table 1 Stakeholders preferences for technical versus economic criteria Sector

Government

Private sector

NGO

Academic

1st group

5

1/3

1/5

1

2nd group

1

3

1

1/7

3rd group

5

5

3

7

4th group

3

5

3

1/5

Table 2 Experts preferences after ranking

Ranked experts’ preferences First group Second group Third group Fourth group

expert expert expert expert

0.20

0.33

1 1

5

0.14

1

3

3

5

5

7

0.20

3

3

5

government, private, academic, and NGO stakeholders. Next, the grouped responses were ranked in increasing order for each individual set (as shown in Table 2) and the importance set for each pairwise comparison, obtained using the MRSS technique. The overall priorities of the main criteria are shown in Table 3, and the highest rank is for environmental criteria that got a weight = 0.41, followed by social criteria (weight = 0.31) while the lowest priority criteria is technical with importance weight = 0.07. These results can be explained as the wastewater treatment option should be located and designed to protect human health and to conserve the environment. Therefore, environmental criteria gained the first rank with top priority and this may as a result of worldwide trend of environment preservation calls. Furthermore, protecting human and the environmental health reduces the local’s objections to the project and widen it publicity. On the other hand, technical issues that are related to construction and operation aspects are not a big concern in Jordan, since it is opened to the world and can invite local and international companies to construct and operate the wastewater treatment plant. Specially under the umbrella of public privet partnership. Furthermore, the required human capacity can be gained by targeted training and, thus by proper system Table 3 Final priorities (weights) of the main criteria

Main criteria

Final priority

Technical

0.07

Economic

0.21

Environmental

0.41

Social

0.31

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Table 4 Weights of all main and sub-criteria Group

Criteria

Priority normalized by group

Final priority

Technical (7%)

Removal efficiency

0.22

0.02

Ease of construction

0.08

0.01

Ease of operation

0.32

0.02

Economic (21%)

Environmental (41%)

Social (31%)

Future improvement

0.38

0.03

Capital cost

0.09

0.02

Operational cost

0.25

0.05

Lifetime

0.66

0.14

Land use impact

0.15

0.06

Sustainability

0.18

0.07

Outflow-emission

0.67

0.27

Public acceptance

0.72

0.22

Aesthetic

0.09

0.03

Usability and compatibility

0.19

0.06

design, planning and training of the plant operator, thus it got the lowest importance. However, in rural and tripe areas it is important to gain the social approval, thus social criteria gained the second highest importance that is not far from environment. However, the overall criteria weights that are related to decentralized wastewater treatment plant are presented in Table 4. The overall highest rank is for outflowemission (27%) because of its direct effects on human and environment. For instance, the quality of outflows directly related to anticipated impacts on surface and groundwater as well as on soil pollution by control of water effluent quality. In addition, to nuisance associated to bad smell and air pollutants. Thus, it is directly related to public acceptance, government licencing and monitoring. The second heist important sub criterion is the public and society approve and acceptance that is essential for wastewater treatment project. Followed by project lifespan that is important aspect to minimize the cost of construction of new plant. As well as to reduce the complexity associated to sitting and construction new wastewater treatment plant. The least important criteria are the construction method, ease of operation and removal efficiency that can be controlled by proper planning and training of the plant staff. The Suitability Index (SI) of the best DWWTP option is shown in Table 5. The table is obtained by multiplying the score of each option by its weights from Table 4, then the final score (SI) for every option is found by calculating the overall weighted score. Constructing a wastewater treatment plant for a cluster of houses seems the most feasible option to be implemented for DWWTP option. It can deduct the construction cost and cope with future demand especially in a hilly region with relatively high variations in its land topography. In addition, it reduces the impacts to the surrounding

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Table 5 Final rankings and SI for the potential DWWTP option Sub-criteria

DWWTP at household level (septic tank)

DWWTP for cluster of household

One DWWTP for an entire village

Removal efficiency

0.02

0.08

0.08

Ease of construction

0.02

0.02

0.02

Ease of operation

0.09

0.07

0.07

Future improvement

0.03

0.08

0.10

Capital cost

0.09

0.02

0.02

Operational cost

0.16

0.16

0.16

Lifetime

0.28

0.70

0.70

Land use impact

0.06

0.25

0.25

Sustainability

0.07

0.29

0.29

Outflow-emission

0.27

1.36

1.09

Public acceptance

0.45

0.91

0.91

Aesthetic

0.05

0.10

0.08

Usability and compatibility

0.18

0.24

0.18

Sum (SI)

1.78

4.26

3.93

by controlling the emission and serving for longer life because it serves limited number of households.

4 Conclusions The decision-making process that is related to environmental issue is complex and has high uncertainty degree as well as it involves multiple criteria and conflict of interests among stakeholders. Therefore, an appropriate hierarchical structure must be constructed for the evaluation criteria, and the expert groups must be determined. However, increasing the stakeholders participation in decision making process is essential to justify the concluded decision, increase its acceptance among public as well as improve the quality of the decision. Moreover, it minimizes the risk associated with uncertainty and avoids production of identical decision. However, conflict of interest among stakeholders complicates the process, thus implementing MRSS that is a statistical tool guarantees to gather unbiased representation of the study society. Consequently, integrating AHP-MRSS enhances handling of the vagueness and imprecision associated with the pairwise comparison process. In addition, it assists the decision makers to more confidently justify the obtained results and reduce the funds and expertise. The proposed approach may serve as a guide for applying a MCDM process for the complex environmental issue.

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References Abdel-Basset M, Mohamed M, Smarandache F (2018) An extension of neutrosophic AHP–SWOT analysis for strategic planning and decision-making. Symmetry 10:116 Afzali A, Samani J, Rashid M (2011) Municipal landfill site selection for Isfahan city by use of Fuzzy logic and analytic hierarchy process. Iranian J Environ Health Sci Eng 8:11–15 Coteur I, Marchand F, Debruyne L, Dalemans F, Lauwers L (2016) A framework for guiding sustainability assessment and on-farm strategic decision making. Environ Impact Assess Rev 60:16–23 Demesouka O, Vavatsikos A, Anagnostopoulos K (2013) Suitability analysis for siting MSW landfills and its multicriteria spatial decision support system: method, implementation and case study. Waste Manage 33:1190–1206 Deshpande J, Frey J, Ozturk O (2006) Nonparametric ranked-set sampling confidence intervals for quantiles of a finite population. Environ Ecol Stat 13:25–40, 2006/03/01 Holman N (2008) Community participation: using social network analysis to improve developmental benefits. Environ Plann C, Gov Policy 26:525 Hossain S, Muttlak H (2006) Hypothesis tests on the scale parameter using median ranked set sampling. Statistica 66:415–434 Husby CE, Stasny EA, Wolfe DA (2005) An application of ranked set sampling for mean and median estimation using USDA crop production data. J Agric Biol Environ Stat 10:354–373 Ibrahim K (2011) On comparison of some variation of ranked set sampling. Sains Malays 40:397– 401 Ibrahim K, Syam M, Al-Omari AI (2010) Estimating the population mean using stratified median ranked set sampling. Appl Math Sci 4:2341–2354 Kushwah SPS, Rawat K, Gupta P (2012) Analysis and comparison of efficient techniques of clustering algorithms in data mining. Int J Innovative Technol Exploring Eng (IJITEE) ISSN:2278–3075 Mode NA, Conquest LL, Marker DA (2002) Incorporating prior knowledge in environmental sampling: ranked set sampling and other double sampling procedures. Environmetrics 13:513–521 Murray R, Ridout M, Cross J (2000) The use of ranked set sampling in spray deposit assessment. Aspects Appl Biol 57:141–146 Sener ¸ S, ¸ Sener ¸ E, Nas B, Karagüzel R (2010) Combining AHP with GIS for landfill site selection: a case study in the Lake Bey¸sehir catchment area (Konya, Turkey). Waste Manage 30:2037–2046 Siedentop S (2010) Locating sites for locally unwanted land uses: successfully coping with NIMBY resistance. Methods and techniques in urban engineering, pp 611–635 Vaishnavi B, Yarrakula K, Karthikeyan J, Thirumalai C (2017) An assessment framework for precipitation decision making using AHP. In: 2017 11th international conference on intelligent systems and control (ISCO), pp 418–421 Wolfe DA (2012) Ranked set sampling: its relevance and impact on statistical inference. ISRN Probab Stat 2012 Younes MK, Basri N, Nopiah ZM, Basri H, Abushammala MF (2015) Use of a combination of MRSS-ANP for making an innovative landfill siting decision model. Math Probl Eng 2015

Study on Preparation of Selective Nickel Ion Exchange Membrane by Ion-Imprinting Technique Jih-Hsing Chang, Shan-Yi Shen, Chian-Yu Lin, Lien-Hsuan Chou, Yu-Chun Li, and Hisen-Chin Yen

Abstract This study applied an ion-imprinting technique to create nickel recognition sites on a cation exchange membrane which can solely allow nickel ions to pass through. Such nickel selective membrane can effectively separate specific metal ions, that is, it can avoid other heavy metals with similar molecular weight and the same valence to penetrate the membrane. In order to separate Ni2+ ions from wastewater containing Ni2+ and Cu2+ , an electrodialytical system is used with the nickel selective membrane. Experimental results show that the adsorption efficiency of self-manufacturing membrane increased with nickel ions concentration, the 90% removal efficiency can be obtained. The highest adsorption capacity has reached around 63 mg/g at the nickel concentration of 400 mg/L, which is significantly higher than the commercial cation exchange membrane. Meanwhile, the prepared nickel selective membrane majorly adsorbed the nickel ions when copper and nickel ions are presented in the wastewater simultaneously. The separation and recovery efficiency of nickel ions can rapidly reach around 50% and 70%, respectively, by the electrodialytical system with such selective membrane operate data voltage of 50 V for 60 min. Keywords Adsorption · Heavy metals · Ion-imprinting · Nickel · Selective membrane

1 Introduction In Taiwan, the surface treatment industry has the largest output value in metallic products, which can reach around NT$157 billion. Surface treatment techniques include electroplating, electroless plating, chemical polishing, anodizing, sandblasting/beading, and etching. In these techniques, electroplating accounts for 47%, which is the highest ratio among them. The source of wastewater is considered to be the J.-H. Chang · S.-Y. Shen (B) · C.-Y. Lin · L.-H. Chou · Y.-C. Li · H.-C. Yen Department of Environmental Engineering and Management, Chaoyang University of Technology, 41349 Wufeng, Taiwan e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_6

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wastewater discharged regularly from the tank and the cleaning wastewater of each unit process in the electroplating process. The main pollutants such as oil, acid, alkali, impurities, suspended solids, chromate, cyanide, and heavy metals are in the wastewater. Due to the use of the many inorganic compounds in the electroplating process which produced the high concentration of copper, zinc, total chromium, nickel, cadmium, and hexavalent chromium in the wastewater. At the same time, most of the electroplating factories are located in the farmland in Taiwan, the wastewater discharge from upstream and is used for irrigation at the downstream. Consequently, it leads farmers to introduce highly concentrated heavy metals wastewater into farmland, which damages the environment, agricultural land, and food crop safety. So far, the heavy metals pollution has become a global problem; the industrial activity caused damage to rivers, lakes, oceans, and to hazards human health (Wang et al. 2018). Nickel is a glossy, off-white metal with a hard and malleable disposition and exhibits a fibrous structure. However, when humans are inadvertently exposed to organic nickel, it leads to an increase in blood sugar and urine sugar, which causes the brain, pulmonary edema, liver disease, even severe cancer which leads to death within a week. Nickel metals wastewater treatment techniques include chemical precipitation, electrolysis, ions exchange, adsorption, reverse osmosis, and membrane filtration. Common conventional methods such as chemical precipitation that using the pH adjustment to convert heavy metal ions into hydroxides, sulfides, carbonates or other less soluble compounds, and then remove them by physical methods such as precipitation, flotation or filtration (Chen et al. 2018); the industry has used a precipitation method with cerium oxide gel to recover nickel ions from wastewater (Wang et al. 2019). In recent years, the advanced oxidation processes (AOPs) like photocatalysis, electrochemical membrane, and UV/H2 O2 have been used to treat organometallic compounds, not only to destroy metal compounds but also efficiently recover metals (Chen et al. 2017). Besides, biosorption is also used as a wastewater treatment process (Chojnacka and Mikulewicz 2019). Currently, the industry mostly uses the electrodialysis (ED) technique that an electrochemical membrane process to treat heavy metal wastewater, which uses an electric field as a driving force to separate and concentrate ions. The ED can selectively separate charged substances, the driving process is not easily fouled (Luiz et al. 2019), and the ED can be combined with solar energy and without adding chemicals. In addition, ED is a mature technique with potential and efficiency due to its sludge free and environmentally friendly and has been widely used in many fields such as desalination, wastewater and brackish water treatment (Ye et al. 2019; Strathmann 2010). The ED uses electric energy to drive ions for penetrating the cation or anion exchange membrane, causing the ions to migrate in opposite polar directions to separate ions in the water. However, take cation exchange membrane (CEM) as an example, when the system is introduced to the CEM, the cation has similar permeation properties that will cause all cations in the water to penetrate the CEM and move toward the cathode, and which make it unable to selectively separate for specific metals. Since the conventional CEM unable to selectively recover the

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target metal ions, it is necessary to increase the operation time to improve the treatment efficiency. Moreover, the application of an electric field for a long time causes a continuous hydrolysis reaction on the surface of the membrane, which the generated H+ and OH− may lead to an excessively high acid-basic degree, cause damages on the membrane surface, fouling and scaling in this system. In order to effectively achieve the goal of the separation of specific metal ions, an ion-imprinting technique can be used to generate recognition sites in the template membrane, thereby promoting the effect of metal separation (Wang et al. 2018). The ion-imprinting technique uses ions as a template to form a chelate compound by electrostatic interaction, coordination, and other reaction. Then elutes the template ions with an acidic reagent after polymerization, and finally prepares a three-dimensional imprinted material in corresponding to the target metal ion, in which the pore structure will have selective recognition properties for the template ions (Vatanpour et al. 2011). Assume a specific CEM is used to separate metal ions, it can avoid heavy metals with similar molecular weight and same valence penetrate the membrane, the specific metal ions in the wastewater can be quickly separated, and the ion exchange membrane can improve in the use and time. However, relevant research on this subject is rarely discussed and described. Therefore, in this study, a nickel selective membrane is prepared by the ion-imprinting technique. The surface structure and elemental composition are analyzed after preparation. The adsorption efficiency and metal selectivity of nickel selective membrane for nickel ion are investigated. Finally, Nickel selective membrane is combined with the ED technique to treat nickel wastewater, to obtain the selective and treatment efficiency of heavy metals.

2 Materials and Methods 2.1 Chemicals and Equipment All chemicals in this study are shown in Table 1. The copper and nickel standard solutions are used as the calibration curves of heavy metal. Other chemicals are used to prepare nickel selective membrane. The impurities on the surface of the ion exchange membrane were removed by the ultrasonic cleaner (DELTA DC400H); the concentration of the heavy metals was analyzed by flame atomic absorption spectrometer (FAAS, PerkinElmer AAnalyst 400); Direct current power supply (GPR-35H20D) applied an electric field to carried out the electrodialysis operation.

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Table 1 The used chemicals in this experiment Chemical

Formula

Source

Purity (%)

Cu standard solution

Cu

America

100

Ni standard solution

Ni

America

100

Dithizone

C13 H12 N4 S

England

85

Methacrylic acid

C4 H6 O2

Japan

99

Ammonia water

NH4 OH

Japan

28

Ethylene glycol

C10 H14 O4

China

98

Azodiisobutyrodinitrile

C8 H12 N4

England

99

Chloroform

CHCl3

England

99.8

Nickel nitrate

Ni(NO3 )2 · 6H2 O

Japan

98

Hydrochloric acid

HCl

England

37

2.2 Preparation of Nickel Selective Membrane The preparation process of the nickel selective membrane is mainly divided into two steps in this study. Firstly, the nickel-dithizone complex is synthesized, then, ionimprinted is used. The detailed procedure is described as following: the preparation process must first synthesize nickel-dithizone. In the beginning, the dithizone of 3.076 g was added slowly to the ammonia water (0.8 M), then, the nickel nitrate solution (0.12 M) was added with vigorous stirring. The precipitate was produced and dried in an oven for later use. Secondly, take 0.608 mL of methacrylic acid, 6.796 mL of ethylene glycol, 0.36 g of azodiisobutyrodinitrile and the dried nickel-dithizone complex is added to 25 mL of chloroform. The mixture is completed according to the above procedure and ultrasonically shaken for 60 min to assure thoroughly mixed. Then, the commercial cation exchange membrane (CEM) with length 11.5 cm * width 10.5 cm was placed within the above mixture for 30 min, then taken out. The membrane is then placed in an airtight glass plate and heated at 95 °C for 72 h. Finally, the membrane was soaked in 1 M hydrochloric acid for 60 min to remove nickel ions on the surface. After that, washed the membrane with deionized water, and dried it at room temperature to complete the preparation. Figure 1 shows the original commercial CEM and selfmanufacturing nickel selective membrane. The finished membrane will be observed using a field emission scanning electron microscope (FESEM, JEOL JSM-6700F) to obtain the surface morphology and composition after preparation.

2.3 Adsorption and the Electrodialytical Experiment To understand the adsorption performance of the self-manufacturing nickel selective membrane, the membrane with a size of 5.75 cm in length and 5.25 cm in width

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Fig. 1 The photo of commercial CEM (left) and nickel selective membrane (right)

is tested. The nickel ion concentration was 20, 50, 100, 200, and 400 mg/L, and the stirring was continued at a speed of 300 rpm. All adsorption time is controlled within 90 min and takes the sample every 30 min for nickel ion concentration analysis. Figure 2 is the system of the ED for recycling the heavy metal wastewater in this study. The experiment of the ED was carried out in a PVC tank with a size of 12 cm × 12 cm × 11 cm (length * width * height), and the anode and cathode electrode was graphite electrode. Both electrodes are 10 cm in length, 8.5 cm in width, and 0.3 cm in thickness. The electrode distance is 5 cm in between and operates at 50 V of constant voltage. The self-made nickel selective membrane is placed in the center of the reaction tank, to separate a wastewater end and recyclate end. To obtain the selectivity of the nickel selective membrane for wastewater containing different

Fig. 2 The diagram of the ED system

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heavy metal, the wastewater with the same concentration (20 mg/L) of copper and nickel ions is tested in the wastewater. The recyclate end is the 0.01 sulfuric acid solution. The volume of both ends is 300 mL and the sample is analyzed every 10 min during the 60 min treatment.

3 Results and Discussion 3.1 Surface Morphology of Membrane Figure 3 shows the surface morphology of CEM and self-manufacturing nickel selective membrane by FE-SEM. It can be clearly noted that the surface of CEM has larger particles and pores, high particle inhomogeneity, and poor surface flatness. In addition, the nickel selective membrane is coated with a polymer layer of selective for nickel ions, which is much flatter than the surface of CEM. The difference in surface morphology is presumably due to CEM allowing various cations to pass through that resulting in large pores and uneven distribution. In contrast, the surface of the nickel selective membrane is relatively close, and the adhesion of complex layer on the surface allowing specific nickel ions to pass through, therefore the overall homogeneity is high. Figure 4 shows the surface element diagram of CEM and nickel selective membrane by energy dispersive spectrometer (EDS). According to analysis results, it was found that the surface elements of the CEM were composed of carbon, fluorine, sulfur, and sodium. The nickel selective membrane is mainly composed of carbon, chlorine, sulfur, and nickel. Among these, the nickel and chlorine elements may be caused by the residues of chemicals used in the preparation of the selective membrane.

Fig. 3 The surface morphology of commercial CEM (left) and nickel selective membrane (right)

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Fig. 4 The diagram of EDS spectra (left: commercial CEM; right: nickel selective membrane)

3.2 Adsorption Efficiency of Membrane Since the released of the negative charge ions on the cation exchange membrane without power supply, the membrane will adsorb the positive charge ions in the solution to maintain the electrical neutrality. Once the electric field is supplied, the positive charge ions will be adsorbed rapidly on the membrane and moves toward the cathode. Therefore, more positive ions adsorbed will increase the removal efficiency of heavy metals. To understand the adsorption efficiency of the nickel selective membrane for nickel ion, different nickel concentrations are tested and compared with CEM. Before the adsorption experiment on different concentrations, around 150 mg/L of nickel ions is tested to obtain the adsorption reaction. The results show in Fig. 5. It was found that the adsorption efficiency of the selective membrane reached around 88% at the adsorption time of 90 min, the nickel concentration decreased from 143 to 18 mg/L. The adsorption saturation of selective membrane is at a reaction time of 90 min. The result shows that selective membrane could effectively absorption

Nickel concentration (mg/L)

200 180 160 140 120 100 80 60 40 20 0

0

30

60

Time (min) Fig. 5 The concentration variation of nickel with treatment time

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Adsorption efficiency (%)

68 Nickel selective membrane Cation exchange membrane

100 80 60 40 20 0

0

20

50

100

200

400

Nickel Concentraiton (mg/L)

Fig. 6 The absorption efficiency of different nickel concentration

nickel ions, which benefited the recycling of wastewater that contained nickel heavy with the ED process. As a consequence, the subsequent adsorption experiment was controlled at 90 min. Figure 6 shows the adsorption efficiency of nickel ions on nickel selective membrane and CEM at different concentrations. When the nickel concentration is lower than 50 mg/L, it is notable found that have not significant adsorption efficiency for both membranes. For the CEM, the adsorption efficiency of nickel increases slowly with the concentration of nickel ions, and the highest adsorption efficiency of nickel is about 18% at the concentration of 400 mg/L. In comparison, the adsorption efficiency of the nickel selective membrane is higher significantly than that of the CEM at 100 mg/L of concentration, the highest adsorption efficiency reaches around 90% in the concentration of 200 mg/L. At the high concentration of 400 mg/L, the adsorption efficiency is still up to 60%. From the result, most of the nickel concentration could be removed without the electronic field for the nickel selective membrane. The results show that the adsorption efficiency is relatively high under the high concentration of nickel ions, and the nickel selective membrane has higher adsorption efficiency than the CEM. Because the nickel selective membrane has a nickel-imprinting site on the surface, these sites can adsorb nickel ions more effectively than the CEM, thereby enhancing the adsorption efficiency. The further calculation shows the adsorption capacity of the membrane for nickel, the nickel selective membrane has reached to 63 mg/g, which is about 3 times (22 mg/g) of the CEM.

3.3 Removal Efficiency of the ED Process Figure 7 shows the removal efficiency of copper and nickel ions in wastewater under the nickel selective membrane combined with the ED system. It shows that the nickel

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30

Concentration (mg/L)

25 20 15 10 5 0

Ni 2+ 2+ Cu

0

10

20

30

40

50

60

Time (min)

Fig. 7 The concentration variation of heavy metals in the ED process

selective membrane can effectively remove nickel ions while copper ions have no obvious effect. The nickel ion decreased from the initial 21–11 mg/L within 60 min of the ED operation, and the removal efficiency is around 50%. This indicates that the nickel selective membrane has favorable selectivity, as nickel ions can move effectively through the membrane to the recyclate end. Meanwhile, the concentration of nickel ions of recyclate end is observed having increased from 0 to 7 mg/L, the recovery efficiency can be up to 70% after 60 min of treatment time (data not shown). It is known from the results that the nickel selective membrane not only effectively passing specific nickel ions through the membrane, but recovering from the recyclate end, which indicate the feasibility of the nickel selective membrane. This result will provide another choice to industrial wastewater treatment techniques for removing the nickel-metal wastewater.

4 Conclusion Based on experimental results, several conclusions can be drawn: 1. In this study, the nickel selective membrane has successfully prepared by an ion-imprinting technique in which the membrane surface is flat and uniform. 2. Nickel selective membrane can effectively adsorb nickel ions in wastewater; the highest adsorption efficiency is up to 90%. The adsorption capacity is 63 mg/g, which is about 3 times that of CEM. 3. The nickel selective membrane has favorable selectivity to nickel ions and has little adsorption effect on other ions, indicating that the membrane is effective on separation.

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4. The nickel selective membrane can effectively treat nickel ions in wastewater. Giving that 50% removal efficiency in the wastewater end and 70% recovery efficiency of in the recyclate end, after 60 min of treatment time.

References Chen Y, Zhao X, Guan W, Cao D, Guo T, Zhang X, Wang Y (2017) Photoelectrocatalytic oxidation of metal-EDTA and recovery of metals by electrode position with a rotating cathode. Chem Eng J 324:74–82 Chen Q, Yao Y, Li X, Lu J, Zhou J, Huang Z (2018) Comparison of heavy metal removals from aqueous solutions by chemical precipitation and characteristics of precipitates. J Water Process Eng 26:289–300 Chojnacka K, Mikulewicz M (2019) Green analytical methods of metals determination in biosorption studies. Trends Anal Chem 116:254–265 Luiz A, McClure DD, Lim K, Coster HGL, Barton GW, Kavanagh JM (2019) Towards a model for the electrodialysis of bio-refinery streams. J Membr Sci 573:320–332 Strathmann H (2010) Electrodialysis, a mature technology with a multitude of new applications. Desalination 264(3):268–288 Vatanpour V, Madaeni SS, Zinadini S, Rajabi HR (2011) Development of ion imprinted technique for designing nickel ion selective membrane. J Membr Sci 373(1–2):36–42 Wang Z, Kong D, Qiao N, Wang N, Wang Q, Liu H, Zhou Z, Ren Z (2018) Facile preparation of novel layer-by-layer surface ion-imprinted composite membrane for separation of Cu2+ from aqueous solution. Appl Surf Sci 457:981–990 Wang R, Ng DHL, Liu S (2019) Recovery of nickel ions from wastewater by precipitation approach using silica xerogel. J Hazard Mater 380 Ye ZL, Ghyselbrecht K, Monballiu A, Pinoy L, Meesschaert B (2019) Fractionating various nutrient ions for resource recovery from swine wastewater using simultaneous anionic and cationic selective-electrodialysis. Water Res 160:424–434

Degradation of Phenol by Three-Dimensional Electrode-UV Photo-Oxidation System Fuchen Ban, Qiu Jin, and Meiran Li

Abstract Aiming at the treatment of phenol simulated wastewater by the threedimensional electrode-UV photo-oxidation system, the effects of electrolyte dosage, electrode spacing, voltage, initial pH, and aeration amount on phenol removal rate were analyzed. The optimum process conditions for the three-dimensional electrodeUV photo-oxidation treatment of phenol were determined as follows: electrolyte dosing amount of Na2 SO4 is 1 g/L, main electrode electrode distance is 7 cm, voltage is 15 V, initial pH is 3.0, and aeration is 11 L/min. Under this optimized condition, the phenol removal rate reached 83.35%. The degradation process of phenol was analyzed by UV-visible absorption spectrum. A kinetic model for the degradation of phenol by three-dimensional electrode-UV photo-oxidation method was established. The three-dimensional electrode-UV photo-oxidation method can effectively reduce organic matter in wastewater and has a better treatment effect on phenol. By further optimizing the reaction conditions, it can be applied to the practice of phenol wastewater treatment engineering. Keywords Three-dimensional electrode · Ultraviolet light · Phenol · Degradation · Decolorization rate

1 Introduction Phenolic compounds are a highly teratogenic carcinogen. After being exposed to phenolic organic substances, the human body is likely to cause cell damage or inactivation, and it is also easy to transfer and accumulate in the environment. Traditional biochemical methods can only transfer such pollutants, secondary pollution still exists (Lyu et al. 2019), and poor adaptability to changes in wastewater concentration. In recent years, the electrochemical method is gradually being widely used due to its advantages of strong oxidation ability, simple operation, and no secondary F. Ban (B) · Q. Jin · M. Li School of Municipal and Environmental Engineering, Shenyang Jianzhu University, 110168 Shenyang, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_7

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pollution (Na et al. 2010). Three-dimensional electrode technology, by filling granular materials between two-dimensional electrodes, makes up for the shortcomings of the traditional two-dimensional electrode method, increases the effective area of the electrode, reduces energy consumption, and improves the mass transfer rate. The combination of three-dimensional electrode technology and photo-oxidation technology has become the further research direction of many scholars (You et al. 2017; Deng et al. 2015; Liang and Zhu 2016; Cheng and Han 2016). The strong oxidation of ultraviolet light can effectively use light energy to degrade organic pollutants, while the redox effect of electrodes can generate free radicals with strong oxidizing and catalytic activity, thereby improving the removal effect of organic matter. In view of this, this experiment combines the ultraviolet photooxidation technology with the three-dimensional electrode technology, and studies the effects of various reaction factors on the phenol removal rate with phenol simulated wastewater as the research object, and determines the optimal reaction conditions for the three-dimensional electrode-ultraviolet oxidation method.

2 Materials and Methods The experiment uses a self-made three-dimensional electrode-UV photoreactor, as shown in Fig. 1. The electrolytic cell is made of plexiglass. The length × width × height is 140 mm × 120 mm × 200 mm. The effective volume is 2 L. The

Fig. 1 Sketch map of test device

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ultraviolet lamp is fixed in the electrolytic cell by an iron stand. As an electrocatalytic anode, titanium-based DSA material has high oxygen evolution overpotential, high electrochemical stability and corrosion resistance. In this test, the anode is a RuO2 – IrO2 /Ti plate, the cathode is a titanium plate, and the particle electrode is placed in a reactor. The phenol wastewater was sampled every 10 min, and the test was performed for a total of 60 min. CODcr was determined by a fast closed catalytic digestion method, and 4-aminoantipyrine direct spectrophotometry was used to calculate the phenol degradation rate.

3 Results and Discussion 3.1 Electrode Material Characterization It can be clearly seen in Fig. 2a that the main component of the RuO2 –IrO2 /Ti electrode is Ti matrix, which is coated with Ru and Ir oxides. Titanium has good electrical conductivity, high mechanical strength, low density, stable chemical properties, and is not easy to be oxidized. Ru and Ir oxides are currently the best known electrocatalysts and have high oxygen evolution overpotential. Therefore, the current efficiency of electrolytic organic matter is high. As can be seen in Fig. 2b, the titanium substrate has a uniform, dense coating and small intermediate cracks, so it can effectively prevent the diffusion of oxygen to the substrate, reduce the formation of the TiO2 insulating layer, and reduce the interface resistance. Defect sites on the RuO2 –IrO2 /Ti electrode surface can strengthen the reaction of H2 O near the electrode to lose electrons, and promote the formation of OH. Fine seed particles are deposited on the substrate to obtain a uniform layer, which can improve the electrode’s activity to a certain extent. At the same time, it can be seen that the electrode layer has a rough

Fig. 2 Energy spectrum diagram of the surface composition and SEM micrograph of RuO2 –IrO2 /Ti electrode

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surface and a large specific surface area, which meets the requirements of porous electrodes.

3.2 Effect of pH and Voltage Test reaction conditions were controlled as follows: phenol concentration was 150 mg/L, COD concentration was 378.5 mg/L, electrode plate spacing was 10 cm, voltage was 25 V, aeration amount was 9 L/min, and particle electrode dosage was 35 g/L. Electrolyte Na2 SO4 was added at a concentration of 1 g/L and left under magnetic stirring for 30 min to eliminate the effect of electrode adsorption on the removal of phenol. The reaction was performed for 60 min under UV light irradiation. The curve of the effect of pH on the phenol COD removal rate is shown in Fig. 3a. When pH = 3.0, the COD removal rate of phenol was 68.35%. The acidity and alkalinity in the solution have a great influence on the reduction reaction of oxygen. In the acidic solution, the reduction reaction of oxygen will generate H2 O2 , and the amount of H2 O2 directly affects the amount of strong oxidizing OH generated in the subsequent reaction. O2 + 2H+ + 2e → H2 O2

(1)

H2 O2 + H+ → OH + H2 O

(2)

2H+ + 2e− aq → H2

(3)

The lower the pH, the greater the H+ concentration, the more severe the hydrogen evolution side Reaction 3, which will seriously affect the formation of OH and H2 O2 ; the higher the pH value, the lower the H+ concentration, and the reaction system is insufficient to provide the Reaction 1. At the same time, the higher pH value will

Fig. 3 Effect of pH (a) and electrolytic voltage (b) on phenol COD removal rate

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make H2 O2 ineffective decomposition (Juang et al. 2016). From the above analysis, it is known that in the electrochemical reaction, the acidic conditions are favorable for the removal of organic matter, and the initial pH requirements are very strict. To reduce the effect of side reactions, the initial pH was set to 3.0 in this experiment. Voltage is the basis of the three-dimensional electrode system electrolysis and the driving force for repolarization of particle electrodes. Appropriate voltage can improve the removal rate of organic matter. As can be seen from Fig. 3b, the change in voltage has a significant effect on the decolorization rate of phenol. The degradation rate of phenol increased significantly with increasing voltage, reaching a maximum of 77.54% at 15 V. Analyzing the reason, as the voltage gradually increases, the effect of the electric field is strengthened, the number of repolarized activated carbon increases, the effective electrode area increases, the electrochemical reaction kinetics increases, and the decolorization rate of phenol is increased (Dai et al. 2015). When the voltage continues to increase, the decolorization rate tends to decrease. The greater the tank voltage, the greater the energy consumption. This shows that there are limit values for the electrochemical reactor when the internal conditions are fixed, that is, there is a limit maximum for the degree of organic matter removal. Above the limit value, excess electrical energy will promote the side reactions of hydrogen evolution and oxygen evolution of the cathode and anode, which will reduce the decolorization rate of wastewater. Comprehensive consideration, the appropriate voltage for the experiment is 15 V.

3.3 Effect of Plate Spacing, Na2 SO4 Dosage and Aeration It can be seen from Fig. 4a that when the distance between the cathode electrode plate and the anode electrode plate is 3 cm, the decolorization rate of phenol is relatively low. With the increase of the distance between the electrode plates, the decolorization rate of phenol gradually increased. The degradation effect of phenol was the best when the electrode distance was 7 cm. In this process, the resistance changes with the change of the pole spacing. When the plate spacing is small, the resistance is relatively small. The increase of the current will reduce the number of particle electrode polarizations, which will affect the degradation rate of organic matter and the decolorization rate. Subsequently, the pole spacing gradually increased, and the resistance also increased. At this time, the solution had a good mass transfer effect, which was conducive to the degradation of organic matter, so the decolorization rate was significantly improved (Palma-Goyes et al. 2014). When the electrode spacing is greater than 7 cm, the decolorization rate of phenol will decrease. The reason is that when the cell voltage is constant, the distance between the plates is too large, the resistance is too large, and the electric field strength is reduced, which further increases the distance of mass transfer, slows down the migration rate of the organic molecules, and affects the decolorization effect of phenol. Therefore, 7 cm is selected as the optimal plate spacing.

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Fig. 4 Effect of plate spacing (a), Na2 SO4 dosage (b) and aeration (c) on phenol COD removal rate

It can be seen from Fig. 4b that the dosage of the electrolyte Na2 SO4 has a great influence on the decolorization rate of phenol. With the increase of the amount of Na2 SO4 added, the decolorization rate of phenol first increased and then decreased. When its mass concentration increased from 0 to 1 g/L, the decolorization rate of phenol increased from 33.07 to 68.77%, and the decolorization effect was significantly improved. The decolorization effect was the worst when Na2 SO4 was not added. The decolorization rate of phenol reaches the maximum at 1 g/L. The reason is that with the increase of the amount of electrolyte added, the electrical conductivity of the wastewater increases, which enhances the conductivity of the wastewater and facilitates the degradation of organic matter. In the case of other reaction conditions unchanged, when the amount of Na2 SO4 added exceeds 1 g/L, the decolorization effect of phenol immediately shows a downward trend. At this time, the continuous increase of electrical conductivity causes the side reactions such as hydrogen evolution to intensify (Cotillas et al. 2018). Not only the power consumption of electrolysis, but also a large number of air bubbles, reducing the mass transfer rate, is not conducive to the progress of the reaction. To sum up, the suitable dosage of Na2 SO4 in the experiment is 1 g/L.

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During the electrolysis process, the air provided by the air compressor enters the bottom of the reactor through the air inlet pipe and enters the electrolytic tank through the air distribution plate through the air distribution plate. The introduced air not only supplements the consumed oxygen for the reaction but also increases the stirring effect. The aeration can effectively reduce the short-circuit current of the particle electrode, and improve the electrolytic efficiency and mass transfer speed. It can be seen from Fig. 4c that the increase in the aeration amount significantly improves the decolorization effect of the phenol. When the aeration volume flow increased to 11 L/min, the decolorization rate reached a maximum of 83.35%. The incoming air provided the cathode with sufficient oxygen to react to form H2 O2 , which in turn promoted the generation of hydroxyl radicals. With the increase of the aeration volume, the decolorization rate continues to increase. When the aeration volume flow reaches 15 L/min, the decolorization effect has a downward trend, because the aeration volume is too large, the stirring effect is enhanced, the mass transfer speed is too fast, and the shorten The reaction time of the organic matter on the surface of the particle electrode is affected (Isarain-Chávez et al. 2017), thereby affecting the degradation efficiency of the organic matter. Therefore, comprehensive consideration, the optimized experimental aeration volume flow should be 11 L/min.

3.4 Reaction Kinetics of Three-Dimensional Electrode-UV Photo-Oxidation System The first-order reaction kinetics equation was best to fit reaction kinetics of threedimensional electrode/UV oxidation system. The reaction rate constant of k is affected by several factors, such as voltage (U), plate spacing (M), Na2 SO4 (Q), pH (D) and aeration (F) under certain phenol concentration (Suhadolnik et al. 2016). Then the apparent correlation could be established: k = f(U, D, M, Q, F) = µUa Db Mc Qd Fe where μ, a, b, c, d, e are constant. In order to get these constant, above results were used to statistical analysis. Then the reaction rate constant of k can be expressed as: k = 1.05 × 10−12 U1.7784 D1.2289 M0.9941 Q0.9210 F1.4613 So kinetic equation of degradation phenol in three-dimensional electrode–UV oxidation system can be expressed as:   Ct = C0 exp −1.05 × 10−12 U1.7784 D1.2289 M0.9941 Q0.9210 F1.4613 t

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From the above equation, the order of factors affecting the reaction rate is as follows: voltage > plate spacing > Na2 SO4 > pH > aeration under certain reaction conditions. Comparison between kinetic model and experimental data, the actual value was well fitted to this dynamic model.

3.5 UV-Vis Spectral Analysis of Phenol Degradation Process In this experiment, the scanning range of UV-visible absorption spectrum was selected to be 200–400 nm. Due to the π → π* transition in phenol in aqueous solution, there are two typical absorption peaks in the ultraviolet region, which are at the wavelengths of 269 nm (B band) and 210 nm (E band). The wavelength of 269 nm (B band) was determined experimentally (Sarmento et al. 2016). By observing the UV-Vis spectra before and after the treatment with the phenol solution, it can be found that the absorption peak of the solution at 269 nm gradually weakens with the extension of the treatment time. After 60 min of treatment, there was only slight absorption here, indicating that the phenol in the water was oxidized by OH, the chromophore was separated from the parent chain, and the benzene ring structure was gradually destroyed. The ring intermediate or final product has no absorption in the ultraviolet region, leading to a significant decrease in absorbance, indicating that phenol has been basically mineralized into the final product through catalytic degradation; no other absorption peaks appear at 280–400 nm, indicating that phenol did not have Converted into other macromolecular organic compounds with absorption in this wavelength range (Luca et al. 2015; Li et al. 2014) (Fig. 5).

Fig. 5 The ultraviolet absorption spectrum of phenol solution with different electrochemical oxidation times (a: 0 min, b: 15 min, c: 30 min, d: 45 min, e: 60 min)

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4 Conclusions In the three-dimensional electrode-ultraviolet oxidation reaction, changes in electrolyte dosage, main electrode electrode spacing, voltage, initial pH, and aeration amount have a great effect on the decolorization effect of phenol wastewater. Threedimensional electrode-UV photo-oxidation method for phenol wastewater with a mass concentration of 150 mg/L. The optimal process conditions are: electrolyte Na2 SO4 dosage is 1 g/L, electrode electrode spacing is 7 cm, voltage is 5 V, initial pH is 3 and the aeration is 11 L/min. Under this optimized condition, the decolorization rate of phenol reached 83.35%. The three-dimensional electro-Fenton method for oxidative degradation of phenol complies with the first-order kinetic model. The overall reaction fitting equation is Ct = C0 exp(−1.05 × 10–12 U1.7784 D1.2289 M0.9941 Q0.9210 F1.4613 t), and it is verified that the fitting effect is good. Through ultraviolet-visible absorption spectrum detection and analysis, it is inferred that the concentration of phenol in oxidative degradation is continuously reduced, and the degradation effect is better; at the same time, a small amount of quinone compounds are generated during its degradation. Acknowledgements This work was financially supported by Liaoning Natural Science Fund Project under Grant number 2014020073.

References Cheng ZL, Han S (2016) Preparation and photoelectrocatalytic performance of N-doped Tio2 /NaY zeolite membrane composite electrode material. Water Sci Technol 73(3):486–492 Cotillas S, Llanos J, Cañizares P, Clematis D, Panizza M (2018) Removal of Procion Red Mx-5B dye from wastewater by conductive-diamond electrochemical oxidation. Electrochim Acta 263:1–7 Dai Q, Zhou J, Meng X, Feng D, Wu C, Chen J (2015) Electrochemical oxidation of cinnamic acid with Mo modified PbO2 electrode: electrode characterization, kinetics and degradation pathway. Chem Eng J S1385894715017258 Deng YJ, Lu Y, Liu JK, Yang XH (2015) Production and photoelectric activity of P and Al co-doped ZnO nanomaterials. Eur J Inorg Chem 2015(22):3708–3714 Isarain-Chávez E, Baró MD, Rossinyol E, Morales-Ortiz U, Sort J, Brillas E et al (2017) Comparative electrochemical oxidation of methyl orange azo dye using Ti/Ir-Pb, Ti/Ir-Sn, Ti/Ru-Pb, Ti/Pt-Pd and Ti/RuO2 , anodes. Electrochim Acta S0013468617310976 Juang Y, Liu Y, Nurhayati E, Thuy N, Huang C, Hu CC (2016) Anodic fabrication of advanced titania nanotubes photocatalysts for photoelectrocatalysis decolorization of Orange G dye. Chemosphere 144:2462–2468 Li XY, Cui YH, Feng YJ (2014) Reaction pathways and mechanisms of the electrochemical degradation of phenol on different electrodes. Water Res 39(10):1972–1981 Liang F, Zhu Y (2016) Enhancement of mineralization ability for phenol via synergetic effect of photoelectrocatalysis of g-C3 N4 film. Appl Catal B 180:324–329 Luca CD, Ivorra F, Massa P, Fenoglio R (2015) Iron–alumina synergy in the heterogeneous Fentontype peroxidation of phenol solutions. Chem Eng J 268:280–289

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Lyu J, Han H, Wu Q, Ma H, Ma C, Dong X et al (2019) Enhancement of the electrocatalytic oxidation of dyeing wastewater (reactive brilliant blue KN-R) over the Ce-modified Ti-PbO2 electrode with surface hydrophobicity. J Solid State Electrochem Na N, Xue-Wei D, Xiao-Li D, Jia LI, Liang S (2010) Catalytic wet air oxidation for degradation of reactive brilliant blue KN-R. Environ Sci Technol 33(12):99–101 Palma-Goyes RE, Silva-Agredo J, González I, Torres-Palma RA (2014) Comparative degradation of indigo carmine by electrochemical oxidation and advanced oxidation processes. Electrochim Acta 140:427–433 Sarmento AP, Borges AC, De Matos AT, Romualdo LL (2016) Phenol degradation by Fenton-like process. Environ Sci Pollut Res 23(18):18429–18438 ˇ M (2016) Mechanism and kinetics of phenol photocatalytic, Suhadolnik L, Pohar A, Likozar B, Ceh electrocatalytic and photoelectrocatalytic degradation in a TiO2 -nanotube fixed-bed microreactor. Chem Eng J S1385894716308348 You H, Wu Z, Jia Y, Xu X, Xia Y, Han Z et al (2017) High-efficiency and mechano/photo- bi-catalysis of piezoelectric-ZnO@ photoelectric-TiO2 , core-shell nanofibers for dye decomposition. Chemosphere 183:528–535

Material Flow Analysis of CRT Monitor, Electric Fan and Refrigerator Through the Primitive E-waste Dismantling in Buriram Province, Thailand Sangsuree Srisa-ard, Penpato Siriruttanaprasert, Thapanee Piboon, Tassanee Prueksasit, and Narut Sahanavin Abstract The great number of small-entrepreneurs for electronic waste (e-waste) dismantling in the rural community are located in the northeastern of Thailand especially in Ban Mai Chiyaphot district, Buriram province. The observational study on the amount of e-waste entry this area was conducted during February–July, 2019. The top three ranked of e-waste amount found in this area were electric fan (1400– 7000 units/month), CRT monitor (100–3400 units/month) and refrigerator (30–2700 units/month), respectively. Material flow analysis was implemented to investigate the flow of valuable material such as recyclable plastic, valuable ferrous and non-ferrous metals, and non-valuable materials. The result showed that valuable material, i.e. precious metals, and recyclable plastic, could be obtained from CRT monitor, electric fan and refrigerator at 34% 94% and 84% (w/wt), respectively. The rest non-valuable materials such as glass, plywood, polyurethane foam that could not be sold in the local market would then be disposed mixed with the municipal solid waste at the open dump site. Moreover, illegally breaking of glass monitor and open burning of non-recycle plastics like polyurethane foam at this site can finally increase potential risks to the environment and human health of the local people in this area. Keywords Electronic waste · CRT monitor · Electric fan · Refrigerator · Material flow analysis

S. Srisa-ard (B) · P. Siriruttanaprasert · T. Piboon Center of Excellence on Hazardous Substance Management (HSM), Chulalongkorn University, Bangkok 10330, Thailand e-mail: [email protected] T. Prueksasit Department of Environmental Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand N. Sahanavin Department of Public Health, Faculty of Physical Education, Srinakharinwirot University, Bangkok 26120, Nakhon-Nayok, Thailand © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_8

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1 Introduction Electrical and electronic equipment waste (WEEE) or e-waste is a serious problem in Thailand. The rapid technology growth in consumer electronics products has created e-waste as the main source, combined with illegally smuggling from other countries. E-waste has then become one of the most serious management problems in Thailand. According to the reports published by Pollution Control Department (PCD) in 2015 and 2018, the amount of e-waste had been produced around 384,233 tons and increased to 414,600 tons from 2015 to 2018 (Meester 2019). The CRT monitor was found the most contribution of e-waste, followed by refrigerator, washing machine, desktop computer, CD/DVD player, and digital camera, respectively (Pollution Control Department 2019). In the present, WEEE is a key resource in the circular economy as it has been given a high amount of valuable materials (Meester 2019). Up to now, however, Thailand has no properly e-waste management enforcement and still has a wide gap in the complete management system. A large proportion of e-waste has been handled by the informal sector, which implemented in rural communities (Thongkaow and Prueksasit 2017). The great number of small-entrepreneurs for e-waste dismantling in the rural community is located in the northeastern of Thailand especially in Kalasin province, Buriram province, and Ubon-Ratchathani province (Vassanadumrongdee et al. 2015). The dismantling method in the informal sector for valuable material removal usually used low technology investment, inappropriate equipment, and less operation cost. Primitive methods are operated in the dismantling process by using a hammer, chisels screwdrivers, and bare hands to separate a different material (Chi et al. 2011). According to such a method, the dismantling process is able to cause serious environmental problems and adverse health impacts. For example, crushing CRT monitors can release heavy metals such as lead and mercury; refrigerator dismantling may emit coolant chemicals that led to contaminate in the environment. Moreover, the separating method for copper from cable and electronic wire by open burning is considerable a typical one. Open burning of the cable and unwanted material may then emit toxic air pollutants to the environment (Chi et al. 2011), while residue waste after burnt is left with general municipal waste in the public area of the community. Material flow analysis (MFA) is one of the most widely accepted and utilized tools to measure input–output materials and examine the pathways of valuable and non-valuable materials of the dismantling process of each e-waste type. In this study, CRT monitor, electric fan, and refrigerator in the dismantling community located in Ban Mai Chiyaphot district, Buriram province, was then investigated. This research aims to evaluate the material flow, sorted valuable materials, and the debris left from dismantling by informal separators. These data would enable the local administrative organizations to gain insight into a particular dismantling method and the tendency of the amount of all relevant usable and unusable materials that occurred in the area. The result data would then be taken to design good guidance or practice for e-waste management in informal dismantling sectors in rural areas.

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2 Material and Methods The data of the target e-waste types that imported to the study area (Ban Mai Chiyaphot district, Buriram province) were collected from the monthly record of e-waste purchasing by small-entrepreneurs of e-waste dismantling located at Ban Mai Chiyaphot district, Buriram province during February–July, 2019. The e-waste type, including CRT monitor, electric fan and refrigerator, were selected for this study because there were the top three ranked in the e-waste amount found in this area. A material flow analysis was used to optimize the mass balance of CRT monitor, electric fan and refrigerator. The data was collected using observation. Each e-waste was weighed before dismantling process, and both valuable and non-valuable materials after dismantling were weighed in any part of the material separately with electric balancer; OHAUS model T24PE (0.1 mg readable) for the large part, OHAUS model V22PWE15T (0.1 mg readable) for small pieces. The sample number of e-waste dismantled in this observation was 34 of electric fans, 34 of CRT monitors, and 17 of refrigerators. The weight of e-waste before dismantling from the beginning until the end of the process was measured and recorded, and all data of the sorted components would be analyzed and reported in terms of mean and SD.

3 Result and Discussion 3.1 Material Flow Analysis of Electric Fan The separation process of the unused electric fan was shown in Fig. 1. The e-waste dismantling workers used a hammer, chisel, screwdriver, scissor for physical dismantling, and burned some materials on the open areas. Firstly, the grill and blade, control panel and motor set were separated. After the separation process, the percentage of separated materials was calculated as summarized in Table 1. Valuable materials such as plastic, steel, copper, aluminum and wire were 50.89 ± 8.74%, 31.36 ± 7.49%, 4.52 ± 2.25% 4.84 ± 2.98% and 2.48 ± 1.20% (w/wt), respectively. Plastic was the main proportion of valuable materials that were obtained from fan blade, body, motor housing and plastic grill nut. From the previous research, the plastic types separated from the electric fan consists of polypropylene (43%), polyamide (19%), acrylonitrile butadiene styrene (17%) and polycarbonate (10%) (Taurino et al. 2010). Steel material was obtained from grill, motor shaft with support pin and nut. Copper material and aluminum were obtained from the motor set. While Non-value material such as non-recyclable plastic and plywood was accounted for 1.84 ± 1.54% and 4.07 ± 3.29%, respectively. Non-recyclable plastic, thermosetting plastic (Osborne Industries, Inc. 2019), and plywood from a base of the fan could not be sold at the

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Fig. 1 The material flow of the electric fan dismantled through a primitive method Table 1 The percentage of material dismantled from electric fans

Percentage (%) N = 34 Valuable materials

94.09

Plastic

50.89 ± 8.74

Steel

31.36 ± 7.49

Copper

4.52 ± 2.25

Aluminum

4.84 ± 2.98

Wire

2.48 ± 1.20

Non-valuable materials

5.91

Non-recyclable plastic

1.84 ± 1.54

Plywood

4.07 ± 3.29

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local market of this study area and would be disposed with a mixed municipal solid waste at open dumpsite.

3.2 Material Flow Analysis of CRT Monitor A CRT monitor was separated through the process, as shown in Fig. 2. The dismantling process of this e-waste type was the same method as mentioned for an electric fan. A frame that is the outer part of the CRT monitor was firstly taken off. Then the inner parts were separated such as a speaker, yoke TV, printed circuits boards (PCBs), and CRT monitor. The percentage of separated material after finish all separation processes was quantified, as shown in Table 2. The percentage of valuable materials such as plastic, steel, print circuit board (PCB), wire, copper and aluminum were 14.53 ± 6.22%, 11.06 ± 3.56%, 4.57 ± 8.82%, 1.44 ± 1.15% 1.04 ± 3.00% and 0.48 ± 0.42% (w/wt), respectively. The main valuable material component of

Fig. 2 The material flow of the CRT monitor dismantled through a primitive method

86 Table 2 The percentage of material dismantled from CRT monitors

S. Srisa-ard et al. Percentage (%) N = 34 Valuable materials

33.12

Plastic

14.53 ± 6.22

Steel

11.06 ± 3.56

PCB

4.57 ± 8.82

Wire

1.44 ± 1.15

Copper

1.04 ± 3.00

Aluminium

0.48 ± 0.42

Non-valuable materials

66.88

Glass

66.46 ± 0.29

Waste

0.42 ± 0.68

the CRT monitor was plastic that was obtained from the frame (outer part) and yoke. From the previous research, Polyphenylene ether/polystyrene (63%), polycarbonate/acrylonitrile butadiene styrene (32%) and polyethylene terephthalate (5%) was a typical plastic composition of the CRT monitor (Taurino et al. 2010). The separated steel parts came from speaker, yoke, nuts and inner part of CRT monitor, and copper was derived from a copper coil of yoke and wire. Whilst, non-valuable materials such as glass and non-recyclable plastics were 66.46 ± 0.29% and 0.42 ± 0.68%, respectively. It was shown that the waste glass is the main component of the CRT monitor which the dismantler would break this part to get the steel for sale. The waste glass and non-recyclable plastics which were non-value materials could not be sold, and would then be thrown away into the municipal solid waste at the open dumpsite of the communities.

3.3 Material Flow Analysis of Refrigerator The separation process of the waste refrigerator is shown in Fig. 3, and the physical dismantling using a hammer, chisel, screwdriver, scissors, wrench and open burning of some materials was carried out. The dismantling was started by taking out the basket and tray inside a refrigerator. Next, the door and frame, which is the outer part of the refrigerator were removed. Then, the inner parts of the cooling system were separated. From all processes, the separated materials were calculated in the percentage contribution as shown in Table 3. The %contribution of valuable materials, including steel, plastic, copper, aluminum and wire, were 59.36 ± 5.08%, 16.67 ± 6.16%, 4.94 ± 0.33%, 3.31 ± 3.40% and 0.60 ± 0.42% (w/wt), respectively. The result showed that steel was a major separated material of the refrigerator. This steel was taken from the door part, the outer part, nut and electric motor compressor. From the previous research, the plastic that used for manufacturing a refrigerator consists of polystyrene (31%), polyamide (19%), acrylonitrile butadiene styrene

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Fig. 3 The material flow of the refrigerator dismantled through a primitive method

(26%), polyurethane (22%) and polyvinyl chloride (6%) (Taurino et al. 2010), which were obtained from basket and tray part and all inner surface of the refrigerator body. Aluminum material could be separated from the cooling system. For nonvaluable materials, the proportions of polyurethane foam (PU Foam), condenser, rubber and aluminum foil could be determined as 13.49 ± 4.73%, 0.82 ± 0.92%, 0.50 ± 0.52% and 0.31 ± 0.40% (w/wt), respectively. Interestingly, the large pieces of PU Foam could not be sold resulting in the largest proportion of non-valuable material found in this area. A few rubber materials were obtained from door seal and aluminum foil around a capillary tube, which is very small pieces after separation. From the observation, a condenser was separated from the cooling system without

88 Table 3 The percentage of material dismantled from the refrigerator

S. Srisa-ard et al. Percentage (%) N = 17 Valuable material

84.88

Steel

59.36 ± 5.08

Plastic

16.67 ± 6.16

Copper

4.94 ± 0.33

Aluminium

3.31 ± 3.40

Wire

0.60 ± 0.42

Non-valuable material

15.12

PU foam

13.49 ± 4.73

Condenser

0.82 ± 0.92

Rubber

0.50 ± 0.52

Aluminium foil

0.31 ± 0.40

the appropriate method, such as not wearing personal protective equipment, and the e-waste workers could then expose to some toxic chemicals (e.g. R-600a, R-22, R290) released during the dismantling through either inhalation or dermal absorption. With respect to a large amount of PU foam discarded mixed with the municipal solid waste at the open dumpsite, this material has become a big problem in the carrying capacity of the dumpsite space. Consequently, the illegally burning of PU foam has been intentionally done by the e-waste dismantlers, which could pose to harm the environment and the health of the local community finally. The amount of some isolated materials from CRT monitor, electric fan, and refrigerator through the dismantling processes were widely varied, with a high standard deviation, this might result from different design and sizes of each product brands and models. It is noteworthy that there should have some room of uncertainty factors for future works on the predictions of both valuable and non-valuable materials amount retrieved from this informal disassembly of e-waste.

4 Conclusion The data regarding the amount of each valuable material suggested that these three types of e-waste could generate a good income from this informal dismantling. However, there still were some materials that could not be sold and had been handled without a good management system. The good management system should be applied, e.g. (1) All small wire from all electronic appliances should be further dismantling by wire stripping machines to get all valuable materials, and (2) Other wastes should be deposited into sanitary landfills especially designed for the e-waste, or informal separators should collect the residue wastes and send to industrial waste treatment plant and disposal service provider.

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Acknowledgements The research was financially supported by National Research Council of Thailand (NRTC): Thailand Research Challenge Program for WEEE and Hazardous Waste. The author also acknowledges the help of local organizations officers and informal separators in the study area.

References Chi X, Streicher-porte M, Wang MYL, Reuter MA (2011) Informal electronic waste recycling: a sector with special focus on China. Waste Manag 31:731–742 Meester SD et al (2019) Using material flow analysis and life cycle assessment in decision support: a case study on WEEE valorization in Belgium. Resour Conserv Recycl 142:1–9 Osborne Industries, Inc. (2019) https://www.osborneindustries.com/news/difference-betweenthermoplastic-thermosetting-plastic. Last accessed 19 Dec 2019 Pollution Control Department (2015) Thailand state of pollution report 2015 of pollution control department. Bangkok, Thailand Pollution Control Department (2019) Thailand state of pollution report 2019 of pollution control department. Bangkok, Thailand Taurino R, Pozzi P, Zanasi T (2010) Facile characterization of polymer fractions from waste electrical and electronic equipment (WEEE) for mechanical recycling. Wastes Manag 30–2601 Thongkaow P, Prueksasit T, Siriwong W (2017) Contribution title. In: 139th the IIER international conference proceedings Osaka, Japan, pp 12–15 Vassanadumrongdee S, Tanwattana P, Damrongsiri S (2015) A survey of environmental impact of an electronic waste dismantling community in Bangkok. J Environ 11:1–23

Heavy Metal Contamination of Surface Water and Groundwater from the Waste Electrical and Electronic Equipment (WEEE) Recycling Area in Buriram, Thailand Nathida Kongsricharoen, Jayrisa Champa, Navaporn Kanjanasiranont and Tassanee Prueksasit Abstract In this study, the concentrations of heavy metals (Cd, Cr, Ni, Pb, Cu, As, Mn and Zn) were investigated around e-waste dismantling areas. The first area was in Dang-Yai subdistrict (DY), Ban Mai Chaiyaphot district and the second was in Ban Pao subdistrict (BP), Phutthaisong district. Both areas were in Buriram province, Thailand. Concentration of eight heavy metals in surface water (SW) and groundwater (GW) samples during the dry season were measured using Inductively Coupled Plasma Optical Emission spectrometer (ICP-OES) and Inductively Coupled Plasma Mass spectrometer (ICP-MS), respectively. Results show that the surface water was contaminated with Mn, As, Pb and Cu. For Mn (0.395 mg/L) indicated the highest concentration around the WEEE dumping site, while the groundwater was slightly contaminated with Mn (0.020 mg/L) in the domestic water supply. In addition, the contamination of Cu and Pb found in DY-SW1 could be from WEEE. Although, the concentration of all heavy metals under investigation were within the permissible levels. However, continual monitoring of the contaminated surface water and groundwater is necessary to prevent the dissemination of heavy metals. Keywords Heavy metals · E-waste · Water · WEEE

N. Kongsricharoen (B) · J. Champa Center of Excellence On Hazardous Substance Management (HSM), Chulalongkorn University, Bangkok 10330, Thailand e-mail: [email protected] N. Kanjanasiranont Faculty of Environment and Resource Studies, Mahidol University, Nakhon Pathom 73170, Thailand T. Prueksasit Department of Environmental Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_9

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1 Introduction In recent years, electronic appliances have been used by all countries around the world and the number of various kinds of consumer electronic appliances has increased. These appliances are among the most important things that assist people through daily life. Use of electronic appliances is high, and the habits related to their use are changing rapidly. Although electronic appliances make our lives easier, they consist of components with valuable and non-valuable materials as well as some toxic substances. After electronic appliances are expired, damaged and out of date, they become electrical and electronics waste or e-waste, and the disposal and recycling of e-waste affect both the environment and human health (Santhanam et al. 2014; Xinwen et al. 2017). E-waste comes from three sources: consumption, production and importation. According to the 2018 Thailand State of Pollution Report (Pollution Control Department Ministry of Natural Resources 2018), the volume of municipal hazardous waste was estimated to be 638,000 tons, a 3% increase from the previous year. Approximately 65% of it was from Waste Electrical and Electronic Equipment (WEEE) accounting for 414,600 tons, and the rest was household hazardous waste such as batteries, dry cell batteries, chemical containers, and spray bottles, accounting for about 223,400 tons or 35%. Only 83,600 tons or 13% of e-waste, however, was properly disposed. The amount of industrial waste in the management system was 22.02 million tons (Pollution Control Department 2018). This result is still deficient, since there is no regulation to separate hazardous waste from general solid waste, as well as no regulations that would enforce the private sector to be responsible for WEEE management. In 2018, the draft of the Waste Electrical and Electronic Equipment Management Act (B.E…) was passed by the Council of Ministers on December 25, 2018. Previous reports have shown that Thailand’s generated e-waste will likely increase due to the lack of knowledge and understanding on how to discard out-of-date appliances, and the lack of procedures for disposal and recycling for proper e-waste management. Thailand has become one of the largest dumping sites for e-waste from developing countries since China banned the import of plastic waste and ewaste generated through the recycling of e-waste and the massive illegal importing of electronic and plastic scraps/waste from overseas. Buriram province has the second-largest e-waste dismantling community in Thailand. The people have adapted the way they earned their livelihood from farming to dismantling e-waste for higher financial returns. The local sector is more efficient at the collecting and pre-processing stages. They start the process of dismantling e-waste (e.g., TVs, refrigerators, washing machines, computers, VCD/DVD players, and air conditioning units) (Pattida and Tassanee 2017) by cutting, smashing and open burning to separate and collect valuable materials. Dismantling processes use a primitive method without any control. After sorting valuable materials, the residue waste is disposed of at an open dumping site. The heavy metals such as Mn, Cu, and

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Zn in e-waste can leach out from the dumping site, contaminating groundwater and surface water (Electrical and Electronics Institute 2007). Therefore, the main objective of this research is to study the heavy metal contamination of surface water and groundwater, which were near current e-waste dismantling areas and around WEEE dumping sites in Dang-Yai subdistrict and Ban Pao subdistrict.

2 Materials and Methods 2.1 Study Area The study area is located in two dismantling areas. The first area is in Dang-Yai subdistrict, Ban Mai Chaiyaphot district, and the second is in Ban Pao subdistrict, Phutthaisong district. Both areas are in Buriram province in northeastern Thailand. The study areas have a tropical savanna climate, with an average annual temperature of 27 °C. The winter season is from December to mid-February. The dry season is from mid-February to May, and the rainy season is from May to October. A map of the study is shown in Fig. 1. According to the preliminary survey in 2019, Dang-Yai subdistrict was made up of 9 areas and 1,146 households, 90 households of which contained local e-waste dismantling workers; meanwhile, Ban Pao subdistrict was made up of 12 areas and 1,456 households, 66 households of which contained local e-waste dismantling workers. A number of local e-waste dismantling workers in Dang-Yai subdistrict and Ban Pao subdistrict were 156 householders. Both areas have the highest density of local people working in e-waste dismantling in Buriram province. This study focused on important surface water, where used in agricultural farms and groundwater, where the arears usually rely on for irrigation. Sources near operational e-waste dismantling areas (Fig. 2) and around WEEE dumping sites (Fig. 3), where burning of e- waste has been carried out.

2.2 Sample Collection and Preparation All the samples were collected on 28th–30th April 2019 in the dry season. Samples were collected from 15 sampling stations. A total of nine surface water sampling stations and six groundwater sampling stations were selected for this study. The sampling locations of the study areas in Dang-Yai subdistrict and Ban Pao subdistrict are shown in Figs. 4 and 5, respectively. The sampling locations are summarized in Table 1. All surface water samples were collected by a well bucket that was about 30–50 cm in deep, below the surface. They were then transferred into acid cleansed 500 mL polypropylene bottles. All the groundwater samples were collected by acid

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Fig. 1 Map of the study areas in Dang-Yai subdistrict and Ban Pao subdistrict, Buriram Province, Thailand

cleansed 1,000 mL polypropylene bottles. After that, concentrated nitric acid was added in each polypropylene bottle to achieve a pH~2.

2.3 Sample Digestion and Metal Analysis All chemicals (concentrated HNO3 and concentrated HCl) used in study were analytical grade reagents, which were distilled to purified and deionized water; 18 megohm

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Fig. 2 The e-waste dismantling areas in (a) Dang-Yai subdistrict and (b) Ban Pao subdistrict, Buriram province, Thailand

Fig. 3 The WEEE dumping sites in (a) Dang-Yai subdistrict and (b) Ban Pao subdistrict, Buriram province, Thailand.

(Elga Purelab Ultra, UK) was used for solution preparation. The water samples were digested by using the hotplate digestion method (Achaya 2016). Teflon beakers were used for digestion. All the Teflon beaker and flasks were cleaned, soaked in 10% HNO3 for more than 24 h, and then rinsed with deionized water and dried. For the metal analysis, 100 mL of surface water or 200 mL of groundwater were transferred to a Teflon beaker, and then 2 mL of concentrated HNO3 and 1 mL of concentrated HCl were added into each beaker. The beaker was closed and digested with a hotplate at 85 °C to evaporate the sample to a low volume, and then it was cooled to room temperature. Then the sample was filtered using a syringe filter PTFE membrane, with a pore size of 0.45 µm and diameter of 13 mm (Millex-LH), and diluted to 10 mL with 1% HNO3 , after which it was stored in a 10 mL bottle. All surface water samples were measured using an inductively coupled plasma optical emission spectrometer (ICP-OES) and all groundwater samples were determined using an inductively coupled plasma mass spectrometer (ICP-MS).

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Fig. 4 The sampling locations of the study area in Dang-Yai subdistrict, Buriram Province, Thailand. and indicate the sampling station for surface water and groundwater, respectively.  and indicate the WEEE dumping site and e-waste dismantling area, respectively (Source from Google Earth Pro Program (Date of access: November 1st, 2019))

3 Results and Discussion 3.1 Concentrations in Surface Water and Groundwater The average concentrations in the surface water samples were 0.007 and 0.152 mg/L, for As and Mn, respectively. The highest concentration of heavy metals were found in DY-SW1 due to the fact that this area around WEEE dumping site, which were 0.017, 0.068, 0.014 and 0.395 mg/L, for Pb, Cu, As and Mn, respectively. The results indicated that the contamination of the heavy metals may have been from e-waste that leached from the dumping site. At the DY-SW3 station, which was nearest to the e-waste dismantling area, the concentration in the surface water samples were 0.004 and 0.095 mg/L, for As and Mn, respectively. The heavy metals concentrations of Pb and Cu were 0.017 and 0.068 mg/L, respectively at DY-SW1. The concentrations of heavy metals Cd, Cr, Ni, and Zn were not detected. However, the distance difference was not significant, The total heavy metals concentrations are shown in Fig. 6 For As concentration in the surface water samples, these were 0.014, 0.004, 0.006, 0.004 and 0.007 mg/L at DY-SW1, DY-SW3, DY-SW5, DY-SW6 and DY-SW7,

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Fig. 5 The sampling locations of the study area in Ban Pao subdistrict, Buriram Province, Thailand. and indicate the sampling station for surface water and groundwater, respectively.  and indicate the WEEE dumping site and e-waste dismantling area, respectively (Source from Google Earth Pro Program (Date of access: November 1st, 2019))

respectively (Fig. 7). However, As can be usually found in the natural water. The As concentration was within the permissible levels. Mn was determined the highest concentrations (0.395 mg/L) at the DY-SW1 station, which was around the WEEE dumping site. In contrast, Mn was detected slight concentrations: 0.095, 0.118, 0.070, 0.220, 0.004, 0.163 and 0.020, mg/L at DYSW3, DY-SW5, DY-SW6, DY-SW7, BP-SW8, BP-SW9 and DY-GW1, respectively. Exposure to this heavy metal could be from burning and dismantling of computer

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Table 1 Description of the sampling locations Samples

Locations

DY-SW1

Ditch, around WEEE dumping site

DY-SW2

Pond, next to WEEE dumping site

DY-SW3

Pond, the nearest e-waste dismantling area

DY-SW4

Pond, next to e-waste dismantling area

DY-SW5

Canal, next to WEEE dumping site

DY-SW6

Canal, next to WEEE dumping site

DY-SW7

Canal, next to WEEE dumping site

BP-SW8

Pond, the farthest station from WEEE dumping site

BP-SW9

Pond, next to e-waste dismantling area

DY-GW1

Domestic water supply, far from WEEE dumping site and e-waste dismantling area

DY-GW2

Domestic water supply, the nearest WEEE dumping site

DY-GW3

Domestic water supply, next to e-waste dismantling area

DY-GW4

Domestic water supply, next to e-waste dismantling area

BP-GW5

Domestic water supply, next to WEEE dumping site and the farthest station from e-waste dismantling area

BP-GW6

Domestic water supply, next to e-waste dismantling area

Fig. 6 The total concentrations of heavy metals in surface water and groundwater

monitors (Olafisoye and Adefioye 2013). In addition, Mn can be usually found in the natural water. The concentration of Mn is shown in Fig. 8 The observed values of all samples were within the permissible levels for the Thailand’s surface water quality standards and classification (Pollution Control Department 1994). Meanwhile, only one of Mn was detected in the groundwater, at a concentration of 0.020 mg/L. The heavy metal concentrations were lower than groundwater

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Fig. 7 The concentration of As in surface water

Fig. 8 The concentration of Mn in surface water and groundwater

quality standards set by the Ministry of Natural Resources and the Environment, Thailand (Pollution Control Department 2000). Comparisons of the hot spots near the WEEE dumping site and e-waste dismantling areas and the farthest station from them revealed significant heavy metal concentration differences. Also, all heavy metal concentrations did not exceed the Thailand quantity standard limits.

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4 Conclusions The results show that the surface water was contaminated with Mn, As, Pb and Cu. While Mn (0.395 mg/L) was presented at its highest concentrations around the WEEE dumping site (DY-SW1). The concentration of heavy metals was various depend on the site of surface water. The groundwater was only slightly contaminated by Mn, with 0.020 mg/L in the domestic water supply. The heavy metals concentrations in both the surface water and groundwater could be from the leaches from WEEE, geology of water sources and catchment area. However, Mn and As can be always found in the natural water. According to on site survey data, the e-waste dismantling activity could not be performed continuously because people work during planting and harvest season, which result in the lack of dismantling workers. Even though the concentration of all heavy metals under investigation were within the permissible levels, it is recommended that the hot spots such as those around the WEEE dumping sites and around the e-waste dismantling area should be selected for continual monitoring and better management planning in order to prevent the dissemination of heavy metals in the future. Therefore, the groundwater samples should be collected more than 1,000 mL for better concentration of the samples for the detection of heavy metals. Acknowledgements This research was financially supported by the National Research Council of Thailand (NRCT): Thailand Research Challenge Program for WEEE and Hazardous Waste and the Center of Excellence on Hazardous Substance Management (HSM) (HSM-PJ-CT-19–02). We would also like to thank local organization officers for their support.

References Achaya W (2016) heavy metals contamination in water reservoirs and aquatic animals in the area of gold mine. Master’s degree thesis. Chulalongkorn University, Thailand Electrical and Electronics Institute (2007) Country report on the Indian electronics sector issues and capacity building needs in relation to international and national product-related environmental regulations and other requirements, Thailand Olafisoye OB, Adefioye T, Osibote OA (2013) Heavy metals contamination of water, soil, and plants around an electronic waste dumpsite. Pol J Environ 22(5):1431–1439 Pattida T, Tassanee P, Wattasit S (2017) Material flow of informal electronic waste dismantling in rural area of northeastern Thailand. In: 139th international proceedings. The IIER International Conference, Japan, pp. 12–15 Pollution Control Department (2000) Groundwater quality standards. Ministry of natural resources and environmental, Thailand Pollution Control Department Ministry of Natural Resources (2019) Booklet on Thailand State of Pollution 2018, Thailand Pollution Control Department (2018) Thailand State of Pollution Report, Thailand Pollution Control Department (1994) Surface water quality standards. Ministry of natural resources and environmental, Thailand

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Santhanam N, Melvin S, Ramalingam C (2014) Electronic waste-an emerging threat to the environment of urban India. J Environ Health Sci & Eng 12(1):36 Xinwen C, Martin S, Mark W, Markus R (2017) Informal electronic waste recycling: a sector review with special focus on China. Waste Manag 31(4):731–742

A New Method to Evaluate Tight Oil Reservoir of Chang7 Member in Zhidan Area Li Kang, Dang Hailong, Kang Shengsong, Chang Bin, and Wang Weibo

Abstract At present, tight oil reservoir is a hot spot in the field of oil and gas development, and the effective development of such reservoir needs reservoir evaluation as a guide. Based on the experimental research on the core of tight oil reservoir of Chang7 member in Zhidan area, combined with the method of statistics and numerical simulation, eight parameters of “sand thickness, crude oil viscosity, main throat radius, starting pressure gradient, clay mineral content, rock brittleness index, percentage of movable fluid and reservoir pressure coefficient” are selected, and then, the new method of tight oil reservoir evaluation is obtained after normalizing the each parameter. Through this method, the tight oil reservoir of Chang7 member in Zhidan area can be divided into Class I, Class II and Class III, of which Class I and Class II reservoirs currently have certain development value. When development this kind of tight oil reservoir, large-scale hydraulic fracturing is required, which may affect or destroy the existing original forest protection area, water resource protection area and farmland. Therefore, after considering the environmental safety factors, the reservoir is classified and evaluated, and the development block is selected. Keywords Tight oil reservoir · Chang7 member · Reservoir evaluation · Parameter selection · Evaluation method · Environmental protection

1 Preface Tight oil reservoir is the main force to replace conventional oil reservoir and plays an important role in relieving energy pressure in China (Zou et al. 2014a, 2018; Sun et al. 2010). At present, the oil-gas with the overburden permeability less than 0.1 × 10−3 μm2 and enriched in non-shales such as clastic rock and carbonate rock are defined as tight oil and gas (Zou et al. 2017). Compared with conventional reservoirs, tight oil reservoirs are generally characterized by poor physical properties, strong heterogeneity and low single reservoir L. Kang (B) · D. Hailong · K. Shengsong · C. Bin · W. Weibo Shaanxi Yanchang Petroleum (Group) Co., Ltd, Xi’an 710075, Shaanxi, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_10

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coefficient, which makes it difficult to select high-quality reservoirs or evaluate them (Tan et al. 2016; Lu Tao et al. 2015). Therefore, conventional parameters such as physical properties, sedimentary facies, sand distribution characteristics and other parameters are no longer completely applicable to the evaluation of tight oil reservoirs (Levorsen 1967). Based on the conventional reservoir evaluation method, this paper builds a new evaluation parameter system, and finally, evaluates the tight oil reservoir of Chang7 member in Zhidan area.

2 Selection of Tight Oil Reservoir Evaluation Parameters The factors that affect the development of conventional reservoirs are physical properties, pore structure, fluid properties and percolation characteristics. However, due to the complex pore structure, poor grade and fracture development in tight reservoirs, it is difficult to supplement the reservoir energy, which makes the effective development of tight reservoirs is more difficult (Yang et al. 2006; Zhang et al. 2015). Therefore, it is necessary to increase parameters such as sand thickness, crude oil viscosity, main throat radius, starting pressure gradient, clay mineral content, rock brittleness index, percentage of movable fluid and reservoir pressure coefficient during the comprehensive evaluation.

2.1 Reservoir Physical Properties and Pore Throat Characteristics This time, the steady-state permeability and porosity of 67 cores of Chang7 member in Zhidan area are tested. The core permeability is distributed between 0.003 and 30.06 × 10−3 μm2 , and the proportion of permeability lower than 0.1 × 10−3 μm2 is up to 63.64%, with an average of 0.12 × 10−3 μm2 . Porosity is between 2.86 and 17.62%, with an average of 8.93%. The permeability and porosity show a good exponential relationship (Fig. 1). It can be seen from the figure (Figs. 2 and 3) that the main throat and the average throat radius increase with the increase of permeability, w the average throat is below 0.4 μm and the main throat is between 0.1 and 1 μm.

2.2 Sand Thickness and Distribution According to the statistics of 304 the well logging curves, the sand thickness of the Chang7 member in this area ranges from 1.1 to 37.1 m, with an average of 12.9 m, and the average sand thickness in the ZF area is the largest, up to 18.3 m (Table 1).

A New Method to Evaluate Tight Oil Reservoir … Fig. 1 Relationship between core porosity and steady-state permeability in Zhidan area

Fig. 2 Relationship between core average throat radius and permeability in Zhidan area

Fig. 3 Relationship between core main throat radius and permeability in Zhidan area

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Table 1 Statistical table of average sand thickness of each block in Zhidan area Block

Wells (no.)

Average sand thickness (m)

ZF

81

18.3

HC

69

15.8

LH

57

8.1

FC

46

9.6

SN Total/average

51

12.2

304

13.5

2.3 Starting Pressure Gradient The non-linear percolation law of 4 tight rock samples in tight oil reservoir of Chang7 member in Zhidan area was studied. The permeability distribution is between 0.05 and 0.69 × 10−3 μm2 . The test results are as follows (Table 2). It can be seen from Table 2 that the true starting pressure gradient is smaller than the pseudo-starting pressure gradient, and the lower the permeability is, the higher the true starting pressure gradient and the pseudo-starting pressure gradient are. At the same time, the equation of fluid motion will be change, and the effective permeability of the liquid phase is no longer be a constant, which will change with the change of the pressure gradient (Fig. 4). According to the relationship between the pseudo-starting pressure gradient and permeability, the starting pressure gradient of different permeability level oil reservoirs can be calculated. It can be seen from Table 3 that the minimum starting gradient pressure of 0.1 × 10−3 μm2 reservoir in Zhidan area is 0.192 MPa/m, and the pseudo-starting pressure gradient is 0.361 MPa/m; When the permeability is as low as 0.01 × 10−3 μm2 , the minimum starting pressure gradient is 0.588 MPa/m, and the pseudo-starting pressure gradient is as high as 1.745 MPa/m, so the reservoir is very difficult to development. Table 2 Non-linear percolation test results of tight oil reservoir of Chang7 member Block

Well name

SN

X42

LH HC ZF

Porosity (%)

Permeability (10−3 μm2 )

True starting pressure gradient (MPa/m)

Pseudo-starting pressure gradient (MPa/m)

9.09

0.05

0.269

0.763

X53

9.66

0.08

0.23

0.437

X245

10.54

0.11

0.189

0.25

X47

13.47

0.69

0.074

0.106

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Fig. 4 Relationship between different levels of permeability samples and the pressure gradient

Table 3 Calculation results of starting pressure gradient with different permeability levels

Permeability (10−3 μm2 )

True starting pressure gradient (MPa/m)

Pseudo-starting pressure gradient (MPa/m)

0.01

0.588

1.745

0.05

0.269

0.581

0.1

0.192

0.361

0.3

0.113

0.17

0.5

0.088

0.12

2.4 Brittle Index of Reservoir Rock The analysis of reservoir rock brittleness index is mainly to evaluate the compressibility of the reservoir, which is an important basic work for the reconstruction of tight oil reservoir, and also an important basis for the evaluation of fracturing effect of tight oil and shale reservoirs (Slatt et al. 2012; Trammel 2012; Leshchyshyn and Pierre-gilles 2010; Zahid et al. 2007; Hull et al. 2013). The mineral content of 10 typical samples in this area is analyzed and tested. The Chang7 member is mainly composed of clay, quartz, potash feldspar, plagioclase, and calcite; among them, the clay minerals range from 6 to 25%, with an average of 12.9%. In this paper, the mineralogical method of measuring the brittleness index of reservoir rock is improved, and the Young’s modulus and Poisson’s ratio (Formula 1) of the mineral are added, thus, the accuracy of the result is improved. B=

Vquartz ∗ Vquartz ∗

Y Mquar t z P Rquar t z

Y Mquar t z P Rquar t z

+ Vcalcite ∗

Y Mcalcite P Rcalcite

+ Vclay ∗

Y Mclay P Rclay

∗ 100

(1)

In the formula, V—mineral content; YM—Young’s modulus; PR—Poisson’s ratio.

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Table 4 Rock brittleness index of tight oil reservoir of Chang7 member in Zhidan area Permeability (10−3 μm2 )

Rock brittleness index (%)

9.31

0.02

63.7

71

10.23

0.119

64.4

69

9.87

0.089

66.1

70

8.86

0.096

67.4

30

2.86

0.003

54.3

35

8.3

0.009

56.8

SN

41

9.57

0.011

58.8

39

9.18

0.013

56.5

ZF

51

10.32

0.148

85.1

53

10.95

0.09

81.2

8.94

0.059

65.4

Block

Core no.

LH

32

HC FC

Average

Porosity (%)

According to the calculation, the average value of the brittleness index of the reservoir rock in this area is 65.4% (Table 4), so the fracture network is easy to form during the fracturing of Chang7 tight oil reservoir in Zhidan area.

2.5 Movable Fluid Research Nuclear magnetic resonance (NMR) experiment is used to measure the percentage of movable fluid in the core of the reservoir. And the NMR experiment can measure the core resonance signal, the T2 relaxation pattern of core NMR can be obtained by recording the transverse relaxation time of core NMR signal (Bai et al. 2016; Voloitin et al. 2001; Liu et al. 2003). Because different permeability corresponds to different pore radius, the content of movable fluid corresponding to different pore intervals can be researched accordingly. It can be seen from Figs. 5, 6 and Table 5 that the movable fluid of Chang7 tight oil reservoir in Zhidan area mainly comes from sub-micron pores, and the movable amount in the nano-level is about 9%; the movable fluid percentage of oil reservoir below 0.5 × 10−3 μm2 is 46.37%, that of oil reservoir below 0.1 × 10−3 μm2 is 35.14%, and that of oil reservoir below 0.05 × 10−3 μm2 is 29.39%.

2.6 Formation Pressure Coefficient Formation pressure coefficient refers to the ratio of the reservoir formation pressure to hydrostatic column pressure at the same depth, and its value reflects the development ability of the reservoir relying on natural energy. Due to the poor physical properties

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Fig. 5 The changing characteristics of the percentage of movable fluid in different throat

Fig. 6 Relationship between movable fluid and permeability

Table 5 Percentage of movable fluid at different permeability levels Permeability (10−3 μm2 )

0 and kLa2 = 0. This means the aerobic tank is placed in front of the anoxic tank. The simulation of the WWTP with the data in Fig. 4, the total cost after optimization is 4,094,095,8 (e year−1 ) (Fig. 5). Simulation with the data in Fig. 6 shows that most of the effluent concentrations are below the effluent standards, only TN is close to its upper bound, this also demonstrates the optimization achieved goals. The total cost of the WWTP after optimization is significantly reduced compared to the actual case of Benchmark WWTP (Fig. 7). This allows saving about 41% of the total cost of the WWTP.

7 Conclusion After optimization, the effluent concentrations from the WWTP meets the permissible standards. Only TN concentration is close to its upper bound. This proves that the WWTP is most likely to cause pollution, so the optimization results are reasonable.

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Fig. 6 Effluent concentrations after optimization

Fig. 7 Total cost before and after optimization

The optimization result shows that there is no need to build many reactors, but only 2 tanks will provide the highest economic efficiency. In addition, a noteworthy note is that the obtained results show the aerobic tank are placed in front of anoxic tank. This is slightly different from the majority of WWTPs in reality that often arrange anoxic tank in front of aerobic tank. It can be explained that the Benchmark WWTP handles urban wastewater, has a high concentration of TN, so it is necessary to carry out the nitrification process in aerobic tank before moving to anoxic tank to carry out the anti-nitrate process to reduce total nitrogen in wastewater.

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References Alasino N, Mussati MC, Scenna N (2007) Wastewater treatment plant synthesis and design. Ind Eng Chem Res 46(23):7497 Alex J et al (2001). The COST simulation benchmark: description and simulator manual. COST Action 624 and COST Action 682 Alex J et al (2008) Benchmark Simulation Model No. 1 (BSM1). IWA Taskgroup on Bechmarking of Control Stategies for WWTPs Chachuat B, Roche N, Latifi MA (2001) Dynamic optimisation of small size wastewater treatment plants including nitrification and denitrification processes. Comput Chem Eng 25:585–593 Chachuat B, Roche N, Latifi MA (2005a) Long-term optimal aeration strategies for small-size alternating activated sludge treatment plants wastewater treatment plants. Chem Eng Proc 44:593– 606 Chachuat B, Roche N, Latifi MA (2005b) Optimal aeration control of industrial alternating activated sludge plants. Biochem Eng J 23:277–289 Cuthrell JE, Biegler LT (1987) On the optimization of differential-algebraic process systems. AIChE J 33(8):257 Cuthrell JE, Biegler LT (1989) Simultaneous optimization and solution methods for batch reactor control profiles. Comput Chem Eng 13(1/2):49 Fikar M, Chachuat B, Latifi MA (2005) Optimal operation of alternating activated sludge processes. Cont Eng Pract 13:857–861 Gillot S et al (1999) Optimization wastewater treatment plant design and operation using simulation and cost analysis. In: 72nd annual conference WEFTEC 1999. New Orleans, USA, 9–13 Oct 1999 Goh CJ, Teo KL (1988) Control parameterization: a unified approach to optimal control problems with general constraints. Automatica 24:3–18 gProms, Process Systems Enterprise (1997–2009). www.psenterprise.com Gujer W, Henze M, Mino T, van Loosdrecht MCM (1999) Activated Sludge Model No. 3. Water Sci Technol 39(1) Henze M et al (1987) Activated Sludge Model No. 1. Technical Report No. 1. IAWQ, London Henze M et al (1995) Activated Sludge Model No. 2. IAWQ Scientific and Technical Report No. 3. London, UK Henze M et al (1999) Activated Sludge Model No. 2D, ASM2D. Water Sci Technol 39(1):165–182 https://iwa-mia.org/benchmarking/ Teo KL, Goh CJ, Wong KH (1991) A unifed computational approach to optimal control problems. Wiley, New York

E-waste Dismantling Community Toward Circular Economy with Ineffective Hazardous Waste Management: A Case Study in Buriram Province, Thailand Mongkolchai Assawadithalerd, Sangsuree Srisa-ard, Pensiri Akkajit, and Tassanee Prueksasit Abstract Thailand situation on Electronic wastes (E-wastes) management is one of critical issue nowadays due to the fact that the legislation of Waste from Electrical and Electronic Equipment (WEEE) using the Extended Producer Responsibility (EPR) principle is on consideration process. However, the existing WEEE in Thailand is expected 414,600 ton/year, and this must require an effective dismantling system. It was observed that the wastes generally find in the second hand stores and household WEEE dismantling. In this research, estimated annually amounts of plastic, steel, aluminium, copper, and alloy were 27.74, 35.68, 2.78, 0.05, and 3.44 tons, respectively using data of June 2019 as based month that effectively recycle followed circular economy. In addition, the results showed that average monthly income of sampling population (301.09 USD/capita) was comparable to database from local government officer (550.25 USD/household or 166.68 USD/capita), which show higher income than the average monthly income of people in Buriram province. Increasing revenues are related to the amount of secondary feedstock in circular economy, especially valued metals and recyclable plastics which in turn promotes the better quality of life. The 7 kinds of WEEE, which were television, fan, refrigerator, washing machine, rice cooker, iron, and kettle have been collected the content of vendible wastes. The challenges on non-value waste management is confronting, especially a large amount of broken CRT monitors, insulated rigid PU foam from refrigerators, and burned plastic shell of cable wire in opened dump site.

M. Assawadithalerd (B) · S. Srisa-ard Center of Excellence on Hazardous Substance Management (HSM), Chulalongkorn University, Bangkok 10330, Thailand e-mail: [email protected] P. Akkajit Faculty of Technology and Environment, Prince of Songkla University, Phuket Campus, Phuket 83120, Thailand T. Prueksasit Department of Environmental Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_12

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Keywords E-wastes · WEEE · Dismantling · Income · Environmental impacts

1 Introduction Circular economy is currently influenced on waste management strategy to promote wastes as secondary raw materials (Li and Yu 2011; Sarkis 2008) that replace primary raw material consumption. The ore extraction for metal production generally requires much more energy, water, and chemicals therefore, using wastes as secondary feedstock in supply chain is advantageously on natural resources depletion (Andersen 2007). In addition, the problems of huge volume of wastes in landfill and open dumping probably decrease due to waste recycling under the circular economy policy. Nowadays, wastes are valuable and are concerned about environmental management which people is more active and susceptible on reuse and recycle material than the past. This indicated that the management of recycling for upcycling is essential in this time in order to drive the environmental awareness and economic opportunity. E-wastes in Thailand are lacking of legislation on e-wastes management right now, however, the relevant law on e-waste management has been drafted which the key point of sustainable improvement and control the problems of e-wastes in future using Extended Producer Responsibility (EPR), which is now implemented across many countries such as Korea, Japan, Hongkong, France, and Australia etc. (Hyunmyung and Yong-Chul 2006; Li 2013). During on the process of law consideration, the 414,600 tons of e-wastes in Thailand (Pollution Control Department 2018) that was informed by Pollution Control Department in 2018 was challenging to handle in the suitable approaches without legislation tool. Surprisingly, not over 15% of collected e-wastes were managed effectively and legally. The situation of e-waste in Thailand is the same as in many developing countries, in which e-waste is sold to merchants as second hand electronic equipment that is useful for being a compartment in case of repair and for dismantling prior to sell as recyclable materials. Small business in household level typically achieved on e-wastes separation at Dang Yai sub-district, Buriram province located in the northeast Thailand. The economic benefits from value parts are able to increase quality of life through the greater income. The amount and price of copper, aluminum, steel (ferrous), alloys, and recyclable plastics are the significant part that induce the entrepreneur still works in this career. Although e-wastes are defined to be hazardous wastes due to their toxicity, the informal manual dismantling will not be severely if the entrepreneurs comprehensively understand about the toxic substances in each part of e-wastes. However, they, in fact, did not concern and focused only on the money they will obtain. Thus, ewastes in the perspective of socioeconomic need to study the received benefits that can be present and communicate to the entrepreneurs for further effective policy to express their responsibility. This business responded to global policy, but it can not deny that environmental contamination and health risk are serious limitation.

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Based on sustainability, entrepreneurs achieved in business model referred to economy aspect is the major focus, while, social and environment aspects are confronting high risk on ecological and health risk. The proposed aims of this research concentrate on (1) studying the potential of recycle wastes such as copper, aluminum, steel (ferrous), alloys, and recyclable plastics of each type of e-wastes that are television, fan, refrigerator, washing machine, rice cooker, iron, and kettle (2) prediction of possible secondary feedstock in study area that turns to industrial sector based on primary data and (3) to investigate livelihood benefit in term of monthly income using face-to-face interview. These are not cover all perspectives to manage e-wastes, which generally handle in state of art to collaborate in all stakeholders, but at least this research presents positively the household-based business model that promote waste utilization with global waste management policy.

2 Methodology 2.1 Study Area and Behavior of Entrepreneurs The informal e-waste dismantling in Buriram province divided into two sub-districts which are Dang-yai and Ban-pao. The two third of dismantler is approximately 105 entrepreneurs in Dang-yai, therefore, this area was focused in this study. Behavioral trading e-wastes of entrepreneur by daily purchasing in nearby area including nearby provinces, took e-wastes back using a 4-wheel truck, and stored in their house everyday. The uniformity of purchasing was found continuously for 2–3 days. Later on, they spent time to physically dismantle the value wastes using small compliances for 5–10 days depended on amount of e-wastes and labors including their cash flow. Then, the platform of this behavior was rotated by following loop as usual excepted for the village had special occasions, religious ceremony, and also funeral ceremony. Selling individual parts must be considered about price to gain the best benefits. Sometimes, the price of separated materials was cheap, they decided to stop their business and waiting for increase of price. Less entrepreneur, especially the large business with stability of cash flow, still dismantle and stocked their separated parts until the price increased. Waste stream of non-value wastes after dismantling also observed.

2.2 Data Collection for Recyclable Materials The primary data was collected in 7 kinds of e-wastes, which were television, fan, refrigerator, washing machine, rice cooker, iron, and kettle. These are first seven orders mainly found in e-waste dismantlers. The weight of each part was collected by

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calibrated 2-digit balance. Criteria regarding to collect the mass of each parts must be dry, different brands, and different models. This would be considered to be a possible representative of each kind. These data were analyzed as a set of database to present in percentage of secondary material that effectively led to circular economy. In fact, the obstacle that generally found was making the appointment to entrepreneurs due to pattern of household based business that seems like a kind of lazy business by non-strict owner. Another primary data that was collected from entrepreneur was randomly taken into the account the daily amount of each kind of e-wastes (n = 45). The number of study e-wastes were informed in monthly period of June, 2019. That was very difficult to predict the high accuracy of e-waste mass through recycling process.

2.3 Data Collection for Income from e-waste Dismantling Entrepreneurs The secondary data governed by province municipality has been collected for being the representative of citizen’s income in Burirum province that normally found in term of annual income, however, the primary data by face-to-face interview was observed in term of monthly income. This two types of income both in provincial level income and e-waste dismantler’s income were normalized as an average monthly income that was comparable in the same base.

3 Results and Discussion The study of mass is complicated due to the various size of e-wastes that was complicated to gain an average of each parts, thus this research collected the various size to be significant representatives.

3.1 Potential Wastes Toward Circular Economy The estimation of number of e-wastes in monthly was calculated based on behavior of all dismantlers (N = 100). For one month, they purchased e-wastes generally 9 times per month. Each time was randomly accounted for all study e-wastes (n = 45) in June 2019. The average monthly weight per household of 7 e-wastes was predicted as following equation M(kg/household) = 393F + 164IR + 151RC + 114TV + 76WM + 76REF + 18KE

(1)

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Table 1 Average mass per unit and percentage by mass of potential recyclable materials in various e-wastes

CRT TV

Average weight (kg/unit)

Recyclable materials (wt%) Recyclable plastic

Steel

Aluminium

Copper

18.27 ± 7.77

14.53

11.06

0.48

1.04

Alloy 4.57

2.73 ± 0.96

50.89

31.36

4.84

4.52

2.48

Refrigerator

32.55 ± 3.89

16.67

59.36

3.31

4.94

0.6

Washing machine

25.96 ± 10.20

50.30

36.76

4.02

3.78

1.53

1.92 ± 0.78

14.98

56.00

3.00

1.41

24.14

Fan

Rice cooker Iron

0.81 ± 0.53

2.64

30.12

0

2.86

34.11

Kettle

1.52 ± 0.28

43.02

37.66

0

0.45

16.06

where; all capital letters mean to average weight per unit (as presented in Table 1) of fan (F), iron (IR), rice cooker (RC), CRT TV (TV), washing machine (WM), refrigerator (REF), and kettle (KE) including M refer to monthly mass of 7 e-wastes per household. Based on actual number of entrepreneurs in Dang-yai sub-district, the predicted monthly e-wastes in study area were possibly 725–966 tons at population of 100 entrepreneurs with 10% uncertainty. This can be assumed that this area promoted recycle wastes as secondary material in circular economy approximately at least 8700 tons/year based on data in June 2019. Individual material was calculated the possibility of recyclability as presented in Table 2. Annually amounts of plastic, steel, aluminium, copper, and alloy were 27.74, 35.68, 2.78, 0.05, and 3.44 tons, respectively. Alloy in this research has defined to the value metal both in each compartment and a set of metal in printed circuit board (PCB) that need specific technology for metal recovery at 30 wt% Kaya 2016). Normally, PCB in this area was sold to recovery plant located in Chonburi and Rayong province. The non-vendible components as depicted in Table 3 such as thermosetting plastic, ply wood, PU foam, rubber, and other wastes are applicable on waste to energy (WtE) as an effective RDF (refuse derived fuel) due to its heating content. The glass from kettle can also be recycled, unfortunately, no vender purchases it in this area. However, the coolants or refrigerants must be seriously concerned because R12-CFC, R134A-HFC, and R143A-HFC affect to global warming with the GWP (global warming potential) of 10,200, 1300, and 4800 (as CO2 equivalent) Values 2014). Some interviewer reported that the refrigerants can sell to specific vendor Table 2 Monthly and annually mass of recyclable materials from all e-wastes in study area Mass (kg)

Plastic

Steel

Aluminium

Copper

Alloy

Monthly

2312.11

2973.19

231.82

4.57

286.51

Annually

27,745.32

35,678.30

2781.82

54.84

3438.15

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Table 3 Percentage by mass of non-vendible materials in various e-wastes Non-vendible materials (wt%) Plastic

Plywood

Glass/CRT monitor

PU foam

Coolant

others

CRT TV

1.43

0

66.47

0

0

0.42

Fan

1.84

4.07

0

0

0

0

Refrigerator

0

0

0

13.49

0.82

0.81

Washing machine

0

0

0

0

0

3.61

Rice cooker

0

0

0

0

0

0.46

Iron

22.37

0

0

0

0

7.96

Kettle

0

0

0.52

0

0

2.33

Table 4 Monthly and annually mass of different waste management for all e-wastes Mass (kg)

WtE

Secure Landfill

Properly reuse

Recycle

Properly dispose

Monthly

547.87

1384.42

21.31

0.14

20.29

Annually

6574.44

16,613.09

255.70

1.71

243.42

who recycle it by repairing the compressor. The CRT monitor provides the greatest health risks due to mercury toxicity in CRT tube that must be properly disposed in secure landfill. The saline (1.08 wt%) in washing machine may found in model containing centrifugal system should be aware by directly pouring in agricultural soil or fresh water for aquatic farm. In addition, plastics contained flame retardants were not identified in this research, but thermal process have to avoid that might cause the emission of PAHs, dioxin, furans, and their derivatives (Bakhiyi 2018). Waste management hierarchy emphasizes on reuse, recycle, and recovery (Parajuly and Wenzel 2017) that were proposed in Table 4. Glass in kettle is heat resisted glass, which can be recycle for 1.71 kg/year. Whereas, coolant potentially proper reuses about 255 kg/year. The mixture of all polymer for energy recovery are 6.5 tons/year. The saline 243 kg in each year may require evaporation process as pretreatment before disposal. The large amount of CRT monitor is severely in environment with over 16 tons/year even disposed by secure landfill. Thus, safety dismantling technology for CRT monitor is needed as soon as possible.

3.2 Livelihood Benefits of e-waste Dismantlers The average income of dismantler in Dang-yai sub-district was calculated using primary data (n = 100) from face-to-face interview that showed approximately 301.09 USD/capita/month. The secondary data from government officer showed income 60,382 Baht/capita/year or 166.68 USD/capita/month. Although survey data is higher

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Fig. 1 Overall limitations regarding to e-waste management in Thailand among all stake holders

than secondary data about 2 times, it is comparable due to the fact that data from government officer is normalized by 3.301 person per household. Provincial monthly income with unofficial report is still lower than 301.09 USD. This may imply that career of e-waste dismantling showed positive benefit via the increase of income. However, the safe dismantling and environmental concerning should be more aware to prevent health risk impact, especially in children and senile. As seen in the family of dismantler, the members of family were employed by themselves in their birthplace and stay with their family that took time for taking care their child and parents. This might be showed a perspective of better quality of life.

3.3 Limitation and Concerns E-waste or WEEE (waste electrical and electronic equipment) management issues is globally critical concerns nowadays. There are many stakeholders relied on ewaste management that also provide many limitations. This research focused on three stakeholders as showed in Fig. 1.

3.3.1

Government Sector

Ineffective dismantlers act like the people that segregate the wastes in household prior put into the suitable garbage, but the most importance of non-value wastes that always defines as toxic substances or hazardous waste is not fully completed loop of waste management. All entrepreneurs need only recyclable wastes and educated on the harmful of e-waste dismantling, but they do not have the appropriate route to handle the non-recyclable waste. Policy of WEEE management has been promoted in

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Thailand, however, the direction of management measures has not obviously clarified yet. Regulation tool on e-waste management is on the process of development and present management may not be effectively since problems of e-wastes always found, especially environmental problems.

3.3.2

Industrial Sector

For this sector, it can be defined as the investor who may be a new industry focusing on environment-friendly waste processor, or existing potential waste processor that would like to expand their market. In case that e-wastes have been managed by these industries, the pollution from e-waste dismantling activities would be more possibly controlled than household dismantler by legal tracking. Feedstock of e-wastes is a major factor that must be known for investment of waste processor. Cost-effective on logistics also a master concern. Distance for logistics between sources of e-waste and waste processor affects to the variable cost of e-waste management. Additionally, price of secondary materials from wastes substantially indicates to a minor concern. Subsidy from government may be considered to support waste processor sector in order to prevent further environmental impacts.

3.3.3

Ineffective e-waste Dismantler (Small Household Business)

Informal or ineffective dismantling business showed benefits in which the area is not urbanized, low income, and near the pool of e-wastes. This can be implied that the people who is e-waste dismantler in that area are the hero who save the world involving circular economy based policy. In this case, win–win solution pushes up the success in small business level. As this sector remain the conventional performance in e-waste dismantling without environmental awareness, hero becomes criminal suddenly. The previous evidence showed heavy metal contamination in top soils that potentially distributed to food chain. Then, the health risk impact, especially the dismantler (Puangprasert and Prueksasit 2019) would be affected in consequence. In present, the study area is aware on an open dump site containing burdens of insulated polyurethane (PU) foams from refrigerators, CRT monitors, and ashes burned wires as shown in Fig. 2 and 3. Such problems on air pollution was being faced by burning for volume reduction of PU foams and copper recovery of small wires. Cracking of CRT monitor was harmful resulting from mercury and lead emission. Additionally, the other villagers both in this area and nearby area who did not do e-waste dismantling business inequitably affected from poisonous gas emission.

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Fig. 2 Open burning of small wire in open dump site to recover coper and alloy with ashes in previous burning

Fig. 3 Preparing CRT monitor cracking in open dump site containing a lot of PU foam

4 Conclusion The e-waste management in the study area is not absolutely concerned about environmental impacts. The unique handling that found in the study area is indicated the success of recycle wastes as secondary feedstock in circular economy because of household level business. This village performed like a combination of small unit in waste processor factory that separate the recycle wastes and sell it for their income. In case that, there is no 100 entrepreneurs in this village, the e-wastes probable mixed in municipal wastes that became more hazardous in expansion. The amount of recyclable wastes from e-wastes and the increase of income compared to province level indicated obviously the importance of circular economy that effectively changed the attitude of consumers to encourage the use of secondary materials. Dismantlers play a significant role combating the large volume of e-wasted so long as they halted open burning and CRT monitor cracking. CRT monitors in open dump site in this study area were critical due to its amount and cost of proper disposal. Burning of small wire was still be a problem in this area. Thus, the best practice of e-wastes dismantler

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with environmental concern was required to communicate for all entrepreneurs so that they still safely did the career and got income including saved the world at the same time. Acknowledgements The authors gratefully thank Center of Excellence on Hazardous Substance Management (HSM) on facilitating the research with financial support by National Research Council of Thailand (NRCT) under Thailand Research Challenge Program for WEEE and Hazardous Waste Project. We also acknowledge all e-wastes dismantlers in Dang-yai sub-district, Buriram province for given relevant data in this research.

References Andersen MS (2007) An introductory note on the environmental economics of the circular economy. Sustain Sci 2(1):133–140 Bakhiyi B et al (2018) Has the question of e-waste opened a Pandora’s box? An overview of unpredictable issues and challenges. Environ Int 110:173–192 Global Warming Potential Values (2014) Hyunmyung Y, Yong-Chul J (2006) The Practice and challenges of electronic waste recycling in Korea with emphasis on extended producer responsibility (EPR). In: Proceedings of the 2006 IEEE international symposium on Electronics and the environment, 2006 Kaya M (2016) Recovery of metals and nonmetals from electronic waste by physical and chemical recycling processes. Waste Manage 57:64–90 Li J, Yu K (2011) A study on legislative and policy tools for promoting the circular economic model for waste management in China. J Mater Cycles Waste Manage 13(2):103 Li J et al. (2013) Regional or global WEEE recycling. Where to go? Waste Manage 33(4): 923–934 Parajuly K, Wenzel H (2017) Potential for circular economy in household WEEE management. J Clean Prod 151:272–285 Pollution Control Department (2019) Booklet on Thailand state of pollution 2018 Puangprasert S, Prueksasit T (2019) Health risk assessment of airborne Cd, Cu, Ni and Pb for electronic waste dismantling workers in Buriram province Thailand. J Environ Manage 252:109601 Sarkis J (2008) Information technology and systems in China’s circular economy: implications for sustainability. J Syst Inf Technol 10(3):202–217

Possible Impact of Future Dams on Suspended Sediment Load Changes Zuliziana Suif, Yoshimura Chihiro, Nordila Ahmad, and Maidiana Othman

Abstract This paper was evaluating the impact of dams on suspended sediment load in Mekong River Basin. In this study, the dam module integrates with the distributed process-based sediment transport model to evaluate the potential future dams in the main stream impacts on suspended sediment load changes. The magnitude of sediment dynamics change is demonstrated with different dam scenarios analysis which are 3 existing, 5 under construction and 11 planned dams. The results from simulations show the reductions in annual suspended sediment load are range from a 20–33% for existing dams, 30–67% for under construction dams and 67–75% for planned dams. Moreover, the depletion on suspended sediment concentration are even greater (33–78%) due to the impact of future dams. Overall, the changes in sediment load and concentration can have significant indication for designed reservoirs and related sediment management. Keywords Dams · Suspended sediment load · Mekong river basin · Dam module

1 Introduction Nowadays, the large river basin is encounter major land use changes as land clearing, water redirection and hydropower construction, as an impact of high population and increasing need for economic expansion. Especially, hydropower development and reservoirs of large dams has been rapid in large river basins such as Mekong River Basin (Barlow 2008). Most remarkably development of large hydropower dams is significant for economic expansion and hence, extensive plans are on-going to build. Building of levees for land reclamation and flood control, bank protection works, construction and operation of upstream reservoirs, hydraulic and dredge mining, Z. Suif (B) · N. Ahmad · M. Othman Department of Civil Engineering, Faculty of Engineering, National Defence University of Malaysia, Kuala Lumpur, Malaysia e-mail: [email protected] Y. Chihiro Department of Civil Engineering, Tokyo Institute of Technology, Tokyo, Japan © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_13

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water export projects and all had an impact in river hydrology and sediment transport. High sediment loads, shows in major problems for water resources development, through siltation of water diversion and irrigation schemes, reservoir sedimentation, and as well increasing the cost treating water abstracted from a river. The possible sediment load changes in the future changes should be notify as a major aspect for river basin management. Thus, studies on sediment detachment, transport and deposition is attracting a number of researchers in recent years. They have addressed the impacts of dams on discharge and sediment transports. However, only a few reports such studies at large river basin. The purpose of this study is to evaluate the future impact of dams in the main stream on the suspended sediment dynamics in Mekong River Basin. The magnitude of sediment dynamics change is demonstrated with different scenarios. In this study, the past suspended sediment load and concentration in the Mekong was simulated to the conservations from 1991 to 2000. Using the estimated model parameters, the sediment dynamics processes were then projected for the 2040s and 2090s, considering the expected changes on dams existing, construction and planned.

2 Study Area In this study focused Mekong River Basin and area of 795,000 km2 (MRC 2003) (Fig. 1). The dry season is from November until April. The rainy season remains from May to October with the average rainfall about 80–90% of the total annual. Moreover, the minimum annual rainfall is 1000 mm yr−1 and the maximum is 4000 mm yr−1 (Kite 2001). The Mekong River Basin is populated about 60 million people and is considered as one of the most culturally diverse regions of the world. Acrisols were found the major soil type (MRC 2003) in this basin, which are have a high clay aggregation and are very weathered and leached. The features consist low ability to sustain agricultural plant growth and high sensitivity to soil loss. Currently, there are 28 dams in Mekong Basin, three of which are located in China part in Mekong mainstream and the rest are located in tributaries. There are great plans to build reservoirs in the tributaries within Lao PDR, Vietnam and Cambodia (MRC 2008). Moreover, there are divers plans to build dams along mainstreams in Lower Mekong Basin and Upper Mekong Basin. Eleven dams are proposed (nine in Lao PDR and two Cambodia) in the Lower Mekong Basin and five are being built or designed in addition to the three existing ones.

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Fig. 1 The Mekong River Basin

3 Methodology In this study, the current sediment dynamics was simulated by applying the integrated sediment transport model to the observation from 1991 to 2000 as a baseline scenario (Suif et al. 2017). Using the calibrated model parameters, suspended sediment load (SSL), river discharge and suspended sediment concentration (SSC) were then projected for the 2040s and 2090s, considering the expected changes in dams factor. Fourteen scenarios were examined to estimate the impact of dams on sediment dynamics.

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3.1 Distributed Sediment Model and Dam Module The processes of sediment dynamics were modelled; process-based distributed sediment transport (Suif et al. 2018) and integrated with a physical-based distributed hydrological model (DHM) (Yang et al. 2000). The momentum and energy and continuity equations are resolve using river routing model and hillslope mode. The integrated distributed model uses a sub grid parameterization scheme, a basin subdivision scheme, a physically based hillslope simulation and kinematic wave flow routing in river network (Yang et al. 2001). The sediment transport on hillslope and rivers was separately modelled and connected each other. Sediment and hydrological processes are simulated on daily time-step. Further details can be described by Yang et al. (2000) for hydrological model and Suif et al. (2018) for sediment model. Furthermore, the distributed sediment model was used the calibrated parameters (Suif et al. 2018). This integrated model was calibrated and applied at Mekong River Basin on assessment of sediment dynamics. In this study, dam module already integrated with the distributed sediment model. For the dam module simulation, the dam release at each dam was assumed equal with inflow, assumed no water withdrawal. All the dams effect (existing, construction and planned) will be assumed as no withdrawal because according to the available hydropower development plans (ADB 2004), the main purpose of dam in Mekong River Basin is purposely for hydropower. Further, there are no plans show that the dams would have a channel for sediment discharge to flush the trapped sediment during the flood (ADB 2004). It is, therefore, practicable to assume that majority of the dams are purposely for hydropower and assumed no water withdrawal in this study. Conventionally, in hydropower dam, the water level of river is keep raises to create falling water and in effect to store energy. Due to an excessive number of reservoirs considered in our study, the approximation to predict individual reservoir sedimentation, trap efficiency (TE) by Brune (1953). The method predicts TE as an indicator of local residence time in years (TR ), defined by dividing the effective volume by local mean annual discharge;  TR,i = Vi Qi

(1)

where TR,i is a residence time of reservoir i, Vi is an operation volume of reservoir i, and Qi is discharge at reservoir i. After the local residence time was estimated, Brune’s method was used to calculate the individual reservoir trapping efficiency (TER,i ); TER,i = 1− 0.05α



TR

 (2)

where TER,i is trapping efficiency for reservoir i, and α was set to 1 (constant), representing the median curve in Brune’s method (Brune 1953). The Brune method was used over other empirical methods because this method is widely used and found to provide sensible calculates of long-term, mean TE (Vorosmarty et al. 2003).

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Table 1 Scenarios to evaluate dams on suspended sediment dynamics in the Mekong River Basin Dam scenario (D) Baseline (no dam)

D1

Past observation (CPO)

Past model (CPM)

D1-CPO

D1-CPM

3 dams (existing)

D2

D2-CPO

–

8 dams (D2 + under construction)

D3

D3-CPO

–

19 dams (D3 + planned)

D4

D4-CPO

D4-CPM

3.2 Dam Scenarios The location of each existing, construction and planned reservoirs (MRC 2008) in the mainstream of Mekong River are presented in Fig. 1. Four different scenarios were used for investigating the effects of the dam which are D1: baseline (without dam), D2: three existing dams, D3: scenario D2 and 5 construction dams and D4: scenario D3 and 11 planned dams. Eleven dams are planned (nine in Lao PDR and two in Cambodia) in Lower Mekong Basin and five dams are being built or designed in Upper Mekong Basin in inclusion to the three existing ones (Xiaowan, Manwan and Dachaosan). The Mekong basin was separated into two part, Upper Mekong Basin (UMB) and Lower Mekong Basin (LMB). The existing and planned dams in UMB and LMB are shown in Table 1. The cumulative storage of all the existing and planned reservoirs was obtained from Mekong River Comission (2008) and Kummu et al. (2010).

4 Results and Analysis 4.1 Impact of Dams on River Discharge, SSL and SSC The annual average of river discharge remains same in each dam scenario because no water withdrawal was assumed in each scenario Fig. 2a. The annual average SSL show changes with dam scenario at three observation station, Chiang Sean, Khong Chiam and Phnom Penh Fig. 2b. It shows decreasing trends in scenario D2 to D3 and D4. However, the SSL at each station have a slightly change effect from scenario D2, shows the existing dam in upper part of Mekong River not much effect to the downstream SSL. But, obviously, the increasing number of dam affected the amount of SSL (scenario D3 and D4) by decreasing with ranging 67–78% from scenario without dam. The change scale is rise in upstream and is reduced as it passes downstream, expected due to different rate of rainfall changes and soil loss from sub basin to sub basin. Figure 2c show the annual average of SSC show the same trend with SSL which are decreasing from D1 to D4. This implies that the impact of dams on the SSC is same as SSL because of the reduction sediment load as it transported downstream due

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Fig. 2 Annual average (1991–2000) of a river discharge, b suspended sediment load, and c suspended sediment concentration at Chiang Sean, Khong Chiam and Phnom Penh stations in dam scenarios

to deposition losses. These will also cause to the reduction in sediment concentration. Also, the SSC profile shows the decreasing trend from upstream to the downstream. Overall, increasing number of dam is linearly decreased with SSL and SSC. In general, a rise in river discharge will increase the daily, annual and seasonal SSL. While a reduce in river discharge will reduce the SSL for all scenarios. Except for D2 scenario (3 existing dams), which show slightly decrease from D1 scenario (without dam). For all scenario (D1, D2, D3 and D4), in the month of September, SSL is highest due to high river discharge in this month. Alike to SSL, the different in simulated SSC between scenarios is significant, which indicate increasing number of dams will decrease the amount of SSC. Changes in SSL and SSC in the future might

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have consecutions for associated sediment management and designed reservoirs. For instance, increased SSL can an augment numerous problems related to highly loss of reservoir storage through siltation and sedimentation of river, an incorporated loss of transport capacity and increase quality of river water (Walling 2008).

4.2 Sediment Trapped in Each Reservoir The three existing reservoirs have the capability to trap around 0.2 × 107 to 0.6 × 107 ton yr−1 of estimated sediment load in the Mekong Basin Fig. 3a. In scenario D2-CPO, the total annual sediment accumulation in three reservoirs is 3.8 ton yr−1 . Most of the sediment load is potentially trapped by Manwan dam with TE is 96% than two others existing reservoirs, Dachaosan and Jinghong. Figure 3b shows the estimated sediment accumulation for existing and under construction reservoirs varies greatly in each year. The accumulation sediment in reservoirs under this scenario (D3-CPO) is increased by 60% from the under scenario D2-CPO with total annual of accumulation estimated sediment is 10.37 ton yr−1 . In scenario D4-CPO (existing, under construction and planned dams) Fig. 3c accumulation estimated sediment was highest with potential total annual sediment 31.94 ton yr−1 . Therefore, resulted low of SSL at Phnom Penh station (lowest point in this study). In general, the results confirm that the potential sediment trap in reservoirs was increased with increasing number of dams especially, when all planned reservoirs are installed. Moreover, the mass balance for each scenario have a slightly error between estimated accumulation in reservoirs with SSL at the Phnom Penh station (outlet). This is probably due to deposition sediment in river part. According to the calculation, sediment was mostly deposited in river part under scenario D2-CPO and D3-CPO. While, the sediment deposited in river is less occurring under scenario D4-CPO, it shows that chances of sediment to be trapped in reservoir are higher than in river part.

5 Conclusion This study assessed the impact of dams on sediment load and concentration in Mekong River Basin. In this study a multi dam scenarios approach for the examination of this potential effect on suspended sediment load and concentration. The distributed process-based model is used as to simulate the potential impacts of present and future dams on sediment load and concentration changes. Calibration and validation for both discharge and sediment from the distributed model were applied in the target river basin. In general, sediment load and concentration are decreased due to increasing number of dam. While, higher discharge and sediment are expected when the rainfall

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(a)

Acc. SSL in 3 reservoirs SSL at Phnom Penh (D2-CPO) SSL at Phnom Penh (D1-CPO)

(b)

Acc. SSL in 8 reservoirs SSL at Phnom Penh (D3-CPO) SSL at Phnom Penh (D1-CPO)

8 6 4 2 0

SSL (107 ton y -1)

10 8 6 4 2 0 10

(c)

Acc. SSL in 19 reservoirs SSL at Phnom Penh (D4-CPO)

8

SSL at Phnom Penh (D1-CPO)

6 4 2

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0

Year Fig. 3 Annual sedimentation in reservoirs for dam scenarios. a 3 dams (D2-CPO), b 8 dams (D3-CPO), c 19 dams (D4-CPO)

is high, during wet season. The results show that wide uncertainties exist in projected future hydrological variables (i.e., rainfall, discharge and sediment) due to differences between the climate model projections. The findings of this paper may be supportive to river basin decision makers, development planners and other communities when designing and applying a proper large river basin water management plan as well as sediment management plan to accommodate to dams.

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References ADB (2004) Cumulative impact analysis and Nam Theun 2 contributions, final report. Prepared by NORPLAN and EcoLao for Asian Development Bank, p 143 Barlow C (2008) Dams, fish and fisheries in the Mekong River Basin. Catch and Culture 14(2) Brune GM (1953) Trap efficiency of reservoirs. Trans Am Geophys Union 34:407–418 Kite G (2001) Modelling the Mekong: hydrological simulation for environmental impact studies. J Hydrol 253:1–13 Kummu M, Lu XX, Wang JJ, Varis O (2010) Basin-wide sediment trapping efficiency of emerging reservoirs along the Mekong. Geomorphology 119:181–197 MRC (2003) State of the basin report 2003. Mekong River Commission, Phnom Penh, p 300 MRC (2008) Existing, under construction and planned/proposed hydropower projects in the Lower Mekong Basin. Map produced by the Mekong River Commission Suif Z, Yoshimura C, Saavedra O, Ahmad N, Hul S (2017) Suspended sediment dynamics changes in Mekong River Basin: possible impact of dams and climate change. Int J GEOMATE 12(34):140– 145 Suif Z, Yoshimura C, Ahmad N, Hul S (2018) Distributed model of hydrological and sediment transport process in Mekong River Basin. Int J GEOMATE 14(42):134–139 Vorosmarty CJ, Meybeck M, Fekete B, Sharma K, Green P, Syvitski JPM (2003) Anthropogenic sediment retention: major global impact from registered river impoundments. Global Planet Change 39:169–190 Walling DE (2008) The changing sediment load of the Mekong River. Ambio 37:150–157 Yang D, Herath S, Oki T, Musiake K (2000) A geomorphology-based hydrological model and its applications. In: Singh VP, Frevert DK (eds) Mathematical models of small watershed hydrology and applications. Water Resources Publications, Littleton, Colorado, USA, Chap 9, pp 259–300 Yang D, Herath S, Oki T, Musiake K (2001) Application of distributed hydrological model in the Asian Monsoon Tropic Region with a perspective of coupling with atmospheric models. J Meteorol Soc Jpn 79(1B):373–380

Application of Activated Carbon and PGα21Ca to Remove Methylene Blue from Aqueous Solution Le Thi Xuan Thuy, Le Thi Suong, Le Phuoc Cuong, and Tatjana Juzsakova

Abstract The article deals with the treatment of methylene blue (MB) contamination of effluents and the MB removal capacity of various materials such as activated carbon (AC), PGα21Ca, sulfate heptahydrate (SH), aluminium sulfate octadecahydrate (ASO) and poly aluminium chloride (PAC). The combination of PGα21Ca and AC was investigated in the treatment procedure and this combination improves the MB removal efficiency at short sedimentation time. The optimal conditions to reach a removal efficiency of 94.44% were as follows: weight ratio of AC to PGα21Ca 2:1, total dosage of materials 0.6 g/L, contact time 10 min and initial MB concentration 10 mg/L. It was shown that the successful operation of the treatment process for textile industrial wastewater can be provided by the combination of PGα21Ca and AC. The combination can ensure that the color of wastewater and pH value after the wastewater treatment complies with the specifications of the National Technical Regulation on the Effluent in Viet Nam. Keywords Methylene blue · Activated carbon · PGα21Ca

1 Introduction Textile industry is one of the most important industries in developing countries and has been established long time ago because of the urgent need for human garments. Textile industry has complex technological lines due to the application of different types of production, different raw materials and chemicals to produce numerous L. T. X. Thuy (B) · L. P. Cuong University of Science and Technology, The University of Danang, 54 Nguyen Luong Bang Street, Lien Chieu, Danang 550000, Vietnam e-mail: [email protected] L. T. Suong SusTech Green Environment Company Limited, Danang 550000, Vietnam T. Juzsakova University of Pannonia, 10 Egyetem St, Veszprem 8200, Hungary © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_14

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products with diversified colors (Shiva 2016; Stephen and Chen 2011). Textile industrial wastewater exhibits high temperature and contain salts, acids, bases, additives, organic compounds, suspended solids and dyestuffs (Dao et al. 2015). In particular, the presence of dyestuffs in the wastewater will reduce the concentration of dissolved oxygen in the water, affect the growth and development of aquatic plant and animal species, and the ability of self-purification of water resources reduces (Chaodao et al. 2017). Methylene blue (MB) is a cationic dye, which is commonly used as the coloring agent for dyeing wool, silk and cotton. However, this dye has adversely effect on human beings and animals including irritation of mouth, throat, stomach and skin with symptoms of nausea, vomiting, quadriplegia, redness and itching skin (Baybars et al. 2012). Therefore, removal of MB dye from wastewater before discharging the treated waste water into the water bodies is the great concern of scientific researchers. Several methods such as ion exchange, oxidation, electrolysis, biodegradation, flocculation, coagulation and adsorption have been applied for dye removal from wastewater. Among these physicochemical techniques, the adsorption and coagulation are widely used because of their simplicity, ease of operation and high removal efficiency (Ashraf et al. 2011; Chaodao et al. 2017; Ta and Chi 2016). The biggest disadvantage of the coagulation method, however, is that it creates a large amount of sludge with high moisture content, while the separation of the adsorbents after pollutants adsorption can be implemented. Thus, the combination of adsorption and coagulation will solve the problems of dye treatment in textile wastewater to increase the capacity of MB removal, shorten the time of sedimentation and reduce the amount of sludge.

2 Experimental 2.1 Materials Methylene blue trihydrate (C16 H18 CIN3 ·3H2 O) (hereinafter referred to as MB), sulfate heptahydrate (SH) (FeSO4 ·7H2 O) and aluminium sulfate octadecahydrate (ASO) (Al2 (SO4 )3 ·18H2 O) were purchased from Xilong, China. Poly aluminium chloride (PAC) [Al2 (OH)n Cl6−n ]m was acquired from Grasim Industries Limited Chemical Division, India. Activated carbon (AC) was purchased from Kanto Chemical Co. INC, Tokyo, Japan and PGα21Ca were purchased from Nippon Poly-Glu Co. Ltd., Osaka, Japan.

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2.2 Methodology 2.2.1

MB Removal by Using Various Materials

0.1 g of SH, ASO, PAC, PGα21Ca and AC were added to 50 mL of MB solution with 20 mg/L initial MB concentration. Then, the solution was shaken for 10 min, allowed to settle for 5 min and then equilibrium MB concentration was determined.

2.2.2

MB Removal from Wastewater by AC and PGα21Ca

Batch experiments were carried out by changing different parameters like weight ratio of AC and PGα21Ca, dosage of AC and PGα21Ca and contact time. Different initial MB concentrations in the range of 10–160 mg/L were used in the experiments as well. Parameter established in the previous run as optimal parameter was chosen for the next experiment. The equilibrium MB concentration was analyzed by UV–VIS spectrophotometer, Genesys 10S, Thermo Scientific, USA at 400 nm wavelength. The removal efficiency of MB (H) was determined by the following equation. H=

C0 − Ce × 100 (%) C0

(1)

where: C0 and Ce are the initial and equilibrium MB concentrations (mg/L).

2.2.3

Removal of Dye Pollutant from Wastewater by AC and PGα21Ca

The optimal parameters determined during the preliminary experiments were applied for the blue dye (MB) removal from wastewater at DANATEX Co. in Da Nang City. The color of the treated wastewater was determined by HANNA HI 96727 measurement and the values obtained were compared with the specifications of National Technical Regulation on the Effluent of Textile Industry in Viet Nam (Decree No. 13/2015 (Column B).

2.2.4

Preparation of MB Calibration Curve

50 mL MB solutions in concentrations of 0, 10, 20, 40, 60, 80 and 100 mg/L were prepared from the stock solution.. After reading the absorbance and knowing the MB concentration of samples the equation of calibration curve was set up (Fig. 1).

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Fig. 1 The equation of calibration

3 Results and Discussion 3.1 MB Removal Capacities of Different Materials To evaluate the MB removal capacities of the above mentioned materials 2 g/L of each material was added into the MB solution having initial MB concentration was of 20 mg/L. The mixture was shaken for 10 min and was left for settling. Then equilibrium MB concentration was measured (Fig. 2). Among the materials studied, the AC exhibits the highest MB removal efficiency and it was the only adsorbent which changed the colour of the mixture. However, its particle size results in a slow sedimentation. This is a serious disadvantage for the collection of the sediment. On the other hand, PGα21Ca generated big flocs and after the coagulation the flocs separated easily from the solution. Therefore, the combination of PGα21Ca and AC was investigated for MB removal. MB removal capacity of AC and PGα21Ca.

Fig. 2 The MB concentrations before and after adding SH, ASO, PAC, PGα21Ca and AC

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Fig. 3 The MB removal efficiency and equilibrium MB concentration with weight ratio of AC and PGα21Ca

3.2 MB Removal Capacity of AC and PGα21Ca 3.2.1

Effect of Weight Ratio of AC and PGα21Ca on the MB Removal Efficiency

The weight ratio of AC and PGα21Ca is the most important parameter that influences the MB removal. When AC is the dominant component, the excessive amount of AC cannot be embedded onto the surface of the formed flocs from the coagulation process of PGα21Ca material. However, lower amount of AC leads to the low MB removal efficiency. Thus, appropriate weight ratios of AC and PGα21Ca will ensure the effective MB removal, reduced sedimentation time and sludge generation. Figure 3 shows the fluctuations in the MB removal efficiency and the equilibrium MB concentrations with different weight ratio of AC and PGα21Ca. The equilibrium MB concentrations changed between 1.296 and 5.741 mg/L. When the weight ratios of AC and PGα21Ca were 2:1 and 1:2, the MB removal efficiency reached the highest level of 93.46%. However, AC can enter into the flocs formed from PGα21Ca at any weight ratio. Thus, the weight ratio of 2:1 between AC and PGα21Ca was chosen for the following experiments because the cost of AC is significantly lower than that of PGα21Ca.

3.2.2

Effect of the Function of Dosage on the MB Removal Efficiency

The dosage of AC and PGα21Ca is a crucial factor. The low mixture dosage can result in reduced MB removal. However, the increase of the mixture dosage doesn’t result in significant changes in MB adsorption/removal as it can be seen in Fig. 4. When excess dosage is used wasting of the materials must be considered. The experiment was conducted at dosage of 0.05, 0.06, 0.1, 0.2, 0.3, 0.4 and 0.5 g AC and PGα21Ca with the weight ratio of 2:1. The mixtures were added sequentially

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Fig. 4 The MB removal efficiency and equilibrium MB concentration with the different dosage of AC and PGα21Ca

to 50 mL solution with 20 mg/L initial MB concentration. After being shaken for 10 min, the solution was settled down for 5 min, filtered through filter paper, and MB concentration of supernatant was measured. As can be seen in Fig. 4, the MB removal efficiency changed between 85.98 and 95.33% and equilibrium MB concentrations were between 0.926 and 2.778 mg/L in the function of AC and PGα21Ca dosage. The mixture dosage of 0.06 g was selected for the following investigations since this resulted in the highest MB removal efficiency.

3.2.3

Effect of the Contact Time on the MB Removal Efficiency

In order to investigate the effect of contact time, AC and PGα21Ca were added sequentially to 50 mL of MB solution. The total weight of two materials was 0.06 g in weight ratio of 2:1. The contact time of AC and MB solution was set for 5, 10, 15 and 20 min, after that, the contact time of PGα21Ca was set for 10 min. Finally, the MB solution was allowed to settle for 5 min, and was filtered through filter paper before MB analysis. The MB removal efficiency and equilibrium MB concentration with the different contact time of AC and with the different contact time of PGα21Ca is given in Fig. 5 and Fig. 6. The MB concentration decreased sharply to 1.4 mg/L, and the MB removal efficiency reached to 93.46% at 5 min. When the contact time was increased, the removal efficiency remained at the same level, and the highest of MB removal was 97.20% for 10 and 15 min. Therefore, the contact time of AC (10 min) was chosen for the next experiments. The result of Fig. 6 indicated that the removal efficiency was 84.11% and the equilibrium MB concentration was 3.148 mg/L. The MB removal efficiency increased to 95.33% and the concentration decreased to 0.9 mg/L at 10 min. The highest of removal efficiency was 97.20% and the equilibrium MB concentration was 0.556 mg/L at 15 min. Since no significant difference in the MB

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Fig. 5 The MB removal efficiency o and equilibrium MB concentration with different contact time by using AC

Fig. 6 The MB removal efficiency and equilibrium MB concentration the different contact time by using PGα21Ca

removal efficiencies were observed at 10 and 20 min, the contact time of 10 min (PGα21Ca) was selected for the next experiments.

3.2.4

Effect of Initial MB Concentration on the MB Removal Efficiency

When the initial MB concentration changes, the mass of required materials also will have to be changed. In particular, the relationship between the mass of materials to be used and the equilibrium MB concentration will be useful in the industrial applications. In this experiment, 0.06 g of AC and PGα21Ca with the weight ratio of 2:1 was dispersed to 50 mL solution having an initial MB concentration (10, 20, 40, 80, 160 mg/L). The mixture was shaken for 10 min, it was settled for 5 min and filtered through filter paper. Following this the MB concentration of supernatant was measured.

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Fig. 7 The MB removal efficiency and equilibrium MB concentration with the different initial MB concentration

The MB removal efficiency and the equilibrium MB concentration with initial MB concentration from 10 to 160 mg/L are given Fig. 7. When initial MB concentration increased, the removal efficiency of MB reduced from 92.88 to 52.66%. It indicates that the adsorption of MB on the surface of AC was high when the initial MB concentration was low, However, the surface of AC became saturated if the MB concentration was increased. Hence, the MB removal efficiency decreased when the initial MB concentration increased. The highest MB removal efficiency was 92.88% at the initial MB concentration of 10 mg/L.

3.3 Application of Dye Removal Using AC and PGα21Ca in the Wastewater from DANATEX Co., Lien Chieu Industrial Zone, Da Nang City, Viet Nam 3.3.1

Dye Removal Using the Real Samples

The dyestuff contaminated wastewater was collected from the input of the wastewater treatment system of DANATEX Company, Lien Chieu Industrial Zone, Da Nang City, Viet Nam at 13:00 Sep 7th, 2017. The pH of the real sample (8.0) was within the limits as specified by the National Technical Regulation on the Effluent of Textile Industry in Viet Nam (Decree No. 13/2015, column B). The color of wastewater was 1000 Pt-Co. It was 4 times higher than stipulated by the Vietnamese Standard. On the basis of the optimal parameters determined previously 0.12 g of AC and PGα21Ca was added to 100 mL wastewater in the following AC and PGα21Ca weight ratios: 2:1, 1:1, 1:2, 1:3, 1:4, 1:5. The contact time of each AC and PGα21Ca dosage was 10 min. After the treatment, the mixture was shaken for 10 min, it was allowed to settle down, and color scale was determined.

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Fig. 8 The MB removal efficiency and color scale after treatment with the different weight ratio of AC and PGα21Ca

The result shows that the color scales of all samples were within the limits of Vietnamese Standard. When the AC and PGα21Ca weight ratio was changed from 2:1 to 1:5, then the MB removal efficiency increased from 88 to 95%, and the color scale changed from 50 to 120 Pt-Co Fig. 8. The color scale was according to the specifications of the Standard at weight ratio of 2:1, However, AC still was observed as suspended material in the solution. It is suggested that the real sample contained not only dyestuff but also organic compounds, detergents, suspended solids from the processes of starching, weaving and product completion. Flocs were formed not only from AC and PGα21Ca, but also from the reaction of variety of other compounds and PGα21Ca. Hence, there was not sufficient amount of PGα21Ca for coagulation of AC, and this resulted in the suspension of AC at AC and PGα21Ca weight ratio of 2:1. When the weight ratio of AC and PGα21Ca was increased from 1:1 to 1:5, the effective coagulation process between AC and PGα21Ca took place, and the sedimentation of AC was faster compared to the sample with AC and PGα21Ca weight ratio of 2:1. The highest removal efficiency was 95% in case of sample with weight ratios of 1:1 and 1:2. Thus, the ratio of 1:1 was selected for the further investigation regarding the dye removal.

3.3.2

Dye Removal from the Wastewater of DANATEX Co.

The dyestuff containing industrial wastewater was provided by the DANATEX Co. and the experiments were carried out in the pilot scale at the University of Science and Technology, the University of Da Nang, Viet Nam. The optimal parameters were determined in the experimental system in laboratory pilot including the material dosage, stirring speed and settling time. Dye containing wastewater was pumped from the inlet tank into the reactor, the materials were measured in exactly and were fed into the reactor automatically. The stirrer provided efficient mixing of the wastewater with AC and PGα21Ca to prevent settling. After a

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Table 1 Parameters of the pilot scale

Parameters

Unit

Value

Wastewater volume

L

20

Dosage of AC

g

12

Dosage of PGα21Ca

g

12

AC stirring time

min

10

PGα21Ca stirring time

min

10

Settling time

min

10

Stirring speed

rpm

80

Table 2 The wastewater quality after treatment in the dye removal pilot facility Parameters

Unit

pH

–

Color

Pt-Co

Before treatment

After treatment

8

7

1000

100

Column B of National Technical Regulation on the Effluent of Textile Industry in Viet Nam No. 13/2015 5.5–9 200

set up time of stirring, the flocs settled to the bottom. After settling the treated water was pumped into the outlet tank, and electromagnetic valve opened automatically to release the flocs into the sludge tank. The dye removal pilot facility operated according to the parameters as given in Table 1 and the results are shown in Table 2, respectively. After the treatment in the automatic dyestuff removal pilot facility, the color of wastewater decreased by 10 times, from 1000 (Pt-Co) to 100 (Pt-Co), and pH value reached a value of 7. These figures are in compliance with the specifications set forth in Column B of National Technical Regulation on the Effluent of Textile Industry in Viet Nam (Decree No. 13/2015). This result demonstrated the efficiency of the combination of AC and PGα21Ca in removal of dyestuff from wastewater and compensated the disadvantages of each material. Moreover, by using the automatic dyestuff removal system a significant improvement can be achieved in the environmental management of the Company which can significantly contribute to a higher life quality for the people living in Da Nang Region.

4 Conclusions Application of an adsorbent (AC) and a coagulant (PGα21Ca) in order to remove methylene blue dyestuff from textile industrial effluent is a significant step to protect the surface waters of Da Nang Region. By the combination of an adsorbent and a coagulant and by the determination of the operational parameters the MB dyestuff

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containing effluent can be efficiently treated. The optimal conditions to reach the highest MB removal efficiency (94.44%) are as follows: weight ratio of AC and PGα21Ca 2:1, total dosage of materials 0.6 g/L, contact time 10 min at initial MB concentration of 10 mg/L. The parameters of the treated wastewater satisfied the specifications of the National Technical Regulation on the Effluent of Textile Industry in Viet Nam (Decree No. 13/2015). Acknowledgements We appreciate to GSGES Funding of Kyoto University—Japan to sponsor us to do this study and highly regard to University of Science and Technology, The University of Danang for supporting us in items, machines and laboratory. The authors would like to thank Professor Ákos Rédey, professor emeritus, Institute of Environmental Engineering, University of Pannonia, Veszprém, Hungary for his valuable comments for the paper.

References Ashraf S, Elmira P, Manouchehr N, Mokhtar A (2011) Removal of Co(II) from aqueous solution by electrocoagulation process using aluminum electrodes. Desalination 279:121–126 Baybars AF, Cengiz O, Mustafa K (2012) Cationic dye (Methylene blue) removal from aqueous solution by montmorillonite. Bull Korean Chem Soc 33:3184–3190 Chaodao L, Jianjiang L, Shanman L, Yanbin T, Bangce Y (2017) Synthesis of magnetic microspheres with sodium alginate and activated carbon for removal of methylene blue. Materials 10(84):1–14 Dao MT, Phan TTS, Ngo KD (2015) Studies on textile wastewater treatment by using aluminium sunlfate octadecahydrate and sulfate heptahydrate mixture. Marit Sci Technol J 41:20–24 Shiva DA (2016) Application of magnetic-modified Fe3 O4 nanoparticles for removal of crystal violet from aqueous solution—kinetic, equilibrium and thermodynamic studies. J Appl Chem Res 10:65–74 Stephen B, Chen BH (2011) Dye adsorption characteristics of magnetite nanoparticles coated with a biopolymer poly(γ-glutamic acid). Biores Technol 102:8868–8876 Ta WS, Chi KL (2016) Removal of dye adsorption: a review. Int J Appl Eng Res 11:2675–2679

Characterization of Erosion of the Sand Bed Near Wide Piers Nordila Ahmad, Zuliziana Suif, and Maidiana Othman

Abstract The characteristic of erosion of the scour hole around the submerged wide pier in alluvial bed play an important role in resolving the bridge failure problem. The present study investigates the erosion/local scour around single wide piers at different pier geometry under varying pier width. Experiments were carried out on mobile bed channel having mean sediment size 0.23 mm and 0.80 mm with geometric standard deviation (σg ) of 1.30 and 1.26. The cylindrical and rectangular piers are vertically embedded in the mobile bed channel having flow depth of 25 cm in clear water conditions, U0 /Uc = 0.95. It was found that the length of the sediment deposited downstream of the wide piers was about 5–11ds . The height of the mound, sand deposited downstream of the pier, was about 0.3–0.55ds and the width of the scour hole around the wide bridge piers was about 3–7ds . A significant relationship has found between the morphometry of scour and deposition with pier Reynolds number, especially when morphometric variables were combined. The relationship between normalised Vsrectangular /Vscircular and Vrrectangular /Vrcircular and pier width, b also obtained. The study reveals that the volumes depend on b. Keywords Erosion · Deposition · Wide piers

1 Introduction Predicting the scour morphometry appears to be relevant in river control engineering, in which the flow resistance is constrained or even significant obstructions downstream of bridges are involved. Moreover, understanding of the scour morphometry of scour holes and deposition is useful in evaluating and validating numerical and theoretical models. Currently, the researches and experimental data on scour morphology around piers under steady flow, mostly at wide piers, are still lacking. Studies on flow around bridge piers and the evolution of scour holes and sediment ridges N. Ahmad (B) · Z. Suif · M. Othman Department of Civil Engineering, Faculty of Engineering, National Defence University of Malaysia, Kuala Lumpur, Malaysia e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_15

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are related to a variety of scientific disciplines, where it consists of oceanography, geomorphology, fluid mechanics, sedimentology and hydraulic engineering. Nevertheless, several researches have systematically investigated the development of scour holes and sediment ridges under controlled boundary conditions. Previous studies activities basically concentrated on mechanisms of local scour around 2D piers (t > y, where t = pier height, and y = water depth), since they are often responsible for bridge failure (e.g. Melville and Coleman 2000; Richardson and Davis 2001), while studies on processes of both downstream deposition and scour are limited (e.g. Kirkil et al. 2008). Ettema et al. (2011) presented how at wide piers (i.e. for a small ratio of flow depth to pier diameter) the sediment deposition bar at the pier tail water affects the development of scour hole. The bar alters the flow field, causing flow to be diverted to the side of the bar, thereby reducing the erosion over the bar. As turbulence is generated by the flow around the pier gradually it erodes the bar and the scour hole deepens. Euler and Herget (2012) give laboratory data and analyses on deposition and local scour around non-submerged and submerged cylinders. Their tests were carried out in a small flume with rectangular shape (5 m long and 0.32 m wide). The sediment ridges morphometric variables were explained from the obstacle Reynolds number. As yet, the researches and data sets on morphology of scour around piers under steady flow, mainly at wide are scarce. Therefore, investigation of erosion and deposition around bridge piers is deemed vital. The objective of this research was to evaluate the morphometric characteristics and correlation of the sediment ridges and scour holes around the wide piers under equilibrium morphodynamic conditions. Moreover, the implication of characterization of scour hole around submerged pier condition have significant importance in high flood situations which brings about the information of rate of sediment transport around the vicinity of piers.

2 Experimental Set-Up and Data Collection 2.1 Experimental Flume The tests were performed in the laboratory with flume of 1.5 m wide, 2.0 m deep and 50 m long. A test section that was 10 m long and 0.4 m deep was filled with uniform sediment (d50 = 0.23 mm and 0.80 mm). The location of the sand bed recess is about 13.5 m downstream of the flume inlet and the pier model was installed in the middle of sediment section at approximately 17.5 m downstream of the flume inlet. A single pier model was fixed at the centre of the flume width. The pier shapes were circular and rectangular with different widths. To simulate the real case in rivers, the flume was flooded for two hours in order to remove air from the voids of bed sediments and increase the degree of compaction. In order to measure the local scour depth (ds ) with time, the flume test section was equipped with a movable carrier and a vernier point gauge. The vernier point gauge was installed with steel rails, which allowed it

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Fig. 1 Particle size distribution of the bed material: a Sample 1; b Sample 2

to be moved across the cross section and along the flume wall. The carrier could also be moved along the flume scour depth and water depth in the flume. As a result, the local scour depth data could be collected until an equilibrium time was achieved.

2.2 Bed Sediment Two sediment samples from bed materials were prepared for grain size analysis. Figure 1 shows the particle size distribution presented in a semi-logarithmic plot for both sediment types.

2.3 Pier Models Two general pier shapes were used: circular and rectangular. The chosen of size of pier is according to the pier aspect ratio (L/b) = 10 where L = pier length and b = pier width. This is decided based on the calculation made for selected bridges in Malaysia where the ratio for these bridges is approximately equal to 10. Five different pier widths were fabricated for both pier shapes, thus the total model of piers came to10. The height of the pier was 750 mm for all shapes. The material used in the fabrication of the pier model was mild steel. Each pier came with a 4-mm-thick base to support the pier. The pier widths (b) for each pier shape consist of 60, 76, 102, 140, and 165 mm.

3 Results and Discussion Results for ten experiments on wide piers are given in Table 1 along with test durations, sediment sizes, and dimensionless parameters. The variation of maximum

0.165

0.140

0.102

0.076

0.060

0.165

0.140

0.102

0.076

0.060

0.165

0.140

0.102

0.076

0.060

0.165

0.140

0.102

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

b (m)

1

Run

0.80

0.80

0.80

0.23

0.23

0.23

0.23

0.23

0.80

0.80

0.80

0.80

0.80

0.23

0.23

0.23

0.23

0.23

mm

Sediment d50

1.26

1.26

1.26

1.30

1.30

1.30

1.30

1.30

1.26

1.26

1.26

1.26

1.26

1.30

1.30

1.30

1.30

1.30

σg

Table 1 Experimental data sets

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

y (m)

Water depth

0.36

0.36

0.36

0.36

0.36

0.36

0.36

0.36

0.27

0.27

0.27

0.27

0.27

0.27

0.27

0.27

0.27

0.27

U (m/s)

Velocity

Flow

0.380

0.380

0.380

0.380

0.380

0.380

0.380

0.380

0.285

0.285

0.285

0.285

0.285

0.285

0.285

0.285

0.285

0.285

Uc (m/s)

Critical velocity

21

21

25

13

20

20

20

21

13

17

19

20

18

13

22

22

23

23

(h)

Test duration

0.148

0.185

0.244

0.089

0.125

0.159

0.196

0.257

0.065

0.073

0.116

0.133

0.182

0.071

0.106

0.125

0.167

0.197

ds (m)

Equilibrium scour depth

2.45

1.79

1.52

4.17

3.29

2.45

1.79

1.52

4.17

3.29

2.45

1.79

1.52

4.17

3.29

2.45

1.79

1.52

y/b

0.95

0.95

0.95

0.95

0.95

0.95

0.95

0.95

0.95

0.95

0.95

0.95

0.95

0.95

0.95

0.95

0.95

0.95

U/Uc

128

175

206

261

330

443

609

717

75

95

128

175

206

261

330

443

609

717

b/d50

Dimension less parameters

R

R

R

R

R

R

R

R

C

C

C

C

C

C

C

C

C

C

Sh

(continued)

1.45

1.32

1.48

1.48

1.65

1.56

1.40

1.56

1.08

0.96

1.14

0.95

1.10

1.18

1.39

1.23

1.19

1.19

ds /b

162 N. Ahmad et al.

0.060

Sh pier shape

0.076

20

b (m)

19

Run

0.80

0.80

mm

Sediment d50

Table 1 (continued)

1.26

1.26

σg

0.25

0.25

y (m)

Water depth

0.36

0.36

U (m/s)

Velocity

Flow

0.380

0.380

Uc (m/s)

Critical velocity

14

20

(h)

Test duration

0.090

0.105

ds (m)

Equilibrium scour depth

4.17

3.29

y/b 0.95

0.95

U/Uc 75

95

b/d50

Dimension less parameters

1.50

1.38

ds /b

R

R

Sh

Characterization of Erosion of the Sand Bed Near Wide Piers 163

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N. Ahmad et al.

Fig. 2 Variation of scour depth with pier width

scour depths, ds , with pier width, b, are demonstrated in Fig. 2. The pattern shows that greater pier size causes deeper scour depth.

3.1 Analysis of Bed Levels Around Wide Piers This section demonstrates the effect of wide pier shapes on frontal scour holes and sediment ridges from two sediment sizes. Table 2 presents the measurements of the frontal scour holes and sediment ridges after equilibrium time was reached. For reasons of practicality, in all other runs measurements were concentrated on certain morphometric parameters only (maximum depth of scour, distance between pier and upper rim of the scour hole at the plane of symmetry, scour hole width, length of ridge at the plane of symmetry, and maximum ridge length and height), as illustrated in Fig. 3. The measurement of frontal scour holes and sediment ridges downstream was recorded for each experiment after the flume was carefully drained. The measurements were conducted following a regular monitoring of the bed level in an XY-grid with a spacing of 5 cm apart for the area close to the pier and 10 cm apart downstream of the pier. It was found that the length of the sediment deposited downstream of the wide piers was about 5–11ds . The height of the mound, sand deposited downstream of the pier, was about 0.3–0.55ds . Furthermore, the width of the scour hole around the wide bridge piers was about 3–7ds . In order to demonstrate the scour contours, the measured XYZ-coordinates were imported into Surfer software (version 10) where the grid-files were calculated. Figure 3 shows an example of local scour contours created by Surfer software. It has long been known that any relationship or equation would be useful in dimensionless parameter. Euler and Herget (2012) had used pier Reynolds number for characterising the morphology of scour and deposition. They found a significant relationship between the morphometry of scour and deposition with pier Reynolds number, especially when morphometric variables were combined. Therefore, in order to obtain the relationship of scour hole geometry around wide piers, dimensionless

(mm)

0.23

0.23

0.23

0.23

0.23

0.80

0.80

0.80

0.80

0.80

0.23

0.23

0.23

0.23

0.23

0.80

0.80

0.80

0.80

0.80

b (m)

0.165

0.140

0.102

0.076

0.060

0.165

0.140

0.102

0.076

0.060

0.165

0.140

0.102

0.076

0.060

0.165

0.140

0.102

0.076

0.060

R

R

R

R

R

R

R

R

R

R

C

C

C

C

C

C

C

C

C

C

Pier shape

0.073

0.085

0.120

0.150

0.198

0.072

0.102

0.129

0.159

0.209

0.065

0.073

0.116

0.133

0.182

0.071

0.106

0.125

0.167

0.197

d s (m)

Scour depth

Equilibrium

0.16

0.2

0.35

0.37

0.4

0.15

0.17

0.26

0.45

0.61

0.14

0.18

0.25

0.28

0.31

0.15

0.22

0.33

0.38

0.4

(m)

ls

Frontal scour hole

l s scour hole length; ws scour hole width; l r ridge length; and wr ridge width

d50

Pier width

Sediment

Table 2 Measurement of the sediment ridge and frontal scour hole

0.48 0.46

0.073

0.85

1.03

1.13

0.31

0.38

0.57

0.79

0.97

0.4

0.42

0.63

0.66

0.83

0.25

0.28

0.35

0.45

0.67

(m)

ws

0.085

0.120

0.150

0.198

0.072

0.102

0.129

0.159

0.209

0.065

0.073

0.116

0.133

0.182

0.071

0.106

0.125

0.167

0.197

(m)

ds

0.70

1.00

1.08

1.39

1.42

0.26

0.34

0.42

0.79

0.97

0.80

0.90

1.10

1.35

1.41

0.5

0.88

0.92

0.97

1.04

(m)

lr

Sediment ridge hr

0.034

0.051

0.057

0.063

0.087

0.030

0.051

0.065

0.075

0.096

0.04

0.062

0.052

0.065

0.072

0.03

0.042

0.064

0.074

0.08

(m)

wr

0.4

0.51

0.67

0.78

0.78

0.43

0.52

0.63

0.74

1.07

0.19

0.39

0.44

0.51

0.53

0.28

0.36

0.41

0.52

0.57

(m)

Characterization of Erosion of the Sand Bed Near Wide Piers 165

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N. Ahmad et al.

Fig. 3 Scour morphometry

ls ws

PIER

lr

wr

equilibrium scour hole width, ws /b with pier Reynolds number, Rep where Rep = Ub/ν were applied. It was found that the dimensionless equilibrium scour hole width, ws /b, decreased as Rep increased, as shown in Fig. 4. The best fit is obtained by the following relationship: ws = −5.03 ln Rep + 14.41 b

(1)

However the correlation of determination, R2 = 0.68 after combining data sets for both of pier shapes and sediment sizes, d50 = 0.23 mm and d50 = 0.80 mm. In a similar manner, the dimensionless parameter of frontal scour hole length, ls /b, was plotted with Rep and it was demonstrated that ls /b is also decreased with Rep . Curve fitting of the measured data set Fig. 5, for circular and rectangular piers for all sediments sizes provided the following expression: ls = 0.016Re2p − 0.422Rep + 4.36 b

(2)

Characterization of Erosion of the Sand Bed Near Wide Piers

167

Fig. 4 Normalised equilibrium scour hole width, ws /b as a function of pier Reynolds number, Rep

Fig. 5 Normalised equilibrium frontal scour hole length, ls /b as a function of pier Reynolds number, Rep

With a correlation of determination, R2 = 0.70. Next, the volume, V, for each scour hole and sediment ridge was calculated for rectangular and circular piers. Figure 6 shows the topography of the scour profile at b = 0.006 m in three-dimensional presentations. The volumes were calculated according to the morphometric parameters (ls , ds , ws , lr , hr , wr ), as presented in Table 2. The volumes were computed with scour depth multiplied by scour width and length. Furthermore, the same calculation was used for sediment ridge volume. Chreties et al. (2008) investigated scour holes at two-dimensional piles and confirmed a significant relationship between hs , ws , and ls . All of these morphometric variables offer the advantage that they are easy to measure in the field. Therefore, the volume of scouring is presented as Vs = ls * hs * ws , while the volume of sediment ridge is Vr = lr * hr * wr . From those data, the relationship between the volume of scouring and the sediment ridge with pier width, b, were demonstrated. Figure 7 shows the relationship between normalised Vsrectangular /Vscircular and Vrrectangular /Vrcircular and

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Fig. 6 The topography of scour profiles at rectangular pier for d50 = 0.23 mm

Fig. 7 Linear relationship between normalised Vsrectangular /Vscircular and Vrrectangular /Vrcircular versus pier width, b, for both sediment sizes for a scour hole and b sediment ridge

pier width, b, for both of the sediment sizes. All patterns show that the volumes depend on b.

4 Conclusions The present study on scour hole characterization around submerged piers in single wide piers has been carried out under clear water condition with varying pier width. The morphology of erosion/local scour and deposition, volume of sediment erosion/depositions were presented in both cases for all sediment sizes. From the present experimental investigation; the following conclusions were drawn.

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169

1. The width of the scour hole around a wide bridge pier is about 3–7ds while the length of sediment deposited downstream of the wide piers is about 5– 11ds . Furthermore, the height of the mound (sediment ridge), sediment deposited downstream of the pier is about 0.3–0.55ds . 2. The best fit is obtained from relationships between the dimensionless equilibrium scour hole width, ws /b, and the dimensionless frontal scour hole length, ls /b. The results prove that the scour geometry for wide piers decreased with increasing pier Reynolds number, Rep . Simple novel relationships are proposed to evaluate the frontal scour hole width and length for wide piers, as presented in Eqs. (1) and (2). and 3. The relationship between normalised Vsrectangular /Vscircular Vrrectangular /Vrcircular and pier width, b, for both of the sediment sizes found that all patterns show that the volumes depend on b. The experimental results in the present study provides insight on erosion and deposition characteristics around wide bridge piers condition. To improve understanding of the discussed geometry of scour hole width and sediment ridge length, more experimental work is needed. This is necessary in order to give better validation of the previously shown predictor expressions for scour geometry.

References Chreties C, Simarro G, Teixera L (2008) New experimental method to find equilibrium scour at bridge piers. J Hydraul Eng 134:1491–1495 Ettema R, Constantinescu G, Melville B (2011) Evaluation of bridge scour research: pier scour processes and predictions. NCHRP Web-Only Document 175, Transportation Research Board of the National Academies, Washington, DC Euler T, Herget J (2012) Controls on local scour and deposition induced by obstacles in fluvial environments. CATENA 91(1):35–46 Kirkil G, Constantinescu SG, Ettema R (2008) Coherent structures in the flow field around a circular cylinder with scour hole. J Hydraul Eng 134:572–587 Melville BW, Coleman SE (2000) Bridge scour. Water Resources Publications, LLC, Colorado, U.S.A., p 550 Richardson EV, Davis SR (2001) Evaluating scour at bridges, 4h edn. Hydraulic Engineering Circular No. 18 (HEC-18), Federal Highway Administration, Washington, D.C.CONFERENCE 2016, LNCS, vol 9999. Springer, Heidelberg, pp 1–13

Hydraulic Ram Pump: A Practical Solution for Green Energy Maidiana Othman, Nur Fadzatul Huda Abd Halimee, Muhammad Nizam Mohammad Sobri, Zuliziana Suif, and Nordila Ahmad

Abstract Hydraulic ram pump is one of the easiest and most environmentally friendly systems that no internal power is needed to drive water. This study investigated the performance of hydraulic ram pump system using alterative materials of air chamber, i.e. PET bottle and PVC pipe were examined in the laboratory. It was found that the hydraulic ram pump system the deliver flow rate Qd , and the pump efficiency increase with increasing of supply head, Hs. Furthermore, the efficiency of 70% and a minimum of 30% occurring at a discharge of 5.5 L/min and 0.5 L/min respectively. The system using PET bottle as an air chamber performed better than PVC pipe materials. Thus, the hydraulic ram pump had the potential to be used to manage the problems related to the acceptable water for drinking, agriculture and many more Keywords Hydraulic ram pump · Efficiency · Green energy · Water security

1 Introduction Over the past few years, green energy or renewable energy sources have gained massive attention to every level of communities around the world to embrace their environmental responsibilities. Renewable energy is energy that produced from renewable resources including solar, wind, geothermal, biomass and water. Besides, renewable energy is better for the environment and produces less emissions than conventional energy sources, this energy will continuously have replenished. Hydraulic ram pump (HRP) is one of the easiest and most environmentally friendly systems that no internal power is needed to drive water (Inthachot et al. 2015). The first application of pump using the water hammer effect was reported in year of 1775 built by John Whitehurst (1775). The hydraulic ram pump can be placed near the river, so that water can be supplied to agricultural activity area and water pools activity. This system can be more cost-effective compare to the pump system using M. Othman (B) · N. F. H. A. Halimee · M. N. M. Sobri · Z. Suif · N. Ahmad Universiti Pertahanan Nasional Malaysia, Kem Sg Besi, 57000 Kuala Lumpur, Malaysia e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_16

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power motor-driven. The pump application of HRP can help to solve the problems related to lacking of adequate water for drinking, agriculture and many more. Ideally, the pump system need to be located at a lower level than the water resource. This situation is important to create the free flow of water to give the flowing water some velocity. The HRP system setting-up was very straightforward and the system consisted of drive pipe, ram body, air chamber, impulse valve, delivery valve, and delivery pipe (Berganta et al. 2006). The working principle of HRP system is using ‘water hammer effects’. The valve will be close to halt the water flowing from a supply to the higher hydraulic head (Karassik et al. 2001; Azevedo Netto 1998; Chen et al. 2016). The main component in the system is waste valve where to allow ‘water hammer’ effect to occur. Several studies were carried out on the building of HRP system (Inthachot et al. 2015; Azevedo Netto 1998; Asvapoositkul et al. 2019; Januddin et al. 2018). The main factors that affect the performance of HRP system are types valve and valve positioning. Other factors that may affect the performance of HRP including type of air chambers, durability, volumetric and mechanical efficiency, and also lifetime of the piston pump (Azevedo Netto 1998; Chen et al. 2016). The main purpose of air chamber in the HRP system is to establish the continuity of water flow at adequate pressure. The volume of air chamber has no significant effect on the operation characteristics of the HRP. But in order to improve the performance of HRP efficiency, it is necessary to take in the pressures that occur during the operational of the pump. This also agreed by (Inthachot 2015) as the volume of air chamber is not critical factor on the functioning of the pump. Hydraulic ram pump air chamber can be constructed using low cost PVC tubes and polyethylene (PET) bottle as an alternative for the normal cast iron air chamber (International Development Research Centre 1985). Reference Bosa (2019) investigate the effect on the angle of inclination on the air chamber position using three alternative materials (i.e. PET bottle, polyvinyl chloride tube and the cylinder of fire extinguisher). Hence, the objective of this study is to identify the potential of HRP system by evaluating the performance of the system using experimental works.

2 Experimental Study The performance of the lab-scale of hydraulic ram pump was tested by setting up the lab-scale hydraulic ram pump system in the laboratory as shown in Fig. 1. The following materials were used to build the system: a 1.5 L PET bottle, PVC pipe, 0.5in. male threaded coupler, 0.5-in. valve, 0.5-in. tee, 0.5 in. 90° elbow, 0.5-in. brass swing check valve, water jet, water container. The performance of the hydraulic pump using two different materials of air chamber, i.e. PET bottle (Fig. 2a) and PVC pipe (Fig. 2b) are measured in the experiment. The delivery flow rate, Qd and the water flow at the drive pipe Qs were determined by measuring the time required for a certain quantity of water. The total head at the

Hydraulic Ram Pump: A Practical Solution for Green Energy

173

Fig. 1 Hydraulic ram pump system setting up in the laboratory works

Fig.2 The hydraulic ram pump using a PET bottle air chamber b PVC pipe air chamber

drive or supply pipe, Hs and at the delivery pipe Hd were calculated from the measures flow rate. The efficiency of the pump was analyzed. The HRP system will pump water using water jet.

2.1 Hydraulic Ram Pump Performance The pump draws on the energy from the supply head, Hs with a large quantity of water, Qs to a delivery head, Hd which is higher than the supply head with a low quantity of water Qd . The operational of HRP system continuously with no other external input and the flow is intermittent. The power used to drive the pump is: Pows = ρgQs Hs the power added to the fluid is

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Powd = ρgQd Hd The efficiency of the pump is defined as η=

Powd ρg Q d Hd Qd Hd = = = = Q ∗ H∗ Pows ρg Q s Hs Qs Hs

where H* = head ratio = Hd /Hs and Q∗ = flow rate ratio =

Qd Qw ≈1− Qs Qs

3 Result and Discussion The performance of hydraulic ram pump using two different materials of air chamber, i.e. PET bottle and PVC pipe was measured. During testing, the water level of the supply tank need to be constant while the water flow was varied for each supply head condition. Figure 3 shows the variations of supply flow rate, Qs and delivery flow rate, Qd when Hs = 0.5 m and 1.0 m. It reveals that higher supply head, tend to increase the delivery flow rate Qd and supply flow rate Qs. Similar performance of

Fig. 3 Supply flow rate versus delivery flow rate at different head, hs = 0.5 m and 1 m for HRP

Hydraulic Ram Pump: A Practical Solution for Green Energy

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Fig. 4 Efficiency versus flow rate ratio at different head, hs = 0.5 m and 1 m for HRP system

HRP system stated in Asvapoositkul (2019). Therefore, we can expect higher power added to the water at higher supply head. Figure 4 illustrates the hydraulic ram pump efficiency for two different supply heads using PET bottle and PVC pipe as an air chamber. The results show that the system efficiency achieved a peak near the maximum flow rate ratio for each of supply head. It should be noted that, the increment of supply head height will also increase the velocity. Hence, the momentum of water in the drive pipe also rise. The results show that the efficiency of the system reaches a peak near the maximum Q* for each supply head. At higher supply head, the velocity and momentum of water in the drive pipe getting improved. The result reveals that the increment in the supply head will increase the pump flow rate, Qd, delivery power and pump efficiency. Therefore, the supply head must be as large as possible. Therefore, we can expect higher power added to the water at a higher supply head. Reference Bosa (2019) discusses the structural properties of PET bottle influenced the performance of air chamber and provided higher yields compared to the other alternative materials (i.e. PVC tube and fire extinguisher tube). According to Bosa (2019), expanding and contracting of air chamber using PET bottles help to represses the water contained in the air chamber. The properties of polyethylene terephthalate

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are a semicrystalline polymer that composed of crystalline and amorphous regions which increase in the properties of impact resistance, fatigue and stretching.

4 Conclusion The performance of the laboratory scale of hydraulic ram pump has been investigated in this study. The experimental works on the HRP were conducted by each of following factors could be varied independently. From the results, it’s shown that the deliver flow rate Qd , and the pump efficiency increase with increasing of supply head, Hs. Furthermore, the efficiency of 70% and a minimum of 30% occurring at a discharge of 5.5 L/min and 0.5 L/min respectively. The system using PET bottle as an air chamber performed better than PVC pipe materials.

References Asvapoositkul W, Juruta J, Tabtimhin N, Limpongsa Y (2019) Determination of hydraulic ram pump performance experimental results. Hindawi. Adv Civ Eng 2019. ID 9702183. Azevedo Netto JM Hydraulics manual. Edgard Blücher, São Paulo 8th edn, p 669. (English) Berganta A, Simpsonb AR, Tijsselingc AS (2006) Water hammer with column separation: a historical review. J Fluids Struct 22:135–171 Bosa IR, LoMonaco PAV, Haddade IR, Barth HT, Roldi V, Vieira GHS, Neto AlC (2019) Efficiency of hydraulic ram pumps made with alternative materials. J Exp Agric Int 31(4):1–7. JEAI.47167. Chen J, Ma J, Li J, Fu Y (2016) Performance optimization of grooved slippers for aero hydraulic pumps. Chin J Aeronaut 29(3):814–823 International Development Research Centre (1985) Proceeding of a workshop on hydraulic ram pump (Hydram) technology Inthachot M, Saehaeng S, Max JFC, Müller J, Spreer W (2015) Hydraulic ram pumps for irrigation in Northern Thailand. Agric Agric Sci Procedia 5:107–114 Januddin FS, Huzni MM, Effendy MS, Bakri A, Mohammad Z, Ismail Z (2018) Development and testing of hydraulic ram pump (Hydram): Experiments and Simulations. IOP Conf Ser: Mater Sci Eng (2018) 012032. https://doi.org/10.1088/1757-899X/440/1/012032 Karassik IJ, Messina JP, Cooper P, Heald CC (2001) Pump handbook, 3rd edn, McGraw-Hill Whitehurst J (1775) Account of a Machine for Raising Water, executed at Oulton, in Cheshire, in 1772. Philos Trans R Society Lond: R Soc 65:277–279. https://doi.org/10.1098/rstl.1775.0026 Wu D, Liu Y, Li D, Zhao X, Li C (2017) Effect of materials on the noise of a water hydraulic pump used in submersible. Ocean Eng 131:107–113

Assessment of the Rudnaya River Geochemical Barriers Water Composition Using Physico-Chemical Modeling Method (Dalnegorsk Ore District, Russia) Konstantin R. Frolov Abstract The article shows the results of Rudnaya River geochemical barriers water composition assessment with the physicochemical modeling method. 24 cases of physichochemical (redox) type barriers were simulated and considered. The obtained pH characteristics of geochemical barriers water have neutral level, maximum TDS are observed in January 2019. The investigation shows seasonal quantitative and qualitative composition geochemical barriers for two drainage discharges of Dalnegorsk ore region tailing dumps. The qualitative distribution of elements and pieces for in situ Rudnaya River water composition are shown. Keywords Acid mine drainage · Drainage water · River water · Water composition assessment · Geochemical barrier · Tailings dumps · Physicochemical modeling · Supergenesis · Hypergenesis

1 Introduction The Dalnegorsky ore district is located on the coast of the Sea of Japan in the valley of the Rudnaya River, the central part of the Sikhote-Alin Ridge. The mining began here at the end of the nineteenth century. Nowadays, the mining technogenic system was formed. It consists of four tailing dumps of the Krasnorechensk Concentrating Mill (KCM) and the Central Concentrating Mill (CCM). More than 10 million tons of sulphide-rich tailings are stored there. As a result of weathering agents (water, atmospheric air, temperature fluctuations) and tailings interaction, technogenic (slurry and drainage) waters with a high concentration of toxic elements such as Fe, Zn, Pb, Cu, S, and As are formed in tailings dams (Jambor 1994; Zvereva 2008). In accordance with the work of Perelman (Perel’man 1986), the points of the technogenic water release into the surface water bodies are geochemical barriers of a physicochemical (redox) type. At these barriers, the precipitation of dissolved elements in the form of solid phases occurs, but all the rest chemical compounds are K. R. Frolov (B) Far Eastern Federal University, 8 Sukhanova St., Vladivostok 690090, Russia e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_17

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remains in the water solutions and migrate further along the watercourses. Ecological and chemical studies of geochemical barriers water using modern methods of atomic emission and mass spectrometry can reliably establish the quantitative characteristics of such solutions. At the same time, it is impossible fully determine their qualitative component composition using these methods. In previous work (Frolov et al. 2019), physicochemical modeling was performed to establish the qualitative composition of slurry and drainage waters of the KCM tailings dumps. The goal of this study is to assess the qualitative composition of geochemical barriers water at points where the Dalnegorsky district tailings dumps drainage waters releases into the natural waters of the Rudnaya River. On the basis of this goal, the following tasks were formulated: (1) to form the physicochemical models of KCM and CCM tailings dumps drainage waters interaction with the waters of the Rudnaya River for the summer, autumn, and winter season; (2) to verify the results of physicochemical modeling; (3) to assess the qualitative water composition of the Rudnaya River geochemical barriers on the basis of simulation results.

2 Materials and Methods The physicochemical modeling was performed in Selektor software complex. The program implements a convex programming approach to the calculation of equilibrium in heterogeneous systems by minimizing thermodynamic potentials. One of the key features of this product is the calculation of complex chemical equilibria in isobaric-isothermal, isochoric and adiabatic conditions in multisystem. An aqueous solution of electrolyte, gas mixture, liquid and solid hydrocarbons, minerals in forms of solid solutions and one-component phases, melts and plasma can be calculated at the same (Chudnenko 2010; Helgeson et al. 1981). In the Selektor the thermodynamic properties calculations are performed depending on temperature, pressure and activity coefficients. In order of thermodynamic functions isothermal changes calculation, the equations of condensed phases volume change depending on temperature and pressure, as well as semi-empirical states of gases in standard terms are used. Thermodynamic properties of the aqueous solution components in the temperature range until 1000 °C and pressure until 5000 Bar are calculated by the modified HKF-model (Helgeson-Kircham-Flowers) (Helgeson et al. 1981; Tanger and Helgeson 1988). The water solution activity coefficients are calculated using the Helgeson modification of Debye-Huckel equation (Helgeson et al. 1981).

Assessment of the Rudnaya River Geochemical Barriers …

179

2.1 Experiment To consider geochemical barriers, two-reservoir models of the flow reactor type were formed in the Selektor software package. The phase composition of models is presented in Table 1. The first reservoir contained a chemical composition of 10.00 kg of atmosphere and 1.00 kg of the KCM new tailing dump or the CCM new tailing dump drainage water. Under natural conditions, the amount of river water is multiple times higher than the amount of tailing dumps drainage water, thus the variants of tailings drainage waters diluting with river waters by 1, 10, 100 and 1000 times were considered. For this, the contents of the first reservoir was moved to the second reservoir with the chemical composition of 1.00, 10.00, 100.00 and 1000.00 kg of river water. The chemical composition of atmosphere was calculated based on Horn work (1969): Ar0.3209 C0.01036 N53.9478 O14.48472 . In order to form the models, the KCM new and CCM new tailing dumps drainage water and the Rudnaya River water samples were taken by the author in Dalnegorsk ore region (in August and October 2018, January 2019). All the samples were cleared through a 0.45 µm membrane filter. The chemical composition was determined at FEGI FEB RAS Analytical Chemistry Laboratory by ICP-AES (on the iCAP 6500duo), ICP-MS (on the Agilent 7700) and liquid ion chromatography (on the Shimadzu LC-20) methods. The results of the analysis are shown in Tables 2, 3 and 4. The average temperatures of water samples in August (18.4 °C) and October 2018 (13.6 °C), January 2019 (3.0 °C) were set in models. Standard Selektor thermodynamic data package (Gibbs energy, enthalpy, entropy, coefficients for the heat capacity equation) and relevant literature information were used (Johnson et al. 1992; Shock 2012; Yeriomin 2011; Charykova et al. 2010).

3 Results and Discussion In total, 24 cases of physicochemical (redox) class geochemical barriers were considered in this work. Table 1 Phase composition of geochemical barriers models

Reservoir

Phase

Mass, kg

Reservoir #1

Atmosphere

10.00

Tailings dump drainage water Reservoir #2

Rudnaya River water

1.00 1.00–1000.00

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Table 2 Chemical composition of KCM new tailing dump drainage water, µg/L Chemical component

August 2018

October 2018

January 2019

Al

16.38

2.40

7.90

Mn

878.24

1626.97

641.90

Fe

91.48

9.45

1.99

Cu

1.52

0.82

2.16

Zn

392.70

637.27

20.95

As

1.46

0.45

1.86

Pb

0.30

0.07

0.48

Ca

29,840.00

32,580.00

30,740.00

K

1135.00

410.00

2444.00

Mg

7831.00

8345.00

7626.00

Na

3374.00

4843.00

4300.00

Si

4394.00

4661.00

4663.00

SO4 2−

38,400.00

34,560.00

29,975.00

Table 3 Chemical composition of CCM new tailing dump drainage water, µg/L Chemical component

August 2018

October 2018

January 2019

Al

51.52

89.32

11.65

Mn

328.53

222.74

1.16

Fe

16.63

28.49

2.02

Cu

38.65

238.75

320.10

Zn

24.00

315.75

0.80

As

49.27

15.73

11.50

Pb

1.08

0.08

0.05

Ca

83,930.00

113,300.00

124,200.00

K

28,610

31,250.00

12,650.00

Mg

2377.00

3058.00

10,480.00

Na

37,560.00

40,000.00

39,910.00

Si

4416.25

4087.00

1400.00

SO4 2−

334,080.00

276,480.00

251,775.00

3.1 Geochemical Barrier “KCM New Tailing Dump Drainage—Rudnaya River” The seasonal quantitative composition of the geochemical barrier “KCM new tailing dump drainage—Rudnaya River” water is presented in Table 5; it contains elements with concentrations more than 0.001 µg/L.

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Table 4 Chemical composition of Rudnaya River water, µg/L Chemical component

August 2018

October 2018

January 2019

Al

28.65

39.62

49.21

Mn

231.11

783.21

834.5

Fe

61.49

197.23

10.88

Cu

3.72

2.86

7.79

Zn

748.81

1981.00

2904.00

As

0.53

0.42

0.35

Pb

0.84

1.72

0.21

Ca

144,40

22,230.00

37,010.00

K

586.70

580.00

842.10

Mg

4521.00

7500.00

12,060.00

Na

4429.00

4720.00

6432.00

Si

9223.02

9350.00

3476.00

SO4 2−

32,900.00

67,700.00

26,390.00

Table 5 “KCM new tailing dump drainage—Rudnaya River” quantitative composition, µg/L Component

August 2018

October 2018

January 2019

Na

709.00–7730.00

869.00–9470.00

887.00–9660.00

Mg

1120.00–12,100.00

1440.00–15,600.00

3130.00–34,100.00

Si

1240.00–2180.00

1270.00–1670.00

839.00–883.00

Mn

101.00–1100.00

219.00–2390.00

58.60–638.00

K

157.00–1700.00

89.90–980.00

103.00–1120.00

Ca

4030.00–23,700.00

4980.00–29,100.00

8510.00–15,100.00

Cu

0.48–10.00

0.34–4.00

0.57–6.25

Zn

104.00–1130.00

238.00–2590.00

2.36–25.70

Pb

0.10–1.14

0.16–2.00

0.04–0.42

S

2160.00–23,600.00

3090.00–33,700.00

1310.00–14,200.00

As

0.18–1.98

0.08–1.00

0.26–2.88

Aqueous solution characteristics pH TDS, µg/L

7.59–8.31

7.62–8.31

8.17–8.63

27,500.00–192,510.00

33,790.00–238,860.00

75,200.00–266,050.00

0.001–0.05

0.01–0.06

0.28–0.33

Minerals, g Calcite CaCO3

182

K. R. Frolov

In all cases, the aqueous solution has a neutral pH level; lowest values of hydrogen index are observed when drainage is diluted 1000 times with river water. At the same time, the pH in August 2018 (7.59–8.31) is lower than in October 2018 (7.62–8.31) and January 2019 (8.17–8.63). The maximum total dissolved solids (TDS) is observed in January 2019 (266,050.00 µg/L) and still reaches 75,200.00 µg/L after 1000 times dilution. Minimum TDS level is presented in August 2018 (27,500.00–192,510.00 µg/L). In cases when tailing dumps drainage is diluted with river water from 1 to 100 times, the Calcite CaCO3 mineral crystallizes from saturated solutions, its maximum mass is observed in winter (0.28–0.33 g). Concentrations of sulfide ores elements Cu, Pb, and As on geochemical barriers reach several µg/L, and Na, Mg, Si, Mn, K, Ca, S reach thousands and tens of thousands µg/L. 1000 times dilution of KCM tailing dumps drainage with Rudnaya River waters reduces the concentration of all the chemical elements for 10 times. Depending on the season, the elements are distributed as follows (µg/L): (a) maximum concentration in August 2018—Si (1240.00–2180.00) and K (157.00–1700.00); (b) maximum concentration in October 2018—Mn (219.00–2390.00), Ca (4980.00–29,100.00), Zn (238.00–2590.00), Pb (0.16–2.00) and S (3090.00–33,700.00); (c) maximum concentration in January 2019—Na (887.00–9660.00), Mg (3130.00–34,100.00), Cu (0.57–6.25), and As (0.26–2.88). All the elements above are presented in forms that are shown in Table 6. In all cases, the following pieces are presented on the geochemical barrier: metal ions— Na+ , Mg2+ , K+ , Ca2+ , Mn2+ ; silicate, sulfate, hydrogen silicate and hydrogen arsenate; carbonates and hydrogen carbonates of Mg, Ca, Zn and Pb; sulfates of Na, Ca, and Mn. At the same time, CuHCO3 + and KSO-4 species are observed on the barrier only in August 2018, and MnSO4 —in August and October 2018. Depending on the season, maximum concentrations of dissolved pieces are distributed as follows (µg/L): (a) in August 2018—K+ (156.00–1700.00), SiO2 (2620.00–4440.00), HSiO3 − (30.40–290.00), CaCO3 (5.07–670.00), and CuHCO3 + (5.15–10.00); (b) in October 2018—Ca2+ (4920.00–26,900.00), Mn2+ (1180.00– 2290.00), SO4 2− (9150.00–96,400.00), ZnHCO3 + (460.00–5010.00), PbHCO3 + (0.20–2.16), NaSO4 − (46.20–170.00), CaSO4 (153.00–5990.00), and MnSO4 (70.40–240.00); (c) in January 2019—Na+ (887.00–9640.00), Mg2+ (3100.00– 32,800.00), HAsO4 2− (0.4–4.81), MgCO3 − (22.30–1790.00), MgHCO3 + (90.40– 2590.00), and CaHCO3 + (278.00–636.00).

3.2 Geochemical Barrier “CCM New Tailing Dump Drainage—Rudnaya River” The seasonal quantitative composition of the geochemical barrier “CCM new tailing dump drainage—Rudnaya River” water is presented in Table 7.

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Table 6 “KCM new tailing dump drainage—Rudnaya River” qualitative composition, µg/L Component

August 2018

October 2018

January 2019

Na+

709.00–7700.00

869.00–9430.00

887.00–9640.00

Mg2+

1120.00–12,000.00

1440.00–15,400.00

3100.00–32,800.00

K+

156.00–1700.00

89.90–970.00

103.00–1120.00

Ca2+

3990.00–22,200.00

4920.00–26,900.00

7880.00–14,400.00

Mn2+

100.00–1060.00

1180.00–2290.00

58.50–628.00

SiO2

2620.00–4440.00

2700.00–3410.00

1750.00–1750.00

6420.00–67,800.00

9150.00–96,400.00

3810.00–42,000.00 56.50–169.00

SO4 2− HSiO3

−

30.40–290.00

30.00–192.00

HAsO4 2−

0.30–3.31

0.13–1.67

0.4–4.81

MgCO3 −

118.00–240.00

138.00–250.00

22.30–1790.00

CaCO3

5.07–670.00

5.98–640.00

118.00–554.00

MgHCO3 +

7.49–370.00

10.90–490.00

90.40–2590.00

CaHCO3 +

19.80–514.00

27.70–640.00

278.00–636.00

CuHCO3

+

5.15–10.00

Mn2+ > ZnHCO3 + > CaSO4 > MgCO3 > K+

Ca > S > Si > Mg > Na > K > Zn > Mn

Na+ > SO4 2− > Ca2+ > SiO2 > Mg2+ > ZnHCO3 + > K+ > MgCO3 > Mn2+

Elements

Pieces

October 2018

August 2018

Component

Na+ > Ca2+ > SO4 2− > Mg2+ > SiO2 > CaHCO3 + > CaSO4 > CaCO3 > K+

Ca > Mg > S > Na > Si > K

January 2019

Table 9 Distribution of elements and pieces on “KCM new tailing dump drainage—Rudnaya River” geochemical barrier

186 K. R. Frolov

Ca > S > Na > Si > Mg > K > Zn Na+ > SO4 2− > Ca2+ > SiO2 > Mg2+ > CaSO4 > K+ > ZnHCO3 + > CaHCO3 +

Ca > Na > S > Mg > Si > K > Zn

Na+ > Ca2+ > SO4 2− > SiO2 > Mg2+ > K+ > CaCO3 > CaSO4 > HSiO3 − > ZnHCO3 + > NaSO4 −

Elements

Pieces

October 2018

August 2018

Component

Na+ > SO4 2− > Ca2+ > K+ > SiO2 > CaSO4 > Mg2+ > CaHCO3 +

Ca > S > Na > K > Si > Mg

January 2019

Table 10 Distribution of elements and pieces on “CCM new tailing dump drainage—Rudnaya River” geochemical barrier

Assessment of the Rudnaya River Geochemical Barriers … 187

188

K. R. Frolov

Following to the Table 10, the water of CCM geochemical barrier have almost the same common elements as it was seen in case of KCM barrier. Nevertheless, in August 2018 Si have the forms of SiO2 and HSiO3 − , and CaSO4 have the concentration above 100 µg/L all the year round. Mg is presented in solution in form of Mg2+ .

4 Conclusions In order to assess the qualitative composition of Rudnaya River geochemical barriers, 24 cases of physichochemical (redox) type barriers were simulated and considered. The pH characteristics of KCM and CCM geochemical barriers of the Rudnaya River has a neutral level. In all cases maximum total dissolved solids are observed in January 2019 (75,200.00–266,050.00 and 88,930.00–523,100.00 µg/L for KCM and CCM respectively). When tailing dumps drainage is diluted from 1 to 100 times with river water, the Calcite CaCO3 mineral crystallizes from saturated solutions. The “KCM new tailing dump drainage—Rudnaya River” geochemical barrier have the maximum seasonal concentrations of elements in the following forms: August 2018, K–K+ , Si–SiO2 and HSiO3 − ; October 2018, Ca–Ca2+ and CaSO4 , Zn–ZnHCO3 + , Pb–PbHCO3 + , S–SO4 2− ; January 2019, Na–Na+ , Mg–Mg2+ , MgCO3 − and MgHCO3 + , Cu–CuHCO3 + , As–HAsO4 2− . Maximum seasonal concentrations on the “CCM new tailing dump drainage— Rudnaya River” geochemical barrier have elements in the following forms: August 2018, Si–SiO2 and HSiO3 − ; October 2018, Mg–Mg2+ , Ca–Ca2+ and CaSO4 , Zn–ZnHCO3 + , Pb–PbHCO3 + , and S–SO4 2− ; January 2019, Na–Na+ and NaSO4 − , Mn–Mn2+ , K–K+ and KSO4 − . The comparison of obtained results with in situ Rudnaya River water composition showed that concentrations of sulfide ores and host rock elements are similar in cases of 1000 times KCM new and CCM new tailing dumps drainage dilution with Rudnaya River water. According to this, the KCM geochemical barrier has the following qualitative distribution of pieces: in August 2018—Na+ > SO4 2− > Ca2+ > SiO2 > Mg2+ > ZnHCO3 + > K+ > MgCO3 > Mn2+ ; in October 2018—Na+ > SO4 2− > Ca2+ > SiO2 > Mg2+ > Mn2+ > ZnHCO3 + > CaSO4 > MgCO3 > K+ ; in January 2019—Na+ > Ca2+ > SO4 2− > Mg2+ > SiO2 > CaHCO3 + > CaSO4 > CaCO3 > K+ . Distribution of pieces on CCM geochemical barrier is observed as: in August 2018—Na+ > Ca2+ > SO4 2− > SiO2 > Mg2+ > K+ > CaCO3 > CaSO4 > HSiO3 − > ZnHCO3 + > NaSO4 − ; in October 2018—Na+ > SO4 2− > Ca2+ > SiO2 > Mg2+ > CaSO4 > K+ > ZnHCO3 + > CaHCO3 + ; in January 2019—Na+ > SO4 2− > Ca2+ > K+ > SiO2 > CaSO4 > Mg2+ > CaHCO3 + . Acknowledgements The reported study was funded by RFBR according to the research project № 18-35-00114.

Assessment of the Rudnaya River Geochemical Barriers …

189

References Charykova M, Krivovichev V, Depmeir W (2010) Thermodynamics of arsenates, selenites, and sulfates in the oxidation zone of sulfide ores: I. Thermodynamic constants at ambient conditions. Geol Ore Deposits 52(8):689–700 Chudnenko K (2010) Thermodynamic modeling in geochemistry: theory, algorithms, software, applications. Geo, Novosibirsk Frolov K, Lysenko A, Pyatakov A (2019) A Study of the qualitative chemical composition of technogenic waters in the tailing dumps of the Russian Southern Far East in a wide temperature range using the physicochemical modeling method. IOP Conf Ser: Earth Environ Sci 272(2):022124 Helgeson H, Kirkham D, Flowers G (1981) Theoretical prediction of the thermodynamic behavior of aqueous electrolytes at high pressures and temperatures: calculation of activity coefficients, osmotic coefficients, and apparent molal and standard and relative partial molal properties to 600°C and 5 Kb. Am J Sci 281:1249–1516 Horne R (1969) Marine chemistry. The structure of water and the chemistry of the hydrosphere. Wiley, New York Jambor J (1994) Mineralogy of Sulfide-rich tailings and their oxidation products. Mineral Assoc Can 22:59–102 Johnson J, Oelkers E, Helgeson H (1992) SUPCRT92: a software package for calculating the standard molal thermodynamic properties of minerals, gases, aqueous species, and reactions from 1 to 5000 bar and 0–1000 °C. Comput Geosci 18:899–947 Perel’man A (1986) Geochemical barriers: theory and practical applications. Appl Geochem 1(6):669–680 Shock E (2012) SUPCRT 1992–1998 Database Geopig, Arizona State University. http://geopig. asu.edu/sites/default/files/slop98.dat. Last accessed 2013/08/12 Tanger J, Helgeson H (1988) Calculation of the thermodynamic and transport properties of aqueous species at high pressures and temperatures: revised equations of state for the standard partial molal properties of ions and electrolytes. Am J Sci 288:19–98 Yeriomin O (2011) Calculation of standard thermodynamic potentials for Na-zeolites with the use of linear programming problems. Int J Geosci 2:227–230 Zvereva V (2008) Environmental consequences of the hypergene processes at tin ore deposits of the Far East. Dal’nauka, Vladivostok Zvereva V, Krupskaya L (2012) Anthropogenic waters in the Komsomolsk, Kavalerovskii, and Dalnegorsk mining areas of the Far East and their impact on the hydrosphere. Russ J Gen Chem 82(13):2244–2252

Spatial and Temporal Variations of PM2.5 in the Vicinity of Expressways in Bangkok, Thailand Navaporn Kanjanasiranont, Tassanee Prueksasit, Narut Sahanavin, and Songkrit Prapagdee

Abstract The ambient air concentrations of PM2.5 were investigated in Bangkok’s urban and suburban expressways during the peak and off-peak period traffic congestion. The locations of the selected study areas were Leab Mae Nam (Inner Bangkok), Ram Intra (Outer Bangkok) and Jatuchot Expressways (suburban) which consisted of six sampling sites for each expressway toll. The sampling sites where located close to the expressway tolls were detected the greatest average concentrations of PM2.5 which showed the values of 44.79, 24.17 and 33.41 µg/m3 for Leab Mae Nam, Ram Intra and Jatuchot Expressways, correspondingly. Conversely, the sampling sites situated far from the expressway tolls were investigated the lowest mean levels of PM2.5 that illustrated the values of 12.72, 13.97 and 20.89 µg/m3 for Leab Mae Nam, Ram Intra and Jatuchot Expressway tolls, respectively. The distance between the expressways and sampling sites was influenced on PM2.5 concentrations, which indicated that the longer distance from the expressway tolls, the lower level of PM2.5 . Moreover, statistical analysis of the PM2.5 data showed an insignificant difference among the three expressway tolls. For this reason, the results displayed a similar pattern to PM concentrations in urban and suburban expressway tolls. In terms of peak and off-peak periods, PM2.5 values of the three expressway tolls showed a significant difference. Normally, most PM2.5 derives from the combustion of gasoline and diesel fuel in vehicle engines. Therefore, the levels of PM2.5 in peak periods tended to be greater than those observed in the off-peak period. N. Kanjanasiranont (B) Faculty of Environment and Resource Studies, Mahidol University, Nakhon Pathom 73170, Thailand e-mail: [email protected] T. Prueksasit Department of Environmental Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand N. Sahanavin Department of Public Health, Faculty of Physical Education, Srinakharinwirot University, Nakhonnayok 26120, Thailand S. Prapagdee Environmental Research Institute, Chulalongkorn University, Bangkok 10330, Thailand © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_18

191

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Keywords PM2.5 · Expressway · Bangkok

1 Introduction Bangkok is Thailand’s Capital City, which is the most populated city in the country. Bangkok’s transportations systems play a key role in releasing many kinds of air pollutions, including particulate matter (especially fine particles). Particulate matter 2.5 (PM2.5 ), the air pollutants mainly generated from road vehicles and combustionrelated pollutants (traffic production), are by far the extensive contributors to ambient air pollution in high traffic density areas (Hagler et al. 2010; Li et al. 2004; Lin et al. 2018; Spinazzè et al. 2015). Exposure to PM2.5 have a high potential risk to cause acute and chronic adverse health effects such as asthma attacks, high blood pressure, ischemic strokes, heart diseases, lung cancer, respiratory diseases and neurodegenerative diseases (Finn et al. 2010; Kioumourtzoglou et al. 2016; Lelieveld et al. 2015). Moreover, it shows the relation between an increase in various health problems of roadside residents and the PM2.5 concentration near the roadway. The associated factors influencing the traffic PM2.5 profiles were the amount of vehicle urban road construction, the type of roadway, daily variation (daytime and nighttime), meteorological condition, temporal variation and spatial distribution (Qiu et al. 2017). Furthermore, traffic flow and vehicle speeds can also be attributed to traffic-related PM2.5 . In addition, the distance from the roadway has crucial impacts on the variation of PM2.5 levels. The different types of roads (such as urban road, rural road, residential street, highways and expressways) tend to have their own characteristics of traffic pollution due to the exhaust emission levels. For the trafficflow patterns, the high traffic density of urban roadside generally shows the set of driving patterns as a combination of the congested and stop-and-go traffic, whereas the flow situation highway or expressway was always free. The variety of the trafficflow patterns can, in turn, be a cause of variation in PM2.5 concentrations. Many research studies were monitored roadside PM2.5 concentrations and their major associated factors (such as land use, traffic volume, population density, meteorological data) (Xu et al. 2016). The PM2.5 levels are depended on not only traffic density but also by distances from the roadways, spatial and temporal variations. However, few studies mentioned on PM2.5 generated from elevated expressways, especially for spatial and temporal variations of PM2.5 . Hence, the main objectives of this study were to investigated the levels of PM2.5 in the vicinity of expressways toll at Bangkok’s urban and suburban during peak and off-peak traffic congestion period. Additionally, the PM2.5 values at the different distances from the expressways were also observed.

Spatial and Temporal Variations of PM2.5 in the Vicinity …

193

Table 1 The description of sampling sites of PM2.5 sampling ExpresswayToll

Sampling Site

Distance from ExpresswayToll (m)

Leab Mae Nam (L)

L1 (IN)

15

L2 (IN)

50

L3 (IN)

150

L4 (OUT)

0

L5 (OUT)

80

L6 (OUT)

170

R1 (IN)

0

R2 (IN)

50

R3 (IN)

100

Ram Intra (R)

Jatuchot (J)

R4 (IN)

400

R5 (OUT)

50

R6(OUT)

200

J1 (IN)

0

J2 (IN)

150

J3 (IN)

500

J4 (OUT)

0

J5 (OUT)

300

J6 (OUT)

680

Location of sampling sites

2 Experiment 2.1 Sampling Locations In this study, the air samples were collected for both urban and suburban expressways in Bangkok, Thailand. Threeexpressway tolls were selected as the study areas, including Leab Mae Nam (L) (Inner Bangkok), Ram Intra (R) (Outer Bangkok) and Jatuchot (J) (suburban) expressway tolls. At each expressway toll, six sampling sites were assigned for the PM2.5 sampling that included both inbound (IN) and outbound (OUT) traffic directions as explained in Table 1.

2.2 Air Sampling Procedure The sampling of PM2.5 in the ambient air using Polytetrafluoroethylene (PTFE Teflon) filter with a diameter of 46.2 mm. PM2.5 samples were collected using the ambient air PM2.5 sampler operated at a flow rate of 16.67 l/min for 6 h. The samples

194

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Table 2 The schedules of PM2.5 sampling and traffic volume Expressway toll L

Period traffic congestion Peak

Off-peak

R

Peak

Off-peak

J

Peak

Off-peak

Sampling date

Sampling period

Traffic volume (vehicles/hour) Inbound

Outbound

3 June

12.00–18.00

7757

6834

4 June

13.00–19.00

7066

7866

5 June

13.00–19.00

7152

7566

2–3 June

23.00–05.00

1650

1899

3–4 June

23.00–05.00

1718

1966

4–5 June

23.00–05.00

1606

1617

6 May

07.00–13.00

4436

5097

7 May

06.00–12.00

6703

3458

8 May

06.00–12.00

6402

3328

5–6 May

23.00–05.00

1455

2167

6–7 May

23.00–05.00

1011

1169

7–8 May

23.00–05.00

1003

1233

20 May

14.00–20.00

2784

2539

21 May

06.00–12.00

3678

2830

22 May

06.00–12.00

2940

2923

19–20 May

23.00–05.00

361

638

20–21 May

23.00–05.00

292

269

21–22 May

23.00–05.00

208

297

and the number of vehicles were monitored during peak and off-peak traffic congestion period in order to compare the difference of PM2.5 level between these two periods(see Table 2). All samples of this study were collected in 2018.

3 Results and Discussion 3.1 Ambient Air Concentrations of PM2.5 The average ambient air concentrations of PM2.5 are illustrated in Fig. 1. At Leab Mae Nam (L) expressway toll, the detected PM2.5 levels of this sampling sites were (in order of highest to lowest concentration) L4 (44.79 µg/m3 ), L1 (29.80 µg/m3 ), L5 (18.86 µg/m3 ), L6 (12.72 µg/m3 ), L2 (12.06 µg/m3 ) and L3 (7.55 µg/m3 ). The concentrations of PM2.5 at L1 and L4 had higher than those found in other sampling sites. The outbound direction showed a greater PM2.5 value than that observed in the inbound direction. In view of the peak and off-peak period of L sampling sites, the differences exist amongst PM2.5 concentrations of the sampling sites which situated

Spatial and Temporal Variations of PM2.5 in the Vicinity …

Fig. 1 The levels of PM2.5 at all sampling sites

195

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close to the expresswaytoll in different periods. The levels of PM2.5 in peak hours were higher than those monitored in off-peak hours due to the greater number of vehicles in the peak periods which was about 3.8–4.8 fold (see Table 2). Nevertheless, there was no difference in PM2.5 value of the sampling sites located well away from the L expresswaytoll (L6) between peak and off-peak period traffic congestion. The statistical analysis revealed the insignificant differences in PM2.5 values between L1 and L4 because these two sampling sites were located on and closet to the road. The sampling site of L1 had a higher concentration of PM2.5 , than those found in L2 and L3, and the statistical analysis showed the significant differences in the PM2.5 levels among these different sampling sites. However, there was no significant difference in PM2.5 levels between L2 and L3. Similar to the inbound direction, the greatest mean concentration of PM2.5 was found at L4 with the significant differences between L4 and L5; L4 and L6. Nonetheless, there was no significant difference in PM2.5 levels between L5 and L6. Considering Ram Intra (R) expresswaytoll, the sampling sites located close to the expressway toll were detected the greatest average values of PM2.5 , whereas, the sampling sites which situated far from the expressway toll were investigated the lowest mean levels of PM2.5 . Thus, the highest average concentration of PM2.5 was found at R5 (35.76 µg/m3 ), followed by R1 (24.17 µg/m3 ), R2 (22.08 µg/m3 ), R3 (18.16 µg/m3 ), R6 (12.31 µg/m3 ) and R4 (11.71 µg/m3 ), correspondingly. The distance between R expresswaytoll and the sampling sites had an effect on the PM2.5 levels in peak and off-peak traffic periods. The concentrations of PM2.5 of the sampling sites where located near R expresswaytoll (R2 and R5) in peak and off-peak traffic periods were extremely different, while PM2.5 values of the sampling sites where situated far from R expresswaytoll (R4 and R6) were slightly different between peak and off-peak periods. The same as L expressway toll, the number of vehicles in the peak periods was greater than those of off-peak hours, i.e., 2.4–6.7 fold, the greater PM2.5 valuesin peak hours were then observed. Six samples of PM2.5 were collected at Jatuchot (J) expressway toll that represented for the suburban area. The highest mean PM2.5 concentration was found for J1 (29.11 µg/m3 ) which is followed by J4 (26.64 µg/m3 ), J5 (19.05 µg/m3 ), J2 (14.22 µg/m3 ), J6 (12.32 µg/m3 ) and J3 (7.09 µg/m3 ), respectively. The results also showed the same trend as L and R expressway, the greater distance from the expressway tolls, the lower levels of PM2.5 were observed. Similarly, the number of vehicles of J sampling sites in peak hour were greater than those monitored in off-peak hour about 3.9–14.1 fold, which contributed to greater PM2.5 values in the peak period. For peak and off-peak traffic hours of the J expresswaytoll, the PM2.5 concentrations collected in the peak traffic period were higher than those found in the off-peak period, except for J2 and J3. The sampling locations of J2 and J3 were situated quite away from J expresswaytoll. Hence, PM2.5 values observed in the off-peak period were greater than those found in the peak period causing by the other sources of PM2.5 , except for the traffic source itself. The possible sources of PM2.5 were biomass burning, open burning and etc. These PM2.5 values were depended on the activities of people and the area characteristic. For inbound direction, the differences in PM2.5 concentrations among J1, J2, and J3 sites were statistically significant. In contrast to

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197

Table 3 PM2.5 concentrations in other studies Location

Sampling site

Average concentration (µg/m3 )

Reference

Bangkok, Thailand

L expressway toll

21.7

This study

R expressway toll

21.2

J expressway toll

22.8

Macau

Roadside

31.4

Song et al. (2014)

Seoul, Korea

Roadside

31.1

Kim et al. (2012)

Hong Kong

Roadside

31.1

Ai et al. (2016)

Singapore

Roadside

28.9

Zhang et al. (2017)

Wuhan, China

Urban area

74.0

Xu et al. (2017)

Maryland, USA

Highway

13.0

Ginzburg et al. (2015)

inbound direction, the statistical analysis showed an insignificant difference for the sampling sites of outbound direction (J4, J5 and J6). In comparing mean PM2.5 levels with other studies (see Table 3), the concentrations of PM2.5 for each expressway tolls (L, R, and J) were calculated average of a set of PM2.5 values in both peak and off-peak periods throughout the sampling date. The highest concentration of PM2.5 was found in China (high traffic volume) followed by Macau, Seoul, Hong Kong, Singapore, Thailand, and America.

3.2 Temporal and Spatial Variation in PM2.5 The temporal variation of PM2.5 was studied for both weekdays and weekends. In addition, the spatial variation of PM2.5 was determined for both peak and off-peak hours as the percentage of the decreasing in PM2.5 value at a different distance from the expressway toll (see Table 4). The peak/off-peak PM2.5 ratio (P/O ratio) of all expressway tolls in weekday demonstrated that the sampling sites situated close to the L expressway toll expressed higher P/O ratios than those found for the sampling sites which located far away from the expressway toll (L1, L4, R1, R5, J1 and J4). Based on the weekend, the tendency of P/O ratios was illustrated the same way as weekday, except for J (OUT). Accordingly, the background values of PM2.5 at J6 areas might play a key role in this sampling site. For the percentage of decreasing in PM2.5 value at different distances from the expressway toll, the comparison between two sampling points which situated the nearest and longest distance from the expressway toll was examined. The spatial variation indicated the link between concentrations and distance to the expressway toll because the concentrations are variables in space. As depicted in Table 4, PM2.5 levels were decreased with increasing distance from the sources. The sampling sites located far away from the expressway tolls (L3, L6, R4, R6, J3 and J6) had the tendency to present greater percentage of decreasing in PM2.5 value for both peak and off-peak periods.

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Table 4 The peak/off-peak PM2.5 ratio and the percentage of decreasing in PM2.5 value at different distances from expresswaytoll Expressway toll

Sampling site

Peak/Off-peak ratio

% Decreasing of PM2.5 value at different distances from expressway toll

Weekday

Weekend

Peak

Off-peak

L

L1 (IN)

3.68

0.89

–

–

R

J

L3 (IN)

1.76

5.08

24.00

63.3

L4 (OUT)

1.93

2.23

–

– 62.71

L6 (OUT)

1.66

0.30

76.07

R1 (IN)

1.89

1.30

–

–

R4 (IN)

1.25

0.47

56.78

26.85

R5 (OUT)

5.41

1.29

–

–

R6(OUT)

2.35

0.47

73.64

18.22

J1 (IN)

1.82

1.85

–

– 28.34

J3 (IN)

0.72

1.05

66.00

J4 (OUT)

1.22

3.28

–

–

J6 (OUT)

1.65

8.79

10.22

42.25

4 Conclusion The highest average concentration of PM2.5 in peak period was demonstrated at Leab Mae Namexpressway toll (inner city of Bangkok) followed by Jatuchot (suburban) and Ram Intra (outer city of Bangkok), correspondingly. For off-peak period, Jatuchot expressway toll expressed the greatest mean level of PM2.5 followed by Ram Intra and Leab Mae Nam, respectively. The concentrations of PM2.5 in peak hour at all sampling sites demonstrated greater value than those shown in off-peak hours due to a higher traffic volume. Moreover, the distance had an impact on PM2.5 values at all expressway tolls in order to decrease the levels of PM2.5 when the longer distance from the sampling site. Higher P/O ratios were observed at the sampling sites that situated close to the expressway tolls. Acknowledgements This study was financially supported by Expressway Authority of Thailand (EXAT).

References Ai ZT, Mar CM, Lee HC (2016) Roadside air quality and implications for control measures: a case study of Hong Kong. Atmos Environ 137:6–16

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Finn D, Clawson KL, Carter RG, Rich JD, Eckman RM, Perry SG (2010) Tracer studies to characterize the effects of roadside noise barriers on near-road pollutant dispersion under varying atmospheric stability conditions. Atmos Environ 44:204–214 Ginzburg H, Liu X, Baker M, Shreeve R, Jayanty RKM, Campbell D, Zielinska B (2015) Monitoring study of the near-road PM2.5 concentrations in Maryland. J Air Waste Manage Assoc 65:1062– 1071 Hagler GSW, Thoma ED, Baldauf RW (2010) High-resolution mobile monitoring of carbon monoxide and ultrafine particle concentrations in a near-road environment. Air Waste Manage Assoc 60(3):328–336 Kim JY, Lee JY, Kim YP, Lee SB, Jin HC, Bae GN (2012) Seasonal characteristics of the gaseous and particulate PAHs at a roadside station in Seoul, Korea. Atmos Res 116:142–150 Kioumourtzoglou MA, Schwartz JD, Weisskopf MG, Melly SJ, Wang Y, Dominici F, Zanobetti A (2016) Long-term PM(2.5) exposure and neurological hospital admissions in the Northeastern United States. Environ Health Perspect 124(1):23–29 Lelieveld J, Evans JS, Fnais M, Giannadaki D, Pozzer A (2015) The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525:367–371 Li Z, Hopke PK, Husain L, Qureshi S, Dutkiewicz VA, Schwab JJ, Drewnick F, Demerjian KL (2004) Sources of fine particle composition in New York city. Atmos Environ 38:6521–6529 Lin MY, Guo YX, Chen YC, Chen WT, Young LH, Lee KJ, Wu ZY, Tsai PJ (2018) An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements. Sci Total Environ 636:1139–1148 Qiu Z, Xu X, Song J, Luo Y, Zhao R, Xiang B, Zhou W (2017) Pedestrian exposure to traffic PM on different types of urban roads: a case study of Xi’an, China. Sustain Cities Soc 32:475–485 Song S, Wu Y, Zheng X, Wang Z, Yang L, Li J, Hao J (2014) Chemical characterization of roadside PM2.5 and black carbon in Macao during a summer campaign. Atmos Pollut Res 5:381–387 Spinazzè A, Cattaneo A, Scocca DR, Bonzini M, Cavallo DM (2015) Multi-metric measurement of personal exposure to ultrafine particles in selected urban microenvironments, Atmos Environ 110:8–17 Xu G, Jiao L, Zhao S, Yuan M, Li X, Han Y, Zhang B, Dong T (2016) Examining the impacts of land use on air quality from a spatio-temporal perspective in Wuhan, China. Atmosphere 7:62 Xu G, Jiao G, Zhang B, Zhao S, Yuan M, Gu Y, Liu J, Tang X (2017) Spatial and temporal variability of the PM2.5 /PM10 ratio in Wuhan, Central China. Aerosol Air Qual Res 17:741–751

Geographic Information System and Integrated Spatial Analysis on Area Selection for WEEE Collection Site at Buriram Province, Thailand Komsoon Somprasong, Suthathip Chitwiwat, Mongkolchai Assawadithalerd, and Tassanee Prueksasit Abstract Thailand is now confronting the problem in ineffective management of Waste Electrical and Electronic Equipment (WEEE). This mismanagement can cause many negative effects to both environment and the quality of life of the residential area nearby. One of the common mismanagements for WEEE in Thailand is mostly occurred for WEEE waste collection centers, leading to high possibilities in contamination and spreading of hazardous materials. In this study, the waste collecting area in the north-eastern part of Thailand were selected as an investigation area for monitoring and analyzing the collection-route of the WEEE. Geographic Information System (GIS) were applied to demonstrates the collecting route of the wastes and the precise location of the stakeholders in waste collection and transportation based on recorded GPS data from 9 GPS device, attached to 12 pick-up trucks. The results showed that WEEE was transported from nearby provinces which mainly were Khon Kaen, Chaiyaphum, and Nakhon Ratchasima. Spatial analysis, considered to be one of the most effective geographical method, were applied to designate the suitable area or coordinates based on significant criteria which was regulations regarding to water resources, residential area, and airport. The information of waste collecting route, land use, water resources and regulations were integrated in order to specify the suitable area for the developing of the official WEEE waste collection center. The results of the study indicate that current wastes collection area and transportation should be diminished and the suitable area for setting up the new center can be specified.

K. Somprasong (B) Department of Mining and Petroleum Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand e-mail: [email protected] S. Chitwiwat · M. Assawadithalerd The Center of Excellence on Hazardous Substance Management (HSM), Chulalongkorn University, Bangkok, Thailand T. Prueksasit Department of Environmental Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_19

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Keywords GPS · Spatial multi-criteria decision making · WEEE · Environment site

1 Introduction The dismantling of WEEE in Thailand are normally found ineffective way by the community that bought the WEEE in term of second-hand electrical equipment. These low portions turned into repairing or selling as are selling product or storing up as a part in further maintenance, whereas larger portion was dismantling to gain value materials e.g. plastics, aluminum, iron, and copper etc. Due to lacking legislation to directly handle WEEE, the people were practically allowed to work in this kind of occupation. However, the related regulation has been drafted that encourage Extended Producer Responsibility (EPR) to improve WEEE management problems, it is not launched yet. Thus, the emerging improvement should be considered during approved regulation becoming effective. There are many sites in Thailand that community has a career on WEEE dismantling, the one is at Buriram province, which can be divided into two subdistricts that are Dang Yai and Ban Pao. This area has informal dismantle the WEEE over 20 years. Currently, the wastes from WEEE dismantling such as CRT monitors, insulated polyurethane foam, and burned ash from small copper wire are supposed to be affected on polluted environment by open dumping and open burning. In a purpose of conducting the better schemes for the management of WEE in this area the appropriate area for the relocation of the collecting and dumping of those materials must be defined. Site selection is one of the necessary process in starting-up, expansion or replacement of both business and construction facilities. This decision-making technique were developed into multi-criteria decision making (MCDM) which has been adopted to serve various purposes in scientific field since the 1970 s (Kumar et al. 2017; Sánchez-Lozano et al. 2013). In site selection and decision-making procedure, large numbers of geographical data were utilized which means that they can be considered to be spatial decision problem (Sánchez-Lozano et al. 2013). Geographic Information Systems (GIS), containing a high capability in the analysis of spatial data with user-friendly interfaces (Somprasong 2019; Cinderby 1999), can be assigned in conjunction with conditions, theories and equations to support the scientific-MCDM process with high efficiency and quality. This integrated technique is based on the spatial data management and analysis. Spatial analysis defined as a type of geographical analysis that can explain anthropogenic activities and natural incident with spatial expression as mathematics and geometry. For example, (Al-Yahyai and Charabi 2015) conducted a spatial-MCDM on solar energy resource assessment for large PV farms implementation in Oman. The implementation, based on this integrated method, showed that 0.5% of the total land area demonstrate a high suitability level. The MCDM method, based on GIS application, also demonstrates a satisfied result when supporting environmental aspects.

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Zhang et al. (2009) blends two aggregation techniques with spatial-MCMD and indicates that 0.3% of their study area is unsuitable for municipal landfill. By using risk perception-scores as weights in spatial-MCMD, developed a new decision framework for risk assessment for monitoring of sea-level rising and land subsidizing for a time span of 50 years. It can be implied that under some of limitations, a spatial-MCDM can enables the decision maker to develop a suitable land use policy. Since the capability of GIS for spatial-MCMD can be a suitable tool to evaluate and develop better schemes in the management of WEEE in the study area, so it was applied in association with spatial data and regulations in a purpose of determining the appropriate location for settling up of WEEE collecting site. The proposed location, retrieved from the spatial-MCMD integrated with existing environmental regulation regarding to landfill site characterization, were then assessed with the current situation in the study area for a further management policy.

2 Methodology The ineffectiveness of WEEE-collection system in the study area can be a cause of hazardous effect on both environmental and health of the adjacent population, the integrated method to define a proper management schemes for this area has been conducted according to Fig. 1. The study commenced with the data acquisition for spatial conditions which were based on the related regulations for the environment and waste disposal. Those conditions were then transformed into the information layers as inputs for spatial analysis in GIS. The suitable site for development as WEEE waste collection site were compared in association with the location of the subsisted dumpsite and the data of the transportation of WEEE in the study area, recorded using 12 GPS devices. As a study results the comparison of those requisite data were then assessed as a development schemes for the WEEE-management in the study area.

Fig. 1 Workflow of this study

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Fig. 2 Location of the study area

2.1 Site Description Ban Pao and Dang Yai district locates in the northeastern parts of Thailand. This area considered to be one of the condense WEEE-involved business in Thailand. These two areas work for WEEE dismantling approximately 170 households. The environment was concerned since they inappropriately manage the wastes after separating valuable materials. The nonvalue is mainly impact to the environment because of open dumping and open burning. However, heavy metals potentially distribute to ecological part of food chain such as surface water that is a major source of water supply, ground water, agricultural soils, and ambient air. The area of these two districts are determined to approximately 60 km2 in total. The area contains both urban water resources and cultural tourist spot as can be followed in Fig. 2.

2.2 Data Acquisition and Preparation The data acquisition was conduct under the purpose of retrieving for regulations to create spatial conditions in form of raster analysis layer. Since there is no straight regulation in controlling of WEEE in Thailand, the spatial conditions, assigned in this study were integrated from other related regulations as can be presented in Table 1. The allocations which can be transform into spatial relationship were selected and given as distribute data for spatial analysis in GIS application.

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Table 1 Spatial condition for GIS data input Relate regulation

Distance from the boundary (m)

Watershed Class 1 and 2 (Siriratpiriya 2014)

Not allow

Domestic and international airport (Siriratpiriya 2014)

5000

Archaeological and historical site (Pollution Control Department 2004)

1000

Conservation area (Pollution Control Department 2004)

1000

Local community (Pollution Control Department 2004)

1000

Water resources (Pollution Control Department 2004)

1000

Geological fault (Pollution Control Department 2004)

1000

Sandstone bedrock (Pollution Control Department 2004)

Not allow

2.3 Spatial Multi-criteria Decision Analysis Since the spatial analysis for site selection can functionality provide and display the indispensable geographic information in supporting of multi-criteria decision making (MCDM) schemes, the appropriate site for substitution of the existing dumpsite with WEEE waste collection center can be determined by integrating the controlled and essential conditions into the raster calculation layer, following the process established in Fig. 2. These criteria, based on the regulation, were defined as geo-referenced data in form of buffer area and then were further integrated using raster calculation according to (1). Aopt refers to cell value of the target area. Ra is the radius of the buffer zone, retrieved from spatial analysis, while x stands for the regulatedallowance radius. This logical calculation can categorize the unqualified sites out of the calculation layers. The appropriate location, conforming with the required criteria can be determined in form of illustration map.  Aopt =

1 X min < Ra ≤ X max 0 otherwise

(1)

2.4 WEEE Collection Route Tracking and Management Scheme Assessment With a purposed of pursuing the distribution and accumulation of WEEE in the study area, the GPS tracking devices were installed to twelve selected trucks of the local waste collectors in the study area. These commercial GPS-tracking devices were assigned for gathering the route of wastes transportation in form of geo-referenced point of the distributer and existing collection center. The route tracking process were conduct during June to September of 2019 for clarifying the common route in waste

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collection and transportation within the study area. These recorded data were then transformed into spatial layers for further analysis. The analyzed data, containing qualified location for WEEE collection center, the transportation route of wastes and the location of previous dump site were integrated toward the assessment for proper management of WEEE in the study area.

3 Results and Discussion 3.1 WEEE Transportation Routing The transportation routes and significant positions in WEEE handling-activity, attained from GPS are demonstrated in Fig. 3. The dense numbers of waste collector and separator can be noticed in two area which classified as “Group A” in the middle part of the study area and “Group B” in the southern part of the study area. The settlement of these two groups’ location of activity are instigated by the location of the existed dumping area for WEEE in Baan Pao and Dang Yai district. Furthermore, the record from GPS designates that the collecting of WEEE were taken place from these locations to other area beyond the study boundary, including nearby provinces which mainly are Khon Kaen, Chaiyaphum, and Nakhon Ratchasima as can be seen in Fig. 4. According to the GPS-tracking system, the derivation position of WEEE collection and transportation route were mostly located in the central area of Group B’s dump yard. The longest distance for transportation of WEEE can be detected as 32.52 km to the southern side of the study boundary in Buriram, while the shortest distance was determined to be 4.97 km from the derivation position.

Fig. 3 Spatial analysis framework for site selection of WEEE collection center

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Fig. 4 Significant location of WEEE business activity in the study area

3.2 Spatial Multi-criteria Decision Analysis’ Results Figure 5 demonstrates the result of the spatial-MCMD analysis using GIS and the allocation from Thai regulations. The qualified area for setting up of the WEEE collection site was demonstrated as pink area in the decision map. It reveals that the current locations of Group A and Group B, where large numbers of WEEErelated activity in the study area are not located in the suitable area according to the regulations. Consistent with the analysis, the proper location for establishing the new collection center were classified into 4 groups as can be follow in Table 2. Almost all of the locations, derived from the analysis, can be accessed by the public transportation route except the area S-2 where is classified as a remote area in Baan Pao district. In consistent with Table 2, all of the qualified areas were located close to Group A with the average displacement of 4.04 km, while the average 6.38 km range of displacement can be measured from Group B to these locations. Final results of the suitable location for WEEE center are shown in Figs. 6 and 7.

3.3 WEEE Management Schemes The qualified locations were analysed in association with other support information, provided by both literatures and interviews. Based on waste separators who generate

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Fig. 5 WEEE transportation route, retrieved from GPS-tracking device

Table 2 Location for establishing the new collection center Appropriate area

Land use type

Distance from Group A (km)

Distance from Group B (km)

S1

Active paddy field

6.44

8.10

S2

Active paddy field

4.31

8.23

S3

Active paddy field Disturbed deciduous forest

1.62

6.72

S4

Active paddy field Eucalyptus Disturbed deciduous forest

4.22

2.40

Average

4.04

6.36

wastes, the significant criteria were considered such as the possible environmental impact, occurred by transportation route, the distance between the groups of the and the proposed relocation area. Therefore, two options are proposed to be the suitable area for the relocation in WEEE management schemes in the current study site. The first option for the relocation were strictly concerned on the environmental effect

Geographic Information System and Integrated Spatial Analysis …

Fig. 6 Results from Spatial-MCMD analysis

Fig. 7 The study results from spatial-MCMD analysis

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from the separation activities. Area S-1 and S-4, accessible by moving pass through the main river in Dang Yai district, is not appropriate for setting up of the collection centre due to the risk in contamination from the of WEEE from the transportation. In a purpose of avoiding the possible impact, the suggested locations are then considered to be S-2. In addition, the second option for consideration on the location of the new centre were based on the distance of the activities and the purposed locations. Area S-2 and S-4, located near the activity area of Group A and Group B respectively, can be selected locations due to the shortage range of the relocation.

4 Conclusion The GIS application for spatial multi-criteria decision making were assigned in a purpose of determining the appropriate location for the setting up of new WEEE collection center. The results indicate that current activity in handling of WEEE in the study area are not in the acceptable condition, based on the spatial allowance from the related regulations. Four qualified area were suggested and analyzed to specify the most appropriate area for the settlement and the results indicated that the southeastern are of the study boundary is the most applicable area for establishment of the new WEEE collection center. Additionally, the study has also revealed that spatialMCMD, couple with GIS application, can become a useful tool in environmental management and perform a capability in customizing this integrated method to be utilized in other similar circumstances. Acknowledgements All research work was conducted under “Thailand Research Challenge Program for WEEE and Hazardous Waste” project. The authors gratefully acknowledge the financial and support provided by National Research Council of Thailand (NRCT). We would like to express our sincere gratitude to the Center of Excellence on Hazardous Substance Management (HSM), Chulalongkorn University for invaluable supports of facilities and scientific equipment.

References Al-Yahyai S, Charabi Y (2015) Assessment of large-scale wind energy potential in the emerging city of Duqm (Oman). Renew Sustain Energy Rev 47:438–447 Cinderby S (1999) Geographic information systems (GIS) for participation: the future of environmental GIS. Int J Environ Pollut 11(3):304–315 Kumar A, Sah B, Singh AR, Deng Y, He X, Kumar P, Bansal RC (2017) A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew Sustain Energy Rev 69:596–609 Pollution Control Department (2004) State of solid waste 2004, http://infofile.pcd.go.th/mgt/ pollution2547. Last access 13/08/2019

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Sánchez-Lozano JM, Teruel-Solano J, Soto-Elvira PL, García-Cascales MS (2013) Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain. Renew Sustain Energy Rev 24:544–556 Siriratpiriya O (2014) Municipal solid waste management in Thailand: challenges and strategic solution. In: Municipal Solid Waste Management in Asia and the Pacific Islands. Springer, Singapore, pp. 337–354 Somprasong K (2019) Application of integrated spatial approaches for studying the vegetation alternation’s effect on the reclaimed land of contaminated zinc mine. In: IOP Conference Series: Earth and Environmental Science, vol 307, issue 1, IOP Publishing Zhang J, Xia LJ, Du WL, Wang JA, Zhang CR, Ji SS, Yu B (2009) Community structure and timespace distribution characteristic of airborne fungi in a municipal landfill site. Huan jing ke xue = Huanjingkexue 30(11), 3184–3189

Simulation and Optimization of Hoa Cam Wastewater Treatment Plant Dinh Huan Nguyen and M. A. Latifi

Abstract The study carried out measurement survey of HoaCam wastewater treatment plant (WWTP) in DaNang City, Vietnam. Based on ASM1 model, simulation of this wastewater treatment plant is implemented to determine relative parameters of the model in Vietnamese conditions closest to the measurement results. After that, optimization of the design and operation of the WWTP to have more efficiency. The simulation results show that it is quite accurate with the actual operation conditions of HoaCam WWTP. The optimization results show that the construction size is greatly reduced compared to the actual design of the HoaCam WWTP. This shows that the HoaCam plant has a waste of investment and energy consumption without meeting discharge standards. Keywords Simulation · Optimization · Wastewater treatment plant · WWTP · ASM1 model

1 Introduction As in any field, simulation and optimization are important nowaday; it helps to quickly identify processes that vary according to different operating processes. Therefore, simulation and optimization of WWTP are important to modernize in design and operation. However, this field is quite limited. But, until now there are a few models that are highly appreciated by scientists and are used to simulate WWTPs, like ASM1 (Henze 1987), ASM2 (Henze 1995), ASM2d (Henze 1999), ASM3 (Gujer et al. 1999). Some authors based on ASM1 model to simulate some actual WWTPs, the results show that the measurement data and simulation theory are quite similar, but the parameters applied in ASM1 has been adjusted to suit the actual conditions, like (Alex 2001). D. H. Nguyen (B) University of Science and Technology—The University of Danang, Danang, Vietnam e-mail: [email protected] M. A. Latifi University of Lorraine, Nancy, France © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_20

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In Vietnam, the optimization of WWTP has not been studied in depth and methodical, only perform measurement experiments of some main components in the system (Manh and Thuy 2014). While the efficiency of wastewater treatment depends on a series of technological processes. Besides, the theory related to optimal simulation only stops at a single level of each segment without connecting to a complete model (Nguyen and Hai 2003), so it is difficult to use a computer to support for the simulation and optimization. The simulation problem needs to go along with the actual measurement process in order to adjust the model parameters accordingly. These parameters include operational parameters, dimensions as well as other conditions of the WWTP. When having the suitable parameters, we use this model to develop different scenarios for the WWTP, have strategies and solutions in design, operation and improvement to ensure effluent output meets discharge standards as well as save operating costs. Concerning the optimization of the wastewater treatment plant, which has not been extensively investigated and very few works have been recently devoted to dynamic optimization of these plants (Chachuat et al. 2001, 2005a, b; Fikar et al. 2005). Gillot et al. (1999) presented a method to determine the cost of the WWTP with fairly accurate results. Alasino et al. (2007) also focused on reducing construction cost for a WWTP, this study is more general because the description of the WWTP is understood as a superstructure, consist a settler and some reactors with different dimensions and assumed for many possible operations. In our study, we focus on the simulation of a WWTP in Vietnam, it’s the HoaCam WWTP. The aim is to determine the parameters of ASM1 model for the WWTP in conditions of Vietnam, to compare with other countries. Then, optimization of the WWTP to have good results for the HoaCam WWTP to have economy and efficiency in the wastewater treatment field.

2 Case Study In our research, case study is the HoaCam WWTP in DaNang City, Vietnam. The design capacity of the WWTP is 2000 m3 /day, with some main components as shown in Fig. 1.

Influent

Anaerobic

Aerobic

Secondary

tank

tank

settler

Fig. 1 The HoaCam WWTP

Effluent

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Actual current operation of the HoaCam WWTP is about 600 m3 /day. In fact, due to the low BOD5 and COD concentration, the WWTP does not use anaerobic tank but only aerobic tank, secondary settler and some lakes to meet the standard rules. The measurement of influent of the WWTP are shown in Fig. 2, which continuously for 2 days, every 2 h. The effluent of WWTP is measured and presented as shown in Fig. 3.

Fig. 2 Influent measurement of WWTP

Fig. 3 Effluent measurement of WWTP

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Preliminary assessment shows that this WWTP is built too large, the hydraulic retention time in the biological tank is too long, not enough nutrients to maintain the life of microorganisms. Therefore, costly operation, but the treatment efficiency is not high, sometimes exceeding the discharge standards. So, the simulation and optimization of WWTP are necessary to show the inadequacies and propose appropriate design parameters.

3 Simulation of HoaCam WWTP 3.1 Simulation Model The ASM1 if applied to simulate the WWTP. The model is presented as: • Flow rate:   Q1 = Qo + Qr , m3

(1)

• Concentrations in the biological tanks: dZ1 1 = (Q Za + Qr Zr + Q0 Z0 + r1 Z1 − Q1 Z1 ) dt V1 a

(2)

1 dZ2 = (Q Z1 + r2 V2 − Q2 Z2 ) dt V2 1

(3)

Q2 = Q1

(4)

• Disolved oxygen in the aerobic tank:    dSO,2 1  = r2 V2 + KL a2 V2 Ssat O − SO,2 − Q2 SO,2 dt V2

(5)

where: Qo , Zo : flow rate and concentration of influent; Q1 , Z1 : flow rate and concentration in anaerobic tank; Q2 , Z2 : flow rate and concentration in the aerobic tank; Qr , Zr : flow rate and concentration of recycle flow; r1 , r2 : coefficients of anaerobic and aerobic tanks; V1 , V2 : volume of anaerobic and aerobic tanks; SO,2 : oxygen concentration in the aerobic tank; SOsat : saturation oxygen; kL a2 : oxygen diffusion coefficient. The physical and biological processes of the model depend heavily on the relevant conversion factors. Basically, there will be some standard parameters for treatment in biological tanks, but there are some parameters that vary greatly depending on the actual structure of the WWTP. Therefore, it is necessary to determine the model parameters to suit the WWTP for reliable simulation results.

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Fig. 4 Determination of model parameters

3.2 Determination of Parameters There are 5 parameters of chemical equilibrium in the biological tanks of a WWTP (YA , YH , fP , iXB , iXP ) and 14 parameters of dynamic equilibrium (μH , KS , KO,H , KNO , bH , ηg , ηh , kh , KX , μA , KNH , bA , KO,A , ka ). The relationship of these parameters is presented in Henze (1987). The simulation of WWTP is to determine the chemical and kinematic coefficients so that when calculating the concentration of substances of the WWTP, it will have an approximation to the measured values. gProm is used to define these parameters. Here we programmed the simulation algorithm based on ASM1 model that is described in Fig. 4.

3.3 Simulation Software The tool to optimize here is the gProms (www.psenterprise.com) (gProms 1997) which is a modelling, simulation and optimization standalone toolbox which is capable of performing large-scale simulation and optimization of complex processes. Its features include solving systems of DAEs, automatic root finding of switching functions when considered process model is of hybrid nature, as well as automatic parametric sensitivity equations generation and evaluation which proves to be very useful for the purpose of process optimization. Nowaday, gProms representative office is located in some developed countries to bring control technology into the automatic manufacturing industry.

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Fig. 5 Error of influent flow rate between simulation and measurement

3.4 Simulation Results 3.4.1

Simulation of Influent Datas

Using gProms for simulation of flow rate, the result is shown in Fig. 5. Figure 5 shows that, the simulation results of flow rate have very small errors compare with measurement data, beside that the gProms software has the ability to soften the data variation to better match the actual measurement data. The simulation results of flow rate are used directly for gProms software to simulate the WWTP and optimize it to have reasonable dimensions and efficient effluent. Similarly, the influent concentrations are simulated, their results shown in Fig. 6. The results show that the variations of influent concentrations are different from the variation of flow rate. This does not mean that the more flow rate is, the higher the pollution is, but depends on the characteristics of wastewater. The simulation results show what period the pollutants exceed the permissible limits, thereby making the operation more effective. However, after the simulation, it is necessary to have additional measurements to make it more reliable.

3.4.2

Parameter Results

Use the simulated influent data to run the program with the default parameters of the ASM1 model to get the effluent concentrations. Compare the simulation results

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Fig. 6 Influent simulation results

with the measurement results, make adjustments to the relevant parameters so that the two results are closest to each other. The adjustment is done automatically based on the optimization toolkit of gProms. However, during the identification process, there will be steps to identify the high and low sensitivity parameters. Low sensitivity parameters use the model’s default coefficients, with high sensitivity parameters that will be determined and effluent by the computer. The parameter results are: YH = 0.41; iXB = 0.0036; bH = 1.2; fns = 0.031. Compared to the default parameters of ASM1 model, the determined parameters are different but not much, only the value of fns is quite large difference (0.031 compared with 0.00228). This proves that parameters of biological process in aeroten tank are quite similar, there is little difference between theory and reality.

3.4.3

Simulation of Effluents

Using the determined parameters for the model with the actual influent measurement data, simulation to get the effluents. The simulation results of aerobic tank shown in Fig. 7. The results show that, although the influent values of COD and BOD are different, but after the aeroten tank, this variation is quite similar. This is perfectly reasonable, because after being decomposed by the microorganisms in the aerobic tank, these values vary in uniformity. TN in the output exceed the permissible limits.

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Fig. 7 Effluent simulation results of aerobic tank

4 Optimization of HoaCam WWTP 4.1 Optimization Methodology The methods of optimization are divided in two groups, sequential and simultaneous methods (Cuthrell and Biegler 1987, 1989). Simultaneous methods are based on complete discretization of state and control variables. Orthogonal collocation methods are usually used and the resulting nonlinear programming problem (NLP) is solved using a gradient-based method. In the sequential methods, the most common approach used is the control vector parameterization (CVP). The state variables are not approximated (Goh and Teo 1988; Teo et al. 1991). In this work, the sensitivity method is used through gOpt software of gProms environment. 4 decision variables are involved and seven equidistant time intervals are used. The resulting number of control parameters is then 28. In addition to the process model equations, four infinite dimensional constraints are considered. The Bolza optimal standard is as follows:     Min Jo = G o x t f , t f +

t f Fo (x, u, t)dt

u(t),t f

(6)

0

x(t) ˙ = f (x(t), u(t), p, t)

(7)

x(t0 ) = x0

(8)

Bound limits:     Ji = G i x t f , t f +

ti Fi (x, u, t)dt

(9)

0

where: x ∈ Rnx : vector of state variable; u ∈ Rnu : vector of control variable; p ∈ Rnp : vector of parameter; t f : time. The resolution algorithm was implemented by gProms software, programmed to suit the WWTP under study. It shows the process to be achieved when the design and

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Fig. 8 Resolution algorithm for WWTP

operation parameters are within the recommended theoretical standards. The model only concentrated in the main stage is activated sludge in anaerobic, aerobic and sedimentation settler. The optimization process can be shown in Fig. 8.

4.2 Optimization Results The objective of optimization is to determine the continuous aeration profile and internal recycle value which minimize the aeration energy under specified constraints on effluent COD, BOD5 , TN and TSS. The optimization problem is formulated as:  Min

Q r , K L ai (t),Vi , i=1−2

 SOsat Vi K L ai (t)dt J= T × 1, 8 × 1000 i=1 2

(10)

Subject to: – Process model equations (see Sect. 3.1) – Effluent constraints: COD = 75 mg/m3 ; BOD5 = 30 mg/m3 ; TN = 20 mg/m3 ; TSS = 50 mg/m3 .

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Fig. 9 Optimization results

where: Qr : flow rate of recycling. kLai : oxygen diffusion coefficient in i-th tank. Vi : volume of i-th tank. Ssat O : saturation oxygen. T: time. With the initial design data of 2000 m3 /day of HoaCam WWTP, based on the ASM1 model, the results of optimizing the design and operationare presented in Fig. 9. Here we will get both the size of the WWTP (aeroten tanks, secondary settler) as well as operating parameters: aeration supply 1.63 mg/l, activated sludge circulation 1680 m3 /day, mud removal out of secondary settler is 44 m3 /day. The optimization results of design and operation show that the dimensions of aerobic tank and secondary settler are much smaller than the actual WWTP. About the effluent concentrations, COD and BOD5 , TSS satisfy the discharge standard, only TN is equal to the allowable discharge threshold, this proves that the WWTP has reached optimal design and operation.

5 Conclusions The study identified the model parameters to simulate the WWTP close to the actual conditions without complicated and expensive measurements. Simulations help us to know the effluent concentration of WWTP at different periods. The results also show that the concentration of COD, BOD5 does not exceed the standard, while TN is exceeded, the reason is due to excessive aeration causing nitrification in the WWTP. The optimization results of design and operation show that the actual size of HoaCam WWTP is too large, causing investment and operation costs, while the effluent concentrations do not meet standard rules.

References Alasino N, Mussati MC, Scenna N (2007) Wastewater treatment plant synthesis and design. Ind Eng Chem Res 46(23):7497 Alex J et al (2001) The COST simulation benchmark: description and simulator manual. COST Action 624 and COST Action 682

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Chachuat B, Roche N, Latifi MA (2001) Dynamic optimisation of small size wastewater treatment plants including nitrification and denitrification processes. Comput Chem Engng 25:585–593 Chachuat B, Roche N, Latifi MA (2005a) Long-term optimal aeration strategies for small-size alternating activated sludge treatment plants wastewater treatment plants. Chem Engng Proc 44:593–606 Chachuat B, Roche N, Latifi MA (2005b) Optimal aeration control of industrial alternating activated sludge plants. Biochem Engng J 23:277–289 Cuthrell JE, Biegler LT (1987) On the optimization of differential-algebraic process systems. AIChE J 33(8):257 Cuthrell JE, Biegler LT (1989) Simultaneous optimization and solution methods for batch reactor control profiles. Comput Chem Eng 13(1/2):49 Fikar M, Chachuat B, Latifi MA (2005) Optimal operation of alternating activated sludge processes. Cont Engng Pract 13:857–861 Gillot S et al (1999) Optimization wastewater treatment plant design and operation using simulation and cost analysis. In: 72nd annual conference WEFTEC 1999, 9–13 October, New Orleans, USA Goh CJ, Teo KL (1988) Control parameterization: a unified approach to optimal control problems with general constraints. Automatica 24:3–18 gProms (1997–2009) Process systems enterprise. www.psenterprise.com Gujer W, Henze M, Mino T, van Loosdrecht MCM (1999) Activated sludge model no. 3. Water Sci Technol 39(1) Henze M et al (1987) Activated sludge model no. 1. Technical Report 1, IAWQ, London Henze M et al (1995) Activated sludge model no. 2. IAWQ Scientific and Technical Report No. 3, London, UK Henze M et al (1999) Activated sludge model no. 2D, ASM2D. Water Sci Technol 39(1):165–182 Manh LD, Thuy LTL (2014) Study on beer wastewater treatment Nguyen NX, Hai PH (2003) Theory and modeling of biological wastewater treatment process Teo KL, Goh CJ, Wong KH (1991) A unified computational approach to optimal control problems. Wiley, New York

Escherichia coli (E. coli) as an Indicator of Fecal Contamination in Groundwater: A Review Farhan Mohammad Khan and Rajiv Gupta

Abstract Escherichia coli or E. coli bacteria is related with the coliform group and is a more accurate fecal contamination indicator than other coliform bacteria; its existence indicates the potential presence of harmful bacteria causing diseases, and also indicates the extent and nature of the pollutants. E. coli bacteria can live in water for 4–12 weeks and currently serve as an indicator bacteria of fecal contamination in drinking water, due to the accessibility of simple, inexpensive, fast, sensitive, and precise detection techniques. According to the laboratory experiment based techniques, 24–48 hours are required for the bacterial concentration to be reported. Continuous monitoring of water quality is required. Techniques are not yet available to classify many pathogenic bacterial strains, and it sometimes takes days to weeks to achieve the results. To overcome these challenges, cost-effective and time-consuming techniques are needed to detect, count, and identify specific bacterial strains. Public health depends on online water quality monitoring, which is dependent mainly on analyzing fecal indicator bacteria, and health safety requires an indicator of fecal contamination so that it is not necessary to examine drinking water to overcome waterborne disease-related problems. This paper will brief the classification, sources, survival of E. coli bacteria, and their correlation with groundwater water quality parameters. Keywords Escherichia coli · E. coli · Indicator · E. coli bacteria · Water · Bacteriological analysis · FCB · Classification

1 Introduction Water is more essential to human life. Adequate, accessible, and secure supply for consumers is needed. Improving access to clean drinking water would carry essential health benefits. Efforts should be made to achieve the cleanest possible water quality for drinking (WHO 2011). In the present situation, people are struggling to obtain F. M. Khan (B) · R. Gupta Department of Civil Engineering, BITS Pilani, Pilani, India e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_21

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access to water. The infectious diseases associated with water consumption are typically polluted with human or animal feces. Some of the significant health disorders are caused by micro-organisms such as bacteria, pathogens, etc. because they can live, reproduce and disperse in water systems (Payus et al. 2018). Approximately 1.7 billion children under the age of five in developing countries died from diarrhea, primarily from consuming contaminated water reported by the World Health Organization (WHO) in 2011. 525,000 children die worldwide a year in 2018 due to poor water quality, sanitation, and hygiene, mainly due to infectious diarrhea. Worldwide, 1.9 billion people use contaminated water (World Health Organization 2017). Approximately 37.7 million people in India suffer from waterborne diseases every year and 1.5 million children have died from diarrhea. Fecal matter is the primary source of water-borne bacteria causing the disease. E. coli bacteria are found in the intestines of men and animals that release into the atmosphere as fecal material, and E. coli is commonly used as an indicator of pollution impacting rivers, sea beaches, reservoirs, groundwater, surface water, recreational water. In the last five years since 2017, India has caused 10,738 deaths. The highest deaths from diarrhea were recorded in Uttar Pradesh followed by Assam, West Bengal, Delhi and Madhya Pradesh (CBHI National Health Profile 2018). In India, 19% of the population washes hands with soap and water that associate with excreta, but 26% drink water that is usually polluted with E. coli (World Health Statistics 2017). 44% of people have access to piped water, of which only 32% is treated. People have no access to water, thereby increasing the possibility of infection (India Water Portal 2019). Water helps to maintain the moisture of the body’s internal organs (Gerald 2011), it also protects the usual volume and uniformity of blood and lymph fluids (Dooge 2001), controls body temperature and eliminates toxins from the body through urine, sweat, and respiration (Comprehensive Assessment of Water Management in Agriculture 2007), which are essential for controlling skin functions (Burton et al. 1987). It can lead to diseases like diarrhea, kidney failure and can cause death if not immediately treated (David and Haggard 2011). To facilitate the removal and control of water pollution, WHO, EPA, and IS 10500: 2012 establish microbiological water quality. According to drinking water quality standards, E. coli bacteria in 100 ml of water sample shall not be detectable (Indian Standard Drinking Water Specification 2012). The standards of water quality are summarized in Table 1 (Indian Standard Drinking Water Specification 2012). Table 1 Bacteriological drinking water quality (Indian Standard Drinking Water Specification 2012) S. No

Organisms

Requirements

1

(a) E. coli or thermotolerant coliform bacteria (b) Total coliform bacteria

Shall not be detectable in 100 ml water sample

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2 Classification of E. coli E. coli bacteria include gram-negative, non-spore, rod-shaped pathogenic bacteria that generate gas in prescribed growth media after fermentation within 48 h at 35 °C. In 1982, Escherichia coli was first recognized as a human pathogen. It can be categorized into three classes of commensal, diarrheagenic, and extraintestinal groups. The E. coliare Citrobacter, Enterobacter, Hafnia, Klebsiella, and Escherichia coli, where E. coli is the most common bacteria that usually survive in the gastrointestinal tract of warm-blooded animals. Some bacterial strains are harmless, like the commensal classes, but there are some infectious types. Diarrheagenic strains can cause diseases such as diarrhea, hemorrhagic colitis, hemolytic uremic syndrome, inflammatory colitis, and dysentery. The extraintestinal strains can cause urinary tract infections, septicemia, and neonatal meningitis. E. coli is a non-spore forming and rod-shaped bacteria with a diameter of around 0.5 µm and a length of between 1.0 and 3.0 µm. E. coli bacteria are capable of surviving 4–12 weeks in water. The bacteria can be exhibited to be undergoing different stresses, and they are well known to be able to live below freezing temperatures (Nevers and Boehm 2011). Various classifications have been established for coliform bacteria. The MacConkay (MacConkey 1909) identified 128 different types of coliform in 1909, and 256 types of coliform were identified in 1909 by Bergey and Deehan (Bergey and Deehan 1908). However, in the 1920 s, coliform variation indicated reactions from indole and Voges-Proskauer that are among the most significant tests used to identify fecal contamination (Hendricks 1978). The combined evolution of E. coli variation, soil coliforms and common variation in the IMViC (Indole, Methyl red, Voges-Proskauer, and Citrate) tests. Figure 1 (Monk 2013) shows the characterization of E. coli pathotypes based on conditions that support growth.

3 Sources of E. coli Sewage discharges are usually linked to the sources of E. coli bacteria, classified into three general categories: human, animal and plant. Human sources include failed septic systems, urban landfills, and sewage sludge land applications. E. coli also originates from diverse animal sources, including domestic animals, wildlife, poultry and manure land use, pasture and feedlots. Drinking water originates from groundwater and surface streams. Sources of surface water consist of rivers, ponds, and lakes where groundwater from wells or boreholes are drained and then drilled into aquifers. Safe water availability almost inaccessible due to bacterial and chemical contamination (Cabral 2010). Contamination is caused by water draining from the soil or animals and birds, as well as by waste leakage, sewer overflow due to storm events and contaminated water release into the water sources (Cornejova et al. 2015; Pandey et al. 2014). Sewage treatment plants (STP) are among the sources of pathogenic E. coli bacteria introduced into the river systems (Eichmiller et al.

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Fig. 1 Characterizing of E. coli pathotypes based on conditions that support growth (Monk 2013)

2013; Anastasi et al. 2012). Low rates of contact with contaminated water in rivers (Madoux-Humery et al. 2016) or beaches (Boehm and Sassoubre 2014) were essential and resulted in gastrointestinal disorders. To assess the likelihood of various activities, i.e., swimming, boating, etc., the volume of consumed water is most significant (Rompré et al. 2002). Although E. coli bacteria usually do not cause serious diseases but are used to indicate the possible presence of pathogenic bacteria and viruses (Dorevitch et al. 2012).

4 E. coli as an Indicator of Fecal Contamination The presence of indicator bacteria shows the occurrence of contamination, and it also shows the extent and nature of the pollutants. Indicator bacteria do not cause disease associated with pathogens, E. coli as an indicator bacteria is a micro-organism whose presence indicates fecal contamination. E. coli bacteria can survive in water for 4–12 weeks and at present, due to the availability of accessible, inexpensive, fast, sensitive and accurate detection methods, and it is an active bacterial pollution indicator. Total coliform (TC) is not considered to be an indicator organism. Figure 2 (Price and Wildeboer 2017) shows a water contamination monitoring approach. The ideal indicator organism has the following characteristics (Tallon et al. 2005):

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Fig. 2 Approach for monitoring of water contamination (Price and Wildeboer 2017)

• Indicator organisms should exist when there are pathogenic strains. • The number of indicator organism counts correlates with the extent of the pollution. • The number of indicator organism counts should be higher than that for pathogenic strains. • Also, the indicator organism should not grow in water. • Indicator organisms should have a survival time greater than or equal to pathogenic strains. • The laboratory tests should detect indicator organisms easily and quickly. • The indicator organism should be harmless to humans.

5 Correlation Between Physicochemical Water Quality Parameters and E. coli The existence of bacteria in sources of water usually increases with decreased temperature. Some factors that affect the existence of E. coli include dissolved organic

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carbon content, intensity of the sunlight (Medema et al. 2003). In a comparative study on the growth of 10 different bacterial strains. E. coli, Citrobacter freundii, Klebsiella pneumonia, and Enterobacter cloacae subsp were identified by Boualam et al. (2002). After 96 h of incubation, only cloacae remained cultivable. In a previous study, Boualam et al. (2003) found that only Citrobacter freundii and Enterobacter cloacae subsp. Cloacas were found alive after 28 days. Baudišová (1997) performed a comparative study on the survival of fecal coliforms, total coliforms and E. coli in polluted and unpolluted river water, and found that all bacteria lived for several months under polluted water conditions, but that the elimination of all bacteria was significantly faster under unpolluted water conditions. Total coliforms lasted the most prolonged and E. coli the shortest. Table 2 (Medema et al. 2003) shows the reduction times for E. coli in surface water. The existence of bacterial strains in groundwater was affected by some factors that are linked with soil. Bacteria had to infiltrate through the soil to enter the groundwater with low temperature, high soil humidity, acidic or alkaline soil pH, and organic carbon (Medema et al. 2003). In a study reported by Doran and Linn (Doran and Linn 1979) in eastern Nebraska for three years, runoff from a cow-calf pasture was observed. The number of fecal streptococci was higher in runoff from the ungrazed region, exposing the wildlife contributions. Table 3 (Medema et al. 2003) shows the disappearance rates of E. coli in groundwater sources. The pollutants carried in runoff originates from urban and sub-urban non-point sources (EPA 2009). Many studies have been reported significantly of correlations between various water quality parameters and E. coli bacteria. The properties of electrolyzed oxidizing water and chemically modified solutions for the E. coli O157: H7 bacteria Table 2 Times for reduction of fecal coliform in surface water (Medema et al. 2003)

Table 3 Disappearance rates of fecal coliform in groundwater sources (Medema et al. 2003)

Bacterial group

Time for 50% reduction in concentration (days)

Total coliforms

0.9

E. coli

1.5–3

Enterococci

0.9–4

Clostridium perfringens

60 – >300

Salmonella

0.1–0.67

Shigella

1

Bacterial group

Disappearance rate (per day)

E. coli

0.063–0.36

Fecal streptococci

0.03–0.24

Clostridium bifermentans spores

0.00

Salmonella enterica subsp. enterica serovar Typhimurium

0.23–0.22

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inactivation were studied by Kim (2000). Inactivation of E. coli occurred within 30 s after electrolyzed oxidizing water was applied, and solutions containing 1% of bromine and chlorine were added to the chlorine neutralization buffer solution. Residual chlorine was added to reduce oxidation-reduction potential (ORP). Iron has been found to be the only effective solution for inactivating E. coli and then having high residual ORP readings. The study recommended that electrolyzed oxidizing water might be simulated by chemical modification of deionized water, whereas ORP of the solution was the critical factor affecting bacterial inactivation. Hughes (2003) studied the impacts of temperature, water salinity, solar radiation, sea ice conditions, and fecal contamination on the E. coli count around Rothera Point, Adelaide Island, and Antarctic Peninsula during February 1999 to September 1999. In summer i.e. February, due to the effects of solar radiation and high station population, presumptive E. coli counts were low, the daily amount of solar radiation was high and the estimated E. coli counts were low. In winter i.e. April, E. coli counts were high because migrant wildlife had increased fecal matter and the intensity of solar radiation dropped by 95%. By late winter i.e. September near the station sewage outfall, E. coli counts were high but the E. coli counts in North Cove were high as compared to February. Solar radiation was found to be the leading factor in the estimation of E. coli counts at sea. Water depth, temperature, and salinity also affect fecal bacterial viability by increasing cell inactivation. The effect of pH and chlorine on E. coli O157: H7 and Listeria monocytogenes was explained by Park (2004). The results revealed that both Escherichia coli and Listeria monocytogenes were sensitive to residual chlorine and chlorine level of electrolyzed water, and electrolyzed water bactericidal activity increased with decreased pH of water for both Escherichia coli and Listeria monocytogenes. The study recommends the application of electrolyzed water with residual chlorine greater than 2 mg/l to achieve complete inactivation of E. coli and Listeria monocytogenes within a pH range between 2.6 and 7.0. Roslev et al. (2004) studied the effect of oxygen on the survival of E. coli bacteria in drinking water which is not disinfected. E. coli ATCC 25922 has shown a decline in growth, both reduced and biphasic. The survival of fecal enterococci, somatic coliphages and coliforms were also seen to be reduced in aerobic conditions, and oxygen was the main factor for E. coli growth in drinking water which is not disinfected. Juhna (2007) studied the effects of phosphoric addition on E. coli bacteria survival. Higher concentrations of phosphorus increased the life of cultivable E. coli bacteria in water and biofilms. The study found that higher concentrations of phosphorus in water increased the cultivability of E. coli in the water distribution system. Ellie (2007) showed a direct correlation with an R2 value of between 0.6 and 0.8. Turbidity from the six sites ranged from 5.7 to 120 NTU, with an average of 12–17 NTU. The E. coli ranged from 20 to 25,000 cfu/100 mL with 180 to 340 cfu/100 mL as geometric mean. A direct correlation between E. coli and turbidity was observed. Higher standards of turbidity can be used to predict increased levels of E. coli bacteria. Kreske (2008) identified E. coli O157: H7 ability to grow in acidified vegetable products at pH 3.2 and 3.7, with specific dissolved oxygen content and a range of ionic strengths between 0.086 and 1.14. The study revealed that in acid solutions under anaerobic conditions, E. coli survived significantly better

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than under aerobic conditions. E. coli strain decreased by 1.55 log Cfu/ml for all acid solutions that were evaluated in the absence of oxygen. Kalantari and Ghaffari (2008) investigated the effects of iron, cadmium and chromium on E. coli bacteria growth. In the series of experiments, E. coli cultivated for 5 h at 37 °C in a nutrient broth added with Fe+2, Fe+3, Cr+3, Cd+2. After every half hour, the bacterial growth was measured using a spectrophotometer. Results indicated that bacterial growth decreased with a concentration of 1 mM/L of Fe+3 and 0.5 mM/L Fe+2, but the growth was completely affected by 1 mM/L concentration of iron (II). Chromium also exhibited growth effects, while cadmium exhibited poisonous effects. Cr+3 and Cd+ showed antagonism to the growth of bacteria with iron. Than (2011) reported the growth of Escherichia in water under different temperatures ranges from 0 to 70 °C at the laboratory of microbiology in the Department of Zoology, University of Yangon. The bacteria cell growths were recorded as 1.28 × 108 Cfu/ml at 20 °C, 3.25 × 108 Cfu/ml at 30 °C and 4.85 × 108 Cfu/ml at 40 °C on nutrient agar. The bacterial count at 37 °C was 4.98 × 108 Cfu/ml. Bacterial colonies formation were not observed under the temperature of 50, 60, and 70 °C. The recorded data revealed that E. coli was found to grow at temperatures between 20 and 40 °C. Sinaga et al. (2016) observed the counts of E. coli in sources of well water and the factors correlated with bacterial growth. Water samples from 5 wells were collected to test the concentrations of total E. coli bacteria, mercury inorganic nitrogen compounds, total phosphorus, dissolved oxygen, pH and salinity. Results showed that E. coli and mercury-contaminated the drinking water resources at the Sekotong regency, as well as mercury and salinity, showed an inverse correlation with E. coli growth. Whereas pH supports the E. coli survival at the range of 6.05–6.50, but no correlation to the growth of E. coli was found between total phosphorus and inorganic nitrogen compounds. However, the growth of E. coli was positively related to phosphorus concentration in water but negative to nitrate concentration. Kim et al. (2018) observed the growth features of foodborne pathogens in a laboratory medium incubated at a range of temperatures 25–45 °C and pH levels 3–10. Results showed that when subjected to pH 3 and 4 at any temperature measured, the concentration of all bacteria were restricted to about 3 log Cfu/ml and all pH at 45 °C. The results showed that at pH 6, growth rates of E. coli and Salmonella were approximately three and half to four times faster than that of Listeria and at pH 7, the growing rates of Bacillus, E. coli and Salmonella were significantly higher than those of Listeria and Staphylococcus. At pH 8, the growth rate of Bacillus was the highest as compare to Salmonella, E. coli, Listeria and Staphylococcus. E. coli and Salmonella were less prone than other bacterial classes to acidic environments at pH 5–6, while Bacillus was the least prone to alkaline environments at pH 8–9.

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6 Conclusion Measuring contamination from bacteria in groundwater resources has become a critical issue. Various physical, chemical, and bacteriological water quality parameters have an effect on the growth of E. coli and it is the best and most precise indicator of fecal contamination in drinking water. To mitigate the severe effects on human health, responsive and continuous online monitoring is needed for early warning of contamination with E. coli.

References Anastasi EM, Matthews B, Stratton HM, Katouli M (2012) Pathogenic Escherichia coli found in sewage treatment plants and environmental waters. Appl Environ Microbiol 78:5536–5541 Baudišová D (1997) Evaluation of escherichia coli as the main indicator of faecal pollution. Water Sci Technol 35:333–336 Bergey DH, Deehan SJ (1908) The colon-aerogenes group of bacteria. J Med Res 19:175–200 Boehm A, Sassoubre LM (2014) Enterococci as indicators of environmental contamination. In: Gilmore MS, Clewell DB, Ike Y, Shankar N (eds) Commensals to leading to causes of drug resistant infections. Massachusetts Eye and Ear Infirmary, Boston, p 101 Boualam M, Mathieu L, Fass S, Cavard J, Gatel D (2002) Relationship between coliform culturability and organic matter in low nutritive waters. Water Resour 36:2618–2626 Boualam M, Fass S, Saby S, Lahoussine V, Cavard J, Gatel D, Mathieu L (2003) Organic matter quality and survival of coliforms in low-nutrient waters. J Am Water Works Assoc 95:119–126 Burton GA, Gunnison D, Lanza JR (1987) Survival of pathogenic bacteria in various freshwater sediments. Appl Environ Microbiol 53:633–638 Cabral JPS (2010) Water microbiology. Bacterial pathogens and water. Int J Environ Res Public Health 7:3657–3703 CBHI National Health Profile (2018) 13th issue Comprehensive Assessment of Water Management in Agriculture (2007) Water for food, water for life: a comprehensive assessment of water management in agriculture. Earthscan, London and International Water Management Institute, Colombo Cornejova T, Venglovsky J, Gregova G, Kmetova M, Kmet V (2015) Extended spectrum betalactamases in escherichia coli from municipal wastewater. Ann Agric Environ Med 22:447–450 David MM, Haggard BE (2011) Development of regression-based models to predict fecal bacteria numbers at select sites within the Illinois River watershed, Arkansas and Oklahoma, USA. Water Air Soil Pollut 215:525–547 Dooge JCI (2001) Integrated management of water resources. In: Ehlers E, Krafft T (eds) Understanding the earth system: compartments, processes, and interactions. Springer, Heidelberg, p 116 Doran JW, Linn DM (1979) Bacteriological quality of runoff water from pasteureland. Appl Environ Microbiol 37:985–991 Dorevitch S, Pratap P, Wroblewski M, Hryhorczuk DO, Li H, Liu LC, Scheff PA (2012) Health risks of limited-contact water recreation. Environ Health Perspect 120:192–197 Eichmiller JJ, Hicks R, Sadowsky MJ (2013) Distribution of genetic markers of fecal pollution on a freshwater sandy shoreline in proximity to wastewater effluent. Environ Sci Technol 47:3395– 3402 Ellie LB (2007) The correlation of fecal coliform and turbidity of the little Tallapoosa River in the West Georgia Region. In: GSA Denver annual meeting, 28–31 Oct 2007

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EPA (2009) Source water protection practices bulletin. Managing stormwater runoff to prevent contamination of drinking water; Office of Water (4606); United States Environmental Protection Agency (EPA), Washington, DC, USA Gerald P (2011) Water science. University of Washington. Available Online at http://faculty. washington.edu/ghp/researcthemes/water-science Hendricks CW (1978) Exceptions to the coliform and the fecal coliform tests. In: Berg G (eds) Indicators of viruses in water and food. Ann Arbor Sci 99–145 (Ann Arbor, MI) Hughes KA (2003) Influence of seasonal environmental variables on the distribution of presumptive fecal coliforms around an antarctic research station. Appl Environ Microbiol 69:4884–4891 India Water Portal (2019) Available Online at https://www.indiawaterportal.org/ Indian Standard Drinking Water Specification (2012) (Second Revision), IS 10500:2012 Juhna T (2007) Effect of phosphorus on survival of escherichia coli in drinking water biofilms. Appl Environ Microbiol 73:3755–3758 Kalantari N, Ghaffari S (2008) Evaluation of toxicity of heavy metals for Escherichia coli growth. Iran J Environ Health Sci Eng 5:173–178 Kim C (2000) Roles of oxidation–reduction potential in electrolyzed oxidizing and chemically modified water for the inactivation of food-related pathogens. J Food Prot 63:19–24 Kim C, Wilkins K, Bowers M, Wynn C, Ndegwa E (2018) Influence of pH and temperature on growth characteristics of leading foodborne pathogens in a laboratory medium and select food beverages. Austin Food Sci 3:1031 Kreske AC (2008) Effects of pH, dissolved oxygen, and ionic strength on the survival of escherichia coli O157:H7 in organic acid solutions. J Food Prot 71:2404–2409 MacConkey A (1909) Further observations on the differentiation of lactose-fermenting bacilli with special reference to those of intestinal origin. J Hydrol 9:86–103 Madoux-Humery A-S, Dorner S, Sauve S, Aboulfadl K, Galarneau M, Servais P, Prevost M (2016) The effects of combined sewer overflow on riverine sources of drinking water. Water Resour 92:218–227 Medema GJ, Shaw S, Waite M, Snozzi M, Morreau A, Grabow W (2003) Catchment characteristics and source water quality. In: WHO, OECD (eds) Assessing microbial safety of drinking water. Improving approaches and method. IWA Publishing, pp 111–158 Monk JM (2013) Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. In: Presented at the Proceedings of the National Academy of Sciences of the United States of America, 10 Dec 2013 Nevers M, Boehm A (2011) Modeling fate and transport of fecal bacteria in surface water. In: Sadowsky M, Whitman R (eds) The fecal bacteria. ASM Press, Washington, DC, pp 165–188 Pandey PK, Kass PH, Supir ML, Biswas S, Singh VP (2014) Contamination of water resources by pathogenic bacteria. AMB Express 4:51 Park H (2004) Effects of chlorine and pH on efficacy of electrolyzed water for inactivating escherichia coli O157:H7 and listeria monocytogenes. Int J Food Microbiol 91:13–18 Payus C, Haziqah N, Basri N, Wan VL (2018) Faecal bacteria contaminations in untreated drinking water (Groundwater well and hill water) from rural community areas, pp 215–218 Price GR, Wildeboer D (2017) E-coli as an indicator of contamination and health risk in environmental waters. Recent advances on physiology, pathogenesis and biotechnological applications, pp 125–139 Rompré A, Servais P, Baudart J, de-Roubin MR, Laurent P (2002) Detection and enumeration of coliforms in drinking water: current methods and emerging approaches. J Microbiol Methods 49:31–54 Roslev P, Bjergbæk LA, Hesselsoe M (2004) Effect of oxygen on survival of faecal pollution indicators in drinking water. J Appl Microbiol 96:938–945 Sinaga DM, Robson MG, Gasong BT, Halel AG, Pertiwi D (2016) Fecal coliform bacteria and factors related to its growth at the sekotong shallow wells, West Nusa Tenggara, Indonesia. Public Health Indonesia 2:47–54

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Sea Ice Variability of the Amur Estuary: Survey Data Analysis Zinaida Verbitskaya, Maxim Medvedev, and Maria Kotelnikova

Abstract The sea ice variability of the Amur estuary was analyzed for the period 1970–2018 to identify trends and patterns in ice period statistics. Important aspect of this research is a demonstration of the impact of global climate change on the local ice situation in the Sea of Okhotsk. Time series of ice characteristics in the Amur estuary from the five monitoring stations (Baidukov Island, Ozerpah, Pronge cape, Jaore and Lasarev cape) were analyzed. Six main characteristics were selected for analysis: maximum ice thickness, date of persistent ice formation; date of final ice freezing; date of fast ice breaking, date of complete ice clearance and number of days with ice during the ice period. Baseline data for annual time series of these characteristics was taken from annual hydrological monitoring tables. It was found that all the ice period characteristics in the Amur estuary have temporal long-term trends defined by the global warming in the Sea of Okhotsk region. One of the clearest warming process trends appears to be the increasing autumn ice forming period. Negative linear trend was found for the maximum ice thickness. Keywords Ice forming · Fast ice breaking · Global warming · Correlation analysis

1 Introduction The Sea of Okhotsk is a non-arctic partially freezing sea in which seasonal ice cover is formed. Sea ice variability of the Sea of Okhotsk has recently attracted interest of climate scientists from all over the world. Ice cover in the Sea of Okhotsk and the Amur estuary plays pivotal role in the Far Eastern climate system. Also, this issue Z. Verbitskaya (B) Far Eastern Regional Hydrometeorological Research Institute, 24 Fontannaya St, 690091 Vladivostok, Russia e-mail: [email protected] M. Medvedev Far Eastern Federal University, 8 Sukhanova St, 690090 Vladivostok, Russia M. Kotelnikova Lavrentyev Institute of Hydrodynamics, Lavrentyev pr. 15, 630090 Novosibirsk, Russia © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_22

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has become of crucial importance with the accelerated shelf development and the increasing volumes of hydrocarbons extraction in the Sea of Okhotsk. Amur estuary is the part of the Amur River forming a gulf between Asia and the northern part of Sakhalin Island. The Amur estuary also includes a part of the Sakhalin Bay and the northern part of the Tatar Strait of the Sea of Japan. This study analyzes the temporal variability of some characteristics of the ice period. We introduce three phases, namely, the period of formation (autmn), the period of persistent (or almost persistent) ice cover (winter) and the period of ice cover destruction (spring) characterizing ice cover variability as well as the duration of the entire period from the date of the beginning of the ice cover formation until the date of complete ice clearance. For the three studied periods, the beginning is in autumn, and the end is in different seasons (autmn-winter; spring; spring-summer), but each of these periods is a part of the “single object” of the study and to break it with the conditional division of years by the date December 31 appears to be inappropriate. In correlation analysis all series must be assigned to one calendar year, despite the fact that the studied process covers two consecutive calendar years. We choose the year of the process beginning. This approach is somewhat different from the traditional analysis of the ice period in a specific ice season (autumn—the period of ice cover formation, spring—the period of its destruction). Here, the ice period is considered as a single process consisting of successive periods, the duration of which is analyzed in correlation with the maximum intensity of the phenomenon (maximum ice thickness). The same approach is used in (Verbitskaya et al. 2017; Pishchalnik et al. 2015a; Frolov et al. 2009). Six characteristic of the ice period were chosen for the analysis: the maximum ice thickness, the date of persistent ice cover formation, the date of final ice freezing, the date of the first fast ice breaking, the date of complete ice clearance and the number of days in the ice period. Series of values of the duration of the following periods were calculated based on the observation data: from the date of persistent ice formation to the date of final freezing (autmn); from the date of final freezing to the date of first fast ice breaking (winter); from the date of fast ice breaking to the date of complete ice clearance (spring); from the date of persistent ice cover formation to the date of complete ice clearance (full period); from the date of final freezing to the date of complete ice clearance (winter and spring). In correlation analysis all characteristics were attributed to the year of the beginning of the ice period in order to preserve the temporal integrity of the object of study. For every observation station the cross-correlation coefficients between all six characteristics were calculated to determine dependences of the considered characteristics, as well as the correlation coefficients for each of the characteristics between the observation points to determine the distribution of parameters over the territory. For these calculations the series of dates limiting the periods of the ice period were converted into the series of the number of days from the date of the September (September 22) or March (March 22) equinoxes and were assigned to the year of the beginning of the ice period.

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2 Sea Ice Dynamics Over the Observation Period The duration of the ice period in the Amur estuary is about seven months: the persistent ice cover formation starts in late October—early November, and the complete ice clearance usually occurs in the second half of May. The key factor of ice formation in the Sea of Okhotsk is the influence of the winter monsoon that delivers cold air masses to the water area and also causes a constant ice drift from the North to the South. For each of the selected observation stations the series of dates of ice formation and the series of dates of total ice clearance for the entire observation period were analyzed. To evaluate the dynamics of the ice variability over the Amur estuary we averaged calculated characteristics over five observation points. The main statistical characteristics of the averaged series are listed in Table 1. The average value for each characteristic was calculated only if the observation data from at least four stations was available for the corresponding year. The persistent ice cover formation in the Amur estuary begins in late October— early November. Landfast ice in the Amur estuary usually appears in mid-November. In March the fast ice occupies almost the entire Amur estuary with the exception of its southernmost part. The thickness of landfast ice varies from year to year and reaches up to 1.3–1.6 m. According (Pishchalnik et al. 2015a), on average, landfast ice is kept in the Amur estuary for 203 days, the landfast ice breaking usually occurs in the third decade of May. The average of the dates of persistent ice cover formation in the Amur Estuary falls on the first decade of November and the average final freezing on the third decade of November. These indicators vary slightly from station to station and from year to year though the standard deviation is small. The date of persistent ice cover formation averaged over the estuary shows a significant upward trend for the last 48 years (Fig. 1). An upward trend in the date of persistent ice cover formation is clearly observed for all stations. Also, a significant negative trend can be noted for the length of the period from the date of final ice freezing to the date of the fast ice breaking for all stations of the Amur estuary. Moreover, this trend is remarkably steepening that may indicate an acceleration of climate changes in the region. At the same time, the duration of the periods from the date of persistent ice formation to the date of final freezing and from the date of fast ice breaking to the date of complete ice clearance increase. The reverse trend is observed for values of the date of complete ice clearance. The process of fast ice breaking starts in the second decade of May and the complete ice clearance of the estuary occurs at the end of the third decade of May, and sometimes even in the first decade of June (Fig. 2). These data also document downward linear trends in the duration of the periods from the first appearance of ice to the complete ice clearance and of from the fast ice breaking to the complete ice clearance for all stations and the Amur estuary as a whole (Fig. 3).

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Table 1 Statistical characteristics of averaged series Parameter

Sample range

Average

Standard error

Min

Max

Max-min

First ice occurrence

Oct.21

Nov.16

26

Nov.02

5.5

Persistent ice cover formation

Oct.29

Nov.18

20

Nov.06

5.3

First ice freezing

Nov.08

Nov.29

21

Nov.20

4.8

Final ice freezing

Nov.13

Dec.08

25

Nov.25

5.6

Dates

First ice breaking

May.10

May.23

13

May.16

12.2

Complete ice breaking

May.12

May.26

14

May.19

3.6

First ice clearance

May.14

May.31

17

May.22

3.7

Complete ice clearance

May.14

May.31

17

May.23

4.1

Periods From the date of persistent ice cover formation to the date of final ice freezing

11

28

18

19

4.5

From the date of final ice freezing to the date of first ice breaking

159

191

31

173

7.1

From the date of first ice breaking to the date of complete ice clearance

3

11

8

7

2.0

178

214

36

199

7.6

184

213

29

200

7.2

87

141

54

117

12.2

From the date of persistent ice cover formation to the date of complete ice clearance Characteristics Number days with ice during ice-period Maximum ice thickness (cm)

Of all the considered characteristics of the ice period in the Amur estuary the ice thickness varies most noticeably from year to year for each station and on average. Figure 4 shows graphs of the dynamics of ice thickness averaged over the Amur estuary for the past 48 years. The average ice thickness over the Amur estuary for this period is 117 cm. A strong downward linear trend in the maximum ice thickness is observed. The presence of sufficiently thick ice in the region of the Amur estuary and Sakhalin gulf can be explained by relatively small depths and significant desalination of this region due to the runoff of the Amur River, which creates a significant density

Sea Ice Variability of the Amur Estuary: Survey Data Analysis

Fig. 1 Average date of persistent ice cover formation on Amur Estuary (1970–2018)

Fig. 2 Average date of complete ice clearance on Amur Estuary (1970–2018)

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Fig. 3 Average ice cover period duration on Amur Estuary (1970–2018)

Fig. 4 Average ice thickness over the Amur estuary (1970–2018)

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gradient of the underlying water and prevents heat from reaching the surface during ice formation. The negative trend in the average maximum ice thickness over the Amur estuary appears to start in 1984. This trend can be considered as an evidence of global changes occurring in the atmosphere and hydrosphere. The spectral analysis of these series shows periods of 6–9 years and 11–15 years with strong linear trends. For the longer periods no series were found with correlation coefficients more than 0.75.

3 Correlation Analysis Results To study the dependencies between the considered characteristics of the ice period the cross-correlation coefficients between all observation series were calculated. The calculation results are listed in Tables 2 and 3. The duration of various periods were calculated using the observation data of the dates of fast ice breaking persistent ice formation, final freezing and complete ice clearance. In this tables with bold we mark coefficients that absolute values more than 0.7. The dates of the final destruction of landfast ice were not considered because in the Amur estuary the period of ice cover destruction is very short (usually not more than 1–2 days), that is significantly less than the duration of period from the date of the onset of persistent ice formation to the date of final freezing (8–28 days) and period from the date of final freezing to the date of the first fast ice breaking (178–211 days) (Table 2). The cross-correlation coefficient between the date of the fast ice breaking and the date of complete ice clearance is high for each observation point and on average over the territory (Table 3) and together with the duration of period from the date of the fast ice breaking to the date of complete ice clearance show a very good agreement of dates and their trends. The cross-correlation coefficients analysis has shown that the number of days with ice in the ice period is correlated significantly with the calculated values of the duration of the period from the date of persistent ice formation to the date of the complete ice clearance. This result allows us to use only one of these characteristics in further studies—the duration of period from the date of persistent ice formation to the date of the complete ice clearance, which has a clear physical meaning and more uniform values of the cross-correlation coefficients between the observation data from different stations and averaged series than the number of days with ice in the ice period (Table 3). In addition, the number of days with ice in the ice period is significantly correlated with the duration of period from the date of final freezing to the date of fast ice breaking for all stations (except Lazarev Cape station) and on average over the territory. This result is expected (the average duration of this period in the Amur estuary is close to the average duration of the entire ice period), but not quite obvious, since the calculation of the duration of this period was carried out on the basis of independent observations.

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Table 2 Correlation coefficients between number of days with ice during the ice period and other observed parameters Station parameter

Baidukov isl.

Oserpakh

Pronge cape

Jaore

Lasarev cape

Average

Persistent ice cover formation

−0.47

−0.73

−0.63

−0.88

−0.62

−0.75

Final ice freezing

−0.25

−0.46

−0.32

−0.25

−0.10

−0.39

First ice breaking

0.62

0.62

0.73

0.45

0.41

0.73

Complete ice clearance

0.78

0.65

0.79

0.75

0.72

0.82

From the date of persistent ice cover formation to the date of final ice freezing

0.14

0.26

0.28

0.62

0.16

0.36

Ot date of final ice freezing to the date of first ice breaking

0.49

0.70

0.61

0.44

0.24

0.67

From the date of first ice breaking to the date of complete ice clearance

0.48

0.10

0.44

0.40

0.33

0.41

From the date of persistent ice cover formation to the date of complete ice clearance

0.74

0.94

0.93

0.95

0.88

0.94

Date

Period

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Table 3 Cross-correlation coefficients between periods duration and the dates limiting the period’s parts Station parameter

Baidukov isl.

Oserpakh

Pronge cape

Jaore

Lasarev cape

Ave.

Duration from the date of persistent ice cover formation to the date of complete ice clearance Date of persistent ice cover formation

−0.88

−0.81

−0.83

−0.93

−0.75

−0.86

Date of final ice freezing

−0.26

−0.53

−0.36

−0.27

−0.14

−0.52

Date of the first ice breaking

0.54

0.58

0.68

0.46

0.36

0.64

Date of complete ice clearance

0.70

0.65

0.73

0.78

0.78

0.77

Duration from the date of persistent ice cover formation to the date of final ice freezing

0.45

0.26

0.22

0.64

0.17

0.33

Duration from the date of final ice freezing to the date of the first ice breaking

0.46

0.74

0.61

0.46

0.26

0.80

Duration from the date of the first ice breaking to the date of complete ice clearance

0.46

0.18

0.01

0.42

0.43

0.33

Duration from the date of persistent ice cover formation to the date of final ice freezing Date of persistent ice cover formation

−0.54

−0.37

−0.35

−0.64

−0.16

−0.36

(continued)

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Table 3 (continued) Station parameter Date of final ice freezing Duration from the Date of final ice freezing to the date of the first ice breaking

Baidukov isl.

Oserpakh

Pronge cape

Jaore

Lasarev cape

Ave.

0.66

0.50

0.73

0.50

0.91

0.51

−0.56

−0.40

−0.61

−0.32

−0.81

−0.34

Duration from the date of final ice freezing to the date of the first ice breaking Date of persistent ice cover formation

−0.31

−0.54

−0.43

−0.41

−0.31

−0.64

Date of final ice freezing

−0.90

−0.84

−0.88

−0.86

−0.92

−0.85

Date of the first ice breaking

0.49

0.62

0.62

0.61

0.49

0.63

Date of complete ice clearance

0.46

0.55

0.53

0.39

0.15

0.56

Duration from the date of persistent ice cover formation to the date of final ice freezing

−0.56

−0.40

−0.61

−0.32

−0.81

−0.34

Duration from the date of the first ice breaking to the date of complete ice clearance Date of the first ice breaking

0.01

−0.22

−0.25

−0.38

−0.45

−0.06

Date of complete ice clearance

0.52

0.33

0.23

0.46

0.52

0.41

0.62

0.35

0.28

0.22

Date of persistent ice cover formation Date of final ice freezing

0.68

0.17

(continued)

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Table 3 (continued) Station parameter Date of the first ice breaking

Baidukov isl. 0.01

Oserpakh

Pronge cape

Jaore

Lasarev cape

Ave.

−0.06

−0.08

−0.30

−0.26

−0.16

0.68

0.86

0.87

0.75

0.48

Date of complete ice clearance Date of the first ice breaking

0.44

Thus, in the Amur estuary it is sufficient to consider two temporal characteristics of the ice period: from the date of persistent ice formation to the date of final freezing and from the date of final freezing to the date of complete ice clearance, which practically coincides with duration and dynamics of period from the date of final freezing to the date of fast ice breaking. For the dates limiting the periods under consideration, high values of the correlation coefficients were obtained only for the pair “the date of the fast ice breaking and the date of complete ice clearance” for each station and on average for the Amur Estuary. Also, significant correlation was found for the pair “date of the persistent ice formation and date of final freezing” for the averaged series. Noteworthy is the complete absence of significant correlations between the maximum ice thickness and all other characteristics under consideration. Thus, the correlation analysis confirms that in the Amur estuary the following set of independent observed characteristics of the ice period can be distinguished: the date of persistent ice formation, the date of final freezing, the date of complete ice clearance, the maximum ice thickness and corresponding calculated periods that have a clear physical meaning: period from the date of persistent ice formation to the date of final freezing, period from the date of final freezing to the date of complete ice clearance.

4 Analysis and Interpretation of Temporal Trends of Ice Period Characteristics The data in Table 4 for all characteristics of the ice period in the Amur Estuary indicate a manifestation of global warming. The only detrended time series is the duration of period from the date of fast ice breaking to the date of complete ice clearance (spring). Linear trends equations for all considered ice period characteristics are listed in Table 4. Here “y”—means dependent variable, “t”—means serial number of year in time line. The dates of the persistent ice formation and final freezing tend to shift towards the end of the calendar year of the beginning of the ice period (upward linear trend),

Period duration (« y » − days)

Number days with ice

y = −0.27 t + 208

y = −0.16 t + 200

y = −0.19 t + 199

y = −0.29 t + 205

y = −0.20 t + 201

y = −0.26 t + 203

y = −0.22 t + 205

y = −0.17 t + 200

y = −0.17 t + 200

y = −0.54 t + 211

y = −0.21 t + 203

y = −0.31 t + 204

Baidukov

Oserpakh

Pronge cape

Jaore

Lasarev cape

Average

y = 0.10 t + 16

y = 0.16 t + 15

y = 0.02 t + 14

y = 0.04 t + 11

y = 0.1 t + 9

y = 0.06 t + 30

y = −0.24 t + 181

y = −0.3 t + 176

y = −018 t + 183

y = −0.33 t + 185

y = −0.28 t + 188

y = −0.32 t + 173

y = −0.36 t + 187

y = −0.44 t +186

y = −0.34 t +191

y = −0.34 t +187

y = −0.28 t +191

y = −0.33 t +178

y = −0.03 t + 6

y = −0.13 t + 10

y = −0.07 t + 8

y = 0.07 t + 3

y = 0.00 t + 3

y = 0.01 t + 7

First ice breaking —complete ice clearance (spring)

y = −0.39 t + 120

y = −0.61 t +118

y = −0.42 t + 120

y = −0.13 t +126

y = −0.08 t + 116

y = −0.65 t + 117

Maximum ice thickness (« y » − cm)

Final ice freezing—complete ice clearance (winter and spring)

y = −0.13−t + 64

y = −0.13 t + 65

y = −0.17 t + 67

y = −0.08 t + 59

y = −0.03 t + 57

y = −0.13 t + 71

Complete ice clearance

Final ice freezing—first ice breaking (winter)

y = −0.1 t + 58

y = −0.04 t + 55

y = −0.11 t + 59

y = −0.15 t + 56

y = −0.03 t + 54

y = −0.14 t + 65

First ice breaking

Persistent ice cover formation—final ice freezing (autmn)

y = 0.14 t + 59

y = 0.16 t + 43

Average

Persistent ice cover formation—complete ice clearance (full period)

y = 0.07 t + 57 y = 0.25 t + 61

y = 0.36 t − 673

y = 0.19 t + 53

y = 0.13 t − 218

Pronge cape

y = 0.11 t − 162

y = 0.26 t + 48

y = 0.14 t − 240

Oserpakh

Lasarev cape

y = 0.18 t + 74

y = 0.11 t − 174

Baidukov

Jaore

Final ice freezing

Persistent ice cover formation

Date (« y » − days)

Table 4 Linear trends equations for ice situation parameters in the Amur Estuary (1970–2018)

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and the dates of the fast ice breaking and the complete ice clearance tend to shift to the beginning of the next calendar year. In this case, the shift rate of the date of final freezing is higher than the shift rates of all other dates. The duration of the entire ice period decreases for all observation stations and on average over the estuary (Table 4). The average values of the maximum ice thickness have strong downward trend more due to the decrease in this characteristic at southern stations (Jaore and Lazarev Cape) and to a lesser extent due to the dynamics of the maximum ice thickness at stations located close to the mouth of the Amur River (Ozerpakh and Prunge Cape).

References Frolov IE, Gudkovich ZM, Karklin VP et al (2009) Climate change in Eurasian Arctic shelf Seas. Praxis Publishing Ltd., Chichester, p 164 Pishchalnik VM, Romanyk VA, Minervin IG, Batuhtina AS (2015a) Analysis of dynamics for anomalies of the ice cover in the Okhotsk Sea in the period from 1882 to 2015. Izv TINRO 185 Verbitskaya EM, Verbitskaya ZV, Romanski SO, Medvedev MA (2017) Dynamics for ice cover characteristics in Amur estuary in the period from 1976 to 2017 with coastal observind points data. Meteorol gidrology

Project Management Affecting the Productivity and Sustainability of a Green Building: A Literature Review Yuxin Dong

Abstract The green building industry has experienced a rapid growth over the past decade. A large body of studies have evaluated the technical factors, that affecting the productivity and the sustainability of green buildings. However, few of them focuses on nontechnical factors. Even though, the practices of design and construction form up the skeleton of a green building, the project management process plays the role of soft tissue which links the parts of skeleton and ensures the smoothness and durability of its activities. With comparisons against traditional constructions, this study aims to sum up the factors affecting the sustainability and productivity of green building constructions from the aspects of project management based on previous studies, and to understand the role of project management in achieving the sustainable objectives. In summary, the major factors affecting the productivity of green construction project are summarized as (1) supervision of labor; (2) sequencing of work; (3) capability of project manager; (4) communication; (5) efficiency of instruction; (6) planning of site layout and (7) inspection delay. Moreover, this study also identifies the roles of project management in each stage of a green construction project to achieve the sustainable objectives, including (1) effective coordination and communication; (2) benchmarking and supervision; (3) commissioning and (4) documentation. Keywords Green construction projects · Project management · Sustainability · Productivity · Critical factors

1 Introduction Although construction industry plays a substantial role in driving nation’s economic growth, providing employment opportunities especially for developing countries (Peng et al. 2016; Yan et al. 2017). However, the rapid development of construction industry also brings about serious energy-consuming problems, wastes disposal Y. Dong (B) Department of Civil and Environmental Engineering, Virginia Tech, 19-901 Jinggongguan, Shidaihuayuan South St. Shijingshan District, Beijing, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_23

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problems and GHG emission problems. Based on the report released by United Nations Environment Programme (UNEP 2011), global buildings are responsible for up to 40% of total energy and resource consumption, including nearly 70% of the cement products, 25% of the steel products, meanwhile, producing 30–40% of all solid wastes, and emitting 35–40% of GHG. Excess exploitation caused serious environmental problems which aroused the concern and vigilance of the public. To reduce the consumption of resource, retard the greenhouse effect and furthermore, keep the environment sustainable, a series of effective strategies are urgently in need. With three dimensions, environmental, economic, and social sustainability, being concerned, green building has been widely accepted by countries around the globe as an effective and workable strategy (Chan et al. 2009). Green building technologies are used to enhance the sustainability of environment, benefits the community of human being and reduce life-cycle costs (Berardi, 2013). As defined in previous research, green buildings provide the required building performance criteria throughout its entire building life cycle, from preconstruction to disposal. Moreover, the project management should be better referred to as nontechnical-related issues, or a process, rather than a technique. This study aims to sum up the factors affecting the sustainability and productivity of green building constructions from the aspects of project management based on previous studies.

2 Project Management of Green Building Construction Rather than a technique, project management should better be considered as a process which is applied in the whole life cycle of a project. For a traditional construction project, the design and construction process usually includes the following stages: (1) the clients identify their needs and approximate budget; (2) a group of architects, structural engineers, equipment engineers (electricity network, water-piping, AC system, etc.) and project managers figure out the building envelope, make sure the design is applicable, permitted by laws and regulations and under the budget. (3) the construction team constructs the building under the supervision of the project manager who ensures the quality of the building to meet the requirements that the construction process is under budget and on schedule. However, there is a big difference between the design of a green building and that of a traditional building due to the integration of the green building system. To be environmentally responsible throughout a building’s life cycle, a green building has an integrated system of structures (Environmental Protection Agency 2016), providing a better building performance. To achieve the green objectives, project management should be taken into consideration in green building rating systems which are used to evaluate the performance of green features. In fact, many mainstream rating systems, e.g. Green Globe, LEED 2.2 and BCA Green Mark 3.0, identify project management as a major rating area. In summary, Green Globe allocates 62.7% of total credit on project management process and 37.3% on project

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management practices, while LEED 2.2 and BCA Green Mark 3.0 only allocates 20–30% of credits on process. Although, different rating systems allocate credits with different preferences, generally, project management play significant roles in green building rating system. An effective project management package fulfilling the requirements of green building should satisfy the following objectives: (1) project management process throughout the lifecycle of the project to achieve the sustainability of the green building. (2) delivery of aspiration of different parties, clients, architects, engineers, to negotiate the design and budget and push the project forward. (3) construction management practices to improve the productivity of the project.

3 Project Management Affecting Sustainability Green buildings, fulfilling the three aspects of sustainable development which are economic, social and environmental, can successfully contribute towards sustainability by tailoring a building to the site condition, the local climate, surrounding environment and culture of community. With reduction of resource consumption, augment of resource supply, application of environmentally friendly features, green construction can benefit the human being, community, environment and reduce life-cycle costs (Czuchry and Yasin, 2003). To achieve the sustainability of green constructions, project management should be taken into consideration in green building rating systems which are used to evaluate the performance of green features. According to a previous study (Darko et al. 2017), assessment areas forming the rating systems can be reallocated into eight sustainability categories. In this study, factors affecting sustainability from the aspects of project management based on different green rating systems will be focused. In fact, many mainstream rating systems, e.g. Green Globe, LEED 2.2 and BCA Green Mark 3.0, identify project management as a major assessment area. Different parties, including clients, architects, engineers, project managers and other stakeholders, should contribute their knowledge into the process to identify the goals of sustainability, such as site conditions, indoor environmental quality, water-efficiency and environmentally responsible activities. However, problems still exist at each transfer stage of the construction period, where there is a high risk of dropping the expectation of sustainability goals (Zhao et al. 2019). As the project moves on, increasing sustainability goals are dropped, which may cause the project be far away from what has been expected, usually in a negative manner, when it is handed over to the client at the end of the construction period. Hence, high green performance standards should be set at the start of the projects to leave enough space for the drop of sustainability goals. In addition, under the umbrella of project management, effective coordination, benchmarking, commissioning and documentation, which are highlighted in different green assessment systems such as LEED 2.2, BCA Green Mark 3.0, and Green Globes, play significant roles in the performance and maintenance of green construction. As discussed previously, a green building should be better considered as a process which consists of operation, maintenance, defects control and contractual

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Fig. 1 Critical factors that affect sustainability

issues and provides the users with continuous experience of green features, rather than a product forming with green technologies (Robichaud and Anantatmula, 2011). In this way, commissioning, which is used to ensure the green building system can be operated and maintained in accordance with the user’s expectation, is indispensable for users seeking green certification. Specifically, project manager should ensure that each part of the green building system has been installed and operated properly, and they can interact well with others. Also, project manager should make sure that all the documentation of the green system including operations and maintenance is provided to the users for continuous improvements and the users are trained to perform the system conveniently and effectively (Doloi et al. 2012). Critical factors that affect sustainability are displayed in Fig. 1.

4 Project Management Affecting Productivity Productivity measures the efficiency of resource usage and the effectiveness of construction in achieving the output. Different from a traditional construction project, a green building has a more integrated system which consists of energy-efficient, water-efficient, and environmentally friendly features. Compared to a traditional building, the capital cost of a green building is higher because of the complexity of

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the construction system. As for the schedule problem, a previous study conducted by Hwang and Leong (2013). In this way, the traditional project management techniques have difficulty in handling the complexity of green building construction system (Mokhlesian and Holmén, 2012; Pulaski and Horman, 2005). As summarized in a previous study (Gündüz et al. 2013), there are typically 26 factors affecting the productivity of a green construction project, which can be grouped into five major categories (1) project factors; (2) manpower factors; (3) management factors; (4) technical factors and (5) external factors. This study will focus on the project management factors affecting the productivity of green construction projects by contrast with that of traditional construction projects. Under the category of management factors, there are seven factors, including supervision of labor, sequencing of work, capability of project manager, communication, instruction of construction, planning of site layout and inspection delay (Fox et al. 2002). Based on the results of the research (Mojahed and Aghazadeh, 2008), the top three factors most likely affecting the productivity of green building construction projects are (1) workers’ experience; (2) capability of project manager; and (3) technology of construction. Project management rank the second place, which is more likely to affect the productivity of green construction projects than the technology. Hence, through the entire lifecycle of a green construction project, project manager needs to take a more competent role, managing the project in a more efficient and effective manner as the system is more integrated than that of the traditional construction projects. Critical factors that affect productivity are displayed in Fig. 2.

Fig. 2 Critical factors that affect productivity

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5 Conclusion Compared to a traditional construction project, a green construction includes a more integrated system which typically causes a higher investment and longer period of construction. Many studies have been conducted to evaluate the technical factors, that affecting the productivity and the sustainability of green buildings. However, few of them focuses on nontechnical factors. In fact, project management plays a significant role in solving the potential cost and schedule problems, furthermore, ensuring that the green objectives can be fulfilled. Rather than a technique, project management should better be considered as a process throughout the whole lifecycle of a green construction, negotiating with different parties, fixing errors at each stage of the project and improving the final performance of the system. Even though, the practices of design and construction form up the skeleton of a green building, the project management process plays the role of soft tissue which links the parts of skeleton and ensures the smoothness and durability of its activities. Based on factor analysis, the major factors affecting the productivity of green construction project are summarized as (1) supervision of labor; (2) sequencing of work; (3)capability of project manager; (4) communication; (5) efficiency of instruction; (6) planning of site layout and (7) inspection delay. As a project manager, both the planning and the construction phase of a green building should be under control. By communicating with clients and designers, the project manager should comprehend the intentions and needs of clients, then convey them to the designers with professional interpretation and develop an economical and workable plan to achieve the goals. During the construction phase, project manager should supervise the site work and provide proper instructions to ensure the project to be accomplished under the budget, on schedule and well performed. Moreover, this study also identifies the roles of project management in each stage of a green construction project to achieve the sustainable objectives, including (1) effective coordination and communication; (2) benchmarking and supervision; (3) commissioning and (4) documentation. Unlike the project management process for a traditional construction project, project manager should also put their eyes on the post-occupancy period. During the commissioning, the project manager should ensure that each part of the green building system has been installed and operated properly in accordance with the user’s expectation. Moreover, the project manager is supposed to provide the instruction and maintenance for the usage. Indeed, there are still some limitations in this field of study. The project management system has not been consummated perfectly because of the short period of development. In the future, researchers will acquire more information of the operation and maintenance phase and feedback from users’ experience. Studies would be conducted on the phases of operation and maintenance for further improvements.

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References Berardi U (2013) Clarifying the new interpretations of the concept of sustainable building. Sustain Cities Soc 8:72–78 Chan EH, Qian QK, Lam PT (2009) The market for green building in developed Asian cities—the perspectives of building designers. Energ Policy 37(8):3061–3070 Czuchry AJ, Yasin MM (2003) Managing the project management process. Ind Manag Data Syst Darko A, Chan AP, Owusu-Manu DG, Ameyaw EE (2017) Drivers for implementing green building technologies: an international survey of experts. J Cleaner Prod 145:386–394 Doloi H, Sawhney A, Iyer KC, Rentala S (2012) Analysing factors affecting delays in Indian construction projects. Int J Project Manag 30(4):479–489 Fox S, Marsh L, Cockerham G (2002) How building design imperatives constrain construction productivity and quality. Eng Constr Archit Manag 9(5–6):378–387 Gündüz M, Nielsen Y, Özdemir M (2013) Quantification of delay factors using the relative importance index method for construction projects in Turkey. J Manag Eng 29(2):133–139 Hwang BG, Leong LP (2013) Comparison of schedule delay and causal factors between traditional and green construction projects. Technol Econ Dev Econ 19(2):310–330 Mojahed S, Aghazadeh F (2008) Major factors influencing productivity of water and wastewater treatment plant construction: evidence from the deep south USA. Int J Project Manag 25(2):195– 202 Mokhlesian S, Holmén M (2012) Business model changes and green construction processes. Constr Manag Econ 30(9):751–775 Peng J, Du Y, Liu Y, Hu X (2016) How to assess urban development potential in mountain areas? An approach of ecological carrying capacity in the view of coupled human and natural systems. Ecol Ind 60:1017–1030 Pulaski MH, Horman MJ (2005) Continuous value enhancement process. J Constr Eng Manag 131(12):1274–1282 Robichaud LB, Anantatmula VS (2011) Greening project management practices for sustainable construction. J Manag Eng 27(1):48–57 Yan J, Zhao T, Lin T, Li Y (2017) Investigating multi-regional cross-industrial linkage based on sustainability assessment and sensitivity analysis: a case of construction industry in China. J Cleaner prod 142:2911–2924 Zhao X, Tan Y, Shen L, Zhang G, Wang J (2019) Case-based reasoning approach for supporting building green retrofit decisions. Build Environ 160:106210

An Automatic Method for Drainage Basin Spatial Range Delineation Using DEMs Xinming Li, Ding Li, Chengzhi Qin, A.-Xing Zhu, and Lin Yang

Abstract Basin spatial range data is widely used in hydrological, ecological and environmental fields, and is an important basic geographic data. At present, it is not very convenient to obtain the spatial data of the basin. The generation of spatial range of river basin needs some professional knowledge. The process is not highly automated. With the development of intelligent geoscience calculation and the emergence of more application requirements, it is of great significance to realize the intelligent retrieval of basin spatial data. In this paper, the method of automatic watershed spatial range determination is proposed. The method modifies the related steps manually operated now in the existing watershed algorithm. A set of automatic processing flow is designed, according to vector data, DEM data is automatically cut and drainage outlet position is automatically determined. The automatic generation of basin range data from vector data is realized, which overcomes the previous cumbersome shortage of spatial range data acquisition. Keywords River basin · Automatic conversion · Spatial range

X. Li · D. Li · C. Qin · A.-X. Zhu State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China X. Li · D. Li · C. Qin College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China A.-X. Zhu Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China L. Yang (B) School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_24

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1 Introduction Drainage basin data is an important fundamental data for many geography studies. It plays an important role in flood risk management, ecological risk assessment, regional watershed management, land resource planning management and many other geographic analysis aspects (Yang et al. 2004; Huang and Vardossy 2014). Since geoscience analysis is increasingly applied to various fields, more and more non-professionals need to obtain corresponding geospatial data, it is important to develop an intelligent tool for people without professional skills to obtain accurate watershed spatial range data. With the rapid development of earth observation technology and GIS technology in recent years, different kinds of high-precision digital elevation model (DEM) appear, and more and more easy to obtain. DEM data plays an increasingly important role in the simulation of environment, hydrology and earth system, and the application of DEM based extraction method for basin range gradually becomes the mainstream. However, at present, the acquisition of basin spatial range data is still inconvenient. Based on DEM, watershed automatic extraction methods can be divided into the following three categories (Jing et al. 2009; Xiaomeng et al. 2013): (1) scan the DEM data in a rectangular space to determine the depression, and the grid unit located in the depression is marked as a component of water system (Tribe 1992). (2) slope flow simulation method, which simulates the movement of water flow in the real physical world, determines the flow direction of each grid unit in DEM, then determines the upstream water supply area according to the flow direction of grid unit, and finally obtains the basin range and river network data (O’Callaghan and Mark 1984; Jenson and Dominique 1988) (3) the third method is valley line search method, which is based on DEM. It can directly identify the ridge line and river valley of the terrain, divide the river basin through the ridge line, and extract the river network through the valley line (Yoeli 1984; Matsuura and Aniya 2012). The most commonly used of the above three methods is the slope flow simulation method based on O’callaghan et al. Various tools with in geographic information systems have been developed in order to automate this process. One of the most commonly used watershed characterization tools is the Spatial Analyst extension from the commercial software ESRI ArcGIS. Other software like QGIS, GRASS GIS, tauDEM also provides some toolkits which can generate the spatial range of a basin. However, the existing watershed generation methods still cannot achieve complete automation, and some of the steps still need human participation. For example, the first step is to select the appropriate DEM according to the target watershed, and the location of the water outlet also needs to be determined manually. The existence of these problems leads to the need of certain professional knowledge and software knowledge for the acquisition of watershed data. At the same time, most of the current basin generation tools are attached to large-scale GIS software, which is difficult to realize the deployment of server-side and the combination of intelligent geoscience computing platform. In

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this study, we developed a new algorithm to generate the spatial range of a target basin, which its vector data is given, and undertake some test of this new method.

2 Automatic Generation of Watershed Spatial Range Based on DEM 2.1 DEM Cutting and Pretreatment Direct use of all DEM for calculation will reduce the calculation efficiency, and DEM needs to be filled and excavated. In the study, the initial DEM Spatial range is determined to select the rectangular space with longitude and latitude extending 0.5° in four directions of the vector data, which is the initial calculation range to cut DEM. This range can directly calculate the results of most small watersheds, and the calculation time can be completed in a short time. If the DEM range can be calculated and the correct watershed space range can be obtained, the subsequent clipping operation will not be needed. However, when using the outlet location and flow direction map to deduce the confluence range, it may appear that the confluence range is larger than the DEM range of the initial clipping. In this case, the DEM of the initial clipping cannot be used to calculate the correct spatial range of the watershed (Figs. 1 and 2).

Fig. 1 DEM range is too small to get the correct basin

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Fig. 2 Expanding DEM cutting range to get correct drainage basin

As for the fact that the range of initial DEM clipping is smaller than that of real watershed space, it is necessary to expand the selection range of DEM to clip a larger range of DEM, and to recalculate the flow direction and confluence information. In this paper, the directional exponential expansion method is used to re select the spatial range of DEM. In the code implementation, the depression filling algorithm uses the morphology.reconstruction algorithm in the Skimage package.

2.2 Calculation of Flow Direction and Cumulative Amount of Confluence Because some flat areas may appear after preprocessing the grid, i.e. the elevation of the surrounding grid is not less than this grid, but the elevation of some grids is equal to that of the grid, so special treatment is needed for the flow direction of the flat area. In this study, the optimized D8 algorithm proposed by Jurgen Garbrecht (Pelletier 2013) is used to deal with the flow direction in the flat area. The main idea is to give a small amount of elevation value to the grid near the downstream area and the upstream area to make it have a certain elevation difference. Under the assumption that the precipitation in the whole area is uniform, the accumulated catchment is calculated according to the flow direction. By selecting a certain threshold value, the pixel with high cumulative flow can use the result of flow to create a river network, and then determine the corresponding outlet location.

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Fig. 3 Buffer range and flow data

2.3 Determination of Water Outlet Location and Vectorization of Drainage Area In this study, river and lake reservoir are considered as reference geographical features to determine the location of water outlet. Based on the vector data file, the default parameter range of 500 m is set as the buffer zone, and the location with the largest flow position in the buffer zone is selected. The location and the original vector data point location are used as the outlet to calculate the spatial range of the watershed. If the reference feature is river we also need to determine is this river flows into other high-level rivers in the buffer zone. If this river flows into other river, the cell ahead of the convergence point will be selected as the pour point (Fig. 3). After the position of the outlet is obtained, the grid in the basin is obtained and assigned uniformly by iterative operation combined with the flow direction data. In the vectorization process, the raster file is transformed into column vector coordinates by using the method of raster.features.shapes in the Rasterio package in Python, and finally stored as a polygon ShapeFile file.

3 Case Analysis 3.1 Study Area This study takes the basin range of Hailong reservoir in Jilin Province as an example. Hailong reservoir is located in Xiaoyang Township, Meihekou, Jilin Province, on the Yangshu River, a tributary of Huifa River (Fig. 4).

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Fig. 4 Image of Hailong reservoir and its surroundings

3.2 Experiment Process This program needs a vector data as input data. We got the vector data file of Hailong reservoir from OpenStreetMap. Based on this vector data, after running the algorithm four files are created automatically: clipped DEM file containing the feature, flow direction data file, cumulative confluence flow file and basin range file. Figures 5, 6, 7 and 8 present the result after running the program.

3.3 Experimental Results The basin range of Hailong reservoir is presented in Fig. 8. Through the above case, we can see that this algorithm can effectively achieve automatic watershed spatial range acquisition according to the input vector data correctly.

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Fig. 5 DEM of Hailong reservoir

Fig. 6 Flow direction data results

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Fig. 7 Cumulative flow data results

Fig. 8 Spatial range results of Hailong reservoir basin

4 Discussion In this paper, the method of automatic watershed spatial range determination proposed in this study considers the related steps that still need more manual operation

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in the existing watershed algorithm, and proposes the automatic method for DEM data preprocessing and watershed outlet location determination, which overcomes the previous cumbersome shortage of spatial range data acquisition. In the next step, we will focus on how to combine the above method with the gazetteer and text analysis technology to realize the intelligent retrieval of a target drainage basin spatial data. Acknowledgements This study is supported by the National Natural Science Foundation of China (Project No. 41971054; 41471178; 41530749)

References Huang Y, Vardossy A (2014) Improving the transferability of hydrological model parameters under changing conditions. In: EGU general assembly conference Jenson K, Dominique FO (1988) Extracting topographic structure from digital elevation data for geographical information system analysis. Photogram Eng Remote Sens 54(11):1593–1600 Jing L, Zheng Z, Jiangang Z, Xiangyu M (2009) Hydrological feature extraction of Taihu Lake basin based on DEM. Environ Sci Manag 34(05):138–142 Matsuura T, Aniya M (2012) Automated segmentation of hillslope profiles across ridges and valleys using a digital elevation model. Geomorphology 177–178:167–177 O’Callaghan JF, Mark DM (1984) The extraction of drainage networks from digital elevation data. Comput Vis Graph Image Process 28:323–344 Pelletier JD (2013) A robust, two-parameter method for the extraction of drainage networks from high-resolution digital elevation models (DEMs): evaluation using synthetic and real-world DEMs. Water Resour Res 49(1):75–89 Tribe AS (1992) Automated recognition of valley lines and drainage networks from grid digital elevation models: a review and a new method. J Hydrol 139(1–4):263–293 Xiaomeng S, Jianyun Z, Chesheng Z, Jiufu L (2013) Research progress of digital watershed feature extraction based on DEM. Prog Geogr Sci 32(01):31–40 Yang D, Koike T, Tanizawa H (2004) Application of a distributed hydrological model and weather radar observations for flood management in the upper Tone River of Japan. Journal 18(16):3119– 3132 Yoeli P (1984) Computer-assisted determination of the valley and ridge lines of digital terrain models. Int Yearb Cartogr 24:197–205

Estimation of Flow from Hunza Watershed Under Possibly Changed Climatic Conditions Muhammad Zaeem Rana and Rana Muazzam Ali

Abstract University of British Columbia watershed model is a modelling software which creates a computational representation of watershed behavior of the flows of rivers. The study was oriented to develop a daily watershed outflow of rivers resulting from glacier melt, snowmelt and rainfall for Hunza watershed, to estimate the flows under changed meteorological conditions. The discharge data was obtained from Danyor gauging station. Daily maximum and minimum temperatures and daily precipitation data was obtained from Ziarat meteorological station. The calibration was performed for the year 1999 to get the best coefficients of efficiency and determination. The results recorded for coefficient of efficiency and coefficient of determination were same for year 1999 i.e. 0.94. The calibrated model was then validated for year 1995 & 2000. The results showed that the values of coefficient of efficiencies were 0.92 & 0.87 and coefficient of determination were 0.88 & 0.72 for the years 1995 and 2000 respectively. River flow for year 2080 of Hunza catchment was estimated by assuming 100% glacier meltdown. Arshad in 2006 predicted future increase in daily temperature and precipitation by about 4.67 °C and 3.95% respectively for year 2080. These results were used in this study and scenario modelling was performed for the year 2080 by taking assumed changes in glacier extents i.e. ranging from 0% change (no reduction in glacier) to 100% change (means no glacier). The modelling results showed that there is a decrease in water river flow. The discharge for year 2080 (assuming no glacier) was 64.9% less than the discharge observed for no reduction in glacier in 1999. Keywords Watershed · Modelling · Calibration · Validation · Coefficient of efficiency · Coefficient of determination

M. Z. Rana (B) · R. M. Ali University of Engineering and Technology, Taxila, Pakistan e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_25

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1 Introduction The University of British Columbia Watershed Model (UBCWM) describes and forecast watershed behaviour in mountainous areas (Quick and Pipes 1972a, b). The model was considered to estimate the daily flows for Fraser River system in British Columbia whose major contribution of the discharge is from snowmelt. As the amount of precipitation also drives the flows in rivers, a system is highly necessary which could forecast the flows in river to prepare for any unwanted circumstances. Different models have been created to estimate the discharge of rivers using meteorological data and watershed description. The hydrological modelling is a process in which certain linked set of equations are developed that help to describe a certain phenomenon taking place in real hydrological system (Naeem 2015). Singh and Woolhiser (2002) stated that the hydrological cycle’s land phase processes are quantified by the hydrological modelling (Figs. 1 and 2). The model calibration is altering the parameters in such a way to get best model fitness with the observed data. Validation of computer models is conducted after the development and making of the model with the goal of producing an accurate and credible model Schlesinger et al. (1979). Models are increasingly being used to solve problems and to aid in decision-making. UBCWM is a hydrologic model designed for forecasting runoff from mountainous watersheds (Quick and Pipes 1977). The model splits watersheds into elevation bands (up to eight), and model parameters can be set within each band. UBCWM climatic inputs include maximum and minimum daily air temperature, and daily precipitation. Outputs include total daily discharge, and discharge from rainfall-runoff, glacial melt, and snow melt (Fig. 3). Daily maximum and minimum temperatures and daily discharge data were used as basic input to University of British Columbia Watershed Model (UBCWM) to estimate the river flows for present flow and for future flow i.e. 2080. The project is an interesting and significant contribution in field of water resources and irrigation engineering. This provides an insight to estimate the future floods and flows through knowing the meteorological data. UBCWM is vital from research perspective, in knowing the losses of water. And in minimizing those losses and it also helps us gather information in design of hydraulic structure so that economy and efficiency could be achieved together.

1.1 Model Description The UBCWM contains three sub model. The first model is linked to metrological data and it allocates the point data of precipitation and temperature over the mid-elevation points of each elevation band in the watershed. The precipitation is measured by critical temperature and falls either in the form of snow or rainfall. The melting of snow cover area and glacier extent is also controlled by this model. The second model deals with the soil moisture and specifies the behavior of watershed. It

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Fig. 1 Schematic flow structure of UBCWM Source Quick and Pipes (1977)

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Fig. 2 Details of Hunza River Basin. a Basin boundary map showing meteorological stations, river network Source Shrestha Methodology Data Collection Land Data & Elevation Bands

Input Data Preparation

Data Digitization ADM, BDM, ADQ, BDQ Files

Model Calibration 1999

Watershed Delineation Elevation Bands

WAT File

Model Validation (1995 & 2000)

Scenario Modeling Reduced Glaciated Extents No Change in Extent 25 Percent Reduction 50 Percent Reduction 75 Percent Reduction 100 Percent Reduction

Fig. 3 Flow chart of methodology. Source (Gübelin 1982)

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divides the watershed inflows into three types: fast as surface, medium as interflow, slow as upper groundwater. The third model is related to routing and it generates the distribution of runoff components. It is based on linear storage reservoir theory. It assures the conservation of mass and water equilibrium. The UBCWM is conceptual and continuous hydrological model. It is a semi-distributed model. The UBCWM has been successfully applied for real time forecasting and research studies in different climatic regions of the world. The model has also been performing well in the Himalayas and Karakoram ranges in India and Pakistan (Khan 2014).

2 Methodology 2.1 Study Area Hunza is a mountainous valley situated in the Gilgit-Baltistan province at an elevation of 2500 m (8200 ft). The mountainous terrain has an elevation, ranging from 2000 to 6610 m above sea level which is calculated by GIS. Hunza has a dry Mediterranean climate with almost no rainfall during summers. In the winter, the night time temperature occasionally drops to −10 °C, while in summers it raises to 25 °C, Winter snowfall in the watershed can be quite heavy with an accumulation of up to 2 feet being quite common, at higher elevations snowfall can reach as high as 20 m (70 ft). Hunza River is the principle river of Hunza Watershed. It is formed by the confluence of the Kilikand Khunjerab (gorges) which are fed by glaciers. It is joined by the Gilgit River and the Naltar River before it flows into the Indus River.

2.2 Data Collection Temperature and precipitation data were collected from Pakistan Meteorological Department (PMD) Islamabad. That was from Ziarat station. The available span of temperature & precipitation data was from 1990 to 2004. This data included daily maximum and minimum temperature. The data is required to make input files for UBCWM, based on which it calculates the discharge. This daily discharge data was collected from Surface Water Hydrology Project (SWHP) Lahore for the same length of time. The data included daily discharge of river water at Danyor gauging station. This data is used to calibrate and verify the results of UBCWM. So firstly, average discharge of all the years was determined from 1990 to 2004 in MS excel and a line was drawn representing the average discharge as shown in Fig. 4. The year which was closest to the average line was chosen to calibrate as it would lead to more accurate and credible results.

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Fig. 4 Average discharge of 14-year flow data

2.3 Processed Land Satellite Data and Area Elevation Bands The Hunza watershed was explained using ArcGIS to obtain glaciated area of snow packs in Hunza watershed and was distributed in elevation bands to obtain the required information of watershed for UBCWM. Area elevation bands were calculated. Figure 5 was obtained after entering Hunza shape files in GIS where we convert them into area elevation bands. In Fig. 5 the green colour in legend depicts the lowest elevation point where as purple tint is a sign of highest elevation (Table 1).

Fig. 5 Area elevation band

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Table 1 Coordinates and elevations of stations Stations

Latitude

Longitude

Elevation (m)

Danyor Gauging station

35° 56

74° 23

1450

39

19

2156

Ziarat Meteorological station

74°

36°

Source Wikipedia

Table 2 Description of area elevation bands Band No.

Area (Km2 )

Glaciated area (Km2 )

Elevation ranges (m)

1

234.98

0

1818.375–2617.125

2

773.24

0

2617.125–3415.875

3

1934.1

0

3415.875–4212.625

4

3746.12

0

4214.625–5013.375

5

5080.32

2711.39

5013.375–5812.125

6

1638.1

1638.1

5812.125–6610.875

7

327.51

327.51

6610.875 above

From Table 2; the total glaciated area is calculated to be 4677 km2 and the total area of Hunza Watershed is 13734.37 km2 . Hunza Watershed is divided into 7 bands and each band is placed in ascending elevation ranges. The glaciers lie in band 5, 6, & 7 only. Total considered glacier area was 4688 Km2 according to (Akhter et al. 2008).

2.4 Data Digitization Before loading these files in UBCWM the data is arranged in adaptable format in Ms Excel. The first file containing meteorological data has maximum and minimum temperature in the first and second column followed by temperature and precipitation in Celsius and mm respectively. The file is then saved in comma delimited format i.e. (.csv) Now, we must go to the file where it is saved and change its extension from .csv to ADM as UBCWM can only read a file with this extension. Similarly, another excel file for daily discharge data for the whole year is treated in similar manner except the extension would be ADQ. The extensions of the input data files are Watershed parameter (WAT), Binary Daily Meteorological (BDM) and Binary Daily Flow (BDQ), respectively.

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2.5 ADM, ADQ & BDM, BDQ Files The binary daily meteorological (BDM) and binary daily flow (BDQ) are the extensions of the input files in UBCWM. The user supplies the filename for these, e.g. STRMFLOW.BDQ. Output data files are given the extension Binary Daily Calibrated (BDC) or Statistics (STA).BDC and STA files are used by the graphics and statistics programs to provide information to the user. Within the Watershed Model, streamflow and meteorological information is contained in an ASCII file having the extension *.ADQ and *.ADM respectively, which is then converted into a binary file *.BDQ & *.BDM. The file name is entered by the user and should identify the watershed with which it is associated. The Watershed Model will handle data sets from a maximum of five meteorological (ADM) stations and one stream flow (ADQ).

2.6 WAT File The WAT file describes an individual watershed and governs the execution of the Watershed Model. Also known as the watershed file is run with the BDQ & BDM files into the software. Wat file contains all the data of the watershed model which includes time and date run control, Meteorological and flow data, Elevations and parameters of AES stations, Distribution of meteorological variables, Snowmelt functions, Water distributions, initial conditions, Initial values of the outflows from the routing storages & recorded flow files.

2.7 Calibration and Model Validation Calibration of a system for a given watershed involves comparing stream flow forecasts produced from the watershed description in the WAT file and from the ADM files, with actual stream flow recorded in the ADQ file. If forecast results are not consistent with historical information, the semi-automatic calibration routine is used to adjust the values of relevant parameters in the WAT file. The Iteration continues until the forecast results closely reflect recorded stream flow. The calibration of model was done on average year i.e. 1999. After calculating average flows, the parameters of WAT file were adjusted by hit and trial to get maximum efficiency. Validation of a model is done to determine the accuracy of the models representation. The model used in this study was validated on years 1995 and 2000 which are close to the average discharge lines after year 1999. Therefore, it is necessary to perform calibration on years closer to the average lines as it would lead to more valid results.

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2.8 Different Parameters in Stages in Calibration We have divided our Parameters into 2 stages, which are used in the calibration process. The first stage parameters, contain the very important parameters, which control the amount and distribution of precipitation, both rain and snow. The second stage parameters are the main runoff response of the system and controls the nonlinear behaviour of the watershed. The default values that have been determined from studying many watersheds usually giving reasonable results. Given Below are the values of parameters used in WAT file to get maximum efficiency.

2.9 Scenario Modelling for 2080 After calibrating the model for year 1999 and validating it on years 1995 and 2000, the model can be used to estimate the river flows for the year 2080. It is presumed that the glaciers are reduced to zero for this year with reduction from 0–100%. Flows are calculated for each 25% for reduction of glacier and discharges were calculated respectively. Temperature and Precipitation data was also altered for 2080 scenario according to the study of (Arshad 2006). Temperature was increased by 4.67 °C & Precipitation by 3.95%. Assumed Scenario for glacier reduction. For the different assumptions of glaciers reduction for year 2080 in table. After each reduction, the glaciers were distributed in the following 7 area elevation bands (Tables 3, 4 and 5).

3 Results and Discussions 3.1 Calibration Result for 1999 The results obtained by running the ADQ & BDQ files with WAT file showed varied results in the start, but after adjusting the parameters of the WAT file, far better results were obtained with coefficient of Determination and coefficient of efficiency closer to 1. The results for 1999 calibration are mentioned below in Fig. 6 and Table 6. Table 6 representing the quantified results for the Fig. 6 (Table 7).

3.2 Evaluation This calibration shows that model can be used in Hunza watershed of Pakistan efficiently, the coefficient of determination is 0.94. Coefficient of determination is a statistical measure of how well the regression line approximates the real data points. After the values are calibrated, validation of the model is performed to check. The

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Table 3 Values of parameters used in WAT file to get maximum efficiency Parameter range Stage 1 Parameters

Stage 2 Parameters

Description EOLMID

–

Elevation at which POGRADM is effective

EOLHI

–

Elevation above which POGRADU is effective(m)

POSREP

−1.0 to 1.0

Adjustment to precipitation when average temperature < (Snowfall)

POGRADL

0–20

Precipitation gradient factor (%) for elevations below EOLMID

POGRADM

0–20

Precipitation gradient factor (%) for elevations below EOLHI

POGRADU

0–20

Precipitation gradient factor (%) for elevations above EOLHI

POPERC

0 to 50

Ground water percolation in mm/day

PORREP

−1.0 to 1.0

Adjustment to precipitation when average temperature > AOFORM (rain)

PODZSH

0.0–1.0

Deep zone share fraction

POUGTK

0.0–1.0

Volumes of runoff and the shapes of the groundwater recession flows

POEGEN

–

Adjust soil moisture deficit production

Source UBCWM Manual Table 4 Remaining glaciated area after assumed glacier reductions Sr No.

%Age reduction

Remaining glacier area (Km2 )

1

0

4637.99

2

25

3478.49

3

50

2318.99

4

75

1159.4975

5

100

0

Table 5 Elevation bands after assumed glacier reductions % Age reduction

Band 1 (Km2 )

Band 2 (Km2 )

Band 3 (Km2 )

Band 4 (Km2 )

Band 5 (Km2 )

0

0

25

0

50

0

75 100

Band 6 (Km2 )

Band 7 (Km2 )

0

0

0

2672.3

1638.1

327.51

0

0

0

1512.89

1638.1

327.51

0

0

0

353.39

1638.1

327.51

0

0

0

0

0

831.99

327.51

0

0

0

0

0

0

0

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Fig. 6 Calibration of year 1999

Table 6 Statistics of year 1999

Table 7 Statistics of year 1995, 2000

Coefficient of determination

0.94

Coefficient of efficiency

0.94

Actual observed discharge at gauging station (cms/d)

307.18

Calculated discharge by UBCWM (cms/d)

293.30

Year

1995

2000

Coefficient of determination

0.92

0.87

Coefficient of efficiency

0.88

0.72

Actual observed discharge (cms/d)

276.4

282.96

Calculated discharge by UBCWM (cms/d)

309.70

319.74

coefficient of efficiency of 1 corresponds to a perfect match of modelled discharge to the observed data (Figs. 7 and 8).

3.3 Validation Results The calibrated model was validated to ensure its effectiveness in all scenarios. As there is a tendency of temperature and precipitation to vary indefinitely, so it is necessary to validate the model. Model was validated on 1995, 2000 years. Following are the graphical results for these years. The COD & COE of the year 1995 & 2000 are shown in Table 8. The calculated

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Fig. 7 Validation of year 1995

Fig. 8 Validation of year 2000 Table 8 Quantified results of flows for 2080 scenario

Assumed Percentage Reduction in glaciated area (%)

Discharge (m3 /s)

0

431.68

25

334.42

50

237.16

75

180.50

100

151.35

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discharges are greater than the actual discharges observed at the gauging station.

3.4 Scenario Modelling Results After assuming different scenario in 2080 that if the glacier remains same in 2080 as was in year 1999 which was our calibrated year then the flow is given in Fig. 9 and if glacier reduces 25% in year 2080 than year 1999 then the flow is given in Fig. 10 and if reduces to 50, 75, 100% then the flow is given in Figs. 11, 12, 13 respectively. The flow trend for all assumed glacier reduction is given in Fig. 14. For zero percent glacier reduction the flow data and meteorological data is modified according to the study of (Arshad 2006). The COE & COD of the graph is 0.70 and 0.91 with flow data of 431.68 m3 /s.

Fig. 9 0% glacier reduction

Fig. 10 25% glacier reduction

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Fig. 11 For 50% glacier reduction

Fig. 12 For 75% glacier reduction

Fig. 13 For 100% glacier reduction

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Fig. 14 Discharge trend after assumed glacier reduction

3.5 Evaluation of Figure From Fig. 10 we can see that the glacier has been reduced by 25% i.e. from 4677 km2 to 3478.49 km2 . The calibrated results show the COE & COD to be 0.88, However the flow is reduced, due to the decrease in glacier area. The new generated flow is estimated to be 334.42 m3 /s. The COE & COD for 50% glacier reduction is 0.64 and 0.77. The flow is reduced to 237.16 m3 /s. The flow for 75% glacier reduction is 180.50 m3 /s, COE & COD are 0.27 & 0.47 respectively given below in the Fig. 12.

3.6 Overall Evaluation The flow for 100% reduction in 2080 is estimated to be 151.35 m3 /s, with COE & COD to be −0.02 & 0.14. It is observed that the flow has been reduced from 431 to 151 m3 /s. The COE and COD are also reduced as the glacier got reduced gradually due to the assumption, and it would lead to more changed atmospheric conditions which effects the flow.

4 Discussion Firstly, average of all years was calculated as explain earlier then the year which was closest to average line was chosen to calibrate the model. That was year 1999 the results of that model showed that model can be used in Hunza watershed of Pakistan efficiently after that it required validation to ensure the effectiveness of calibrated model. Model was validated on two years that were 1995 & 2000. After

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getting satisfactory results the calibrated model can be used for further analysis. Then from previous studies glacier reduction and modified temperature and modified precipitation were assumed for year 2080 as explain earlier. After that if the glacier remains same as was in 1999 the discharge will be maximum because the increase of temperature and precipitation and in future (2080) and discharge will decrease when reduction of glacier will 25% for 50% glacier reduction discharge will further decrease similarly discharge will decreases for 75% glacier reduction and discharge will be minimum for 100% glacier reduction.

5 Conclusions Temperature and precipitation of Hunza is Increasing with the rise in global warming global annual land mean precipitation showed a small but uncertain upward trend over the twentieth century of approximately 1.1 mm per decade. Glacier is also reducing due to the increase in global warming. Geographic Information System (GIS) is very useful in describing the characteristics of watershed otherwise; characteristics like watershed delineation, area, elevation bands, hypsometric elevation mean must be computed manually which are subjected to human error and physically very hard job to perform. After examining the statistical and graphical analysis of Hunza Watershed, it is concluded that UBCWM is well calibrated and validated.

6 Recommendations Glaciers are melting due to increase in temperature and global warming and precipitation is increasing with the passage of time so it is quite possible that the discharge will increase in the future to some extent it will not remain the same as was in past as while doing this project the calculated flow of year 1995 was minimum and was maximum of year 2000 from which it could be seen that with the passage of time flow is increasing by keeping all these things in mind water resources must be modified to bear the future flow. The capacity of water reservoirs should be increase to the worst condition also the water resources should made to preserve the water. An important factor that can cause abrupt changes in environment of Hunza Valley is the development of China Pakistan Economic Corridor (CPEC). It is predicted that the amount of vehicular transport will drastically increase in a span of 5–10 years, causing a significant change in environment leading to more glacier melt. This external factor is thus included as a major factor which can cause change in flows and study needs to be done on this area.

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References Akhtar M (2008) The impact of climate change on the water resources of Hindukush–Karakorum– Himalaya region under different glacier coverage scenarios. J Hydrol 355:148–163 Arshad M (2006) A remote sensing technique detecting and identifying water activity sites along irrigation canals. Am J Environ Eng Sci Gübelin E (1982) Die Edelsteinvorkommen Pakistans. 1. Teil. Die Rubine aus dem Hunzatal. LAPIS 7(5):19–26 Khan SA (2014) Assessment of flows in a glaciated region-Shigar River Basin, Pakistan technical. J Univ Eng Technol Taxila 19(I):38–40 Naeem UA (2015) Ranking sensitive calibrating parameters of UBC watershed model. KSCE J Civil Eng 19(5):1538–1547 Quick MC, Pipes A (1972a) Daily and seasonal forecasting a water budget model, in role of snow and ice in hydrology. In: Proceedings of the UNESCO/WMO/IAHS Symposium, Banff, September 1972, IAHS Publication no. 106, pp 1017–1034 Quick MC, Pipes A (1972b) Daily and seasonal runoff forecasting with a water budget model. In: Role of snow and ice in hydrology proceedings of the UNESCO/WMO/IAHS Symposium, Banff, pp 1017–1034 Quick MC, Pipes A (1976) A combined snowmelt and rainfall runoff model. Canadian J Civil Eng 3(3):449–460 Quick MC, Pipes A (1977) UBC watershed model. Hydrol Sci Bull 153–161 Schlesinger S (1979) Terminology for model credibility. Simulation 32(3):103–104 Shrestha M (2015) Integrated simulation of snow and glacier melt in water and energy balance-based, distributed hydrological modelling framework at Hunza River Basin of Pakistan Karakoram region: integrated snow and glaciermelt model. J Geophys Res Atmos Singh V, Woolhiser D (2002) Mathematical modelling of watershed hydrology. J Hydrol Eng 7(4):270–292

Optimizing Industrial Facility’s Demand for Combined Heat-and-Power (CHP) Stanislav Chicherin, Lyazzat Junussova, Timur Junussov, and Chingiz Junussov

Abstract There is no work able to handle with minimizing costs of a CHP baseddistrict energy system in the face of uncertain manufacturing currently available. This study is therefore has a goal of establishing a methodology applicable to future supporting engineers and decision makers, asked to combine heat and electricity generation for optimising a DH system’s structure and production rates of an industrial facility. Both objective functions are expressed as the sum because they represent the total annual thermal energy and electricity. Yearly heat and electricity values are calculated and shown, that represents the correlation and the flexibility of the generation. It can be observed that the higher electricity demand is compensated by a higher electricity production at a CHP plant, thus reducing the payback period for generation equipment. In case of the increased heat production from a CHP, the level of electricity does not obligatory increase. The most relevant option in comparison to other values of electricity delivery concerning the introduction of thermal energy output is also identified. Although countries try to achieve energy efficiency targets, ordinary industrial facilities in the world still largely depend on production rates and associated heat loads. CHP plants perform as the best option to be facility of the next generation although, but there is still a tremendous potential to use an optimizing approach in a DH system. This assessment shows the way to control the combined generation of power and heat within a complex industrial facility and to make it smoother. Keywords District heating · DH · Load · Profile · Electricity · Generation · Production · Output · Volume · Manufacture

S. Chicherin (B) Omsk State Transport University, Omsk, Russia e-mail: [email protected] L. Junussova · T. Junussov · C. Junussov Almaty University of Power Engineering and Telecommunications, Almaty, Kazakhstan © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_26

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1 Introduction Shan et al. (2017) introduce the district heating (DH) availability indicators to assess the ability of meeting demand and the impact of faulty components on a DH substation. To review detection methods Zhou’s paper (2018) is intended. The ordinary engineering and feasibility studies usually do not take into account reliability aspects (Badami et al. 2018). The way how results of the present paper could be implemented is given in Guelpa et al. (2017) showing the adverse affect of improper load assessment. In case of low heat consumption, energy generated by a heat pump unit is cheaper even in a residential building (Junussova et al. 2018). The amount of energy required per unit of economic output is handled here as a constant value. The specific units have been drawn from Russian Standards, that consider data shown for every type of heat and electricity production. One can find the similar approach in nearly every paper. In Björnebo et al. (2018) with heat demand known, the capital costs of the distribution system are determined empirically taking into account materials and construction specific costs. In Kabalina et al. (2018) the avoided CO2 emissions are estimated through the annual emissions factors. Miscellaneous fitting techniques are used (Noussan et al. 2017); in this paper a least-square one is applied. Schweiger et al. (2017) and Delangle et al. (2017) reviewed different optimization methods and objective functions applicable to DH networks. Coss et al. (2018) use holistic design approach to present a multi-objective performance assessment conducted with the help of a Pareto optimum. A few techniques are applied to analyze combined electricity and DH networks fed by a single Combined Heat-and-Power (CHP) plant (Liu et al. 2016). In Brange et al. (2016) options of the wind power use significantly depends on share of energy in the present energy mix a natural gas based CHP substituted. Sayegh et al. (2018) discuss types of heat pump technologies including a heat pump run by power coming from a CHP plant and a low-potential source of heat—return line of a DH network fed by a CHP plant as well. In Cai et al. (2018) and Vivian et al. (2018), some houses are assumed to be so called Energy Hubs consuming district heat, electricity and natural gas. The temperature at a CHP plant outlet is expected to evolve according to the changing heating needs (varying mostly with an outdoor temperature) and circumstances under which a DH system operates (Ayele et al. 2018). The amount of crucial elements of a DH network increases once the weather conditions become harsh, especially if it happens sharply as often in Russia (Chicherin 2017). According to the operation practice, acceptable time of a malfunction is shorter as the outdoor temperature declines (Chicherin 2016). As shown in Hou et al. (2018), CHP plant provides both steam and electricity to Ningbo Hi-Tech District, China, where industrial facilities and commercial objects are situated. Although Hou et al. (2018) present connection between the base year and the new configurations, their assessment of an available energy resource is directly linked to the expansion of the existing district energy system. In Ayele et al. (2018) each case study is assumed to have different flows of the primary fuel and amount of

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energy generated. Each topology represents a typical configuration of an industrial facility.

2 Materials and Methods The system was designed for various volumes of manufacture, and supply/return temperatures are not discussed because they fall outside the scope of this paper. The electricity and heat demand profiles were simulated using commercial software Microsoft Excel, loads are received from the local utility company. Facility’s design of a network and data to assess losses have been identified with the help of outlines of an industrial enterprise in the suburbs of Omsk, Russia using Wikimapia. The amount of energy required for industrial production is treated here as a discrete variable (Hou et al. 2018, Junussova et al. 2019). Assumed stochastic inputs for the whole facility are categorized as shown in Table 1. Industrial consumers tend to have various heat demand profiles (Chicherin et al. 2018) than residential ones because of a shift schedule and presence of a ventilation (Brange et al. 2016). Both objective functions are expressed as the sum be-cause they represent the total annual thermal energy Q and electricity E 

Q → min

∪



E → min

(1)

To define minimum and maximum constraints on the derivatives of the decision variables, the following equations are introduced ⎧ ⎪ ⎪ ⎨



Q actual ≥0  Q actual ≥ Q demand  ⎪ E actual ≥0 ⎪  ⎩ E actual ≥ E demand

(2)

The driving energy is assumed to be generated at a CHP plant and transmitted by the electricity grid. Yearly electricity use (kWhE + 03) is defined as follows Table 1 Energy usage—the base case Volumes of production (pcs)/Option

1

2

16

21

28

1st Product

0

5360

5540

0

3150

2nd Product

0

5610

3550

0

5070

3rd Product

0

5500

4900

0

5020

4th Product

5880

0

0

3420

0

5th Product

3060

0

0

3260

0

6th Product

5230

0

0

5070

0

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E y = e P R P R · 10−3

(3)

where e P R specific energy needs of the pcs considered (kWh), PR annual production (pcs). Yearly electricity consumption for lighting (kWhE + 03) E yL I G = e L I G E y · 10−2

(4)

where e L I G share of electricity consumption for lighting. Daily lighting load supplied to the customers varies proportionally to the weather conditions from 0 to 25% per day. Here 7.5% implies that less than a tenth of the total thermal energy in average was delivered to the lighting purpose. Yearly peak electricity use (kW) Py =

Ey h

(5)

where h cumulative yearly hours (h). With these parameters known, the peak electricity demand was calculated empirically for each location with Eq. 6 PL I G =

E yL I G hLIG

(6)

where h L I G cumulative yearly hours of electricity consumption for lighting (h). In fact, in a new building the lighting is responsible of up to 5% of the energy bills, while in an industrial facility this percentage is assumed to differ. Thus, another formulae represents human behavior although it still takes into account the average loads to calculate the total yearly heat demand (kWh) E yD = ee H

(7)

where ee electricity consumption per year per employee (kWh) H number of employees. Peek domestic need (kW) PD =

E yD hD

(8)

where h D cumulative yearly hours of electricity consumption for domestic needs (h). According to the assumption, that partial load behavior limits CHP efficiency, the perfect demand profile for an assumed heat and electricity consumption is represented as a steady state pattern with a design power equal to the annual experience (Chicherin 2018). This value is 8760 h since a plant operates the whole year. Usually this is

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Table 2 Average values used in the calculations Type of service

h D (h)

ee (kWh)

Residential and public buildings lighting

2250

200

Street lighting

2000

65

Household appliances (both private and public sector)

3500

120

Electrified transport

5750

25

Water and sanitation

4500

55

Service loads (both private and public sector)

3750

160

unachievable; however the demand profile of is not limited to the certain values given in Table 2. The heat demand is denoted by Qy with superscripts meaning the variety of purposes which are met by DH. The heat supply for technological process is covered via steam (GJ) Q y = q P R P R · 10−3

(9)

where q P R specific heat use per unit of production (GJ). The hourly thermal power is equal to the ordered thermal power divided by the cumulative yearly hours (GJ/h) Qh =

Q y · 103

(10)

h

The total heat demand depends on the building structure connected to the DH network (GJ) Q Sy H = q S H P R · 10−3

(11)

where q S H specific heat use for SH (GJ). The values of specific units for this study were obtained from the previous paper (Chicherin 2016), refer to Table 3. Q hS H =

Q Sy H · 103

(12)

hSH

Table 3 Peak loads and specific energy needs of the pcs considered Type of product

e P R (kWh)

q P R (GJ)

q S H (GJ)

h (h)

1st Product

30,000

6.9

4

3000

3rd Product

25,000

3.6

4

3000

5th Product

38,000

9.6

8

3000

Peak heat load for SH of industrial facilities (GJ/h)

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where h S H hourly heat use over a year (h). For instance, it is 6 months or 4320 h in northern Italy (Badami et al. 2018). Residential and commercial buildings’ demand and their corresponding shares for space heating and domestic hot water (GJ) are considered as Q yD = q D H

(13)

where q D heat consumption for domestic purposes per year per employee (GJ). The ordinary experience in Russia and China has been to charge the heat consumption by an annual fixed price depending on a type of consumer, whereas heat meters are not commonly used (Werner 2017). In Ayele et al. (2018), the Jacobian matrix are discussed to develop formulas applied for modeling the combined electricity and DH systems.

3 Results and Discussion The annual simulation outcome from all the cases is summarized in Fig. 1. Based on the results for each regarded scenario from the investigated period, it can be observed that the relative heat demand (in %, the amount of heat taken per unit output) decreases for 5% (percentage points) on average for the increase from 2420 to 7065 GJ. The system greatest absolute value is reached for total production of 14,050 pcs and equals 7065 GJ. However, the value of total production may also grow as the yearly heat remains a relatively constant: compare points associated with production 10,300 and 16,620 units. The revenues increase for higher values of total production (refer to 16,470 pcs); such growth is due to higher CHP efficiency and decrease in relative heat demand value.

Fig. 1 Dynamic yearly heat of the studied cases

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Fig. 2 Cogeneration electricity delivery as a function of E y

Because of the losses, heat production has to be higher than the design demand, which is especially notable after the application of renovation measures. The highest relative heat losses usually correspond to the year with the lowest heat demand (Andri´c et al. 2018). As known (Kabalina et al. 2018), a CHP system must distribute significant amounts of heating or cooling to be economically feasible. As shown in Fig. 2, the curve is nearly a straight line if the operational performance and the whole system are more heavily influenced by primary (technology) load. From Fig. 2, it can be observed that the higher electricity demand is compensated by a higher electricity production at a CHP plant, thus reducing the payback period for generation equipment (Chicherin et al. 2019). The movement of the selected solutions towards their ideal value is represented through the fine line in Fig. 2. It must be noted that this curve only represents the objective functions tendency towards its ideal state, thus the actual solutions on the in- and outside fronts do not lie on that line. We can look, for example, at that for the case of 1098,479 MWh yearly electricity output, with 824,466 MWh consumption of electricity for technological process, and compare with ones of 507,074 and 317,090 MWh, respectively. The former case results in higher CHP capacity, and suggests a strong opportunity for the advanced DH system and feeding lots of electricity to the grid. Once again, the results are summarized as the fine line in Fig. 2, whose parameters after linear interpolation are presented as y = f(X) equation. Subject to the fit level in terms of root-mean-square error (RMSE), Eq. (14) is obtained. 

E = 0.88E y + 296, 642

(14)

RMSE equals 0.65. Despite the strong correlation, a CHP plant should be sized properly to avoid unreasonable harmful effect on the environment and in line to the local regulation (Chicherin 2019). This conclusion fits in well with the work of Björnebo et al. (2018) that noted an influence of the increasing use of fossil fuel on

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a greenhouse gas abatement cost increase when replacing conventional (in the U.S.) heating with DH.

4 Conclusions Although countries try to achieve energy efficiency targets, ordinary industrial facilities in the world still largely depend on production rates and associated heat loads. CHP plants perform as the best option to be facility of the next generation although, but there is still a tremendous potential to use an optimizing approach in a DH system. Load diversity is recognised as a common issue in most industries, therefore it requires a heating and electricity control strategies to operate a plant with profit. This assessment shows the way to control the combined generation of power and heat within a complex industrial facility and to make it smoother.

References Andri´c I, Fournier J, Lacarrière B, Le Corre O, Ferrão P (2018) The impact of global warming and building renovation measures on district heating system techno-economic parameters. Energy. https://doi.org/10.1016/j.energy.2018.03.027 Ayele GT, Haurant P, Laumert B, Lacarrière B (2018) An extended energy hub approach for load flow analysis of highly coupled district energy networks: Illustration with electricity and heating. Appl Energy 212:850–867 Badami M, Fonti A, Carpignano A, Grosso D (2018) Design of district heating networks through an integrated thermo-fluid dynamics and reliability modelling approach. Energy 144:826–838 Björnebo L, Spatari S, Gurian PL (2018) A greenhouse gas abatement framework for investment in district heating. Appl Energy 211:1095–1105 Brange L, Englund J, Lauenburg P (2016) Prosumers in district heating networks—a Swedish case study. Appl Energy 164:492–500 Cai H, You S, Wang J, Bindner HW, Klyapovskiy S (2018) Technical assessment of electric heat boosters in low-temperature district heating based on combined heat and power analysis. Energy 150:938–949 Chicherin SV (2016) New approach to determination of corrosion damage degree of pipeline system elements. Bull Tomsk Polytech Univ Geo Assets Eng 327 Chicherin SV (2017) Unlocking a potential of district heating network efficient operation and maintenance by minimizing the depth of a trench system. Bull Tomsk Polytech Univ Geo Assets Eng 328 Chicherin S (2018) Low-temperature district heating distributed from transmission-distribution junctions to users: energy and environmental modelling. Energy Procedia 147:382–389 Chicherin SV (2019) Comparison of a district heating system operation based on actual data—Omsk city, Russia, case study. Int J Sustain Energy 38:603–614 Chicherin S, Volkova A, Latõšov E (2018) GIS-based optimisation for district heating network planning. Energy Procedia 149:635–641 Chicherin S, Junussova L, Junussov T (2019) Minimizing the supply temperature at the district heating plant—dynamic optimization. E3S Web Conf 118

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An Analytical Approach to Sustainable Beneficial Use of Dredged Materials in Yangon River, Myanmar Khin Myat Noe and Kyoungrean Kim

Abstract Yangon Port is the primary maritime transport to international trades through Yangon river waterway. High rate of sediment deposition in a confluence of rivers, namely an inner bar, has hindered navigation channel approaching Yangon Port. Thus, maintenance dredging at inner bar has been carried out annually. Owing to Myanmar Port Authority assuming dredged materials as wastes and focusing on ship to pass easily, dredged materials are directly dumped into open water area closed to dredging area without any disposal site monitoring and assessment of sediment contamination. This research aims to minimize volume generated annually by appropriate beneficially use-methods approaching sustainable dredging practices. Thus, sediment samples were collected from 3 different locations in inner bar area and analyzed following U.S. EPA test methods or other relevant methods. Particle size distribution, organic matters, heavy metals, and persistent organic pollutants were evaluated.14% of sand, 32% of silt, and 54% of clay were obtained from particle sizing results. Lower values of heavy metals, Polycyclic aromatic hydrocarbon, and Polychlorinated biphenyls than sediment quality criteria of various countries were found. Therefore, these sediments may be supposed being non-contaminated and reused without additional pollution-control treatments. Particle separation and dewatering techniques were described as cost-effective methods in order to save dredging cost and to produce commercial products. Legislation on disposal at sea, national building code standards for building materials, and sustainable dredging procedures were stated. K. M. Noe Student, Ocean Science, KIOST School, University of Science and Technology (UST), 217, Gajeong-dong, Yuseong-gu, 34113 Daejeon, Republic of Korea e-mail: [email protected] K. M. Noe · K. Kim Marine Environmental Department, Korea Institute of Ocean Science and Technology (KIOST), 385, Haeyang-ro, Dongsam-dong, Yeongdo-gu, 49111 Busan, Republic of Korea K. Kim (B) Professor, Integrated Ocean Science Department, KIOST School, University of Science and Technology (UST), 217, Gajeong-dong, Yuseong-gu, 34113 Daejeon, Republic of Korea e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_27

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Keywords Yangon river · Inner bar · Dredged materials · Particle separation techniques · Beneficial use

1 Introduction Sediments are originally come from the land, are transported by river flow, and then deposit at the bottom of riverbed/ seabed. They are composed of organic matters, nutrients, and minerals effectively giving to human and aqueous environment. Indeed, sediments at the bottom of the rivers, lakes, estuaries, and sea areas are valuable soil resources (U.S EPA 2004).Dredging is essential for maintenance of navigational channel or waterway for port development in relation with country economy. Thus, marine sediments are excavated and generally disposed at sea as wastes, however, these are not waste. All types of dredged materials can be applied as a potential resource for different purposes such as engineering uses and marine habitats (Mulligan et al. 2010). Accordingly, beneficial use of dredged materials is an alternative way of reduction in dredging volume. The Yangon river is about 40 km long (25 miles) (Lwin and Khaing 2012). One of distributaries of Ayeyarwady river is Yangon river connecting Hlaing river and Pan Hlaing river at upper part and the joining of tributaries such as Bago river and Pazundaung creek meets downstream flow of Yangon river creating a large water volume and thenthe river freely flows into the Gulf of Martaban. Along the Yangon riverbank, Yangon Port divided into Yangon Inner Harbor and Thilawa Port area is the primary maritime transport to international trades accounting for 90% of country’s imports and exports. Myanmar Port Authority (MPA) officially allows the approachable ship specifications such as LOA-167 m, Draft-9 m, and DWT15,000 for Yangon Inner Harbor and LOA-200 m, Draft-9 m, and DWT-20,000 for Thilawa Port (Thein and Yang 2019).Nowadays, total 24 terminals (18 terminals at Yangon Inner Harbor and 6 terminals at Thilawa Port) can operate 24 ocean-going vessels simultaneously (Thein and Yang 2019).Consequently, waterway maintenance is a necessity for Yangon river for port development. Therefore, the maintenance dredging has been performed periodically to get sufficient water depth at inner bar area shown in Fig. 1 by trailing suction hopper dredgers owned by MPA for over 50 years. Because the inner bar is a major inefficient obstacle that makes a bottleneck towards the country’s economy (De Koning and Janssen 2015). The volume generated annually is assumed to be about 1.4 million m3 as a result of periodic dredging activities at inner bar (Aung 2013). Dredged materials are directly disposed at an open water area where is 200 m downstream near the confluent area of rivers (JICA 2016) by opening the doors in the hopper bottom. MPA assumes that the open water area is being deep and stronger clean flow washing out sediments to the downstream of the river and does not disturb river navigational channel (JICA 2016). Practically, that huge amount of dredged materials may be a tendency to reaccumulate to the inner bar area because the current dredging site and the present dumping area are very closed. A few researchers (Aung 2013; De Koning and Janssen

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2015; and JICA 2016) focused only on the sedimentation problems and sediment transport modeling studies along the Yangon river. High rate of sediment deposition at inner bar was found as a result of sedimentation studies. Also, (Kyi and Myint 2017) collected water samples along lower part of the Hlaing river which is upstream of Yangon river and analyzed water quality. But there was no feasibility study related to potential impacts on river flow due to disposal of dredged materials at open water area and environmental impacts at the disposal area. Despite the lack of required information, a case study for the assessment of sediment quality in dredged materials was mainly performed in this research in order to make the preliminary framework for sustainable beneficial use of dredged materials in Yangon river, Myanmar.

2 Materials and Methods 2.1 Site Characteristics There are two constraint shallow areas: an inner bar near the confluence of the rivers shown in Fig. 1 and an outer bar at the river mouth. The shape of dredged navigational channel at inner bar is about 4.2 m depth and 100 mwidth. Besides, the ocean-going vessels (9 m draft or lower) are waiting at low tide condition (about 1 m depth from Chart Datum (CD)) at river mouth and passing through the inner bar for calling Yangon port during high tide (about 5.13 m depth from CD). Also, the average tidal difference between the rainy season and the dry season is approx. 1 m. Therefore,

Fig. 1 Map of sampling points, Yangon river

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MPA dredgers maintain the present water depth daily around the inner bar area along the river channel (JICA 2016).

2.2 Sampling and Pretreatment Sediment samples should be collected in a specific/desired area for various reasons including physical, chemical, and biological characteristics in sediment. Compatibility with choosing sampling devices available and collecting techniques is related to sampling locations, sampling purposes, and characteristics of sediment. In this research, three sampling points were randomly selected around the inner bar area. Sampling point locations are shown in Fig. 1. The amount of each sample collected will depend on the number of analysis items and the detection limit of the analyses(Mulligan et al. 2010).Sediment samples were collected by using a hand corer (about 1 m in height) by diver. Field measurements and observations were noted during sampling shown in Table 1. In the field manual records, sample locations were recorded by Global Positioning System (GPS)and weather conditions, tidal level, current local situations, and date and time were recored by taking photographs. Physical characteristics such as presence of debris, color, odor, and sediment properties were observed (Batley and Simpson 2016). Much amount of silt and clay particles were found. After sampling, pretreatment procedures were started step-by-step. sediment samples obtained were transported with a dry ice container to a laboratory of Chemistry department at Yangon University and then storedat −20 °C in a deep freezer to reduce loss of volatiles and to minimize bacterial activity (Batley and Simpson 2016). These samples were well dried by a freezing drying method in a laboratory of KIOST and then thoroughly homogenized by using a quartz motor. Table 1 Field records during sampling Sampling method

Date and Time

Weather condition

Color

Water depth

Odor

Diving

25.07.2019 09:00 am

Sunny day

Light grey

5. 20 m

No smell

Diving

25.07.201 09:24 am

Sunny day

Light grey

5.36 m

No smell

Diving

25.07.2019 10:29 am

Sunny day

Light grey

5.00 m

No smell

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2.3 Evaluation Items and Analytical methods To determine sediment qualities, dry samples obtained were analyzed using spectrophotometer (DR 5000, Hach, USA) and heat block (DR 200, Hach, USA) according to the following analysis items based on U.S.EPA methods such as 10129, 10067, and 8000 methods for organic matters and other relevant methods for harmful chemicals and particle size distribution. They are: 1. Physical parameters: water contentand particle size distribution 2. Chemical parameters: Total organic carbon (TOC), Nutrients (CODMn and CODCr ), Heavy metals (Cu, Pb, Ni, Zn, Cd, Cr, and As), and Persistent organic pollutants (POPs) (Polychlorinated biphenyls (PCBs) and Polyaromatic hydrocarbons (PAHs) (Mulligan et al. 2010). Average values of each sample for all organic contents were calculated and compared with that of standard solution to check accurancy. Heavy metals were determined using inductively coupled plasma mass spectrometer (Nexion 300D, Perkin Elmer, USA) followed by US. EPA methods. PCBs and PAHs were also experimented using gas chromatography coupled with mass spectrometer (7890 GC/5975CMSD, Agilent, USA) followed by US. EPA method 8082A and 8310 respectively. Particle size distribution was analyzed by laser diffration particle size analyzer (Beckman, USA).

3 Results and Discussion 3.1 Characteristics of Samples The results analyzed from physical and chemical characteristics are shown in Fig. 2 and Table 2. As a result of organic matter analysis, it was found that CODCr values for three sampling points were 235 mg/l, 212.5 mg/l, and 75 mg/l respectively and TOC values for all points were within the range of 3% to 5% of sediment. This TOC result is accepted for beneficial use as the conditions of beneficial use are dependent on the content of organic matters in sediment. In the CODCr test, the standard solutions were analyzed for accuracy check that the result was 99.76%. Also, a blank sample was tested for the determination of any contaminant from sample preparation and extraction or analysis. The water content in all samples was 40.73, 40.28, and 31.40%. And the concentrations of heavy metals, PAHs, and PCBs were very low as described in Table 2. According to Table 2, it was evaluated that sediments at inner bar in Yangon river may be uncontaminated and suitable for beneficial uses. In addition, the concentration of toxic chemicals and persistent pollutants was compared with sediment quality standards in dredged materials from various countries to ensure being non-contamination

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Table 2 Harmful chemicals of sediment samples Specification

Sample 1

Sample 2

Sample 3

Cd (mg/kg)

0.0000

0.0000

0.0000

Zn (mg/kg)

0.0527

0.0588

0.0464

Ni (mg/kg)

0.0736

0.0764

0.0623

As (mg/kg)

0.0013

0.0016

0.0008

Cr (mg/kg)

0.1109

0.1234

0.1194

Cu (mg/kg)

0.0185

0.0205

0.0154

Pb (mg/kg)

0.0228

0.0242

0.0209

Total PAHs* (µg/kg)

0.1040

0.1644

0.0921

Total PCBs (µg/kg)

Lower than the detection limit

* 16PAHs: naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, bene[a]anthracene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, indeno[1,2,3-cd]pyrene, dibenz[a,h]anthracene, benzo[ghi]perylene

in dredged materials from inner bar, although the Myanmar government has not stipulated sediment quality criteria in dredged materials yet. Table 3 expresses the comparison of sediment quality standards including the average concentration values in heavy metals at inner bar area. Figure 2 shows that the average values of particle size distribution for all samples were 14% of sand (>75 µm), 32% of silt (32–75 µm), and 54% of clay (92% for both RT and RC in Phases 1 and 2. Its removal efficiency was improved and almost stable in Phase 2, at about 94%. Total nitrogen (TN) removal was also high in both Phases 1 and 2, which was almost similar in Phase 1, about 65% for RT and RC, then increased in Phase 2 to a stable value of 71.5%. Incident light intensity and change

Fig. 2 DOC and nutrients removals by AB-AGS in RT and RC throughout the 3 phases’ operation

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in upflow air velocity did not seem to have a great impact on TN removal even though the removal rates were more stable in Phase 2 when the upflow velocity was controlled at 0.5 cm/s. In Phase 3, however, the removal rates decreased drastically, although they recovered to about 50% when the HRT was increased to 24 h. A relatively higher TN removal was observed in the study, which might be attributed to the high nitrogen removal capacity of algae, even under no aeration condition (Zhao et al. 2019; Nambiar and Bokil 1981). Figure 2 also shows the phosphorous (P) removal efficiencies by the two reactors throughout the experimental period. As it can be seen, RT always exhibited a slightly higher P removal efficiency than RC during the whole experiment. In Phase 1, the average removal efficiency was 34.9% in RT, which was 30.4% in RC. During Phase 2, the average P removal rate was further improved in RT, up to 36.9%, and the difference in P removal efficiency became more obvious as the average removal efficiency remained at 30.6% in RC. Previous work has shown that aeration rate doesn’t have significant impact on P removal efficiency (Wang et al. 2015), which is mainly attributed to abiotic precipitation and biotic uptake of phosphorous by organisms (Nurdogan and Oswald 1995). Algae are capable of absorbing and assimilating large quantities of phosphorous (Solovchenko et al. 2016). Algae also seem to have a positive impact on the activity of polyphosphate accumulating organisms (PAOs). Thus, the slight increase in phosphorous removal in Phase 2 might be contributed by the increased growth of algae as indicated by the Chl a concentration. In Phase 3, there seems to be a decrease in P removal efficiency, even though a small improvement was observed when the HRT was prolonged to 24 h. The promotion of P uptake by PAOs requires alternative anaerobic and aerobic periods. Under the anaerobic/anoxic conditions, PAOs can uptake organic substances, while the bacteria release P into the bulk liquor. During the aerobic period, the organic substrate can be converted to cellular matter and energy; and PAOs may take up P from the bulk liquor to fulfil their nutrients requirement. Thus, in Phase 3, possibly due to the lack of an aerobic period and probably due to insufficient production of oxygen by the algae in AB-AGS, P removal rate was affected to a great extent. The relationship between upflow air velocity and airflow rate. In the context of this study, the upflow air velocity is defined as the velocity with which air rises in the fluid medium, i.e., water. The upflow air velocity depends on the cross-sectional area of the reactor. Airflow rate is the measurement of the amount of air per unit of time that flows through a particular device. Airflow rate divided by the cross-sectional area gives the upflow air velocity. Due to the different cross-sectional areas of the RT and RC, the upflow air velocity was kept constant for both reactors in the individual phase, leading to different air flow rates in the two reactors. It was noted that due to the virtue of its geometry, RT requires an air flow rate that is 3.25 times lower than RC at both upflow velocities. It was also noticed that even under much lower aeration rates, the performance of RT was almost similar to RC. Thus, RT reduces the aeration requirement due to its reactor design.

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4 Conclusions This study investigated and compared the performance and stability of AB-AGS in tubular and column batch reactors under decreasing upflow air velocities from 1 to 0 cm/s. The tubular reactor system was successfully established, which showed similar performance to the column reactor even though the tubular reactor was operated at 3.25 times lower aeration and had a lower biomass concentration in the reactor as compared to the column reactor. Higher Chl a concentration was detected in RT, suggesting more algae growth in RT as compared to RC. Stable granule properties were obtained in RT and RC. Both RT and RC showed similar and excellent organic and nitrogen removal rates during Phases 1 and 2, while RT showed better P removal most probably due to its enhanced algal growth. During Phase 3, under no aeration condition, some decrease in stability and performance was noticed in both reactors. When the HRT was prolonged from 16 to 24 h, the nutrients removal efficiency of both reactors seemed to recover to some extent. After comparing the performance of RT and RC during the different phases, it was found that AB-AGS could maintain its best performance and stability in Phase 2 when the upflow air velocity was controlled at 0.5 cm/s for the two reactors. Thus, the tubular reactor can be a viable versatile reactor design for low aeration and efficient wastewater treatment process due to its almost similar nutrients removal (and even better P removal) efficiency as compared to the traditional column reactor at reduced air flow rates.

References Abouhend AS, McNair A, Kuo-Dahab WC, Watt C, Butler CS, Milferstedt K, Hamelein J, Seo J, Gikonyo GJ, El-Moselhy KM, Park C (2018) The oxygenic photo granule process for aeration free wastewater treatment. Environ Sci Technol 52:3503–3511 Adav SS, Lee DJ (2008) Extraction of extracellular polymeric substances from aerobic granule with compact interior structure. J Hazard Mater 154:1120–1126 APHA (2012) Standard methods for the examination of water and wastewater. American Public Health Association/American Water Work Association/Water Environment Federation, Washington DC Boelee NC, Temmink H, Janssen M, Buisman CJN, Wijffels RH (2014) Balancing the organic load and the light supply in symbiotic microalgal-bacterial biofilm reactors treating synthetic municipal wastewater. Ecol Eng 64:213–221 Brenner K, You L, Arnold FH (2008) Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol 26:483–489 de Bruin LMM, de Kreuk MK, van der Roest HFR, Uijterlinde C, van Loosdrecht MCM (2004) Aerobic granular sludge technology: an alternative to activated sludge? Water Sci Technol 49:1–7 Franca RDG, Pinheiro HM, van Loosdrecht MCM, Lourenco ND (2018) Stability of aerobic granules during long-term bioreactor operation. Biotechnol Adv 36:228–246 Ghangrekar MM, Asolekar SR, Joshi SG (2005) Characteristics of sludge developed under different loading conditions during UASB reactor start-up and granulation. Water Res 39:1123–1133 He Q, Chen L, Zhang S, Chen R, Wang H, Zhang W, Song J (2018) Natural sunlight induced rapid formation of water-born algal-bacterial granules in an aerobic bacterial granular photo-sequencing batch reactor. J Hazard Mater 359:222–230

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Hudayah N, Suraraksa B, Chaiprasert P (2019) Impact of EPS and chitosan on enhancement of anaerobic granule quality during simultaneous microbial adaptation and granulation. J Chem Technol Biotechnol 94(11):3725–3735 Li X, Peng Y, Ren N, Li B, Chai T, Zhang L (2014) Effect of temperature on short chain fatty acids (SCFAs) accumulation and microbiological transformation in sludge. Water Res 41:34–45 Miao L, Wang S, Cao T, Peng Y, Zhang M, Liu Z (2016) Advanced nitrogen removal from landfill leachate via Anammox system based on sequencing biofilm batch reactors (SBBR): effective protection of biofilm. Biores Technol 220:8–16 Nambiar KR, Bokil SD (1981) Luxury uptake of nitrogen in flocculating algal-bacterial system. Water Res 15:667–669 Nurdogan Y, Oswald WJ (1995) Enhanced nutrient removal in high-rate ponds. Water Sci Technol 31:33–43 Solovchenko A, Verschoor AM, Jablonowski ND, Nedbal L (2016) Phosphorus from wastewater to crops: an alternative path involving microalgae. Adv Biotechnol 34(5):550–564 Tang CC, Zuo W, Tian Y, Sun N, Wang YW, Zhang J (2016) Effect of aeration rate on the performance and stability of algal-bacterial symbiosis system to treat domestic wastewater in sequencing batch reactors. Biores Technol 222:156–164 Tay JH, Liu QS, Liu Y (2004) The effect of upflow air velocity on the structure of aerobic granules cultivated in a sequencing batch reactor. Water Sci Technol 49(11–12):35–40 Wang X, Tian Y, Zhao X, Peng S, Wu Q, Yan L (2015) Effects of aeration position on organics, nitrogen and phosphorus removal in combined oxidation pond-constructed wetland systems. Biores Technol 198:7–15 Xu L, Weathers PJ, Xiong XR, Liu CZ (2009) Microalgal bioreactors: challenges and opportunities. Eng Life Sci 9(3):178–189 Zhao Z, Liu S, Yang X, Lei Z, Shimizu K, Zhang Z, Lee DJ, Adachi Y (2019) Stability and performance of algal-bacterial granular sludge in shaking photo-sequencing batch reactors with special focus on phosphorous accumulation. Biores Technol 280:497–501

Spatial Variation of Heavy Metals Contamination in Soil at E-waste Dismantling Site, Buriram Province, Thailand Nisakorn Amphalop, Tassanee Prueksasit, and Mongkolchai Assawadithalerd Abstract The uncontrolled informal e-waste dismantling activities in the rural areas of Buriram province, Thailand, have posed the heavy metal emissions and contaminated in the soils. Spatial variation of heavy metals contamination in soil at the e-waste dismantling site in Daeng Yai subdistrict, Ban Mai Chaiphot district, Buriram province, was then investigated in April 2019. The surface soil was taken from e-waste and non-e-waste dismantling houses, open dumping and burning site, and reference site located 5 km away from the e-waste dismantling site. The heavy metals (As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn) in the samples were extracted using acid digestion and analyzed by ICP-OES. The mean values of all heavy metals, except As, were highest in the burning site where the open-burning of wires and dumping of unwanted electronic materials are carried out, followed by those in e-waste and non-e-waste dismantling houses. The concentration of all heavy metals in the burning site was significantly higher than that found in the reference area (p < 0.05). The concentration of all heavy metals were still compliance with Thai standard for residential and agricultural soils; however, Cu concentration (1,117.87 ± 2.09 mg/kg soil) at the burning site exceeded the Intervention Values of Netherlands (190 mg/kg soil). The results suggest that informal e-waste dismantling activities could lead to the contamination of heavy metals in soil. Therefore, a good manner of the environmental management system should be recommended for protecting the soil pollution from e-waste dismantling activities in the long run. N. Amphalop Hazardous Substance and Environmental Management (IP-HSM) Graduated School, Chulalongkorn University, Bangkok 10330, Thailand T. Prueksasit (B) Department of Environmental Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand e-mail: [email protected] Research Program of Municipal Solid Waste and Hazardous Waste Management, Center of Excellence on Hazardous Substance Management, Bangkok 10330, Thailand M. Assawadithalerd Center of Excellence on Hazardous Substance Management (HSM), Chulalongkorn University, Bangkok 10330, Thailand © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_30

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Keywords E-waste · Soil contamination · Heavy metals

1 Introduction E-waste generation continues to be a global concern since the rapidly growing of technological advancement in electronic equipment has resulted in more people replacing their old equipment with new ones. Furthermore, the decreasing costs of electronic equipment have led to more purchases (Leung 2019). In Thailand, e-waste was growing rapidly with an increasing rate of at least 7–8% between 2012 and 2015 (Withayaanumas 2019). By 2020, e-waste generation of computers has been forecasted to reach approximately 7.5 million units, while industrial e-waste has been estimated to be around 11,000 tons per year (Manomaivibool 2011). In addition, the amount of 13.42 million mobile phones, and 3.65 million portable audio players have been expected to be found in 2021 (Withayaanumas 2019). Few amount of e-waste in Thailand is managed and recycled properly, but a large proportion of them are disposed and transported to family-run e-waste recycling/dismantling workshops where household areas are used to store and dismantle e-waste in order to obtain precious metals and valuable materials for sale. The techniques used to dismantle and recycle e-waste in the workshops are carried out by primitive methods. These include physical disassembly of e-waste like smashing, cutting, breaking, open-burning of wire cables to recover copper, splitting air-conditioner compressors to extract copper and ferrous, and open-burning of abandoned components. After sorting, the precious materials are sold, while the unwanted and residue waste is discarded in the open dump site of municipal solid waste (Thongkaow 2017; Vassanadumrongdee 2017). Electronic equipment contains valuable materials such as Pt, Cu, Au, Fe, Al, plastic, and glass which can be separated and sold as secondary raw materials. Meanwhile, various toxic metals like Pb, Cu, Cd, Cr and Hg are also contained in the equipment. The uncontrolled recycling and informal dismantling activities have caused the release of the metal contaminating in soils at e-waste dismantling sites, e-waste burning sites and also in surrounding e-waste sites including pond areas, agricultural lands, and vegetable gardens via atmospheric deposition, and runoff (Jun-hui 2009; Luo 2011; Quan 2015; Tang 2010). In the past several years, there have been e-waste dismantling activities in Daeng Yai subdistrict, Buriram Province, Thailand. The e-waste dismantling activities are normally operated at household areas and dump sites. The activities carried out at the household area consist of smashing and separating of electric motors, printed circuit boards contained in washing machines, refrigerators and air-conditioners in order to get copper and steel. The burning of wires and breaking of cathode ray tube (CRT) screens aiming to recover copper, steel, and aluminum are implemented at the dump sites, where the glass, insulation foams, and other unwanted materials are dumped on land. As e-waste houses are sparsely located in the villages, and dump sites are located in the field nearby the land served for agricultural activities,

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heavy metals from incineration and dismantling of e-waste could enter the soils that is served for the agricultural purposes including rices. Eventually, the heavy metal could enter into food chains and into ecosystems. As a result, information on heavy metal contaminations is necessary to be investigated in the e-waste sites. Up to now, there has been no study conducting on the heavy metal contamination in the Daeng Yai e-waste dismantling sites. This study is a preliminary investigation that aims (1) to investigate the total concentrations of heavy metals in soils at the e-waste site, and (2) to evaluate the potential ecological risk of heavy metals posed to the e-waste site.

2 Materials and Methods 2.1 Study Area The study area is in the e-waste dismantling village in Daeng Yai subdistrict Buriram Province, Thailand. There have been 105 households involved in uncontrolled/unauthorized e-waste handling for more than ten years. E-waste dismantling activities, like manually dismantling of e-waste to obtain valuable metals and dumping of unwanted electrical parts in backyards, are performed in the villages where e-waste and non-e-waste dismantling house are located. In addition, the open burning site, where the burning of e-waste has been carried out, is located among paddy fields in the village, and electronic debris is also disposed in the open burning site. The reference site is located in northwestern of the study area over 5 km distance where e-waste dismantling activity has not been carried out. Thus, there were four different sampling sites in the study (e-waste dismantling house, non-e-waste dismantling house, open burning site, and reference site) (see Fig. 1).

Fig. 1 The location of the study area and the sampling sites

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Table 1 Description of sampling points Sampling sites

Description

GPS coordinates

E-waste dismantling house

This site is the house served as a storage area of e-waste; physical dismantling of e-waste is operated to obtain the valuable metals, and unwanted materials of e-waste are dumped in household areas

15° 34 47.27 N 102° 53 28.98 E

Non-e-waste dismantling house

This site is the house of non-e-waste dismantling people which located among the e-waste dismantling houses

15° 34 49.94 N 102° 53 21.18 E

Open-burning site

Open burning of wires is performed to recover copper, and the sites serve for disposal of unwanted parts and CRT screens

15° 34 48.59 N 102° 52 47.06 E

Reference site

This site approximately 5 km away from the village

15° 36 26.54 N 102° 51 44.73 E

2.2 Soil Sampling For each sampling site, the surface of soils was cleaned to remove debris and large materials, and then a 2 m × 2 m quadrat was made. Three sub-sample soils in the depth intervals of 0–15 cm. were collected diagonally by a shovel and then mixed to provide a kilogram of composite soil, and were kept in clean polyethylene bags. To avoid cross contamination, the shovel was carefully cleaned with deionized water (DI water), and then make it dry by dried wipes prior to sampling study soils. The soil sampling was conducted in the dry season (April, 2019). The geo-referenced coordinate of sampling location coordinates recorded using a handheld GPS as shown in Table 1.

2.3 Soil Analysis Soil preparation Large materials and stones in the soil samples were removed. The soil samples were air-dried at room temperature, and grounded with a porcelain mortar and pestle. The grounded soil was passed through 2 mm mesh sieve, and then collected in a polyethylene bag in a desiccator for further analysis. Total heavy metal analysis The glassware used in the analysis were soaked by a 10% HNO3 acid solution before use, and then rinsed with DI water. 0.5 g of the sample was transferred into a microwave vessel and then 12 mL of aqua regia (9 mL of 37% HCl: 3 mL of 65% HNO3 ) was added following U.S. EPA method 3050b (U.S. EPA 1996). The digested samples were filtered through a filter paper No. 42 with

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2.5 µm pore size into 25 mL-flasks and were adjusted the volume by DI water and stored in polyethylene bottles prior to heavy metal analysis. The digested samples were analyzed for As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The soil samples were analyzed in triplicate for quality control. The blank determination was carried out without soil samples. The final concentrations of the heavy metals in soil samples were calculated using Eq. 1. Heavy metal in soils (mg/kg soil) = (A × B)/(1000 × C)

(1)

where A is the heavy metal concentration measured from ICP-OES (µg/L), B is the final volume of 25 mL (mL), and C is the soil weight (g).

2.4 Data Analysis Potential ecological risk assessment To exhibit the potential risk as a result of the heavy metals in the soil, the potential ecological risk index (PERI) was applied in this study. This index is used to represent the sensitivity of the biological community to the heavy metal in the soil and express the potential ecological risk affected by the heavy metal contamination (Hakanson 1980). The ecological risk factors (Er) of each heavy metal, and the PERI, which is a summation of Er, can be calculated following Eqs. 2, and 3, respectively.   Er = Tr × Csample /Cbackground

(2)

PERI = Er1 + Er2 + · · · + Ern

(3)

where Tr is the biological toxic response factor of each metal (As = 10, Cd = 30, Cu = Ni = Pb = 5, Cr = 2, and Mn = 1, and Zn = 1) (Hakanson 1980), Csample is the heavy metal amount in the soil (mg/kg), and Cbackground is the heavy metal concentration in the reference area (mg/kg). The calculation of Er and PERI posed by the heavy metals can be categorized and summarized the risk levels as presented in Table 2. Table 2 Potential ecological risk levels

Er

PERI

Ecological risk levels

Er < 40

PERI < 150

Low

40 ≤ Er < 80

150 ≤ PERI < 300

Moderate

80 ≤ Er < 160

300 ≤ PERI < 600

Considerable

160 ≤ Er < 320

PERI > 600

Very high

Er > 320

–

Extreme

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Statistical analysis The mean of each total heavy metal concentration among different sites was statistically compared using one-way ANOVA by Tukey HSD method. The mean of heavy metal concentration of the reference site and the other sampling site were compared by t-test. SPSS (version 23) software for Windows was adopted for statistical analysis at p < 0.05.

3 Results and Discussion 3.1 Heavy Metal Concentration in the Study Site The heavy metal concentration in the study area showed a wide range (Table 3). As concentration ranged between 6.03 and 28.92 mg/kg, and those of other were 0.40 and 2.30 mg/kg for Cd, 14.19 and 66.13 mg/kg for Cr, 1.06 and 1117.87 mg/kg for Cu, 39.37 and 399.56 mg/kg for Mn, 4.19 and 17.98 for Ni, 10.56 and 298.11 mg/kg for Pb, and 3.27 and 263.22 mg/kg for Zn. Comparison of heavy metals in the sampling sites to the reference site, all heavy metals in the burning site were significantly higher (p < 0.05) than the heavy metals in the reference site. However, no statistical difference was reported in the concentration of any heavy metal in either non- or e-waste dismantling houses with the reference site. A particular trend of all heavy metals, except As, was found regarding the different sampling sites; all heavy metals was found highest in the open-burning site, the e-waste dismantling site, and none-waste dismantling site, respectively. The results implied the range of heavy metal concentration was affected by the e-waste dismantling activities and e-waste burning activities. With this result, the burning site could be considered as a hotspot since the greatest concentrations of all heavy metals were found in the site. Similarly, the ewaste sites that the incineration of e-waste was operated to obtain valuable metals in China and Nigeria were found to have higher heavy metal concentrations compared with e-waste storage and dismantling sites (Luo 2011; Isimekhai 2017). Comparing the total heavy metal concentrations with guideline values, there was no heavy metal concentration exceeded the Thai standard for residential and agricultural soils, but the concentration of Cu at the burning site (1,117 mg/kg) exceeded the Dutch intervention value. This suggested that remediation in the site should be implemented.

3.2 Heavy Metal Contribution at the Sampling Sites The contribution of heavy metals at different sampling sites was shown in Fig. 2. The results revealed that Mn accounted for the highest percentage of the reference site (46.3%), non-e-waste dismantling house (47.3%), and e-waste dismantling house (41.8%). This indicates that Mn was the main contributor in these sampling sites,

0.40 ± 0.07 2.30 ± 0.03 0.65 ± 0.01

28.92 ± 0.58

11.07 ± 0.23

NA

Open burning

Reference site

Thai standard for residential and agricultural soils (Thailand standard level for residential soil 2074)

29

55

– Optimum value

– Action/intervention value (VROM 2000)

Dutch soil quality standards 12

0.8

37

0.49 ± 0.07

E-waste dismantling house

6.03 ± 0.71

Cd

10.39 ± 0.11

As

Non-e-waste house

Site

380

100

300

29.25 ± 2.74

66.13 ± 1.58

14.19 ± 0.75

24.20 ± 1.92

Cr 1.06 ± 0.11

21.93 ± 1.14

190

36

NA

9.34 ± 1.00

1,117.87 ± 2.09

Cu

Table 3 The heavy metal concentrations (mg/kg) at different sampling sites (mean ± SD)

NA

NA

1800

66.38 ± 4.24

399.56 ± 107.16

39.37 ± 1.88

97.97 ± 7.58

Mn

210

35

1600

6.56 ± 0.13

17.98 ± 2.97

4.19 ± 0.27

5.67 ± 0.18

Ni

530

85

400

14.00 ± 0.07

298.11 ± 20.85

10.56 ± 0.58

20.38 ± 1.76

Pb

720

140

NA

6.15 ± 0.18

263.22 ± 65.95

3.27 ± 0.56

57.56 ± 3.57

Zn

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Fig. 2 Heavy metal contribution at the sampling sites

and also means that Mn was the most abundant heavy metals in the sites. Mn loading in soils generally comes from natural sources, such as the weathering of parent materials. In addition, Mn was found to have the highest concentration in the earth’s upper crust compared to other elements (527 mg/kg of upper crust) (Wuana 2011; Alloway 2012). For the burning site, Cu accounted for 50.9%, indicating that Cu was the main contributor at the burning site. The reason for this result is that while the open-burning of electrical wires is being operated, scarps of the wires that contain a large amount of copper could directly fall onto soil surface, and the smoke and particulate matter containing copper could be released into the atmosphere and eventually fall onto soil surface via an atmospheric deposition (Luo 2011; Pradhan 2014). For this reason, a high concentration of Cu could be highly accumulated in the open-burning site. Cu and Zn contributions were highly found in the e-waste dismantling house and the burning site; this might be caused by Cu and Zn that are normally used for the production of copper wires, CRT screens, and printed circuit boards, which are the most imported products in the study area, so the recycling and dismantling of these types of products could emit the scarps and particulate matters containing Cu and Zn onto the soils (Thongkaow 2017).

3.3 Potential Ecological Risk Assessment Assessment of ecological risk posed by the heavy metals in the study area, Hakanson’s method mentioning the abundance and the toxicity of heavy metals to ecosystem in the study area was adopted. Table 4 shows the individual ecological risk factor of heavy metals (Er) and potential ecological risk index (PERI) in the study site. The Er

5.45

26.11

E-waste dismantling house

Open burning

9.38

As

106.47

22.82

18.49

Cd

Individual risk factor (Er)

Non-e-waste dismantling house

Site

Table 4 Potential ecological risk at the study site Cr

4.52

1.65

0.97 598.28

11.74

0.57

Cu

Mn

6.02

1.48

0.59

13.71

4.32

3.19

Ni

106.50

7.28

3.77

Pb

42.80

9.36

0.53

Zn

904.41

64.09

37.49

PERI

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of all heavy metals in both non- and e-waste dismantling houses, and the Er of As, Cr, Mn, and Ni in the open burning site are lower than 40, suggesting that the metals have posed a low potential ecological risk. Conversely, for the burning site, Zn with Er of 42.80 has caused a moderate potential ecological risk to the ecosystem; Cd and Pb have posed considerable potential ecological risk in the burning area with the Er of 106.47 and 106.50, respectively. In addition, Cu in the burning site has Er of 598.28, indicating that Cu could pose an extreme potential ecological risk into the ecosystem at the burning site. Therefore, the Cu in the site is of great concern, and Cu should be remediated and controlled due to its ecological hazard. Comparison of Er among the sites, the burning site showed a higher value of Er in all metals, meaning that the burning site was the most concerned site in the study area because of the ecological hazard resulting from all metals released from the open-burning of wires and the dumping of the unwanted parts of e-waste. The PERI was the summation of pollution from various metals. The PERI in nonand e-waste dismantling houses were less than 150, which can be concluded that the non- and e-waste dismantling houses had low potential ecological risk. On the other hand, the PERI in the burning site was 598.28, which was lower than 600, suggesting that a considerable potential ecological risk was found in the site. In the burning site, the PERI was mainly contributable to the highest Er of Cu, Cd and Pb. The results were consistent with the activities carried out at the burning site, which mainly were the smashing of CRT screens, and the burning of electric wires. These activities release particulate matters containing metalloids and many kinds of heavy metals, especially Cu emitted from the wires, and Cd and Pb released from the CRT screens, into the air. The heavy metal-containing particles are then deposited onto the surface of soils via the atmospheric deposition causing heavy metal accumulations in the soils (Luo 2011; Oguri 2018). Therefore, the burning of e-waste in the study area could be noted to affect the higher ecological risk from various kinds of heavy metals.

4 Conclusions and Recommendations The release of heavy metals into soils at the study area has been caused by informal e-waste recycling and dismantling activities. The heavy metal concentrations in the e-waste dismantling site were greater than those in the non-e-waste dismantling site. Comparing to the regulatory guidelines, the concentration of Cu at the burning site exceeded the Dutch intervention value. The heavy metal contribution revealed that Mn can be naturally found in the study area, while Cu, and Zn are the main contributors in the e-waste dismantling house and the e-waste burning site. The ecological risk assessment reported that Cu posed an extreme potential ecological risk to the soil of the burning site because of the highest total concentration detected. Moreover, a soil remediation action, together with appropriate practices of e-waste recycling, should be implemented to control the contamination of heavy metals in the study area, especially for Cu.

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Acknowledgements The study was financially supported by National Research Council of Thailand (NRCT) under Thailand Research Challenge Program for WEEE and Hazardous Waste. The publication of this article was partially supported by the Research Program of Municipal Solid Waste and Hazardous Waste Management, Center of Excellence on Hazardous Substance Management (HSM), the S&T Postgraduate Education and Research Development Office (PERDO), the Office of Higher Education Commission (OHEC). The authors would like to acknowledge Associate Professor Apichat Imyim, Department of Chemistry, Chulalongkorn University, for helping with the heavy metal analysis by ICP-OES.

References Alloway BJ (2012) Heavy metals in soils: trace metals and metalloids in soils and their bioavailability, 3rd edn. Springer Science & Business Media, Dordrecht Hakanson L (1980) An ecological risk index for aquatic pollution control. A sedimentological approach. Water Res 14(8):975–1001 Isimekhai KA (2017) Heavy metals distribution and risk assessment in soil from an informal E-waste recycling site in Lagos State, Nigeria. Environ Sci Pollut Res 24(20):17206–17219 Jun-hui Z (2009) Eco-toxicity and metal contamination of paddy soil in an e-wastes recycling area. J Hazard Mater 165(1):744–750 Leung A (2019) Chapter 15—environmental contamination and health effects due to e-waste recycling. Butterworth-Heinemann, Oxford Luo C (2011) Heavy metal contamination in soils and vegetables near an e-waste processing site, South China. J Hazard Mater 186(1):481–490 Manomaivibool P (2011) Extended producer responsibility in Thailand. J Ind Ecol 15(2):185–205 Oguri T (2018) Exposure assessment of heavy metals in an e-waste processing area in northern Vietnam. Sci Total Environ 621:1115–1123 Pradhan JK (2014) Informal e-waste recycling: environmental risk assessment of heavy metal contamination in Mandoli industrial area, Delhi, India. Environ Sci Pollut Res 21(13):7913–7928 Quan SX (2015) Spatial distribution of heavy metal contamination in soils near a primitive e-waste recycling site. Environ Sci Pollut Res 22(2):1290–1298 Tang X (2010) Heavy metal and persistent organic compound contamination in soil from Wenling: an emerging e-waste recycling city in Taizhou area, China. J Hazard Mater 173(1):653–660 Thailand standard level for residential soil. http://www.onep.go.th/topics/20748/. Last accessed 2018/05/13 Thongkaow P (2017) Material flow of informal electronic waste dismantling in rural area of northeastern Thiland. In: 139th the IIER international conference, Osaka, Japan U.S. EPA (1996) Method 3050B: acid digestion of sediments, sludges, and soils. Environmental Protection Agency, Washington Vassanadumrongdee S (2017) Heavy metal contamination in soil in e-waste recycling area in Thailand. http://www.eric.chula.ac.th/download/ew58/ew_pocd.pdf. Last accessed 2017/11/16 VROM (2000) The circular on target values and intervention values for soil remediation. Ministry of Housing, Spatial Planning and the Environment, The Netherlands Withayaanumas S (2019) E-waste management in Thailand. https://tdri.or.th/wpcontent/uploads/ 2018/04/wb133.pdf. Last accessed 2019/06/6 Wuana RA (2011) Heavy metals in contaminated soils: a review of sources, chemistry, risks and best available strategies for remediation. ISRN Ecol 20

Research on the Influence Factors of Degradation of Pyrimidine with Anaerobic Bacteria Dexin Lin, Yong Wang, Dexin Wang, Fei Yang, Li-ping Sun, and Xuesong Yi

Abstract Anaerobic activated sludge was got from Hybrid Loop Anaerobic Baffled Reactor (HLABR), and then pH, alkaline, concentration of pyrimidine, several ions such as Ca2+ , Cu2+ , Fe2+ were studied as influence factors in static experiment within 24 days. The results were shown as follows: the removal rate of pyrimidine could reach 60% during the range of pH 5.5–9.0 and the pyrimidine concentration of 200– 2000 mg/L; pyrimidine couldn’t be degraded when alkaline over 2000 mg/L; Ca2+ had no obvious effect on sludge activation, Cu2+ leaded to low sludge activation at the concentration of 10 mmol/L; Fe2+ coexisted facilitated the removal rate by microbe degradation and the removal rate reached 97.2% at the concentration of 10 mmol/L. Keywords Pyrimidine · Biodegradation · Anaerobic bacteria · Ion · Influence factors

1 Introduction Acetyl-pyrimidine (2-methyl-4-amino-5-acetylamidemethyl-pyrimidine, pyrimidine for shot in this paper) is used as an important intermediate for the synthesis of pharmaceutical, it has a extensive application in the production of varieties of antibiotics and bacteriophages. However, due to its long duration and difficulties in degradation in the environment, this industry has nearly been shut down in other countries and moved mainly to China as a result. So China’s production of pyrimidine accounts for the world’s total output more than 80% (Feng and Sun 2005). Because of D. Lin · D. Wang · F. Yang · X. Yi (B) School of Environmental Science and Engineering, Hainan University, 570228 Haikou, China e-mail: [email protected] Y. Wang State Key Laboratory Breeding Base of Marine Genetic Resources, Third Institute of Oceanography, SOA, 361005 Xiamen, China L. Sun Department of Municipal and Environmental Engineering, Tianjin Chengjian University, 300384 Tianjin, China © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_31

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that, this industry has done great damage to China’s ecological environment affected people’s physical and mental health seriously. Pyrimidine has a ring structure with a stable nature and it’s difficult to be removed from water by traditional wastewater treatment processes, what’s more, it also can not be degraded by common enzymes. Chinese scholars Feng Lei-yu and Sun Li-ping et al. used coagulation-sedimentation, chemical oxidation processes for treatment of this wastewater. Though these methods were effective, the cost was very high and secondary pollution could emerge easily. They just transferred the contaminations from one phase to another without eliminating them from environment forever. Therefore, using microbe to eliminate the pyrimidine from the environment will be the main trend (López-Vinyallonga 2010; Yi et al. 2012; Sun et al. 2014). The preliminary studies of our laboratory indicated that the pyrimidine was not biodegraded easily in aerobic circumstance, but under the anaerobic condition it could be biodegraded by the hydrolytic acidification bacterium though the degradation rate was slow. This study investigated the factors of anaerobic sludge in the stable-running HLABR reactor with a view to have a clear understanding of the anaerobic degradation of pyrimidine, provide a scientific basis for the anaerobic biodegradation research of pyrimidine.

2 Materials and Methods 2.1 Experimental Apparatus and Reagents Main reagents: 2-methyl-4-amino-5-acetylamidemethyl-pyrimidine (purity > 95%), H2 SO4 , NaOH, NaHCO3 , FeSO4 , etc. All the chemicals were analytical grade. Main apparatus: Multi-340iPH meter; UV2550 UV-Vis Spectrophotometer; High Performance Liquid Chromatography (Agilent1100) with automatic injector, using C-18 reverse column.

2.2 Experimental Procedure A certain concentration of pyrimidine solution was prepared and put into the anaerobic reactor bottles supplementing with the right amount of nutrients and trace elements. The pH value was adjusted to the required range using NaOH or H2 SO4 . After 24 days of shaking culture at Constant temperature of 33 °C, the remaining concentration of pyrimidine in the water samples was measured, and the removal rate was also be examined.

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2.3 Measurement of Pyrimidine A certain volume of samples were sacrificed. After high speed centrifugation (12,000 r/min, 10 min), the supernates were injected into the HPLC (Agilent 1100) to measure the concentration of pyrimidine. Methanol water mixture (75:25, v/v) was used as the mobile phase at a flow rate of 1 mL/min. The injection volume for all the samples was 20 microliters and the wavelength for the UV detector was 232 nm, and the retention time was about 5 min (Li et al. 2016). Chose C-18 reverse column as separate column.

3 Results and Discussion 3.1 The Effect of PH Value on the Anaerobic Biodegradation of Pyrimidine The conditions of the experiment were controlled as follows: the reactors were filled with pyrimidine solution of which the concentration was 1000 mg/L and the right amount of nutrients and trace elements. The pH was controlled at 1.0, 3.0, 5.5, 7.0, 9.0 and 11.0 respectively using H2 SO4 and NaOH. The temperature was kept at 33 °C, after 24 days of shaking culture, the concentration of pyrimidine was measured and the removal rate was also investigated. The experimental results were shown in Fig. 1. From Fig. 1 we could see that the pH value had a great influence on the removal of pyrimidine. The removal rate was relatively low when pH < 5.5. It was just 1.3% when pH = 1.0. When the pH was adjust to the range of 5.5–11.0, the removal rate could be kept more than 60%. When pH = 9.0, the highest removal rate could be acquired and it was 88.23%. These indicated that under the condition of overacid, the charges in the membrane could be changed easily. And this would inhibit the acidification of the hydrolytic acidification bacterium,influencing the activity of enzyme in the process of metabolism. In addition, pyrimidine molecule changed

Fig. 1 The effect of pH value on the anaerobic biodegradation of pyrimidine

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its existing space configuration and structure under the acid condition, resulting in an increase in its toxicity and a change in microbe’s living circumstance (Cutler and Zimmerman 2011). All of these were the limiting factors of the low removal efficiency of pyrimidine.

3.2 Effect of Alkalinity on the Anaerobic Biodegradation of Pyrimidine According to the experimental methods of 1.2, the concentration of influent pyrimidine was fixed at 1000 mg/L, pH was adjusted to 7.0. The alkali concentrations were 0, 500, 1000, 2000, 3000, 5000 mg/L (in CaCO3 ) respectively. The static experiment was conducted in the constant temperature incubator (33 °C) for 24 days. The results were shown in Fig. 2. As could be seen from Fig. 2, alkalinity had a great effect on the removal of pyrimidine. If the alkali concentration was too high, the degradation of pyrimidine would be inhibited. After 24 days’ culture and the alkali concentrations were 2000, 3000, 5000 mg/L respectively, the rate of pyrimidine degradation were 16.2%, 14.1%, 3.8% respectively. This result showed that the pyrimidine was nearly not degraded. The reason accounting for it was that high alkali concentration had inhibiting effect on the activity of biological enzyme. When the alkalinity was below 1000 mg/L, the removal rate of pyrimidine was more than 80%.

Fig. 2 Effect of alkalinity on the anaerobic biodegradation of pyrimidine

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Fig. 3 Effect of concentration of pyrimidine on its anaerobic biodegradation

3.3 Effect of Concentration of Pyrimidine on Its Anaerobic Biodegradation Figure 3 showed that the results of degradation after 24 days static culture in the constant temperature incubator. The pH value was 7.0, the alkali concentration was 500 mg/L (in CaCO3 ), and the concentrations of pyrimidine were 200, 500, 1000, 2000, 4000, 10000 mg/L respectively. From Fig. 3 we could know that as long as the concentration of pyrimidine was less than 10000 mg/L, when the concentration of pyrimidine increased from 200 to 10,000 mg/L, the biodegradation rate would drop from 100 to 55%. However, the net removal volume increased indicating that if the circumstance was appropriate, the relatively stable pyrimidine-degrading enzyme system would be formed in the microorganisms after a long period of domestication and inducement, and this enzyme system could easily attack the sensitive bonds in pyrimidine and break them down. It also indicated that the biological toxicity of pyrimidine was weak, so there was little negative influence on the microorganisms. Therefore, when increased the concentration of pyrimidine, microorganisms remained a relatively high treatment capacity and efficiency.

3.4 Effect of Metal Ions on the Anaerobic Biodegradation of Pyrimidine This section of experiment was conducted mainly to investigate the influence of Ca2+ , Cu2+ , Fe2+ on the bacterium which could degrade pyrimidine. The reactor bottles were filled with pyrimidine solution of 1000 mg/L and trace elements of 1 mL. The pH was adjusted to 7.0. Adding three above ions with concentrations of 0, 0.01, 0.1, 1, 10 mmol/L respectively to the reactors. The temperature was kept at 33 °C, after

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Fig. 4 Effect of metal ions on the anaerobic biodegradation of pyrimidine

24 days of shaking culture, the concentration of pyrimidine and its removal rate were examined. The changes of sludge activity before and after culture were investigated at the same time. The experimental results were shown in Fig. 4. The influences of different metal ions on the pyrimidine biodegradation were shown in figure and the conclusions were as follows: 1. When Ca2+ worked alone, the removal rate of pyrimidine did not change obviously along with the increase of its concentration, and was always in a state of relative stability (about 80%). Through analysis, the reasons might be: When the concentration of Ca2+ < 0.1 mmol/L, it could promote the growth of microorganisms, and degradation capacity would drop slightly. At the same time, as the results of May 16 showed (did not provide in this paper), even if the concentration of Ca2+ was relatively high, the inoculation sludge did not trend to be inorganic obviously, the VSS/SS was stable at around 0.75. This indicated the concentration of Ca2+ had no obvious influence on the biological degradation of pyrimidine. 2. When Cu2 + worked alone, as the concentration of Cu2 + increased, there was a slight decrease in the removal rate of pyrimidine. In addition, considering the datum of May 16, VSS/MLSS decreased from the initial 0.72 to 0.45. This showed that when the concentration of Cu2 + was too high, it could enter into cells, combine with–SH of enzyme and this would make enzyme loss its activity (Guo et al. 2012), thus micro-organisms would be inhibited or even killed, causing the activity of sludge decreased (El-Baradie et al. 2014; Li et al. 2015). The removal rate of pyrimidine did not significantly lower, which might due to the adsorption of biological debris.

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3. When Fe2 + worked alone, with the increase of Fe2 + concentration, the removal rate of pyrimidine showed a trend of increased first and then decreased, and when the concentration was 10 mmol/L, the removal of pyrimidine was as high as 97.2%. Through analysis, the reason accounting for this phenomenon was: Fe2+ could promote the growth of microorganisms when the concentration was low; however when the concentration of Fe2+ increased, microorganisms were induced in the environment, the gradual strain variation occurred and this could promote their growth and enhance the content of iron in the microorganisms without changing its biodegradation capacity. Iron had a certain role in coagulation, thus the removal of the pyrimidine increased correspondingly.

4 Conclusions The high alkali concentration had negative influence on the degradation of pyrimidine. The removal rate of pyrimidine was more than 60% when the pH value was in the range of 5.5–9.0. When the alkalinity exceeded 2000 mg/L, pyrimidine was nearly not degraded. When the concentration of pyrimidine was between 200 and 2000 mg/L, its removal rate could all exceed 60%. The effects of some common trace heavy metal elements on the anaerobic degradation of pyrimidine were as follows: Ca2+ had no obvious effect on the sludge activation. When the concentration of Cu2+ was too high, the activity of sludge would decrease because of the microbial protein denaturation. The low concentration of Fe2+ could promote the growth of microorganisms. When increased Fe2+ concentration, microorganism would have a positive mutation resulting in that the amount of iron in the organism increased without changing its biodegradation capacity. Because iron had a certain role in coagulation, the removal rate of pyrimidine would increase. When the concentration of Fe2+ was 10 mmol/L, the removal rate was up to 97.2%. Acknowledgements The authors gratefully acknowledge the financial support provided by National Natural Science Foundation of China (Grant No. 51968017), Hainan University (Grant No. KYQD (ZR)1846); We also appreciate the editors’ valuable comments very much, which are helpful to improve the quality of our present study.

References Cutler TD, Zimmerman JJ (2011) Ultraviolet irradiation and the mechanisms underlying its inactivation of infectious agents. Anim Health Res Rev 12(1):15–23 El-Baradie K, El-Sharkawy R, El-Ghamry H (2014) Synthesis and characterization of Cu(II), Co(II) and Ni(II) complexes of a number of sulfadrug azodyes and their application for wastewater treatment. Spectrochim Acta Part A Mol Biomol Spectrosc 121(5):180–185 Feng LY, Sun LP (2005) Oxidation of acetyl-pyrimidine wastewater by Fenton process. China Water Wastewater 21(12):41–43

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Guo C, Xu J, Zhang Y (2012) Hierarchical mesoporous TiO2 microspheres for the enhanced photocatalytic oxidation of sulfonamides and their mechanism. RSC Adv 2(11):4720–4727 Li M, Zhao F, Sillanpää M (2015) Electrochemical degradation of 2-diethylamino-6-methyl-4hydroxypyrimidine using three-dimensional electrodes reactor with ceramic particle electrodes. Sep Purif Technol 156(2):588–595 Li D, Sharp JO, Drewes JE (2016) Influence of wastewater discharge on the metabolic potential of the microbial community in river sediments. Microbial Ecol 71(1):78–86 López-Vinyallonga S, Arakaki M, Garcia-Jacas N (2010) Isolation and characterization of novel microsatellite markers for Arctium minus (Compositae). https://doi.org/10.1007/s12686-0140342-1 Sun W, Deng Y, Liu J et al (2014) Electrochemical behavior and voltammetric determination of p-methylaminophenol sulfate using LiCoO2 nanosphere modified electrode. Thin Solid Films 564(1):379–383 Yi T, Barr W, Harper WF (2012) Electron density-based transformation of trimethoprim during biological wastewater treatment. Water Sci Technol J Int Assoc Water Pollut Res 65(4):689–696

Spatial-Temporal Changes of Wetland Landscape Patterns in the Eastern Shandong Peninsula Xinmeng Shan, Luyang Wang, Ning Xu, Yamin Lv, Jin Tang, and Jiahong Wen

Abstract Shandong Peninsula has rich wetland resources, and human activities had significant impacts on wetland resources and wetland landscape. Based on the Landsat TM/ETM+/OLI remote sensing images of 1990, 2000, 2010 and 2018, this paper uses GIS spatial analysis and landscape ecology methods to analyze the spatial-temporal characteristics of wetland landscape pattern in the eastern Shandong Peninsula and its driving forces. The results show that: (1) The wetland in the study area decreased slightly by 31.22 km2 (accounting for 2% of the total wetland area) from 1990 to 2018. However, the natural wetlands decreased dramatically while the artificial wetlands increased greatly. Over the past 30 years, up to 37.46% of the natural wetlands was converted to artificial wetlands. Among them, the change of beach wetland and reservoir pit was the most significant, and the transfer probability was 34.82% and 21.26%, respectively. (2) The shape of the wetland landscape was reduced in complexity, and the types of wetland landscape patches tended to be diversified first and then tended to be evenly distributed, and the degree of fragmentation of the wetland landscape was reduced. (3) Population and socio-economic factors are the main factors affecting the change of wetland landscape pattern. Climate factors have a great impact on natural wetlands, especially the changes of river wetland landscape. In addition, policy factors have also accelerated changes in the landscape pattern of the wetlands in the eastern Shandong Peninsula. Keywords Wetland · Landscape pattern · Spatial-temporal change · Driving factor · Shandong Peninsula · China

X. Shan · L. Wang · N. Xu · Y. Lv · J. Tang · J. Wen (B) School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_32

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1 Introduction Wetland is one of the highest biodiversity and productivity ecosystems on the planet, and it is called the three major ecosystems along with forests and oceans. It has important functions and values in terms of material production, energy conversion, and maintaining regional ecological balance (Bai et al. 2005; Jiang et al. 2017). In recent years, due to the rapid growth of population and economic level, humans’ unreasonable over-use of natural resources in wetlands has caused the shrinkage and degradation of wetland areas, and the deterioration of wetland ecosystems, threatening regional ecological security (Zorrilla-Miras et al. 2014; Sun et al. 2018; Feng et al. 2018). Therefore, an in-depth analysis of the spatial-temporal characteristics of wetland landscape change is the scientific basis for understanding the wetland ecological evolution law, wetland ecological risk management, and formulating wetland protection countermeasures. The landscape pattern usually refers to the spatial structural characteristics of the landscape, which is the spatial arrangement and combination of the shape, size and quantity of the landscape (Lei et al. 2016). It is formed under the combined action of biological factors, natural factors, and human interference (Zhang et al. 2017a, b). The research of wetland landscape pattern unifies the spatial characteristics and spatial processes of wetland landscape, which can better reveal the structural composition and spatial allocation relationship of wetland landscape (Yu et al. 2009) and then analyze the dynamic evolution of natural systems and socioeconomic systems in wetland landscape patterns (Wang et al. 2012; Liu et al. 2012; Fan et al. 2014), landscape diversity (Liu et al. 2004), wetland landscape simulation and prediction (Li et al. 2013; Zhang et al. 2014a; Zhao et al. 2017), driving factors that cause the evolution of wetland landscape patterns in wetlands, contribute to the protection and sustainable use of wetland resources. In recent years, the scholars in China have explored aspects such as ecological risk assessment (Xie 2008) and wetland ecosystem service values (Li et al. 2018, 2019a, b), which have provided effective reference for the evolution, simulation and risk management of wetland patterns. At the same time, the driving mechanism of wetland landscape change and the spatiotemporal changes of human influence have been paid more and more attention (Xu and Dong 2013; Zhang et al. 2014b; Xiao et al. 2015; Gong et al. 2016; Lin et al. 2018; Zeng et al. 2015; Yan et al. 2017). The Shandong Peninsula is rich in wetland resources. The eastern region faces the sea with three rivers. It is an important wetland distribution area in Shandong Province. In recent years, the wetland landscape pattern in the eastern region of the Shandong Peninsula has changed significantly (Yan et al. 2017). Previous studies have shown that due to drought (Xu and Han 2018), population growth, economic development and other factors, the area of natural wetlands in the region has gradually shrunk, and artificial wetlands have gradually increased. The coastal tidal flats in the region serve as the boundary between the ocean and the mainland. Marine aquaculture (Wang et al. 2017), coastal tourism and other human activities are frequent. The landscape of wetlands is strongly changed, disturbing the habitats of aquatic and

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aquatic plants, and destroying the balance of the original wetland ecological service functions. At present, the research on the wetland resources of Shandong Peninsula is mainly concentrated in the Yellow River Delta region (Yu et al. 2013; Lu et al. 2018), and the research on the wetland landscape pattern in the eastern region with a large population density and a rapid economic growth rate has not been in-depth. Previous studies have focused on wetland changes in the eastern Shandong Peninsula before 2010 (Zeng et al. 2015). The eastern part of Shandong Peninsula is an important component of Shandong wetlands, especially coastal wetlands. It is an important ecological barrier for coastal development. In recent years, human activities (tourism industry, aquaculture) in the coastal zone have been frequent, and the wetlands in the study area have been seriously disturbed. There are also studies that analyze the wetlands from the seas more extensively (Zeng et al. 2015; Kong et al. 2016), and they pay insufficient attention to beach wetlands. The tidal flat, as the boundary between the ocean and the continent, on the one hand, provides abundant material resources for human beings, on the other hand, it also provides a variety of ecosystem services, such as biodiversity protection functions, which play an important role (Fang et al. 2016). At the same time, artificial salt marshes, including salt pans and breeding ponds, are mostly concentrated in reservoir pits. Studying the conversion relationship between them is of great significance for maintaining the ecological service functions of wetlands. This paper takes the eastern part of Shandong Peninsula as the research area, and uses GIS spatial analysis and landscape ecology-related research methods to analyze the characteristics and driving forces of wetland landscape pattern in the past 30 years, and to reveal its evolution mechanism, so as to provide scientific basis for rational use, wetland ecological risk management and formulation of wetland protection countermeasures.

2 Data and Methods 2.1 Study Area The study area is located in the eastern part of Shandong Peninsula, between 119° 34 –122° 42 E, 36° 16 –38° 23 N, with a total area of 20,028.52 km2 , including Yantai City, Weihai City and the partial area of Laizhou Bay and Dingzi Bay (Fig. 1). The study area is facing the sea in three. The north faces the Liaodong Peninsula across the Bohai Bay, the east faces South Korea across the sea, the south faces the Yellow Sea, and the west connects Weifang and Qingdao. The climate belongs to a warm temperate continental monsoon climate, and the wetland resources are abundant. Rain-source rivers and coastal wetlands in the monsoon region are the main wetland types in the region. Inter-annual variation of river runoff is large, with a difference of up to 10 times between high and low years. The annual flow distribution

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Fig. 1 Location of the study area

is uneven, with 70–80% concentrated in July to September each year. The research area has a superior geographical location, vast coastal resources, excellent natural ports and bays along the coastline, and extensive tidal flats, which provide excellent conditions for the development of fisheries, salt industry, and aquaculture. In recent years, the economy has developed rapidly, with a large population density of 454.33 (persons/km2 ). The level of urbanization has continued to increase, and its overall economic strength and social development level have been among the highest in the country.

2.2 Data The data used in this work mainly include remote sensing data, meteorological data and economic data. Landsat image data was downloaded from a geospatial data cloud website (http:// www.gscloud.cn/). The selection of remote sensing data takes into account the period, coverage and data quality of satellite images, and selects 8 scenes from 1990, 2000, 2010, and 2018, and the Landsat TM/ETM+/OLI remote sensing is acquired from June to October, and the images have a spatial resolution of 30 m. Based on a full analysis of the current status of wetland resources in the study area, wetlands are classified into natural wetlands and artificial wetlands according to the types of wetlands in China classified by the wetland convention and Lu (1996), of which natural wetlands are divided into river wetlands and beach wetlands artificial wetlands are divided into reservoir pit, salt pan and breeding pond. The ENVI software was used

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Fig. 2 The spatial distribution of wetland types in eastern Shandong Peninsula from 1990 to 2018

to enhance the image data for band combination, density segmentation, and independent component analysis. Using ArcGIS software, referring to historical images of Google Earth, and interpreting and extracting the research areas according to the classification system and interpretation marks, 1990 and 2000, 2010 and 2018 wetland information. Using field survey data to evaluate and correct the interpretation results, the classification accuracy can reach 89.6%, and a total of four phases of wetland landscape distribution maps have been formed (Fig. 2). The meteorological data of the study area from 1990 to 2018 originated from China Meteorological Data Network (http://data.cma.cn/), including six stations: Changdao Station, Longkou Station, Yantai Station, Laiyang Station, Haiyang Station, Fushan station. The economic data comes from the 1990–2018 statistical yearbooks of the two cities. The economic data mainly includes GDP, total population, total output value of agriculture, forestry, animal husbandry and fishery, total aquatic product output, agricultural population, non-agricultural population, total households, and wastewater discharge; Meteorological data includes temperature and precipitation.

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2.3 Methods 2.3.1

Wetland Type Transfer Matrix

The transition probability matrix, also called the transition matrix, was proposed by the Russian mathematician Markov. The Markov model is used to construct the transition probability matrix of land use types in the study area, which can describe the structure, characteristics and direction of land use type changes in the area. This method can not only reflect the structure of land use types at the beginning and end of the study period, but also reflect the source and composition of each land use type during the study period (Fang et al. 2016). Its mathematical expression is: ⎡

P11 ⎢ .. ⎣ .

... .. .

⎤ P1n .. ⎥ . ⎦

(1)

Pn1 . . . Pnm

Among them, P represents the area of each wetland type, and include beach wetlands, river wetlands, reservoirs and ponds, breeding ponds, and Yantian five wetland types; n is the total number of wetland types, i and j represent the beginning and end of the study period, respectively. This study includes three research sections, from 1990 to 2000, 2000 to 2010, and 2010 to 2018. Pi j is the transfer probability of type i wetland type to type j wetland type.

2.3.2

Landscape Pattern Index

According to the characteristics of the study area, the landscape pattern index is selected at the landscape type level: the number of patches (NP), the patch area (CA), and mean patch size (MPS). At the landscape level, it is selected: edge density (ED), The number of patches (NP), mean patch size (MPS), area-weighted average patch fractal dimension (AWMPFD), Shannon’s diversity index (SHDI) and Shannon’s evenness index (SHEI), the meaning of each landscape index can be refer to previous studies (Zeng et al. 2015).

2.3.3

Driving Force Analysis

There are many driving factors that cause changes in the landscape pattern of wetlands. They are usually divided into two categories, one is natural factors, including climate, hydrology, soil, etc. the other is human factors, including population, economic development level, urbanization level, technology, policies and culture, etc. The landscape pattern of the wetland ecosystem is the result of the combined effects

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of the natural environment and human behavior. In this study, indicators that are easy to quantify and compare as driving force indicators are summarized and combined (Zeng et al. 2015). The principal component analysis method is an analysis and statistical method that converts multiple related elements into several unrelated comprehensive indicators, which is widely used in the analysis of driving force factors for wetland landscape changes.

3 Results and Analysis 3.1 Analysis of Area Change of Wetland Landscape Type Total area of wetlands in the eastern part of the Shandong Peninsula decreased first and then increased, and the overall trend decreased from 1990 to 2018. In the past 30 years, the total area of wetlands decreased by 31.22 km2 (Table 1). The area of natural wetlands in this area continued to decrease, and the area of artificial wetlands increased overall from 1990 to 2018 (Fig. 3). Among the natural wetlands, the area of beach wetland decreased from 234.13 km2 (accounting for 14.37% of the total wetland area) in 1990 to 152.52 km2 (9.54%) in 2018, a decrease of 81.61 km2 , and the decline rate in 3 decades was 34.86%, with a significant area reduction. The decline rate of river wetland area in the past 30 years was 28.80%, and the area decreased by 144.92 km2 . Among the artificial wetlands, the increase in the area of salt pans was the most prominent, increasing from 95.96 km2 (5.89%) in 1990 to 200.71 km2 (12.56%) in 2018, increasing the area by more than twice; the proportion of the area of the breeding ponds to the total area of the wetland was stable at about 30%, its area has not changed much; the increase in the area of reservoirs and pits is larger than that of breeding ponds, with a growth rate of 24.51% in the past 30 years, which is 14 times the growth rate of breeding ponds (1.7%).

3.2 Analysis of Probability Change of Wetland Type Area Transfer in Eastern Shandong Peninsula From 1990 to 2018, 37.46% of the natural wetlands were converted into artificial wetlands, of which beach wetlands were the main source of artificial wetlands, with a transfer probability of 32.72%. The changes in beach wetlands and breeding ponds are the most significant, with 34.82% of tidal beach wetlands and 21.26% of breeding ponds converted to other types of wetlands, with a transfer probability of more than 50%. The conversion from non-wetlands to wetlands is weak, with a transfer probability of only 2.26%. From 1990 to 2000, 15% of the natural wetlands were converted into artificial wetlands, and only 4.49% of the artificial wetlands were

95.96

458.32

Salt pan

Breeding pond

1629.37

337.75

234.13

Beach wetland

Reservoir pit

503.21

River wetland

100.00

28.13

5.89

20.73

14.37

30.88

1501.65

489.65

133.82

234.46

266.46

377.28

Area (km2 )

Area (km2 )

Percentage (%)

2000

1990

100.00

32.61

8.91

15.61

17.74

25.12

Percentage (%)

1537.87

482.29

206.24

323.30

158.69

367.36

Area (km2 )

2010

100.00

31.36

13.41

21.02

10.32

23.89

Percentage (%)

1598.15

466.11

200.71

420.52

152.52

358.29

Area (km2 )

2018

100.00

29.17

12.56

26.31

9.54

22.42

Percentage (%)

1.92

1.7

109.16

24.51

−34.86

−28.80

1990–2018 (%)

Rate of change

Note The rate of change = (A2018 − A1990)/A1990 × 100%, of which A1990, A2018 represent the wetland area in 1990 and 2018, respectively

Total

Artificial wetland

Natural wetland

Wetland type

Table 1 The area of the wetland types and their percentage of the total in the eastern Shandong Peninsula from 1990 to 2018

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Fig. 3 Changes of wetland area in the eastern Shandong Peninsula from 1990 to 2018

converted into natural wetlands; from 2000 to 2010, 2.16% of the natural wetlands were converted into reservoir pits and 4.32% were converted into salt pans, 14.72% were converted into breeding ponds. From 2010 to 2018, natural wetlands were mainly transformed into salt pans and breeding ponds, with the transfer probability of 11.13% and 10.04%, respectively. The conversion from artificial wetlands to natural wetlands is very weak, with only 2.43% of the artificial wetlands converted into natural wetlands (Fig. 4).

3.3 Analysis on the Change of Landscape Pattern Index in Shandong Peninsula 3.3.1

Changes in Landscape Pattern Index at Landscape Level

In this study, six landscape indices: edge density (ED), number of patches (NP), mean patch size (MPS), area-weighted mean patch fractal dimension (AWMPFD), Shannon’s diversity index (SHDI), Shannon’s evenness index (SHEI) change trend from 1990 to 2018 shown in Fig. 5. It can be seen from Fig. 5 that the number of patches decreases first, then increases and then decreases. From 2000 to 2010, the wetland plaques increased rapidly, and the deceleration from 2010 to 2018 was relatively gentle. The average patch size increased first, then decreased, and then increased gradually, and the mean patch size decreased significantly from 2000 to 2010, indicating that landscape fragmentation has intensified. Edge density increased first and then decreased. The complexity of wetland landscape shapes decreased and the degree of fragmentation decreased. The area-weighted mean patch fractal dimension generally showed a downward trend, indicating that wetland landscapes are greatly affected by human activities. The Shannon diversity index and Shannon evenness index increased first and then decreased, indicating that the types of wetland landscape patches in the study area

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Fig. 4 Probability matrix of wetland type transition in the eastern part of Shandong Peninsula in four periods. The abscissa is the wetland type, and the ordinate is the conversion percentage from the beginning state to the end state during the study period, that is, the transition probability

Fig. 5 Trends in landscape level at 1990–2018

tended to diversify first and then to a balanced distribution, but also reflected the reduction of the fragmentation of wetland landscape from the side.

3.3.2

Changes in Landscape Pattern Index at Landscape Type Level

The dynamic changes of the number of patches (NP), patch type area (CA), and mean patch size (MPS) in the study area of the three periods from 1990 to 2018 are shown in Table 2. As can be seen from study data, the area of river wetlands and beach wetlands have been significantly reduced, which mainly due to the frequent human

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Table 2 Landscape type level index from 1990 to 2018 Wetland type

1990 [year]

2000 [year] NP (piece)

CA (km2 )

2.69

210

377.28

1.80

234.13

2.13

94

266.46

2.83

7147

337.75

0.05

4989

234.46

0.05

Breeding pond

346

458.32

1.32

499

489.65

0.98

Salt pan

40

95.96

2.40

40

133.82

3.35

Total

7830

1629.37

5832

1501.67

Wetland type

2010 [year]

Natural wetland

Artificial wetland

Natural wetland

Artificial wetland

NP (piece)

CA (km2 )

River wetland

187

503.21

Beach wetland

110

Reservoir pit

MPS (km2 )

2018 [year]

NP (piece)

CA (km2 )

River wetland

207

367.36

Beach wetland

65

Reservoir pit Breeding pond Salt pan

Total

MPS (km2 )

NP (piece)

CA (km2 )

1.77

218

358.29

1.64

158.69

2.44

59

152.52

2.59

9000

323.30

0.04

8590

420.52

0.05

365

482.29

1.32

471

466.11

0.99

30

206.24

6.87

21

200.71

9.56

9667

1537.88

9359

1598.15

MPS (km2 )

MPS (km2 )

activities on the coast, economic activities such as aquaculture and salt exposure have resulted in the continuous expansion of the area of breeding ponds and salt pans. In the past 30 years, the growth rates of reservoir pits, breeding ponds, and salt pans were 2.76, 0.26, and 3.49 km2 /a. The total area of natural wetlands decreased from 737.34 km2 in 1990 to 510.81 km2 in 2018, with the largest decrease between 2000 and 2010, reaching 11.77 km2 /a; the total area of artificial wetlands increased from 892.03 km2 in 1990 to 1087.34 km2 in 2018, the largest increase between 2000 and 2010, reaching 15.39 km2 /a. In terms of the number of patches, the total number of patches in the wetlands in the study area decreased first, then increased and then decreased over the past 30 years. Artificial wetlands have more patches than natural wetlands, and the number of patches in reservoirs pits has an absolute advantage, exceeding 85% of the total, followed by breeding ponds and river wetlands. The mean patch size of wetland types in the study area generally decreased first and then slowly increased from 1990 to 2018, indicating that the degree of landscape fragmentation has decreased (Table 2). Except for beach wetlands, the mean patch

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size of the salt pan is large, which indicates that the area has a well-developed breeding industry and good clustering. The mean patch size of the reservoir pits is the smallest, indicating that it is most affected by human activities and the landscape is the most fragmented.

4 Discussion 4.1 Driver Analysis The factors affecting the wetland landscape change include natural and man-made factors. Principal component analysis was performed on ten measurable impact factors, and the analysis result was reduced to two principal components. The first principal component explained 73.13% of the total variable, and the second principal component explained 14.46% of the total variable. The load of each variable is shown in Table 3. According to Table 3, the factors with a larger load on the first principal component are agricultural population (10,000 people), total output value of agriculture, forestry, animal husbandry and fishery (10,000 CNY), wastewater discharge (10,000 tons), non-agricultural population (10,000 people) and GDP (100 million CNY), these factors reflect the development level of population, economy and agriculture, so the first main component mainly reflects population and socio-economic factors. The factors with larger loads on the second principal component are annual precipitation Table 3 Factor load matrix after rotation Influence factor

First principal component

Second principal component

Agricultural population (10,000 people)

−0.986

−0.043

Gross output value of agriculture, forestry, animal husbandry and fishery (10,000 CNY)

0.963

−0.003

Waste water discharge (10,000 tons)

0.961

0.083

Non-agricultural population (10,000 people)

0.959

0.039

GDP (100 million yuan)

0.941

0.097

Aquatic product production (tons)

0.934

−0.070

Total number of households (households)

0.777

−0.118

Annual precipitation (mm)

0.043

0.817

Annual temperature (°C)

0.227

−0.732

Total population (10,000 people)

0.388

0.455

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(mm) and annual temperature (°C), so the second principal component mainly reflects climate factors.

4.2 Influence of Population and Socio-economic Factors on Wetland Landscape Changes On the scale of several decades, the magnitude of change in wetland landscape caused by natural factors is relatively small, and the change in wetland landscape caused by human activities has become the main factor. Through principal component analysis, it was found that population and socio-economic development were the main factors affecting the wetland landscape change in Shandong Peninsula. From 1990 to 2018, the urbanization of the Shandong Peninsula area developed rapidly, the agricultural population continued to decline, the urban population increased rapidly, the economy developed rapidly, and the agricultural, forestry, animal husbandry, and fishery output values and GDP increased significantly. Correlation analysis was made between the population and socio-economic principal component analysis scores and various types of wetland areas, and it was found that population and socioeconomic factors had a significant negative correlation with changes in the area of river wetlands and beach wetlands, and significantly changed with the area of reservoir pits and breeding ponds relationship. This shows that urbanization and economic development have promoted the reduction of the area of natural wetlands and the expansion of the area of artificial wetlands. Regional economic development and urbanization have concentrated population, resources and industries, changed land use methods and people’s values, and affected changes in wetland landscapes. As a weak link in the land use structure, natural wetlands are bound to be greatly impacted in the early stage of rapid urbanization. The pace of urbanization is accelerating, a large number of agricultural population enters the city, and the urban construction area continues to expand; the level of industrialization has increased, wastewater discharge has increased, the discharge of pollutants has caused changes in the physical and chemical properties of the soil and changes in the landscape structure, the ecological environment has been continuously degraded, and wetland functions have continued to decrease. As natural wetlands decrease and artificial wetlands increase, the conversion of natural wetland area to artificial wetland area is more significant.

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4.3 Influence of Climate Factors on Wetland Landscape Changes Climate change is an important factor affecting the evolution of wetlands, especially the evolution of natural wetlands, which is greatly affected by precipitation and temperature (Gong et al. 2016). Through principal component analysis, it was found that the annual precipitation was the largest load in the second principal component, with a load factor of 0.858, indicating that there is a positive correlation between precipitation and changes in wetland landscape. Precipitation is an important recharge method for wetland water resources, and the change in wetland area is closely related to precipitation. The load factor of the annual average temperature in the second principal component is −0.742, indicating that there is a negative correlation between the temperature and changes in the wetland landscape. Compared to precipitation, the annual average temperature has a smaller effect on the change in wetland area. It can be seen that the inter-annual changes in the eastern part of the Shandong Peninsula have been more obvious, with abundant and dry alternately appearing in the past 30 years. The year of high water was 2007, with annual rainfall as high as 874.71 mm, and the dry season in 1999 and 2000. The annual precipitation was only 371.31 mm and 473.2 mm, respectively. The area of river wetland in 2000 decreased by 125.93 km2 compared with 1990, which indicates that the river wetland area is significantly affected by fluctuations in precipitation. The change of air temperature mainly affects the change of wetland landscape through the amount of evaporation, that is, the process of surface evaporation accelerates as the temperature rise, the amount of water in the wetland decreases, and the area of the wetland decreases. For example, the temperature in the eastern part of the Shandong Peninsula was high in 1999 and 2000, and a large amount of water vapor evaporated, which accelerated the shrinkage of river wetlands in 2000. From 1990 to 2018, the average annual temperature generally showed an upward trend, which accelerated surface evaporation and accelerated the shrinkage of natural wetland areas.

4.4 Impact of Policy Factors on Wetland Landscape Changes In addition to population and socio-economic development and climate factors, policy factors play an important role in the change of wetland landscape pattern. Since the Shandong Province proposed the strategy of “Sea Shandong” in 1991, the State Council formally approved the “Development Plan of the Shandong Peninsula Blue Economic Zone” in 2011, and the “Action Plan for the Construction of a Strong Marine Province in Shandong” in 2018, The industry has developed rapidly, occupying large areas of natural tidal flats and beaches to build farms and salt pans, and at the same time establishing ports to better develop fisheries, the establishment of large-scale reservoir pits, and the increase in the area of reservoir pits. From 1990

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to 2018, the area of reservoir pits increased by 104.75 km2 , and the fishery output value increased by 16.5 times. From 2000 to 2018, the area of reservoir pits was accelerating, especially during the period of 2010 to 2018, the area of reservoir pits increased by 12.15 km2 /a, and during the same period, the annual precipitation generally showed a downward trend, and the area of reservoir pits was not reduced. The main reason for the reverse increase is that the Shandong Provincial Government planned a large number of new or expanded water storage facilities such as reservoirs to meet the needs of social and economic development in the water resources planning projects during the 15th to 12th Five-Year Plan period. In summary, population and socio-economic development have been the dominant factors influencing the wetland landscape change in the eastern Shandong Peninsula in the past 30 years; climate change is closely related to changes in wetland landscape, especially the large fluctuations in precipitation have a significant impact on changes in natural wetland areas. In addition, the change of the wetland landscape in the eastern part of the Shandong Peninsula is also affected by a series of policy factors.

5 Conclusions In this study, four Landsat TM/ETM+/OLI remote sensing images from 1990, 2000, 2010, and 2018 were selected. Based on the established interpretation landmarks, different types of wetland landscapes in the eastern Shandong Peninsula were extracted, and their spatial and temporal patterns of wetland landscapes were extracted. The changes and their driving mechanisms are studied, and the main conclusions are as follows: (1) The total wetland area decreased first and then increased from 1990 to 2018. The area decreased by 31.22 km2 , accounting for 2% of the total area. However, the conversion from natural wetlands to artificial wetlands was significant. Beach wetlands and reservoir pits have the most significant changes, with a transfer probability of more than 50%, which is mainly transferred to other wetland types. The area of river wetland accounted for the largest proportion (30.88%) in 1990, and the area of breeding ponds accounted for the largest proportion (29.17–32.61%) in 2000, 2010 and 2018. (2) From the change of landscape pattern index at the landscape level, the complexity of the shape of the wetland landscape is reduced, and the types of wetland landscape patches first tend to diversify and then to a balanced distribution. From the analysis of the change of landscape pattern index at the landscape type level, except for beach wetlands, the mean patch size of salt pans in artificial wetlands is large, indicating that the area has a well-developed breeding industry and good clustering, indicating that human activities are not only the destruction of wetland ecological environment also protects the wetland ecological environment through the construction of artificial wetlands. The mean

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patch size of the reservoir pits is the smallest, indicating that it is most affected by human activities and the landscape is the most fragmented. (3) The change of the landscape pattern of the wetland ecosystem in the study area is the result of the combined effects of population, socioeconomic factors and climate factors. Population and socio-economic factors are the main factors affecting the wetland landscape change in the eastern part of Shandong Peninsula. Among the population and socio-economic factors, the number of agricultural population and the total output value of agriculture, forestry, animal husbandry and fishery have a greater impact on changes in wetland area. The interannual variation of precipitation in climatic factors has a greater impact on the area change of river wetlands in the eastern Shandong Peninsula. In addition, a series of policies such as “Sea Shandong” also affect the change of wetland landscape. Author Contributions X. S. analyzed the data and wrote the paper; J. W. conceived and designed the research and edited the paper; Y. L. and N. X. and reviewed and edited the paper; J. T. collect the data. Funding This research was funded by the National Key Research and Development Plan (Grant number: YS2017YFC1503001), National Natural Science Foundation of China (Grant number: 71603168).

References Bai JH, Ou YH, Yang ZF, Cui BS, Cui LJ, Wang QG (2005) Change in wetland landscape patterns: a review. Prog Geogr 24(04):36–45 Fan Q, Du T, Yang J (2014) Analysis of landscape pattern evolution of Nansi Lake wetland from 1982 to 2012. Resour Sci 36(4):865–873 Fang ZX, Li GQ, Feng L, Zheng P, Wang MM, Yang H, Zhang W (2016) Characteristics of landscape pattern change of wetlands in Jiaozhou Bay. Wetland Sci 14(02):276–281 Feng YJ, Liu Y, Tong XH (2018) Spatio-temporal variation of landscape patterns and their spatial determinants in Shanghai, China. Ecol Ind 87:22–32 Gong N, Niu ZG, Qi W, Zhang HY (2016) Analysis of driving forces of wetland changes in China. J Remote Sens 20(02):172–183 Jiang WL, Lv JX, Wang CC, Chen Z, Liu YH (2017) Marsh wetland degradation risk assessment and change analysis: a case study in the Zoige Plateau, China. Ecol Ind 82:316–326 Kong XX, Li GQ, Chen BY, Shi C, Lv T (2016) Evaluation and comparative study of ecosystem services of wetlands in eastern part of Shandong Peninsula. Wetland Sci Manag 12(03):23–27 Lei K, Pan HY, Lin CY (2016) A landscape approach towards ecological restoration and sustainable development of mining areas. Ecol Eng 90:320–325 Li XG, Liang CH, Wang YS (2013) Prediction of wetland landscape pattern in Liaohe River Delta based on CA-Markov model. Environ Sci Technol 36(5):188–192+200 Li YT, Ge ZQ, Wang X, Wang ZM, Du ZY, Liu DX (2018) Evaluation of the service function value of coastal natural wetland ecosystem in Shandong Province. Chin J Ecol 37(02):106–113 Li Z, Jiang WG, Wang WJ, Lv JX, Deng Y (2019a) Study on the wetland leading service function of Jing-Jin-Ji urban agglomeration based on the ecosystem service value. J Nat Resour 34(8):1654– 1665

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Li YT, Du ZY, Wang X, Yang QS, Chen ZQ, Sun YY, Liu DX (2019b) Evaluation of wetland ecosystem services in Yellow River Delta Nature Reserve. Mar Environ Sci 38(5):761–768 Lin WP, Cen JW, Xu D, Du SQ, Gao J (2018) Wetland landscape pattern changes over a period of rapid development (1985–2015) in the Zhoushan Islands of Zhejiang Province, China. Estuar Coast Shelf Sci 213:148–159 Liu HY, Lv XG, Zhang SK (2004) Study on the diversity of wetland landscapes in the Sanjiang Plain and its 50-year changes. Chin J Ecol 24(7):1472–1479 Liu DY, Wang Q, Du LF (2012) Landscape pattern and waterbird habitat change of Tianjin wetland from 1999 to 2007. 10(3):350–358 Lu JJ (1996) Classification of coastal wetlands in China. Environ Guide 01:1–2 Lu XN, Huang Y, Hong J, Zeng DY, Yang LQ (2018) Temporal and spatial pattern evolution of wetland landscape in the Yellow River Delta based on landsat. Environ Sci China 38(11):4314– 4324 Sun N, Zhu W, Cheng Q (2018) GF-1 and Landsat observed a 40-year wetland spatiotemporal variation and its coupled environmental factors in Yangtze River estuary. Estuar Coast Shelf Sci 207:30–39 Wang YL, Yu JB, Dong HF (2012) Spatial evolution of landscape pattern of coastal wetlands in the Yellow River Delta. Sci Geogr Sinica 32(6):717–724 Wang M, Li GQ, Liu YJ, Yang H, Zhou Y (2017) Dynamic changes of marine aquaculture water surface in the eastern Shandong Peninsula in the past 20 years. J Appl Oceanogr 36(03):319–326 Xiao C, Xie XF, Wu T, Bian HQ, Xu W (2015) Dynamic changes of landscape pattern and hemeroby in Ximen Island wetland, Zhejiang Province, China. Chin J Appl Ecol 25:3255–3262 Xie HL (2008) Regional ecological risk analysis based on landscape structure and spatial statistics. Chin J Ecol 10:5020–5026 Xu JR, Dong JH (2013) Landscape pattern changes and its driving force of Nansihu wetlands during 1987–2010. Wetland Sci 11(4):438–445 Xu ZH, Han M (2018) Temporal and spatial distribution characteristics of drought in Shandong Province and its correlation with ENSO. Chin J Eco-Agriculture 26(08):1236–1248 Yan WH, Yi HP, Feng L, Cheng ZL, Sun X, You XP (2017) Spatial and temporal changes of wetland resources in Yantai city based on Landsat imagery. Bull Soil Water Conserv 37(02):289–294+301 Yu YL, Zhou YH, Liu CL (2009) Landscape pattern changes and driving forces of wetland along the Yangtze River in Anqing. Resour Environ Yangtze Basin 18(6):522–527 Yu JB, Wang YL, Dong HF, Wang XH, Li YZ, Zhou D, Gao YJ (2013) Estimation of soil organic carbon storage in coastal wetlands of modern Yellow River Delta based on landscape pattern. J Wetland Sci 1:1–6 Zeng YL, Li GQ, Chen Q, Wang YY, Yu XY, Li BM (2015) Analysis of landscape pattern changes and driving forces of wetland in Eastern Shandong Peninsula. Yellow River 37(08):78–82 Zhang HB, Liu HY, Hou MX, Li YF (2014a) Model and simulation of coastal wetland landscape process: a case study of the core area of Yancheng protected area in Jiangsu Province. J Nat Resour 29(07):1105–1115 Zhang XP, Pan DL, Chen JY, Zhao JH, Zhu QK, Huang HQ (2014b) Evaluation of coastline changes under human intervention using multi-temporal high-resolution images: a case study of the Zhoushan Islands, China. Remote Sens 6:9930–9950 Zhang F, Kung HT, Johnson VC (2017a) Assessment of land-cover/land-use change and landscape patterns in the two national nature reserves of Ebinur Lake watershed, Xinjiang, China. Sustainability 9(5):724 Zhang SH, Fan WW, Li YQ, Yi YJ (2017b) The influence of changes in land use and landscape patterns on soil erosion in a watershed. Sci Total Environ 574:34–45 Zhao DD, He HS, Du HB, Zong SW (2017) Research and application of wetland landscape simulation model. Wetland Sci 15(04):562–570 Zorrilla-Miras P, Palomo I, Gómez-Baggethun E, Martín-López B, Lomas PL, Montes C (2014) Effects of land-use change on wetland ecosystem services: a case study in the Doñana marshes (SW Spain). Landscape Urban Plann 122:160–174

Spatial Distribution of PM10 and PM2.5 in Ambient Air at E-waste Dismantling Community in Buriram, Thailand Siriwipha Chanthahong, Tassanee Prueksasit, Narut Sahanavin, and Navaporn Kanjanasiranont

Abstract The spatial distribution of PM10 and PM2.5 was observed at the e-waste dismantling community in Daengyai and Banpao subdistrict, Buriram, Thailand. High and low volume air samplers were used to collect PM10 and PM2.5 in the ambient air, respectively, at e-waste dismantling, non-e-waste dismantling, open dumpsite, and reference area. The sampling was performed during summer and rainy seasons of 2019 for 24 h and seven consecutive days. The average concentrations of PM10 were 49.64 ± 17.71, 55.36 ± 17.46 and 57.61 ± 17.55 µg/m3 at e-waste dismantling, non-e-waste dismantling, and open dump area, respectively. For PM2.5 , the average concentrations at e-waste dismantling, non-e-waste dismantling, and open dump area were 29.71 ± 14.28, 33.81 ± 18.85, 30.68 ± 13.53 µg/m3 , respectively. PM10 levels at open dumpsite were 1.2 and 1.1 times higher than those of e-waste dismantling area and non-e-waste dismantling area. Meanwhile, ANOVA analysis showed no significant differences (p > 0.05) of PM10 concentration between non- and e-waste dismantling, and open dumpsite. The level of PM2.5 at the e-waste dismantling area was only higher than the reference area but lower than non-e-waste dismantling and open dump area. Besides, there was no statistically significant difference between all sampling sites for PM2.5 concentration. PM10 at non-e-waste dismantling area S. Chanthahong Interdisciplinary Program in Environmental Science, Graduate School, Chulalongkorn University, Wang Mai, Pathumwan District, Bangkok 10330, Thailand T. Prueksasit (B) Department of Environmental Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand e-mail: [email protected] Research Program of Municipal Solid Waste and Hazardous Waste Management, Center of Excellence on Hazardous Substance Management, Bangkok 10330, Thailand N. Sahanavin Department of Public Health, Faculty of Physical Education, Srinakharinwirot University, Nakhonnayok 26120, Thailand N. Kanjanasiranont Faculty of Environment and Resource Studies, Mahidol University, Nakhon Pathom 73170, Thailand © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_33

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and open dumpsite were exceeded guidelines of WHO (50 µg/m3 ), and also PM2.5 (25 µg/m3 ) of all sampling areas. In this e-waste community, there is no zoning provided for e-waste dismantling activities. Consequently, e-waste dismantling houses were randomly distributed in the community. Consequently, it can be indicated that e-waste dismantling activities led to more PM10 contribution than PM2.5 . Keywords Spatial distribution · E-waste · Ambient air pollution · PM10 · PM2.5

1 Introduction Electronic waste or e-waste is an end of life’s electronics, electric appliances that are not working, or the user does not want it anymore (Vassanadumrongdee 2019). From the accumulative of e-waste in Thailand over the previous years, there seems like the number still expanding from 359,070 tons to 414,600 tons from 2012 to 2018, respectively (Pollution Control Department 2013; Pollution Control Department 2019). Not only coming from all over Thailand but the increasing of e-waste also derived from developed countries (Senet 2019; Chantanusornsiri 2019). Presently in Thailand, Banmaichaiyaphot district, Buriram Province, is the second largest e-waste dismantling area in Thailand. From the observation in 2019, there are 105 informal separators in Daengyai subdistrict and 68 separators in Banpao subdistrict that have performed or registered as e-waste separator section. The process for separate e-waste in Daengyai and Banpao were similar, they use the informal e-waste dismantling methods include (1) using physically dismantling tools such as hammers, screwdrivers, chisels and bare hands to separate materials (2) burning cables to recover copper, burning unwanted plastics and foams in the open air (Thongkaow et al. 2019). This e-waste contains several hazardous substances (Uddin 2012). Then primitive e-waste dismantling activities can cause emission of hazardous substances contaminated particulate and generate airborne pollution in e-waste dismantling communities. Typically, e-waste dismantling workers and residents can expose to these contaminated particles via inhalation and caused harmful effects to their bodies. According to the strong relationship between PM levels and the adverse health impact, then the monitoring of fine particulate matter concentration in ambient air is a significant factor for clarifying air quality of the concerned area (Gangwar et al. 2016). There is a study found mean concentration of PM at the e-waste recycling site was higher than the surrounding area, which may due to e-waste activities such as dismantling and open burning (Gangwar et al. 2016; Zheng et al. 2016). Fang et al. 2013 also found that PM10 and PM2.5 at the e-waste dismantling workshop were higher than the non-working workshop of the factory for cathode ray tube television recycling (Fang et al. 2013). These contaminated dust can disperse to another area nearby their sources, including non-e-waste dismantling area. Additionally, e-waste dismantling houses in Daengyai and Banpao subdistrict were randomly located neighboring none-waste dismantling houses. Other than e-waste dismantling activities, there are open

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dumping sites in both subdistricts, which can cause air pollution due to dismantler will burn electronic wires and residues to separate the precious metals. According to the above evidence of current e-waste dismantling houses widespread location in the communities in Northeastern Thailand, particular in Banmaichaiyaphot and Phutthaisong District, Buriram province. Also, the concentration and distribution of particulate matter in ambient air at e-waste, non-e-waste dismantling, and open dumpsite in this area have not been studied. Therefore, this study aims to monitor the concentration of PM10 and PM2.5 in the ambient air and to compare between those found at non- and e-waste dismantling and open dump area.

2 Materials and Methods 2.1 Study Area In this study, 7 sampling sites in Buriram province in the northeast of Thailand were chosen as a study area, namely Banmaichaiyaphot district, Daengyai subdistrict e-waste dismantling site (DY01ES), non-e-waste dismantling site (DY02NS), open dumpsite (DY03OD) and reference area (DY00CT). For Phutthaisong district, Banpao subdistrict, sampling sites include e-waste dismantling site (BP01ES), none-waste dismantling site (BP02NS), and open dumpsite (BP03OD). The selection of the reference area, Wat E Sarn primary school, was considered by the area that has not e-waste dismantling activity and far from the e-waste dismantling site (Fig. 1).

Fig. 1 Location of the sampling points in Daengyai and Banpa subdistrict, Buriram Province

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2.2 PM Collection For PM2.5 , 46.2 mm PTFE filters were stored in an electronic dehumidification system desiccator at room temperature and humidity below 30% for at least 2 days approximately. For PM10 , 8 × 10 quartz fiber filter was kept in desiccator at least 24 h before weighing. A mass of PM2.5 on each filter was measured by a 7 digits Ultra-Microbalance (UMX2, Mettler® Toledo) with 0.001 mg sensitivity. The quantity of PM10 was weighed by 4 digits microbalance with 0.0001 g sensitivity. PM2.5 and PM10 were collected during two seasons; dry season (April 2019) and monsoon season (September 2019). The sampling was performed using high and low volume air sampler for PM10 and PM2.5, respectively. Air sampler was performed 24 h at each sampling site for 7 days consecutively. After air samples have been collected, all the filters were returned to weigh at the laboratory. DAVIS Vantage Pro2 wireless weather station was used to collect meteorological data such as temperature, relative humidity, rain rate, wind speed, and wind direction. Meteorological measurement device was installed on a telescopic mast and placed 10 m above the ground at three points, including Dangyai’s open dumpsite (DY03OD), Banpao’s open dumpsite (BP03OD) and reference area (DY00CT).

2.3 Data Analysis Statistical analysis of the data was performed using the SPSS program (version 23). The mean difference in concentrations of PM10 and PM2.5 between non- and e-waste dismantling areas and between seasons were analyzed using t-test method. For the different concentrations of PM10 and PM2.5 at all sampling sites, the One Way ANOVA analysis was performed.

3 Results and Discussion Results of 24-h average PM2.5 and PM10 concentrations in ambient air at e-waste dismantling, non-e-waste dismantling, open dump area, and reference area are shown in Table 1. The PM2.5 concentrations in summer at e-waste dismantling, non-e-waste dismantling, open dump, and reference area were 17.41 ± 3.28, 18.08 ± 4.29, 20.40 ± 6.00 and 16.03 ± 4.21 µg/m3 , respectively. It shows lower concentration when compared with those in the rainy season (42.00 ± 9.34, 49.53 ± 13.68, 40.97 ± 10.78 and 36.66 ± 8.41 µg/m3 , respectively). Similar to PM2.5 results, the levels of PM10 in summer at e-waste dismantling, non-e-waste dismantling, open dump and reference area (38.64 ± 6.93, 46.42 ± 14.79, 49.79 ± 12.94 and 27.29 ± 3.30 µg/m3 , respectively) were also lower than rainy season (60.64 ± 18.51, 64.29 ± 15.59, 65.43 ± 18.45 and 48.71 ± 11.22 µg/m3 , respectively). Both PM2.5 and PM10 concentrations

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Table 1 Summary of average PM concentrations of e-waste, non-e-waste dismantling and reference area (µg/m3 ) Sample sites E-waste dismantling

Non-e-waste dismantling

Open dump area

Reference area

Seasons

PM concentrations (mean ± SD) PM2.5

PM10

Summer

17.41 ± 3.28

38.64 ± 6.93

Rainy

42.00 ± 9.34

60.64 ± 18.51

Mean

29.71 ± 14.28

49.64 ± 17.71

Summer

18.08 ± 4.29

46.42 ± 14.79

Rainy

49.53 ± 13.68

64.29 ± 15.59

Mean

33.81 ± 18.85

55.36 ± 17.46

Summer

20.40 ± 6.00

49.79 ± 12.94

Rainy

40.97 ± 10.78

65.43 ± 18.45

Mean

30.68 ± 13.53

57.61 ± 17.55

Summer

16.03 ± 4.21

27.29 ± 3.30

Rainy

36.66 ± 8.41

48.71 ± 11.22

Mean

26.34 ± 12.47

38.00 ± 13.67

in summer and rainy seasons were highest at open dump area except PM2.5 in the rainy season that highest at non-e-waste dismantling area. These results indicated that the activities such as wire and plastic burning, cathode-ray tube screen (CRT) beating at open dump area had influenced the contribution of PM levels in this study area. For the average PM2.5 in both seasons at e-waste dismantling, non-e-waste dismantling, open dump, and reference area were 29.71 ± 14.28, 33.81 ± 18.85, 30.68 ± 13.53 and 26.34 ± 12.47 µg/m3 , respectively. Also, PM10 concentrations at those sampling sites were 49.64 ± 17.71, 55.36 ± 17.46, 57.61 ± 17.55 and 38.00 ± 13.67 µg/m3 , respectively. The mean concentration of PM10 at open dump area showed the highest level and were about 1.2 and 1.1 times higher than those of e-waste dismantling area and non-e-waste dismantling area, whereas only PM10 at reference area was statistically different (p > 0.05) to e-waste, non-e-waste dismantling and open dump area. While the highest concentration of PM2.5 was observed at non-e-waste dismantling area, but there was no statistically significant difference between all sampling sites. The similarity of PM levels at each sampling point might due to the topography of this study area that was plain in rural habitation, so the distribution of PM could be greater than other areas such as located in a valley or surrounded by hills. Additionally, PM10 at non-e-waste dismantling area and open dumpsite were exceeded the guideline by WHO (50 µg/m3 ), and also PM2.5 (25 µg/m3 ) of all sampling areas. The concentration of PM2.5 and PM10 that higher in the rainy season might due to other anthropogenic sources than e-waste dismantling activities such as forest fire, agricultural waste burning and there still has some people smuggled burning the sugarcane fields, stubble of rice, weeds and some leaf waste in the surrounding

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area at rainy seasons. These anthropogenic sources and also the e-waste dismantling activities within the sampling area could lead to an increase of PM concentration in the rainy season. The highest concentration of PM2.5 and PM10 in this study were lower than the average PM2.5 (256.6–290.8 µg/m3 ) and PM10 (326.3–394.5 µg/m3 ) which was sampled around the CRT recycling factory nearly 8 times (Fang et al. 2013). PM10 levels at the open burning site also lower than the average PM10 (200 ± 3.05 and 195 ± 5.50 µg/m3 ) that was collected at the e-waste burning site in Moradabad, India about 5 and 3 times in summer and monsoon, respectively (Gangwar et al. 2016). Additionally, the result in this study was similar to Xue et al. 2011 that was revealed PM10 distribution in a typical printed circuit board manufacturing workshop also higher than the level observed in the off-site area (Xue et al. 2012). Thus, e-waste dismantling could influence the distribution of PM concentration but also the burning activities that could contribute PM2.5 and PM10 to the ambient air around this study area. The PM concentration was also calculated for investigating the ratios of the concentration measured at the e-waste (EW), non-e-waste dismantling (NEW), and open dump area (OD) compared with the reference area (RF), as given in Table 2. The average of EW/RF ratios were 1.14 and 1.33 for PM2.5 and PM10 , respectively. For those of NEW/RF ratios were 1.24 and 1.51, and for OD/RF ratios were 1.20 and 1.58. This result indicated that the areas involving e-waste dismantling activity, including e-waste dismantling houses, non-e-waste dismantling houses and open dump site could probably influence the higher contribution of PM2.5 and PM10 levels. The meteorological data in the sampling area was also monitored, including temperature, relative humidity, rainfall, and wind speed. There were 32.28 ± 1.07 and 27.89 ± 0.67 °C in summer and rainy seasons, respectively, as shown in Table 3. The relative humidity in summer and rainy seasons were 64.10 ± 5.39 and 62.45 ± Table 2 Ratios of the PM concentration at e-waste, non-e-waste dismantling, and open dump area compared with the reference area Ratios

Seasons

PM2.5

PM10

EW/RF

Summer

1.11

1.42

Rainy

1.17

1.24

Average

1.14

1.33

Summer

1.14

1.70

Rainy

1.35

1.32

Average

1.24

1.51

Summer

1.29

1.82

Rainy

1.11

1.34

Average

1.20

1.58

NEW/RF

OD/RF

EW E-waste dismantling area, NEW Non-e-waste dismantling area OD Open dump area, RF Reference area

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Table 3 Meteorological conditions in summer and rainy seasons in the study area Meteorological parameters

Summer

Rainy

Temperature (°C)

32.28 ± 1.07

27.89 ± 0.67

Relative humidity (%)

64.10 ± 5.39

62.45 ± 1.41

Rainfall (mm)

0.11 ± 0.19

Wind speed (m/s)

1.46 ± 0.49

– 1.30 ± 0.52

Table 4 PM2.5 /PM10 ratios at all sampling points in summer and rainy seasons Periods

PM2.5 /PM10 E-waste dismantling area

Non-e-waste dismantling area

Open dump area

Reference area

Summer

0.46

0.41

0.42

0.58

Rainy

0.74

0.77

0.63

0.75

1.41%, respectively. For wind speed, there were 1.46 ± 0.49 and 1.30 ± 0.52 m/s in summer and rainy seasons, respectively. The rainfall result was 0.11 ± 0.19 mm in summer but there was no rainfall detected in the sampling period of rainy seasons. According to previous studies, the meteorological conditions, as mentioned above, can influence on increasing or decreasing the PM concentrations (Gangwar et al. 2016; Outapa and Ivanovitch 2019; Xu et al. 2017). However, in this study, there was only a slight difference in meteorological conditions between summer and rainy seasons. Thus, it might not much affect on seasonal variation of PM2.5 and PM10 in this study. The relationship of PM levels at each sampling point was also investigated. PM2.5 and PM10 ratios were calculated, as presented in Table 4, in order to characterize the spatial distribution of PM in this e-waste dismantling community. The result showed the proportion of PM2.5 and PM10 in summer were 0.46, 0.41, 0.42 and 0.58 at e-waste dismantling area, non-e-waste dismantling area, open dump area, and reference area, respectively. For the rainy season, PM2.5 and PM10 ratio at e-waste dismantling area, non-e-waste dismantling area, open dump area, and reference area were 0.74, 0.77, 0.63 and 0.75, respectively. The higher PM2.5 and PM10 ratio in the rainy season at all sampling points were indicated that more contribution of PM2.5 occurred in this season, which might cause by more anthropogenic activities, as stated above. Additionally, PM ratios between e-waste dismantling area, non-e-waste dismantling area, open dump area and reference area of summer were similar to each other as same as those ratios in the rainy season.

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4 Conclusion The study presents the spatial distribution of PM2.5 and PM10 concentration in ambient air at the e-waste dismantling community of Dangeyai and Banpao subdistrict, Buriram province. The dismantling activities of e-waste processed by informal separators or in family-run workshops could affect a noticeable contribution of PM10 and PM2.5 concentrations in both summer and rainy seasons. Both PM2.5 and PM10 concentrations in summer and rainy seasons were mostly highest at open dump area that could be influenced by the activities such as wire and plastic burning, cathode-ray tube screen (CRT) beating at the open dump site. The concentration of PM2.5 and PM10 that higher in rainy season might be elevated by other additional anthropogenic sources than only e-waste dismantling activities such as forest fire, agricultural waste burning in the surrounding area. The ratios of e-waste, non-e-waste dismantling, and open dump area compared with reference area giving more than 1 at all sampling points could indicate that e-waste dismantling activities could influence more contribution of PM2.5 and PM10 levels than reference area that not have any e-waste dismantling activities. Additionally, non- and e-waste dismantling houses are located nearby each other in the same community, and the study area is plain terrain, so the PM could be widely and well distributed. The overall result of this study could be implied that e-waste dismantling activities such as using hammers, screwdrivers, chisels, and bare hands to separate materials or burning cables to recover copper, burning unwanted plastics and foams in the open air could distribute the PM into surrounding area at the e-waste dismantling community. Moreover, it is important to verify the source strength of the PM explicitly; further analysis of PM composition and comparison between all sampling sites should be investigated. Acknowledgements This research was financially supported by National Research Council of Thailand (NRCT) under Thailand Research Challenge Program for WEEE ad Hazardous Waste. The publication of this study was partially supported by the Research Program of Municipal Solid Waste and Hazardous Waste Management, Center of Excellence on Hazardous Substance Management (HSM), the S&T Postgraduate Education and Research Development Office (PERDO), the Office of Higher Education Commission (OHEC). Moreover, we are deeply grateful to all local organizations and houses owner for their kindly cooperation and the involved authorities.

References Chantanusornsiri W (2019) https://www.bangkokpost.com/thailand/general/1484385/e-wasteshunned-by-china-piles-up-in-thailand. Last accessed 1 Nov 2019 Fang W, Yang Y, Xu Z (2013) PM10 and PM2.5 and health risk assessment for heavy metals in a typical factory for cathode ray tube television recycling. J Environ Sci 24(4):665–674 Gangwar C, Singh A, Kumar A, Chaudhry AK, Tripathi A (2016) Appraisement of heavy metals in respirable dust (PM10) around e-waste burning and industrial sites of Moradabad: accentuation

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on spatial distribution, seasonal variation and potential sources of heavy metal. IOSR J Environ Sci Toxicol Food Technol 10(6):14–21 Outapa P, Ivanovitch K (2019) The effect of seasonal variation and meteorological data on PM10 concentrations in Northern Thailand. Inter J Geomate 16(56):46–53 Pollution Control Department (PCD) (2013) Thailand state of pollution report 2012. Sor Mongkol Printing, Bangkok Pollution Control Department (PCD) (2019) Thailand state of pollution report 2012. Sor Mongkol Printing, Bangkok Senet S (2019) https://www.euractiv.com/section/circular-economy/news/thailand-new-dumpsitefor-western-e-waste/. Last accessed 27 Nov 2019 Thongkaow P, Prueksasit T, Siriwiong W (2019) Material flow of informal electronic waste dismantling in rural area of Northeastern Thailand. In: 139th the IIER international conference on proceedings. IRAJ, India, pp 12–15 Uddin MDJ (2012) Journal and conference paper on (environment) e-waste management. IOSR J Mech Civil Eng (IOSRJMCE) 2(1):25–45 Vassanadumrongdee S (2019) http://www.eric.chula.ac.th/download/ew58/ew_pocd.pdf. Last accessed 06 Dec 2019 Xu G, Jiao L, Zhang B, Zhao S, Yuan M, Gu Y, Liu J, Tang X (2017) Spatial and temporal variability of the PM2.5 /PM10 ratio in Wuhan, Central China. Aerosol Air Qual Res 17:741–751 Xue M, Yang Y, Ruan J, Xu Z (2012) Assessment of noise and heavy metals (Cr, Cu, Cd, Pb) in the ambience of the production line for recycling waste printed circuit boards. Environ Sci Technol 22(7):356–364 Zheng X, Xu X, Yekeen TA, Zhang Y, Chen A, Kim SS, Dietrich KN, Ho S, Lee S, Reponen T, Huo X (2016) Ambient air heavy metals in PM2.5 and potential human health risk assessment in an informal electronic-waste recycling site of China. Aerosol Air Qual Res 16(2):388–397

Blood Lead and Cadmium Levels of E-waste Dismantling Workers, Buriram Province, Thailand Thidarat Sirichai, Tassanee Prueksasit, and Siriporn Sangsuthum

Abstract E-waste dismantling activities can release heavy metals into the environment. Heavy metals cause environmental problems and adverse health effects, especially exposure to lead (Pb) and cadmium (Cd) of e-waste dismantling workers. The health risk of lead and cadmium from e-waste dismantling was evaluated. Blood lead and cadmium levels, which serve as biomarkers, of e-waste dismantling and none-waste dismantling workers in the local community (Buriram province, Thailand) were determined using inductively coupled plasma mass spectrometry (ICP-MS). These levels were then compared using paired samples Mann-whitney test (U-test) with significance level at p < 0.05. A total of 60 subjects from Daeng Yai Sub-district, Ban Mai Chaiyapot district were selected and divided into two groups, comprising 30 e-waste dismantling workers and 30 non-e-waste workers who live in villages not involved in e-waste dismantling. The results showed that the mean blood lead level of e-waste workers (6.61 ± 3.07 µg/dl) was significantly higher than that of non-ewaste workers (2.73 ± 0.49 µg/dl) with p < 0.05, while the mean blood cadmium level of e-waste workers was slightly lower than that of non-e-waste workers with values of 1.00 ± 0.33 µg/l and 1.17 ± 0.39 µg/l, respectively. These results suggest that e-waste dismantling workers have a higher risk of lead exposure from e-waste

T. Sirichai International Postgraduate Program in Hazardous Substance and Environmental Management (IP-HSM) Graduated School, Chulalongkorn University, Bangkok 10330, Thailand e-mail: [email protected] T. Prueksasit (B) Department of Environmental Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand e-mail: [email protected] Research Program of Municipal Solid Waste and Hazardous Waste Management, Center of Excellence on Hazardous Substance Management, Bangkok 10330, Thailand S. Sangsuthum Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok 10330, Thailand e-mail: [email protected] © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_34

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dismantling activity and therefore workers should be recommended to use personal protective equipment during dismantling activities. Keywords Lead · Cadmium · E-waste dismantling · Biomarker

1 Introduction The rapid growth of consumer electrical appliances and electronic equipment, e.g., televisions, mobile phones, and computers, has led to fast growing waste worldwide. The consumers often change old electrical appliances and electronic equipment before they become non-working, end of useful life and unwanted; finally, it will turn into an electronic scrap called “Electronic waste or E-waste” (Oguri et al. 2018; Rao et al. 2017; Awasthi et al. 2016). Informal electronic waste dismantling poses direct effects on human health with severe consequences (Cayumil et al. 2016); furthermore, sources of exposure to potentially hazardous chemical elements such as chromium, mercury, copper, nickel, and arsenic are informal electronic waste dismantling activities (Grant et al. 2013) including lead and cadmium. Previous studies in China, Ghana, and Vietnam have established that the e-waste activities lead to the elevation of the concentration of lead and cadmium in blood of e-waste dismantling workers (Wittsiepe et al. 2017; Srigboh et al. 2016; Schecter et al. 2017; Mittal et al. 2011). Therefore, informal e-waste dismantling workers easily exposed to toxins emitted from dismantling and resource recovery processes (Orlins and Guan 2016). Lead and cadmium could pose serious health risks to e-waste dismantling workers. Lead is an important toxic in e-waste because lead is used as a component in electrical and electronic equipment. Approximately 2 kg of lead was contained in cathode ray tubes (CRTs) and computer monitors (Herat 2008; Niu et al. 2012), and 1.5–2 kg of lead was contained in an old CRT television (US EPA 2008). Besides, lead is also used in solder circuit boards (Ramesh et al. 2007). A human can adsorb lead through inhalation of lead dust or lead can enter the human body through ingestion of lead-contaminated food/drinks (Patrick 2006). Normally, the respiratory and gastrointestinal systems are routes through which lead can enter the human body. Furthermore, lead can be rapidly absorbed in the respiratory system (Agency for Toxic Substances and Disease Registry 1999). Cadmium (Cd) is a chemical element that is relatively abundant in the environment. Cadmium is generally considered to be toxic and carcinogenic and therefore it is described as toxic to organisms and the environment. Moreover, cadmium is a good electrical conductor and is resistant to corrosion and attack by chemicals. Ordinarily, cadmium is a component of phosphate fertilizers, batteries, cathode ray tubes, and some semiconductors; it is also released during incineration of electronic wastes, and is present in cigarettes (US EPA 2009). Thus, these activities can release cadmium into the environment. Approximately 5– 50% of cadmium enter the human body by inhalation exposure through dust and fumes. As a result, inhalation exposure is the main route of cadmium exposure. In addition, humans may be exposed to cadmium through intake of contaminated food

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at the workplace. Exposure to cadmium can affect tissues such as kidneys, lungs, and bones. Assessments of exposure to environmental pollutants are based on biological monitoring, which is a scientific method to measure and assess human exposure to environmental chemicals. Metabolites in human tissue and body (blood, feces, urine, hairs, and nails, which serve as biomarkers) are used to determine human exposure to heavy metals, which are contaminants in the environment (Król et al. 2013; Haines and Murray 2012; Barbosa et al. 2015). Moreover, biomarkers were used to investigate the routes of exposure and effects, which depend on the toxicokinetics of heavy metals (Sobus et al. 2010). The evaluation of lead and cadmium exposure of humans is normally done using blood lead levels (BLLs) and blood cadmium levels (BCLs). As lead is bound to the hemoglobin component of erythrocytes in red blood cells, the vascular system circulates it to the liver, bones, kidneys, and hair. Blood lead is measured its concentration from human exposure to lead (Sakai 2000). Inhalation is considered the primary route of exposure to cadmium and 100% of cadmium is absorbed into the blood. Cadmium binds to albumin like an enzyme in red blood cells and is transported to the liver (Keil and Mcmillin 2011). To evaluate the health risks of lead and cadmium released from e-waste dismantling activity, we aimed to determine and compare the BLLs and BCLs of e-waste dismantling workers and non-e-waste workers within Buriram province, Thailand. Hopefully, the results from this study would raise awareness among the workers to minimize exposure pathways in order to decrease the involved potential risk from their e-waste dismantling activity.

2 Method 2.1 Study Area This study was conducted at Dang Yai sub-district, Ban Mai Chaiyapot district in Buriram province, Thailand. This area was found to be the second largest electronic waste dismantling community of Thailand. The main electronic waste dismantled by local people are televisions, washing machines, refrigerators, VCD/DVD players, computers, and air conditioning units. In addition, e-waste dismantling in this area generates an average income of approximately 2000–2500 USD/year, which is causing many residents to be interested in e-waste dismantling (Puangprasert and Prueksasit 2019). Moreover, this district consists of nine villages, of which five villages are e-waste dismantling areas, while the remaining four villages are non-ewaste dismantling areas. The main occupations of the local population in non-e-waste dismantling areas are agriculture and merchant.

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2.2 Study Design and Sample The total samples of this study was assigned as 60 subjects, who were divided into two groups: 30 subjects were e-waste dismantling workers (exposed group) and 30 subjects were non-e-waste dismantling workers (control group). All the subjects voluntarily participated in this study. The e-waste dismantling workers dismantled components of electrical or electric equipment by a primitive method, which involves informal methods such as cutting, scattering, and removing valuable materials for sale. These activities result in e-waste workers being directly exposed to heavy metals. The control group consists of people who are not e-waste dismantling workers, live in a non-e-waste dismantling area, and was assigned as approximately five kilometers away from the e-waste dismantling activity. The inclusion criteria for both subjects were male or female, 18–65 years old, non-smoker, and no history of disease associated with the interpretation of blood lead and cadmium levels such as liver disease, kidney disease, and hemolytic anemia or icteric (3+ ).

2.3 Sampling and Data Collection This study involved experiments conducted with human subjects, which consist of blood sample collection and questionnaires. The research ethics on human was approved by the International Conference on Harmonization—Good Clinical Practice (ICH—GCP), Chulalongkorn University, COA No. 217/2561. Blood collection and storage Blood samples (6–8 ml) were collected via venipuncture by nurses from a local hospital. Then, blood samples were separated into two tubes for analyzing blood lead and cadmium levels and creatinine in serum. First, blood samples (3 ml) were stored in trace metal free tubes. Then, ethylenediamine tetra-acetic acid (EDTA) was added as an anticoagulant and blood samples were mixed well. Finally, blood samples were immediately placed in an icebox at the site of blood collection and transferred to keep in a refrigerator at −20 °C (Dix-Cooper and Kosatsky 2018). The remaining 3–5 ml blood samples were stored in a clotted blood tube and left to settle for approximately 20 min. Then, the blood samples were centrifuged using a centrifuge (H-11D, Kokusan Enshinki) at 3000 rpm for 10 min, in which blood samples were separated into red blood cells and serum. Serum samples were immediately placed in an icebox and transferred to keep in a refrigerator at − 20 °C. Finally, plasma and serum samples were stored until the next analysis step. Questionnaire data collection The questionnaires were conducted by personal interview and used for gathering personal information of e-waste workers (exposed group) and non-e-waste workers (control group). The questionnaire of case group was prepared to collect information on their age, gender, weight, high, underlying disease, and occupational disease or symptoms from cadmium and lead poisoning. For

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example, symptoms of lead poisoning are squeamish and vomiting, irritability, muscle pains, and fatigue, while irritation of the respiratory tract, dyspnea, chest pains, headache, and muscular weakness are notified as cadmium poisoning. Moreover, there are working condition including working hours, working years, and working periods. The questionnaire of the control group was used to gather personal information related to their age, gender, weight, height, underlying disease, the main occupation of their family, and drinking water source. Further, some diseases that might affect the detection of lead and cadmium levels in the blood, such as anemia and kidney diseases, were assessed.

2.4 Analysis of Blood Sample for Lead and Cadmium Blood samples were analyzed by Special Laboratory Center Co. Ltd. Sathorn, Thailand. This laboratory is certified by the U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. Blood samples were extracted by acid digestion method following the method instructed by Atlanta (2012). Then, the samples were analyzed by Inductively Coupled Plasma Spectrometry-Mass Spectrometry (ICP-MS) following the method instructed by Centers for Disease Control and Prevention (Centers for Disease Control and Prevention 2004).

2.5 Analysis of Creatinine in Serum The concentration of creatinine in serum samples was analyzed by Health Sciences Service Unit, the Faculty of Allied Health Sciences, Chulalongkorn University, which passed ISO 15189 and ISO 15190 certifications. The samples were determined following the enzymatic method and assay on the ARCHITECT c Systems™ and the AEROSET System.

2.6 Statistical Analysis Statistical analysis of the data was done using SPSS for Windows Release 22.0 (SPSS Inc.). Blood lead and cadmium levels of e-waste and non-e-waste workers are expressed as mean ± standard deviation (SD). The mean blood lead and cadmium levels of both groups were compared by U-test (p < 0.05) and Chi-square was used to investigate the relationship between their blood lead and cadmium levels with associated factors.

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3 Result and Discussion 3.1 Demographic Characteristics and Background Personal Information of Participants Results of the questionnaire survey on the demographic characteristics of the 60 subjects in this study are shown in Table 1. It was found that the majority gender of e-waste workers was 18 females (56.7%) and 12 males (43.3%). The proportion of female and male non-e-waste workers were 24 persons (80%) and 6 persons (20%), respectively. The comparable age of the subjects with the mean ages of e-waste and non-e-waste workers were 47.71 ± 10.24 and 49.20 ± 9.43 years, respectively. Moreover, the ages range of both groups was from 27 to 64 years and 23 to 65 years, respectively. This study found that the average age of the both groups were higher than these subjects of the informal e-waste workers in Ghana (Wittsiepe et al. 2017; Srigboh et al. 2016). Similarly, the age range of e-waste recycling workers in Ghana (15–60 years) is lower than the age range of workers in this study (Srigboh et al. 2016). The average weight, height, and BMI of the e-waste workers were 62.33 ± 8.56 kg, 159.17 ± 6.86 cm, and 24.55 ± 2.56 kg/m2 , respectively, and 60.07 ± 5.82 kg, 160.27 ± 5.56 cm, and 23.45 ± 2.68 kg/m2 for non-e-waste workers. Moreover, the results of the working condition mentioned as working hour (h/day), working day (day/week), and working period (years) are shown in Table 2. The results explained that the average working hours, working day, and working period are 7.27 ± 1.46 h/day, 6.43 ± 1.25 day/week, and 9.52 ± 4.52 years, respectively. The ranges of the working conditions are 4–9 h/day for working hours, 3–7 day/week for working day, and half a year to 20 years for working period. Table 1 Demographic characteristics and background personal information of the participants Characteristics

E-waste workers

Non-e-waste workers

Range

Mean ± SD

Range

Mean ± SD

Age (year)

27–64

47.71 ± 10.24

23–65

49.20 ± 9.43

Weight (kg)

48.00–80.00

62.33 ± 8.56

50.00–72.00

62.33 ± 8.56

Height (cm)

150.00–175.00

159.17 ± 6.86

150.00–172.00

160.27 ± 5.56

BMI (kg/m2 )

19.23–29.43

24.55 ± 2.56

17.65–30.30

23.45 ± 2.68

Table 2 Working conditions of e-waste dismantling workers Factors

Range

Mean ± SD

Median

Working hour (h/day)

4–9

7.27 ± 1.46

Working day (day/week)

3–7

6.43 ± 1.25

7

Working period (years)

0.5–20

9.52 ± 4.52

10

8

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3.2 Blood Lead and Cadmium Levels Blood lead levels of the e-waste and non-e-waste dismantling workers were statistically significantly different. Mean lead levels in the blood of the e-waste dismantling workers (6.61 ± 3.07 µg/dl) were significantly higher than those of the non-e-waste dismantling workers (2.73 ± 0.49 µg/dl) at p < 0.05, as shown in Fig. 1. Surprisingly, the maximum blood lead level (19.50 µg/dl) is three times higher than the mean. Likewise, Wittsiepe et al. (2017) found that informal e-waste processing activities in Agbogbloshie, Accre, Ghana caused the median concentration of lead in the blood of workers to be significantly higher that of non-e-waste workers (88.5 vs. 41.0 µg/l, p < 0.001). Therefore, these activities increased the ambient concentration of heavy metals and contributed to human exposure to toxics. The observed mean blood lead levels of this study are lower than those of the research in Agbogbloshie, Accre, Ghana. Further, blood lead levels of the workers in this study are higher than those of workers in Ghana as reported by Amankwaa et al. (2017). Although blood lead level (BLLs) of e-waste dismantling workers in this study are lower than the Thai Biological Exposure Indices (Thai BEIs) values for blood lead levels of the occupationally exposed (30 µg/dl), the means lead (Pb) from e-waste dismantling activity could affect their health. In contrast to blood lead levels, blood cadmium levels of e-waste and non-ewaste dismantling workers were not statistically significantly different. The mean blood cadmium levels of e-waste and non-e-waste dismantling workers are 1.00 ± 0.33 µg/l and 1.17 ± 0.39 µg/l, respectively. Non-e-waste dismantling workers had higher blood cadmium levels (0.44–1.86 µg/l) than e-waste workers (0.33–1.58 µg/l). Similarly, Wittsiepe et al. (2017) reported that cadmium levels in the blood of the exposed and control groups were not different. When compared to the blood cadmium levels of another study, blood cadmium levels in this study are slightly lower than 1.70 µg/l and 1.40 µg/l recorded for e-waste workers and non-e-waste workers, respectively, at the Agbogbloshie site, Ghana, by Srigboh et al. (2016).

Fig. 1 Comparison of blood lead levels of the control group and exposed group (a) and blood cadmium levels of the control group and exposed group (b)

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The blood cadmium levels of e-waste dismantling workers and non-e-waste dismantling workers in this study are lower than the Thai BEIs value of 5 µg/l (Department of Disease Control, Ministry of Public Health 2016). However, it seems that additional factors besides e-waste dismantling activity affected blood cadmium levels in this study area.

3.3 Creatinine in Serum Creatinine blood test is a method for measuring waste products in the body and kidney function, which means that creatinine in serum can indicate the effects of kidney function. The mean creatinine levels in serum of e-waste dismantling workers and non-e-waste dismantling workers were 0.92 ± 0.23 mg/dl and 1.19 ± 0.28 mg/dl, respectively. The normal range of creatinine is 0.73–1.18 mg/dl for males and 0.55– 1.02 mg/dl for females (Junge et al. 2004). This study found that 70% of the non-ewaste workers have creatinine levels higher than the normal range, while the corresponding value for e-waste workers is only 10%. However, more laboratory testing such as creatinine clearance and blood urea nitrogen (BUN) level is required to confirm that the function of the kidney is impaired.

4 Conclusion The observed blood lead levels of e-waste dismantling workers in Daeng Yai Subdistrict, Ban Mai Chaiyapot district, Buriram province, Thailand were significantly higher than those of non-e-waste workers. However, blood cadmium levels of non-ewaste workers were slightly higher than those of e-waste workers. The mean of both groups was not significantly different. Moreover, blood lead and cadmium levels of both groups were lower than the Thai Biological Exposure Indices value. This indicates that the workers might be exposed to lead from e-waste dismantling activities, which result in an elevated risk of lead exposure. Acknowledgements This study was financially supported by the Research Program of Municipal Solid Waste and Hazardous Waste Management, Center of Excellence on Hazardous Substance Management (HSM), the S&T Postgraduate Education and Research Development Office (PERDO), the Office of Higher Education Commission (OHEC). Moreover, the authors are grateful to all of the participants for the voluntary allowance to collect blood samples and supporting information. Finally, the authors appreciate the continued support of Ban Pao Pattana Health Promoting Hospital, Dang Yai sub-district, Ban Mai Chaiyapot district, and Ban Pao Health Promoting Hospital, Ban Pao sub-district, Putthisong district in Buriram province, Thailand.

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References Agency for Toxic Substances and Disease Registry (1999) Toxicological Profile for Lead (online). Available from: https://www.atsdr.cdc.gov/toxprofiles/TP.asp?id=96&tid=22 Atlanta (2012) Agency for toxic substances and disease registry. Public Health Statement for Cadmium (online). Available from http://www.atsdr.cdc.gov/phs/phs.asp?id=46&tid=15# bookmark03 Awasthi AK, Zeng X, Li J (2016) Relationship between e-waste recycling and human health risk in India: a critical review. Environ Sci Pollut Res 23(18):1–24 Barbosa F, Tanus-Santos E, Gerlach F, Parsons J (2015) A critical review of biomarkers used for monitoring human exposure to lead: advantages, limitations, and future needs. Environ Health Perspect 113:1669–1674 Cayumil R, Khanna R, Rajarao M, Ikram-ul-Haq PS, Mukherjee, Sahajwalla V (2016) Environmental impact of processing electronic waste—key issues and challenges. In: Florin-Constantin Mihai (ed) E-waste in transition—from pollution to resource. InTech, pp 9–35 Centers for Disease Control and Prevention (2004) Laboratory Procedure Manual (online). https:// www.cdc.gov/nchs/data/nhanes/nhanes_09_10/pbcd_f_met.pdf Department of Disease Control, Ministry of Public Health (2016) Thai Biological Exposure Indices: Thai BEIs (online). http://pr.ddc.moph.go.th/pakard/showimg4.php?id=1334 Dix-Cooper L, Kosatsky T (2018) Blood mercury, lead and cadmium levels and determinants of exposure among newcomer South and East Asian women of reproductive age living in Vancouver, Canada. Sci Total Environ 619–620:1409–1419 Grant K, Goldizen F, Sly P, Brune M, Neira M, Berg M, Norman R (2013) Health consequences of exposure to e-waste: a systematic review. Lancet Glob Health 350–361 Haines A, Murray J (2012) Human biomonitoring of environmental chemicals-early results of the 2007–2009 Canadian Health Measures Survey for males and females. Int J Hyg Environ Health 215:133–137 Herat S (2008) Recycling of cathode ray tubes (CRTs) in electronic waste. Clean-Soil Air Water 36:19–24 Junge W, Wilke B, Halabi A, Klein G (2004) Determination of reference intervals for serum creatinine, creatinine excretion and creatinine clearance with an enzymatic and a modified Jaffé method. Clin Chim Acta 344(1–2):137–148 Keil D, Mcmillin AG (2011) Testing for toxic elements: a focus on arsenic, cadmium, lead, and mercury. Labmedicine 42(12):735–742 Król S, Zabiegała B, Namie´snik J (2013) Human hair as a biomarker of human exposure to persistent organic pollutants (POPs). TrAC Trends Anal Chem 47:84–98 Mittal A, Malviya M, John J (2011) High blood lead levels in e-waste recyclers. Epidemiology 22:293 Niu RX, Wang ZS, Song QB, Li JH (2012) LCA of scrap CRT display at various scenarios of treatment. Procedia Environ Sci 16:576–584 Oguri T, Suzuki G, Matsukami H, Uchida N, Tue NM, Viet PH, Takigami H (2018) Exposure assessment of heavy metals in an e-waste processing area in northern Vietnam. Sci Total Environ 621(1):1115–1123 Orlins S, Guan D (2016) China’s toxic informal e-waste recycling: local approaches to a global environmental problem. J Clean Prod 114:71–80 Patrick L (2006) Lead toxicity, a review of the literature. Part 1: exposure, evaluation, and treatment. Alternative medicine review. J Clin Ther 11:2–22 Puangprasert S, Prueksasit T (2019) Health risk assessment of airborne Cd, Cu, Ni and Pb for electronic waste dismantling workers in Buriram Province, Thailand. J Environ Manage 252 Ramesh BB, Parande A K, Basha CA (2007) Electrical and electronic waste: a global environmental problem. Waste Manage Res 25:307–318 Rao MN, Sultana R, Kota SH, Shah A, Davergave N (2017) Solid and hazardous waste management: science and engineering, 1st edn. Butterworth-Heinemann, Oxford

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Major Microorganisms Involved in Nitrogen Cycle in Plateau Cold Region and Its Relationship with Environmental Factors Jianwei Wang, Tianling Qin, Fang Liu, Baisha Weng, Kun Wang, Xiangnan Li, Hanjiang Nie, and Shanshan Liu Abstract In order to find the major microorganisms involved in nitrogen cycle and analysis the relationship with environment factors in plateau cold region, the representative soil types in the Huangshui river basin (above Xining city) were sampled. High-throughput sequencing was used to study the diversity of soil bacteria, and the composition and abundance of bacteria in the samples were analyzed. With the help of significance test regression analysis, the correlation between species abundance and environmental factors was analyzed, and the spatial distribution and causes of soil nitrogen were further analyzed. The results showed that major microorganisms main genera involved in nitrogen cycle are Nitrospira Bacillus, Nocardioides and Bradyrhizobium. And there was a negative correlation between soil nitrogen content and elevation, a significant negative correlation between Bradyrhizobium and soil TN, and a negative correlation between Nitrospira and Nocardioides species abundance. This study makes a preliminary exploration on major microorganisms involved in nitrogen cycle in plateau cold region and its relationship with environmental factors. Keywords Plateau cold region · Spatial distribution of soil nitrogen · Soil bacteria

1 Introduction Nitrogen(N) is an essential nutrient for all organisms. At the same time, nitrogen is a key limiting nutrient element that controls plant growth and primary production in most terrestrial ecosystems (Elser and Hamilton 2007; Stanley et al. 2011). The biogeochemical cycle of nitrogen includes: nitrogen fixation (Postgate 1970), J. Wang · T. Qin (B) · B. Weng · K. Wang · X. Li · H. Nie · S. Liu State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China e-mail: [email protected] F. Liu College of Environmental Science and Engineering, Donghua University, Shanghai, China H. Nie Department of Hydraulic Engineering, Tsinghua University, Beijing, China © Springer Nature Switzerland AG 2020 H.-Y. Jeon (ed.), Sustainable Development of Water and Environment, Environmental Science and Engineering, https://doi.org/10.1007/978-3-030-45263-6_35

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nitrification (Kowalchuk and Stephen 2001), denitrification (Brandes et al. 2010; Zumft 1997; Lipschultz et al. 1981) and amination. Microbes drive the above processes through enzymes in their bodies (He and Zhang 2009; Falkowski 1997). The involvement of microbes in the nitrogen cycle is shown in Fig. 1 (Francis et al. 2007). Microorganism is the driving pump of nitrogen cycle, biological nitrogen fixation inputs nitrogen and denitrification outputs nitrogen, so as to maintain the dynamic balance of nitrogen cycle and nitrogen (Li et al. 2002). Research on nitrogen has been the focus of scholars at home and abroad. SCOPE (Scientific Committee on Problems of the Environment) has highlighted the “cycle and transformation of nitrogen” in the scientific plan for 1998–2001, and many core projects of IGBP have also Nitrogen biogeochemical cycle as its main research content (Gruber and Galloway 2008). Scholars explored the distribution of soil nitrogen and its related microbes from different perspectives. Land use is the main factor in the distribution of nitrogen-functional microbial communities in soil (Blaud et al. 2018). The community structure, diversity and abundance/biological stock of soil microorganisms are related to soil organic matter (Blaud et al. 2012, 2014), soil porosity (Kravchenko et al. 2014) and tillage (Helgason et al. 2010; Laine et al. 2018). Global warming will also affect the nitrogen cycle (Dawes et al. 2017). However, there are few studies on the distribution of soil nitrogen and the distribution of microbial communities on the slopes of plateau cold region. The Qinghai-Tibet Plateau, known as the roof of the world, is one of the most peculiar natural resources with the most peculiar ecological environment and the most abundant biological resources. At the same time, the microbial community structure and diversity of the Qinghai-Xizang

Fig. 1 Nitrogen cycle process. Notes amo represents ammonia monooxygenase;hao represents bacterial hydroxylamine oxidoreductase;nir represents nitrite reductase;nor represents nitric oxide reductase

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Plateau are very different from those of other regions, so it is of great scientific research value (Zhou 2014). The Huangshui river basin (Feng et al. 2013) (above Xining city) selected in the study is a first-level basin in the upper reaches of the Yellow River basin. Its average elevation is about 3000 m. It is located in the transitional zone between the Qinghai-Tibet Plateau and the Loess Plateau. It has its unique geographical location and ecological function, and plays an important role in the stability of plateau ecosystem. Therefore, the soil on the slope of Huangshui river basin (above Xining) was sampled, the spatial distribution of nitrogen elements and the distribution of microbial community in this area were analyzed, and the causes of formation were analyzed from the aspect of environmental factors. In order to provide basis and support for ecosystem protection and restoration in Huangshui river basin.

2 Materials and Methods 2.1 Sample Layout and Sample Collection According to the representative principle, the main soil types in the basin are selected for sampling. Through the ArcGIS platform, the main soil types in the basin are analyzed as calcareous soil, alpine soil and aqueous soil. The proportions of the three soil types in the whole watershed are 49.29%, 45.51% and 1.1% respectively. Based on the above situation, combined with the Google Earth software and the actual situation of the road, the sampling points are set up in the watershed, as shown in Fig. 2. From 9 to November 2018, samples were taken from Datong, Haiyan and Huangzhong counties in Qinghai Province. The principle of sampling is to select a soil mixture of 1 m below the surface of 10 cm. if the soil layer is less than 1 m, select the soil below the surface 10 cm to the deepest soil layer. When sampling, use the tool to plan the vertical section, as shown in Fig. 3a–c. Put on clean gloves. Then scrape the profile soil from top to bottom with a blade. collect it in a sterile sample bag. Then place it in the car refrigerator and transport it to the laboratory for high-throughput sequencing. In the second step, the soil sample was taken in an aluminum box at a section every 20 cm for the determination of soil water content. Finally, 100 g of mixed soil samples were taken and collected in a sterile bag and taken back to the laboratory to determine soil pH, total carbon content (TC), TN content (TN), total phosphorus content (TP), and soil organic matter (OM). As shown in Fig. 2, the S2, S3 and S6 sample soil types are calcareous soil, the S4 and S5 sample soil types are alpine soil, and the S1 sample soil type is aqueous soil.

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Fig. 2 Layout map of soil sample point on slope

Fig. 3 a Aqueous soil. b Calcareous soil. c Alpine soil

2.2 Test Parameters and Test Methods 2.2.1

Soil Physicochemical Properties

The soil physical and chemical parameters tested in this study include: soil weight moisture content, TC, TN, TP, pH and OM. The soil moisture content is obtained by drying the aluminum box soil sample. Isoprime 100 instrumentation method for

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Table 1 Basic situation of sampling points and basic properties of soil samples Soil number

Elevation/meter

Soil type

OM (g/kg)

pH

TP (g/kg)

TN (g/kg)

TC (g/kg)

S1

3050

Aqueous soil

9.42

8.6

0.42

0.646

24.7

S2

2823

Calcareous soil

45.1

8.0

0.56

2.365

29.3

S3

2630

Calcareous soil

14.2

8.1

0.52

0.953

21.9

S4

3160

Alpine soil

11

8.2

0.53

0.712

24.2

S5

3020

Alpine soil

40.1

8.0

0.41

1.965

25.5

S6

2380

Calcareous soil

20

8.3

0.49

1.163

35.1

S6

2380

Calcareous soil

20

8.3

0.49

1.163

35.1

determination of TC. Determination of pH by Glass electrode method (GB/T 69201986) Determination of TP with ammonium molybdate spectrophotometric (GB/T 11893-1989). Determination of TN by Alkaline Postassium Persulfate Digestion-UV Spectrophotometric Method. The basic situation of the sampling points is shown in Table 1.

2.2.2

16S RRNA Gene Sequence Analyses

The PCR reactions were conducted using the following program: 3 min of denaturation at 95 °C, 27 cycles of 30 sat 95 °C, 30 s for annealing at 55 °C, and 45 s for elongation at 72 °C, and a final extension at 72 °C for 10 min. PCR reactions were performed in triplicate 20 µL mixture containing 4 µL of 5 × Fast Pfu Buffer, 2 µL of 2.5 mM dNTPs, 0.8 µL of each primer (5 µM), 0.4 µL of Fast Pfu Polymeraseand 10 ng of template DNA. The resulted PCR products were extracted from a 2% agarose gel and further purified using the Axy Prep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using Quanti Fluor™-ST (Promega, USA).

2.3 Analysis Method Firstly, the spatial distribution of nitrogen in slope soil was analyzed, then the correlation between nitrogen and soil microbes was analyzed, and the correlation between soil microbial and soil environmental factors was further analyzed. Finally, the spatial distribution of nitrogen in slope soil was analyzed from the perspective of soil microbial. Analyze according to the process of “phenomenon—cause—result”.

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(1) Nitrogen spatial distribution analysis method based on regression analysis With the help of SPSS and EXCEL software, regression analysis and single factor analysis of variance were used to analyze the spatial distribution characteristics of nitrogen on soil slope from three aspects: elevation, soil type and land use. (2) Analysis method of soil bacterial community structure characteristics Based on the results of high-throughput sequencing, the genera containing norank, unclassified and unidentified in the taxonomic notes were classified as others. The abundance of genus was calculated and the heat map was drawn, and the dominant genus in the soil samples was screened to obtain the distribution characteristics of the soil bacterial community on the slope. Combined with the previous research results and literature review, the dominant bacteria related to nitrogen cycle were screened. (3) Correlation analysis method of soil nitrogen content, environmental factors and dominant species (participate in the nitrogen cycle) community abundance With the help of the data analysis tool in EXCEL 2016, the regression analysis of nitrogen and dominant bacteria (participate in the nitrogen cycle), nitrogen and environmental factors, dominant bacteria and environment factors were carried out, and Duncan post-test is passed was carried out. If the 0.05 significant level test is passed, it is considered that there is a significant correlation between the two variables, and the regression equation of the two variables is further established to study their changing rules.

3 Results 3.1 Characteristics of Bacterial Community Structure in Slope Soil 3.1.1

Overall Soil Bacterial Community Structure

The operational taxonomic unit (OUT) clustering of each sample and the corresponding species taxonomic lineage were counted. From 6 soil samples, the number of OUT was 12,650, distributed in 57 phyla, 112 classes, 146 orders, 298 families, 509 genera and 804 species. 99.3% of the sequences can correspond to phylum level, and 61.1% of the sequences can correspond to the genus level. Screen the bacteria with abundance greater than 1% and draw a heat map (Fig. 4). It can be seen from the figure that Actinobacteria, Proteobacteria, Acidobacteria and Chloroflexi are absolutely dominant, and the abundance is more than 10%. Furthermore, the heat map of the genus of the first 20 abundance was drawn (Fig. 5) to prepare for the follow analysis.

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Fig. 4 Abundance heat map of soil bacterial phylum

Fig. 5 Heat map of the first 20 abundance of soil bacteria genus

3.1.2

Analysis of Distribution Characteristics of Bacteria Associated with Nitrogen Cycle

The genus of bacteria associated with the nitrogen cycle in the top 20 abundance includes: Nitrospira, Bacillus, Nocardioides and Bradyrhizobium. The regression of the above four genera and elevation showed that the above four genera did not pass the significant level test of 0.05, indicating that the spatial distribution characteristics of the above four species were not obvious in this study.

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3.2 Correlation Between Soil Nitrogen Content and Community Abundance of Dominant Genus (Participate in the Nitrogen Cycle) Significant analysis showed that soil total nitrogen was negatively correlated with Bradyrhizobium (Table 2); Bradyrhizobium was positively correlated with soil OMC, and other genera were not significantly associated with environmental factors, but Nitrospira was negatively correlated with Nocardioides (Table 3).

4 Discussion Soil nitrogen is an essential element for plant growth and development. Revealing that the spatial distribution characteristics of soil total nitrogen plays an important role in ecological protection spatial distribution of nitrogen has a certain law. Through Table 2 Significant test results of nitrogen and dominant bacteria Name of genus

r

p

Nitrospira

0.17

0.74

Bacillus

0.04

0.94

Nocardioides

0.32

0.54

−0.83

Bradyrhizobium

0.036

* and italics indicate significant 0.01 ≤ P < 0.05

Table 3 Significant test results between microbial genus and environmental factors Nitrospira r

Bacillus p

r

p

Nocardioides

Bradyrhizobium

r

p

r

0.02

p

OGM

0.24

0.65

0.06

0.91

0.26

0.61

−0.885*

TC

0.15

0.78

−0.61

0.20

0.05

0.92

−0.25

0.64

TP

−0.39

0.44

−0.30

0.57

0.39

0.45

0.38

0.46

pH

−0.41

0.42

−0.42

0.40

0.09

0.86

0.49

0.32

Humid

0.13

0.81

−0.21

0.68

0.30

0.56

−0.59

0.21 0.25

Temave

−0.55

0.26

−0.57

0.23

0.36

0.49

0.56

Nitrospira

1.00

–

0.44

0.38

−0.872*

0.02

−0.47

0.35

Bacillus

0.44

0.38

1.00

–

−0.44

0.38

−0.16

0.77

Nocardioides

−.872*

0.02

−0.44

0.38

1.00

–

0.00

1.00

Bradyrhizobium

−0.47

0.35

−0.16

0.77

0.00

1.00

1.00

–

* and italics indicate significant 0.01 ≤ P < 0.05

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geostatistical analysis, soil nitrogen content has a certain correlation with soil physical and chemical properties and spatial geographic elements. From the perspective of nitrogen cycle, soil microbial community distribution is the key factor affecting soil nitrogen distribution, and soil microbial community distribution is closely related to environment. Therefore, the spatial distribution of nitrogen is studied, the distribution characteristics of microbial communities are analyzed, and the correlation between microbial distribution and environmental factors is sought to provide support for the study of soil nitrogen cycling mechanism.

4.1 Correlation Analysis Between Bradyrhizobium and TN Soil physicochemical properties are an important factor affecting nitrogen-fixing microbial communities (Liu et al. 2017). This study found the dominant genus Bradyrhizobium (Vaninsberghe et al. 2015) associated with nitrogen cycling in soil microbe. There was a significant negative correlation between the genus and TN through a significant test (P < 0.05). Studies have shown that Bradyrhizobium has a high nitrogen-fixing potential (Sarr et al. 2016) and increases the total nitrogen content in the roots of plants (Muñoz et al. 2016). It indicates that Bradyrhizobium causes more nitrogen in the soil to be transferred to the plants, which leads to a decrease in total nitrogen content in the soil. On the other hand, nitrogen-fixing microorganisms have a more competitive advantage in nitrogen-deficient soils. Changes in soil nitrogen content can affect the competition between nitrogen-fixing microorganisms and other soil microorganisms, thus affecting the community composition of nitrogen-fixing microorganisms (Zheng et al. 2015). This further indicates that Bradyrhizobium is negatively correlated with soil total nitrogen content.

4.2 Negative Correlation Analysis Between Nitrospira and Nocardioides Nitrospira takes up nitrification in the soil nitrogen cycle, and Nitrospira converts nitrite to nitrate (Daims et al. 2015). Nocardioides (Sánchezmarañón et al. 2017; Ahn et al. 2014) undertakes denitrification in the soil nitrogen cycle and reduces nitrate to nitrite (Zhang 2018). Studies have shown that Nocardioides and Nitrospira have no significant correlation with total nitrogen (Chen et al. 2018; Sanchez-Maranon et al. 2017) which is consistent with the findings of this paper. However, Nocardioides and Nitrospira are the opposite processes of the same chemical process, and they may mutually inhibit each other, so they have a negative correlation.

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5 Conclusion The content of nitrogen in slope soil of Huangshui river basin (above Xining city) decreased with the increase of elevation. The dominant soil bacterial communities were Actinobacteria, Proteobacteria, Acidobacteria and Chloroflexi. The main genera involved in nitrogen cycle are Nitrospira Bacillus, Nocardioides and Bradyrhizobium. There was a significant negative correlation between Bradyrhizobium and soil total nitrogen, and Nitrospira was negatively correlated with Nocardioides species abundance. This study makes a preliminary exploration on the spatial distribution and causes of soil nitrogen on the slope in plateau cold region, and draw some superficial conclusions, which provides the direction for further analysis in the later stage. In the next step, we will conduct a special study on the spatial distribution of nitrogen on soil slopes with different soil layer thickness, different surface cover types and whether there are human activities. To explore the distribution and function of microbial community in different situations, increase the test of ecological stoichiometric characteristics, and analyze the interaction mechanism among soil nitrogen, soil microorganisms and their environmental factors. It provides support for the study of soil nitrogen cycle mechanism in plateau cold region. Funding This work was supported by the National Key Research and Development Project (No. 2017YFA0605004), the National Science Fund for Distinguished Young Scholars (No. 51725905), the National Key Research and Development Project (No. 2016YFA0601503) and the Basic Scientific Research Business Expense of Yellow River Institute of Hydraulic Research (Grant no. HKY-JBYW-2016-25).

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