Advances in Water Resources Management for Sustainable Use [1 ed.] 9813364114, 9789813364110

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Lecture Notes in Civil Engineering

Pankaj Kumar Roy Malabika Biswas Roy Supriya Pal   Editors

Advances in Water Resources Management for Sustainable Use

Lecture Notes in Civil Engineering Volume 131

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

Lecture Notes in Civil Engineering (LNCE) publishes the latest developments in Civil Engineering - quickly, informally and in top quality. Though original research reported in proceedings and post-proceedings represents the core of LNCE, edited volumes of exceptionally high quality and interest may also be considered for publication. Volumes published in LNCE embrace all aspects and subfields of, as well as new challenges in, Civil Engineering. Topics in the series include: • • • • • • • • • • • • • • •

Construction and Structural Mechanics Building Materials Concrete, Steel and Timber Structures Geotechnical Engineering Earthquake Engineering Coastal Engineering Ocean and Offshore Engineering; Ships and Floating Structures Hydraulics, Hydrology and Water Resources Engineering Environmental Engineering and Sustainability Structural Health and Monitoring Surveying and Geographical Information Systems Indoor Environments Transportation and Traffic Risk Analysis Safety and Security

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Pankaj Kumar Roy Malabika Biswas Roy Supriya Pal •

•

Editors

Advances in Water Resources Management for Sustainable Use

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Editors Pankaj Kumar Roy School of Water Resources Engineering Jadavpur University Kolkata, West Bengal, India

Malabika Biswas Roy Department of Geography Women’s College, Calcutta Kolkata, West Bengal, India

Supriya Pal Department of Civil Engineering National Institute of Technology Durgapur Durgapur, West Bengal, India

ISSN 2366-2557 ISSN 2366-2565 (electronic) Lecture Notes in Civil Engineering ISBN 978-981-33-6411-0 ISBN 978-981-33-6412-7 (eBook) https://doi.org/10.1007/978-981-33-6412-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

This book is an outcome of the International conference on “Sustainable Water Resource Management under Changed Climate” organized by the School of Water Resources Engineering, Jadavpur University in joint collaboration with Department of Geography, Women’s College, Calcutta and National Institute of Technology, Durgapur, held in March 2020. The book has been edited to present the content of the conference where water resources management takes the prime role. We are much enraptured to introduce Advances in Water Resources Management for Sustainable Use, a text providing immense contribution to the field of water resources management exploring the relevant areas of physical, social, ecological, climatological and economic issues to safeguard the human and environmental demands and wastewater reuse including treatment technologies. The world population has been rapidly increasing up by 81 million people per year leading to the induced consumption of water resources growing with many more folds in agriculture and industrial hippodrome. No intervention has made an injunction leading to the greater impact upon socioeconomic development and public health than the provision of safe drinking water and proper sanitation facilities. There has been an enlarging gap between water demand and water availability which has been playing a pivot role in many developing economies as an urgent issue to solve with. Therefore, the objective of corroborating the water security for human world remains a significant provocation for most of the developed and developing nations. In order to endure the economic growth amidst increasing utilization pressures, particularly in the urban and rural ecosystems, a holistic and integrated perspective is a need to deal with. Thereby a collaborative venture is required as the solution of water crisis mitigation solely depends on full commitment of the stakeholders as well as indignation of knowledge and technologies pertaining to the field. This book will culminate every aspect of the integrated water resources management which is a driving process that encourages the unified action leading to the development and management of air-, water- and land-related resources without compromising the adversities and sustainability of the ecosphere. Thus, implementing integrated water resources management and conservation helps to protect v

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Preface

the world’s environment, stimulate economic advancement and sustainable agricultural innovations, encourage democratic consideration in governmental sectors and improve human overall health. At a global level, water policy, management and mitigation are uprising to reflect the intrinsically interconnected nature of hydrological processes, and sustainable water resources management is emerging as the most thriving alternative against the traditional style that dominated in the past. All organisms in an eco-system are dependent on water. The population of one organism rises or falls, depending on the availability of water. Water affects the ecosystem directly; the approach of sustainable development and ecological interdependencies of water and life is discussed as one of the important chapters in this book. The book includes lab results as well as case studies. It provides the significance of sustainable water use, as well as the recommendations and solutions for policy making and sustainable water management. Main issues highlighted in the book include the emerging issues of surface and groundwater dependent ecosystems, the impacts of climate change on water resource, water treatment and technological advancement, conjunctive use of surface and groundwater, water supply and network analysis, application of remote sensing and GIS in the water sector. The issues are contemporary, international and regional in level that will ensure the interdisciplinary nature of the water science. The book culminates how we can make a progressive move and solve scientific issues in a sustainable way. We believe that this book is a pioneering contribution that will inculcate knowledge among the community of researchers, technicians, managers and policy makers and is a must read.

Kolkata, India Kolkata, India Durgapur, India

Editors Prof. (Dr.) Pankaj Kumar Roy Dr. Malabika Biswas Roy Dr. Supriya Pal

Acknowledgements

It is our pleasure to present this Springer book chapter entitled Advances in Water Resources Management for Sustainable Use consisting of selected papers from this International Conference on Sustainable Water Resources Management under Changed Climate, 2020, organized by School of Water Resources Engineering, Jadavpur University in joint collaboration with Women’s College, Calcutta, Department of Geography and National Institute of Technology Durgapur. The conference focuses on issues related to water resource management in an interdisciplinary format which provides a common platform for deliberations to broad sections of the academic community. We would like to thank the chief patron of the conference—Prof. Suranjan Das, Hon’ble Vice Chancellor, Jadavpur University, patrons—Prof. Anupam Basu, Director, NIT Durgapur, Dr. Mahua Das, Principal, Women’s College, Calcutta, Dr. Pradip Kumar Ghosh, Pro-VC, Jadavpur University, Prof. Chiranjib Bhattacharjee, Pro-VC, Jadavpur University, president—Prof. Asis Mazumdar, organizing secretary—Prof. Pankaj Kumar Roy, joint coordinators—Dr. Supriya Pal, Dr. Hirok Chaudhuri, Dr. Mrinal Kanti Mandal, NIT Durgapur and other organizing joint secretary and working committee members for their relentless support. We would like to take this opportunity to thank all of the participants in the conference—invited speakers, presenters and audience alike. We would also like to extend our gratitude to the eminent reviewers of the original abstracts and the papers submitted for consideration in this volume for having so generously shared their time and expertise.

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Acknowledgements

Finally, we would like to acknowledge the help of all the research scholars involved in structuring this book and, more specifically, to the reviewers that took part in the review process. Without their support, this book would not have become a reality. Editors Prof. (Dr.) Pankaj Kumar Roy Dr. Malabika Biswas Roy Dr. Supriya Pal

Contents

Part I 1

2

3

4

5

Slope Stability Analysis Under Critical Conditions of Geogrid Reinforced Canal Embankment . . . . . . . . . . . . . . . . . . . . . . . . . . . Arindam Karmakar, Md. Nishat Afsar, and Supriya Pal Stability Analysis of a Riverbank for Different Microstructural Arrangements of the Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Debasish Biswas, Arijit Dutta, Sanchayan Mukherjee, and Asis Mazumdar Trend Analysis of Highly Cited Papers on Sustainable Watershed Management: A Bibliometric Review . . . . . . . . . . . . . . . . . . . . . . . Malabika Biswas Roy, Sudipa Halder, Arnab Ghosh, Snehamanju Basu, and Pankaj Kumar Roy Tri-Decadal Visualization Analysis on River Health Studies: A Global Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Malabika Biswas Roy, Swetasree Nag, Arnab Ghosh, and Pankaj Kumar Roy Role of Inland Dredging for Integrated Water Management . . . . . Mridul Kumar Sarkar

Part II 6

Emerging Issues on Surface Water Management Under Change Climate 3

11

23

39

57

Integrated Groundwater Management: An Over View of Challenges and Issues

A Study on Porous Bituminous Pavement as an Alternative Method for Ground Water Management . . . . . . . . . . . . . . . . . . . . Debashish Karmakar, Bappi Das, and Manish Pal

75

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7

Contents

Socio-economic Assessment of Arsenic and Iron Contamination of Groundwater and Feasibility of Rainwater Harvesting (RWH): A Case Study of Amdanga Block, North 24 Parganas, West Bengal, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Satabdi Biswas and Anupam Debsarkar

Part III 8

9

85

Water Quality Assessment and Prediction Modelling

Efficacy Assessment of Amended Laterite Soil as a Subsurface Liner to Attenuate Migration of Contaminants in Leachate of Ash Pond Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Avishek Adhikary, Shyamal Kumar Dutta Mazumdar, and Supriya Pal

99

Flood Mapping and Prediction Using FLO-2D Basic Model . . . . . . 109 Diptarshi Mitra, Dipankar Das, and Asim Ratan Ghosh

10 Perspectives on Chemical Warfare and Emergence of Antibacterial Resistance in Water Environment . . . . . . . . . . . . . 121 Minakshi Ghosh and Pankaj Kumar Roy 11 Removal of Hazardous Dyes from Waste Water in a Green and Cost-Effective Way . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Paramita Das and Chiranjib Bhattacharjee 12 Development of Low-Cost Arsenic Removal Process by Using Ion-Exchange Resins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Priyabrata Mondal, Pankaj Kumar Roy, Nil Sadhan Mondal, Saurabh Kumar Basak, and Arunabha Majumder 13 Water Quality Index Is an Important Tool of Groundwater: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Sanjib Das, Pankaj Kumar Roy, Gourab Banerjee, and Asis Mazumdar 14 A Critical Review of Various Arsenic and Iron Removal Plants Installed in North 24 Parganas District of West Bengal, India . . . . 169 Saurabh Kumar Basak, Pankaj Kumar Roy, Nil Sadhan Mondal, Arunabha Majumder, and Asis Mazumdar 15 Water Pollution in Damodar River Basin—A Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Trina Dutta, Hirok Chaudhuri, and Chiranjit Maji 16 Study of Groundwater Quality in a Part of North 24 Parganas, Under Gangetic West Bengal and Highlighting the Extent and Magnitude of Arsenic Contamination . . . . . . . . . . . . . . . . . . . 217 Yuvaraj Mondal, Pankaj Kumar Roy, Arunabha Majumder, and Susanta Ray

Contents

Part IV

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Modelling Extreme Climate Events: Intensity and Magnitude of Drought, Flood and Cyclone

17 Characteristics of Precipitation in the Changing Climatic Scenario in India: A Critical Observation . . . . . . . . . . . . . . . . . . . . 231 Rupam Sahu and Pankaj Kumar Roy 18 Assessment of Drought Using Multi-parameter Indices . . . . . . . . . . 243 Shuvoshri Bhattacharya, Sudipa Halder, Swetasree Nag, Pankaj Kumar Roy, and Malabika Biswas Roy Part V

Water Supply Network Analysis and Necessary Management

19 Laboratory Evaluation of Crumb Rubber Modified Asphalt Using Over Burnt Brick Aggregate . . . . . . . . . . . . . . . . . . . . . . . . . 259 Machavarapu Suresh, Anibrata Debnath, and Manish Pal 20 Experimental Investigation of Hot-Mix Asphalt Using Recycled Concrete Aggregate and Waste-Polymers . . . . . . . . . . . . . . . . . . . . 269 Machavarapu Suresh, Polisetty Uma Maheswara Manikanta, and Manish Pal 21 Comparative Study of Arsenic Removal Using Different Coagulants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Nil Sadhan Mondal, Pankaj Kumar Roy, Asis Mazumdar, and Arunabha Majumder 22 Removal of Hexavalent Chromium by Carbonaceous Material Derived from Sawdust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Vijoyeta Chakraborty, Papita Das, and Pankaj Kumar Roy Part VI

Water for People Water for Society

23 An Economic and Institutional Review of Water User Associations (WUAs) in Odisha . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Arnab Roy, M. N. Venkataramana, and G. Sagar 24 Impact of Heavy Metal Exposure on Newborn and Pregnant Women Associated with Leukocyte Carcinoma . . . . . . . . . . . . . . . . 311 K. Manoj Kumar and Anita Mukherjee 25 Trending Nature of Indian and Egyptian Independent Floodplain Research on River Ganga and Nile: A Bibliometric Analysis . . . . . 319 Malabika Biswas Roy, Arnab Ghosh, Abhishek Kumar, and Pankaj Kumar Roy 26 Study of Gumti Wetland in Connection with Its Socio-Economic Status: A Step Towards Sustainable Management Practices . . . . . . 333 Mihir Pal, Malabika Biswas Roy, and Pankaj Kumar Roy

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Contents

27 A Stochastic Approach to Evaluate Drinking Water Availability Status—A Case Study on Patharghata GP, Rajarhat CD Block, North 24 Paraganas, West Bengal, India . . . . . . . . . . . . . . . . . . . . 347 Ratnadeep Ray, Panchali Majumdar, and Madhusree Palit Part VII

Application of Remote Sensing and GIS in Water Sector

28 A SWOC Analysis and Smart Land Use Modelling for Chandipur-Erashal Census Town Cum Growth Center Due to Its Sustainable Journey Stimulating Regional Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Harekrishna Manna, Rabin Das, and Jibanananda Samanta 29 Remote Sensing of Water Quality Parameters Along the West Bengal Coast of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 Narendra Gonapa, Chiranjivi Jayaram, and K. Padma Kumari 30 Identification of Critical Watersheds Based on Morphometric Analysis and Prioritization of Sagar Island, India . . . . . . . . . . . . . 401 Sk Mohinuddin, Pankaj Kumar Roy, Malabika Biswas Roy, and Tuhin Ghosh 31 Soil Loss Estimation for Sustainable Watershed Conservation in Semi-arid Bengal Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 Sudipa Halder, Malabika Biswas Roy, Shuvoshri Bhattacharya, Souvik Mondal, and Pankaj Kumar Roy 32 Assessment of Topographic Complexity Zone of a Drainage Basin Using Geographic Information System . . . . . . . . . . . . . . . . . . . . . . 441 Swetasree Nag, Malabika Biswas Roy, Shuvoshri Bhattacharya, Souvik Mondal, and Pankaj Kumar Roy Part VIII

Ecological Interdependencies of Water and Life

33 An Overview of Construction Demolition Waste Management in India: Sustainable Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 Anshuman Pal and Pankaj Kr. Roy 34 An Evaluation of Engine Performance of a Compression Ignition Engine with Biodiesel Produced from Different Kinds of Feedstock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Apurba Sharma, Nabanita Banerjee, and Tushar Jash 35 Variation in Fuel Consumption with Load in Private Cars—Scenario in Real-Time Traffic Conditions . . . . . . . . . . . . . . 481 Atanu Dutta, Deepanjan Majumdar, and Tushar Jash

Contents

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36 Reflection of Soil–Water Relationship Under Different Land Use Pattern: A Case Study of Neora River, West Bengal, India . . . . . . 495 Debanjana Chatterjee, Malabika Biswas Roy, and Pankaj Kumar Roy 37 Agrivoltaic: A New Approach of Sustainable Development . . . . . . . 513 Kunal Chowdhury and Ratan Mandal 38 Feasibility Study on Energy Generation from Municipal Organic Waste Through Biogas Production . . . . . . . . . . . . . . . . . . . . . . . . . 523 Pramita Deb Sarkar, Pankaj Kumar Roy, Deep Ranjan Pal, and Malabika Biswas Roy 39 Plant Micronutrient Relationship with Water and Soil in Backdrop of Global Food Security Issue . . . . . . . . . . . . . . . . . . . 533 Sritama Chatterjee, Malabika Biswas Roy, Arunabha Majumder, and Pankaj Kumar Roy Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547

Editors and Contributors

About the Editors Prof. (Dr.) Pankaj Kumar Roy is currently the Dean of Interdisciplinary Studies, Law and Management of Jadavpur University and Professor and Joint Director of the Department of School of Water Resources Engineering, Jadavpur University (JU). He is also the Director of School of Environmental Studies, JU. He earned his doctorate in Water Resources Engineering and Management from Jadavpur University which was mainly concentrated on modelling hydrological regime under impact of climate change scenario. His thrust area includes hydraulics and water resources engineering, fluid mechanics, climate change and its impact on water resources engineering, surface water pollution and its mitigation, groundwater dynamics, watershed technology and management, water conservation technology, hydro-geological investigation, surface and groundwater interaction, solid waste management, environmental modelling, wastewater characteristics, etc. He is the recipient of several fellowships and awards such as Young Scientist (Institute of Engineers, Government of India), 1st National Doctorate Fellow by AICTE at JU (Government of India), Internship Fellowship (University of Pisa, Italy) and IEI Young Engineers Award (Institution of Engineers (India), Government of India). He has acted as the invited speaker and the chair-person in as many as 20 reputed national and international seminars, conferences and workshops. He was the co-ordinator of international Conference on

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Sustainable Water Resources Management conducted by School of Water Resources Engineering, Jadavpur University under collaboration with Women’s College Calcutta and National Institute of Technology, Durgapur. He has completed 20 national and international research projects funded by DST, PHED, etc., and has conducted 30 consultancy research items. Currently he is the principal investigator of 4 research projects funded by NRDMS, DST, RUSA 2.0, DST, Government of West Bengal, etc. He has more than 20 years of teaching and research experience. He is the reviewer of two reputed journals, namely, Groundwater for Sustainable Development, Elsevier and Environmental Science and Technology, ACS publications. He has published more than 140 articles in national and international journals and published 13 research articles as book chapters. He has acted as a sectional recorder of Engineering Science section of The Indian Science Congress Association, for the period of 2018–19 and 2019–20. He is now the member of as many as 10 number of learned societies. Dr. Malabika Biswas Roy is now currently working as an Assistant Professor and head of the Department of Geography, Women’s College Calcutta. She has acquired her Ph.D. from School of Water Resources Engineering, Jadavpur University. Her doctoral research is mainly focused on Participatory Management of Ecosystem Services: A Case of wetland of West Bengal. She has received many prestigious awards like RULA International Innovation and Betterment Award for Young Scientist of the Year (2018), 2017 by The Institution of Engineers (India), Young Scientist of the Year Award (2016) by IFEE and Kolkata, India and Bharat Jyoti Award (2014). She has an expertise in Wetland Studies, Hydrology, Fluvial Geomorphology, Forest Hydrology, Biodiversity, Water Quality Modelling and Impact of Climate Change on Wetland System. She has over 16 years of teaching and research experience and provides doctoral guidance to students. She has presented papers at many international-national conferences and has more than 63 national and international journal papers. She has more than 24 research articles in edited volumes as book chapters. She has given a number of lectures at national

Editors and Contributors

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and international conferences as an invited speaker and co-chaired many session on issues related to water resources. She was an invited speaker in 2nd World Congress on Climate Change, held in Berlin, Germany. She has field experience in various studies related to environmental studies and is currently acting as the co-investigator of Central DST, NRDMS funded project on coastal vulnerability entitled “Scientific Assessment of Coastal Vulnerability and Sustainable Ecosystem Management under Changed Climate Scenario”. She has completed one minor and one major research project as the principal investigator funded by UGC, Government of India and has completed 7 projects as a team member. At present she is the member of many learned societies of national and international repute. She is an active member of International Water Association. She was also the convener of the International Conference on Sustainable Water Resources Management under Changed Climate, 2020, organized by the School of Water Resources Engineering, Jadavpur University, in collaboration with Women’s College Calcutta and National Institute of Technology, Durgapur. She is the reviewer of two reputed journals, namely, Wetland (Springer) and Environment, Development and Sustainability (Springer). Dr. Supriya Pal is presently working as Associate Professor in the Department of Civil Engineering, National Institute of Technology Durgapur, West Bengal, India. He is having a teaching, research and industrial experience of more than 18 years in the field of geotechnical and geo-environmental engineering. He has done Ph.D. in Civil Engineering from Jadavpur University, Kolkata, India, and obtained Master of Civil Engineering from the same institution in the specialization of geotechnical engineering. He has a long-term working experience in the research areas: geotechnical engineering, geo-environmental engineering and solute transport through porous media, industrial wastewater treatment and electro-kinetic treatment of contaminated land. Recently he started research activities in the field of stability analysis of ash dykes, scientific study on open cast mine slope stability and metal extraction from fly ash. He has research collaboration with Far Eastern

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Editors and Contributors

Federal University, Russia (in metal extraction technology from fly ash); Tomsk Polytechnic University, Russia; Hohai University, East China University and Technology, China; Federal University of Rio De Janerio, Brazil; Institute for Water and Wastewater Technology, Durban University of Technology, South Africa (through BRICS NU Programme of Water Resources and Pollution Treatment). Presently he is working as investigator of three research projects funded by M/s Essar Oil, India; BRICS, BRICS STI Framework Programme; and RIG, MHRD. He has more than 25 publications in peer-reviewed international and national SCI, Scopus and Web of Science indexed journals and published 5 research articles as book chapters. He delivered more than 25 invited talks in various conferences, seminars, workshops and technical meetings held in India and abroad. Presently he is serving as the coordinator and ITG (International Thematic Group) member of the BRICS Network University Programme of NIT Durgapur in the thematic area of “Water Resources and Pollution Treatment”. He is also acting as Member of National Coordination Committee of BRICS Network University Programme under the Department of Higher Education, MHRD, Government of India. He is also acting as coordinator of State Technical Agency, Pradhan Mantri Gram Sadak Yojana (PMGSY), under the authority of the Ministry of Rural Development, Government of India.

Contributors Avishek Adhikary National Institute of Technology, Durgapur, West Bengal, India Gourab Banerjee School of Water Resources Engineering, Faculty of Interdisciplinary Studies, Law and Management, Jadavpur University, Kolkata, India Nabanita Banerjee School of Energy Studies, Jadavpur University, Kolkata, India Saurabh Kumar Basak School of Water Resources Engineering, Jadavpur University, Kolkata, India Snehamanju Basu Jadavpur University, Kolkata, West Bengal, India

Editors and Contributors

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Chiranjib Bhattacharjee Department of Chemical Engineering, Jadavpur University, Kolkata, India Shuvoshri Bhattacharya School of Water Resources Engineering, Jadavpur University, Kolkata, West Bengal, India Debasish Biswas Department of Mechanical Engineering, Kalyani Government Engineering College, Kalyani, India Satabdi Biswas Department of Geography, Mrinalini Datta Mahavidyapith, Kolkata, West Bengal, India Vijoyeta Chakraborty School of Water Resources Engineering, Jadavpur University, Kolkata, India Debanjana Chatterjee School of Water Resources Engineering, Jadavpur University, Kolkata, India Sritama Chatterjee School University, Kolkata, India

of

Water

Resources

Engineering,

Jadavpur

Hirok Chaudhuri Department of Physics and Center for Research on Environment and Water, National Institute of Technology Durgapur, Durgapur, West Bengal, India Kunal Chowdhury Depatment of Renewable Energy, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India; School of Energy Studies, Jadavpur University, Kolkata, India Bappi Das National Institute of Technology Agartala, Jirania, Tripura, India Dipankar Das Department of Science and Technology and Biotechnology, Government of West Bengal, Kolkata, India Papita Das Department of Chemical Engineering, Jadavpur University, Kolkata, India Paramita Das Department of Chemical Engineering, Jadavpur University, Kolkata, India Rabin Das Department of Geography (UG and Mahavidyalaya, Purba Medinipur, West Bengal, India

PG),

Bajkul

Milani

Sanjib Das School of Water Resources Engineering, Faculty of Interdisciplinary Studies, Law and Management, Jadavpur University, Kolkata, India Anibrata Debnath Department of Civil Engineering, NIT Agartala, Agartala, Tripura, India Anupam Debsarkar Department of Civil Engineering, Jadavpur University, Kolkata, West Bengal, India

xx

Editors and Contributors

Arijit Dutta Department of Mechanical Engineering, Kalyani Government Engineering College, Kalyani, India Atanu Dutta School of Energy Studies, Jadavpur University, Kolkata, India Trina Dutta Department of Chemistry, JIS College of Engineering, Kalyani, Nadia, West Bengal, India Arnab Ghosh School of Water Resources Engineering, Jadavpur University, Kolkata, West Bengal, India Asim Ratan Ghosh Department of Science and Technology and Biotechnology, Government of West Bengal, Kolkata, India Minakshi Ghosh School of Water Resources Engineering (S.W.R.E), Jadavpur University, Kolkata, India Tuhin Ghosh School of Oceanography Studies, Jadavpur University, Kolkata, India Narendra Gonapa School of Spatial Information Technology, Institute of Science and Technology, JNTU, Kakinada, Andhra Pradesh, India Sudipa Halder School of Water Resources Engineering, Jadavpur University, Kolkata, India Tushar Jash School of Energy Studies, Jadavpur University, Kolkata, India Chiranjivi Jayaram Regional Remote Sensing Centre—East, NRSC/ISRO, Kolkata, India Arindam Karmakar Department of Civil Engineering, National Institute of Technology, Durgapur, West Bengal, India Debashish Karmakar National Institute of Technology Agartala, Jirania, Tripura, India Abhishek Kumar Ballia Water Centre, Ballia, Uttar Pradesh, India K. Manoj Kumar Institute of Public Health and Hygiene, Mahipalpur, New Delhi, India Chiranjit Maji Department of Physics, National Institute of Technology Durgapur, Durgapur, West Bengal, India Deepanjan Majumdar School of Energy Studies, Jadavpur University, Kolkata, India Panchali Majumdar East Calcutta Girls’ College, Kolkata, India Arunabha Majumder School of Water Resources Engineering, Jadavpur University, Kolkata, India

Editors and Contributors

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Ratan Mandal School of Energy Studies, Jadavpur University, Kolkata, India Polisetty Uma Maheswara Manikanta Department of Civil Engineering, NIT Agartala, Agartala, India Harekrishna Manna Department of Geography (UG and PG), Bajkul Milani Mahavidyalaya, Purba Medinipur, West Bengal, India Asis Mazumdar School of Water Resources Engineering, Faculty of Interdisciplinary Studies, Law and Management, Jadavpur University, Kolkata, India Shyamal Kumar Dutta Mazumdar National Institute of Technology, Durgapur, West Bengal, India Diptarshi Mitra West Bengal State Council of Science and Technology, Department of Science and Technology and Biotechnology, Government of West Bengal, Kolkata, India; Salt Lake City, Kolkata, India Sk Mohinuddin School of Water Resource and Engineering, Jadavpur University, Kolkata, India Nil Sadhan Mondal School of Water Resources Engineering, Jadavpur University, Kolkata, India; Jangipur Government Polytechnic, Murshidabad, India Priyabrata Mondal School of Water Resources University, Kolkata, India

Engineering, Jadavpur

Souvik Mondal School of Water Resources Engineering, Jadavpur University, Kolkata, West Bengal, India Yuvaraj Mondal Regent Education and Research Foundation, Kolkata, India Anita Mukherjee Institute of Public Health and Hygiene, Mahipalpur, New Delhi, India Sanchayan Mukherjee Department of Mechanical Government Engineering College, Kalyani, India

Engineering,

Kalyani

Swetasree Nag School of Water Resources Engineering, Jadavpur University, Kolkata, West Bengal, India Md. Nishat Afsar Department of Civil Engineering, National Institute of Technology, Durgapur, West Bengal, India K. Padma Kumari School of Spatial Information Technology, Institute of Science and Technology, JNTU, Kakinada, Andhra Pradesh, India Anshuman Pal Jadavpur University, Kolkata, India

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Editors and Contributors

Deep Ranjan Pal W.B.P.H & I.D.C. Ltd., Kolkata, West Bengal, India Manish Pal National Institute of Technology Agartala, Jirania, Tripura, India; Department of Civil Engineering, NIT Agartala, Agartala, Tripura, India Mihir Pal Department of Physics, Kabi Nazrul Mahavidyalaya, Sonamura, West Tripura, India Supriya Pal Department of Civil Engineering, National Institute of Technology, Durgapur, West Bengal, India Madhusree Palit Sarojini Naidu College for Women, Kolkata, India Ratnadeep Ray JIS University, Kolkata, India Susanta Ray Council of Scientific and Industrial Research, Indian Institute of Chemical Biology, Kolkata, India Arnab Roy Department of Agricultural Economics, University of Agricultural Sciences, Bangalore, India Malabika Biswas Roy Department of Geography, Women’s College Calcutta, Kolkata, West Bengal, India Pankaj Kr. Roy Jadavpur University, Kolkata, India Pankaj Kumar Roy School of Water Resources Engineering, Faculty of Interdisciplinary Studies, Law and Management, Jadavpur University, Kolkata, West Bengal, India; School of Water Resources Engineering, Jadavpur University, Kolkata, West Bengal, India; Faculty of Interdisciplinary Studies, Law and Management, Jadavpur University, Kolkata, India G. Sagar Department of Agricultural Economics, University of Agricultural Sciences, Bangalore, India Rupam Sahu School of Water Resources Engineering, Jadavpur University, Kolkata, India Jibanananda Samanta Department of Geography (UG and PG), Bajkul Milani Mahavidyalaya, Purba Medinipur, West Bengal, India Mridul Kumar Sarkar Excavation and Equipment Manufacturing (P) Ltd., Kolkata, India; Australian Maritime College, University of Tasmania, Hobart, Australia Pramita Deb Sarkar Department of Civil Engineering, Netaji Subhash Engineering College, Kolkata, India

Editors and Contributors

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Apurba Sharma P.K. Sinha Centre for Bio Energy and Renewables, IIT Kharagpur, Kharagpur, India Machavarapu Suresh Department of Civil Engineering, NIT Agartala, Agartala, Tripura, India M. N. Venkataramana Department of Agricultural Economics, University of Agricultural Sciences, Bangalore, India

Part I

Emerging Issues on Surface Water Management Under Change Climate

Chapter 1

Slope Stability Analysis Under Critical Conditions of Geogrid Reinforced Canal Embankment Arindam Karmakar , Md. Nishat Afsar, and Supriya Pal

Introduction Slopes are one of the most dangerous and critical geotechnical structures. Failure of such structures as in dams, embankments, mines, roads often leads to catastrophic events with many casualties and financial losses. Thus, design and construction of slopes with proper safety margin is a big challenge for the design engineers [1]. Geosynthetics materials are widely used to encounter geotechnical problems in weak soil. The geogrid is one of the most used geosynthetics material for slope stability in the canal embankments. Geogrid is a two-dimensional flexible material that is usually consisted of polymeric elements such as polyethylene, polyester, polypropylene, etc., which in turn improves the tensile strength of geogrid. Inclusion of geogrid is done in such a way that the soil particles are interlocked better thereby giving strength in the soil [2]. Use of geosynthetic material as reinforcement in steep slopes or soil embankments can increase efficiency in construction works, serviceability of the structure, and resistance against seismicity [3]. Also, geogrids can be used to minimise the lateral displacement and settlement of the subsoil in embankments [4]. Numerical modelling studies using geotextiles, geogrid, and steel strip materials were carried out earlier by researchers to check their adequacy for improvement of soil bearing capacity as well as reduction of displacement values. It is reported that A. Karmakar (&)
Md. Nishat Afsar
S. Pal Department of Civil Engineering, National Institute of Technology, Durgapur, West Bengal 713209, India e-mail: [email protected] Md. Nishat Afsar e-mail: [email protected] S. Pal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_1

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geogrid reinforced soil provides better interlocking properties and thereby enhances the soil strength to support foundation structure in soft soil [5]. The slope stability of a canal embankment depends on its geometry, embankment and foundation materials geotechnical properties, and the hydrostatic condition. The pore water pressure and the external forces such as earthquake, surcharge load, etc., are the reason behind the instability and failure of the slope. Both drained and undrained conditions are considered to analyse the stability of the embankment slopes, either with classical analysis or with finite element method. Where classical analysis is based on limit equilibrium method (LEM) and finite element method is based on the strength reduction method (SRM). SRM is used globally nowadays for analysing slope stability using finite element method because of its several merits in comparison to the traditional limit equilibrium method [6]. The rapid drawdown or sudden drawdown of the water level in the canal is considered the most critical condition in the stability analysis of canal slopes. The excess pore water pressure that develops inside the canal embankment dissipates over time and the rate of dissipation depends on the drawdown rate and the permeability of embankment material. There are some other critical conditions such as slow drawdown, high reservoir, low reservoir, etc., under which the stabilities of the canal slope sections are examined using finite element method-based software PLAXIS 3D. The total stress analysis concept is considered in undrained condition to examine the short-term stability of slopes. Whereas, the effective stress analysis concept under drained condition is used for the long-term stability evaluation. The numerical modelling techniques of slope stability analysis have gained popularity in recent years due to the advancement of finite element methods as well as requires lesser time to solve complex problems. In the present study, numerical modelling is carried out using PLAXIS 3D software package to compute the stresses, displacements, stains, pore water pressure generation, and their dissipation pattern in the geogrid reinforced canal embankment [7]. The strength reduction technique is used considering Mohr–Coulomb elasto-plasticity to evaluate the factor of safety of the embankment slopes [8] and thereby to assess the efficacy of the geogrid as reinforcement in canal slopes to arrest instability. Regarding the slope stability analysis, the ‘safety factor’ remains the primary factor which indicates the condition of a slope with respect to the failure condition [9].

Description of the Project Site An 8 km long water supply canal is running from Durgapur barrage to Durgapur thermal steel power plant (DTSP). This canal is located at Durgapur city, West Bengal, India. The purpose of this canal is to cater to daily needs of water to the DTSP plant. During the survey of the canal, it has been found that the side slopes of the canal are distorted and also damaged at various points and requires immediate attention to rectify and make a stable slope (Fig. 1.1).

1 Slope Stability Analysis Under Critical Conditions of Geogrid …

(a)

5

(b)

Fig. 1.1 a and b View of the canal (project site)

Methodology Laboratory Tests Soil samples have been collected from the slope and crest of the embankment at each 500 m interval throughout the canal by Augar boring method. Laboratory tests have been performed for the determination of engineering properties of the collected soil samples at Soil Mechanics and Foundation Engg. Laboratory, NIT Durgapur, India. The tests are performed following the guidelines depicted in BIS:2720 as shown in Table 1.1.

Numerical Modelling In the present study, the performance of the geogrid reinforced canal embankment has been analysed through numerical modelling using a finite element-based Table 1.1 Engineering properties of the soil samples Sl. No.

Property of soil

Subsoil

Embankment

1 2 3 4 5 6 7 8

Unsaturated unit weight (cunsat) (kN/m3) Saturated unit weight (csat) (kN/m3) Young’s modulus (E) (kN/m2) Poisson’s ratio (µ) Cohesion (c) (kN/m2) The angle of internal friction (u) (°) Dilantancy angle (w) (°) Permeability (k) (m/day)

19 22 5.6e4 0.28 2 37 7 0.03

17 21 2.3e4 0.3 7 34 4 0.5

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PLAXIS 3D software. Figure 1.2 shows the complete structure of the canal cross-section along with the canal bed. The FEM analysis simulated various water levels (High, low) and drawdown conditions and the factor of safety at the end of each condition have been observed. The optimization of the canal design has been done by finite element analysis with dimensions of 6 m in height with the side slope of 1:1 and 4 m deep subsoil thickness. The groundwater table has been considered at a depth of 1.5 m below the canal bed. The high reservoir and low reservoir levels are considered at 4 and 1 m above the canal bed. Rapid and slow drawdown both the cases have been evaluated at a rate (R) of 1 and 0.1 m/day, respectively. The most critical condition is when rapid drawdown occurs because excess pore water pressure is generated inside the embankment, which eventually reduces the shear resistance of the embankment. Time duration in case of rapid drawdown is considered as three days, whereas in the case of the slow drawdown, it is taken as 30 days. Mesh diagram of the canal cross-section before and after the application of geogrid as reinforcement is shown in Fig. 1.3 (Table 1.2).

Fig. 1.2 Numerical Model of the canal using PLAXIS 3D

(a)

(b)

Fig. 1.3 Mesh Diagram of the canal a without geogrid b with geogrid

1 Slope Stability Analysis Under Critical Conditions of Geogrid … Table 1.2 Biaxial Geogrid Properties

7

Axial Stiffness (kN/m)

Ultimate Tensile Strength (kN/m)

500

45

Results and Discussion At first, the stability analysis has been done under the existing in situ conditions of the canal, and it is found that the safety factor is below 1.5 in case of rapid drawdown. Then, two layers of geogrid have been inserted as reinforcement material. Considerable improvement has been noticed in all four critical conditions. After generating the finite element mesh in the canal embankment, PLAXIS software gives no. of nodes for calculating pore water pressure, displacement, and stress–strain values for the whole structures. As depicted in Fig. 1.4, after installation of geogrid, total displacement has increased from 1.378 to 2.475 mm. As depicted in Fig. 1.5, after the installation of geogrid, it is observed that principal effective stress has decreased from 5.638 to 2.367 kN/m2. Figure 1.6 indicates that after the insertion of two consecutive geogrid layers, pore water pressure has increased from 1.402 to 1.876 kN/m2. Various numerical models have been analysed by varying the spacing and position between two geogrid layers. The dissipation of pore water pressure inside the canal has improved after inserting the geogrids in a proper position (Table 1.3). The stress–strain curve indicates that the application of geogrid has improved Young’s modulus of the embankment material as shown in Fig. 1.7. It is observed that variation in stress is negligible, but the strain values of the embankment materials have decreased considerably in all the critical conditions after the insertion of two consecutive geogrid layers as shown in Fig. 1.8.

(a)

(b)

Fig. 1.4 Total displacement of the canal a without geogrid b with geogrid

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(a)

(b)

Fig. 1.5 Principle effective stress in the canal a without geogrid b with geogrid

(a)

(b)

Fig. 1.6 Dissipation of Pore water pressure in the canal a without geogrid b with geogrid

Table 1.3 Factor of Safety in the canal embankment before and after installation of geogrid Sl. No.

Conditions

Without Geogrid (F.O.S)

With Geogrid (F.O.S)

1 2 3 4

High reservoir Rapid drawdown Slow drawdown Low reservoir

1.884 1.463 1.5 1.508

2.141 1.531 1.531 1.605

Fig. 1.7 Stress versus Strain curve of the canal before and after installation of geogrids

1 Slope Stability Analysis Under Critical Conditions of Geogrid …

9

Fig. 1.8 Strain versus Steps curve of the canal before and after installation of geogrids

Conclusions The design and stability of canal embankment side slopes with the inclusion of geogrid layers as reinforcement has been evaluated in the present study. Laboratory test results have been considered as the input parameters in the PLAXIS 3D software using Mohr–Coulomb failure criteria. SRM has been used to find out the variation in the factor of safety, modulus of elasticity, and strain with varying the number of geogrids. The following inferences can be drawn from the present study: • The safety factor for the canal embankment has been determined as 1.531 in the case of the most critical condition (rapid drawdown) after reinforced with geogrid. • The factor of safety and modulus of elasticity of embankment material have increased considerably in all cases viz. (high reservoir, rapid drawdown, slow drawdown, low reservoir) after installation of geogrid as reinforcement material in the canal-side slopes. • It is essential to analyse any canal section for the drawdown conditions especially, for the rapid drawdown case as there is a considerable chance for the development of excess pore water pressure inside the embankment slopes because of the sudden change in water level along the canal bed, which affects the stability of the slopes by reducing the shear resistance of the embankment. • The application of geogrid layers has increased the shear resistance of the side slopes by improving the interlocking of soil particles. The dissipation of water inside the embankment has improved and reduces the chances of failure by improving the factor of safety. • It can also be concluded that better is the permeability of the embankment material, better will be the stability of the slopes because water will dissipate in a better way which in turn will reduce the excess pore water pressure.

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• However, dynamic analysis is to be performed to assess the performance of geogrid reinforced canal slopes under seismic excitations. Acknowledgements The authors of the paper are grateful to the Department of Civil Engineering, NIT Durgapur, West Bengal, India, from the bottom of their heart for ensuring all the essential help & support to perform the current research work. Authors would like to convey their vote of sincere thanks to the Director, NIT Durgapur, West Bengal, India, for his constant inspiration throughout the current study. Conflict of Interest Not applicable. Funding Information Not applicable.

References 1. Liu H (2012) Long-term lateral displacement of geosynthetic-reinforced soil segmental retaining walls. Geotext Geomembr 32(2012):18–27 2. Rai R, Khandelwal M, Jaiswal A (2011) Application of geogrids in waste dump stability: a numerical modeling approach. J Environ Earth Sci 66:1459–1465 3. Song F, Liu H, Ma L, Hu H (2018) Numerical analysis of geocell-reinforced retaining wall failure modes. Geotext Geomembr 46:284–296 4. Rowe KR, Liu WK (2015) Three-dimensional finite element modelling of a full-scale geosynthetic reinforced, pile-supported embankment. Can Geotech J 5. Evirgen B, Tuncan M, Tuncan A (2017) Modelling study on the geotextile, geogrid, and steel strip reinforced slopes. Çukurova Univ J Fac Eng Archit 32(4):227–240 6. Matsui T, San CK (1992) Finite element slope stability analysis by shear strength reduction technique. Soils Found 32(1):59–70 7. Berilgen MM (2006) Investigation of stability of slopes under drawdown conditions. Comput Geotech 34:81–91 8. Chen X, Huang J (2011) Stability analysis of bank slope under conditions of reservoir impounding and rapid drawdown. J Rock Mech Geotech Eng 3:429–437 9. Cheng YM, Lau CK (2014) Slope stability analysis and stabilization, 2nd edn. Taylor and Francis Group, New York, pp 17–97

Chapter 2

Stability Analysis of a Riverbank for Different Microstructural Arrangements of the Particles Debasish Biswas, Arijit Dutta, Sanchayan Mukherjee, and Asis Mazumdar

Introduction Riverbank stability is a very important factor for society as the bank erosion destroys the agricultural land as well as the civilization that develops alongside the river. Stability of the bank surface depends on many factors due to dynamic nature of the river flow. This makes the stability analysis quite complex for the researchers. Therefore, it is a huge challenge for the researchers to develop a generalized concept for stability analysis. A prolonged and thorough observation to study riverbank erosion has been made by many researchers [1–3]. These observations are basically case studies for a particular river system which are time-consuming and cost investment as well and less significant to develop generalized concept. So it becomes necessary to analyse in such a manner which will develop generalized concept with least investment. Theoretical analysis of the microstructural arrangement of the particle comes into consideration to serve the purpose. Also, simulations to correlate riverbank retreat with erosion and mass loss have been made [4], and that too for cohesive riverbanks. To find out the particle escape velocity through analysis of particles in microscopic level an analytical model (Truncated Pyramid Model) has been developed [5]. Escape velocity variation with particle size has been investigated in a subsequent research work [6]. Influence of pore water pressure and hydrostatic force on the stability of the bank surface has been studied by some researchers [7, 8]. In another published work, riverbank erosion has been quantified with a subsequent D. Biswas (&)
A. Dutta
S. Mukherjee Department of Mechanical Engineering, Kalyani Government Engineering College, Kalyani 741235, India e-mail: [email protected] A. Mazumdar School of Water Resources Engineering, Jadavpur University, Kolkata, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_2

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risk analysis [9]. A recent work has dealt with a process-based model to study the riverbank stability [10]. In the present study, both “Truncated Pyramid Model (TPM)” and “Simple Cubic Model (SCM)” have been investigated in microscopic level to determine particle escape velocity. Three different sizes (300, 400 and 500 lm of radii) of the particles have been considered in fully submerged condition for both rising and falling water levels. The forces that are considered in the present analysis are the inter-granular force of cohesion, pore water pressure force, force due to water of the sediment particle and the hydrostatic force. Principle of conservation of angular momentum has been applied in microscopic level to determine escape velocity and the variation of the same with inter-particle distance has been quantified and plotted. The effect of water entrapped volume on capillary cohesion has been studied by [11]. Relationship for finding the force of cohesion between two different size grains has been developed by them, which is as follows: FCs ¼ pr 

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Rs1  Rs2  ½cs þ expfas  ðDs  Rs Þ þ bs g

ð2:1Þ

Here, the force of cohesion acting between two sediment grains of radii Rs1 and Rs2 ðRs1 6¼ Rs2 Þ is denoted by FCs with inter-granular distance of Ds, Rs denotes the larger of the two radii and r is the surface tension. The parameters as, bs and cs are the functions of water entrapped volume, angle of contact and radius of the grains in the following manner:  as ¼ 1:1 bs ¼

8s R3s

0:53

      8s 8s 0:148 ln 3  0:96 /2  0:0082 ln 3 þ 0:48 Rs Rs

ð2:2bÞ

 cs ¼ 0:0018 ln

 8s þ 0:078 R3s

ð2:2aÞ

ð2:2cÞ

Here, 8s denotes inter-particle liquid bridge volume and (/) denotes the angle of contact. Pore-pressure force between two unsaturated grains has been analysed by [12] and they developed an equation to describe the pore-pressure force as follows: FPs ¼ Pw  p  r22 þ 2  r  p  r2

ð2:3Þ

Here, Pw is the pore water pressure, and r2 ¼

Rs ½sin b þ cos b  1 cos b

Here, b Water content index angle.

ð2:4Þ

2 Stability Analysis of a Riverbank for Different Microstructural …

13

Fig. 2.1 Particle arrangement in TPM with adjacent particle’s magnified view

Method of Analysis and Model Framework Microscopic arrangement of the particles in TPM and SCM are shown in Figs. 2.1 and 2.2. In Figs. 2.1 and 2.2 Ds is the inter-particle distance between the particles. In TPM, particles settle down on the top of the two particles with small volume of water bridge between them. And, in SCM, each particle settles down at the top of the individual particle with a small volume of water bridge. Water bridge volume which is considered in this analysis is 20 nl. Being top most and left most, particle 11 is the most unstable. So the particle 11 is considered for the analysis of the forces which are acting on it and the escape velocity of the particle 11 has been determined. Free body diagrams of the particle 11 in TPM and SCM are shown in Figs. 2.3 and 2.4, respectively. In both the figures point A is the point of contact with the particle beneath. So the point A is the instantaneous centre of rotation as particle tends to topple about this point. So the conservation of angular momentum principle has been applied about the point A for both the arrangements. In TPM hydrostatic force will act on the particle from the left side as shown in Fig. 2.3 as at the right side of the particle water volume is negligible, where as in SCM hydrostatic force will act on both sides of the particle as shown in Fig. 2.4.

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Fig. 2.2 Particle arrangement in SCM

Fig. 2.3 Free body diagram of the particle 11 in TPM with different forces acting upon it

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Fig. 2.4 Free body diagram of the particle 11 in SCM with different forces acting upon it

Generalized Equations for Particle Escape Velocity Considering planar analysis hydrostatic force in horizontal direction can be written as FX ¼ FH ¼ Pavg  AX ¼ q  g  hc  AX

ð2:5Þ

Hydrostatic force in vertical direction (upward) FY ¼ P  AY ¼ q  g  h  AY

ð2:6Þ

Weight of the fluid block (downward) FW ¼ m  g ¼ q  g  V

ð2:7aÞ

  pR2 FW ¼ q  g  FE  AE  s 6

ð2:7bÞ

For TPM

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For SCM  FW ¼ q  g 

R2s

pR2  s 4

 ð2:7cÞ

Net hydrostatic force in vertical direction (upward) FV ¼ FY  FW

ð2:8Þ

Resultant hydrostatic force FRH ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðFH Þ2 þ ðFV Þ2

ð2:9Þ

And the direction of the resultant hydrostatic force with horizontal direction h ¼ tan

1



FV FH

 ð2:10Þ

Now applying conservation of angular momentum principle in TPM, angular acceleration can be determined as follows     FWs11  AC þ FCs  AE þ AD þ FRH  AP þ FPs  AE þ AD ¼ IA  as ð2:11aÞ pffiffiffi  pffiffiffi  pffiffiffi pffiffiffi 3Rs 3Rs 3Rs 3Rs Rs þ þ FRH  Rs sinð60  hÞ þ FPs  þ ) FWs11  þ FCs  2 2 2 2 2   7 4 3 2 pR q  Rs  as ¼ 5 3 s s

ð2:11bÞ So the expression for the angular acceleration of the particle in TPM will be as ¼

FWs11 2

pffiffiffi pffiffiffi   þ FCs  3 þ FRH sinð60  hÞ þ FPs  3   7 4 3 5 3 pRs qs  Rs

ð2:11cÞ

For SCM angular acceleration can be determined as follows (Taking moments about A) FCs  AO þ FH1  AP þ FPs  AO  FH2  AM ¼ IA  as

ð2:12aÞ

2 Stability Analysis of a Riverbank for Different Microstructural …

17

) FCs  Rs þ FH1  Rs sinð90  h1 Þ þ FPs  Rs  FH2  Rs sinð90  h2 Þ   7 4 3 pRs qs  R2s  as ¼ 5 3 ð2:12bÞ So the expression for the angular acceleration of the particle in SCM will be as ¼

FCs þ FH1  sinð90  h1 Þ þ FPs  FH2  sinð90  h2 Þ   7 4 3 5 3 pRs qs  Rs

ð2:12cÞ

And the expression for impending acceleration (in m/s2) of the particle will be f s ¼ as  R s

ð2:13Þ

And the expression for escape velocity (in m/s) of the particle proposed by [13] would be Vescape ¼ ð2  Rs  fs Þ0:5

ð2:14Þ

Here, Rs is expressed in mm.

Input Parameters for Analysis The input parameters value have been considered as follows: q = 1000 kg/m3 and qs = 2650 kg/m3; Surface tension coefficient (r) = 0.073 N/m; Liquid bridge volume (8s ) = 20 nl; Angle of water content index (b) = 45°; / = 0° (for pure water); Pw = 10 kPa.

Results and Discussion Stability of the riverbank has been analysed with different particle sizes for both TPM and SCM for both water level rising and water level falling. From Figs. 2.5 and 2.6 it is seen that inter-particle distance has an adverse effect on the particle escape velocity. Particle escape velocity decreases continuously with an increase in particle size as seen in Figs. 2.4 and 2.5; therefore, the stability declines. Particle escape velocity is less in case of water level falling compared to the case when

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Fig. 2.5 Vescape versus Ds for fully submerged particle with water level rising a For TPM b For SCM

2 Stability Analysis of a Riverbank for Different Microstructural …

19

Fig. 2.6 Vescape versus Ds for fully submerged particle with water level falling a For TPM b For SCM

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water level is rising for the same size of the particle as seen in Figs. 2.4 and 2.5. If a comparison is made between TPM and SCM, in both the cases as seen in Figs. 2.4 (a, b) and 2.5 (a, b), TPM is more stable in both cases of rising and falling water level.

Conclusions From the present study, it can be concluded that • Larger particles are less stable as the force of cohesion is less in case of larger particles, which, in turn, reduces the stability of the bank surface. • Increase in inter-granular distance reduces escape velocity. The force of cohesion between the particles becomes weak with increase in inter-granular distance which takes its toll on the bank stability. • It is clear that particle escape velocity is higher when the water level is on the rise because of the favourable moment helping the particles to bind together. • The above analysis shows that TPM makes more stable bank surface than SCM, in both the situations involving rising and falling water level. The present work has dealt with the variation of escape velocity vis-à-vis several parameters of significance. The work may, further, be extended to determination of volumetric erosion rate of the bank in macro level, provided the rate of deposition of sediments on the riverbank is known. In that case, it would be easier to apply the outcome of the analysis in macro-level field studies pertaining to specific riverbanks.

References 1. Odgaard AJ, Mosconi CE (1987) Streambank protection by Submerged Vanes. J Hydraul Eng ASCE 113(4):520–536 2. Carroll RWH, Warwick JJ, James AI, Miller JR (2004) Modeling erosion and overbank deposition during extreme floods conditions on Carson river, Nevada. J Hydrol 297(1–4): 1–21 3. El Kadi Abderrezzak K, Moran AD, Mosselman E, Bouchard JP, Habersack H, Aelbrecht D (2014) A physical, movable-bed model for non-uniform sediment transport, fluvial erosion and bank failure in rivers. J Hydro-Environ Res 8(2):95–114 4. Darby SE, Rinaldi M (2007) Coupled simulations of fluvial erosion and mass wasting for Cohesive river banks. J Geophys Res 112(F3):1–15 5. Mukherjee S, Mazumdar A (2010) Study of effect of the variation of inter-particle distance on the erodibility of a riverbank under cohesion with a new model. J Hydro-Environ Res 4 (3):235–242 6. Mukherjee S (2011) Application of truncated pyramid model in determination of escape velocity of particles of different diameters in varying conditions. Int J Soft Comput Eng (IJSCE) 1(5):75–79

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7. Osman AM, Thorne CR (1988) Riverbank stability analysis, I: theory. J Hydraul Eng 114 (2):134–150 8. Darby SE, Thorne CR (1996) Stability analysis for steep, eroding, cohesive riverbanks. J Hydraul Eng 122:443–454 9. Nardi L, Campo L, Rinaldi M (2013) Quantification of riverbank erosion and application in risk analysis. Nat Hazards 69:869–887 10. Klavon K, Fox G, Guertault L, Langendoen E, Enlow H, Miller R, Khanal A (2017) Evaluating a process-based model for use in streambank stabilization: insights on the bank stability and toe erosion model (BSTEM). Earth Surf Proc Land 42:191–213 11. Soulie F, El Youssoufi MS, Cherblanc F, Saix C (2006) Capillary cohesion and mechanical strength of polydisperse granular materials. Eur Phys J E 21:349–357 12. Likos JW, Lu N (2002) Hysteresis of capillary cohesiom in unsaturated soils. In: 15th ASCE engineering mechanics conference Columbia University, 2–5 June, New York, NY 13. Duan JG (2005) Analytical approach to calculate rate of bank erosion. J Hydraul Eng 131 (11):980–989

Chapter 3

Trend Analysis of Highly Cited Papers on Sustainable Watershed Management: A Bibliometric Review Malabika Biswas Roy , Sudipa Halder , Arnab Ghosh, Snehamanju Basu, and Pankaj Kumar Roy

Introduction Sustainable watershed management is an important concern under changed climate scenario. The main theme of the sustainable watershed management is to conserve the present environmental stability for meeting future needs. Sustainable management in water resources is at urgent need. Loss of storage due to siltation, fall of groundwater table, recession of glaciers, incidence of drought and floods, quality degradation, salinity ingress, recycling and reuse and drying of rivers makes it important to analyse and conserve a watershed in a sustainable way. Reddy et al. [1] found watershed management will carter to the soil moisture enhancement which is deteriorating at an alarming rate. In 2017, Zhou et al. [2] examined surface water resources by using Soil Accounting Matrix (SAM) in Gaotoi country of China. Zhang et al. [3] made a study on green space water use for the purpose of maintaining and enhancement of water security of China. Integrated water management scheme is needed for the improvement of irrigational efficiency and increase of infiltration rate of rainfall. Lang et al. [4] advocated that water yield in a basin can M. B. Roy (&) Department of Geography, Women’s College, Calcutta, Kolkata 700003, India e-mail: [email protected] S. Halder
A. Ghosh
P. K. Roy School of Water Resources Engineering, Jadavpur University, Kolkata 700032, India e-mail: [email protected] A. Ghosh e-mail: [email protected] P. K. Roy e-mail: [email protected] S. Basu Jadavpur University, Kolkata, West Bengal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_3

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be simulated by using VEST (Integrated Volume of Ecosystem Services and Thradeffs) and it provided fruitful results in water resources management over the Saucha river basin of China. The sustainability of the water resource can be degenerated by applying rainwater harvesting techniques. Sanchez et al. [5] found rooftop harvesting an important technique for analysing the physicochemical and microbiological contamination of a particular river basin and it is an important criterion for sustainable watershed management. Watershed management is now based on quantitative and qualitative methods and approaches. In 1992, the concept of integrated watershed management and resource management (IWRM) for meeting the requirements of different uses of water and aquatic zones was introduced. It is an important approach which brought fruitful results while applying in the Giffre watershed [6]. Amisigo et al. [7] made an assessment of the impact of projected climate change on the crop production and water availability of Volta basin in Ghana by using Water Evaluation and Planning (WEAP) software provided useful results in allocation of water resources. Application of GIS is now being seen frequently used in the water resources management. Vehhilainen and Johvansuu [8] prepared a map-based user interface to visualise the changes of variables simulated by watershed models in three levels of sub-basin division of Finland. Tropical Watershed is effected by numerous problems that disturb its sustainability such as land degradation, water scarcity, water pollution, sedimentation and several socio-economic pressure [9]. Watershed sustainability index can be calculated based on the basin hydrology, environment, life and policy (HELP) condition which was applied in the Batang Merao watershed in Indonesia. Future watershed needs an understanding of the climate change impacts which requires incorporation of technological advancements and cross-disciplinary approaches so that it can continue to serve the future needs [10]. Sustainable watershed management is an important approach specially where there is a variation in the topography from the source to its mouth. The approaches of the management need different techniques to deal with a watershed where a river is flowing through a hard rock terrain in its upper course then flowing through a lateritic soil cover on middle catchment and ultimately changes its character into a tidal reach in its lower catchment. Thus a water budget estimation should be an early step in identifying the variation of water availability throughout the catchment. Watershed vulnerability can be managed sustainably if the assessment tools are chosen based on the indicators [11]. Hydrological modelling is now being used at an increased rate to understand the natural process and human activities that make watershed vulnerable [12]. A coupled watershed modelling system of CE-Qual-W2 and SWAT has been applied to test the possible sources of seasonal excess nutrient concentration in lake Waco reservoir of Central Texas which brought fruitful results for enhancing the watershed health [13]. Extensive degradation of forest and increased runoff is now an important phenomenon in most of the watersheds and thus need continuous assessment. According to Ghanbarpour et al. [14] stream flow prediction is critical for proper water resources planning and management in a watershed scale and used four modelling techniques like ARMA, ANN, SWRRB and IHACRES to compare the stream flow in Kasilian watershed of North of Iran. Stream flow evaluation is

3 Trend Analysis of Highly Cited Papers on Sustainable Watershed …

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important for the estimation of the groundwater storage capacity and water availability for the surrounding inhabitants. Therefore, the difficulties while evaluating the groundwater potential of various aquifer units can be overcome by using surface water data [15]. These groundwater water resources are now contaminated from various natural and manmade sources which needs proper management. Licciardello et al. [16] found high electrical conductivity and chloride concentration in the groundwater of Ragusa province which can cause health and environmental problems. Thus before recharging the groundwater storage, treatment is needed in order to reduce the influence of agricultural waste into it. According to Souissi et al. [17] artificial recharge potential sites can be identified through remote sensing and Geographical Information System (GIS) and provided fruitful results while assessing the groundwater recharge patterns in Jeffara aquifer of Gabe, Tunisia. According to Adeleke et al. [18] estimation of groundwater recharge and capability of aquifers are essential in water resources investigation of a watershed. They used empirical methods to estimate recharge in Ogun-Oshun River of Nigeria and found that taking precipitation as 100% there was 11% of recharge and 73% as evapotranspiration and others as flow as surface runoff. Rainwater harvesting can be an important technique to manage watershed in a sustainable way. Gopal and Sah [19] made a study on the conservation and management of the River Yamuna which showed rapid degradation of water quality, loss of fishing and changes in the biotic community. They emphasised on treating River Yamuna as an eco-complex phenomenon and conservation of water quality and biota is urgently needed. Excessive Siltation may also lead to the degradation of the river health condition hampering the normal ecological flow. Roy et al. [20] made an investigation in Ichamati River located at the Indo-Bangladesh border which showed almost degradation due to poor implementation of watershed management schemes. They made a study on water quality parameters along with the siltation depth and found a degradation in quality from upstream to downstream reaches. Khan et al. [21] made a study for identification of controlling factors of sediment dynamics under natural flow regime to understand the hydrological alteration and future climate change of Ganga River. Areas suitable for rainwater harvesting technique can be identified by calculating the water budget of a river basin and identification of the recharge potential areas can be made through remote sensing and GIS and it provided fruitful results in Devak-Rui watershed of Jammu Himalaya [22]. Therefore, before moving towards managing a watershed in a sustainable manner it is important to have a throughout knowledge of past and future works related to it. Bibliometric analysis can be an important tool to fulfil such research targets. Zyound and Hanusch [23] made a bibliometric analysis to track the ongoing activities in managing vulnerable groundwater scenario of Arab. Wang et al. [24] through bibliometric analysis found that most of the researchers are paying attention to the groundwater and water quality parameters. The drying and contamination of surface water has put immense burden over the groundwater resource. Maassen [25] made a bibliometric analysis to identify the trend and research gaps in the field of international waste water irrigation techniques, an important and modern method to manage water resources in a sustainable way.

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Based on the current scenario of water resources vulnerability in the important river basins of the world the objective of this paper is bibliometric analysis of the articles related to sustainable water resources management encompassing both surface and sub-surface flow and storage characteristics to track the current and ongoing research projects related to it and its future trends.

Methodology The methodology of the study can be divided into two phases. The 1st phase includes the collection and downloading of data from online version of Science Citation Index Expanded from 1956 to 2018 and the 2nd phase includes execution and analysis of data through statistical methods using Excel and XLSTAT software addin. Articles amounting to 200 have been downloaded along with its citation by inputting the key words like Watershed, Sustainable management, Water stress, Groundwater potential and Water resources vulnerability. Throughout analysis of the articles has been made on every head which includes the analysis of the author’s status, theme of articles, subject and category, country and area of research, journal genre, methodology used in the articles, modern software used, etc. Papers from the years 1956–2018 have been download and analysed. Impact factors of the journals have been recorded from each journal’s own website. Mann Kendall test statistics have been calculated to find out the trend in research publication related to watershed management. Mann Kendall is a useful non-parametric statistical analysis to determine spatio-temporal trend within a dataset. Algorithm used for Mann Kendall test statistics S, Var and standardised test statistics Z are as follows, [26] S¼

n¼1 X n X

  sig Xj  Xi

ð3:1Þ

i¼1 j¼i þ 1

8   < þ 1if Xj Xi [ 0 Sgn Xj  Xi ¼ 0 if Xj  Xi [  0¼0 : 1 if Xj  Xi [ 0 



Var(SÞ ¼ 1=8 ½nðn  1Þð2n þ 5Þ 

a X

to ðto  1Þð2to þ 5Þ

ð3:2Þ

ð3:3Þ

P¼1

8 S1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi if S [ 0 > < VARðSÞ Z ¼ 0 if S ¼ 0 > Sþ1 ffi : pffiffiffiffiffiffiffiffiffiffiffiffiffiffi if S\0 VARðSÞ

ð3:4Þ

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27

In these equations Xi and Xj are the time series monitoring data in sequential order, n is the length of the time series, to is the number of the tie in the pth value and m is the numbers of tied variables. V is the Variance within the data set if | Z| > Z1-a/2, (Ho) is rejected and a significant trend exists within the data. The positive value of the Z means upward rising and a negative value means downward trend. Here the test has been performed at a significance level of 0.05 in XLSTAT software. The magnitude of the trend has been analysed using the Sen’s slope estimator as developed by Sen [27]. The algorithm used in the calculation is given in the following Ti ¼

Xi þ Xk jk

ð3:5Þ

In this equation, the Xi and Xk represent the values of the data at the time j and k, respectively. Let’s consider  Qs ¼

TðN þ 1Þ=2 N is odd  1=2 TN=2 þ TðN þ 2Þ=2 N is even

ð3:6Þ

A positive Qs value represents an increasing trend; a negative Qs value represents a decreasing nature of the trend.

Results and Discussion A Bibliometric analysis of efficient documents between the years 1956 and 2018 has been conducted by making treacherous search in different publisher websites and journal pages. Articles amounting to 200 from reputed publishers based on the thematic phrase Sustainable Watershed management has been identified and downloaded. A thorough critical analysis starting from its citation criteria up to its methodological base has been done with utmost precision.

Document Type Most of the documents pertaining to the sustainable watershed management are of research article type published in reputed journals. The articles are mainly published in the English language. Of them, few are published in multilingual heads having English and other languages like Spanish or French.

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Publisher Contribution The one main role in documenting a research article is played by the publishers. They bring some of the furnished articles through their enlisted journals in the light of education to enrich the knowledge of inhabitants and to find out the research gaps. The top publishers publishing the research topic on watershed has been chosen and analysed. Among them the top publishing research article is Elsevier (Fig. 3.1). Among the 200 studied articles almost 85 articles have been published by journals under Elsevier. Followed by Elsevier, Springer also publishes high impact factor journals. Almost 49 articles have been under Springer. Followed by the above-mentioned journals Taylor & Francis, Philica, Current Science published 36, 1, and 1, respectively, among the 200 reviewed documents under watershed management. Watershed documents of the highly impacted journals have been studied and a scientific analysis has been made to rank them accordingly. Among them reviewed articles published in different journals have been ranked based on their impact factor. The data sets have been obtained from their respective websites. As seen in Table 3.1, Landscape and Urban Planning of the Elsevier journal ranked 1st with impact factor of 4.563. In the 2nd place is the Water Resources Research Journal of American Geophysical with impact factor of 4.397. The Sustainability of Water Quality and Ecology journal of Elsevier ranked 16th with low impact factor of 0.70.

Thematic Basis of the Articles Watershed management deals with multiple and overall aspects of a resource region of the earth surface. It includes the analysis and management of physical as well as the non-physical parameters of a region. Thousands of works have been made on different watershed of the world and hundreds of articles have been reviewed of which two hundred articles have been chosen. By thorough study of the articles, 13 themes have been identified such as water pollution, groundwater, soil erosion,

Fig. 3.1 Reputed Publishers’ outcome

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Table 3.1 Articles published in reputed journals Important Journals Published Articles on Sustainable Watershed research

Impact Factor (up-to-date)

Number of papers

Publisher

Hydrology Science Journal Environmental Management Journal of Hydrology International Journal of Remote Sensing Journal of Hydrologic Engineering

2.22 1.65 3.48 1.72 1.34

8 13 5 10 15

Environmental monitoring and assessment Urban Water Applied Geography International Journal of Environmental Science and Technology Land Use and Policy Bulletin of Environmental Contamination and Toxicology International Soil and water Conservation Research Sustainability of Water Quality and Ecology Hydrology Science Journal Environmental Modelling and software Landscape and urban planning Water Resources Research

1.68

12

Taylor & Francis Springer Elsevier Taylor & Francis American Society of Civil Engineers Springer

2.65 2.56 1.76

11 7 9

Taylor & Francis Elsevier Springer

4.01 1.59

4 13

Elsevier Springer

1.37

14

Elsevier

0.70

16

Elsevier

2.22 4.40 4.56 4.39

8 3 1 2

Taylor & Francis Elsevier Elsevier American Geophysical Union

climate change, land degradation, runoff, etc. (Fig. 3.2). The increase in the population pressure has led to increase in the pollution of an area. Most of the research works also deal with this research topic to bring it down and manage it sustainably. Water scarcity is going to increase more in the near future. It requires more attention to deal with. The next theme worked on is groundwater vulnerability. The over extraction of the groundwater has led to the decrease in the groundwater level and its fluctuation. It is one of the important supply of drinking water to millions of people. It was also a burning topic for the researchers.

Important Subject Categories Pertaining to the Articles Important Subjects that are of concern about the watershed research include Hydrology, Forest Hydrology, Sedimentology, Environmental Science and

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Fig. 3.2 Graph plot of number of articles respect of research theme

Table 3.2 Important subject categories under watershed research Important subject categories Related to Sustainable Watershed Research

Total articles

Rank

Hydrology Forest Hydrology Sedimentology Environmental Science Geomorphology

104 3 1 84 8

1 4 5 2 3

Geomorphology. The highest articles come under the subject category of the Hydrology branch of science with 104 research documents. The Subject Environmental Science also includes 84 articles under it. Others follow accordingly in Table 3.2.

Citation Analysis of the Research Articles Citation is an important part of research articles which provides its validity and reliability, acceptance and true application in the global real-life scenario. Citation is an important parameter to be evaluated for any research article and greater the resourcefulness of a paper greater will be the increase in citation per year of it. The 200 studied research articles have been classified based on the respective years of publication and mean citation has been calculated. The group of years has been divided into three phases, i.e. 1950–1970, 1970–2010 and 2010 to present (Fig. 3.3). The mean citation of the articles published in the years 1950–1970 was about 25, 1970–2010 it was about 33.15 and from 2010 to present it was about 112.62, respectively. It has also been seen that with the increase in the articles per year the mean citation has also been increasing.

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Fig. 3.3 Graph and Bar plot of articles per citation in three phases

Result and Discussions Trend Analysis of the Research Articles The publications of the research articles has increased in recent years. In the early era, important research discoveries also took valuable position in research history. Researches on watershed management have been made since the nineteenth century. But the environmental problems associated with watershed management increased in the recent era with massive climate change effect. For detecting and estimating a trend in time series of selected value of observation of the data sets to detect the presence of positive or negative trends Mann Kendall statistical trend can be used [28]. Mann Kendall statistical trend test has been performed to find out if there is any trend among the articles and published years accordingly. The interpretation of Table 3.3 has to do with the hypothesis of the analysis. Ho: There is no trend in the series of groundwater level data Ha: There is a trend in the series of groundwater level data. The assumption is that as the computed p-value is higher than the significance level alpha = 0.05, the null hypothesis cannot be rejected and the alternative hypothesis is to be rejected. In Table 3.3 the result shows that the computed p-value is 1.000 which is very high compared to the significance level alpha 0.05, hence the null hypothesis cannot be should be rejected and have to deny the alternative

Table 3.3 Mann Kendal test results

Kendall’s tau Mann Kendal test statistics Variance (S) p-Value (one-tailed) Alpha

0.549 197.000 2487.000 1.000 0.05

M. B. Roy et al.

Numb er of ArƟ cles

32

Fig. 3.4 Trend analysis of the articles respect to years

hypothesis, respectively. The trend analysis (Fig. 3.4) show no trend between the two independent parameters. The Sen’s slope estimate also validates it with value of 0.004.

Author’s Contribution from Different Continents Authors plays a significant role in highlighting their works on a particular topic through extensive research. The bibliometric survey on the watershed management showed that most of the research works came from Asia with 131 productions of articles occupying the rank 1. Table 3.4 shows that the single author, collaborative and joint author’s contribution (Fig. 3.5) is also highest in the Asian countries respect to others. Followed by Asia, North America took the 2nd position. Figure 3.4 shows the contribution of authors in three different aspects, i.e. single, national collaboration and international collaborative respect to total articles reviewed. The number of national collaborations is highest with 150 research papers.

Methodology Used in the Reviewed Articles The research methodology of the reviewed articles include diversity in project execution from laboratory method to field verification. But most of the works are survey-based as watershed management requires intensive field investigation and information related to the physical and non-physical aspects of the basin area. Survey method can be seen in about 120 articles out of 200 reviewed journals

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Table 3.4 Top continents with research publication and authors on watershed management Continent

TP

IPR

RPS

RPC

RPJ

Num ber of arƟcles

Asia 131 (1)62 (1)12 (1)21 (1)88 North America 38 (2)18.09 (2)4 (2)12 (2)25 Europe 33 (3)15.71 (2)4 (3)8 (3)21 South America 5 (4)2.38 (3)1 (4)2 (4)3 Australia 3 (5)1.42 (3)1 (5)1 (5)1 IPR: Total production in percentage and rank; RPS: Total single-author publication percentage and rank; RPS: Total Collaborative author publication percentage and rank; RPJ: Total joint author publication percentage and rank

Fig. 3.5 Author’s role in total publication

(Fig. 3.6). In the recent era, the models are also extensively used in the research work of sustainable watershed management. Predictions and simulation related to flow in channel now can be made through different types of modelling software. From the bibliometric survey it can be seen that Ground Water Modelling (GMS) software is mostly used in watershed management which is based on groundwater potential assessment. HEC-RAS and HEC-HMS (Fig. 3.8) are also two important flow simulation and flow modelling software prominently used in the research articles. Soil and Water Assessment (SWAT) helps in modelling different parameters of watershed. In the recent century, remote sensing and GIS has taken a predominant role characterisation of watershed morphology (Fig. 3.7). In GIS part, the most used software was TNT MIPS with 31% of the articles and in statistical part it was Statistical Package for Social Science (SPSS) software, respectively, with 28% of research articles (Fig. 3.8).

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Fig. 3.6 Methods used in the articles

Fig. 3.7 Types of GIS/statistical software used in articles

Page Count of the Research Articles Page count is an integral part of the articles. From the survey different volumes of articles have been found. Page count has been shown with help of the statistical diagram (Fig. 3.9). Most of the articles have page count within 10–15 starting from 5 to 30 pages.

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Fig. 3.8 Types of Models used in articles

Fig. 3.9 Page count respect to articles

Conclusion • In this paper investigation on watershed management has been made from the year 1956–2018. From the analysis it has been found that there is no such trend in the publication of research articles through the years and the Mann Kendall test proves it. Watershed management is now becoming an important issue due to global climate change and population explosion,

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• From the analysis it has been seen that most of the articles has been published by the Elsevier and it is also an important publisher that covers different research topic throughout the world besides environmental aspects. • The climate change has put on massive effect on the surrounding environmental scenarios putting stress on different aspects of the watershed which requires early management. From the analysis it has been found that problems related to water scarcity and water pollution are highest over the period. • Countries all over the world showed concern over the watershed management and are using multiple and varying methodology to investigate and provide solution towards conservation and management in a sustainable way.

References 1. Reddy VR, Saharawat YS, George B (2017) Watershed management in South Asia: a synoptic review. J Hydrol 551:4–13 2. Zhou Q, Denge X, Wu F (2017) Impacts of water scarcity on socio-economic development: a case of study of Gaotoi Country, China. Phys Chem Earth, 1–10 3. Zhang X, Mi F, Lu N, Yan N, Kugleerova L,Yuan S, Peng Q, Ma OZ (2017) Green space water use and its impact on water resources in the capital region of China. Phys Chem Earth, 1–10 4. Lang Y, Sang W, Zhang Y (2017) Responses of the water-yield ecosystem service to climate and land use change in Sancha River basin, China. Phys Chem Earth S1474-7065(17):30018– 30019 5. Sánchez AS, Cohim E, Kalid R (2015) A review on physicochemical and microbiological contamination of roof-harvested rainwater in urban areas. Sustain Water Qual Ecol. https:// doi.org/10.1016/j.swaqe.2015.04.002. (In press) 6. Charnay B (2011) A system method for the assessment of integrated water resources management (IWRM) in mountain watershed areas: the case of the “Giffre” watershed (France). Environ Manage 48:189–197. https://doi.org/10.1007/s00267-011-9683-7 7. Amisigo BA, Mcclurkey A, Swanson R (2015) Modeling impact of climate change on water resources and agriculture demand in the volta basin and other basin systems in Ghana. Sustainability 7:6957–6975 8. Vehvilainen B, Lohvansuu J (1996) Watershed simulation and forecasting system with a GIS-oriented user interface. Application of geographic information systems in hydrology and water resources management. IAHS Publ. no 235 9. Firdaus R, Nakagoshi N, Idrisi A (2014) Sustainability assessment of humid tropical watershed: a case of Batang Merao watershed, Indonesia. Procedia Environ Sci 20:722–731 10. Wang G, Mang S, Cai H, Liu S, Zhang Z, Wang L, Innes LJ (2017) Integrated watershed management: evolution, development and emerging trends. J Res 27(5):967–994 11. Juwana I, Muttil N, Perera BJC (2012) Indicator-based water sustainability assessment—a review. Sci Total Environ 438:357–371 12. Wu Y, Chen J (2013) Analyzing the water budget and hydrological characteristics and responses to land use in a monsoonal climate river basin in South China. Environ Manage 51:1174–1186 13. White JD, Peochnow SJ, Filstrup CT, Scott JT, Byars BW, Flynn LZ (2010) A combined watershed–water quality modeling analysis of The Lake Waco reservoir: I. Calibration and confirmation of predicted water quality. Lake Reserv Manage 26:147–158

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14. Ghanbarpour RM, Amiri M, Zarei M, Darvari Z (2012) Comparison of stream flow predicted in a forest watershed using different modelling procedures: ARMA, ANN, SWRRB, and HACRES models. Int J River Basin Manage 10:281–292 15. Mero F, Gilboa Y (1974) A methodology for the rapid evaluation of groundwater resources, Sao Paulo state. Brazil Hydrol Sci Bull 19(3):347–358 16. Licciardelo F, Antoci ML, Brugaletta L, Cirelli GL (2011) Evaluation of groundwater contamination in a coastal area of South-Eastern Sicily. J Environ Sci Health Part B 46:498– 508 17. Souissi D, Msaddek MH, Zoushi L, Chenini I, May ME, Dlala M (2018) Mapping groundwater recharge potential zones in arid region using gis and landsat approaches, southeast Tunisia. Hydrol Sci J 63:251–268 18. Adeleke OO, Makinde V, Eruola AO, Dada OF, Ojo AO, Aluko TJ (2015) Estimation of groundwater recharges in Odeda local government area, Ogun State, Nigeria using empirical formulae. Challenges 6:271–281 19. Gopal B, Sah M (2009) Conservation and management of rivers in India: case study of river Yamuna. Environ Conserv 20:243–254 20. Roy PK, Pal S, Roy MB, Ray D, Majumder A (2014) Variation of water quality with siltation depth for Ichamati along international border with Bangladesh using multivariate statistical techniques. J Inst Eng (India) 95:97–103 21. Khan S, Sinha R, Whitehead P, Sarkar S, Jin L, Futter NM (2018) Flows and sediment dynamics in the Ganga river present and future climate scenarios. Hydrol Sci J. https://doi. org/10.1080/02626667.2018.1447113 22. Jasrotia AS, Majhi A, Singh S (2009) Water balance approach for rainwater harvesting using remote sensing and GIS techniques, Jammu, Himalayas, India. Water Res Manage 23:3035– 3055 23. Zyoud SH, Hanusch DF (2016) Estimates of Arab world research productivity associated with groundwater: a bibliometric analysis. Appl Water Sci, 1255–1272. https://doi.org/10.1007/ s13201-016-0520-2 24. Wang MH, Li J, Ho YS (2011) Research articles published in water resources journals: a bibliometric analysis. Desalin Water Treat 28:353–365. https://doi.org/10.5004/dwt.2011. 2412 25. Maassen S (2016) Bibliometric analysis of research on wastewater irrigation during 1991– 2014. Irrig Drain 65:644–653 26. Mann HB (1945) Non-parametric tests against trend. Econometrica 13:163–171 27. Sen PK (1968) Estimates of regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389 28. Mustapha A (2013) Detecting surface water quality trends using Mann-Kendall tests and Sen’s slope estimates. Int J Adv Innov Res 2:108–114

Chapter 4

Tri-Decadal Visualization Analysis on River Health Studies: A Global Perspective Malabika Biswas Roy , Swetasree Nag , Arnab Ghosh , and Pankaj Kumar Roy

Introduction The impact of global climate change poses a serious threat to the water resources of the world. Both the ground and surface water hydrology alteration has turned to be an immediate challenge to humankind. Such kinds of alterations in water resources may result in environmental degradation in terms of quality, quantity, flow pattern, water availability, and other environmental aspects. Furthermore, overexploitation and unscientific use of water resources may lead to almost no-flow condition in a river during the dry season period [28, 31, 32]. Keeping this detrimental issue in mind, the survival of rivers has turned to be an immediate challenge to humankind [16]. To sustain a river basin for the long run, priority-wise assessment of the critical areas has been regarded as an important approach to maintain the physical environment and overall health status of the area [25]. Rivers are the lifeline of human society. From the period of human civilization, river water plays a vital role in controlling its ecological, social, and economic value for the environment. But excessive human interruptions are deteriorating the fitness of rivers in terms of their physical, chemical, and biological health. At such juncture, the concept of river health has arrived which focused on the maintenance of the inherent character of the river system and also the management and restoration into an undisturbed environment (Clean Water Act, 1972). To protect the dynamic nature of the river system Nandi et al. [26] proposed a comprehensive model on River Health Assessment (RHA) which encompasses numerous influential aspects of river system management like Catchment Health (CH), Floodplain Health (FPH), River Channel Health (RCH), Flow Health (FH), River M. B. Roy (&) Department of Geography, Women’s College, Calcutta, Kolkata, West Bengal, India e-mail: [email protected] S. Nag
A. Ghosh
P. K. Roy School of Water Resources Engineering, Jadavpur University, Kolkata, West Bengal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_4

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Water Quality Health (QH), and Biotic Health (BH). Humans are the prime users of river water. Various point and non-point sources of contamination are responsible for the degeneration of the river health condition. Figure 4.1 (https:// bewaterfriendly.com/our-water/watersheds/) shows the major contributing source of waste materials coming from industries, agricultural fields, and domestic sectors which pollutes the river water quality and hampers the ecology. This impure surface water is percolating into the deeper layer of the aquifer and contaminating the groundwater sources which are again used by the inhabitants for their daily uses. Through such kind of surface and groundwater interaction, pollutants are degrading both surface and groundwater hydrology. Not only that, construction of dams and reservoirs, channel diversion, and river bed modification are also detrimental for maintaining the catchment health as well as the biotic health causing a no-flow condition of the river system. Several researchers are focusing on the assessment of ecological and environmental flow to sustain the health of the river in the long run. Yang et al. [34] conducted a study to determine the minimum required flow levels to maintain the different ecological functions and sustain the river for a long period. Minjian et al. [24] carried out a study on the Liao basin in China to calculate the minimum flow that will sustain the low flow channel and also sustain the aquatic ecosystem. Verma et al. [33] also conducted a study on environmental flow variability using three different hydrological methods in the sub-watershed of the Damodar River basin. However, the development of holistic methodologies by a combination of multidisciplinary information has great potential to quantify the environmental flow requirements [10]. Karimi and Eslamian, [13] conduct a study by applying a newly developed hydrological method, i.e. Flow Duration Curve (FDC) shifting to investigate potential environmental flows in a river reach of Shahr Chai River in Iran. The results were also compared with the other methods to validate the minimum flow to maintain the basic functions of the river ecosystem. But the direct consequences of human activities affect the river channels through the flow variability and water consumption pattern [9]. Apart from this, Liu et al. [20] considered the climatological extremities which affect water scarcity observed over the Euro-Mediterranean region. To understand the behavior of total stream flow for changing climatic scenarios, the use of a daily snowmelt runoff model has also been analyzed for the enhancement of the health of a river basin [28]. Other than that, Makokha et al. [22] addressed various indices to minimize the effect of climatological harshnesses like rainfall, snow, and glacier melt (RSC) standardized anomaly index. Other than the climatic hazards, various anthropogenic activities are also responsible to modify the natural flow of water and degrade its overall health status [37]. Various structural constructions such as a dam, barrages impound the natural flow of a river and increase the bed load materials within the channel, which ultimately promotes the formation of bars and increase the channel sinuosity [5, 36]. Pal et al. [27] stated that the asymmetric pattern of channel width and depth, riverbank instability, and huge siltation are the major outcome of the strong human interference on the channel flow. Such river impounded structures also affect the natural flow rate of the river and may hamper its discharge capacity [8].

4 Tri-Decadal Visualization Analysis on River Health Studies …

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Fig. 4.1 Surface and Groundwater interaction

So, focusing on these above-mentioned problems related to water resource management or maintaining the health of riverine hydrological condition, relatively growing research interest has been observed in this field. Therefore, to observe the research trend of those studies bibliometric research can be implemented. To assess the global research trend on a specific discipline or to observe the publication material qualitatively and quantitatively and also to predict the future research trend, a bibliometric study can be regarded as an important tool to fulfill such kind of research goal. Bibliographic studies have been done through various aspects like publication classification, citations, co-authorship details, important keywords, productive countries, etc. [1]. Zhai and Ho [35] made a bibliometric study on controlled publications. Zhang et al. [37] studied bibliometric analyses on ecosystem services to visualize the global trend and researchers’ network on that particular topic. Liao et al. [19] have done a study focusing on the development and trend of research on medical big data through bibliometric analyses. Not only that, for the assessment of the most influential document in a specific study or the frame the structure of the field of research Aibar et al. [2] executed bibliometric analyses on socially responsible funds. All these studies reveal a panoramic view of the overall research growth in any kind of scientific discipline which may also provide the future research trend as well. In this present work, the study focuses on a bibliometric analysis of the global research trends in the field of river health assessment based on highly cited articles

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for the last three decades (1990–2020). However, there are various dichotomies regarding the definition of highly cited articles. Here, a citation threshold value of more than 100 times has been taken to refer to the highly cited articles [3, 7, 14]. This kind of study will provide a systematic and conceptual review that will encourage future researchers to conduct various studies scientifically.

Methodology The data used in this study has been downloaded from Scopus online databases, one of the largest databases from Elsevier having extensive coverage of research publications and information related to all the cited authors in the reference section more accurately [38]. However, to avoid the impact of other sources on the output of the analysis, Web of Science; Google Scholar has not been considered in this particular study. The authors have accessed the Scopus database on 17 May 2020 to gather research publications on river health for the last 30 years. All the publications related to river health studies have been derived using the term ‘river health’ in the title section. The document types have been defined to all types, which show a total of 3726 documents. By adopting the threshold value of more than 100 times citation [3, 7, 14], 147 publications have been retrieved, which shows 3.95% to the total publications. The list of various types of retrieved documents is shown in Table 4.1. Major information like authors name, affiliations, subject, publication year, journal name, citations, and countries have been subsequently extracted for fulfilling the objective of this paper. Various indices like Total Citation since Publication to the end of 2020 (TC 2020), Citation per Publication (CPP), Number of Author per Publication (AU/TP) has been calculated as well [35]. Finally, a set of network analysis has been performed here using VOSviewer software (www.vosviewer.com). This software has been developed by Van Eck and Waltman in the year 2010 and recognized as a powerful tool to analyze and visualize different kinds

Table 4.1 Highly cited publications output Document Type

*TP

TP%

TC 2020

AU

AU/TP

Article 124 84 28,773 476 3.84 Conference Paper 13 8.84 34 23 0.57 Review 7 4.76 10 8 1.14 Book Chapter 2 1.36 4 6 3.00 Book 1 0.68 5 5 5 Total 147 100 – – – *TP: Total number of Publications; TP%: Total number of Publication in %; TC 2020: Total Citation since publication to the end of 2020; AU: Number of Authors; AU/TP: Number of Author per Publication

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of bibliometric information [2, 19, 30, 37]. The following network analysis has been employed using this advanced software system—Co-authorship network analysis, Co-occurrence of all keywords, Citation analysis of source, authors, and countries accordingly. Finally, to understand the year-wise research growth or the emergence of river health studies, a non-parametric Mann-Kendall statistical technique has been executed on the number publication within the period of 1990– 2019 [23, 29]. Due to the incompletion of the year 2020, the data of this current year has been excluded from the trend analysis. This method assumes two types of hypothesis conditions—null hypothesis (no trend) and the alternative hypothesis (increasing/decreasing trend). Applying the significance level at 0.05, the Mann-Kendall S statistics have been computed using Eq. 4.1. S¼

n¼1 X n X

  sig Xj  Xi

ð4:1Þ

i¼1 j¼i þ 1

where Xj and Xi are the independent variables and n is the period of the dataset. Also to find out the magnitude and intensity of the trend, Sen’s slope [29] has been estimated to apply Eq. 4.2. Ti ¼

Xi þ Xk jk

ð4:2Þ

where Xi and Xk represented as variables of the dataset.

Results and Discussion Year-Wise Publication Output Figure 4.2 shows a year-wise publication related to river health studies around the world. About 3729 documents have been identified from the Scopus database from 1990 to 2020. It has been observed that the annual number of documents gradually increases with the expansion of the period (Fig. 4.2). In addition to that, due to the combined effect of rapid publication throughout the period, the citation per publication (CPP) has also varied in each year (Fig. 4.2). In the twenty-first century, river health-related works have been growing progressively, accelerated by the development of modern methods and techniques of research work. Among 3726 documents, highly cited publications were sorted out by applying a threshold of more than 100 times cited documents as mentioned earlier. In doing so about 147 publications have been retrieved from the mentioned database. The types of highly cited publications with their percentage of publication, Total Citation of 2020, Number of Authors, and Number of Author per Publication have been

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Fig. 4.2 Year wise variation in total publication output

tabulated in Table 4.1. Table 4.1 shows that among the 147 extracted documents, articles were the mostly dominated document type with the number of 124 and 84% of total publications, followed by conference paper (8.84%), review (4.76%), etc. Based on the number of the authors, per publication variation in authors number has also been calculated for each document type shown in Table 4.1.

Citation Analysis Citation analysis has been considered as one of the significant methodologies adopted in bibliometric research. This kind of analysis can help to visualize the most influential documents in any field of research following which some advanced research trends or some innovative management approach can be established [2]. Using VOSviewer software the following types of analysis have been done. In this work, more than 100 times cited publications on river health studies have been selected for qualitative analysis. Table 4.2 shows the top 20 journals on river health having a high citation index where freshwater biology journals ranked 1st both in terms of document publication and citation score. All the journals listed in the following table having maximum citation scores indicate more productive journals for publications on river health research. Most of the journals identified with high document publications and citation value are focuses on interdisciplinary studies in connection with the environment, ecology, and aquatic environment which ultimately promotes the priority areas of river health studies on which the researchers and scientists are working. Figure 4.3 shows a network analysis of most cited sources of river health studies where the size of the bubble indicates the number of citations received. Different colors represent different clusters of sources which mainly formed based on their similarity of occurrence. As per the size of the bubble, the journal freshwater biology has the dominant one which received a maximum citation from the others and also has the highest link strength (183) with 35 documents followed by the journal of the North American Benthological Society

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Table 4.2 Top 20 highly cited journals on river health studies Sl No

Journal Name

No. of Documents

Citation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Freshwater biology Hydrobiologia Journal of the North American Benthological Society Ecology Canadian journal of fisheries and aquatic sciences Ecological applications Bioscience Environmental management Science of the total environment Limnology and oceanography Transactions of the American fisheries society Freshw. biol. Geomorphology River research and applications Water resources research Regulated rivers: research and management Nature Oikos Archiv fur hydrobiologie Marine and freshwater research

35 7 8 2 1 4 1 2 4 1 1 1 1 3 1 1 1 2 1 4

983 632 594 387 285 221 208 191 179 159 145 133 123 123 123 116 113 111 108 90

having link strength of 51 with 8 documents and Hydrobiologia having link strength of 42 with 7 documents. Author co-citation analysis has also been done on highly cited publications related to river health studies which can provide a scenario of the intellectual structure of any scientific publication [33]. A total of 480 authors have been identified in the 147 highly cited publications on river health. The VOSviewer software creates 15 clusters of all 480 authors which are signified by individual colors (Fig. 4.4). The size of the orange cluster shows the author ‘allanj.d’ has got the maximum citation (3179) from all the 147 highly cited publications having total link strength of 104. Such kind of link strength represents a strong co-citation relationship with other connected 85 authors. Similarly, the author named as ‘bunns.e’ and ‘arthingtona.h’ has got the following positions based on their citation score. To interpret the interconnection between all authors or the co-authorship network in detail, another analysis has been performed in VOSviewer software. Among all 480 authors identified from the highly cited publications on river health, 78 authors have a minimum of 2 documents published together (Fig. 4.5). The clusters show among these 78 authors, 59 are well connected. The size of the red cluster indicates that the author named as ‘bunns.e’ has the highest link strength

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Fig. 4.3 The journal citation network analyses on river health related publications

Fig. 4.4 The author’s citation network analyses on river health-related publications

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Fig. 4.5 The co-authorship network analyses on river health related publications

(25) with the 16 authors followed by the blue cluster surrounded by the author named as ‘pont d’ having a link strength of 18 with other 11 authors from violet, blue, and yellow cluster.

Keyword Analysis To visualize the core research terminology used in river health studies authors have done another network analysis map based on co-occurrence of all keywords obtained from the highly cited publications. Such a kind of analysis can help to visualize the research trend in different disciplines around the world [17]. In this study, the co-occurrence of all keywords of river health research has been produced by VOSviewer software shown in Fig. 4.6 from which four types of clusters have been noticed in the form of red, yellow, green, and blue colors. The size of the bubble and words denotes the weights of the words whereas the lines between all keywords represent the frequency of occurrence. The thick line between two keywords indicates more frequently used keywords than the other ones. A keyword used for more than 5 times has been visualized from Fig. 4.6 where the word ‘invertebrata’ (26) has the highest frequency of co-occurrence followed by ‘environmental monitoring’ (21), ‘fish’ (20), ‘rivers’ (19), ‘water quality’ (19), etc. However, Table 4.3 shows the link strength of ‘environmental monitoring’

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Fig. 4.6 The keyword co-occurrence network analyses on river health related publications

(145) has higher than the word ‘invertebrata’ (129) representing the cluster as denoted by green color have more frequency of co-occurrence with the remaining 3 clusters.

Most Productive Countries Based on the number of publications, the top 10 most productive countries have been sorted out and ranked from 1 to 10. Table 4.4 shows their ranking and % of total publications, single-author articles, corresponding author articles, and internationally collaborative articles. The United States ranked 1st in the publication of documents on river health research with 53 publications, followed by Australia (39), France (20), United Kingdom (10), etc. (Fig. 4.7 and Table 4.4). The United States published 36.05% of total publications with the highest citation (13,674) since publication to the end of 2020. In the other categories like CPR%, RPR%, and SPR%, the United States stands in the top position which indicates the highest number of publications through various modern methodologies. To visualize the degree of communication between the countries, a country citation map has been developed by VOSviewer software (Fig. 4.8). The size of the bubble and highest link strength proves that the United States (272) has more

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Table 4.3 Top 10 keywords of highly cited publications Keywords

Frequency of occurrences

Total link strength

Invertebrata Environmental monitoring Fish River Rivers Water quality Biodiversity Ecosystem health Stream Ecology

26 21 20 19 19 19 17 17 15 14

129 145 118 93 117 122 97 91 75 93

Table 4.4 Top 10 most productive countries in the world Country

*TP

TP%

TC 2020

TPR%

CPR%

RPR%

SPR%

CPP

United States 53 36.05 13,674 1(36.05) 1(31.25) 1(27.3) 1(23.8) 258 Australia 39 26.53 8337 2(26.53) 3(18.8) 2(16.5) 3(9.5) 213.77 France 20 13.61 4412 3(13.61) 4(18.8) 3(14.2) 4(9.5) 220.60 United Kingdom 10 6.80 5002 4(6.80) 2(28.6) 4(8.5) 2(21.9) 500.20 Germany 9 6.12 1937 5(6.12) 6(6.25) 5(6.8) 5(9.5) 215.23 New Zealand 4 2.72 1468 6(2.72) 5(12.5) 6(6.8) 6(4.8) 367 South Africa 4 2.72 2148 7(2.72) 7(3.1) 7(4.5) 7(4.8) 537 Netherlands 3 2.04 2149 8(2.04) 8(3.1) 8(2.3) 8(0) 716.33 Sweden 3 2.04 2476 9(2.04) 9(0) 9(2.3) 9(0) 825.33 Canada 2 1.36 1487 10(1.36) 10(0) 10(1.1) 10(0) 743.50 *TP: Total number of Publications; TP%: Total number of Publication in %; TC 2020: Total Citation since publication to the end of 2020; TPR%: Rank and the percentage of Total Publications; CPR%: Rank and the percentage of Internationally Collaborative Articles; RPR%: Rank and the percentage of the Corresponding Author Articles; SPR%: Rank and the percentage of the Single Author Articles; CPP: Citations per Publication (CPP = TC 2020/TP)

research interest on river health studies among the researchers and scientist resultant highest collaborative work with the remaining countries. Furthermore, the authors have also tried to perform an analysis considering the number of researchers working in this field per country. Figure 4.8 shows Australia (53), which has got 2nd ranking in the case of several highly cited publications, has the highest number of researchers involved in river health-related studies. Whereas the United States (49) has acquired the 2nd position followed by France (26), United Kingdom (19) and so on depicting the strength of countries’ scientific productivity.

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Fig. 4.7 The country citation network analyses on river health-related publications

Fig. 4.8 Scientific productivity analysis of the top 10 productive countries

Research Trend Analysis Finally, to detect the statistically significant research trend on river health studies, a rank-based non-parametric Mann-Kendall method has been applied from 1990 to 2019. The year 2020 has been excluded from this analysis because of the

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incompletion of the year. This method is very common in handling environmental time-series data and one of the major usefulness is it does not require a normally distributed dataset. Mann-Kendall [23] formulated such a statistical trend analysis method where the outliers within the dataset do not affect the output [23]. Here, two hypotheses have been taken into consideration, one is the null hypothesis (H0) indicating no trend and another is an alternative hypothesis (H1) depicting an increasing or decreasing trend within the dataset. In this study, the hypothesis has been tested at a significant level of 0.05 for the dataset and the output of the analysis has presented in Fig. 4.9 and Table 4.5. The result of the Mann-Kendall test suggests that the two-tailed p-value (1 m, and an area of 62 km2 (approximately) will be inundated with water level  1 m, where, the total area of the Ghatal block is nearly 230 km2. (All the roads have been considered to be at an average height of 1 m.) Similarly, flood simulation maps can be generated for simulation time of 48 h, 72 h, 96 h and so on. Obviously, in those cases, more areas will be submerged by the flood water. The flood simulation map, in Fig. 9.4, looks a bit blocky as 1 km  1 km grid has been used in the FLO-2D Basic Model for simulation. A grid with sufficiently lower element size, would have smoothened the simulation map, but for running the FLO-2D Basic Model with that grid, a computer with higher computational capacity (than that used in the study) is required. The flood simulation map, and the maps of the roads and the settlements (in Fig. 9.4), can help the concerned administrative authority, in having an advanced understanding of the nature of flood in the study area, and in taking appropriate precautionary measures such that the loss of human lives, property, infrastructure and resources, due to flood, may be avoided, to a great extent. Furthermore, publishing of maps in GeoServer may help the pertinent administrative authority to access them faster, and take early action to tackle the flood problem. Also, if the people of the study area can access these maps (through GeoServer or otherwise), they themselves can take suitable and timely measures for bypassing, as much as possible, the devastation caused by floods. Besides, the maps may encourage other researchers/scientists to undertake research in this field, and propose better methods for producing more accurate flood simulation maps, preferably using lesser amount of input data. An important benefit of using the FLO-2D Basic Model is that it can predict the nature of flood with very little information (i.e., DEM and hydrograph); this makes the prediction process quite simple for the user. However, for more accurate flood simulation and/or tackling more complicated situations, there are provisions in this model for more complicated procedures, but they require data of higher complexity and quantity, and computers of higher computational ability. Actually, this work attempts to simulate flood in the study area with the input data that are not only small in amount, but also reusable. DEM may be used again

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Fig. 9.4 The outcome of flood simulation

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and again, as land surface elevation can be expected to remain constant over time. Even if the DEM includes large man-made structures like huge embankments, then also there is little possibility that they will undergo any major change in the near future. Also, it should be stated that a DEM of lower resolution may lessen the accuracy of the result, but at least give some output (i.e., some idea about flood simulation). And, regarding the hydrograph, it can be said that it is better to have one that reflects recent data, but an older one will at least give a more or less rough estimate of the simulated flood. Thus, this work helps in simplifying and expediting the process of flood simulation, and attempts to overcome the restrictions imposed by the lack of availability of sufficient and/or recent data. Hence, the technique adopted in this study, may be quite helpful in those situations where there is scarcity of relevant datasets, particularly the recent ones. Moreover, the fact that FLO-2D Basic Model, GeoServer, SRTM DEM and LISS-III image are freely available, makes the whole process, used here, quite economical. Flood simulation software systems like, MIKE FLOOD, FLO-2D Pro etc., are much more sophisticated, and they have the capability of handling situations with more complexity and diversity; hence they are often used by various foreign government agencies. But, they are quite costly, and so, it is difficult to use them in low budget studies, like this one. After necessary processing and calculation, it has been found that approximately 76.43% of the area, pertaining to the simulated flood, has been covered by the total inundation (as shown by the maps obtained from Bhuvan) during July–August, 2011, October, 2013, July–August–September, 2015, August, 2016 and July– August, 2017. Thus, the total actual inundation, for the aforesaid periods, corresponds to around three-fourths of the region pertaining to the simulated flood; hence, it can be said that there is a good correlation between the simulated and the total actual inundations. That, nearly one-fourth of the region corresponding to the simulated flood is not actually submerged during the abovementioned periods, is probably because, the extent of real flooding often varies greatly from occasion to occasion. If real flood maps corresponding to other suitable periods were also available, more accurate match might have been possible. The other factors which have also possibly contributed to this discrepancy (i.e., one-fourth of the area pertaining to the simulated flood is not actually inundated), are the use of only the DEM of the study area, and the hydrograph of the Silabati river, for flood simulation, and the allotment of a simulation time of only 24 h. As an example of the nature of similarity between the actual and the simulated floods, the flood simulation map generated by the FLO-2D Basic Model, and the real flood map corresponding to July–August, 2017 (obtained from Bhuvan), are compared in Fig. 9.5. Fig. 9.5 shows some overlap between the simulated flood and the actual inundation.

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Fig. 9.5 The real and the simulated floods

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Conclusions In this work, the extent and nature of flooding in the study area have been predicted, the relevant maps, which convey the prediction related information, have been published in GeoServer to enable wide access, and the effectiveness of the prediction has been tested. Due to the use of (mostly) freely available data and software systems, the whole process, adopted in this study, is quite economical. The flood simulation map would have been smooth, if a grid with element size sufficiently lower than that of the one used in this study, were employed in the FLO-2D Basic Model for simulation. If the factors responsible for causing flood, other than the amount of rainfall, the water carrying capacity of the concerned river/s, and the nature of terrain, were also considered, the influences of the relevant rivers, other than Silabati, were also taken into account, separate data for the amount of rainfall in the study area, were used, and more sophisticated software system were employed, result of higher accuracy would have been obtained. If, in addition to the above, real flood maps corresponding to suitable periods other than those used, were also available, and a higher simulation time were allotted for the flood simulation process, a more accurate validation of the result could have been done. The maps of the roads and the settlements of the study area, and the simulated flood, published in GeoServer, can provide timely warning to the people who are at the risk of being affected by flood, and also to the relevant administrative authority who may take precautionary measures and/or organise relief work. The work done in this study, may be further improved to develop a suitable mobile app which will automatically access the relevant DEM and hydrograph (and, if needed, other relevant) data (from some specified source/s), and will instantly produce an appropriate flood simulation map of any part of West Bengal or India. The data used for simulation may/may not be disclosed to the user. Acknowledgements The authors are thankful to Dr. Soumya Kanti Ghosh, Professor, Department of Computer Science and Engineering, IIT Kharagpur, for his help, support and cooperation in this work. The authors also gratefully acknowledge the help, encouragement and advice, they received from Dr. Arindam Dasgupta, Department of Computer Science and Engineering, IIT Kharagpur, in connection with this project. Besides, the authors are indebted to the Department of Science and Technology and Biotechnology, Govt. of West Bengal, for their kind permission and support, with regard to this study.

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References 1. Dolui G, Ghosh S (2013) Flood and its effects: a case study of Ghatal block, Paschim Medinipur, West Bengal. Int J Sci Res 2(11):248–252 2. Mandal Sahoo P and Sivaramakrishnan L (2014) Vulnerability of flood prone communities in the lower reaches of Shilai River—Ghatal Block, Paschim Medinipur District, West Bengal, India. Int J Dev Res 4(7):1393–1400 3. National Informatics Centre (2018) Egiye Bangla. [Online] Available: https://www. paschimmedinipur.gov.in/node/224 4. The Pennsylvania State University and Utah State University (2020) Water: Science and Society: Hydrograph. [Online] Available: https://www.e-education.psu.edu/earth111/node/ 865 5. FLO-2D Software Inc (2009) FLO-2D reference manual: version 2009 6. FLO-2D Software Inc (2015) FLO-2D workshop lessons 7. Open Source Geospatial Foundation (2014) GeoServer. [Online] Available: http://geoserver. org/about/ 8. Gao P, Carbone GJ, Lu J (2018) Flood simulation in South Carolina watersheds using different precipitation inputs. Adv Meteorol 2018 9. Hadimlioglu IA, King SA, Starek MJ (2020) FloodSim: flood simulation and visualization framework using position-based fluids. ISPRS Int J Geo-Inform 9(3) 10. Fava MC, Abe N, Restrepo-Estrada C, Kimura BYL, Mendiondo EM (2019) Flood modelling using synthesised citizen science urban streamflow observations. J Flood Risk Manag 12(2) 11. Mitra P, De I (2016) Flood prediction modelling of Ghatal block (West Bengal). Int J Adv Res Comput Sci Eng Inf Technol 6(1):1075–1078 12. Wu Y-H, Liu K-F, Chen Y-C (2013) Comparison between FLO-2D and Debris-2D on the application of assessment of Granular Debris flow hazards with case study. J Mt Sci 10(2): 293–304 13. Chakrabarty A (2014) Real time flood inundation modeling for Midnapore-Kharagpur Development Authority (MKDA) planing region of West Medinipur District, West Bengal (India). In: Proceedings of the AfricaGEO conference & exhibition

Chapter 10

Perspectives on Chemical Warfare and Emergence of Antibacterial Resistance in Water Environment Minakshi Ghosh and Pankaj Kumar Roy

Introduction Water disinfection using chlorination and safe water supply to consumers has resolved the issues in most of the rural and urban areas. Improved sanitation due to the usage of chemical disinfectants has markedly decreased the mortality rate due to improper sanitation. Nonetheless, yet another problem has set back which imposed a major threat to the environment. Perpetual usage of the sub-optimal levels of disinfectants persuaded bacterial resistance through mutagenesis [1–3]. When a bacterial species is subjected to chemical warfare which often threatens its extinction, they evolve mechanisms to survive under stress leading to the development of resistance [4–8]. Bacterial tolerance to disinfectants imposes selective pressure on the bacterial cell. It is evident from the experimental investigations that stress response due to the use of disinfectants imposes potential influence on the bacterial cell that aids in the development of antibacterial resistance [9–12]. Antibacterial resistance is a huge concern these days as it often leads to treatment failure. Low-level community-resistant bacteria may go undetected adding a burden on health care cost. This problem threatens to return to pre-antibiotic era. Of all the chemical disinfectants chlorine-based disinfectants are very easily available and commonly used in water treatment. It is reasonably cheap compared to other disinfectants and very easily employed which leaves a residual effect that provides additional protection of water against recontamination. There is no doubt that residual free chlorine is an effective disinfectant for bacteria and viruses, yet it is not always effective against C. parvum and G. lamblia. An additional limitation of M. Ghosh
P. K. Roy (&) School of Water Resources Engineering (S.W.R.E), Jadavpur University, Raja S. C. Mullick Road, Kolkata 700032, India e-mail: [email protected] M. Ghosh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_10

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using chlorine in water treatment is its production of some harmful disinfection by-products (DBPs) that lead to potential public health risks [13]. Hypochlorous acid (HOCl) and hypochlorite (OCl−) ions make up residual free chlorine that has high oxidation potential. It is more effective than any other form of chlorine, like chloramines. Hypochlorous acid (HOCl) is eight times more effective as a disinfectant compared to hypochlorite ions (OCl-) [14, 15]. Chlorination requires contact time for effective cellular inactivation and oxidative damage of the pathogens. Dosages of chlorine and contact time are the two important factors. Chlorine acts on microbial cells by various mechanisms which are as follows: (1) physical, chemical, and biochemical alterations of cell wall structure, (2) terminating vital cellular functions, (3) chlorine disrupt cell wall barrier that targets on specific sites on the cell surface, (4) chlorine acts as an oxidant that ruptures the cells releasing all of the vital cellular constituents from the cell to the exterior, (5) chlorine terminates membrane-associated functions, and (6) inactivates various enzyme activity as it reacts with sulfur-containing, aromatic amino acids leading to bacterial death probably as a result of a chlorine attack on a variety of bacterial molecules or targets, including enzymes, nucleic acids, and membrane lipids. Gram-negative bacteria possess a very different constitution in which cell walls consist of an outer membrane and a cytoplasmic membrane that often help them to resist many chemicals at its low optimal level [2, 8, 12]. The mechanism of resistance to chlorine disinfectants may be acquired or intrinsic, the association of the outer membrane and the porin channels in Gram-negative bacteria with the chlorine-resistant pattern has been less extensively studied. Many pieces of literatures are available on the antibiotic resistance pattern and mechanism of resistance in bacteria [4–6, 16] but the mechanism of resistance toward chlorine agents is not extensively studied therefore only poorly understood. The main objective of the study is to understand the mechanism of action of Gram-negative bacteria in resisting disinfectants like chlorine agents as it is extensively used to maintain a residual effect to protect water from recontamination.

Methodology Four test strains of Gram-negative bacteria often found resistant to disinfection in water system, namely, K. Pneumonia, P. aeruginosa, P. rustigianii, and Pr. myxofaciens were taken under consideration for the present study. All the strains were maintained within an initial load of 5 log CFU/ml [17]. An extensive microbiological culture-dependent technique and molecular biological study have been done for the present experimental investigation. Each strain was subjected to different chlorine disinfectants and biocide; like sodium hypochlorite, calcium hypochlorite, chlorine dioxide gas, and triclosan [18]. Triclosan is a chlorine-based biocide commonly used in healthcare as an antiseptic. The concentration of each chlorine disinfectant and the biocide was set at three ranges, 0.5, 1, and 3 mg/L, respectively. After a contact time of one hour, the percentage (%) log survival (CFU/ml)

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was determined on plate count agar (PCA). The cells that survived the treatment with disinfectants were analyzed for changes in their efflux pump and porin channels. The cDNA analysis was done to find out the expression of efflux pump genes like Acr AB-TO1C, MexAB-OprM, and outer membrane protein gene OmpK [7]. The expression of other resistant genes like blaTEm, blaOXA, NDM-1, Qnr genes, and TetC genes that impart role in antibiotic resistance often co-selected and acquired, were also analyzed by RT qPCR. The PCR products were ran in 2% Agarose gel electrophoresis for confirmation of results [19, 20]. Alternatively, antibiotic susceptibility of all the resistant and susceptible strains was checked by the disc diffusion method on MH agar. The loss of lipopolysaccharides (LPS) was also determined by extracting LPS with an LPS extraction kit (Sigma) and running electrophoresis on 2% SDS PAGE, stained with silver staining method [21]. LPS loss and modification in presence of a low concentration of free chlorine (FC < 0.2 mg/L) may signify developing resistance. A discussion on inactivation of these resistant species through chlorine-bromine shock and ozonation in highly contaminated water environment was also done in the end of the study [14, 22, 23].

Results and Discussion The test result revealed few mechanisms of resistant patterns by Gram-negative bacteria on response to chlorine disinfection (1) activation of the efflux pump system, (2) porin channels, (3) plasma membrane-mediated protein scaffolding, (4) biofilm formation and attachment to surfaces/cellular aggregation (as listed in Fig. 10.2 and Table 10.2). Bacteria react to disinfectants, biocides, and antibiotics diversely; often emerge with resistant mechanisms against toxic chemical substances targeted against its destruction (shown in Table 10.1 and Fig. 10.1). It was evident that the four Gram-negative bacterial isolates were selected for plasmid-encoded acquired resistance against carbapenem, quinolone, and tetracycline on exposure to chemical disinfectants (as in Figs. 10.3 and 10.4). The outer membrane becomes vulnerable and loses its control. The cell’s defensive barrier is broken and at last, loses its selective permeability. The efflux pump AcrAB-ToIC belonging to the resistance-nodulation-division (RND) family is commonly a significant mechanism in strains of the Enterobacteriaceae family. The role of the efflux pump in expelling chlorine agents is ambiguous. Bacteria that survived chlorination showed high AcrAB-ToIC pump activity. MexEF-OprN expression was many folds higher in Pseudomonas. This bacterium also exports HHQ (4-hydroxy-2-heptylquinoline), a precursor of the Pseudomonas quinolone signal-PQS [24]. Additionally, mechanisms such as porin activity (Omp genes) in Proteus and Providencia played a significant role in resistance as it was observed that these isolates conferred fourfold greater porin activity as they survived chlorination. This tripartite complex might have spanned the innermost membrane, then the periplasm, and the outermost membrane consecutively expelling toxic chemicals from the cells. The AcrAB-ToIC pump may also confer a high resistance

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Table 10.1 Effect of disinfectants and biocide on gram-negative bacteria NaOCl

Ca(OCl)2

Disinfectants at concentration of 0.5 mg/L P. rustigianii Resistant Resistant Pr. myxofaciens Resistant Resistant K. pneumonia Resistant Resistant P. aeruginosa Resistant Resistant Disinfectants at concentration of 1.0 mg/L P. rustigianii Resistant Resistant Pr. myxofaciens Resistant Intermediate K. pneumonia Resistant Resistant P. aeruginosa Resistant Resistant Disinfectants at concentration of 3.0 mg/L P. rustigianii Resistant Resistant Pr. myxofaciens Resistant Intermediate K. pneumonia Resistant Resistant P. aeruginosa Resistant Resistant

ClO2

Triclosan

Resistant Resistant Resistant Resistant

Resistant Resistant Resistant Resistant

Resistant Resistant Intermediate Resistant

Resistant Resistant Resistant Resistant

Resistant Intermediate Intermediate Intermediate

Resistant Resistant Resistant Resistant

Table 10.2 Mechanisms involved in resistance against chlorine disinfectants and biocide Isolates* (n = 4)

Chlorine agent and biocide

P. rustigianii

Highly tolerant of biocide, and disinfectant till 3 mg/L High tolerance to NaOCl, and triclosan till 3 mg/L

Pr. myxofaciens

Efflux pump, Porin expression Acr AB system, OmpK expression Acr AB system, OmpK expression

Acr AB Highly system, tolerant to NaOCl, ClO2, OmpK and triclosan expression till 3 mg/L Mex ABP. aeruginosa Highly OprM tolerant to NaOCl, ClO2, system and triclosan till 3 mg/L * Significance of testing- The isolates showed K. pneumonia

Antibiotic-resistant genes (ARG)

Effective removal

Elucidation

Highest blaTEM, blaOXA, TetC, and Qnr gene resistance

5% chlorinebromine shock

Highest tolerance to all chlorine agents, triclosan, and antibiotics

High blaTEM, TetC, and Qnr gene resistance

Ozone gas, 5% chlorinebromine shock

Highest blaTEM, blaOXA, TetC, and blaNDM-1 expression

Ozone gas, 5% chlorinebromine shock 5% chlorinebromine shock

Moderate tolerance to NaOCl, and triclosan. Moderate expression of ARGs High tolerance to NaOCl, ClO2, and triclosan. High expression of ARGs High tolerance to NaOCl, ClO2, and triclosan. High expression of ARGs

Highest blaOXA, Qnr, TetC, and blaNDM-1 gene expression

significant results at P < 0.05

% Log survival (CFU/mL)

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6 5 4

P. rustigianii

3

Pr. Myxofaciens

2

K. pneumoniae

1

P. aeruginosa

0 NaOCl

Ca(OCl)2

ClO2

Triclosan

Disinfectant at concentration 3.0 (mg/ L)

Relative fold change in genes

Fig. 10.1 Log survival of isolates after 1-hr exposure to disinfectants and biocide

25 20 15 10 5 0 P. rustigianii

Pr. K. P. aeruginosa myxofaciens pneumoniae

bla TEM bla OXA NDM- 1 TetC Qnr Acr AB- TO1C MexAB-OprM OmpK Control

Gram negative bacterial strains Fig. 10.2 Relative fold changes in expression of efflux pump, porin, and antibiotic-resistant genes (ARGs)

DNA ladder Control Qnr A (180 kb)

Fig. 10.3 Agarose gel electrophoresis showing QnrA gene expression by P. aeruginosa

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Fig. 10.4 Antibiotic resistant P. rustigianii and P. aeruginosa

against other clinically relevant antibiotics like b-lactams, macrolides, tetracycline, and fluoroquinolones. The antibiotic-resistant genes (ARGs) were found to show fourfold greater activity in Providencia > Pseudomonas > Klebsiella > Proteus species significantly at P-value < 0.05 (as shown in Fig. 10.2). An illustration of QnrA expression by P. aeruginosa has been represented in Fig. 10.3. The fact that P. aeruginosa and K. pneumonia aggregates and forms biofilm also helped these pathogens to withstand chlorine at an optimal concentration. Chlorination often injures the cell as a consequence the cell may lose its outer lipopolysaccharide (LPS) membrane. Species like Pseudomonas and Klebsiella tolerated the injury, showed yet very different mechanisms to resist further damage. They have emerged through multiple mechanisms that include increased production of the capsule (which decreases the binding of certain microbicidal substances that target against its lipopolysachharides—LPS). The SDS PAGE result ensures loss of LPS and the modification of LPS by the strains of Pseudomonas and Klebsiella in presence of a low concentration of free chlorine (FC < 0.2 mg/L). Pseudomonas, Providencia, and Proteus may have acquired antibiotic resistance through plasmid-mediated transfer as bla gene expression is not their common mechanism. Pseudomonas and Providencia were involved in Qnr and high TetC response. TetC response was relatively high in all the test strains, often regulated by transmissible elements.

Conclusions The up regulation of efflux pumps of carbapenem-resistant Enterobacteriaceae (CRE) was not clear until now, in pathogens that regulate chlorine agents following the same pattern. The present study illustrated the role of efflux pumps and porin channels in adding protection against toxic molecules. The efflux mediated response was involved in acquiring resistance against the antibacterial substances and aid in the secretion of quorum-sensing signals. Biofilm forming bacteria provides additional protection against disinfection. The study revealed that at a very low concentration of free chlorine biofilm formation was increased. In the process of acquiring resistance against disinfectants and biocides, all the four test strains

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co-selected for antibiotic-resistant genes (ARGs) as shown by gene expression studies. Methods for total inactivation of these resistant forms were strategized, as they may spread to the environment through water compartments. The present investigation suggests total inactivation of chlorine and triclosan resistant Gram-negative bacteria by 5% chlorine-bromine shock and ozonation [14, 22, 23]. Water compartments suffering from bacterial contamination if left unattained may harbor pathogens that develop antimicrobial resistance in presence of free chlorine [10, 13], such pathogens if not deactivated may spread resistance (via the transfer of plasmid DNA) to other bacteria that are generally not human pathogens [10, 11].

Recommendations A few costlier yet effective treatment methods are nano-sorbents particles, nano-particle enhanced filtration methods, nano-catalysts, nanostructured catalytic membranes, and bioactive nano-particles. It is important to inactivate harmful pathogens and to adopt an approach in large-scale treatment plants so that these antimicrobial-resistant bacteria do not perpetuate in the environment.

References 1. Jin M, Liu L, Wang D et al (2020) Chlorine disinfection promotes the exchange of antibiotic resistance genes across bacterial genera by natural transformation. ISME J 14:1847–1856. https://doi.org/10.1038/s41396-020-0656-9 2. Karumathil DP, Yin HB, Kollanoor-Johny A, Venkitanarayanan K et al (2014) Effect of chlorine exposure on the survival and antibiotic gene expression of multidrug resistant Acinetobacter baumannii in water. Int J Env Res Pub Health 11(2):1844–1854. https://doi. org/10.3390/ijerph110201844 3. Lin W, Zhang M, Zhang S, Yu X et al (2016) Can chlorination co-select antibiotic-resistance genes? Chemosphere 156:412–419. https://doi.org/10.1016/j.chemosphere.2016.04.139 (Epub: 2016 May 15, PMID: 27192478) 4. Russell AD (2000) Do biocides select for antibiotic resistance? J Pharm Pharmaco 52:227– 233. https://doi.org/10.1211/0022357001773742 5. Santajit S, Indrawattana N (2016) Mechanisms of antimicrobial resistance in ESKAPE pathogens. BioMed Res Intern 2016:2475067. https://doi.org/10.1155/2016/2475067 6. Sharma VK, Yu X, McDonald TJ (2019) Elimination of antibiotic resistance genes and control of horizontal transfer risk by UV-based treatment of drinking water: a mini review. Front Env Sci Eng 13(3). https://doi.org/10.1007/s11783-019-1122-7 7. Shin S, Jeong SH, Lee H et al (2018) Emergence of multidrug-resistant Providencia rettgeri isolates co-producing NDM-1 carbapenemase and PER-1 extended-spectrum b-lactamase causing a first outbreak in Korea. Ann Clinic Microbio Antimicrob 17(1):20. https://doi.org/ 10.1186/s12941-018-0272-y 8. Shrivastava RK, Upreti SR, Jain KN et al (2004) Suboptimal chlorine treatment of drinking water leads to selection of multidrug-resistant Pseudomonas aeruginosa. Eco-Toxicol Env Safe 58(1):277–283

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9. McDonnell G, Russell AD (1999) Antiseptics and disinfectants: activity, action, and resistance. Clinic Microbio Rev 12(1):147–179 10. Ridgway HF, Olson BH (1982) Chlorine resistance patterns of bacteria from two drinking water distribution systems. App Env Microbiol 44:972–987 11. Roy PK, Ghosh M (2017) Chlorine resistant bacteria isolated from drinking water treatment plants in West Bengal. Desal Water Treat 79:103–107. https://doi.org/10.5004/dwt.2017. 20697 12. Roy PK, Ghosh M, Biswas Roy M (2018) Selection of multi-drug resistant bacteria from water treatment plants. Desal Water Treat 107:279–284. https://doi.org/10.5004/dwt.2018. 22160 13. Ngwenya N, Ncube EJ, Parsons J et al (2013) Recent advances in drinking water disinfection: successes and challenges. Rev Environ Contam Toxicol 222:111–170 14. Roy PK, Kumar D, Ghosh M, et al (2016) Disinfection of water by various techniquescomparison based on experimental investigations. Desal Water Treat 57(58):28141–28150. https://doi.org/10.1080/19443994.2016.1183522 15. Virto R, Manas P, Alvarez I et al (2005) Membrane damage and microbial inactivation by chlorine in the absence and presence of a chlorine-demanding substrate. App Env Microbiol 71(9):5022–5028. https://doi.org/10.1128/aem.71.9.5022-5028.2005 16. Singer AC, Shaw H, Rhodes V, Hart A (2016) Review of antimicrobial resistance in the environment and its relevance to environmental regulators. Front Microbiol 7:1728. https:// doi.org/10.3389/fmicb.2016.01728 17. Martinez-Hernandez S, Vazquez-Rodriguez GA, Beltran-Hernandez RI et al (2013) Resistance and inactivation kinetics of bacterial strains isolated from the non-chlorinated and chlorinated effluents of a WWTP. Int J env Res pub Health 10(8):3363–3383. https://doi. org/10.3390/ijerph10083363 18. Carey DE, McNamara P J (2015) The impact of triclosan on the spread of antibiotic resistance in the environment. Front in Microbio 5 (780):1–11 19. Miao M, Wen H, Xu P et al (2019) Genetic diversity of carbapenem-resistant Enterobacteriaceae (CRE) clinical isolates from a tertiary hospital in Eastern China. Front Microbio 9:3341. https://doi.org/10.3389/fmicb.2018.03341 20. Speer BS, Shoemaker NB, Salyers AA (1992) American society for microbiology bacterial resistance to tetracycline: mechanisms, transfer, and clinical significance. Clinic Microbio Rev 5(4):387–399 21. Davis MR Jr, Goldberg JB (2012) Purification and visualization of lipopolysaccharide from Gram-negative bacteria by hot aqueous-phenol extraction. J visual exp JoVE 63:3916. https:// doi.org/10.3791/3916 22. Farkas-Himsley H (1964) Killing of chlorine-resistant bacteria by chlorine-bromine solutions. App Microbio 12(1):1–6 23. Alternative drinking-water disinfectants: bromine, iodine and silver (2018) ISBN 978-92-4151369-2 © World Health Organization 24. Lamarche MG, Deziel E (2011) MexEF-OprN efflux pump exports the Pseudomonas quinolone signal (PQS) precursor HHQ (4-hydroxy-2-heptylquinoline). PLoS One 6(9): e24310. https://doi.org/10.1371/journal.pone.0024310

Chapter 11

Removal of Hazardous Dyes from Waste Water in a Green and Cost-Effective Way Paramita Das and Chiranjib Bhattacharjee

Introduction Increasing population and standard lifestyle of mankind directly affects our environment. Enhanced urbanization demands more industries which in turn contaminates our environment mostly in the form of water pollution. The presence of toxic pollutants like synthetic dyes and its adverse effects on living beings is an environmental concern of the twenty-first century worldwide. Synthetic dyes that are responsible for the major contribution toward water pollution mainly come from the effluents of fabric, plastics, paint, dyeing, leather, and paper industries [1]. Majority of the dyes used in the industries are synthetic and hence non-biodegradable. Again small molecular size hinders their separation from contaminated water [2]. These hazardous dyes can easily penetrate the cell membranes and get concentrated inside the cytoplasm. Hence they become threat to the environment and to public health as well [3–5]. Numerous conventional techniques like precipitation, membrane-based separation, chemical coagulation, photo degradation, ion exchange, and flocculation have been thoroughly investigated to remove toxic dyes from wastewater. Although these aforesaid methods have proven themselves as efficient methods, but the initial and operational cost is too high. On the contrary, adsorption is a simple water purification method due to the availability of different adsorbents, its high efficiency, easy handling, and low operational cost [6]. Adsorbents from natural origin are easily procurable, inexpensive, and highly efficient. Nowadays a variety of green and cost-effective adsorbents based on agricultural residues, viz., rice husk, pine sawdust, orange peel, olive mill waste, P. Das
C. Bhattacharjee (&) Department of Chemical Engineering, Jadavpur University, Kolkata, India e-mail: [email protected] P. Das e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_11

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avocado peel, wheat straw, sugarcane bagasse, pomelo peel, etc., have been intensively investigated for wastewater treatment [6, 7]. Camellia sinensis leaves, obtained from the dried and processed leaves of Camellia sinensis plant are consumed by a large number of people in the world [8]. After utilization the used leaves become an agricultural residue which can be utilized to remove toxic dyes from polluted water [9, 10]. The main chemical constituents of Camellia sinensis leaves, i.e., cellulose, hemicelluloses, condensed tannins, lignin, and structural proteins play a pivotal role for chemical as well as physical adsorption of several dyes [11]. Hence, the utilization of waste Camellia sinensis (WCS) leaves as adsorbent for water treatment is an inspiring topic of study among researchers. The present study focuses on the utilization of WCS leaves, a green and inexpensive adsorbent from agricultural waste to separate a cationic dye, Rhodamine B (RhB) from water. It is easily available and can be collected from domestic wastes. The adsorbent was characterized by FTIR, SEM, and TGA. The effects of different parameters like adsorbent dosage, temperature, initial dye concentration, solution pH, and time of contact that affect the removal efficiency were studied in terms of percent dye removal (%R). The motivation behind our study is the introduction of an environment friendly, readily available, and cheap adsorbent for waste water treatment.

Experimental Materials WCS leaves, obtained from domestic wastage (Tetley) were used to prepare the green adsorbent. Rhodamine B dye and all other reagents (AR grade) were purchased from Sigma-Aldrich (Bangalore, India). Deionized water was used for the preparation of the adsorbent as well as dye solutions.

Instrumentation Thermal gravimetric analysis (TGA) of WCS leaves adsorbent was performed in nitrogen atmosphere using a Perkin Elmer instrument (scanning rate: 10 °C/min, temperature range: 30–600 °C). ATR-FTIR spectra of the adsorbent sample before and after dye adsorption were recorded on a Bruker (AlphaII) spectrophotometer. Morphological study of the adsorbent was done by Scanning Electron Microscope (SEM, S-4800, Hitachi). Dye concentration was measured in a UV–vis spectrophotometer (Varian, 50 Bio). A Labman pH-meter was used for pH measurements.

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Preparation of Adsorbent The water-soluble impurities as well as the surface adhered particles that are present in the collected WCS leaves residue was washed several times with distilled water. Then the adsorbent was repeatedly boiled with water and filtered until the filtrate became clear. The washed WCS leaves adsorbent was dried at 60 °C in hot air oven and then cooled at room temperature. Finally, the prepared adsorbent was ground to a fine powder using a mixer grinder, sieved through a sieve of 100 mesh size, and stored in a desiccator for further study.

Adsorption Experiment The adsorption experiment was carried out in a batch process. The experiment was performed by the addition of required amount of the green adsorbent into 20 ml dye solution of known concentration in a 50 ml beaker with continuous stirring for 24 h. The dye loaded adsorbent powder was then separated by centrifugation. Spectrophotometrically, the dye concentration after adsorption was determined at a wavelength of 555 nm. The effects of different operating parameters like the quantity of the adsorbent, solution pH, dye concentration, time of contact, and temperature were thoroughly studied. The effect of solution pH was studied from pH 2 to pH 10 for which 0.1 (M) HCl and 0.1 (M) NaOH solutions were utilized. The dye removal efficiency in terms of percent dye removal (%R) was determined using the following expression [12]. Dye removal; R ð%Þ ¼ ðCi  Ce Þ=Ci  100

ð11:1Þ

where, Ci and Ce are the concentrations (mg/L) of RhB dye before and after the adsorption experiment, respectively.

Results and Discussion Characterization Thermogravimetric Analysis (TGA) Figure 11.1 shows the TGA curve of the WCS leaves sample used for this study. The first weight loss (about 8%) at a temperature range near 100 °C is associated with dehydration of the samples. The maximum weight loss of the WCS leaves is observed between 250 and 370 °C (14–58%) which is due to the degradation of structural sugar components. The weight loss at 150–370 °C and 265–370 °C are

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because of degradation of hemicelluloses and cellulose, respectively. The last weight loss in the range of 370–460 °C (58–71%) was due of degradation of lignin [13].

FTIR Analysis The FTIR spectra of WCS leaves before and after dye adsorption are shown in Fig. 11.2a and b., respectively. The presence of a number of absorption peaks in Fig. 11.2 indicating the complex nature of the natural adsorbent. As shown in the figure the peaks near 2924, 1328, and 1036 cm−1 are indicating the presence of aliphatic C–H group, C–O stretching, and C–O–H stretching vibration, respectively. The natural adsorbent under study before dye adsorption is observed to show peaks near 3409, 2852, 1651, 1522, 1464, 1222, and 1150 cm−1 corresponding to the –OH groups, aliphatic C–H group, C=O stretching, secondary amine group, CH3 symmetric bending, –SO stretching and C–O stretching of ether groups, respectively [9, 14] which have been shifted to 3390, 2845, 1644, 1531, 1451, 1214, and 1155 cm−1, respectively, after adsorption of the dye. Shifting of peak values clearly indicating the adsorption of dye into the adsorbent under study.

Morphological Study by SEM Analysis WCS leaves sample is characterized by scanning electron microscopy (SEM) to study the surface morphology as shown in Fig. 11.3. As observed from the SEM analysis the surface of the WCS leaves contain compact fibers with tunnels, intermittent grooves, and minute pores on the surface. These rough perforated structures of the sample as shown in Fig. 11.3 are mainly responsible for adsorption of dye as they create a route for dye penetration [13]. Fig. 11.1 TGA of WCS leaves

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Fig. 11.2 FTIR spectra of WCS leaves a before and b after dye adsorption

Fig. 11.3 SEM images of WCS leaves

Adsorption Experiment Effect of the Quantity of the Adsorbent Adsorption efficiency of an adsorbent is dependent on quantity of the adsorbent utilized or in other words on the number of active adsorption sites. Figure 11.4a is

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showing the effect of adsorbent dosage on percent dye removal (%R). In the present study adsorbent dose was varied from 0.2 to 6 wt% for 5 mg/L dye solution. The % R was found to increase rapidly from 56.4 to 89.5% with increase in adsorbent dose from 0.2 to 4 wt%, respectively. With further addition of the green adsorbent percent removal remained almost constant.

Effect of Initial Concentration of Dye Solution Adsorption of the dye is also influenced by the initial adsorbate concentrations. Figure 11.4b is showing the effect of initial concentration of RhB dye (3–30 mg/L) on its adsorption onto WCS leaves (4 wt%). From this curve we can see that initially the percent dye removal (%R) increases with increase in the concentration of RhB dye up to 20 mg/L (91.9%). With further increase in dye concentration %R decreases. At the beginning the number of available adsorption sites is high in comparison to the number of RhB molecules present in the solution and hence almost all the adsorbate molecules get adsorbed. After a certain concentration (20 mg/L), no active adsorption sites are left unoccupied as all the active sites are already blocked with the dye molecules. Consequently, no further increase in percent adsorption is observed.

Effect of pH of the Solution Figure 11.4c is showing the variation of percent dye removal against pH of the solution for the cationic dye RhB (5 mg/L) adsorbed onto the natural adsorbent under study (4 wt%). The solution pH was varied from pH 2 to pH 10. Rhodamine B is a cationic dye which exists as positively charged ions in aqueous solution and hence its adsorption on the surface of a given adsorbent is dependent on solution pH as it affects the surface charge of the adsorbent [14]. Initially the percent dye removal (%R) is found to increase with solution pH up to pH 8 and thereafter no significant change is observed as shown in Fig. 11.4c. At lower pH the WCS leaves tend to adsorb H+ ions as the amine groups present in the adsorbent may be protonated as given in Eq. (11.2) and hence become positively charged. Thus, a repulsive force comes into play between the positively charged adsorbent and cationic adsorbate molecules. In addition, the dye cation has to compete with the highly concentrated H+ ions for the unoccupied adsorption sites. Hence %R is insignificant at low pH. Similarly, at higher pH the adsorbent becomes negatively charged due to the adsorption of OH− ions and deprotonation of the carboxyl groups as given in Eqs. (11.3 and 11.4), respectively [14]. Consequently, a significant electrostatic attractive force develops between the negatively charged adsorbent and the cationic adsorbate molecules at higher pH which makes the process of adsorption of RhB dye onto the WSC leaves highly favorable.

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Fig. 11.4 Effect of a quantity of the adsorbent, b initial adsorbate (RhB dye) concentration, c pH of the solution, d temperature, and e contact time on percent dye removal (%R)

ð11:2Þ WCS  COOH ! WCS  COO þ H þ

ð11:3Þ

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WCS  COOH + OH ! WCS  COO þ H2 O

ð11:4Þ

Effect of Temperature The adsorption experiment of RhB dye onto the natural adsorbent was carried out at 30, 40, 50, and 60 °C, respectively, to study the influence of temperature on dye adsorption (Fig. 11.4d). Percent dye removal is found to increase with rise in temperature as shown in the figure. With enhancement of temperature the diffusion rate of RhB molecules through external boundary layer as well as into the internal pores of the adsorbent increases. Again at higher temperature the porosity as well as the pore volume of the adsorbent, i.e., the active sites for adsorption also increases [15]. As a result, %R increases with temperature.

Effect of Contact Time The adsorption experiment was carried out by varying the time of contact of the dye solution with the adsorbent from 0 to 300 min to observe its effect on dye adsorption and percent dye removal (%R) is determined at an interval of 10 min (as shown in Fig. 11.4e). The dose of the adsorbent and the concentration of RhB dye were fixed at 4 wt% and 10 mg/L, respectively, during the experiment. From Fig. 11.4e it is observed that the adsorption process comprises of two distinct stages. In the first stage the amount of dye adsorbed by the natural adsorbent increases sharply with time till the solution attains equilibrium (*180 min). Initially, a large number of vacant active sites are available to adsorb dye molecules hence the rate of adsorption is fast. Thereafter in the next stage the adsorption process slows down as the vacant sites are exhausted due to repulsion between the adsorbed dye molecules and the bulk phase.

Conclusion • In this study a green adsorbent from agricultural residue, WCS leaves has been prepared and utilized to remove a toxic cationic dye, Rhodamine B (RhB) from water. • The prepared natural adsorbent was characterized by SEM, TGA, and FTIR (before and after adsorption of dye). • The effects of different operating parameters namely, quantity of the adsorbent, initial adsorbate concentration, temperature, solution pH and contact time on percent dye removal (%R) were investigated.

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• From this study it was observed that addition of 4wt% adsorbent can remove about 91.9% RhB dye from its solution (20 mg/L) at 30 °C which encourages its future use to remove similar type of pollutants from wastewater. Acknowledgements The first author is highly indebted to University Grants Commission (UGC), for financial support by providing Dr. D. S. Kothari Post-Doctoral Fellowship (No. F.4-2/2006 (BSR)/EN/18-19/0019).

References 1. Katheresan V, Kansedo J, Lau SY (2018) Efficiency of various recent waste water dye removal methods: A review. J Environ Chem Eng 6:4676–4697. https://doi.org/10.1016/j. jece.2018.06.060 2. Castro E, Avellaneda A, Marco P (2014) Combination of advanced oxidation processes and biological treatment for the removal of benzidine-derived dyes. Environ Prog Sustain Energy 33(3):873–885. https://doi.org/10.1002/ep.11865 3. Bhattacharyya R, Ray SK, Mandal B (2013) A systematic method of synthesizing composite superabsorbent hydrogels from crosslink copolymer for removal of textile dyes from water. J Ind Eng Chem 19:1191–1203. https://doi.org/10.1016/j.jiec.2012.12.017 4. Bayramoglu G, Arıca MY (2007) Biosorption of benzidine based textile dyes “Direct Blue 1 and Direct Red 128” using native and heat-treated biomass of Trametes versicolor. J Hazard Mater 143:135–143. https://doi.org/10.1016/j.jhazmat.2006.09.002 5. Bhattacharyya R, Ray SK (2015) Removal of congo red and methyl violet from water using nano clay filled composite hydrogels of poly acrylic acid and polyethylene glycol. Chem Eng J 260:269–283. http://dx.doi.org/10.1016/j.cej.2014.08.030 6. Khan TA, Dahiya S, Ali I (2012) Use of kaolinite as adsorbent: equilibrium, dynamics and thermodynamic studies on the adsorption of Rhodamine B from aqueous solution. Appl Clay Sci 69:58–66. https://doi.org/10.1016/j.clay.2012.09.001 7. Dai Y, Sun Q, Wang W, Lu L, Liu M, Li J, Yang S, Sun Y, Zhang K, Xu J, Zheng W, Hu Z, Yang Y, Gao Y, Chen Y, Zhang X, Gao F, Zhang Y (2018) Utilizations of agricultural waste as adsorbent for the removal of contaminants: a review. Chemosphere 211:235–253. https:// doi.org/10.1016/j.chemosphere.2018.06.179 8. Bharathi KS, Ramesh ST (2018) Removal of dyes using agricultural waste as low-cost adsorbents: a review. Appl Water Sci 3:773–790. https://doi.org/10.1007/s13201-013-0117-y 9. Mokgalaka NS, McCrindle RI, Botha BM (2004) Multielement analysis of tea leaves by inductively coupled plasma optical emission spectrometry using slurry nebulisation. J Anal At Spectrom 19:1375–1378. https://doi.org/10.1039/b407416e 10. Madrakian T, Afkhami A, Ahmadi M (2012) Adsorption and kinetic studies of seven different organic dyes onto magnetitenanoparticles loaded tea waste and removal of them from wastewater samples. Spectrochim Acta A Mol Biomol Spectrosc 99:102–109. https://doi.org/ 10.1016/j.saa.2012.09.025 11. Bulgariu L, Escudero LB, Bello OS, Iqbal M, Nisar J, Adegoke KA, Alakhras F, Kornaros M, Anastopoulos I (2019) The utilization of leaf-based adsorbents for dyes removal: a review. J Mol Liq 276:728–747. https://doi.org/10.1016/j.molliq.2018.12.001 12. Panneerselvam P, Morad N, Tan KA (2011) Magnetic nanoparticle (Fe3O4) impregnated onto tea waste for the removal of nickel(II) from aqueous solution. J Hazard Mater 186:160–168. https://doi.org/10.1016/j.jhazmat.2010.10.102

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13. Patil CS, Gunjal DB, Naik VM, Harale NS, Jagadale SD, Kadam AN, Patil PS, Kolekar GB, Gore AH (2019) Waste tea residue as a low cost adsorbent for removal of hydralazinehydrochloride pharmaceutical pollutant from aqueous media: An environmental remediation. J Clean Prod 206:407–418. https://doi.org/10.1016/j.jclepro.2018.09.140 14. Yadav D, Barbora L, Rangan L, Mahanta P (2016) Tea waste and food waste as a potential feedstock for biogas production. Environ Prog Sustain Energy 35(5):1247–1253. https://doi. org/10.1002/ep.12337 15. Uddin MdT, Islam MdA, Mahmud S, Rukanuzzaman Md (2009) Adsorptive removal of methylene blue by tea waste. J Hazard Mater 164:53–60. https://doi.org/10.1016/j.jhazmat. 2008.07.131

Chapter 12

Development of Low-Cost Arsenic Removal Process by Using Ion-Exchange Resins Priyabrata Mondal, Pankaj Kumar Roy, Nil Sadhan Mondal, Saurabh Kumar Basak, and Arunabha Majumder

Introduction Being a semi-metallic compound of the chemical world, arsenic (As) is toxic and carcinogenic when it excessively transfers through drinking water to a living body. High concentration of arsenic in drinking water has been documented as a major public health concern from past two decades. Over 150 million people in more than 70 countries are probably affected by arsenic poisoning of drinking water [1, 2]. More than 26 million people are badly affected by high arsenic contamination in West Bengal [3, 4]. There are two major reasons behind the mobility of environmental arsenic problem, i.e., geogenic and anthropogenic activities [5]. Arsenopyrites, Realgar, Orpiment are the common ores of arsenic presents in groundwater [6, 7]. As per World Health Organization (WHO) and the Bureau of Indian Standard (BIS) the permissible limit of arsenic in drinking water in India is 10 µg/l. Excessive arsenic in a living body can cause of severe diseases like diarrhea, vomiting, abdominal pain, anaemia, and chronic diseases like hyperpigmentation, melanosis, hyperkeratosis, lung cancer, etc. [8, 9]. Many different technologies, i.e., Oxidation-Coagulation, Electro-coagulation, Ion-exchange, Adsorption, Membrane Separation can be applicable for removal of arsenic [10–12]. Oxidation-Coagulation followed by filtration is the cost-effective and commonly used treatment procedure for water treatment by generating flocs by adding some oxidising agent and coagulant. In aqueous solution anions are attracted by the change of surface charge properties of solids and deliver agglomeration of solids into flocculated particles. In case of arsenic removal aluminium and iron salts are more efficient as coagulant. Aluminium sulphate [Al2 ðSO4 Þ3 :18H2 O] and ferric chloride [FeCl3 ] or ferric sulP. Mondal (&)  P. K. Roy  N. S. Mondal  S. K. Basak  A. Majumder School of Water Resources Engineering, Jadavpur University, Kolkata 700032, India e-mail: [email protected] N. S. Mondal Jangipur Government Polytechnic, Murshidabad 742225, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_12

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phate [Fe2 ðSO4 Þ3 :7H2 O] are commonly used salts for arsenic removal. Chlorine, chloramine, hydrogen peroxide, permanganate, air, and pure oxygen are used as oxidising agent for removal of arsenic [12–14]. It is noticed that adsorption process is well known for its highest removal efficiency. Being an operation friendly, cost-effective procedure, it is widely used for arsenic removal operation. The adsorbents which are commonly used such as activated carbon, activated alumina, iron oxides are easily available in market [10, 15, 16]. Electro-coagulation is a suitable process for arsenic removal, different electrode combination such as mild steel and stainless steel is more efficient than other locally available materials [17]. The use of dual treatment (oxidation-coagulation-filtration and adsorption) method is more operative and dependable as compared to other [18]. In this study, only ion-exchange method is adopted. Anionic resin (AR) and cationic resin (CR), which are cheaply available in local market have used as ion-exchange media in this study. This study of ion-exchange had introduced to evaluate the sorption capacity of the ion exchange materials (AR & CR) and to choose the best alternatives between the single bed and combined bed arrangement.

Ion-Exchange Process Ion-exchange process for removal of arsenic has been found to be most convenient for use in field. These resins are rooted in a cross-linked polymer skeleton, are composed of polystyrene cross-linked with di-vinyl-benzene and charged functional groups are attached to this through covalent bonding [19]. There are four groups according to their charges. Strong acidic (sulfonate, RSO3 H) Weakly acidic (carboxylate, RCOOH) Strongly Basic (quaternary amine, RN þ OH ) Weakly Basic (tertiary amine, RNH3 OH). Cationic exchange resins are negatively charged and attracted by the positively charged ions in the water (e.g., Na+). There are two types of cation exchange resins, weak acidic cation resins (WAC), and strong acidic cation resins (SAC). Anionic Exchange Resin is positively charged, attract negatively charged ions that present in water. There are two types of cation exchange resins, weak base anion resins (WBA) and strong base anion resins (SBA) [20].

Methodology and Materials Anionic Resin WBA resin was collected from the local market which is easily available. This was beads shaped and reddish in colour. In groundwater, arsenic is mainly found as

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 negatively charged (H3 AsO 4 , HAsO2 ) which may be attracted by WBA resin. It is found most effective to remove the arsenic from groundwater. It can be easily regenerated by caustic soda, 10% NaOH solution, and reused [21].

Cationic Resin SBC resin was also collected from the local market. This was also the beads shaped and yellowish in colour. SBC is insufficient to remove total arsenic effectively from the contaminated water. It can also be regenerable by the brine solution (10% NaCl) for reusing [21]. If SBC was used with WBA in a combined filter bed system then the system may give the higher removal efficiency, which is presented in this study.

Reagents Analytical reagent/general reagent graded chemicals are used without any added purification. All the solutions are prepared with ultrapure water in the laboratory. 100 mg/l primary stock solution is prepared by sodium arsenite diluted with ultrapure water, stored in refrigerator and the low concentration arsenic test solutions are freshly prepared from primary stock solution with laboratory tap water for each experiment. Glass and plastic-ware are acid washed before use.

Experimental Setup and Procedure An acrylic column of 75 cm long and 1.0 cm in diameter having a stopper valve was used as gravity filter column. It was designed as a gravity filter by maintaining the outlet flowrate of 535 l/hr/m2. 500 ml of synthetic aqueous solution for each experiment was prepared with various concentrations of arsenic. Primary stock solution was diluted with groundwater (arsenic is absent) for making the synthetic aqueous solution. Combined bed arrangement This filter bed was arranged from the bottom with a 2 cm of gravel, 15 cm of coarse sand, and a total of 25 cm of AR-CR layer in between them (AR-CR) a 1 cm sand layer was given to separate them and at the top of the CR layer a 2 cm sand layer was given. Overall, 1.5 l of arsenic-contaminated water with altered concentrations (75, 112, and 192 ppb) was passed through the filter bed in three numbers of trials. The water quality parameters like arsenic, iron, and pH of treated water sample were analysed which are discussed below.

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Single bed arrangement This setup had the same bed arrangement expect the combined (AR-CR) layer. A 25 cm of AR layer was given instead of combined layer. Overall, 1.5 litres of arsenic contaminated water with different concentrations (78, 131, and 212 ppb) was passed through the filter bed in three number of trials. The water quality parameters like arsenic, iron, pH, TDS, EC, total alkalinity, total hardness, and chloride of treated water sample were analysed which are discussed in results and discussion part (Fig. 12.1).

Analytical Procedure As per standard method, total arsenic and total iron concentration were measured by spectrophotometer, using sliver diethyldithiocarbamate (SDDC) method at 520 nm wavelength and 1, 10-phenanthroline method at 510 nm, respectively. The pH was measured by electromagnetic method. Total dissolve solid (TDS) and electrical conductivity (EC) were measured by ion selective electrode method. Hardness and alkalinity were analysed by standard titration method. Chloride was measured by argentometric method.

Fig. 12.1 Cross-sectional diagram of combined (AR-CR) filter bed and single (AR) filter bed set up

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Results and Discussion Single Bed Arrangement Only WBA resin was used as ion-exchange media. The removal efficiency of this single or separated system is about 93–97%. The pH of treated water was in the acceptable limit of drinking water as seen in Fig. 12.2b. The chloride and TDS concentration in treated water are not able to retain in the range of permissible limit of drinking water standard which is clearly shown in Fig. 12.3a and b.

Combined Bed Arrangement

As Concentration in mg/l

15 gm ion-exchange media (AR-CR) was used to remove arsenic from contaminated water, where 1.5 l of arsenic spiked water passed through the filter with various concentration.

a

0.25 0.2 0.15 0.1 0.05 0

Trial 1

Trial 2

Trial 3

Number of trials

pH level

Raw

Filtered

b

7.8 7.6 7.4 7.2 7 6.8 6.6 6.4 6.2 6

Trial 1

Trial 2

Trial 3

Number of trials Raw

Filtered

Fig. 12.2 In case of single bed arrangement a representation of arsenic removal in anionic resin filter bed, b variation of pH in treated water

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Concentration in mg/l

Concentration in mg/l

144

a

1500

1000

500

0

Trial 1

Trial 2 Number of trials

Trial 3

Alkalinity of Raw

Alikalinity of Treated

Hardness of Raw

Hardness of Treated

Chloride of Raw

Chloride of Treated

b

1800 1780 1760 1740 1720 1700 1680 1660

Trial 1

Trial 2 Number of trials Raw

Trial 3

Filtered

Fig. 12.3 In case of single bed arrangement a Effects of alkalinity, chloride, and hardness, b TDS concentration variation in treated water

After the filtration, the pH of treated water was getting very low, as seen in Fig. 12.4a and b which is not suitable for drinking, so a primary pH adjustment is needed for this system to make it acceptable. In combined bed arrangement the SAC resin which is strong acidic in nature, causes a severe drop in pH level in treated water. For that reason, in case of separated bed arrangement, only SAC resin was not used for this study.

Development of Low-Cost Arsenic Removal Process …

As concentration in mg/l

12

145

a

0.25 0.2 0.15 0.1 0.05 0

Trial 1

Trial 2

Trial 3

Number of trials Raw

Filtered

b

10

pH values

8 6 4 2 0

Trial 1

Trial 2

Trial 3

Number of trials Raw

Treated

Fig. 12.4 In case of combined bed arrangement a representation of arsenic removal in combined system, b representation of pH variation in combined system

Conclusion The beads shaped ion-exchange materials are able to remove arsenic from synthetic contaminated water. SAC and WBA resins are easily available in the local market in reasonable price. The sorption capacity of combined bed arrangement is 70.26 mg/kg. It is very efficient system with respect to the arsenic removal percentage (100%). On the other hand, the sorption capacity and maximum removal efficiency of single bed arrangement is 69.87 mg/kg and 97%, respectively. Although the difference in sorption capacity is very small in both cases, single bed arrangement has been able to maintain the pH level of treated water in the permissible limit of drinking water standard. So, ion exchange process of single bed arrangement of AR may be acceptable for arsenic removal than other removal technologies because of inconsiderable sludge generation and simple regenerating procedure of ion exchange media. This system may be applicable and adaptable for a household, which is cost-effective and user-friendly too.

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Acknowledgements The authors are highly grateful to School of Water Resources Engineering, Jadavpur University for allowing the departmental laboratory to be used for the research work, and they are even more grateful to all the previous investigators of arsenic and its removal process. Funding information This work is supported and funded by the School of Water Resources Engineering, Jadavpur University, Kolkata-700032, India

References 1. Mukherjee A, Sengupta MK, Hossain MA, Ahamed S, Das B, Nayak B, Lodh D, Rahaman MM, Chakraborti D (2006) Arsenic contamination in groundwater: a global perspective with emphasis on the Asian scenario. J Health Popul Nutr 24(2):142–163 2. Shankar S, Shanker U, Shikha (2014) Arsenic contamination of groundwater: a review of sources, prevalence, health risks, and strategies for mitigation. Sci World J 2014:304524. https://doi.org/10.1155/2014/304524 3. Basu A, Sen P, Jha A (2015) Environmental arsenic toxicity in West Bengal, India: a brief policy review. Indian J Public Health 59(4):295–298. https://doi.org/10.4103/0019-557X. 169659 4. Mazumder DNG, Haque R, Ghosh N, De KB, Santra A, Chakraborti D, Smith AH (2000) Arsenic in drinking water and the prevalence of respiratory effects in West Bengal. Int J Epidemiol 29:1047–1052. https://doi.org/10.1093/ije/29.6.1047 5. Kaur S, Kamli MR, Ali A (2011) Role of arsenic and its resistance in nature. Can J Microbiol 57(10):769–774. https://doi.org/10.1139/w11-062 6. Chakraborty M, Mukherjee A, Ahmed KM (2015) A Review of groundwater arsenic in the Bengal basin, Bangladesh and India: from source to sink. Curr Pollut Rep 1:220–247. https:// doi.org/10.1007/s40726-015-0022-0 7. O’Day PA (2006) Chemistry and mineralogy of arsenic. Elements 2:77–83. https://doi.org/10. 1007/s11356-012-1449-0 8. National Research Council (1999) Arsenic in drinking water. Washington, DC: National Academy Press. https://doi.org/10.17226/6444 9. National Research Council, 2001, Arsenic in drink-ing water. Washington, DC: National Academy Press. https://doi.org/10.17226/10194 10. Hao L, Liu M, Wang N, Li G (2018) A critical review on arsenic removal from water using iron-based adsorbents. R Soc Chem Adv 8:39545–39560. https://doi.org/10.1039/ C8RA08512A 11. Nicomel NR, Leus K, Laing GD (2016) Technologies for arsenic removal from water: current status and future perspective. Int J Environ Res Public Health 13:62. https://doi.org/10.3390/ ijerph13010062 12. Zakhar R, Derco J, Cacho F (2018) An overview of main arsenic removal technologies. Acta Chim Slov 11(2):107–113. https://doi.org/10.2478/acs-2018-0016 13. Kumar I, Quaff A R (2018) Comparative study on the effectiveness of natural coagulant aids and commercial coagulant: removal of arsenic from water. Int J Environ Sci Technol 16. https://doi.org/10.1007/s13762-018-1980-8 14. Wickramasinghea SR, Han B, Zimbron J, Shen Z, Karim MN (2004) Arsenic removal by coagulation and filtration: comparison of groundwaters from the United States and Bangladesh. Desalination 169:231–244. https://doi.org/10.1016/j.desal.2004.03.013 15. Hashim MA, Kundu A, Mukherjee S, Ng YS, Mukhopadhyay S, Redzwan G, Gupta BS (2018) Arsenic removal by adsorption on activated carbon in a rotating packed bed. J Water Process Eng 30:100591. https://doi.org/10.1016/j.jwpe.2018.03.006

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16. Ng KS, Ujang Z, Clech PL (2004) Arsenic removal technologies for drinking water treatment. Rev Environ Sci Biotechnol 3:43–53. https://doi.org/10.1023/B:RESB.0000040054.28151.84 17. Mondal NS, Basak SK, Roy PK, Majumder A, Mazumder A (2020) Arsenic removal using electro-coagulation: suitable electrode selection. Environ Asia 13(3):13-25. https://doi.org/10. 14456/ea.2020.38 18. Roy PK, Majumder A, Banerjee G, Roy MB, Pal S, Mazumdar A (2015) Removal of arsenic from drinking water using dual treatment process. Clean Technol Environ Policy 17(4):1065– 1076. https://doi.org/10.1007/s10098-014-0862-0 19. Mahamood JS, Taj N, Parveen F, Usmani TH, Azmat R, Uddin F, (2007) Arsenic, fluoride and nitrate in drinking Water: the problem and its possible solution. Res J Environ Sci 1:179– 184. https://doi.org/10.3923/rjes.2007.179.184 20. Vogle IA (2006) Textbook of quantitative chemical analysis, 5th edn. Wiley, New York, pp 186–194 21. Chowdiah V, Foutch GL (1995) Kinetic model for cationic-exchange-resin regeneration. Ind Eng Chem Res 34:4040–4048. https://doi.org/10.2478/v10026-012-0068-3

Chapter 13

Water Quality Index Is an Important Tool of Groundwater: A Case Study Sanjib Das , Pankaj Kumar Roy, Gourab Banerjee, and Asis Mazumdar

Introduction The water quality of groundwater is the important parameter for use in sector like, industry water supply, agriculture, power generation, etc. What affects the quality of groundwater is a complex process that involves all the environmental and geological forces and reactions working on it right from the moment of its condensation in the atmosphere, following its lifecycle, until it is extracted from a well. Therefore, depending on the area, depth of water table, and the season, the quality scenario of groundwater varies [1]. In Kolkata, most of the groundwater is found in confined and semi-confined aquifer. The fresh groundwater in major parts of the city is found in the Brackish groundwater [2]. Nearly 85% of the groundwater samples of KMC area are found to be safe for drinking purpose. In some places, groundwater is contaminated by the toxic effluents like lead, cadmium, etc., generated by the industrial belts of the city. High concentration of TDS, Iron, and Arsenic are also found in groundwater in some words of KMC area due to geogenic pollution. But, in almost all places the groundwater is free from bacterial contamination uses 80% surface and 20% sub-surface water for public water supply [3]. The groundwater quality is very much dependent on the climate & weather condition, hydrogeological condition, and the adjacent human activities [4]. Artificial neural network model is an effective tool for formulation and measuring the WQI based on different water quality parameters [5]. An innovative approach was undertaken to depict the water quality index of Southern part of the Tamil Nadu, state where the researcher discovers the limitations of different existing Water Quality Index (WQI) methodologies through the developing reliable Drinking Water Quality Indexing (DWQI) system [6]. An investigation was carried S. Das (&)
P. K. Roy
G. Banerjee
A. Mazumdar School of Water Resources Engineering, Faculty of Interdisciplinary Studies, Law and Management, Jadavpur University, Kolkata 700032, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_13

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out to evaluate the priorities in water quality management based on correlations and variations. A water quality assessment strategy was developed on basis of spatial and temporal variation caused by natural and anthropogenic activity. Cluster analysis, water quality index method, sensitivity analysis and correlation analysis are used to study the priorities in pollution control activities [7]. Water quality is an effective tool for revealing the groundwater analysis to assess the scenario in a scattered manner and applied as a prime interpretation tool for adaptation strategy toward agricultural environmental protection policy development [8]. An effective index of river water quality was developed in Taiwan containing an increasing aggregate function of validated for pH, particulate (suspended solids, turbidity), organics (dissolved oxygen, BOD, ammonia), toxic substances, and faecal coliform and total coliform (TC, FC) and temperature. The calibrated values for each groundwater quality parameter are based on predefined rating or graded curves, such that a value of 100 represents excellent or very good water quality and a value of 0 indicates extremely poor or very bad water quality. The index depends also on the geometric means of the calibrated values [9]. Similarly, a study was undertaken to compute the WQI to study the suitability of groundwater considering drinking water perspective in twelve wards of Borough X of KMC in the state of West Bengal in India. In this paper an attempt has made to develop water quality index (WQI) of groundwater in KMC area of West Bengal considering seasonal variation of data and is to devise a simple mechanism for the common people to understand the quality of water, instead of a complex set of data to determine the quality of groundwater.

Methodology Study Area Kolkata Municipal Corporation (KMC) area situated between north latitudes of 22º 28′ 00″ and 22º 37′ 30″ and east longitudes 88º 17′ 30″ and 88º 25′ 00″. KMC area is sub-divided into 141 wards in 15 boroughs. Boroughs X is selected in the present study. Boroughs X is consisting of 12 numbers of wards, namely, 81, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, and 100 [2, 3]. Borough map of KMC are shown in Fig. 13.1. At the time of sample collection, GPS location of the area was measured with the help of GPS meter. The study was carried out during the Thesis work of Master of Engineering [10]. The study area and sample locations are incorporated in Table 13.1. Figure 13.2 generated on the basis of GPS value of the sampling locations with the help of Q-GIS software.

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Fig. 13.1 Borough map of Kolkata municipal area. Source KMC [3]

Table 13.1 Ward wise sampling locations Ward no.

Sampling location

81 89 91 92 93

New Alipore, Deshapran Sasmal Road, Tollygunge Circular Road, etc. Prince Anwar Shah Road, Charu Chandra Place (East), Russa Road (East), etc. Bose Pukur Road, N. K. Ghoshal Road, Baikuntha Ghosh Road, etc. Jheel Road, Sahid Nagar, Gariahat Road (South), Dhakuria Station Road, etc. Golf Green, Jodhpur Park, Bengal Lamp, CIT Market, Central Road, Jadavpur Police Quarter, Jodhpur Colony, Poddar Nagar, Gobindapur, KatjuNagar, etc. Uday Shankar Sarani, Golf Club Road, Jubilee Park, Prince Anwar Shah Road, etc. Azadgarh, Bijoygarh, Golf Green, Tilak Nagar, Regent Place, etc. Regent Estate, Bijoygarh, Baghajatin (E-Kolkata) Market Complex, Baderaipur Road, Bapuji Nagar, Narkel Bagan, Jadavpur, Ibrahimpur Road, etc. N. S. C. Bose Road, Ashok Nagar, Graham Road, Santi Nagar, Moore Avenue, etc. Suryanagar, Bashdroni Bazar, Paddapukur Road, Netaji Nagar, Ashok Avenue, etc. Baghajatin, Ramgarh, Sree Colony, Raipur Road, Jorabagan Road, Vidyasagar, etc. Rathtala Bazar, N. S. C. Bose Road, Naktala, Ganguly Bagan, Raja S. C. Mullick Road, Sudakshina Police Quarter, Garia Bazar, Baishnabghata Lane, etc.

94 95 96 97 98 99 100

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Fig. 13.2 Study area (sampling location)

Sample Collection and Analysis Four hundred groundwater samples were collected from existing tube wells of eighty different locations situated in Borough: X of KMC area in two different seasons and were analysed in School of Water Resources engineering, laboratory as per standard procedure recommended by APHA [11]. About four hundred and eighty (480) numbers groundwater samples (with GPS location) were collected from various random locations in two different seasons, namely, monsoon and post-monsoon situated in twelve Words of Borough: X of KMC area. Water samples were mainly collected from KMC tube wells. Total number of small diameter tube wells run by the KMC in Borough X are 82 in ward 98, 180 in ward 100, 92 in ward 95, 130 in ward 97, 78 in ward 94, 62 in ward 89, 105 in ward 93, 55 in ward 99, 52 in ward 96, 52 in ward 92, 55 in ward 91 and 60 in ward 81, respectively. In this study, only 20 numbers of groundwater samples were collected from each ward. Thus, percentage of water samples tested in each ward are 24% in ward 98, 11% in ward 100, 22% in ward 95, 15% in ward 97, 26% in ward 94, 32% in ward 89, 19% in ward 93, 36% in ward 99, 38% in ward 96, 38% in ward 92, 36% in ward 91 and 33% in ward 81, respectively. First set of water sample collection was started in the month of July, 2013 and ended on first week of December, 2013, i.e., Monsoon period and second set of water sample collection was start in the month of January, 2014 and ended on April, 2014, i.e., post-monsoon period. After sampling, all the water samples are brought to the water quality analysis laboratory of the School of Water Resources Engineering, Jadavpur

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University, where analyses have been done as per the APHA (American Public Health Association) guidelines [11]. pH, Total Dissolved Solids (TDS), Turbidity and Fluoride are measured by pH meter, TDS meter, Turbidity meter, and Fluoride meter, respectively. Chloride is measured by Argentometric method. Total Alkalinity and Total Hardness are measured by Phenolphthalein titration method and EDTA titration method, respectively. Nitrate and Iron are measured by Phenol disulphuric acid method and Spectro-photometric method, respectively. Total Arsenic is measured by Silverdietyldithiocarbonate (SDDC) method. Here, Arsenic Apparatus “Gutzeid” (Modified) is used for the testing of Arsenic.

Determination of Water Quality Index (WQI) With help of method developed by Tiwari T. N., Mishra M. A. [12], Singh D. F. [13], Das K. K., Panigrahi T., Panda R. B. [14] and Das S., Roy P. K., Mazumdar A. [15] was adopted for computation of the groundwater Water Quality Index (WQI). Selected ten water quality parameters, namely, pH, Turbidity, Total Dissolved Solids, Chloride, Total Alkalinity, Total Hardness, Nitrate, Iron (Fe), Arsenic (As), and Fluoride (F) have been considered in this study for calculating the WQI. To determine the WQI, qi, and wi are assigned, where qi and wi stands for quality rating and weight value, respectively. The unit weight of each parameter estimated by the below formula Eq. (13.1), X ðWi Þi Wi ¼ P wi ¼ 1 i as ðwi Þ

ð13:1Þ

To assess the water quality, the weights (Wt) have been considered based on the actual groundwater quality and the weight value recommended by most of the researchers [12–18]. Adopted standards and weight for Water Quality Parameters are depicted in Table 13.2. The quality rating scale for water quality parameters (qi) has been divided into four parts, i.e., Usual (100), Minute (80), Worry (50), and Reject (0), are highlighted in Table 13.3. The sub index (SI) of each groundwater quality parameter is the multiplication value of weightage value (wi) and rating scale (qi). Therefore, the equation of groundwater WQI is considering (wi = 1) depicted below in Eqs. (13.2) and (13.3) P ðSIÞ WQI ¼ P i ð13:2Þ wi

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Table 13.2 Adopted standards and weightage for Water Quality Parameters S. no.

Parameters

Unit

Weightage (Wt)

Unit weightage (Wi)

Standards (BIS:10500:2012)

1 2 3

pH TDS Total hardness (Caco3) Chloride (Cl−) Nitrate (NO3−) Iron as Fe Total alkalinity (Caco3) As F Turbidity

– mg/l mg/l

4 4 2

0.11 0.11 0.06

6.5–8.5 500–2000 300–600

mg/l mg/l mg/l mg/l

2 5 4 2

0.06 0.14 0.11 0.06

250–1000 45–100 0.3–1.0 200–600

mg/l mg/l NTU

6 4 2

0.17 0.11 0.06

0.01–0.05 1.0–1.5 5–10

4 5 6 7 8 9 10

P

Wt = 35

Table 13.3 Quality rating scale (qi) S. no.

Parameter

1 2 3 4 5 6 7 8 9 10

pH Turbidity (in mg/l) TDS (in mg/l) Hardness (in mg/l) Chloride (in mg/l) Iron (in mg/l) Alkalinity (in mg/l) Nitrate (in mg/l) Arsenic (in mg/l) Fluoride (in mg/l)

Degree of pollution rating (qi) Usual (100) Minute (80) 6.5–7.5 7.51–8.0 0–5.0 5.1–7.5 0–500 501–1250 0–300 301–450 0–250 251–600 0–0.3 0.31 – 0.70 0–200 201–400 0–45 46–75 0–0.01 0.011–0.035 0–1.0 1.01–1.30

Worry (50) 8.01–8.5 7.5–10.0 1251–2000 451–600 601–1000 0.71–1.0 401–600 76–100 0.0351–0.05 1.31–1.50

Reject (0) >8.5 >10 >2000 >600 >1000 >1.0 >600 >100 >0.05 >1.50

So, WQI ¼

X

ð qi w i Þ

ð13:3Þ

Now, WQI has been classified based on four scales, namely: Extremely poor, Poor, Good, and Excellent. The selected range is between 0 and 70 for Very poor, 71–80 for Poor, 81–90 for Good, and >90 for Excellent is depicted in Table 13.4. By this classification water, the status of water quality can be determined, whether the water is fit for drinking or not.

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Table 13.4 Classification of WQI S. no.

Scale of water quality index (WQI)

Output status of water quality

1 2 3 4

0–70 71–80 81–90 >90

Extremely poor Poor Good Excellent

After deriving the water quality index, correlation coefficients are obtained from statistical method Pearson Product Moment Correlation (PPMC) with the help of Excel software. It is effective to evaluate the linear relationships between data, where one value is dependent on changing on other value in case of water quality parameters. Commonly used formula [19] is given below for R value. R is also called Pearson correlation coefficient, also called Pearson’s R:    P xi  xaverage  yi  yaverage R ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2  2ffi xi  xaverage  yi  yaverage

ð13:4Þ

Spatial distribution of Iron and TDS parameters are also evaluated with the help of Surfer 10 software, using kinging interpolation method. In kinging method incorporate Spline interpolation and Inverse distance Weighted (IDW) tools for application of deterministic interpolation methods. The method is governed by the surrounding measured data or mathematical formulas depicted in Eq. (13.5) which compute the smoothness of the output surface. The below governing equation [20] was used in prediction for an unknown location, with the help of deriving weights of the surrounding measured values. Z ðs0 Þ ¼

N X

ki zðsi Þ

ð13:5Þ

i¼1

In the Eq. (13.5), z(si) stands for the value at the ith location, ki indicates an unknown weight for the observed value at the ith location and S0 & N represents the prediction location and total number of the observed values. Kinging described as a second family of interpolation methods incorporating statistical or geostatistical model which include automatic correlation. Dependency of statistical sampling among the location points developed a prediction surface and provide some measure of reliability or accuracy of the predictions.

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Result and Discussion Analysis of result of groundwater quality revealed considerable information of water quality in the study area. Tables 13.5 and 13.6 Physico-chemical parameters and sample calculation of WQI results of eight (08) samples of ward 98 during monsoon season. Nitrate and Arsenic found below detection level (BDL). The Quality rating scale for water quality parameters (qi) as described in Table 13.3 are used for all the Physico-chemical parameters. Thus, WQI of Sample ID 98-M-1 is 69.30.[100  0.11) + (50  0.06) + (50  0.11) + (0  0.06) + (50  0.06) + (0  0.11) + (80  0.06) + (100  0.14) + (100  0.17) + (100  0.11) = 69.30], And, accordingly, Water Quality Status of Sample ID 98-M-1 = “Very Poor” (Ref. Table 13.4). Tables 13.7 and 13.8 indicates water quality analysis result and WQI of groundwater samples collected from specified Tubewells of the study area. Figures 13.3 and 13.4 indicates the overall water quality indexing status (based on 480 groundwater samples and 10 quality parameters) of Borough X of KMC in 2013. WQI for these samples revealed that about 34% of collected groundwater samples were categorized as “excellent” and 46% as “good” during monsoon period, whereas 32% of groundwater samples were revealed as “excellent” and 55% of these as “good” during post-monsoon period. Whereas about 01% of collected groundwater samples were categorized as “Extremely Poor” and 19% as “Poor” during monsoon period, and 01% of groundwater samples were revealed as “Extremely Poor” and 12% of these as “Poor” during post-monsoon period.

Spatial Variation of Tube Wells Depth Simultaneously both water quality parameters and the groundwater depth were measured to ensure spatial variability of the data. The notable correlation between

Table 13.5 Physico-chemical parameters Sample ID

pH

Turbidity (NTU)

TDS (mg/l)

Hardness (mg/l)

Chloride (mg/l)

Iron (mg/l)

Alkalinity (mg/l)

Fluoride (mg/l)

98-M-1 98-M-2 98-M-3 98-M-4 98-M-5 98-M-6 98-M-7 98-M-8

7.3 7.18 7.22 7.83 7.61 7.45 7.4 7.4

7.78 5.84 1.12 0.67 2.28 1.93 0.58 0.87

1755 1883 1140 598 1034 1096 1047 1905

800 724 594 320 508 560 554 940

940 1150 540 230 500 530 490 940

1.7 1.8 0.51 1.1 1.14 1.19 1.14 0.31

292 288 272 240 240 284 264 264

0.28 0.34 0.32 0.33 0.34 0.35 0.36 0.14

Turbidity

50.00 80.00 100.00 100.00 100.00 100.00 100.00 100.00

pH

100.00 100.00 100.00 80.00 80.00 100.00 100.00 100.00

50.00 50.00 80.00 80.00 80.00 80.00 80.00 50.00

TDS

0.00 0.00 50.00 80.00 50.00 50.00 50.00 0.00

Hardness

Table 13.6 WQI value and water quality status Chloride 50.00 0.00 80.00 100.00 80.00 80.00 80.00 50.00

Iron 0.00 0.00 80.00 0.00 0.00 0.00 0.00 80.00

Alkalinity 80.00 80.00 80.00 80.00 80.00 80.00 80.00 80.00

Nitrate 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00

Arsenic 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00

Fluoride 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00

WQI 69.30 69.14 90.29 81.71 78.86 81.14 81.14 82.29

Status Extremely poor Extremely poor Excellent Good Poor Good Good Good

13 Water Quality Index Is an Important Tool of Groundwater … 157

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Table 13.7 Water quality analysis result of groundwater of Borough X of KMC Parameters

pH TDS (in mg/l) Turbidity (in mg/l) Chloride (in mg/l) Alkalinity (in mg/l) Hardness (in mg/l) Nitrate (in mg/l) Iron (in mg/l) Arsenic (in mg/l) Fluoride (in mg/l)

During monsoon Minimum range value with respect to municipal ward no.

Maximum value with respect to municipal ward no.

During post-monsoon Minimum range Maximum value value with respect with respect to to municipal ward municipal ward no. no.

6.77 (Ward-95) 200 (Ward-81)

8.69 (Ward-93) 3040 (Ward-93)

6.54 (Ward-98) 215 (Ward-89)

8.46 (Ward-81) 3040 (Ward-93)

0.13 (Ward-97)

7.78 (Ward-98)

0.12 (Ward-94)

8.00 (Ward-98)

12 (Ward-96)

1768 (Ward-93)

12 (Ward-96)

1768 (Ward-95)

16 (Ward-93)

480 (Ward-92)

20 (Ward-93)

488 (Ward-92)

68 (Ward-95)

1260(Ward-93)

68 (Ward-95)

1280 (Ward-93)

BDL

BDL

BDL

BDL

0.02 (Ward-96)

3.04 (Ward-92)

0.20 (Ward-93)

3.00 (Ward-92)

BDL

BDL

BDL

BDL

0.02 (Ward-81)

1.42 (Ward-94)

0.02 (Ward-81)

1.40 (Ward-94)

Table 13.8 Ward wise maximum and minimum WQI found in Borough X of KMC

Ward

WQI (maximum)

WQI (minimum)

81 89 91 92 93 94 95 96 97 98 99 100

95.43 95.43 91.29 94.29 94.14 98.86 98.86 95.43 100.00 92.57 90.86 95.43

79.43 79.43 73.71 73.71 62.29 76.00 64.57 74.86 72.00 64.57 70.86 73.14

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Fig. 13.3 Overall water quality indexing status in monsoon (based on 480 groundwater samples and 10 quality parameters) of Borough X of KMC in 2013

Fig. 13.4 Overall water quality indexing status in post-monsoon (based on 480 groundwater samples and 10 quality parameters) of Borough X of KMC in 2013

groundwater level and sampling location reveals the quantity and quality scenario of groundwater resource in the study area. The spatial variability maps generated applying the ordinary kriging method and revealed that the main groundwater aquifer from where withdrawal occurs within the depth ranges of 60–180 m below ground level in the different part of the study area [2]. The depth of tube wells is varying from 75 m to 90 m in the large part of the study area. In very few places, depth of tube wells is greater than 100 m due to water level fluctuation. Figure 13.5 showed the depth variation of tube wells. Figures 13.6, 13.7, 13.8, and 13.9 showed the spatial variation of TDS and Iron concentration.

Correlation Among Various Groundwater Quality Parameters The correlation coefficient r among different groundwater quality parameters was calculated and depicted in Tables 13.9 and 13.10. The results of correlation matrix analysis revealed that with increase or decrease of value of one parameter is related to other parameters. The correlation is strong when values lie in between ±0.80 and 1.0 and moderate ±0.50–0.8 and week ±0.0–0.5. The pH value is especially

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Fig. 13.5 Contour map of depth of tube well

important parameter in groundwater analysis for identify the acidity and alkalinity and resulting value of acidic and basic reaction or interaction. From the results it was found that pH has negative correlation with other water quality parameters in both monsoon and post-monsoon samples. Iron has the positive week correlation with pH in monsoon period. The higher or strong positive correlation between TDS and chloride (0.90 and 0.91), Total Hardness (0.86 and 0.87) and week correlation between TDS and Turbidity (0.15 and 0.18), Total alkalinity (0.13 and 0.11), Iron (0.12 and 0.17) was found in both monsoon and post-monsoon sample. Only fluoride has negative weak correlation with TDS (−0.20 and 0.21). From the Tables 13.9 and 13.10, it was also found that correlation in monsoon is more for the Chloride. Fluoride has maximum weak correlation in both monsoon and post-monsoon period. Turbidity and Chloride also has weak correlation with other parameters in both seasons.

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Fig. 13.6 Spatial variation of TDS concentration during monsoon

Fig. 13.7 Spatial variation of TDS concentration post-monsoon

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Fig. 13.8 Spatial variation of Iron concentration during monsoon

Fig. 13.9 Spatial variation of Iron concentration during post monsoon

S. Das et al.

pH TDS Turbidity Chloride Total alkalinity Total hardness Iron Fluoride

1 −0.24799 −0.08826 −0.18204 −0.61872 −0.26382 0.149569 −0.1793

pH

1 0.154755 0.902239 0.138728 0.860198 0.120566 −0.2099

TDS

1 0.151804 0.134817 0.11646 0.281745 −0.08915

Turbidity

Table 13.9 Correlation coefficient of monsoon water quality

1 0.040822 0.561873 0.077461 −0.05375

Chloride

1 0.219968 0.062201 0.031755

Total alkalinity

1 0.133008 −0.339

Total hardness

1 −0.22536

Iron

1

Fluoride

13 Water Quality Index Is an Important Tool of Groundwater … 163

pH TDS Turbidity Chloride Total alkalinity Total hardness Iron Fluoride

1 −0.37924 −0.20621 −0.33707 −0.48785 −0.34495 −0.17393 0.081697

pH

1 0.184691 0.918644 0.11713 0.875059 0.175139 −0.21272

TDS

1 0.216863 0.14378 0.111687 0.27102 −0.06008

Turbidity

Table 13.10 Correlation coefficient of post-monsoon water quality

1 0.107507 0.617909 0.162535 −0.0659

Chloride

1 0.105195 0.069922 0.114352

Total alkalinity

1 0.155148 −0.3411

Total hardness

1 −0.19725

Iron

1

Fluoride

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WQI Status The study revealed that almost in all selected wards located in Borough X under KMC, the WQI changes from time to time (monsoon to post-monsoon), and depicted in Table 13.11. The percentage of samples comes under “Excellent” and “Extremely Poor” has been found to be 5 and 10 in ward 98, 15 and 0 in ward 100, 98.86 and 5 in ward 95, 50 and 0 in ward 97, 40 and 0 in ward 94, 50 and 0 in ward 89, 35 and 5 in ward 93, 25 and 0 in ward 99, 40 and 0 in ward 96, 20 and 0 in ward 92, 5 and 0 in ward 91, 25 and 0 in ward 81 of Borough X of Kolkata Municipal Corporation, respectively. The present study also revealed that better WQI is found in post-monsoon season than monsoon season of the water year 2013 for the wards 94, 89, 93, 99, 92, 91, and 81, because of water recharging due to rain.

Table 13.11 Categorical status of WQI in Borough X, KMC S. no.

Ward

WQI

Status

1

98

2

100

3

95

4

97

5

94

6

89

0–70 71–80 81–90 >90 0–70 71–80 81–90 >90 0–70 71–80 81–90 >90 0–70 71–80 81–90 >90 0–70 71–80 81–90 >90 0–70 71–80 81–90 >90

Extremely Poor Good Excellent Extremely Poor Good Excellent Extremely Poor Good Excellent Extremely Poor Good Excellent Extremely Poor Good Excellent Extremely Poor Good Excellent

Poor

poor

Poor

poor

Poor

Poor

% of samples (during monsoon) 10.00 35.00 40.00 15.00 0.00 10.00 50.00 40.00 0.00 5.00% 15.00% 80.00 0.00 5.00 20.00 75.00 0.00 15.00 50.00 35.00 0.00 5.00 45.00 50.00

% of samples (during post-monsoon) 10.00 30.00 55.00 5.00 0.00 10.00 75.00 15.00 5.00 5.00 65.00 25.00 0.00 10.00 40.00 50.00 0.00 10.00 50.00 40.00 0.00 0.00 50.00 50.00 (continued)

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Table 13.11 (continued) 7

93

8

99

9

96

10

92

11

91

12

81

0–70 71–80 81–90 >90 0–70 71–80 81–90 >90 0–70 71–80 81–90 >90 0–70 71–80 81–90 >90 0–70 71–80 81–90 >90 0–70 71–80 81–90 >90

Extremely Poor Good Excellent Extremely Poor Good Excellent Extremely Poor Good Excellent Extremely Poor Good Excellent Extremely Poor Good Excellent Extremely Poor Good Excellent

Poor

Poor

Poor

Poor

Poor

Poor

10.00 10.00 55.00 25.00 0.00 45.00 45.00 10.00 0.00 5.00 40.00 55.00 0.00 30.00 55.00 15.00 0.00 50.00 50.00 0.00 0.00 10.00 85.00 5.00

5.00 15.00 45.00 35.00 0.00 25.00 50.00 25.00 0.00 15.00 45.00 40.00 0.00 10.00 70.00 20.00 0.00 50.00 45.00 5.00 0.00 0.00 75.00 25.00

Conclusion Groundwater is the most valuable and reliable freshwater source and so, investigation of its quality in different regions are very important. In this paper, Kringing interpolation model has been created for measuring the water quality index of KMC area depending on different numbers of water quality parameters like pH. TDS, Iron, Chloride, and the methodology has established its ability to estimate WQI with high sensitivity and accuracy. In this study, groundwater samples are analysed and WQI are estimated to evaluate the suitability of groundwater as drinking water. This study concludes that about 82% of collected groundwater samples from the study area can be treated as safe for drinking as per IS-10500: 2012 [21]. The study also revealed that strong positive correlation between TDS and chloride and TDS and Total Hardness and week correlation between TDS and Turbidity (0.15 and 0.18) and TDS and Iron (0.12 and 0.17) in both monsoon and post-monsoon samples. Here, factors included in WQI are varying based upon the defined water uses of the water body and local demand. Water quality indexing also depicted that

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better WQI is found in post-monsoon season than monsoon season of the water year 2013 for the wards 94, 89, 93, 99, 92, 91, and 81 influenced by recharge. Acknowledgements At the very outset, authors would like to convey their cordial and earnest regards to all members of School of Water Resources Engineering, Jadavpur University for their valuable suggestions. Authors also express their sincere gratitude to Kolkata Municipal Corporation for their wholehearted involvement, support throughout this study.

References 1. Hasan A, Bekhit H, Chapman J (2008) Uncertainty assessment of a stochastic groundwater flow model using GLUE analysis. J Hydrol 362:89–109 2. Booklet of Groundwater Information (2006) Kolkata Municipal Corporation (KMC), West Bengal 3. Report of 3rd Mayors’ Asia-Pacific Environmental Summit (2003) Solid waste management session 4. Honolulu, Hawaii 4. Sim SF, Tai SE (2018) Assessment of a physicochemical indexing method for evaluation of tropical river water quality. J Chem 2018:1–12 5. Ohman F, Alaaeldin ME, Seyam M, Ahmed AN, Teo FY, Fai CM (2020) Efficient river water quality index prediction considering minimal number of inputs variables. Eng Appl Comput Fluid Mech 14(1):751–763 6. Ramesh S, Sukumaran N, Murugesan AG, Rajan MP (2010) An innovative approach of drinking water quality index—A case study from Southern Tamil Nadu, India. Ecol Indic 10 (4):857–868 7. Boyacioglu H, Gündogdu V, Boyacioglu H (2013) Investigation of priorities in water quality management based on correlations and variations. Mar Pollut Bull 69(1–2):369–384 8. Stigter TY, Ribeiro L, Dill AMMC (2006) Application of groundwater quality index as an assessment and communication tool in agro-environmental policies—Two Portuguese case studies. J Hydrol 327(3–4):578–591 9. Liou SM, Lo SL, Wang SH (2004) A generalized water quality index for Taiwan. Environ Monit Assess 96(1–3):35–52 10. Das S (2014) Development of groundwater quality indices in KMC area, West Bengal, India. In: Master of Engineering Thesis, Water Resources and Hydraulic Engineering, Jadavpur University, Kolkata 11. APHA (2012) Standard methods for the examination of water and wastewater, 22nd Edition, published by the American Public Health Association, American Water and Water works Association and Water Environment Federation 12. Tiwari TN, Mishra MA (1985) A preliminary assignment of water quality index of major Indian rivers. Indian J Environ Prot 5(4):276–279 13. Singh DF (1992) Studies on the water quality index of some major river of Pune, Maharastra. Proc Acad Environ Biol 1(1):61–66 14. Das KK, Panigrahi T, Panda RB (2012) Evaluation of water quality index (WQI) of drinking water of Balasore district, Odisha, India. Discov Life 1(3):48–52 15. Das S, Roy PK, Mazumdar A (2013) Development of water quality index for groundwater in Kolkata city, West Bengal, India. ARPN J Eng Appl Sci 8(12):1054–1058 16. Singh A, Minsker BS, Valochhi AJ (2008) An interactive multi-objective optimization framework for groundwater inverse modelling. Adv Water Resour 31(10):1269–1283 17. Thomas A, Tellam J, Greswell R (1991) Development of a GIS based urban groundwater recharge pollutant flux model. UK—A report. School of Earth Sciences, University of Birmingham, Edgbaston, Birmingham, 1–24

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18. Thomas A, Tellam J (2006) Modelling of recharge and pollutant fluxes to urban groundwaters. Sci Total Environ 360(1–3):158–179 19. Wang J (2012) On the relationship between Pearson correlation coefficient and Kendall’s Tau under bivariate homogeneous shock model. ISRN Probab Stat 2012:1–7 20. Shahbeik S, Afzal P, Moarefvand P, Qumarsy M (2014) Comparison between ordinary kriging (OK) and inverse distance weighted (IDW) based on estimation error. Case study: Dardevey iron ore deposit, NE Iran. Arab J Geosci 7:3693–3704 21. IS-10500:2012, Indian standard for drinking water, Bureau of Indian standards, New Delhi, India

Chapter 14

A Critical Review of Various Arsenic and Iron Removal Plants Installed in North 24 Parganas District of West Bengal, India Saurabh Kumar Basak , Pankaj Kumar Roy , Nil Sadhan Mondal, Arunabha Majumder, and Asis Mazumdar

Introduction Arsenic, a naturally found metalloid and widely known as a toxic chemical is becoming a major concerning issue due to its presence in groundwater. The presence of arsenic in nature like soil, water, and the environment is found generally in its two common oxidation state that is arsenite (As III) and arsenate (As V). The presence of arsenic in soil is generally formed by weathering of its parent rock minerals and ores like arsenic pyrites, realgar, orpiment, etc. Contamination of arsenic in groundwater widespread in more than 70 countries all over the world leaving more than 140 million people at high risk [1]. The major incidents related to groundwater contamination by arsenic were reported before 2000 from three Asian countries of Bangladesh, India [2], and China [3]. After 2004 reports of the presence of arsenic in groundwater were found in many other southeast Asian countries like Nepal [4], Pakistan [5], Myanmar [6], Taiwan [7], Afganistan [8], Iran [9], Cambodia [10], Vietnam [11], etc. Contamination of arsenic in groundwater in the Ganga-Brahmaputra fluvial plains in India and Padma-Meghna fluvial plains in Bangladesh with an approximate area covering 569,749 km2 and its impact on residing people’s health has been reported as one of the world’s biggest natural groundwater miseries known to mankind [12]. The people in the rural areas are largely dependent on the groundwater for drinking and domestic use which is found largely contaminated by Arsenic in the arsenic-affected areas. The presence of arsenic contamination in groundwater was initially found in the early ’80s in West S. K. Basak (&)
P. K. Roy
N. S. Mondal
A. Majumder
A. Mazumdar School of Water Resources Engineering, Jadavpur University, Kolkata 700032, India e-mail: [email protected] P. K. Roy e-mail: [email protected] N. S. Mondal Jangipur Government Polytechnic, Murshidabad 742225, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_14

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Bengal when some patients were reported to be affected by arsenicosis, generally happens due to the drinking of arsenic-contaminated water for a long duration. At present 104 blocks in 11 districts are affected by arsenic contamination in groundwater in West Bengal, India where Malda, Murshidabad, Nadia, North, and South 24 Parganas are the most severely affected districts [13]. In West Bengal presence of arsenic in groundwater is generally found in shallow tube wells where depth ranging from 20 to 80 m below ground level. So the installation of deep tube well where the presence of arsenic is very low could be an option to mitigate the arsenic problem. The presence of a high concentration of iron in groundwater of these arsenic affected areas was also reported. The government of West Bengal, India, and Bangladesh in collaboration with the World Health Organization (WHO), United Nations Children’s Fund (UNICEF), World Bank, and various international agencies, and Non-Governmental Organizations (NGOs) launched a 2 phase program to mitigate arsenic contamination in groundwater in various arsenic affected areas of West Bengal and Bangladesh from 1997 [14, 15]. The 1st phase was to mark the tube wells with green if the concentration of arsenic was found 100 and up to 150 >150 and up to 200 >200 and up to 250

2 –

9

1

–

– –

3

–

–

Depth-wise percentage of arsenic in tube-well water Depth (m)  0.01 mg/L As, >0.01–0.05 mg/L in tube-well (above As, in tube-well BDL)

Table 16.2 Depth versus As and Fe concentration

–

3

8

7 1

>0.05 As in tube -well

1

10

27

5 3

BDL

10-50 >50 and up to 100 >100 and upto150 >150 and up to 200 >200 and up to 250 –

–

1

3

– –

3 –

1 1

Depth-wise percentage of iron in tube-well water Depth (m) 0.3– tube-well (above 1 mg/L Fe BDL) in tube-well

–

9

39

9 2

>1 mg/L Fe in tube-well

1

4

5

1 1

BDL

224 Y. Mondal et al.

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225

39 (82.97%) of them contain iron above 1 mg/L, >0.3–1 mg/L is 3 (6.38%) showing >0.3–1 mg/L. 14 number (18.75%) of tube-wells have a depth of >150– 200 m showing iron concentration, 10 number of tube-wells (71.42%): nine number (64.28%) of these showing above 1 mg/L, one tube-well (7.14%) showing >0.3–1 mg/L. Maximum iron concentration is found in wells having depth of 100–150 m. Table 16.2 shows all the relation. Out of 80 groundwater samples collected from tube-wells of various GPs of Habra Block-II, 34 samples contained both arsenic and iron. Regression analysis has been done in order to assess the correlation between the concentration of iron and arsenic present in the groundwater and it is shown below (Fig. 16.3). From the regression model we have found a good relationship between iron and arsenic in groundwater. The regression equation and correlation are depicted in the following table (Table 16.3). It is observed that if arsenic concentration increases then iron concentration also increases and vice versa. In this study it has been found that groundwater of 68 tube-wells is iron contaminated, whereas 34 tube-well water is arsenic contaminated. The study also reveals that all arsenic affected tube-well water is also affected by iron, but all the iron affected tube-well water is not arsenic affected.

Fig. 16.3 Correlation between aarsenic and iron in groundwater at Habra Block-II

Table 16.3 Regression equations, coefficient (R2) and correlation (r) for arsenic versus iron

Name of the area

Regression

R2

Correlation (r)

Banspole GP Bhurkunda Gp Guma-I GP Srikrishanapur GP Habra Block-II

7.364x 39.79x 7.364x 31.32x

1.259 1.417 1.259 2.042

0.592 0.736 0.252 0.864

0.768 0.850 0.50 0.929

32.36x + 0.798

0.434

0.659

+ + + +

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Conclusions The entire study surely ensures that the groundwater under Habra Block-II, North 24 Parganas, West Bengal is highly contaminated with high concentration of arsenic and iron. Most of the GPs have been found having contaminated groundwater with iron (85% tube-wells) and arsenic (42.5% tube-wells). In this eighty tube-well water 3.75% contains arsenic concentrations less than or equal to 0.01 mg/L and 15% are >0.01–0.05 mg/L. In these eighty samples only 2.5% tube-well water has iron concentration  0.3 mg/L. TDS (5%) and Total Hardness (51.25%) are found to be above the desirable limit. pH, Chloride, TDS and Total Hardness are found to be within the permissible limit and sulphate is not found, in the entire study area. The results have been compared with the IS standard [10]. On the basis of seven parameters (pH, TDS, TH, Cl−, As, Fe and Sulphate), out of 80 tube-well samples, only 14 (17.5%) samples are within the permissible limit and five (6.25%) are within the desirable limit and safe for drinking purpose. From the study, a good correlation between arsenic and iron present in groundwater has been derived. It also indicates a good correlation between the groundwater arsenic and iron with respect to change in depth. From the correlation, we can get some ideas regarding the influence of different metals in groundwater and its interaction as well as how these metals contribute to contamination of water. The correlation study is very much effective and adopted to assess groundwater quality. From the present study results it may be concluded that the people in the study areas at the entire Habra Block under North 24 Parganas, West Bengal are at higher potential risk of arsenic toxicity. This may result in revealing of arsenic-caused diseases like keratosis, gangrene, skin cancer, etc., among the people of rural areas. The groundwater tapped from tube-wells in all these GPs is not completely fit for direct drinking purposes and it needs treatment to minimize the contamination. Regular monitoring and analysis of the quality of the groundwater should be carried out in order to assess the concentration of various physicochemical parameters including arsenic and iron. As an alternative, it would be a good option to use surface water resources after proper treatment. Rainwater harvesting needs to be compulsorily implemented in rural areas which may be used both for drinking as well as groundwater recharge purpose after necessary treatment. Towards mitigation of the arsenic toxicity, different approaches may be adopted with the help of proper water-shed management technique. There are not yet effective drugs for the treatment of toxicity caused by arsenic. So, the only way to fight against arsenic toxicity is to adopt preventive measures such as taking safe and pure drinking water and nutritious foods together with regular physical exercise.

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References 1. World Health Organization (2011) Guidelines for drinking water quality, 4th ed, Geneva 2. National Ground Water Association, 601 Dempsey Road, Westerville, Ohio 43081-8978, USA. https://www.ngwa.org/about:National. Last accessed 10 Nov 2020 3. Gleeson T, Befus KM, Jasechko S, Luijendijk E, Cardenas MB (2015) The global volume and distribution of modern groundwater. Nat Geosci 9(2):161–167. https://doi.org/10.1038/ ngeo2590 4. Report of the groundwater resource estimation committee (GEC-2015) (2017) Ministry of Water Resources, River Development & Ganga Rejuvenation Government of India, New Delhi 5. Jha BM, Sinha SK (2007) Towards Better Management of Ground Water Resources in India, Central Ground Water Board, Government of India 6. Mukherjee A, Sengupta M, Hossain MA, Ahamed S, Das B, Nayak B, Lodh D, Rahman MM, Chakraborti D (2006) Arsenic contamination in groundwater: a global perspective with emphasis on the Asian Scenario. International Centre for Diarrhoea Disease Research, Bangladesh, ISSN 1606-0997, June, 24(2):142–163 7. Smith AH, Lingas EO, Rahman M (2000) Contamination of drinking-water by arsenic in Bangladesh: a public health emergency. Bull World Health Organization 78(9) 8. Ghosh NC, Singh RD (2010) Groundwater arsenic contamination in India: vulnerability and scope for remedy. Environ Sci 9. Purkait B, Mukherjee A (2008) Geostatistical analysis of arsenic concentration in the groundwater of Malda district of West Bengal, India. Front Earth Sci 2(3):292–301, China, September 10. Indian Standard (IS) 10500: 2012, Indian Standard, Drinking Water-Specification, Second Rev. (2012) 11. Acharyya SK (2002) Arsenic contamination in groundwater affecting major parts of southern West Bengal and parts of western Chhattisgarh: Source and mobilization process. Current Sci 82(6) 12. Acharyya SK, Shah BA (2007) Arsenic-contaminated groundwater from parts of Damodar fan-delta and west of Bhagirathi River, West Bengal, India: influence of fluvial geomorphology and Quaternary morphostratigraphy. Environ Geo l52:489–501 13. World Health Organization (2004) Some drinking-water disinfectants and contaminants, including arsenic. IARC Monograph on the Evaluation of Carcinogenic Risks to Humans, IARC, 84:271–441 14. Guha Mazumder DN, Ghosh A, Majumder KK, Ghosh N, Saha C, Guha Mazumder RN (2010) Arsenic contamination of ground water and its health impact on population of district of Nadia, West Bengal, India. Indian J Community Med 35(2):331–338 15. NRC (National Research Council) (1999) Arsenic in drinking water. Washington, DC, National Academic Press 16. Guha Mazumder DN, Haque R, Ghosh N, Santra BK, Chakraborty A (1998) Arsenic levels in drinking water and the prevalence of skin lesions in West Bengal, India. Indian Int J Epidemiol 27:871–877 17. Guha Mazumder DN, Dasgupta UB (2011) Chronic arsenic toxicity: studies in West Bengal, India. Kaohsiung J Med Sci 27:360–370 18. Caussy D (2005) A field guide for detection, management and surveillance of arsenicosis cases. WHO Regional Office for South East Asia, WHO Technical Publication, New Delhi, vol 30, pp 19–22 19. Standard Methods for the Examination of Water and Wastewater (2012) American Public Health Association (APHA), 22nd ed

Part IV

Modelling Extreme Climate Events: Intensity and Magnitude of Drought, Flood and Cyclone

Chapter 17

Characteristics of Precipitation in the Changing Climatic Scenario in India: A Critical Observation Rupam Sahu and Pankaj Kumar Roy

Introduction Indian economy largely depends upon the performance of Indian Summer Monsoon Rainfall (ISMR) especially during the four months period, June to September, which contributes nearly 75% of the annual rainfall total. The rainfall pattern in India, in general, is controlled by the topography of the Eastern Himalayas and the Western Ghats and also the geographical position of different zones ranging from tropical to subtropical. Since the second half of the last century, the climatic scenario in India is changing at a rapid pace may be due to uncontrolled removal of the rainforest for expanding the habitable area, fast growth of industrialization along with least control on maintaining the pollution level below the desired limit. Due to this, there is a continuous rise in the average temperature on land and sea surface which coupled with geographic positioning and topographical variations are seen to affect the climatic scenario largely. In this context, it may be noted the occurrences of extreme rainfall in various parts of India in last two decade has become a nightmare. In July, 2005 Mumbai experienced a heavy rainfall event, huge loss of lives and property occurred in Ladakh in August, 2010 due to a flash flood. All these and similar other events influence the researchers to give more importance on studying trends in temporal and spatial heterogeneity of rainfall events in order to assess their behavioural pattern and help the planners with judicious prediction. In order to express the temporal and spatial variability of rain events, their intensity and duration, the whole year is divided into four seasons. These are pre-monsoon (PREM) (March to May—MAM), summer monsoon (SM) (June to September—JJAS), post-monsoon (POSTM) (October to December—OND) and R. Sahu (&)
P. K. Roy School of Water Resources Engineering, Jadavpur University, Kolkata, India e-mail: [email protected] P. K. Roy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_17

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winter (January to February—JF). India is located between 8°4’ N and 37°6’ N latitude and 68°7’ E and 97°25’ E longitude. So, there is a significant spatial variation of rain events and to characterize the nature of variations the whole country has been divided into six regions. These are Peninsula, West Central, North West, North East (NE), Central North East (CNE), Central India (CI) and Hilly Region (Dash et al. [1] ). For each region rain intensity is classified into light (2.5– 7.5 mm per day), moderate (7.6–35.5 mm per day), heavy (64.5–124.5 mm per day), very heavy (124.5–244.5 mm per day), extremely heavy (  244.5 mm per day). Similarly, on the basis of rain duration and intensity the rain spell events are classified as long (LS) (  2.5 mm per day for > 4 consecutive days), short (SS) (  2.5 mm per day for < 4 consecutive days), dry (DS) ( DL

Lengthen telomere

Newborn

Arsenic, chromium > DL Arsenic, lead > DL

Lymphoblastic, myeloblastic leukemia Monoblastic leukemia Acute lymphoblastic leukemia Lymphoblastic leukemia Normal Normal Normal

Lengthen telomere

Pregnant women

Chromium > DL Adult male

Chromium < DL Lead, arsenic < DL Chromium > DL

Lengthen telomere

Lengthen telomere Normal Normal Normal

Adult female Adult Lead with in the DL Normal Normal female DL—detecting limit of heavy metals in Urine samples of pregnant women and newborn; arsenic— (acceptable range: 130–140 lg/L), cadmium—(acceptable range: 3.8–4.4 lg/L), Chromium— (acceptable range: 52.4–55.8 lg/L), and lead—(acceptable range: 35.1–40.6 lg/L)

a

b

Fig. 24.1 a Peripheral blood smear showing leukemia in WBC of individual affected by heavy metal exposure. b Peripheral blood smear showing normal WBC of individual

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Fig. 24.2 Detecting limit (DL) of heavy metals in individuals

Conclusions Water scarcity has left no choice but to consume groundwater in many villages and urban areas in India. Heavy metals are significant as pollutants enter the food chain through direct consumption. Vegetable harvested in wetlands that are watered perpetually with contaminated water is yet another source of the entrance of heavy metals to the bloodstream. When the toxic heavy metals accumulation in the bloodstream over a certain concentration, they become dangerous for the human life. Poisoning by heavy metals like lead, arsenic, cadmium, and chromium may lead to a variety of problems including leukocyte carcinoma. Environmental heavy metal exposure of the human beings through contaminated water and food chain is a huge concern. Toxic effects of heavy metals depend on various routes of its exposure; disruption of intracellular homeostasis includes harm to proteins, enzymes, lipids, and nucleotides (in DNA) via the action of free radicals. The study revealed that an increase in the serum level of heavy metals (of leukocyte carcinoma patients) might have its involvement as a contributing factor of carcinogenicity. The study completed in New Delhi elucidates that newborn and pregnant women are more vulnerable to heavy metal exposure than adult males and females. Among all age groups, heavy metal affects neonates and newborn WBC at a rate two times higher than the adult. At the developing stage, the newborn when exposed to a higher concentration of heavy metals had higher effects on the telomeres of their leukocytes resulting in lymphoblastic leukemia associated with heavy metal poisoning of the blood. Pregnant women in their first trimester may also be at higher risk of developing leukemia due to exposure to heavy metals.

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References 1. Malik DS, Maurya PK (2014) Heavy metal concentration in water, sediment, and tissues of fish species (Heteropneustis fossilis and Puntius ticto) from Kali River, India. Toxicol Env Chem 96(8):1195–1206 2. Wai KM, Mar O, Kosaka S et al (2017) Prenatal heavy metal exposure and adverse birth outcomes in Myanmar: a birth-cohort study. Int J Env Res Pub Health 14(11):1339. https:// doi.org/10.3390/ijerph14111339 3. Rehman K, Fatima F, Waheed I, Akash MSH (2018) Prevalence of exposure of heavy metals and their impact on health consequences. J Cell Biochem 119(1):157–184. https://doi.org/10. 1002/jcb.26234 Epub 2017 Aug 2 PMID: 28643849 4. Bacquart T, Frisbie S, Mitchell E et al (2015) Multiple inorganic toxic substances contaminating the groundwater of Myingyan Township, Myanmar: Arsenic, manganese, fluoride, iron, and uranium. Sci Tot Env 517:232–245 5. Barbu C, Popescu A, Selisteanu D, Preda A (2008) Determination of toxic heavy metals present in Jiu River Water using ICP-MS. Asia J Chem 20(3):2037–2046 6. Jan AT, Azam M, Siddiqui K et al (2015) Heavy metals and the human health: mechanistic insight into toxicity and counter defense system of antioxidants. Int J Mol Sci 16:29592– 29630. https://doi.org/10.3390/ijms161226183 7. Beck S, Wojdyla D, Say L et al (2010) The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bulletin WHO 88:31–38 8. Lawn JE, Wilczynska-Ketende K, Cousens SN (2006) Estimating the causes of 4 million neonatal deaths in the year 2000. Int J Epi 35:706–718 9. Ministry of Health (MOH); The United Nations Children’s Fund (Unicef) (2013) An analysis of arsenic content in drinking water sources of Ayeyarwaddy Region. MOH, Naypyidaw, Myanmar 10. Ministry of Health and Sports (MOHS); ICF International. Demographic and Health Survey (2015–2016): Key Indicators Report. In: Myanmar Demographic and Health Survey (DHS); Ministry of Health and Sports (MOHS), Naypyidaw, Myanmar; The DHS Program ICF International, Rockville, MD, USA, 2016 11. Hanan FA (2012) Dietary habits and relation to cancer disease in different population. Arch Cancer Res 1(1:2): https://doi.org/10.3823/901 12. Rahman A, Persson L, Nermell B et al (2010) Arsenic exposure and risk of spontaneous abortion, stillbirth, and infant mortality. Epidem 21:797–804 13. World Health Organization (WHO), United Nations Children’s Fund (UNICEF) (2004) Low Birth Weight: Country, Regional and Global Estimate; United Nations Children’s Fund (UNICEF), New York, NY, USA 14. World Health Organization (WHO); United Nations Children’s Fund (UNICEF) (2004) Low Birth Weight: Country, Regional and Global Estimate; United Nations Children’s Fund (UNICEF), New York, NY, USA 15. Tun TN (2003) Arsenic contamination of water sources in rural Myanmar. In: Proceedings of the 29th WEDC international conference towards the Millennium Development Goals, Abuja, Nigeria, 22–26 Sep 2003, pp 219–221 16. Van Geen A, Win KH, Zaw T et al (2014) Confirmation of elevated arsenic levels in groundwater of Myanmar. Sci Tot Env 21–24 17. Al-Saleh I, Shinwari N, Mashhour A, Rabah A (2014) Birth outcome measures and maternal exposure to heavy metals, (lead, cadmium and mercury) in Saudi Arabian population. Int J Hyg Env Health 217:205–218 18. Gundacker C, Hengstschlager M (2012) The role of the placenta in fetal exposure to heavy metals. Wien Med Wochenschr 162:201–206 19. Rai PK, Lee SS, Zhang M (2019) Heavy metals in food crops: health risks, fate, mechanisms, and management. Env Int 125:365–385. https://doi.org/10.1016/j.envint.2019.01.067 Epub 2019 Feb 8 PMID: 30743144

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20. Bamji MS (2011) Hazardous metals and minerals pollution in India: sources, toxicity and management. A Position Paper. Indian National, Published by Shri S. K. Sahni, Executive Secretary On Behalf of Indian National Science Academy, Bahadurshah Zafar Marg, New Delhi, Angkor Publishers (P) Ltd., Noida-201301 21. Quansah R, Armah FA, Essumang DK et al (2015) Association of arsenic with adverse pregnancy outcomes/infant mortality: a systematic review and meta-analysis. Env Health Pers 123(5):412–421. https://doi.org/10.1289/ehp.1307894 22. Wai KM, Umezaki M, Kosaka S et al (2018) Impact of prenatal heavy metal exposure on newborn leucocyte telomere length: a birth-cohort study. Env Pol 243(Pt B):1414–1421. https://doi.org/10.1016/j.envpol.2018.09.090 Epub 2018 Sep 21 PMID: 30278415 23. Vahter M (2009) Effects of arsenic on maternal and fetal health. Annu Rev Nutri 29:381–399 24. Zheng G, Zhong H, Guo Z et al (2014) Levels of heavy metals and trace elements in umbilical cord blood and the risk of adverse pregnancy outcomes: a population-based study. Bio Trace Elem Res 160:437–444 25. Blencowe H, Cousens S, Oestergaard MZ et al (2012) National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. Lancet 379:2162–2172

Chapter 25

Trending Nature of Indian and Egyptian Independent Floodplain Research on River Ganga and Nile: A Bibliometric Analysis Malabika Biswas Roy, Arnab Ghosh, Abhishek Kumar, and Pankaj Kumar Roy

Introduction The river famous for its elixir of life and is among the substances. The two rivers have played an integral role, not merely in the development of civilization. Human culture rides upon the river from the start. On the list of planet’s most significant rivers, the in-between Nile and river Ganga are still an essential one. Floodplains are puzzling elements flanking twisting flows in plaited, or anastomosing comes. The majority of the publications of these rivers cover burning off issues linked to the flood plain. Indus and Egyptian culture had endowed using Ganga along with the Nile. These river’s floodplains formed abandoning silt you can utilize for food that is growing. Basin irrigation existed tens of a large number of years ago once again to catch a number of their overflow drinking-water [1]. Later, there appeared a permanent irrigation system to decrease dependence. The irrigation system promotes plants, such as grain and wheat, while a well-balanced water system boosts money vegetation, such as corn and organic cotton. Ordinarily, these floodplains have lately been confronting the issue of overpopulation [2] and also an essential

M. B. Roy (&) Women’s College, Calcutta, West Bengal, India e-mail: [email protected] A. Ghosh
P. K. Roy School of Water Resources Engineering, Jadavpur University, Kolkata-32, India e-mail: [email protected] P. K. Roy e-mail: [email protected] A. Kumar Ballia Water Centre, Ballia, Uttar Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_25

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quantity of flood. As a result of the flow of river water [3] and additionally, due to sedimentation, which communicates the lake path chocked [4]. Designs can also be initiated to minimize the issue. The study attempts to find the issue and potential of the sort of two main factors. Research can be just a means to expand our thinking. It’s also valuable in the event of independent analysis of 2 states, books, leading to their depth of comprehension [5, 6]. The bibliometric investigation gets increasingly more use to make comparisons between two different factors from the sort of research, books, and associations [7]. The very last reviews are contingent on the qualitative, qualitative study of production routines, procured on word-clusters, and take the expressions in essay titles, author keywords, and Keywords Plus, one of the data, other signs [8]. The quantifier of journals by document and record type regularly utilized for information. In the meantime, citations per book (CPP), that might grant an ‘observing gadget’ for its science and administration plan [9], joined in various reviews [10–15]. Whatever the scenario, communitarian distributions may function as an alternative form of non-cooperative newspapers. Its impact of mates out of deductively ‘stronger’ states effects in anyone’s research region. Afterward, they’re to it trusted this to grasp the bonito the nation’s profile, plus something ought to research distributions without outside co-workers [16–24]. In our particular case, it plans to analyse distributions composed by founders from Egypt and India. ‘Independent Research’ intends to clarify that the research of a specific country on the nation’s resource. This research finished river Ganga, and the Nile focused on their view and suggestion. India and Egypt are most similar in civilization. The size and the pattern of the refinements are the same. The present study investigated the thickness of this research about the floodplain of these rivers. The work plans to screen similar river research over the flood plain with the assistance of Ganga and Nile past the years dispersing from 2006 to 2015.

Materials and Methods Data Collection Data found in this research gained by the internet version of this ISI Web of Science: SCI (Science Citation Index). All files from 2006 to 2015 using ‘Nile’ and also ‘Ganga’ from the speech area and then had the next critical words downloaded: Flood Plain, Flood, Sedimentation, Hazard Possibilities, Character of Channel, Bathymetry, Urbanization, Dam, Siltation of all Reservoir, Decay, and Hydrological version. As a whole, the selection criteria met by 820 publications. Upon further examination, just 640 publications classified as ‘articles.’ Others were also a bibliography (35), Conference Paper (46), Bulletin newspaper (23), Review Paper (31), notes, and Project Report (45).

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Methodology The records recovered, also contained the name of authors, article names, book years, journal titles, and authors mentioned to authors’ locations plus a few data, which moved into XLSTAT Programming along with also Microsoft Excel 2010. A few additional coding has been subsequently physically conducted to distinguish the associations of co-creators along with even the active aspects of the publishing journals. It also disseminated the words from the titles of their authors, contact address, name, year of publication, keywords, times mentioned, subject sorts of the guide, titles of journals publishing from the articles, and publisher details. Keywords Plus looks provisions divided by articles’ claims known considerably and by authors within commentaries, and their publication references from the Web of Science increase author keyword order and title-word. Impact facets taken out of the Journal Citation Report (JCR) released in 2005. Aside from ‘Country Independent’ research on the degree of states, we were beside interested in the level of ‘separate’ research. The definition of ‘institutionally’ allocate whether its authors were related to a similar association from Egypt or India and also the description of ‘collaborative article’ hone whether the authors were out of teams. The level of ‘independence’ of a base might communicate with widening single association articles in an organization’s supply, and forcing stations within a country usually, are more autonomous compared to typical as we’ll see later in this newspaper. There is the same notion that talk of ‘author,’ and also author articles make productions. A considerable proportion of those articles, for the large part, means a superb devotion to the research spread as the principal writer frequently expects that will have performed the trials given that an account of and also the corresponding author is often a senior researcher tackling the study. In one single-author article, the writer is author and both original. In just one distribution, the founder base delegated by the company and the author’s establishment.

Results and Discussions Article Characteristics In the beginning, all of the journal articles on both of these rivers split into five key conversation topics (Fig. 25.1). Adhering to Fig. 25.1, it’s apparent the 28% of river Ganga-related research from India depends upon hazard problem, for example, riverbank erosion, flooding, the aftereffect of dams, and individual encroachment together with the river. But in the research on the Nile river out of Egypt compacted on modelling and fluvial character negotiations like fluvial hydraulics along with chronology and sediment transfer (23.3% and 22.7%). This feature indicates the gap in the design of research. Concerning technological production, the 382 lake Nile-related independent articles written roughly 86% of river Ganga-related

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Fig. 25.1 No. of articles on different discussion criteria

separate articles, which equates with regular analysis, production amounts, even when Egypt’s populace (82.06 million) is nearly one-fifth of their Indian populace (1.252 billion). The amount of independent floodplain-related research upon Ganga and Nile lake has increased since 2006 (Fig. 25.2). 820 floodplain articles published between 2006 and 2015 about both of these rivers and the publication reached in the event of the world. There are only 46 articles in 2006. However, it goes 68 in 2008 with a steady growth. From 2011, it rises from 98 to 111 in 2015. The growth styles of floodplain articles, related to Egyptian and Indian investigators, increase rapidly on growing tendency about civilized nation.

Fig. 25.2 Comparision of floodplain-related research in between World and Ganga and Nile

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Yearly Variation in Number and Citations of Article English is the main language for those articles on Ganga and Nile. As the publication of river Ganga-related article grew steadily by 20 from 2006 to 61 in 2015 (Fig. 25.3), the Nile research, publication was much significantly robust, and it started with 25 items in 2006, not precisely appeared at 51 in 2012 and slightly dropped to summit with 47 in 2015 again finally. The article features have been alike in both states between 2015 and 2006. The average authors per paper climbed from 3.1 to 4.7 in river Ganga-related articles, also out of 2.7 to 3.6 in river Nile associated articles. The mean of few cited references per article climbed from 19 to 37 from river Ganga-related substances and from 21 to 34 in river Nile associated items, and also the normal article span ascended from 7.3 to 8.0 pages together with river Ganga associated articles and by 7.1 to 8.3 with river Nile-related substances. Concerning the effects of articles released, older articles mentioned more commonly in rivers using 7.8 citations each paper along with 11.3 citations each paper in 2006 and 6.9 and 10.1 citations each paper in 2015 for both Ganga along with Nile river articles.

Citations Age The citation analysis of those countries changed among (Fig. 25.4) 0.91 citations of river Ganga-related informative article and 1.47 citations for Nile article. Afterward, the citation count annually drops on a yearly basis following the river Nile exhibit an inferior mentioning generally, that will be normally average 5.8 citations each paper, in contrast to 4.1 citations per paper for lake Ganga articles.

Fig. 25.3 Indian and Egyptian article on Ganga and Nile and their citatons

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Fig. 25.4 Citations per article by article age

Web of Science Categories Among the total articles which published in India between 2006 and 2015, the share of river Ganga-related topic is, very few directly (0.21%) and indirectly (3.9%). But in the case of Egypt, the river Nile share is slightly higher than Ganga directly (0.68%) and indirectly (4.23%). There is also a difference in the focus of river Ganga and the Nile-related independent research that can understand by examining Table 25.1 with the list of Web of Science subject categories to which Ganga and the Nile-related publications allocate.

Table 25.1 Top 10 subject categories Web of science category

Ganga with TP (%)

Nile with TP (%)

Geoscience, multidisciplinary Geography, physical Engineering, mechanical Water resource Remote sensing Environmental science Engineering, geological Engineering, environmental Fisheries Economics

73 60 33 45 47 29 17 29 23 23

50 51 83 25 16 13 18 23 16 21

(16.6) (13.7) (7.53) (10.3) (10.7) (6.62) (3.88) (6.62) (5.25) (5.25)

(13.1) (13.4) (21.7) (6.54) (4.18) (3.4) (4.71) (6.02) (4.2) (5.49)

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Journal Categories Tables 25.2 and 25.3 show that the most abundant group for Ganga-related article is ‘geosciences, Multidisciplinary’ and for the Nile is ‘Engineering, mechanical’ with a share of about 16.6% and 21.7%, respectively. The other strongly related river Ganga-related categories are ‘Geography, Physical’ (13.7%) and ‘Remote Sensing’ (10.7%). However, the comparative strongest research class at river Nile-related paper is ‘geosciences, Multidisciplinary’ (14.9%) and ‘Geography, Physical’ (13%). The above results further corroborated by the journals in which river Ganga and the Nile-related articles published most often, showed in Tables 25.2 and 25.3. In the case of Ganga-associated articles, India mainly focuses on its river dynamics and fluvial morphology origin. Quaternary International (12.7% of TP) demonstrate the high number of publications in the Ganga-related articles and Hydrology and Earth System Science (13.3% of TP) documents by many papers in the Nile-related journals. In this unique situation, it is intriguing to note that classification like geosciences, multidisciplinary is most contemplated groupings in both productions and nations, specialists in this field tend to distribute their research in their particular residential and global articles. Likewise, there are just local journals for both countries tend to publish, i.e., Current Science (river Ganga) and Arabian Journal of Geosciences (river Nile). Autonomous research in both nations is in this way vigorously distributed in worldwide outlets.

Table 25.2 Top 5 journals for Ganga-related articles Journal

TP (%)

IF2015

Quaternary international 61 (12.7) 2.062 Journal of geophysical research 53 (12.1) 3.318 Current science 41 (9.4) 0.833 Earth and planetary science letter 35 (7.9) 4.724 International journal of geomatics and geosciences 29 (6.6) 1.7 TP Number of articles, IF2015 Impact Factor by 2005 Edition of Journal Citation Reports®

Table 25.3 Top 5 journals for Nile-related article

Journal

TP (%)

IF2015

Hydrology and earth system science 51 (13.3) 3.99 Sedimentology 47 (12,3) 3.216 Arabian journal of geoscience 40 (10.5) 1.224 Journal of hydrology 36 (9.4) 3.043 Quarternary research 30 (7.8) 2.198 TP Number of articles, IF2015 Impact Factor by 2005 Edition of Journal Citation Reports®

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Keyword Characteristics To further study the contents of river Ganga and the Nile-related articles, the differences also found in article titles, author keywords, and keywords briefly present most exciting results. These two studies have many phrase titles in common, and the most frequent words are: ‘Sediment,’ ‘Morphology,’ ‘Effect,’ ‘Modelling,’ and ‘Influence.’ Most frequent title words in Ganga-related articles are: ‘Discharge,’ ‘Quality,’ ‘Basin,’ ‘Morphometry.’ By contrast, most frequent title words in Nile-related articles are: ‘Flood,’ ‘Blue Nile,’ ‘Dam,’ ‘Quaternary.’ A total 43 words are common in the top 50, and the similarity between the 50 most frequent Ganga-related title words and the 50 most frequent Nile-related title words is 0.45 using the shortest edit script algorithm by Myers, where one means that two strings are identical and 0 means that they are entirely different. Then again, the closeness between the top author keywords in each research is just 0.39 with far fewer keywords (13 out of 50) having a place with both sets, such as, ‘water quality,’ ‘flooding,’ ‘sediment transport,’ ‘morphology.’ The restrictive Ganga-related top author keyword join ‘climate change,’ ‘river dynamics,’ ‘quaternary,’ ‘fluvial geomorphology,’ ‘stream flows to give some examples,’ and the other one incorporates ‘the Nile,’ ‘Blue Nile,’ ‘water quality,’ ‘flooding.’ Lastly, the similarity between the top keyword is higher with 0.4 even with many primary keywords (31 out of 50), which are positioned contrasting by an event. Some of them are ‘sediment’ and ‘flood’ (which are one of the top keywords in both sides), ‘model,’ ‘climate.’ It might reason that to the extent article title words, author keywords, and keywords plus to the concern, the subjects of Ganga and the Nile-related independent articles are extraordinary concerning their need and more comparable if just the overlay of their keywords considers.

International Collaborations Figures 25.5 and 25.6 elaborated the top 5 countries that produced most articles on river Ganga and Nile in the period under study. There are only three countries that produced 75% of the total publications on the Nile with eight nations published 80% of the Ganga-related articles. The distinctive pretended by the two driving organizations in their separate nations likewise exhibited by alternate markers like the share of papers delivered by a single country (single country publication—SP), collaborative countries (collaborative publication—CP), or the share of first author (FP), corresponding writer (RP) articles and TPR (countries the total publication ratio with total production of that particular criteria throughout the world). In the case of river Ganga, most articles were from India (39%). India generated most single country and international collaborative articles pursued by the Bangladesh and USA. For example, the individual country publication of India is 59.1% of all of its items compared to 35.1% of its elements that are collaborative, i.e., published

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Fig. 25.5 Top 5 countries in Ganga and Nile research

Fig. 25.6 TP—number of articles; SP—single country articles; CP—collaborative countries articles; FP—first author articles; RP—corresponding author articles, and R—rank in Ganga and Nile research

together with co-authors from other countries. Similarly, 42.7% of its articles are first author papers and 18.1% applies to corresponding author papers. Compared with other countries, Bangladesh is much higher single country and first author publication than India (60.9% and 47.1%). In the case of river Nile, most articles were from Egypt (40%). Egypt generated most single country and international collaborative articles pursued by Ethiopia and Sudan. By contrast, Egypt is most dominant in the individual country paper

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(76.4%) and less relying on collaborative documents (23.5%). Also, its share of the first author and corresponding author papers is much higher (63.3% and 34.6%) compared to India. These all pointers delineated various parts of research productivity. Many single countries and collective articles teach us concerning the research flexibility of a specific foundation.

Institutional Collaborations Tables 25.4 and 25.5 elaborated the top Indian and Egyptian institutions that produced most articles on river Ganga and Nile in the period under study. There are only 8 Egyptian institutions that produced 60% of the total publications whereas 13 Indian institutions published 75% of the Ganga-related articles. Though the population of Egypt is about 1/5th of India, the independent research of Egyptian institutions is more significant than Indian. So, the Indian research production is lower than Egypt because of its lower participation. The distinctive pretended by the two driving organizations in their separate nations likewise exhibited by alternate markers like the share of publications delivered by a solitary institution (independent papers—IP), a few collaborative institutions (collaborative papers— CP), or the percentage of first author (FP) and a corresponding writer (RP) article. For example, the single institution articles of the IIT, Kanpur, India from 63.3% of all of its items compared to 37.7% of collaborative publication, i.e., published together with co-authors from other Indian institutions. Similarly, 77.7% of its articles are first author papers and 71.7% apply to corresponding author papers. Thus, over 60% of publications produce with a significant contribution of the Indian Institution, exceeding ‘simple’ co-authorship participation. By contrast, the Nile Research Institute is more dominant in single institution papers (78.8%) and less on collaborative documents (23.1%). Also, its share of the first author and corresponding author papers is slightly higher (76.9% and 73.1%) compared to its Indian counterpart and very high in absolute terms. Once more, this exhibits the exceptional status of the Nile research institute in Egyptian independent research. It is Table 25.4 Indian top 3 institution on Ganga research (TP > 40) Institution

TP

TPR (%)

IP (%)

CP (%)

FP (%)

RP (%)

Department of Civil Engineering, 53 1 33 20 41 38 IIT, Kanpur (12.1) (63.3) (37.7) (77.3) (71.7) Department of Geology, Centre of 47 2 20 27 33 26 Advanced Studies, Delhi (10.7) (42.5) (57.4) (70.2) (55.3) Geological Studies Unit, ISI, 43 3 19 24 30 21 Kolkata (9.8) (44.2) (55.8) (69.7) (48.8) TP Number of articles, IP Institutionally independent articles, CP Inter-institutionally collaborative articles, FP First author articles, RP Corresponding author articles, R rank

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Table 25.5 Egyptian top 3 institution on Nile research (TP > 40) Institution

TP

Nile Research Institute, Egypt

52

TPR (%)

IP (%)

CP (%)

FP (%)

RP (%)

1 40 12 40 38 (13.6) (78.8) (23.1) (76.9) (73.1) National Water Research Centre, 48 2 34 14 27 37 El-Kheima, Cairo, Egypt (12.5) (70.1) (29.2) (56.3) (77.1) Faculty of Engineering, Cairo 41 3 29 12 33 19 University, Egypt (10.7) (70.7) (29.3) (80.5) (46.3) TP Number of articles, IP Institutionally independent articles, CP Inter-institutionally collaborative articles, FP First author articles, RP Corresponding author articles, R rank

hard to disambiguate and bring together institutional names accurately after figures in this segment may be viewed as surmised. These all pointers depicted diverse parts of research profitability.

Conclusion and Recommendations Independent research of a country not only depends on global authorization, but also a part of single autonomous publication in a given country. In this manner, we often possibly observe independent research because this type of research done in nationalize areas without outside effects. Consequently, only the study on rivers from attaining significant researchers with the devotion of investigators through individuals believe. It hopes the research profile due to the research of a nation could make the comparison from a research profile and could reflect a country’s research limitation real, especially if open and little states fear. In this independent review research on Ganga and Nile through India and Egypt investigate and made the accompanying fundamental commitments throughout the study. All journal articles published autonomously by India and Egypt in the light of the Ganga and Nile river between the 2006 and 2015 file in the Science Citation Index Expanded. Analysis of independent research, production of both nations do as far as research points, publication outlets, and productive institutions. The two countries vary generously from each other as far as profitability, research points, and besides the effect of their independent research. Egyptian production was just 87% of the Indian creation, which included 438 articles, and both nations depended similarly intensely on ‘geosciences, multidisciplinary’ and ‘Geography, physical’ and many other research fields. However, concerning the distinctions, Egypt, for the most part, focused on ‘Mechanical, Engineering’ concerning dam development on the Blue Nile while India primarily focuses on ‘geosciences, multidisciplinary’ concerning fluvial progression or depositional phase of river Ganga. Nile-related independent research is more often referred to than Ganga-related independent research with 5.8 citations for every paper

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contrasted with 4.1 as are the most exceedingly involved to articles in both nations with 108 aggregate citations of the top Nile-related documents and 43 add up to excerpts of the Ganga-related paper. Scientometric investigations of independent research of nations bode well specifically for small and open economies that are less independent and that, for the most part, teams up widely with outside countries in their research ventures. The analysis attempts to test the issues with the research on both of these rivers in two nations. Primarily the Nile may be the dad of his or her culture. Into climate, a higher civilization must not evolve without it. However, Ganga worshipped by folks and also can be just actually a sacred river. Due to the production Aswan Dam massive amount of pollution raised in the Nile river both also natural and human-made. In Ganga, the water’s calibre reduction because of industrialization and urbanization. In the lower section of Ganga, arsenic’s issue is increasing. Egypt’s climate could be humid type and also, the water of the Nile is vital for their livelihood. However, in the example of river Ganga by enhancing water quality and Indians are attempting to eliminate the issue of flooding. The majority of most Egyptian research is dependent upon the Nile origin in this particular publication. Consequently, they make an effort to tidy and clean out the river, also develop social and economic wealth such as fishing, rail travel, etc., in the historic location. However, in every riverine country, this kind of activity found in the example of river Ganga. Indian govt. develop the Ganga Action Plan (GAP) to clean up the contamination all through the river rarely followed. In this manner, this Ganga’s character will enhance, and also the practice of reincarnation will activate the river. Acknowledgements The authors are showing sincere gratitude to the Digital Library of School of Water Resources Engineering, Jadavpur University, for allowing to access all the GIS and statistical software.

Funding Any government or non-government organization did not fund this research article. Conflict of Interest There is no conflict of interest among the authors.

References 1. Raslan Y (2010) Human impacts on Nile river morphology. In: Fourteenth international water technology conference, IWTC 14 2010, Cairo, Egypt, pp 221–235 2. Omer AYA, Ali YSA, Roelvink JA, Dastgheli A, Paron P, Crosato A (2015) Modelling of sedimentation processes inside Roseires Reservoir (Sudan). Earth Surf Dynam 3:223–238. https://doi.org/10.5194/esurf-3-223-2015 3. Ducassou E, Migeon S, Mulder T, Murat A, Capotondis L, Bernasconi SM, Mascle J (2009) Evolution of the Nile deep-sea turbidite system during the late quaternary: influence of climate change on fan sedimentation. Sedimentology 56:2061–2090. https://doi.org/10.1111/ j.1365-3091.2009.01070.x

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4. Sinha R, Tandon SK, Gibling MR (2010) Shallow sub surface stratigraphy of the Ganga basin, Himalayan foreland: present status and future perspective. Quatern Int 227:81–86. https://doi.org/10.1016/j.quaint.2010.07.015 5. Fiala D, Ho YS (2016) Comparison of Czech and Slovak independent research in the 21st century. Curr Sci 110(8):1524–1531 6. Fiala D, Tutoky G (2017) Computer science papers in web of science: a bibliometric analysis. Publications 2017(5):23. https://doi.org/10.3390/publications5040023 7. Hammouti B (2010) Comparative bibliometric study of the scientific production in Maghreb countries (Algeria, Morocco and Tunisia) in 1996–2009 using Scopus. J Mater Environ Sci 1 (2):70–77 8. Khan MA, HO YS (2011) Arsenic in drinking water: a review on toxicological effects, mechanism of accumulation and remediation. Asian J Chem 23:1889–1901 9. Fu HZ, Ho YS (2015) A bibliometric analysis of the journal of membrane science (1976– 2010). Electron Libr 33(4):698–713. https://doi.org/10.1108/EL-12-2013-0221 10. Ma R, Ho YS (2016) Comparison of environmental laws publications in science citation index expanded and social science index: a bibliometric analysis. Scientometrics 109:227– 239. https://doi.org/10.1007/s11192-016-2010-6 11. Bashar KE, Mutua F, Mulungu DMM, Deksyos T, Shamseldin A (2006) Appraisal study to select suitable rainfall-runoff model(s) for the Nile river basin. proceedings of international conference of UNESCO flanders fust friend/Nile project, 12–15 Nov 2005 12. Bharati L, Lacombe G, Gurung P, Jayakody P, Hoanh CT, Smakhtin V (2011) The impacts of water infrastructure and climate change on the hydrology of the upper Ganges river basin. International Water Management Institute, Colombo, Sri Lanka, p 36 (IWMI Research Report 142). https://doi.org/10.5337/2011.210 13. Chuang K-Y, Huang Y-L, Ho Y-H (2007) A bibliometric and citation analysis of stroke-related research in Taiwan. Scientometrics 72(2):201–212 14. Cluer B, Thorne C (2013) A stream evolution model integrating habitat and ecosystem benefits. River Res Appl 1–20. http://dx.doi.org/10.1002/rra.2631 15. El-Shabrawy GM, Goher ME (2014) Limnology of the river Nile. Anim Res Divers Africa 1–15 16. Fiala D, Ho YS (2015) Twenty years of Czech science: a bibliometric analysis. Malayasian J Libr Inf Sci 20(2):85–102 17. Ghosh A, Roy MB, Roy PK (2017) A bibliometric analysis of highly cited papers on floodplain research in science citation index expanded. Int J Sci Res Rev 6(4):112–127 18. Hassan HA, Al Rasheedey A (2007) The Nile river and Egyptian foreign policy. Afr Sociol Rev 11(1):25–37. https://doi.org/10.4236/gep.2014.25010 19. Kirby M, Eastham J, Mainuddin M (2010) Water-use accounts in CPWF basins: simple water-use accounting of the Nile Basin. CPWF working papers-basin focal projects series BFP 03 20. Roy MB, Ghosh A, Chatterjee D, Roy PK (2018) An overview on river morphology research: a bibliometric analysis. IAETSD J Adv Res Appl Sci 5(2):498–509 21. Sui X, Chen Y, Lu Z, Chen Y (2015) A bibliometric analysis of research paper related to the Mekong river. Scientometrics 105:419. https://doi.org/10.1007/s11192-015-1683-6 22. Van Griensven A, Ndomba P, Yalew S, Kilonzo F (2012) Critical review of SWAT applications in the upper Nile basin countries. Hydrol Earth SystSci 16:3371–3381. https:// doi.org/10.5194/hess-16-3371-2012 23. Ward JV, Tockner K, Arscott DB et al (2002) Riverine landscape diversity. Freshw Biol 47:517–540 24. Wohl E (2014) Time and the rivers flowing: fluvial geomorphology since 1960. Geomorphology 216:263–282. https://doi.org/10.1016/j.geomorph.2014.04.012

Chapter 26

Study of Gumti Wetland in Connection with Its Socio-Economic Status: A Step Towards Sustainable Management Practices Mihir Pal, Malabika Biswas Roy, and Pankaj Kumar Roy

Introduction Ecosystem of any wetland plays a very vital role for sustainable environment by water regulation and purification [11] and also by its response towards carbon storage [9]. But different anthropogenic activities and climatic changes like global warming, greenhouse gas emission, etc. are putting pressure in diverse manner on the health of wetland ecosystem and thereby threatening their sustainability. Due to such pressures there may be alterations in the species composition and obviously in the functioning of wetlands [7]. According to Gorham [9], wetlands become dry due to warming of climate and this may be the cause of alterations in water level; which in turn can be the crucial driver of ecosystem change. The wetlands may also be considered as good natural resources for obtaining several cost-effective and value-added products essential for human welfare. But for a long time, human beings are neglecting the importance of wetland ecosystems. Due to unplanned developments in wetland area ecological disturbance have become a matter of concern for a large number of wetlands worldwide. Expansion of urban area increases the demand of water supply and this demand has led to lowering of underground water level in various regions. Significant damages in wetland ecosystem have been observed due to anthropogenic activities like agriculture, road construction, industries, residential developments, resource extraction, etc. [13].

M. Pal (&) Department of Physics, Ramthakur College, Agartala, India e-mail: [email protected] M. B. Roy Department of Geography, Women’s College, Calcutta, West Bengal, India P. K. Roy School of Water Resources Engineering, Jadavpur University, Kolkata, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_26

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Restoration and conservation strategies of such wetland have now become ultimate necessity for any country. Eco-restoration of wetland ecosystem is very much helpful for providing food, shelter, species richness, and energy in sustainable manner. The structure of wetland communities has been founded to be greatly affected by flooding and drought. Flooding can be the cause of aeration stress and drought may lead to the water supply issue [16]. Temporal and spatial variability of water supply [17] in wetland area is a crucial cause of the inherent changeability of wetland communities. In some wetland area waterlogging issue is greatly affecting the existence of plants also. Water logging phenomena expels air from soil pores and without air roots of plants degenerates and crops dies. Moreover, this effect increases the acidity of soil and reduces the soil temperature. But some species are found in different wetland area to adapt themselves for increasing their tolerance to water logging and drought. The seven major wetlands of Tripura are the Rudra Sagar, Gomti Reservoir (Dumbur Lake), Sipahijala Reservoir, Trishna, Sattar Mia’s Hoar, Batapura Lake, and College Tilla Lake. Out of these wetlands of Tripura, Gumti reservoir is the largest artificial lake of the state and this is as a consequence of the construction of hydroelectric dam on 1974. There exist about 48 islands in the reservoir [6], and thereby has increased the natural beauty of the entire wetland area. Gumti river and its associated wetlands have ecological and economic significance to the community of basin area and as well as to the global community. This wetland area of Tripura is enriched of various types of valuable flora and fauna and all these are very much reliant to this wetland. But deforestation found in several places of this area causes especially the removal of the topmost layer of the basin area which may generate soil erosion and siltation problems. Due to such soil erosion sediment load in the river Gumti has been increasing day by day and due to lack of sufficient amount of water, the river becomes unable to eliminate the deposited sediment from its bed. Gradual rise of river bed has been identified as one of the major causes of flooding in the adjacent wetland area. In this paper estimation of the non-use value of public goods of Gumti basin area has been carried out by estimating Willingness to pay (WTP) for improved environment studies in the platform of the well-known survey method of CVM. A hypothetical market place may be created where the respondents get the opportunity to buy the goods which are not possible to trade in marketplace. WTP may be contingent upon such a hypothetical marketplace generating a newer dimension in this regard is called the contingent valuation method [4]. Restoration of any wetland area is one of the important non-tradable issues of traditional marketplace which may be handled by CVM. This method is very much helpful for cost benefit analysis of environmental goods. Contingent valuation surveys are entirely different from traditional surveys. In CVM, respondents have to decide which goods are preferable for them and for availing those how much amount they would pay. The main crucial factor of this survey method is that it always values goods about which respondents have little experiences. This method is very important for estimating both use and non-use values of environmental

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resources empirically [1]. A very fruitful and systematic study based on CVT has already been carried out on another important Ramsar wetland of Tripura called Rudrasagar wetland, to get the clear picture of WTP [3].

Materials and Methods Study Area Gumti wetland (also known as Dumboor) is a reservoir type wetland of Tripura having size 2328 ha, which draws water from Barak, Raima, and Sarma river basin. Effective area of this wetland is found to vary in monsoon and pre-monsoon period of state. The reservoir area is very much enriched of Macrophytes community and varieties species offishes. This charming water body is actually located in Amarpur Sub Division about 120 km away from Agartala. The Lake is situated in South Tripura at a Latitude of 23° 25′ 45″ and Longitude of 91° 49′ 20″. With a ceaseless spell of lush green vegetation all around stands glorious for her incredibly beguiling magnificence and 48 islands amidst the lake. Presence of seasonal birds,water sports offices etc are extra attractions of this reservoir. The Hydro Power Project close to the lake from where River Gumti starts is called Tirthamukh. 5 km support zone of the examined wetland has been displayed in Fig. 26.1. Post-monsoon and pre-monsoon picture of the wetland as captured as LIS III image has been displayed in Figs. 26.2 and 26.3, respectively.

Fig. 26.1 5 km Buffer area of Gumti Wetland

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Fig. 26.2 LISS-III image of the wetland area in post-monsoon season

Fig. 26.3 LISS-III image of the wetland in pre-monsoon season

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Degradation of Wetland and Growing Threatens From literature review this can be followed that anthropogenic exercises like horticulture, street development, industrial activities, private turns of events, asset extraction, and removal of wastes causes an incredible natural unsettling influence of any wetland biological system and creates its long-term damages [13]. According to Directory of wetlands (1990) reports, the Gomti reservoir in South Tripura district is the only man-made wetland of the state Tripura. During the field survey this has been identified that the main hindrances for degradation in conservation and wise use of Gumti wetland are—the population growth, land use patterns, lack of proper monetary valuation assignment of the goods and services provided by wetland area, lack of public awareness regarding valuation of wetland goods and services but the increasing demands of resources. Contamination of wetlands by horticultural overflows and home-grown and industrial wastes is another undermining issue for this wetland environment. In some cases, this has been identified that deforestation is being performed for collecting soil for brick field, sand for construction of houses, etc. Lifting of uncontrolled and excess amount of water from the river for different purposes has been identified as another threatening issue to sustain the regular supply of water in the river. All the activities responsible for wetland degradation have been summarized in Table 26.1.

Table 26.1 Causes of degradation in Gumti wetland Source of impact

Responsible activities

Natural processes

Erosion, Accretion and Storm events Agriculture Forestry Solid waste disposal Road construction Commercial, residential and Industrial development Flood protection Housing and development For agriculture For aquaculture Pesticides, herbicides and nutrients (domestic sewage, run-off, sediments from dredging and filling) Industrial processes Crop irrigation

Drainage Filling

Channelization Land use change Additions to land/crops Water used from aquifers, reservoirs and rivers

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Methodology Gumti wetland is one of the vital places of Tripura for the tourist attraction. As the ecological and Socio-economic importance of this wetland is very important, we have chosen this wetland to study the Socio-economic scenario of the households. In order to estimate the non-use value of wetland properties beneficial for human beings, contingent valuation method of survey is very useful and this is exactly done by measuring the parameter WTP. A total 100 households have been chosen for the primary data collection. Systematic random sampling method has been adopted for our current study for which concentration has been given to trace out the Socio-economic factors along with perception about the conservation of the stakeholders. In order to explore the Socio-economic profile of households of this wetland, investigation for socio-economic dimensions of the stakeholders of the Gumti wetland has been carried out. In addition, the connection between the financial factors with the view of stakeholders on the improved protection of the wetland has been investigated and the determinants of their Willingness to Pay for the improved preservation have been assessed at long last. The absolute financial estimation of the wetland framework has been assessed inside a structure of Contingent Valuation Method. The basic need to analyze scientific data related with the social science can be suitably fulfilled by using Statistical analysis software package SPSS (Statistical package for social studies). This software package has been used for data accuracy with the help of Anova and analysis of variance for generating the model equation of WTP using the primary data collected in Gumti wetland zone during field survey. In order to generate multiple regression model for any data set by using SPSS, first of all one has to verify these by eight prescribed assumptions to get confirmation whether the analysis is appropriate for getting valid result. The generated linear regression equation of WTP is very much effective for long-term wise use of Gumti wetland area.

Result and Discussion Socio-Economic Profile of Gumti Wetland Socio-economic elements of the stakeholders of the Gumti wetland have been examined by investigating the connection between these elements with the impression of stakeholders on the improved protection of the wetland and explored the determinants of their Willingness to Pay to assess the absolute monetary estimation of the wetland framework inside a structure of Contingent Valuation Method. On the off chance that we focus on the dissemination of Households dependent on the family size, at that point it reveals that lion’s share of families lies in the family size shifts somewhere in the range of 3 and 6. Only 5% of the sample

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families are in the customary gathering family framework, having in excess of six individuals and the quantity of earning members in such family lies below five. For estimating willingness to pay for the improvement of Gumti wetland, a study has also been carried out considering the literacy level of the head of the family. The corresponding data reveals that maximum percent of households are headed by a person having educational level up to primary. Occupation wise classification of the household’s profile explores the detailed scenario of the occupation of the head of the family. Highest percent of the households are monitored by a person who is farmer in profession. 15% of the households are depending on manual labor whereas 10% of the households are depending on fishing activity in Gumti wetland area. As far as income-wise distribution is considered, this has been observed that the levels of annual income among the sample households vary between INR 36,500 and INR 600,000. It has been observed that 15% of the households are having annual income range below 50,000. The average income of the family unit is INR 140,740 with a standard deviation of INR 156,580.20. Only 10% of households are having annual income of INR 400,000 and above.

Stakeholders Perception on the Conservation of Gumti Wetland The most widespread threats as observed throughout the entire Gumti wetland area during field survey are the drainage for agriculture, settlements and urbanization, pollution, hunting, etc. Attentiveness of the example families on protection of the Gumti wetland has been researched for exploring the stakeholder’s perception. As an outcome of verbal discussion with the sample households, their awareness about the conservation issue of wetland area has been assessed. 5% of the sample households suggest that the scenic beauty is the important feature, 24% believe that paddy field preservation is the important issue of the wetland, 9% like to give stress in water preservation issue, whereas 62% households considered all such features of the wetland. In the next phase when investigation is done on interest wise classification of the households, it reveals that 76% of households show high interest in wetland improvement through conservation and management of the wetland, 22% show moderate interest while only 2% show no interest for improvement of conservation of the wetland zone. The distance of households from the center of the wetland is another crucial factor for their interests in conservation issue. While study has been carried out for getting the profile picture of distance wise classification of the perception on wetland improvement, it has been found that there exist high interests among the sample stakeholders residing closer to the wetland for betterment of the wetland and it reduces with their increased distance from the wetland. So this issue has an important role for estimating the quantity Willingness to Pay (WTP) for improvement of wetland. Education is another important element which helps

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people to realize the importance of various positive fronts in our environment. It is normal that there must be immediate and positive relationship between the education and their inclinations for the wetland improvement. An immediate and positive relationship among education and interest for preservation of wetland has been identified in the investigation. By and large, it is seen that the partners having educational level over the essential stage have demonstrated revenue in preservation issue. Level of income of the sample stakeholders must be positively associated with the WTP and this prediction gets support while we follow the obtained profile picture of income-wise classification of stakeholders on wetland development.

The Willingness to Pay: Composition and Distribution The Willingness to Pay (WTP) is characterized by the sum that a consumer wants to pay so as to engross a specific unit of a product. It is connected with the utility of that specific ware and is broadly utilized in financial matters for estimating the purchaser overflow. In the current study, it is utilized to distinguish the measure of cash that the metropolitan partners want to pay to preserve Gumti wetland and consequently to appraise the monetary estimation of this wetland. It is seen from the investigation that 90% of test family units communicated their ability to pay for the improvement of the wetland. As indicated by test information, 10% of test families are not ready to pay for improvement of this wetland. 40% of family units fall under the class where most extreme WTP shifts from INR 200 to INR 300. From the investigation of anticipated level of relationship among WTP and pay it has been distinguished that there exists a positive relationship between pay of a sample family and WTP.

Model Development for WTP Estimation of WTP through CVM is very essential to gather information of the demand side on tariff level of services with consideration to its limitations. Through careful design and execution, various biasing issues of CVM can be minimized [20]. WTP estimated through CVM may be very much helpful for getting basic information for cost benefit analysis and tariff setting [20]. A linear regression model has been developed in this regard. For model development seven parameters have been selected as factors to include social impact on willingness to pay. The WTP might be communicated as the capacity of the socio-economic ascribes of the sample families and distance from wetland and may be represented by the function as,

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WTP ¼ f ðAGE; EDU; FAM; INC; LND; ENV; DISÞ The linear additive form of the same is WTP ¼ a þ b1 AGE þ b2 EDU þ b3 FAM þ b4 INC þ b5 DIS þ b6 LND þ b7 ENV; where, a, b1, b2, b3, b4, b5, b6 and b7 are constants. The illustrative factors alongside their documentations and expected indications of causal connections are accounted for in Table 26.2. The descriptive statistics exploring the mean values and standard deviation of the variables used in the model has been shown in Tables 26.3 and 26.4. The higher standard deviation of WTP is indicating variation in the distribution of variable across households where as the 2nd higher standard deviation is for AGE. The value of R obtained in this model is 0.920, which indicates a strong relationship between dependent and independent variables of the model. R2 value explores the total variance explained by all the predictor variables associated with the regression model. So the value of R2 explores the percentage of variations (allowed limit is from 0 to 1) explained by the model relationship among the selected variables which is found as 0.847 which is good enough for this developed model.

Restoration and Conservative Strategy of Wetland Policy makers must have to adopt comparatively better wetland resource management to restore and conserve this valuable wetland. The generated public fund (i.e. peoples’ WTP) can be utilized for implementation of simple, manageable, and cost-effective wetland restoration policy so that it will be helpful for resilience of this wetland area also. Moreover, the findings of WTP model may motivate the

Table 26.2 Illustrative variables in the model Sl. No.

Variable

Definition

Exp. Sign

1 2 3 4 5 6

AGE EDU FAM INC DIS ENV

+ve +ve +ve +ve −ve +ve

7

LND

The age of the decision maker of the household Education level of the decision maker Family size of the household Logarithm of annual income of the household Distance of the households from Gumti Wetland Whether the respondent is a part of any Environmental organization (1-Yes, 0-No) Total landholding size of the household

+ve

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Table 26.3 Descriptive statistics for Gumti wetland Variables

Mean

Std. Deviation

WTP AGE EDU FAM INC DIS ENV LND

96.0000 49.3000 3.1000 4.8000 3.6000 6.1000 0.6000 3.9000

52.32378 13.09835 1.66333 1.31656 1.17379 3.07137 0.51640 1.66333

Table 26.4 Linear regression results Model

R

R square

Adjusted R square

Std. error of the estimate

1 ANOVAb Model

0.920a

0.847

0.310

43.47754

1

Sum of squares

Regression 20859.407 Residual 3780.593 Total 24640.000 Coefficientsd Model Unstandardized coefficients B Std. Error

df

Mean square

F

Sig.

7 2 9

2979.915 1890.297

1.576

0.442c

Standardized coefficients

t

Sig.

−0.516 −1.085 0.961 1.638 1.566 0.089 −0.758 −1.167

0.657 0.391 0.438 0.243 0.258 0.937 0.527 0.363

Beta

(Constant) −76.879 149.046 AGE −1.697 1.564 −0.425 EDU 17.326 18.036 0.551 FAM 37.993 23.202 0.956 INC 46.084 29.420 1.034 DIS 0.781 8.811 0.046 ENV −33.868 44.663 −0.334 LND −33.300 28.526 −1.059 a Predictors: (Constant), LND, EDU, ENV, DIS, AGE, FAM, INC b Dependent Variable: WTP c Predictors: (Constant), LND, EDU, ENV, DIS, AGE, FAM, INC d Dependent Variable: WTP 1

policy makers to turn the adopted wetland management policy in more holistic manner. Use of remote sensing and geographic information system (GIS) will be very much useful for restoration and management of this wetland. Water resource assessment, water quality mapping and monitoring, flood management, hydrologic modelling, reservoir capacity surveys, environmental monitoring—all these will be fruitful if remote sensing and GIS facilities are implemented in this regard. Remote

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sensing data along with aerial photo-interpretation may be very useful even in the planning of groundwater reconnaissance. Such approach can also discover ground water sources by recognition of geomorphologic units. Projects related to restoration and conservation of wetland may also be monitored by temporal data of satellite. Flood-risk zone and flood-inundated regions of wetland may be easily delineated by using satellite data source. Even the inflow of sewage can be monitored on regular basis from the temporal and spatial resolution data information of satellite. Such types of data information may also be useful for monitoring the point of discharge and source of pollution of wetland zone. Minimization of soil erosion should be ensured with top priority in the followed restoration and conservative strategy of wetland. Afforestation, reduction of overgrazing, reduction of land use change, proper crop management—all these will be very helpful for preventing the soil erosion [8]. Eutrophication may be reduced by increasing soil fertility and resisting soil erosion through tree plantation and crops [21]. In order to carry out the wastewater treatment, sources of contaminants of this wetland should be removed for improving the water quality. Plant-based phytoremediation technology may be adopted for environmental cleanup by detoxifying different contaminants of wetland area in a very cost-effective manner [2, 12, 14, 18]. Climate change issue also has a significant negative impact on productivity of crops in wetland area [10, 19]. However this climate change issue may be compensated by following carbon sequestration process to capture excess CO2 of atmosphere, afforestation, and agroforestry [5]. Finally for overall success in the implementation of conservation policy to restrict environmental degradation of the wetland area, local authorities, communities, and policymakers—all have to involve more actively.

Conclusion Gumti wetland area has wide spectrum of resources and services towards human beings and also this has got importance for its biological, environmental, and fiscal values. This wetland area is very much enriching with different types of food, livestock fodder, fish, fuel wood, medicinal plants, building materials, honey, beeswax, etc. Entire wetland area has great role for maintaining the ground water levels even in low rainfall periods. Recreational activities like fishing, boating, etc. may be encouraged for boosting up the economic status of the area. Agricultural runoffs along with domestic and industrial wastes spreading are the main source of pollution of this wetland area. Deforestation adjacent to the river basin area has been identified as another threatening issue of the wetland. Increased numbers of localization result reduction in green plants and thereby cause deforestation adjacent to the river basin area. More plantations are required to increase the average rainfall which can effect directly in the generation of the new plants. The unrestricted dumping of toxic wastes from industries and sewage found in certain places

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of wetland area can induce the deterioration of physiochemical properties of wetlands. Such type of deterioration can welcome the destruction of aquatic ecosystem of this wetland. The economic value of this wetland is mainly due to maintenance of its water quality, Preserve River, flood control, open space, fish, recreation, forested wetland, and nonuser value. In fact, the valuation of natural resources of any wetland is controversial to some extent as the importance of non-use values may be reflected by public opinion and policy decision. The result of modelled output for estimating WTP suggests that the mean WTP per household in the sample obtained is INR 96.00 with a standard deviation of INR 52.32 and this result also indicates that the Gumti wetland is an important natural resource. Results of such empirical study will be helpful while decision makers set up the well-designed conservation policies for this wetland. In fact, in true sense all environmental resources of this wetland area should be conserved regardless of the costs of strategy. If ecological functioning and resilience of the wetland area become less resilient and stable, then the future generation will experience greater ecological scarcity and obviously then a significant cost burden will be required for maintaining ecosystem function and service provision. Acknowledgements The authors would like to thank the local authorities of Gumti wetland area and other stakeholders for their sincere cooperation and assistance in this research. The authors would also like to thank Civil Engineering Department, NIT Agartala for their various supports and assistance during field survey in estimating the economic value of Gumti wetland. Constant encouragement and guidance from Water Resource Department, Jadavpur University is also acknowledgeable.

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9. Gorham E (1991) Northern peat lands: role in the carbon cycle and probable responses to climatic warming. Ecol Appl 1:182–195 10. Lobell DB, Field CB (2007) Global scale climate–crop yield relationships and the impacts of recent warming. Environ Res Lett 2(1):014002 11. Millennium Ecosystem Assessment (2005) World Resources Institute, Washington, DC. ISBN 1-56973-597-2. 12. Paz-Alberto AM, Sigua GC (2013) Phytoremediation: a green technology to remove environmental pollutants. Am J Clim Change 2:71 13. Prasad SN, Ramachandra TV, Ahalya N, Sengupta T, Kumar A, Tiwari AK, Vijayan L (2002) Conservation of wetlands of India-a review. Trop Ecol 43(1):173–186 14. Rezania S, Ponraj M, Talaiekhozani A, Mohamad SE, Din MFM, Taib SM, Sabbagh F, Sairan FM (2015) Perspectives of phytoremediation using water hyacinth for removal of heavy metals, organic and inorganic pollutants in wastewater. J Environ Manag 163:125–133 15. Russi D, ten Brink P, Farmer A, Badura T, Coates D, Förster J, Kumar R, Davidson N (2013) The economics of ecosystems and biodiversity for water and wetlands. IEEP, London, p 78 16. Silvertown J, Dodd ME, Gowing DJG, Mountford JO (1999) Hydrologically-defined niches reveal a basis for species richness in plant communities. Nature 400:61–63 17. Tallis JH (1983) Changes in wetland communities. In: Gore AJP (ed) Ecosystems of the world 4A, mires: swamp, bog, fen and moor. Elsevier, Amsterdam, pp 311–347 18. Tangahu BV, Abdullah S, Rozaimah S, Basri H, Idris M, Anuar N, Mukhlisin M (2011) A review on heavy metals (As. Int J Chem Eng, Pb and Hg) uptake by plants through phytoremediation 19. Thomason MK, Storz G (2010) Bacterial antisense RNAs: how many are there, and what are they doing? Ann Rev Genetics 44:167–188 20. Yasuo Fujita Y, Fujii A, Furukawa S, Ogawa T (2005) Estimation of willingness-to-pay (WTP) for water and sanitation services through contingent valuation method (CVM)—a case study in Iquitos City. The Republic of Peru. JBICI Review No. 10. pp 59–87 21. Zazai KG, Wani OA, Ali A, Devi M (2018) Phytoremediation and carbon sequestration potential of agroforestry systems: a review. Int J Curr Microbiol App Sci 7:2447–2457

Chapter 27

A Stochastic Approach to Evaluate Drinking Water Availability Status—A Case Study on Patharghata GP, Rajarhat CD Block, North 24 Paraganas, West Bengal, India Ratnadeep Ray, Panchali Majumdar, and Madhusree Palit

Introduction Water is an important part of the social and economic growth of every country. On earth, water supplies are distributed unevenly. More than two billion people live in highly water-stressed regions due to this uneven distribution of water sources. Water has become a valuable asset for most people [20]. This is especially true in Central and Western Asia and North Africa’s populated arid regions, with a water supply of less than 1000 m3/capita/year [21]. The rising population, the drastic rise in demand for water and the shortage of water are all facts of the modern era. Water demand, which calls for wise management of scarce water supplies, is highly likely to increase in the twenty-first century. In a developing nation like India with a high rate of population growth, there is a considerable imbalance between demand and supply of water. An agro-based economy with broad socio-economic fluctuations and evolving state-wide rainfall patterns presents a greater challenge to water supplies. Therefore, there is a need to holistically analyze the water situation across the region. In Indian cities, water usage is much lower than the standards set by the Indian Standards Bureau. Shortage of fresh water, degrading water quality, inability to access safe drinking

R. Ray JIS University, Kolkata, India e-mail: [email protected] P. Majumdar (&) East Calcutta Girls’ College, Lake Town, Kolkata, India e-mail: [email protected] M. Palit Sarojini Naidu College for Women, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_27

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water, instability of management of water resources and the continued absence of political and decision making awareness poses major impediments to countries like India. In order to holistically identify the water situation in five main areas: Income, Access, Power, Usage and Environment [22], the Water Poverty Index (WPI) was created to tackle and fix water problems and inequalities. It has been used on a variety of scales: national [13, 2], river basin [15, 18, 7], regional/district/subdistrict [15] and local [23, 4, 19]. The WPI expanded the concept of water management to include aspects related to Capacity management, Access equity and Environmental conditions, while previous water accounting instruments concentrated primarily on availability and efficiency [22]. The index therefore is a robust measure that attempts to address various pertinent issues attached with water that is often analyzed differently like irrigation in the field of agriculture, health conditions and drinking water. Advantage of using WPI as a water stress index is that in a single numerical representation, it condenses multiple indicators of affecting components varying from physical to socio-economic influences [12]. In this respect it is pertinent that people do not have enough water to meet their basic needs, people may be ‘water poor’ because they can not avail water. In order to get it, they may have to travel a long way or, for various reasons, supplies may be restricted even if they have access to water nearby. Along with that a strong correlation exists between ‘water shortage’ and ‘income poverty’ [22]. In spite of water, people are often reffered as ‘water poor’ because they are of low income and hence do not possess the capability to afford. Even if supply of water is adequate and secure, very low level of income make them unable to compensate for this quality of water and hence force them to resort to not recommended origins of water. The underlying conceptual framework of the index therefore needs to include water availability, water efficiency, efficiency maintenance capability, water use and environmental factors that Affect the quality of water and the ecosystem that water sustains. The interdisciplinary approach to estimating the indices of water scarcity was developed by Sullivan [22]. She supports the view that the combination of physical water stress and scarcity measures with socio-economic variables reflects the vulnerability of poor households. In seeking potable water for the poor, Sullivan reflects on the lack of time and resources. By connecting the physical and social sciences, she developed an index of water scarcity. Nowadays, the WPI approach is widely used to study water scarcity. Cook et al. used the Bayesian Networks to calculate values for the above-mentioned five WPI components that connected water and poverty in the Volta Basin of Ghana. A comparative study was performed by Lawrence et al. [14], showing the WPI from different countries around the globe. Castelazo et al. [1] introduced flood risk vulnerability as a variable into the Capability section for Juarez Municipality in Mexico as part of the Disaster Management sub-component. For certain areas of South Africa, Van der Vyver and Dawid [27] estimated WPI and produced Water

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Scarcity Maps. In the original WPI calculation, Garriga and Foguet [6] incorporated a pressure-state-response feature to create a comprehensive method for policy-makers. Given that many datasets are required by the Sullivan et al. [25] method to calculate WPI sometimes availability of all the datasets may be difficult. A simplified method for calculating WPI was developed by using three parameters: adjusted availability of water (percentage), population having access to clean water and sanitation (percentage) and time and effort taken to collect household water. Also a time-analysis approach by Olotu et al. assumes that water scarcity is directly related to the distance of water source from the households. However, in a critical sense in regard to all these procedural postulations, in this analysis, some methodological alterations were adopted to determine the parameters for calculating WPI in local scale. The fundamental objective of the study is to demonstrate the water availability at community level in terms of water poverty index and socio-economic quality of life as an indicator of income poverty within the Rajarhat CD Block, North 24 Paraganas Patharghata GP.

About the Study Area Rajarhat CD Block is one of the most important administrative units of North 24 Paraganas. It is such a block having the affinity of Bidhannagar Municipal Corporation and New Town, which is the most modern planned and smart urban growth of the greater Kolkata. There are six GPs in Rajarhat Block out of which Patharghata GP is the largest one having the area of about 2007.6 ha and the total population of about 24703 (Census 2011). This GP is composed of nine villages like Akandakeshari, Baligari, Chakpachuria, Chhapna, Ganragari, Kadampukur, Kalikapur, Kashinathpur and Patharghata. Out of these villages Akandakeshari, Baligari, Chakpachuria, Chhapna, Kadampukur and Patharghata are included under the area of New Town planning region. Therefore, it very pertinent that this GP is being facilitated by the urban infrastructural development of New Town. But it is a fact that this region is quite backlogged in regard to the drinking water infrastructural development. Still, no such piped water supply has been developed yet. Community has to depend upon the tube well or the other drinking water sources allotted by different government schemes. Moreover, the chemical quality of the water is not so satisfactory. Besides the interaction between socio-economic quality of the community and the access to the safe water is pertinent over here. So in this present study Patharghata GP has been selected as study area to analyze availability status of drinking water in semi quantitative manner using the independent variables like Access, Environment, Capacity, Use and Resource (Fig. 27.1).

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Fig. 27.1 Location map of the study area

Materials and Methodology The Water Poverty Index (WPI) is an interactive tool applicable primarily at national, local and district level. Setting goals for action and tracking progress towards objectives are also beneficial. An interdisciplinary approach to measuring progress in the water sector needs to be adopted, including both qualitative and quantitative evaluations. The indices of water scarcity are built from a set of variables to capture the nature of what is being calculated. This can be achieved using data on a national scale (a top-down approach) or at a local level using values and parameters calculated locally (a bottom-up approach). The WPI may Use the composite index approach to contain various elements such as Resource, Access, Power, Usage and Climate. The concept of water poverty is assumed to be a function of physical availability of water resources (R), extent of access to water (A), Capacity to manage water (C), ways in which water is used for different purposes (U) and the need to allocate water for ecological services (E). To calculate the WPI those five components are combined to obtain a dynamic and holistic perspective. The final value of WPI has been calculated for a location as described by Lawrence et al. [14]:

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w1  R þ w2  A þ w3  C þ w4  U þ w5  E P ðw1 ; w2 ; w3 ; w4 ; w5 Þ

The weights (w1…w5) applied to each of the five components (R, A, C, U and E) are constrained to be non-negative and sum to unity. All parameters are standardized to fall in the range 0 to 1, where value 0 is assigned to the poorest level (i.e. highest degree of water poverty), and 1 to optimum conditions. ‘Resource’ component measures availability of water resources. In this study to calculate the resource values, location of working tube well has been collected from the WBPHED website and per capita tube well availability as well as mean spacing of those tube wells have been computed. Finally, the resource index has been calculated as: Numbers of tube well  mean spacing between tube well per capita tube well Here mean spacing has been calculated as, rffiffiffiffi A 1:0746  V N where, A is the area of village and N is the number of tube wells. Relative to access to safe water is one of the parameters of ‘Access’ indicator. From Census of India, data (2011) regarding distance to safe water has been used to compute access index. The response parameter has also been structures with respect to the distances between tube well clusters and village centres. Besides in this case Voronoi approach has also been deployed over the tube well locations to conceptualize the utility status of the water sources per village. The ‘Capacity’ indicator has been calculated as the function of economic poverty. Economic poverty is such a parameter which in turn affects health security, literacy status as well as access to the treated sources of water. So here quality of life has been calculated as an important parameter of poverty to establish the Capacity indicator. The ‘Use’ indicator has been calculated on the basis BIS system for the domestic water use and peoples’ perception. Water-use efficiency has been evaluated as a response parameter on a sustained basis. Lastly, the ‘Environment’ indicator has been calculated on the basis of water chemical quality data mainly of arsenic, pH and iron collected from WBPHED. A zonation of safe water availability has been prepared on the basis of permissible chemical range declared by BIS. Where a permissibility limit has been breached, it has been considered unsafe and vice versa.

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Results and Discussion In this present study the level of water availability has been estimated for each village of Patharghata GP using WPI. The resulted scores depict Kalikapur and Ganragari as the most water scared zone having the WPI values of 0.1204 and 0.0897, respectively, followed by Baligari having the WPI value of 0.20801 (Table 27.1). The evaluated cross co-relation between the components has shown a significant relation between WPI-access and WPI-environment. Besides the resource component has also a significant relation with WPI (Table 27.2). So, such a relationship will suggest that these problems should first be tackled in order to improve water poverty in this area. Following the principal of Sullivan et al. [26], in this present study WPI (Fig. 27.7) has been calculated as the function of five components like Capacity, Access, Resource, Environment and Use. In this study access component has been conceptualized as the shortest distance from the village centre to the drinking water source clusters irrespective of the time of travelling for the water sources or the carriage specification. Besides this, that component is also been established with the help of Voronoi generation by which potential service areas of the drinking water sources can be visualized. Higher the polygon area higher will be the service area Table 27.1 Water poverty index of villages under Patharghata GP Villages

Capacity

Access

Use

Resource

Environment

WPI

Akandakeshari Baligari Chakpachuria Chhapna Ganragari Kadampukur Kalikapur Kasinathpur Patharghata

1 0.14307 0.99012 0.16707 0.44774 0.65090 0 0.67434 0.23449

0.13454 0.11410 0.39935 0.18131 0.08136 0 0.11106 0.00031 1

0.28571 0.58571 0.85714 0 0.28571 0.71428 1 0.14285 0.28571

0.24404 0 0.32028 0.14833 0.28804 0.21646 0.26243 0.27073 1

0.53338 0.29516 0.07528 1 0 0.29669 0.06047 0.28619 0.91042

0.42830 0.20801 0.34857 0.66271 0.08976 0.25480 0.12042 0.25165 0.90874

Table 27.2 WPI relations with component Capacity Access Use Resource Environment WPI

Capacity

Access

Use

Resource

Environment

WPI

1.000 −0.129 −0.015 −0.053 0.221 −0.080

1.000 −0.077 0.893 -0.502 0.795

1.000 −0.135 0.640 −0.472

1.000 −0.390 0.679

1.000 −0.906

1.000

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and higher will be the access potentiality. But in his cases the distance factor may not be shortest. As per the shortest distance Patharghata village has been registered as having highest Access Index followed by Chackpchuria, Chhapna, Akhendaswari, Baligari, Kalikapur, Ganragari, Kashinathpur and Kadampukur (Table 27.1). It is very pertinent that as per Voronoi diagram Patharghata village has the access for drinking water to the surrounding villages like Chackpchuria, Baligari, Ganragari and Chhapna; Chackpchuria has the access for drinking water to Kadampukur, Baligari and Patharghata; Chhapna has the access for drinking water to Akhendaswari and Patharghata; Akhendaswari has the access for drinking water to Chhapna and Patharghata; Baligari has the access for drinking water to Chackpchuria and Patharghata; Kalikapur has the access for drinking water to Patharghata, Kashinathpur and Ganragari; Ganragari has the access for drinking water to Patharghata and Kashinathpur; Kashinathpur has the access for drinking water to Ganragari and Kalikapur; Kadampukur has the access for drinking water to Chackpchuria (Fig. 27.2 Voronoi). So it is so distinct that Patharghata village is the most accessible and Kadampukur has least accessibility for the drinking water.

Fig. 27.2 Service area of tube wells

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Fig. 27.3 Per capita resource index

For the resource component only drinking water point sources has been considered as piped water supply is not so developed there. In this study by this component per capita water source availability with respect to its mean spacing has been demonstrated (Fig. 27.3 Facility). In this case also, Patharghata village has been presented as resource rich and Baligari village as resource poor (Table 27.1). These kinds of resource distribution nature are the consequence of population density variation. Baligari has the highest population density than Patharghata but Patharghata has more water source point than Baligari (Fig. 27.4). Regarding the Use component the consuming village is Kalikapur and the lowest is Chhapna (Table 27.1). This kind of consumption variation is probably due to the better accessibility and well distribution of Resource. This component is seen to be very much comparable to the Environment component. There is an inverse relation among Environment component and Use component (Fig. 27.5). Ganragari village has registered the safe environmental status whereas Chhapna is unsafe (Table 27.1).

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Fig. 27.4 Population density (census 2011)

Finally, Capacity is one of the most important components to calculate WPI. This component indirectly represents the ease of availing safe and sufficient water in terms of poverty. Sullivan [24] hypothesized high economic poverty corresponds to the high water poverty. In this study economic poverty has been parameterized by Quality of living index (QLI) (Fig. 27.6 qli). As per QLI Akhendaswari, Chackpchuria, Kadampukur and Kashinathpur have the high QLI whereas Patharghata, Chhapna, Kalikapur and Baligari have the low QLI. But villages having low QLI have shown low WPI and vice versa. In this respect the hypothesis of Sullivan can be alternated. In this respect a model-based analysis has been prompted to calculate the most influential derivative of WPI (Tables 27.3 and 27.4, Fig. 27.7).

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Fig. 27.5 Water quality index

From Tables 27.3 to 27.4, it has been seen that the components are closer to each other with a significant relationship. However as per the Beta Coefficient and P-value it has been seen that Environment component is the most influential over WPI followed by access than the other. On the other hand, Capacity has a lesser impact over WPI and Resource and Use components have almost no impact. Therefore, WPI can be derived as, WPI ¼0:000303 þ ð0:1011  CapacityÞ þ ð0:3551  AccessÞ þ ð0:331  useÞ þ ð0:0296  resourceÞ þ ð0:5488  environmentÞ

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Fig. 27.6 Quality of life index

Table 27.3 Regression coefficients Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations

0.996871839 0.993753464 0.983342569 0.034563909 9

Table 27.4 Coefficients of WPI components

Intercept Capacity Access Use Resource Environment

Coefficients

Standard error

t Stat

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

0.0003 0.1011 0.3551 0.0331 0.0296 0.5488

0.0539 0.0346 0.1060 0.0546 0.1063 0.0601

0.0056 2.9191 3.3493 0.6065 0.2785 9.1287

−0.1712 −0.0091 0.0177 −0.1406 −0.3086 0.3575

0.1718 0.2114 0.6925 0.2067 0.3678 0.7402

−0.1712 −0.0091 0.0177 −0.1406 −0.3086 0.3575

0.1718 0.2114 0.6925 0.2067 0.3678 0.7402

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Fig. 27.7 Water poverty index

Conclusions One of the important factors for regional growth and development is the sustainable supply of water. This is also an important approach to regional viability. In this relation, the WPI has proved to be an appropriate tool for making water policy more efficient. Safe water supply is also one of the important factors in terms of community growth, such as economic prosperity. The WPI was used in this research to determine the status of water scarcity in Rajarhat CD Block’s Patharghata GP. The WPI has proven to be an accurate indicator of the region’s water shortage, and the findings may be used for further policy-making and prioritization. On the basis of the study, the area’s total WPI was evaluated to be 3.3, suggesting water scarcity. Among the nine villages studied, the optimum WPI score was obtained by Patharghata village (0.9087) along with the WPI components excluding power. For Patharghata village (0.0082), the lowest ability value has been recorded. For the studied villages, there is a conventional linear correspondence between WPI and the components like Access, Use, Environment and Resource. Higher the component scores, higher is the optimality in WPI. But Capacity component has an exceptional correspondence with WPI. Villages having low Capacity scores are

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seen to be experiencing optimum WPI scores. In this respect proposition of Sullivan [24] should be alternated and should be modified for the local scale. However, optimal water use, water related infrastructural development, deployment of treated water source and overall socio-economic development can alleviate water scarcity as well as water poverty.

References 1. Castelazo (2007) Incorporating flood vulnerability to the water poverty index in the juarez municipality. UCOWR, Paper 14 Southern Illinios University, Mexico 2. Cho DL, Ogwang T, Opio C (2010) Simplifying the water poverty index. Soc Indic Res 95:257–267. https://doi.org/10.1007/s11205-009-9501-2 3. Cohen A, Sullivan CA (2010) Water and poverty in rural China: developing an instrument to assess the multiple dimensions of water and poverty 4. Cullis J, O’Regan D (2004) Targeting the water-poor through water poverty mapping. Water Policy 6:397–411 5. Ecol Econ (2015) 69:999-1009 Congress UPC Sustainable 6. Garriga RG, Foguet AP (2010) Improved method to calculate a water poverty index at local scale. J Environ Eng 136:1287–1298 7. Gine Garriga (2008) The enhanced water poverty index: targetting the water poor at different scales. A: WISA Biennial Conference. WISA 2010 Biennial Conference Durban 8. GineGarriga R, Perez-Foguet A (2005) The water poverty index: assessing water scarcity at different scales, II 9. Harrington L, Cook SE, Lemoalle J, Kirby M, Taylor C, Woolley J (2009) Cross-basin comparisons of water use, water scarcity and their impact on livelihoods: present and future. Water Int 34(1):144–154 10. Van den Hove S (2006) Between consensus and compromise: acknowledging the negotiation dimension in participatory approaches. Land Use Policy 23:10–17 11. Kim JH, Keane TD, Bernard EA (2015) Fragmented local governance and water resource management outcomes. J Environ Manag 150:378–386 12. Koirala S, Fang Y, Dahal NM, Zhang C, Pandey B, Shrestha S (2020) Application of water poverty index (WPI) in spatial analysis of water stress in Koshi River Basin, Nepal. Sustainability 12(727) 13. Komnenic V, Ahlers R, van der Zaag P (2009) Assessing the usefulness of the water poverty index by applying it to a special case: can one be water poor with high levels of access? Phys Chem Earth 34:219–224 14. Lawrence PM, Jeremy Meigh, Caroline Sullivan (2003) The water poverty index: An international comparison. Keele Economic Research Papers from Centre for Ecology and Hydrology (CEH), Wallingford USA 15. Manandhar S, Pandey VP, Kazama F (2012) Application of water poverty index (WPI) in nepalese context: a case study of Kali Gandaki River Basin (KGRB). Water Resour Manag 24:89–107 16. Molle F, Mollinga P (2003) Water poverty indicators: conceptual problems and policy issues. Water Policy 5:529–544 17. Olotu Y, Akinro AO, MogajiKehinde O, Ologunagba B (2009) Evaluation of water poverty index in Ondo State, Nigeria. ARPN J Eng Appl Sci 4(10) 18. Pandey V, Manandhar S, Kazama F (2012) Water poverty situation of medium-sized river basins in Nepal. Water Resour Manag 26(9):2475–2489 19. Perez-Foguet A, Gine Garriga R (2011) Analyzing water poverty in basins. Water Resour Manag 25(14):3595–3612

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20. Pisani E (1995) The management of water as an essential and rare commodity. Water Int 20:29–31 21. Rijsberman FR (2006) Water scarcity: fact or fiction? Agric Water Manag 80:5–22 22. Sullivan C (2002) Calculating the water poverty index. World Dev 30(7):1195–1210 23. Sullivan CA, Meigh JR, Giacamello AM, Fediw T, Lawrence P, Samad M, Mlote S, Hutton C, Allan JA, Schulze RE, Dlamini D, Cosgrove W, DelliPriscoli J, Gleick P, Smout I, Cobbing J, Calow R, Hunt D, Hussain A, Acreman MC, King J, Malomo S, Tate EL, O’Regan DO, Milner S, Steyl I (2003) The water poverty index: development and application at the community scale. Nat Resourc Forum 27:189–199 24. Sullivan C (2000) The potential for calculating a meaningful water poverty index. Water Int 25. Sullivan CA, Meigh JR, Simon S, Lawrence P, Calow RC, Mckenzie AA, Acreman MC, Moore RV (2000) The development of a water poverty index: a feasibility study; centre for ecology and hydrology/department for international development: Wallingford, UK 26. United Nations World Water Development Report (2003) Water for people, water for life. UNESCO and Berghahn Books, Barcelona 27. Vyver CV, Dawid B Jordaan (2011) Water poverty mapping and its role in assisting water management. Communications of the IBIMA, 13p

Part VII

Application of Remote Sensing and GIS in Water Sector

Chapter 28

A SWOC Analysis and Smart Land Use Modelling for Chandipur-Erashal Census Town Cum Growth Center Due to Its Sustainable Journey Stimulating Regional Development Harekrishna Manna, Rabin Das, and Jibanananda Samanta

Introduction In different developing countries like India urbanization is an important phenomenon. According to the census 2011, the rate of urbanization is 31.16% in India and it increases very rapidly [1]. The different functional activities are key factors for developing any urban area. The urban center is a mother/nodal point in any urban area, from where the development processes are spread out and this development process is known as urbanization. There are several theories of regional development such as Spatial Diffusion Theory [2], Growth Pole Theory [3], Cumulative Causation Model [4], Economic Development Theory [5], Stage of Economic Growth Model [6], and Core-Periphery Model [7] which directly or indirectly explain the relationship between urbanization and development and, thereby, the processes operating in creating regional disparities [8]. In the analytical sense of the life cycle of any urban area, we found mainly four stages like initial stage, acceleration stage, deceleration stage, and terminal stage. Our study area belongs to the second (acceleration) stage naturally [9]. Our study area, Chandipur-Erashal urban cum growth center is reflected as one proto-urban region or center having with its childhood structure and function whereas Erashal has been declared as one of the census towns of Purba Medinipur district in 2011. Our nation, India is a faster country in the technocentric world from the viewpoint of population growth and urbanization. From this perspective, the H. Manna (&)
R. Das
J. Samanta Department of Geography (UG and PG), Bajkul Milani Mahavidyalaya, Purba Medinipur, West Bengal, India e-mail: [email protected] R. Das e-mail: [email protected] J. Samanta e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_28

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development of enormous growth points having urban character is the typical feature of this advanced developing nation. Bengal is not backward from this event also. The explosive population of West Bengal is always finding out the proper shelter and job opportunities in terms of settlement and occupation. Consequently, the transformation of rural landscape into a rurban or urban another has been the way of settling and functioning of a huge population over time. We have chosen such a type of place featured by the rural-urban linkage of a rurban entity. Not only that, now Chandipur-Erashal urban area has existed as the urban foci or development engine to peripheral advancement in terms of the growth of the buffer and hinterland. The behavioral attitude of the selected urban area is just like the growth center to regional development since it influences most of the socio-economic and service sectors facilitated by not the only periphery, but also most of the neighborhood nodes and urban centers as well as growth points. In this perspective, our fieldwork tries to investigate the nature and status of this urban entity cum growth center considering different quantitative and qualitative scales and theories regarding urban growth and regional planning and development. Here lies the essence of this study.

Location of the Study Area Our study area, Chandipur-Erashal Urban region in terms of growth center is reflected as one of the rural-urban landscapes in Purba Medinipur district. Administratively, it has been recognized as one of the census towns of this district in 2019. Geographically, this study area is situated within the extension of 22004’55.51’’N to 22005’47.99’’N latitude and 87051’10.51’’E to 87051’43.65’’E longitude. From the administrative and political point of view, this is the census town (2011) including Brindabanpur-I and Usmanpur Gram Panchayats under Chandipur CD Block in Purba Medinipur district. This rurban area includes Chandipur and Erashal Mouzas from Brindabanpur-I GP and Kalikakhali from Usmanpur GP. Side by side, this region is featured by its buffer having Saripur of Iswarpur GP, Erashal, Chandipur, and Rampur of Brindabanpur-I GP and Kalikakhali of Usmanpur GP. The study area belongs to 577,479 m2 of its spatial existence along with its buffer potentiality of 941,389.6 m2 (Figs. 28.1). Geomorphologically, the study area has existed on Keleghai-Haldi-Hooghly interfluves as well as fluvial plain. The elevation of this region is ranged in between 3.73 m and 9.45 m from Mean Sea Level (MSL) [10]. Geo-environmentally, this study area is reflected as one of the important rural-urban landscapes in the Purba Medinipur district. Geologically, this region is of the most recent fluvial-coastal formation of the Quaternary Age over the South Bengal Basin.

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Fig. 28.1 Location map and spatio-temporal existence of the study area

Aim and Objectives Aim: Assessment of the journey of Chandipur-Erashal Census Town through rurban transformation and justification of its role as growth center to regional development. Specific Objectives: • To estimate the development background, locational advantages, and demographic change of Chandipur-Erashal Census Town as a rurban reality; • To investigate the Spatio-temporal change in LULC and landscape transformation in the study area; • To find out the urbanization and land use patterns along with the landscape typology in the study area; • To estimate the unplanned urbanization and the failure of urban policy in the study area; • To provide the suggestions toward new momentum of the processes and journey.

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Methods and Methodology Our study on “A SWOC Analysis and Smart Land use Modelling for Chandipur-Erashal Census Town cum Growth Center due to Its Sustainable Journey stimulating Regional Development”, has been conducted and completed through some sequential and systematic methods which have been mentioned below: (A) Sampling Techniques: In the case of socio-economic data collection and study we have followed different sampling techniques for household surveys, market surveys, hotel surveys, health surveys, institutional surveys, etc. The major sampling techniques are systematic random sampling, stratified random sampling, and purposive sampling. (B) Database: We have used different primary and secondary databases for statistical and mapping analysis to prepare the field report. Mouza maps, Landsat satellite images, and Google Earth Images have mostly been used for mapping analysis whereas different secondary databases from Govt. Census Report, BDO, BLRO, Panchayet Office, etc. have been used for socio-economic analysis. (C) Mapping Techniques for LULC Analysis: • NDVI (Landsat 8 OLI/TIRS C1 Level-1):

NDVI ¼

NIRðB4Þ  RðB3Þ NIRðB4Þ þ RðB3Þ

where, NIR = Near Infrared R = Red • NDBI (Landsat 8 OLI/TIRS C1 Level-1):

NDBI ¼

SWIR1ðB6Þ  NIRðB5Þ SWIR1ðB6Þ þ RðB5Þ

where, SWIR = Short Wave Infrared MNDWI (Landsat 8 OLI/TIRS C1 Level-1):

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MNDWI ¼

367

GreenðB3Þ  SWIR1ðB6Þ GreenðB3Þ þ SWIR1ðB6Þ

• Demarcation of Concentric (Multi Ring) and Polygonal Buffer and Hinterland: Circle Buffer: Core point to hinterland is 2 km and core point to buffer is 1 km. Polygon Buffer: Growth center Boundary to hinterland is 160 m and growth center to buffer is 80 m (Table 28.1). (D) Landscape Profiling: This is another technique to represent cross-sectional and longitudinal scenario of urban landscape prepared based on GPS Survey, LULC Survey, and Mapping Analysis. (E) Comprehensive Methodological Flow Chart for the Study (Fig. 28.2):

Dignity of Chandipur-Erashal Urban Region and Its Growth Status with Respect to Surroundings According to the previous census of India 2011, the total population of Erashal census town had 5,332 and the sex ratio between males and females was equal. When 2,641 (49.5%) and 2,691 (50.5%) of population were females, the population below 6 years was 601. The total number of literates in Erashal was 4,153 (87.78% of the population over 6 years) (Tables 28.2, 28.3 and Fig. 28.3). The above map shows the nature and status of the Chandipur-Erashal Urban cum Growth center region. With respect to surrounding or neighborhood nodes and urban centers, the Chandipur-Erashal Urban region has been reflected as 1st order core whereas others have been categorized as 2nd order, 3rd order, and 4th order as

Table 28.1 Landsat 8 OLI (operational land imagers) and TIRS (thermal infrared sensor) Band name

Band

Wavelength (lm)

Resolution (m)

Ultra blue Blue Green Red Near infrared (NIR) SWIR1 SWIR2 Panchromatic Cirrus TIRS1 TIRS2

01 02 03 04 05 06 07 08 09 10 11

0.435–0.451 0.452–0.512 0.533–0.590 0.636–0.673 0.851–0.879 1.566–1.651 2.107–2.294 0.503–0.676 1.363–1.384 10.60–11.19 11.50–12.51

30 30 30 30 30 30 30 15 30 100*(30) 100*(30)

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Study Area DelineaƟon

Google Earth & Satellite Image

Land Use & Land Cover (LULC) Cadastral & Other Base Maps

ElevaƟon Number of Household

SpaƟo-temporal Change in LULC

Change in VegetaƟon Change in Water Bodies

Change in Infrastructure

Status of UrbanizaƟon &Urban Development

Change in SeƩlements Change in Commercial ConstrucƟon

DEM & Contour Change in Ecology

Demographic Structure

Census Report, Govt. Project Plan & Other InsƟtuƟonal Secondary Database

Causality &

Relief & Drainage

Problem Assessment

ExisƟng Socioeconomic Structures

Development Level

Assessment of Growth

Infrastructural FaciliƟes

Household, Market, Resort,

Pole cum Urban Foci to Regional Development

Dimensions & Trend of UrbanizaƟon

Tourist & Road Survey

Proposed Way to compute and provide a Sustainability Approach for Growth

Meteorological Data

Pole Development &Childhood

ClimaƟc Data (2012 to 2017)

Urbanization

Fig. 28.2 Methodological flow diagram of the study Table 28.2 Spatio-temporal change in area and population Year

Area (m2)

Population

Annual areal growth rate (%)

1999 95,910 3,703 – 2001* 112,376 4,641 8.58 2004 154,694 4,918 12.55 2009 292,895 5,192 17.87 2011* 375,050 5,332 14.02 2014 554,750 7,101 15.97 2018–’19 577,479 14,461 1.02+ *Indicates census year Source Compilation of primary data and secondary data

Annual population growth rate (%) – 12.67 2.00 1.11 1.35 11.06 14.72

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Table 28.3 Included Mouzas with its area and population Year

Area (m2)

Population

Households

Male population

Female population

Literacy rate (%)

Erashal (CT) Chandipur

3,750,500

5,332

1,143

2,641

2,691

12,097,300

2,343

552

1,186

1,157

4,153 (77.89) 2,025 (86.42) 2,327 (84.34) 2,038 (77.08) 1,100 (79.54) 11,643 (80.51)

Kalika Khali Saripur

1,564,900

2,759

625

1,417

1,342

2,401,500

2,644

561

1,326

1,318

Rampur

1,097,200

1,383

304

709

674

Total

10,111,400

14,461

3,185

7,279 (50.34) Source Compilation of primary data and secondary data

7,182 (49.66)

Fig. 28.3 Status of Chandipur-Erashal growth center with respect to neighborhood growth points and existence of Chandipur-Erashal urban region with respect to regional transport network

per the rule of Core-Periphery Model of Friedman. Here, Nandigram and Haria have been considered as 2nd order, Bhagwanpur, Narghat, Bajkul, Reyapara, Hanschara have been estimated as 3rd order and Magrajpur, and Kalaberia have been reflected as 4th order growth centers, respectively (Fig. 28.4).

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Fig. 28.4 Status map of Chandipur-Erashal urban region with respect to its buffer and hinterland (polygonal and concentric methods)

The study area belongs to 577,479 m2 of its spatial existence along with its buffer potentiality of 1,303,630 m2. The hinterland of this urban area is signified by the whole of the Chandipur Block, a little bit of Nandigram-I and II, Bhagwanpur-I and II, and a little portion of Nandakumar CD Block.

Spatio-Temporal Change in LULC (from 2004 to 2019) of the Study Area The above data table and diagrams reflect the Spatio-temporal change of major land uses in the study area, Chandipur-Erashal Urban region. The changing scenario shows that the road infrastructure has been increased over time whereas the existence of the canal is more or less consistent over time. The amount of agricultural land has been drastically declined from 2004 to 2019. Specifically, before 2011 of recognizing as a census town, the amount of agricultural land was higher whereas it has been declined after 2011 at a quick rate. On the other hand, the magnitude of settlement and other construction growth is moderate to high in the study area. Specifically, after 2011, it has been increased a higher rate due to quick r-urbanization and growth center development. Vegetation cover in the study area

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has also been changed over time. But, this change is mixed in nature. Because, if we consider the time of early 2011, the vegetation cover was between 15 and 16%. But, after 2011, it has been increased to 31.36% which is mainly due to a higher level of spatial change of growth center or urban region. Later on, in 2019, the vegetation cover has been enormously declined again due to urban infrastructural development mainly. The amount of wasteland has been changed from 2004 to 2019 whereas vacant lands have been changed as more or less in amount with its up and down scenario. The amount and magnitude of water bodies have been changed before and after 2011 along with its higher and lower existence maintaining census year. But, after 2011, the existence of water bodies has been squeezed at a higher rate due to different growth center development activities. Overall, the land uses of the study area have been changed spatially and temporally. But, ecologically important land use and land covers have been declined at a higher rate while the urban infrastructure, settlement, and commercial construction have been increased with higher magnitude. So, the Spatio-temporal changes of land uses have been occurred following the general nature of any urban area and also growth center development (Figs. 28.5, 28.6, 28.7 and Table 28.4).

Fig. 28.5 Spatio-temporal existence of study area and change in LULC (2004–2019) and comparative scenario of land uses, NDVI, NDBI, and MNDWI in the study area, 2019

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Fig. 28.6 Spatio-temporal change of settlement and transport in study area (2004–2019)

Fig. 28.7 Spatio-temporal change of water bodies and vegetation in study area (2004–2019)

10.09 4.35 2.37 1.03 4.46 3.99 −0.942 0.888

Amount (%) of major land uses Road Canal Agriculture

2004 1.33 0.08 2009 1.55 0.28 2014 1.52 0.31 2019 1.55 0.29 Mean 1.49 0.24 SD 0.11 0.11 R +0.767 +0.793 0.589 0.629 R2 Source Mapping analysis

Years

18.04 16.36 25.15 24.98 21.13 4.59 +0.832 0.692

Settlement and other construction 15.64 16.12 31.36 15.31 19.61 7.84 +0.235 0.055

Vegetation

5.84 5.44 3.08 0 3.59 2.69 −0.956 0.913

Wasteland

Table 28.4 Amount (%) of major land uses in the study area over time (2004–2019)

1.25 4.17 11.47 6.22 5.78 4.31 +0.665 0.442

Water bodies 19.93 14.50 2.34 5.65 10.61 8.06 −0.880 0.775

Wetland

18.10 28.65 17.31 38.70 25.69 10.10 +0.645 0.416

Vacant land

6.06 4.17 3.48 2.85 4.14 1.39 −0.959 0.920

Play ground

3.84 4.40 1.61 3.41 3.32 1.21 −0.436 0.190

Others

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Nature and Causes of Landscape Transformation Throughout the Time and Driving Factors to Urbanization and Growth Center Development Our field survey tried to investigate the major causes of why this region has been developed as an urban cum growth center region over time. With respect to the lack of literature and deficiency of a good database relating study area and its urbanization, we have conducted a perception study to take the response from the inhabitants and commuters regarding the causality of urbanization or growth center development here. This study has been considered on the target group as older/ senior and experienced people who have been experiencing such events over time. Without them, we have considered the people related to trade and commerce, small businesses, shopkeepers, vendors, etc. from the market area and local and migrated residents who are existing now. We have taken the interviews of different officials from different socio-economic, administrative, and political institutions (Fig. 28.8). The perception study reflects that nodal existence and good accessibility of transport-communication, regional market and business facility, the regional center of basic and modern amenities (goods and services), agglomeration of rural economies (formal and informal), tri-functionaility as a business, residence, and communication, potentiality of buffer and periphery, the interest of local and small entrepreneurs in investment, low land rent at primary stage, development and opportunity of different socio-cultural facilities/services, dignifying the place as important one from the sites of administration, politics, entrepreneurship, trade and commerce and socio-cultural dimensions, regional and occupational immigration, infrastructural development and better occupational cum residential opportunity after 2000 AD, etc. are the major responsible causes to the urbanization and growth center development in the study area. From the perception study, most of the above facts have been dignified as very high, high, and moderately responsible causes for urbanization and growth center development in the study area. The above map composition shows the land use scenario along with the corresponding maps on NDVI, NDBI, and MNDWI. The mapping analysis reflects that the NDVI is lower in the case of a settlement, commercial sectors, transport, and other builtup areas whereas the vegetation area is featured by higher value of NDVI and grazing lands, agricultural lands, and wetlands with water bodies ensure the moderate to the higher value of NDVI. Hence, urbanization indicates a declining trend in vegetation magnitude and also the NDVI. So, the relation between Urbanization cum growth center development and NDVI is inversely proportional to each other. Further, side by side existence of land use and NDBI maps shows that the NDBI is higher in case of settlement, commercial sectors, transport, and other builtup areas whereas it is lower in and on the grazing field, vacant land, wasteland, vegetation cover, wetland, water bodies, etc. Hence, urbanization indicates an

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Fig. 28.8 Major responsible/driving causes/factors for the development of the growth center

inclining trend in construction and concretization magnitude and also the NDBI. So, the relation between Urbanization cum growth center development and NDBI is directly proportional to each other. Another map on MNDWI shows that it is highest in the case of deepwater bodies and wetlands whereas it is higher in the case of grazing field and vegetation cover. It has been reflected as moderate in the case of vacant land, wasteland, playground, etc. while it is moderate to lower in the case of builtup and concretization zones. Hence, urbanization indicates a declining trend in MNDWI. So, the relation between Urbanization cum growth center development and NDBI is inversely proportional to each other.

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Profile-1, 2, 3 and 4: Longitudinal and Cross-sectional Profiles for showing the Urbanization and Landscape Scenario in the Study Area

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Transport Efficiency and Accessibility of Chandipur-Erashal Urban/Growth Pole Region The above map and data table show the Detour Index to assess the efficiency of the transport network of the Chandipur-Erashal urban cum growth pole region. The above statistical and mapping analysis reflects that the transport efficiency is higher at the growth pole region and its nearer surroundings and it has been declined toward far distant nodes and periphery (Fig. 28.9). The above statistical and following mapping analysis reflects that the transport efficiency is higher at the growth pole region including Chandipur, Hanschara, Bajkul, Kalaberia, Magrajpur, etc. whereas it has been declined toward far distant nodes like Haria, Nandigram, Reyapara, Chowkhali, and Bhagwanpur, and adjacent periphery.

Major Existed Problems as Per Field Observation and Survey (a) Possession of a critical population mass at the core and congestion of both settlement and market at the center of urban gravity: There is a positive correlation between the proportion of a population living in the urban area and the level of income. High population concentration in the urban area brings about economies of scale and richer market structures, lower costs of providing public facilities and infrastructure, and faster diffusion of knowledge. However, economic growth is generated at a certain optimal level of urban concentration. Excessive urban concentration creates congestion and higher cost for production and degradation of the quality of life, while

Fig. 28.9 Efficiency of transport network, accessibility of transport network of Chandipur-Erashal urban cum growth pole region

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insufficient urban concentration prevents the synergistic effects of economies of scale and a dense customer base. In the study area, a critical mass population is observed at the core or CBD zone where the market and business-related activities are strong in function. As a result, the core region is faced with critical stress of both commercial and residential activities (Fig. 28.10). (b) A favorable economic environment for fostering growth-mind entrepreneurs and essential urban sprawling destroying buffer and hinterland potentiality: Growth in terms of sprawling is essential in the urban process, but not beneficial at all. Sometimes, it may demerit the potentiality of buffer and periphery. Our study area is also faced with such type of problems regarding sprawling which affects the agricultural economy and ecosystem, wetland, wasteland, vegetation cover, and others. (c) Prospering but problematic in the prevalence of the capacity for innovation: Creating a competitive economy requires not only risk-taking entrepreneurs but also innovative ideas, mobilization of available local, and international knowledge, skills, and technology. These are elements that are mostly fostered in an urban setting [11]. In the case of our study area, the urbanization and growth center development is very much beneficial, but lack of institutional and implementing opportunity, enriched entrepreneurs, better-minded youths, qualitative human resources are not interested to involve in this way of lifestyle named as urban cum growth center development in the study area.

Fig. 28.10 Residential cum CBD congested zone and map No. 16: Illegal, haphazard, and unscientific urban sprawling illegal and haphazard dumping sites in the study area, 2019

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Fig. 28.11 Poor and interrupted drainage in the study area, 2019, map No. 19: traffic congested sectors in the study area, 2019 and map No. 20: Spatio-temporal change of growth center boundary

(d) Drainage Interruption and Poor Drainage-Evil Situation to Sustainable Infrastructure: Urban morphology is advanced by the good introduction and functioning of all essential infrastructural dimensions like drainage, transport, drinking water facility, electricity facility, sanitation facility, waste disposal facility, etc. (Fig. 28.11). In the case of our study area, a very poor drainage situation acutely affects the residential cum central business zone during the rainy season. Illegal horizontal and vertical overcrowding of settlement and market infrastructure without proper drainage facility reflect the poor drainage facility here. There is one north-south canal passing through the growth center region which has been tremendously interrupted by illegal and haphazard settlement expansion and other construction on and along with the canal line locations. Hence, the poor and interrupted drainage condition is one of the resistances to proper infrastructural development in this region. (e) Traffic Congestion and Problematic Transport—The Bottleneck Situation of Urban Dynamics: Strengthening Urban Transport is just like the well-functioning of the artery and vein in a living body. In the case of any urban living entity, the transport network is one dynamic infrastructural character to flow and exchange goods, passengers, services, and other living characters during day and night. In our study area, the main nodes including Nandigram road, Mechheda Road and Digha Road, Bypass nodes, sluice gate or culvert location on the main route, etc. are regularly and frequently featured by conventional traffic congestion and hazardous daily journey moment in the study area. Hence, the transport problem is one of the major problems and issues to its sustainability and potentiality.

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(f) Illegal and Haphazard Dumping Sites—Unhealthy State of Affairs to Urban Livability and Environment: Illegal and haphazard dumping sites for waste disposal of any settlement affect the livability and healthy environment of it. In the case of our study area, there has not existed any fixed dumping place or site for waste disposal. As the result, huge wastes are dumped where and there throughout the study are violating the principle, rule or order of govt. or administration for waste management. So, this is a major problem in the study area now. (g) Other existed problems are: • Unstable economic, and conflicted political institutions; • Unavailability of public facilities/infrastructure, including transportation, potable water, sanitation, and waste management systems; • Inefficient urban governance; • Lack of comprehensive growth management policies for sustainable urban growth, and reduction of social and environmental problems; • Lack of provision of information technology and faster diffusion of knowledge; • Disrespecting the rights of women and the urban poor; • Unavailability of jobs and the city’s ability to match them with available skills, both local and expatriate labor force, etc.

Management of Problems and Managemental Gaps Between Problems and Efforts in the Study Area The following diagram from the Perception Study and Field Work shows the role of different sites for management of the existed problems in the study area. 210 respondents have gifted their responses cum feedbacks on the major managemental efforts for declining the observed socio-economic, cultural, infrastructural, administrative, and environmental problems as per the survey schedule/questionnaire. But, in the first four cases of the management, they did not observe the well or satisfactory efforts from different govt., administrative, political, and non-government sites. Most of the people are not satisfied with them or those institutions. The roles of local administration and selected members from different levels of democracy are not satisfactory here. There is no emphasis on any kind of specific plan or project from all those characters for the far-sighted development of this potential region. The study reveals that some efforts from the individual or personal levels are observed to protect their environment in the study area which is lightening the candle of hope and esteem for the management of the issue in the study area. Hence, this picture is clear cut that the huge gaps in between problems and efforts from different sites are the key problem for the management of the problems and obstacles to the study area and regional development (Fig. 28.12).

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% of the perception of the sample respondents

28

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Role of Govt. & Higher Level Administration

Role of Local Administration

381

Role of Local Political Party & Selected Members

Role of NGOs

Effort from Individual Level

Role of different sites for management of the problems in the study area Over Satisfactory/ Mostly

Satisfactory/ A Lot

Traditional/ Quite a bit

Low/ A little

Very Low/ Not at all

No Remarks

Fig. 28.12 Role of different sites for management of the problems in the study area

Respondent’s Perception on the Expected Management for Well/Sustainable Development The above diagram reflects the managemental ways proposed by the sample respondents (210) in the study areas. It is very interesting that most of the respondents (>50%) have given their proposal for the management of the existed problems in the study area. The responses coming from the perception study show a very high and higher magnitude in most of the cases. They have dignified the roles of government and local administration specifically. It is interesting that most of the people in the study area expect the liberal co-existence of all political parties in one envelop named urban cum growth center development. Since the transport and drainage infrastructure is one of the major problems here, the sample respondents demand its solution urgently. Although the study area has been recognized as the census town as per the 2011 census, it is not provided by any higher level institution having general, technical, management, and socio-cultural education. Hence, most of the people have put their feedback for it. So, the perception study for getting the managemental proposals for recovering the problems in the study area reflects the relevant and most demandable ways to local and regional development which should be emphasized in the schedule of planning, project, and development of local and higher administration, government and selected public representatives [12] (Fig. 28.13).

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65.7

Proposed Managemental Ways as per Percetion Survey

Others

96.7

Efforts to liberal co-existence of all political parties in self of local and regional…

84.3

Introducing, proper implementing and justifying more projects or schemes relating…

80.5

Effort to introduce well urban governance to consider the facts and findings

87.6

Urgent efforts for proper waste dumping sites and sanitation development

84.8

Efforts to establish relevant cultural, technical and management institutions

56.7

Efforts to attract more investment and generate more employment opportunity

95.7

Urgent efforts to reconstruct and expand the transport and drainage infrastructure

86.2

Efforts to specification of residential and business zones separately Documentation of threatening species & special care on its conservation and protection

73.3

Strict restriction on devegetation & wetland filling up

72.4 83.8

Strict restriction on rural land conversion cum land use change & Land Promotary…

90.9

More active role of Local Administration and Selected Members to reduce the Problems…

93.8

More active role of Govt. & Administration by rules and regulation

0

20

40 60 % of the Responses

80

100

Fig. 28.13 Proposed managemental ways as per respondent’s perception

SWOC Analysis of the Study Area See Table 28.5.

Table 28.5 SWOC analysis A. Strength:

B. Weakness:

• Accumulation and concentration of local and regional small businessman, entrepreneurs, and capitalists; • The journey of childhood phase with bright potentiality; • Leading node and growth center with respect to surroundings and neighborhoods; • Large existence of buffer and hinterland; • Site suitability from the view point of its geography, environment, topology, and human resource; • Not manufacture, but market and service based urban development, etc. • Poor and hazardous drainage, sanitation, and transport infrastructure; • Lack of higher educational, technical, and management based education facility; • Residential cum CBD overcrowding and congestion; • Fragile and weak institutional and organizational facility and poor governance; • Not specific waste disposal facility; • Illegal and haphazard growth center sprawling having with illegitimate land business; • Lack of urban or growth center plan, policy, and project for development; • Huge gap among public, politicians, and plan makers, etc. (continued)

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Table 28.5 (continued) C. Opportunity:

D. Challenge:

• • • •

Opportunity for climax development since it is at initial phase; Opportunity to convert into municipality or planned town; Opportunity to develop as regional driving growth center; Opportunity to grow up with scientific and systematic sprawling and expansion; • Opportunity to reflect its optimal livability and healthy environment; • Opportunity to establish as sustainable urban landscape or green city model or planned city in near or far future; • Opportunity to be the large platform of capitalist, entrepreneurs, manufacturers, businessman, etc. • Opportunity to make it as better urban morphology with potential buffer and hinterland, etc. • Traditional political chaos and conflicts; • Dominance of promoters and protractors; • Loosened and fragile administration; • Influence of large towns and cities like Haldia, Contai, and Tamluk; • Lack of plan, policy, and prime interest; • Lack of provision of information technology and faster diffusion of knowledge; • Lack of comprehensive growth management policies for sustainable urban growth, and reduction of social and environmental problems; • Unstable economic and conflicted political institutions; • Unavailability of jobs and the city’s ability to match them with available skills, both local and expatriate labor force, etc.

Suggestions Toward an Anti-sprawl Urban Policy Sprawling is one of the base characters of any urban cum growth center region. In the case of our study area, this is not exceptional also. But, if we consider the growth rate or magnitude toward different directions on and along different routes, this is higher in the case of Chandipur-Mechheda and Chandipur-Digha routes. This is interesting that after recognition of census town, it has been accelerated toward the south along Chandipur-Digha road (Figs. 28.14 and 28.15). Hence, we can recommend not stopping the urban or growth center sprawling, but also introducing the planning controlling this evil process. So, the suggestions toward anti-sprawl urban policy may be made of as follows: • Restricting the illegal land use conversion maintaining the land use policy as per govt. rules and regulation; • Restricting the illegal encroachment and forceful capturing of wetland, vegetation cover, wasteland, agricultural lands, etc. for the haphazard development of the growth center cum urban region;

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Fig. 28.14 Sprawling of the growth center toward different directions (2004–2009 and 2009– 2011)

Fig. 28.15 Sprawling of the growth center toward different directions (2011–2014 and 2014–2019)

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• Stopping the dominance of promoters and protractors in case of land business; • Rectifying the government and administrative negligence in case of unplanned and unscientific sprawling; • Maintaining the ecological footprint and landscape susceptibility in case of free frog urban expansion; • Reconstructing and reforming the urban and growth center morphology adjusting with its functionality; • Synchronization of policy, public, and plan for sustainable growth and expansion of growth center region [13]; • Providing the specific rules for settling as the residents of the migrant people in the study area, [14, 15] etc. Development of a Sustainability Approach: Policy Recommendations and Strategic Options for Sustainable Urban Development: • Adopting a Two-Pronged Approach (Sustainable of Urban and Rural Devt.) • Building Agribusiness as the Initial Industrial Base (Agro based Industry Devt.) • Establishing the Urban Governance and Strengthening Its Capacity (Not Govt. system) • Bridging the Infrastructure and Social Services Gap • Undertake Policy-Relevant Applied Research (Plan and Project) • Promote Fiscal Decentralization to Empower Rural Communities and Local Governments • Encourage Urban Planning Networks to Share Best Practices • Synchronization of Local Administration, Local Representatives, Politicians, Planners and Public • Sustainable adjustment between urban ecology and urban morphology • Applying the Plans, Projects, and Schemes of Planned City, Green City Model, Agropolitan Development, Top-down Approach, [16] etc.

Conclusion Our study area, the Chandipur-Erashal growth pole cum urban region has been experiencing such types of problems mentioned above in the case of any Indian growth center, rural-urban area, and peri-urban areas. The respective local governments (rural and/or urban) within whose jurisdictions the rurban and growth pole area lies should have with them a guiding document (such as a local area plan) that helps in future planning and development of the peri-urban, rural-urban, and growth pole area. As the study area is urbanizing, the concern is growing over the adverse conditions created by uncontrolled growth and unregulated development in the

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urban region. The government’s approach and response over the years have been gleaned from the following: • policy documents (i.e., erstwhile Five-Year plans of the Planning Commission); • legislations (e.g., Seventy-fourth Constitution Amendment Act, 1992); • programs/schemes (Jawaharlal Nehru National Urban Renewal Mission, 2005; Atal Mission for Rejuvenation and Urban Transformation, 2015; National Rurban Mission, 2016); and • initiatives of town and country planning departments, regional planning, and development authorities, state and local governments; • under this scope to facilitate the urban region, the recommendations for more effective governance of the growth pole and rurban region may include; • planning for rural-urban/peri-urban/growth centers areas; • providing a rational regional land use pattern; • formulating an effective regulatory regime; • provision of affordable housing, basic services, regional transport corridors, and facilities [17]; Finally, it may be said that for the comprehensive but sustainable development of the growth center cum urban region, there should be reflected the one and unique effort and role of all functional characters including common people, local administration, local representatives, politicians, plan makers, entrepreneurs, businessman, and other institutional characters.

References 1. Misra HN, Mishra A (2017) Role of small and intermediate towns in regional development: a case study of Raebareli, Sultanpur and Pratapgarh Districts of Uttar Pradesh, India. Environ Socio-economic Stud 12 5(4) 2. Hagerstrand T (1968) Innovation diffusion as a spatial process. University of Chicago Press, Chicago, USA 3. Perroux F (1955) Regional development planning in India –A new strategy. Vikas, New Delhi 4. Myrdal G (1957) Rich lands and poor. Harper and Row, New York 5. Hirschman AO (1958) The strategy of economic development. Yale University Press, New Heaven, Connecticut, USA 6. Rostow WW (1960) The stages of economic growth. Cambridge University, Cambridge, London 7. Friendmann J, Alnso W (1964) Regional development and planning: a reader MIT Press, Cambridge, England 8. Chandramouli C (2011) Census of India: rural urban distribution of population census of India 2011 (Provisional population totals)-our census, our future, Ministry of Home Affairs New Delhi, pp 5 9. Klaassen L (1981) Dynamics of urban development. St. Martin’s Press, New York, USA 10. https://en.wikipedia.org/wiki/Keleghai_River 11. Adedeji O (2011) Rural and urban regional planning (EMT 425:) Lecture Notes, pp 26 12. Haggett P (2001) Geography: a global synthesis[M]. Pearson Hall, New York 13. Jefferson M (1939) The law of prime city. Geographical Review

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14. Harvey DW (1973) Social justice and the city. Arnold, Londan 15. Smith D (1994) Geography and social justice. Blackwell, Oxford 16. Praksh Rao VLS (1973) The process of urbanization. Fulbright Newsletter, March, New Delhi, India 17. Aijaz R (2019) “India’s Peri-Urban Regions: The Need for Policy and the Challenges of Governance”, ORF Issue Brief No. 285, Observer Research Foundation

Chapter 29

Remote Sensing of Water Quality Parameters Along the West Bengal Coast of India Narendra Gonapa, Chiranjivi Jayaram, and K. Padma Kumari

Introduction Consistent and systematic observations are necessary to monitor the spatio-temporal variability of the water quality for efficient management of the coastal ecosystems. The chief objective of any water quality management is concerned with human and ecosystem health. Water quality is important for all living and non-living organisms. The scales of variability range from hourly to the tidal scale that could have profound impact on the region of interest on a wider range. In situ measurements of water quality provide a detailed and accurate estimate of the water bodies. However, these are limited by the spatial and temporal coverage due to the cost and logistical challenges that could be overcome by making use of satellite-based measurements [1]. Variations in the water quality parameters (WQP) alter the optical properties of the water surface that could be detected by the satellites operating in the visible band of the electromagnetic spectrum. However, all the WQP cannot be mapped using remote sensing. Parameters like chlorophyll, coloured dissolved organic matter, turbidity and suspended matter, which are optically active properties are estimated using remote sensing [2]. Though the satellite measurements enhance the spatial coverage, the coarse wavelengths of the ocean colour sensors coarse resolution of the present optical satellite sensors for marine environments have limited capability to monitor the optically complex coastal waters. Despite the improvements with respect to the sensor characteristics, atmospheric correction and the calibration the resolutions remain a bottleneck for nearshore regions [3, 4]. However, the ability of Landsat 8 N. Gonapa (&)
K. Padma Kumari School of Spatial Information Technology, Institute of Science and Technology, JNTU, Kakinada, Andhra Pradesh 533003, India e-mail: [email protected] C. Jayaram Regional Remote Sensing Centre—East, NRSC/ISRO, Kolkata, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_29

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OLI/TIRS to resolve the features at 30 m/100 m, respectively, and a better signal-to-noise ratio (SNR), has made the mapping and estimation of chlorophyll and temperature of nearshore regions compared to the traditional ocean colour sensors like MODIS (1 km), VIIRS (750 m), etc. The turbid coastal waters are in general optically dark targets that require an improved SNR and radiometric resolution to accurately deduce the coastal feature. This has made satellite sensors like Landsat 8 and Sentinel 2, which are meant for terrestrial applications, useful for coastal and nearshore applications [5]. The present study makes use of Landsat 8 OLI for mapping the coastal WQP along the east coast of India with an emphasis on the optically complex West Bengal coast followed by Odisha and Andhra Pradesh. The east coast of India is punctuated by numerous east flowing rivers that drain their waters into the Bay of Bengal (BoB). These river systems include the Ganges-Brahmaputra in the north to Cauvery in the south. The freshwater discharged by the rivers significantly alter the coastal water quality on daily to seasonal scales. The coastal waters of West Bengal are influenced by the interaction between marine waters in the head of the Bay of Bengal and freshwaters from Hooghly river. Odisha coast is mainly changing water quality parameters due to Mahanadi estuary region and Andhra Pradesh coast mainly affected by the freshwater discharge from the Godavari and Krishna rivers. The present study is carried out using the data from Landsat 8 OLI/TIRS data during 2016–2019 period. The change in the water quality parameters is quantified for pre- and post-monsoon seasons.

Data and Methods Landsat 8 OLI/TIRS Level 1C Products Landsat 8 comprises 2 sensors onboard viz., Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). OLI has 9 spectral bands including one panchromatic band, while TIRS has two thermal bands [6]. The datasets were obtained freely from website: https://earthexplorer.usgs.gov/. Band designations and the corresponding central wavelengths of the OLI are given in Table 29.1. The satellite data considered for this study is provided in Table 29.2.

Methodology All the Landsat 8 OLI/TIRS data sets are processed in ENVI software. Atmospheric corrections are made to the data before processing for further procedures. FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) [7] is used for the atmospheric correction of the images. Atmospheric correction involves the removal of the effects on the reflectance values of satellite imagery, due to the

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Table 29.1 The spectral bands and central wavelength of Landsat 8 Band no.

Spectral band designation

Wavelength (nm)

Central wavelength (nm)

01 02 03 04 05 06

Ultra blue Blue Green Red Near infrared (NIR) Short wave infrared (SWIR) 1 Short wave infrared (SWIR) 2 Panchromatic Cirrus Thermal infrared (TIR) 1 Thermal infrared (TIR) 2

435–451 452–512 533–590 636–673 851–879 1566–651

443 482 561 655 865 1609

30 30 30 30 30 30

2107–294

2201

30

503–676 1363–1384 1060–1119

590 1373 1090

15 30 100

1150–1251

1201

100

07 08 09 10 11

Resolution (m)

Table 29.2 Date of acquisition and file name of the Landsat 8 data used in the study No.

Date of acquisition (YYYY– MM–DD)

Path

Row

1 2 3 4

2019–01–30 2019–2–15 2019–11–30 2019–12–16

138/45

Data file name

LC08_L1TP_138045_20190130_20190206_01_T1 LC08_L1TP_138045_20190215_20190222_01_T1 LC08_L1TP_138045_20191130_20191216_01_T1 LC08_L1TP_138045_20191216_20191226_01_T1

atmospheric constituents. To retrieve surface reflectance from RS imagery digital numbers (DN) values of level 1 data is converted to the Top of the Atmosphere (TOA) radiance and further converted to reflectance. TOA brightness temperature is obtained from the spectral radiance of the thermal bands by using the thermal constraints provided in the metadata using the following expression (29.1): TOA brightness temperature ðT Þ in K ¼ K2 lnðK1 Lk þ 1Þ where: Lk = TOA spectral radiance (W m−2 srad−1 lm−1) K1 = thermal conversion constant for the specific band K2 = thermal conversion constant for the specific band

ð29:1Þ

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Fig. 29.1 Flow chart showing various steps involved in processing the data

Cloud and land are masked in the data after the atmospheric correction is carried out and the corresponding remote sensing reflectance (Rrs) is generated for each band of Landsat 8. Estimating the WQP using the expression: Rrs (k) = (k)/p. Detailed methodology is shown in the flowchart (Fig. 29.1).

Estimation of Coastal Water Quality Parameters Chlorophyll-a Concentration Chlorophyll-a is often considered as a proxy for the phytoplankton biomass prevalent in the coastal and open ocean waters. Chlorophyll mainly indicates trophic states and act as a link between productivity and the nutrient concentration of the water column. Chlorophyll mainly reflects at green and absorbs in the blue

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region with maximum energy from wavelengths of violet-blue and orange-red, thus reflecting in the green region. [1]. The OC-2 algorithm that was proposed by O’Reilly et al. [8, 9] is used to derive chlorophyll-a in the present study.

Coloured Dissolved Organic Matter (CDOM) Basically, CDOM is humic rich and influences the optical properties in the natural waters through the decomposition of detritus and other organic materials and it is also called as chromophoric dissolved organic matter, Gelbstoff or yellow substance [10]. Absorption of CDOM is mainly observed at 440 nm, but chlorophyll and CDOM both are inter-linking parameters. Generally, it involves absorbance at a specific wavelength in the range of 250–440 nm.

Turbidity Turbidity is the optical measurement of the clarity of water. The presence of suspended and dissolved sediments makes the water look turbid or murky. These substances scatter the light incident on the water column bringing about differences in the amount of light backscattered from these waters. Turbidity can come from the dissolution of clay, sediments, inorganic matters or organic materials like algae, phytoplankton’s or remained dead or decayed marine organisms. Apart from these, water may appear turbid due to the presence of Coloured Dissolved Organic Matter (CDOM), Flourescent Dissolved Organic Matter (FDOM), etc. A band ratio algorithm of remote sensing reflectance (Rrs (670)/Rrs (670) + Rrs (555)) was developed by Kulshreshtha et al. [11] which estimate TSS and could be correlated with Turbidity positively. Estimation of Total Suspended Matter (TSM) is extensively carried out using satellite observations to discern the sediment transport in a given coastal region, but this is not the sole indicator of turbidity of the water column. Turbidity being an optical property, it is directly linked to the backscattering and reflectance of water than the TSM. Most of the prevailing algorithms are regional and often based on the empirical relation between in situ-based turbidity measurements and satellite reflectance values at various wavelengths [12]. Turbidity is generally expressed in Nephelometric Turbidity Unit (NTU), Formazin Turbidity Unit (FTU), or Formazin Nephelometric Unit (FNU), these are often instrument dependent [13].

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Results and Discussion WQP are generated for pre-monsoon and post-monsoon data sets for the year 2019 (Fig. 29.2) for the coastal West Bengal encompassing the Sundarbans delta region. A clear distinction is observed between the quantitative estimate of CDOM and Turbidity between both the monsoon seasons. From the figure it is observed that the CDOM absorption has shown considerable increase from pre-monsoon season (0.7–1.00 m−1) to post-monsoon (*4.00 m−1), especially in the Hooghly river estuary region compared to the Sundarban region. This infers to the terrestrial CDOM input carried by the numerous minor river streams that discharge into the Hooghly river catchment during the monsoon season. In the Sundarban region, CDOM profusion (*2.5 m−1) is observed in the Matla-Vidyadhari channels and the upper reaches of the inter-tidal rivers, as they carry the discharge from the nearby agricultural fields and narrow creaks within the deltaic system. In the pre-monsoon season, due to the reduced levels of freshwater flux into the estuarine system and the Sundarban delta, the CDOM concentration shows the moderate values and structure of the organic matter prevalent in the coastal and estuarine region. Turbidity also showed analogous distribution like CDOM with higher values up to 25 FTU in the post-monsoon season compared to 10 FTU during pre-monsoon season. Considerable enhancement in the turbidity concentration is observed in the

Fig. 29.2 Pre-monsoon and post-monsoon distribution of CDOM (upper panel) and turbidity (lower panel) for the year 2019

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Hooghly river estuary and upper reaches of the Sundarban region. The open ocean region showed meagre values of turbidity *5 FTU in both the seasons. Total suspended matter for the pre-monsoon season is shown in the Fig. 29.3. Post monsoon season figure is not included here, but the distribution follows a similar pattern as that of turbidity. Figures 29.4 and 29.5 show the chlorophyll

Fig. 29.3 Total suspended matter for the pre-monsoon of 2019

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Fig. 29.4 Chlorophyll concentration (mg m−3) for the pre-monsoon season of 2019

concentration in the Hooghly estuary during pre- and post-monsoon seasons of 2019, respectively; wherein it is observed that the spatial spread is more during pre-monsoon while the values are high in post-monsoon season. This could be attributed to the nutrients brought by the runoff during post-monsoon season.

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Fig. 29.5 Chlorophyll concentration (mg m−3) for the post-monsoon season of 2019

Similar analysis was carried out for other years (2013–2019) and along the coasts of Odisha and Andhra Pradesh. The study demonstrates the successful retrieval of WQP along the east coast of India using high-resolution satellite observations. This could be operationalised for continuous production of water quality maps for the region of interest for better sensitising the coastal and water resource management.

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Conclusions Water quality parameters like CDOM, turbidity, total suspended matter, chlorophyll etc., are derived from the Landsat 8 surface reflectance data for the east coast of India with emphasis on the coast of West Bengal. Analysis was carried out to compare the pre-monsoon and post-monsoon season, which quantified the enhancement of CDOM, turbidity and total suspended matter during the post-monsoon season. Influence of anthropogenic/terrestrial inputs into the Hooghly river estuary is estimated for CDOM. A similar analysis was carried out along the coasts of Odisha and Andhra Pradesh for the period 2013–2019. Acknowledgements The authors thank the USGS (https://earthexplorer.usgs.gov/) service for providing Landsat 8 Level-1 data that is used in this study. GN gratefully thank the Ministry of Human Resource Development, Govt. of India, for providing the GATE fellowship for carrying out his M.Tech degree. The General Manager and Head (Applications) of RRSC-East are thankfully acknowledged for allowing GN to carry out his dissertation and constant monitoring of the project work at the centre.

References 1. Luis KMA, Rheuban JE, Kavanaugh MT, Glover DM, Wei J, Lee Z, Doney SA (2019) Capturing coastal water clarity variability with Landsat 8. Mar Pollut Bull 145:96–104. https://doi.org/10.1016/j.marpolbul.2019.04.078 2. Gholizadeh MH, Melesse AM (2017) Study on spatiotemporal variability of water quality parameters in Florida bay using remote sensing. J Remote Sens GIS 6:1000207. https://doi. org/10.4172/2469-4134.1000207 3. Mouw CB, Greb S, Aurin D, DiGiacomo PM, Lee Z, Twardowski M et al (2015) Aquatic color radiometry remote sensing of coastal and inland waters: challenges and recommendations for future satellite missions. Remote Sens Environ 160:15–30. https://doi.org/10. 1016/j.rse.2015.02.001 4. Trinh RC, Fichot CG, Gierach MM, Holt B, Malakar NK, Hulley G, Smith J (2017) Application of Landsat 8 for monitoring impacts of waste water discharge on coastal water quality. Front Mar Sci 4:329. https://doi.org/10.3389/fmars.2017.00329 5. Vanhellemont Q, Ruddick K (2014) Turbid wakes associated with offshore wind turbines observed with Landsat 8. Remote Sens Environ 145:105–115. https://doi.org/10.1016/j.rse. 2014.01.009 6. Barsi JA, Lee K, Kvaran G, Markham BL, Pedelty JA (2014) Spectral response of the Landsat-8 operational land imager. Remote Sens 6:10232–10251. https://doi.org/10.3390/ rs61010232 7. Cooley T, Anderson GP, Felde GW, Hoke ML, Ratkowski AJ, Chetwynd JH, Gardner JA, Adler-Golden SM, Matthew MW, Berk A, Bernstein LS, Acharya PK, Miller D, Lewis P (2002) FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. In: IEEE international geoscience and remote sensing symposium, 24–28 June 2002, Toronto, Canada. https://doi.org/10.1109/IGRASS.2002.1026134 8. O’Reilly JE, Maritorena S, Mitchell BG, Siegel DA, Carder KL, Garver SA, Kahru M, McClain CR (1998) Ocean color chlorophyll algorithms for SeaWiFS. J Geophys Res 103:24937–24953. https://doi.org/10.1029/98JC02160

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9. O’Reilly JE, Maritorena S, O’Brien MC, Siegel DA, et al (2000) SeaWiFS post launch calibration and validation analyses, Part 3. In: Hooker SB, Firestone ER (eds) NASA Technical Memorandum 2000-206892, vol 11, NASA Goddard Space Flight Center, p 49 10. Hoge FE, Williams ME, Swift RN, Yungel JK, Vodacek A (1995) Satellite retrieval of the absorption coefficient of chromophoric dissolved organic matter in continental margins. J Geophys Res Oceans 100:24847–24854. https://doi.org/10.1029/95JC02561 11. Kulshreshtha A, Shanmugam P (2016) Estimation of turbidity in coastal waters using satellite data. In: Proceedings of SPIE 9878, 987805, remote sensing of the oceans and inland waters: techniques, applications and challenges. https://doi.org/10.1117/12.2223544 12. Dogliotti AI, Ruddick KG, Nechad B, Doxaran D, Knaeps E (2015) A single algorithm to retrieve turbidity remotely sensed data in all coastal and estuarine water. Remote Sens Environ 156:157–168. https://doi.org/10.1016/j.rse.2014.09.020 13. Anderson CW (2005) Techniques of water-resource investigations. 09-A6.7 Chapter A6. Version 2.1, Pubs. USGS, Reston, VA, USA. https://doi.org/10.3133/twri09a6.7

Chapter 30

Identification of Critical Watersheds Based on Morphometric Analysis and Prioritization of Sagar Island, India Sk Mohinuddin, Pankaj Kumar Roy, Malabika Biswas Roy, and Tuhin Ghosh

Introduction A watershed can be identified as a particular topographically delineated area of land on the surface of the earth having a definite water divide or boundary with a single stream outlet. Multiple sources of rain, such as precipitation, snowfall, drizzle, etc., flow to a single point as a result of drainage over a given surface area, i.e., the point of departure enters large streams, rivers, lakes, and oceans [1, 2]. These water bodies are commonly used in agriculture, industry, drinking and various household practices, fisheries, etc. Therefore, to satisfy the need, sufficient water supplies need to be conserved and properly handled. The watershed is assumed to be suitable for management activities. The key problems that play a critical role in the watershed that actively or indirectly impact the health of the watershed are soil degradation and excess drainage [3–5]. Water and soil protection are thus the two most critical factors that rely on the productivity of agriculture and the environment, etc. [6]. Watershed management is a collaborative method of management of land and water resources. The primary goal of the management of watersheds is soil and water management, enhancement of soil water carrying capacity, rainwater recycling, groundwater recharging through the incorporation of a progressive, protective, curative, and corrective method. Watershed management can also be defined as a well-balanced and sufficient use of land and water resources to achieve optimum productivity with minimal natural resource risk. The preservation of soil fertility, the retention of water in basins, catchments, or watersheds, proper stormwater Sk. Mohinuddin (&)
P. K. Roy School of Water Resource and Engineering, Jadavpur University, Kolkata 700032, India e-mail: [email protected] M. B. Roy Department of Geography, Women’s College, Calcutta, Kolkata 700003, India T. Ghosh School of Oceanography Studies, Jadavpur University, Kolkata 700032, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_30

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management, flood protection steps, the removal of sediments in catchment areas, and the improvement in the efficiency of current land use can be accomplished by proper study and management of watersheds [7, 8]. The characteristics of the watershed depend primarily on the area’s scale, form, average slope, drainage density, various land use, soil type, and vegetation cover. Watersheds can range in size from a small number of hectares to a large number of square kilometres [7]. It has been reported that the average area of a watershed should be less than 500 km2 ± 50%, according to the Atlas prepared by AIS & LUS. This was further graded into sub-watersheds (area of 30–50 km2), mini-watersheds (area of 10– 30 km2), and micro-watersheds (area of 5–10 km2) as per the National Remote Sensing Agency [1, 9]. Many studies of morphometric analysis using Geographical Information Systems (GIS) and soil erosion have already been published [10]. In the morphometric study of sub-watersheds of the Pawagada area, Tumkur district, Karnataka, Gajbhiye [11] and Srinivasa et al. [12] used GIS techniques. Chopra et al. [13] conducted a morphometric study of Bhagra-Phungotri and Haramaja sub-watersheds of Gurdaspur district, Punjab. Khan et al. [14] used Remote Sensing (RS) and GIS techniques in the Guhiya basin, India, for watershed prioritization. Research on control dam placement by prioritising micro-watersheds using the sediment yield index (SYI) model and morphometric analysis using GIS was performed by Nookaratnam et al. [15]. Sharma et al. [16] carried out a morphometric study and prioritization of eight sub-watersheds of the Uttala river basin, which is a tributary of the Son River. This study uses updated and integrated ArcGIS hydrology methods to create the drainage network and delineate the borders of watersheds and micro-watersheds. The derived drainage network and micro-watershed boundaries were used as important input parameters for the morphometric analysis. Morphometric analysis is carried out to calculate and analyse the surface, shape, and varying dimensions of the landforms of the earth [17]. It is also useful to define the networks of surface drainage since it gives a detailed overview of different systems of surface drainage [18]. Morphometric analysis plays an important role in the interpretation of a given watershed’s hydrological activity [19–23]. It is possible to divide the different morphometric parameters generally into two categories: (i) linear aspect, (ii) aerial aspect. Linear parameters such as bifurcation ratio, drainage density, texture ratio, overland flow length, stream frequency is directly related to the erodibility of the watershed surface and subsurface soil, while shape parameters such as coefficient of compactness, circularity ratio, elongation ratio are inversely related to this erodibility factor. In addition to multiple morphometric analyses, watershed prioritization plays a very significant role in the conservation of soil and water, as well as in the management of natural resources [2, 15, 24–28]. The morphometric analysis and prioritization, therefore, provides us with immense data on different hydrological parameters, which effectively allows us to preserve the water and soil of a watershed. Sagar Island is a significant island of the Sundarbans and the area has now become vulnerable both geographically and ecologically, which effectively needs to

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preserve the water and soil of the watersheds. That’s why the proposed method has been used for the prioritisation of micro-watershed. In view of the above, this analysis was carried out with the specific goals of: (i) Calculation of the morphometric parameters of the micro-watersheds of Sagar Island using RS and GIS techniques and (ii) Prioritizing micro-watersheds using the compound value method and assessing their environmental planning and management priority rank and category.

Study Area It is known that Sagar Island is one of the largest estuarine islands at the confluence of the Ganges River and the Bay of Bengal. It is bounded by the Hugli River to the North and West, the Muriganga River to the East, and the Bay of Bengal to the South. The island is just 6.5 m above the normal sea level [29]. Tropical cyclonic events and tidal variations adversely influence the island. The area has now been vulnerable as well as geomorphically and ecologically. A ferry service across the Muriganga River links the island with the mainland. The population of the island is 212,037 [30], which relies primarily on agriculture, aquaculture, and seasonal tourism. The region has a gross geographical region of 231.83 km2 (2019) at latitudes of 21° 38′ N to 21° 42′ N and 88° 2′ E to 88° 10′ E (Fig. 30.1). The maximum length of the region and its width is 11 km and 7 km, respectively. There is plain terrain in the whole area and the height ranges from 0 to 14 m. This watershed’s general slope is from North to South. The average annual precipitation in this region is 154–168 mm.

Materials and Method Digitization from a published map was used in this case study to extract stronger and more detailed drainage networks in ArcGIS 10.7. For further research, this delineated drainage was used as the main input. Following the thumb rule of the National Remote Sensing Agency (Fig. 30.2), a total of 25 micro-watersheds were delineated. Remote sensing and GIS approaches have determined the basic parameters of each micro-watershed, such as area, perimeter, stream number, stream order, and elevation. Such parameters are important inputs for morphometric analysis. In this study, Horton’s law of stream order has been followed. Using standard formulas, linear basin parameters such as bifurcation ratio, drainage density, texture ratio, overland flow length, stream frequency, and aerial parameters such as compactness coefficient, circularity ratio, elongation ratio, and shape factor were measured. In the table below, the estimated values of all these morphometric parameters are shown (Table 30.1). The rating was applied to each of the linear and aerial parameters, considering the highest value as the linear parameter rank1 and

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Fig. 30.1 Map of the study area

the lowest value as aerial parameter rank1. By taking the average of all morphometric parameters (both linear and aerial), the additional compound parameter (CP) was estimated, and each of the micro-watersheds was assigned the final ranking. Based on their Cp score, all these micro-watersheds were classified.

Results and Discussion The geospatial technology-clubbed scientific approach provides accurate and reliable knowledge for the conservation of water and soil, which, in many ways, leads to the growth and maintenance of watersheds and their preservation for the availability of water resources for agricultural and other purposes. Morphometric analysis was used for the characterization of micro-watersheds, essentially concerned with the geometry of the watershed and its spatial extent. In the GIS environment, all parameters such as bifurcation ratio, drainage density, texture ratio, overland flow length, stream frequency have a direct relationship with the erodibility of the watershed surface and subsurface soil, while shape parameters such as compactness coefficient, circularity ratio, elongation ratio, and shape factor have been measured. The hydrological activity of a micro-watershed that is closely related to the geo-morphometric parameters was extensively analysed and observed. It is observed that the drainage networks have a maximum and minimum average

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Fig. 30.2 Micro-watershed of the study area

bifurcation ratio (Rb) of 1.88 and 0.00, respectively. Lower Rb value means high drainage density and uniform surface materials where the geology is fairly high are suggested. High Rb value indicates systemic regulation of drainage ways and also implies high average stream flood capacity when many tributary segments flow through comparatively few drainages carrying trunk segments. The maximum and minimum frequency of the drainages are respectively 0.98 and 0.00. The maximum and minimum drainage density values were found to be 1.24 and 0.00, respectively, for the delineated drainages. Based on land use land cover (Fig. 30.3), where the thick vegetation cover has been detected, where relief is low, low drainage density suggests highly permeable sub-soil content. The micro-watersheds were observed to have a maximum and minimum texture ratio of 0.326 and 0.000, respectively.

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Table 30.1 Compound values and priorities depending upon the morphometric ranks

Shape Factor

Bs

Cc

Rc

Linear Parameters Re

Compact Circularit ElongaƟo ness y n Co-eī RaƟo RaƟo

Lo Rf

SW_ID

Dd

Fs

Rb

T Length of AVERAGE over Drainage Drainage Texture BIFURCATION land Density Frequency RaƟo RATIO flow

SW_ID

Shape factor

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

95 100 91 81 85 81 74 90 71 80 83 72 82 83 88 82 96 77

79 77 85 81 87 67 71 76 73 76 80 77 77 90 80 73 76 100

74 77 63 70 60 100 90 79 85 80 72 76 78 57 71 85 78 46

87 85 89 95 92 94 98 89 100 95 93 100 94 93 90 94 86 97

75 71 78 88 83 87 96 79 100 89 85 99 87 85 81 86 74 93

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

21 62 37 30 46 80 52 40 16 29 79 62 47 48 3 50 46 100

11 42 29 52 46 65 62 15 43 19 68 41 31 55 9 15 23 100

21 62 37 30 46 80 52 40 16 29 79 62 47 48 3 50 46 100

18 100 53 0 0 18 18 18 0 0 71 0 0 53 0 0 26 44

15 99 48 78 79 60 40 18 45 0 93 42 52 63 19 0 43 25

19 20 21 22 23 24 25

79 76 90 81 89 85 80

84 84 79 89 72 94 98

65 64 73 57 89 51 47

96 97 90 94 90 92 95

90 93 79 88 80 83 89

19 20 21 22 23 24 25

37 39 41 98 0 0 0

21 25 31 82 0 0 0

37 39 41 98 0 0 0

0 0 18 71 0 0 0

27 0 36 92 0 0 0

SW_ID

CP VALUE

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

50 78 61 61 62 73 65 54 55 50 80 63 60 68 44 54 59 78

19 20 21 22 23 24 25

54 52 58 85 42 41 41

Form factor

Cp VALUE SW_ID 41 24 41 25 42 23 Very High 44 15 50 1 50 10 52 20 54 16 54 19 High 54 8 55 9 58 21 59 17 60 13 61 4 61 3 Medium 62 5 63 12 65 7 68 14 73 6 Low 78 2 78 18 Very Low 80 11 85 22

The maximum and minimum length values for overflow land were found to be 0.621 and 0.00, respectively. The maximum and minimum value of the elongation ratio of the delineated micro-watersheds is 0.80 and 0.67, respectively for the aerial

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parameters. Low values suggest an elongated shape of the tectonic regulation of stream growth. The micro-watersheds’ circularity ratio was observed to be in the range of 0.78–0.35. The lower ratio of circularity reveals the elongated form of the micro-watersheds. It was observed that the form factor was in the 0.50–0.36 range. The high form factor suggests long, narrow micro-watersheds. The coefficient of compactness was found to have a maximum and minimum value of 1.67 and 1.12, respectively. All the micro-watersheds were further evaluated for a better and more detailed evaluation and prioritization was carried out according to the calculated compound parameter value. The priority micro-watersheds were divided into five ranges, i.e., Very low, low, very high, medium, and high. The range of each category is shown in Table 30.2. The prioritized micro-watersheds are shown in Fig. 30.4 based on the morphometric analysis.

Fig. 30.3 Land use land cover of the study area

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Table 30.2 Range of compound parameter Sl no.

Category

Range of compound parameter

1 2 3 4 5

Very low Low Medium High Very high

>77 69–77 60–68 51–59 1 [1]. The AV Park can give the answer on the dual use of the land by another economic approach. When the algebraic sum of income generation from PV power (IPV) and that of income from agriculture (IAg) is greater than that of initial income from agriculture (I0Ag) having the relationship, ½I ðPVÞ þ I ðAgÞ [ I ð0AgÞ

ð37:1Þ

In Indian context, Agrivoltaic system is viable in the regions with high solar radiation level. Western part of India i.e. Gujrat and Rajasthan as well as Ladakh in the northern region is suitable for such application. High initial cost is an obstacle for such systems. Govt. of India is implementing few policies and regulations which will be beneficial for Agrivoltaic systems. RESCO model and KUSUM scheme are two of such government initiatives. Though more endeavour on loan, capital investment, land policy and grid interaction should be emphasized for successful implementation of Agrivoltaic in India [3]. The results of these researchers were conducted mostly in cold climatic countries and in normal land. However, no report is available in salt and marshy land in hot ambience. In the present paper AV studies conducted in salt marshy land to evaluate the AV performance.

Land is a Big Issue Land is a prime asset to a country. Wealth, development, economy all is directly dependent on the available land of a country. Land can be used for various purposes like building complexes, industry, agriculture or to produce electricity. Depending upon the economic structure, land is applied to a particular sector. Like in India

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agriculture is the backbone of economy, so it has most land consumption. On the contrary other sectors also need decent amount of land. If power sector is considered, thermal power plants are the most dominating till now in India taking 3–5 acre or 0.01214–0.02324 Km2 of land per MW of generation and these lands can’t be used for another purpose. While one of the most promising renewable energy that is, PV systems require 5 acre or 0.02324 Km2/MWp for multi crystalline silicon solar cell and 7 acre or 0.02833 Km2/MWp for thin film solar cell which is quite higher than thermal plants. In general, thermal plants require land more than 0.01416 Km2 per MW including the lands of coal mine and the mine area from where coal is extracted can’t be used for other purpose. On the contrary PV plants have clean and static environment which allows the lands under the PV plants to use for different purpose.

Study Area The Solar Park The study is conducted in Gujarat Solar Park situated at western part of India. The Gujarat Solar Park was initiated by the Gujarat Government in 2010 at Charanka village adjacent to little Rann of Kutch in West Gujarat. The park was planned with having the capacity of 500 MWp. The park contained three types of solar module and these are multi crystalline silicon (mc-Si), amorphous silicon (a-Si), and thin film CdTe. Government tendered the expression of interest from the developers for setting up power plants with a minimum capacity of 5 MWp to a maximum capacity of 25 MWp. The required lands for installation of such power plants were allotted. Till date 17 developers set up their power plants totalling to the capacity of 221 MWp. Long term studies indicate that the yearly variation of the radiation level is between 3.92 and 6.68 kWh/m2/day with about 40 cloudy or non-sunny days. So, the plants operate on average 325 days in a year. The DC power outputs from each of PV modules were connected in series and parallel combination in the multi-junction boxes (MJB). The output from MJB is connected to the input of high frequency inverters which are in the ranges of 50 Hz and 250–400 V AC output to convert into AC power. The AC power is then feed into a compact secondary sub-station (CSS) to convert it into 11 kV. The output from CSS is then feed into switch yard to convert it in 66 kV and integrated into the GETCO grid for proper blending (Fig. 37.1).

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Fig. 37.1 Block diagram of the solar park

Energy Generation It was mentioned in the previous section that the output from plants are connected with the inverting system and in absence of solar radiation the inverter went on to the sleep mode. The radiation level increases power generation and at a particular voltage the inverter wakes up and started exporting power to the GETCO grid. Studies indicated the average yearly generation from mc-Si plants are 205.87  106 kWh, a-Si is 87.514  106 kWh, and thin film CdTe is 76.532  106 kWh [4].

Plant Performance It was reported that PV modules used in the plant have the efficiencies in the range of 9–14%. The power conditioning units (PCUs) used for this purpose is 95–98% efficient. The power losses associated are,

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(i) (ii) (iii) (iv)

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Temperature loss, Cable loss, Radiation capturing loss, Mismatching loss.

Data related to the power generation including all the climatic conditions are collected in computer through the software known as Supervisory Control and Data Acquisition (SCADA) system. The data logger monitors the insulation level, module temperature, wind velocity direction, and generation output. Data can be accessed from anywhere in the country through internet networking system. The plants are according to the standard of IEC 61724 as per the International Energy Agency photovoltaic power system (IEA-PVPS) program TASK 2 [5]. The performance of the plant is analysed from the following figure of merits and these are, (i) Array yield (YA) (ii) Reference Yield (YR) (iii) Final yield (YF). From these parameters the following parameters were evaluated, (iv) Capture loss (CL) = [YR − YA] (v) System loss (SL) = [YA − YF]. Finally plant performance was evaluated by estimating, (vi) Performance ratio (PR). The generation diminished at higher ambient temperature. Therefore, one of the most prominent losses in the loss factors are due to rise in temperature. Studies conducted in six power plants of three 25 MWp capacity and three 5 MWp capacity made up from the materials like mc-Si, a-Si, and thin film CdTe. Typical results from present studies are presented in Table 37.1.

Table 37.1 Performance studies on three power plants

System parameters

mc-Si

a-Si

CdTe

Plant Capacity (MWp) Array Yield in a day (YA) Reference Yield/day (YR) Final Yield in a day (YF) Capture Loss (CL) System Loss (SL) Performance Ratio (%) Annual Export in GWh

5 5.27 5.76 4.83 0.49 0.44 83.85 8.83

5 5.14 5.76 4.71 0.62 0.43 81.77 8.61

5 5.20 5.76 4.77 0.56 0.43 82.81 8.71

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Impact of Agrivoltaic The studies also conducted to find out the agricultural production performance of the land under the shadow of the modules. The park was setup on a salt marshy barren land and hardly shrubs were seen at that area before the initiation of the park. Shades created by the PV arrays in the park reduce the evaporation rate of the soil and increases the water storage capacity which as a result increase the greeneries of the park. The shadow effect of the PV arrays also helped the growth of bacteria. Bacteria directly help to implant C and N2 in the land to increase the land fertility and grow vegetation. As commercial cultivation Tomato plants were cultivated over the land under the module and the results showed the yield of tomato is at par with expected results, but the size is comparatively smaller than open land. As the cultivation is done on a land where no cultivation was done before the initiation of the park the term LER which is defined in [1] will be always >1. To evaluate the impact of agrivoltaic, the power generation in terms of parameters defined earlier was calculated with different measuring units mentioned earlier in the energy generation and plant performance part. For agricultural part the performance is evaluated by additional annual income by selling the vegetables grown on that particular park. Studies also conducted to see the variation of radiation level both at adjacent to PV module as well under the PV modules. The results of studies are presented in Table 37.2.

Result and Discussion Comparison of AV Performance Results showed that for power performances mc-Si plants is the best, but in AV performance both a-Si and thin film CdTe gives the better performance than that of mc-Si. This phenomenon can be explained from the available radiation data presented in Table 37.2. The tedler sheet at the back of mc-Si module may be responsible for reducing radiation level below the module than that of a-Si and CdTe. Table 37.2 The variation of radiation level and AV performance PV module materials

Radiation level in W/m2 Top Bottom Ground surface surface surface

Open surface

mc-Si a-Si CdTe

740 740 740

740 740 740

58 68 68

62 70 70

Additional income from crops (%) 30 37 37

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Comparison of Energy Export in GWh Category 4 CdTe a-Si mc-Si 0

2 mc-Si

4 a-Si

6 CdTe

8

10

Category 4

Fig. 37.2 Comparison of Energy Export

Fig. 37.3 Comparison of Radiation Level at Ground Level

Fig. 37.4 Comparison of Additional income from crops

From Fig. 37.2, it is clear that energy performance of three different plants are not varying very much with best performance by mc-Si. But in Fig. 37.3 it is clear that radiation at the ground surface of mc-Si is lower than the a-Si and CdTe panels which results in lower agricultural performance for mc-Si than other two types represented in Fig. 37.4. As the AV performance is cumulative sum of PV and agricultural performance, AV performance of the mc-Si plant is lower than a-Si and CdTe plants.

Change in Soil Characteristics Change in soil surface characteristics below the PV module maybe due to the phenomenon of flow of charge in the humid reorganizing the dissolve ions in the

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soils. At comparatively higher temperature, the top glass surface of the PV panel increases the air temperature and decreases the air mass by reducing the density around the panel and it generates convective air flow over the glass surface of the panel which creates static charges on the glass surface of the panels and the charges are grounded and flows over the soil with the help of water available from rain or panel cleaning. These flowing charges reorganize the soil ion characteristics of the soil. The reorganization may initiate growth of bacteria those are fixing both N2 and C on the soil surface. More studies are on the way in confirming the causes for soil surface change in developing the ambience for growing plants and vegetables.

Improvement of Cation Exchange Capacity In soil matrix, most of the plant nutrients are available in complex compounds which are not usable to the plants. Only a small portion of simple and more soluble nutrients are usable by plants. Therefore, the fertility of a soil is dependent on how easily the complex compounds can be converted to the simpler forms. Mostly a plant takes soil nutrients which are in soluble form. Usually an atom becomes electrically charged when it is in a water solution. The useful ion for plants are aluminium (Al+++), calcium (Ca++), sodium (Na+), magnesium (Mg++), potassium (K+). Absorbing capacity of these nutrients in the form of Cation is called Cation exchange capacity. Proper distribution of these Cations in the soil surface depends on the negative charges. Negative charges hold the Cations with the help of electrostatic force. As discussed earlier, there are flows of charges all over the soil surface of the park which result in the increment of Cation exchange capacity of the soil and make the land useful for cultivation [6].

Improvement of Agricultural Land by Reducing Soil Salinity Soil salinity is referred as salt content in soil. If soil moisture is lowered by evaporation, it increases the concentration of soluble salts in soil like Ca, Mg, K, and Na. High concentration of soluble salts can interfere with the growth of plants. In case of the present study area, the land of the park was initially salt marshy. So, the reduced evaporation rate by the PV panels may decrease the soil salinity of the soil and soil become favourable to grow tomato plants over it.

Two Directional Dependency in AV System One of the obstructions of PV panels to generate electricity is dust deposition on the glass surface of the panels. If non rainy days last for a long time several layers of

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dust deposits on the glass surface and it creates obstruction to the sunlight to pass through the glass surface on the solar cells. It results in reduction of power generation up to several percentages. In AV system plants which grows under the panels holds the dust particles with its parts i.e. leaf, roots and consequently reduce the dust deposition rate on the glass surface of the panels. On the contrary, panels have a direct impact on the formation of local cloud which result in rain and provide water supply for the agriculture. So, in AV systems both PV system and agriculture helps each other and make the overall system more efficient.

Conclusion and Future Forecast The Agrivoltaic system will become a popular application in near future but more studies are required to know about the crops which can be feasible to develop under the shadow of the PV panels that will open up a new area of research. Researchers found that species with low root mass and high net photosynthetic rates are able to grow faster in low light conditions [7] and could be cultivated in AV plants. Some of such plants are Aloevera, Jojoba, and Cabbage [8]. Another good option is to install PV plants in already existing cultivated lands and convert it into an AV system. In this context, there is a good potential for AV system in Tea Gardens. In case of Tea plants shading is required for proper growth of the plants and due to this reason shading trees are provided in the Tea Gardens. So, if PV plant is installed over the Tea plants, it will provide shadow for the plants. But to implement AV system in existing cultivated lands some design parameters must be maintained i.e. the structural heights of the panels, etc. The growth of the plants depends on proper evapotranspiration rates and to maintain that rate PV module structure must be at optimal height with both evapotranspiration rate and Economy of the structure taking into account. If the land of food processing units and factories are concerned, they are already in use to contribute on food security but if PV plants are installed above that land and above the rooftops of factories then it can also be used to generate electricity and contribute to energy security without using any additional land. Another application area of PV system to ensure energy and food security is at different livestock farms. In case of these farms PV panels can give proper shading to the livestock and provide pleasant temperature. Thus, such system will perform sustainable development by contributing both in energy security as well as food security. Acknowledgements The authors wish to express their sincere gratitude to Prof. Biswajit Ghosh for his courageous support to do this work and are also thankful to School of Energy Studies, Jadavpur University for providing facility to complete this work.

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References 1. Dupraz C, Marrou H, Talbot G, Dufour L, Nogier A, Ferard Y (2011) Combining solar photovoltaic panels and food crops for optimising land use: towards new agrivoltaic schemes. Renew Energy 36:2725–2732 2. Marrou H, Dufour L, Wery J (2013) Eur J Agron 50:38–51 3. Santra P, Singh RK, Jain D, Yadav OP (2018) Agri-voltaic system to enhance land productivity and income. Indian Farming 68(09):108–111 4. Elavarthi P (2014) Gujrat Charanka solar park. Int J Mag Eng Technol Manage 1(7):25–32 5. Jahn U, Mayer D, Heidenreich M, Dahl R, Castello S, Clavadetscher L (2000) IEA-PVPS TASK 2: analysis of the operational performance of the IEA database PV systems. In: Proceedings of 16th EUPVSEC, VD 2.28, pp 1–5 6. David B. Mengel, fundamentals of soil Cation Exchange Capacity (CEC), Agronomy Guide, Purdue University Cooperative extension Service 7. Seidlova L, Verlinden M, Gloser J, Milbau A, Nijs I (2009) Plant ecology, 200(2):303–318 8. Lin S, Zhang Q, Chen Q (2007) Shade-tolerance of ten species of garden plants. J Northeast For Univ 35:32–34

Chapter 38

Feasibility Study on Energy Generation from Municipal Organic Waste Through Biogas Production Pramita Deb Sarkar, Pankaj Kumar Roy , Deep Ranjan Pal, and Malabika Biswas Roy

Introduction The World is going to face an energy crisis due to its increased demand and consumption. On the other hand, the reduction of fossil fuels needs to alternate source of energy that can fulfil the energy requirement. Various waste to energy conversion technologies has been introduced for the substitute process of energy generation. Through these technologies, the waste materials are further used as fuel for power production, transportation purpose and chemicals [1]. An Anaerobic digestion process is one of them which produced methane-rich biogas fuel by the degradation of different kinds of wastes. It is a green technology to convert energy from organic wastes to create environmental and public health benefits [2]. In municipal areas, the generated municipal solid wastes have been managed by Municipal Corporation, who have responsible authority for collection to disposal of municipal solid wastes [3]. The MSW is collected mainly from households and municipal areas [4]. The biodegradable municipal organic waste should separate P. D. Sarkar (&) Department of Civil Engineering, Netaji Subhash Engineering College, New Garia, Kolkata, India e-mail: [email protected] P. K. Roy Faculty of Interdisciplinary Studies, Law and Management, Jadavpur University, Kolkata, India e-mail: [email protected]; [email protected] D. R. Pal W.B.P.H & I.D.C. Ltd., Kolkata 700091, West Bengal, India e-mail: [email protected] M. B. Roy Department of Geography, Women’s College, Calcutta, West Bengal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_38

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from non-biodegradable waste at its source point. However, in most municipal areas, municipal organic waste is not separated and disposed of in mixing conditions at the open dumped landfill site. As a massive quantity of organic matter is present in MSW, it is decomposed under bacteriological action and released methane and carbon dioxide (biogas) to air [5]. The significant component of biogas is methane, which is used as fuel for energy generation [6]. By using the municipal organic waste for the generation of energy through biogas results to minimize methane emission from the landfill site and reduce environmental impacts. In different countries of the World, municipal solid waste is used to produce energy to diminish the landfilled organic materials and treated the waste before disposal [7]. In India, the ministry of new and renewable energy (MNRE) has implemented the programme on energy from municipal solid waste, vegetable market and slaughter house waste, cattle dung, etc. [8]. As per annual report of 2019–2020, the ministry has supported the different biogas schemes for deployment of biogas plants in remote, rural and semi-rural areas of the country and central financial assistance (CFA) has provided subsidy for these schemes [9]. According to this report, 12 numbers of projects have been commissioned where having biogas generation capacity of 1805 m3 per day corresponding power generation capacity of 212 KW [9]. Many researchers have completed the experiments on biogas technology and explored the concentration of biogas or energy generation from municipal organic waste [10–15]. pH and temperature are two important factors that affect the anaerobic digestion process [16]. Inoculums also help enhance the biogas generation rate [17, 18]. Kolkata is the largest city and capital of West Bengal. As per the census 2011, the total population is 4,486,679. In this city a huge quantity of municipal solid waste (MSW) is generated per day due to rapid population growth rate. Kolkata Municipal Corporation (KMC) is the urban local body (ULB) who is managed the generated waste under this city. At present, 144 numbers of wards of this city are present under this corporation area and seven numbers wards have practice source segregation. However, a large fraction of municipal solid waste dumped at Dhapa open dumping landfill site without any separation and treatment. The unscientific disposal system creates environmental and human health hazards. The aim of this study to analyse the capability of biogas generation potential from municipal organic waste under KMC area. If the municipal organic waste has potential for biogas production so it can be used for both energy origination and waste quantity minimization or treatment purposes. For this purpose, the separated municipal organic wastes have been collected under the KMC area for experimental purposes and three experiments have been performed in the laboratory by using different inoculums for finding the biogas and methane yield.

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Materials and Methods Sampling The segregated municipal organic wastes as primary feedstock has been collected from the Kolkata Municipal Corporation area and used after dried and blended. For inoculum purposes, the cow dung has been collected from a cattle farm located at Bottala, Santoshpur, Kolkata, West Bengal. At Dhapa open dumping site under KMC the waste is decomposed and generated liquid substance leachate which has been collected for inoculation purposes. Sewage water has been collected from the septic tank at the hostel of Jadavpur University, Kolkata, West Bengal. These inoculums have been used to enhance the rate of degradation of organic matter.

Mixing Proportions Three experiments using different inoculums have been performed in an experimental setup. In each experiment, different inoculums have been mixed with the feedstock in a 1:1 ratio, and after mixing, taken 800 ml slurry for experimental purpose.

Bench-Scale Model Develop for Biogas Generation A bench-scale model has been developed in the laboratory for the experimental purpose of biogas generation. A 1000 ml conical flask has been used as a batch digester, and a rubber cork has been attached for airtight. One end of the glass pipe has been inserted into the conical flask, and the other end has been attached with a plastic pipe. The end portion of the plastic pipe has been inserted into the 500 ml measuring cylinder. The cylinder has been filled up with water and placed inverted in position. The measuring cylinder with the plastic pipe has been placed in a beaker having a capacity of 1000 ml, which is filled up with water. Figure 38.1 represents the schematic diagram of the bench-scale model.

Experimental Operation for Biogas Generation Three experiments have been conducted under batch conditions for 30 days. The details of the experiments are given in Table 38.1. The rate of biogas generation per day has been monitored and measured by the water displacement method. The

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Fig. 38.1 Schematic diagram of the bench-scale model for biogas generation

Table 38.1 Details of experiments Sl. No.

Experiment no.

Mixing proportion

1 2 3

Exp. No. 1 Exp. No. 2 Exp. No. 3

Municipal organic waste: cow dung (1:1) Municipal organic waste: sewage water (1:1) Municipal organic waste: leachate (1:1)

approximate amount of methane and carbon dioxide present in biogas have been obtained by the syringe method.

Water Displacement Method The water displacement method has been used for the measurement of biogas generation and expressed as ml. In the conical flask, produced biogas has been inserted into the measuring cylinder. The volume of water has been displaced by the same volume of the produced biogas. The data of biogas generation has been measured by this method daily. Cumulative and daily biogas yield has been obtained by this method.

Syringe Method The syringe method has been used to measure the approximate amount of methane and carbon dioxide present in produced biogas. Dilute sodium hydroxide (NaOH)

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Fig. 38.2 Biogas collection through syringe

solution has been used for carbon dioxide (CO2) percentage estimation as NaOH absorbs CO2 but does not absorb methane (CH4). This method has been performed at time intervals. The syringe has been attached with a flexible tube and collected the produced biogas (Fig. 38.2). Then NaOH solution has been taken (Fig. 38.3) Fig. 38.3 Collection of NaOH solution

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Fig. 38.4 Blue flame confirms the presence of methane

and mixed up by shaking process. The NaOH solution absorbs CO2, so the rest of the biogas portion indicates the presence of methane. The burning of gas generates a blue flame, indicating the confirmation of methane gas presence (Fig. 38.4).

Analytical Measurements At the beginning and end of the digestion, different parameters like volatile suspended solid (VSS) and chemical oxygen demand (COD) has been measured to obtain the amount of biogas generated during the digestion process. The rate of biogas generation has been analyzed on basis of the destruction of VSS and COD. The other two important parameters like temperature and pH have been monitored in the digester at the time interval.

Results and Discussions The data of per day biogas generation have been collected by water displacement method, which is presented in Figs. 38.5 and 38.6 that represents the cumulative biogas yield of different experiments. By comparing the results of Figs. 38.5 and 38.6, it has been observed that experiment no. 1 gives more biogas yield than others in terms of per day and cumulative. In all experiments, the biogas has been generated per day in the nearest amount and reached the highest value before the 30th day. Initially, experiments no. 2 and 3 produce more biogas per day than experiment no. 1, but finally, it produces the highest amount of biogas. From Fig. 38.6, it has been obtained that the

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Fig. 38.5 Biogas generation per day

Fig. 38.6 Cumulative biogas generation

cumulative biogas volume has been produced by different experiments are 704, 695 and 611 ml, respectively. The percentage of methane and carbon dioxide presence in produced biogas have been found by the syringe method at 10 days interval, and the results are represented in Figs. 38.7 and 38.8. From Fig. 38.7, it has been obtained that experiment 1 generates more amount of methane than others. Volatile suspended solids (VSS) and chemical oxygen demand (COD) have been measured at the time of the pre and post-digestion process. The destruction of volatile suspended solids and chemical oxygen demand measures the rate of biogas generation, which is presented in Table 38.2

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Fig. 38.7 Methane generation (%)

80% 60% 40% 20% 0% Exp 1

Exp 2

Exp 3

% of CO2 generation

Fig. 38.8 Carbon dioxide generation (%)

60% 50% 40% 30% 20% 10% 0% Exp 1

Exp 2

Exp 3

Table 38.2 Biogas production from destruction of VSS and COD Experiment no.

Biogas produced in l/gm of VSS destruction

Biogas produced in l/gm of COD destruction

Exp. No. 1 Exp. No. 2 Exp. No. 3

0.68 0.51 0.42

0.69 0.68 0.67

From Table 38.2, it has been observed that experiment no. 1 produces more biogas in terms of destruction of VSS and COD than others. pH is another important factor that affects the biogas generation rate. Variation of pH during digestion time for experiment number 1 is presented in Fig. 38.9. From this figure, it has been observed that initially, there is decrease in the pH value due to the accumulation of volatile fatty acids. But, after that pH value again increases with an increase of digestion time. All the experiments have been performed under the mesophilic condition as temperature varies from 27 to 30 °C.

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Fig. 38.9 Variation of pH

Conclusion From the finding of this study, it has been concluded that the municipal organic waste under KMC area has potential for biogas generation in presence of inoculums. Cow dung is the best substrate than others to produce maximum amount of biogas and methane. This biogas technology can be adopted for treatment purposes of waste in KMC area in future as this gas is a clean and environment-friendly gaseous fuel for energy generation purposes. It is also a probable solution for waste minimization and utilization. Before the implementation of this technology a pilot-scale model has to be developed for optimum yield of biogas generation and the basis of that results in the biogas plant has to be designed and installed as per requirement. The source segregation practice should be mandatory for all the wards under KMC before the commencement of this processing. By the segregation process, the organic fraction of MSW should be separated that can be processed for the generation of biogas as alternative clean energy to developing a conservation, eco-friendly and sustainable environment. Acknowledgements The authors would like to acknowledge all staff members of Kolkata Municipal Corporation for helping the collection of samples and providing relevant information. Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest.

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References 1. Badgett A, Newes E, Milbrandt A (2019) Economic analysis of wet waste-to-energy resources in the United States. Energy 176:224–234. https://doi.org/10.1016/j.energy.2019.03.188 2. Zhu B, Zhang R, Gikas P, Rapport J, Jenkins B, Xiujin Li (2010) Biogas production from municipal solid wastes using an integrated rotary drum and anaerobic-phased solids digester system. Biores Technol 101:6374–6380. https://doi.org/10.1016/j.biortech.2010.03.075 3. Sharholy M, Ahmad K, Mahmood G, Trivedi RC (2008) Municipal solid waste management in Indian cities—a review. Waste Manag 28:459–467. https://doi.org/10.1016/j.wasman.2007. 02.008 4. Chaudhary SA, Schwede S, Eva Thorin, Yan J (2017) Enhancing biomethane production by integrating pyrolysis and anaerobic digestion processes. Appl Energy 204:1074–1083. https:// doi.org/10.1016/j.apenergy.2017.05.006 5. Xydis G, Nanaki E, Koroneos C (2013) Exergy analysis of biogas production from a municipal solid waste landfill. Sustainable Energy Technologies and Assessments 4:20–28. https://doi.org/10.1016/j.seta.2013.08.003 6. Can A (2020) The statistical modelling of potential biogas production capacity from solid waste disposal sites in Turkey. J Clean Prod 243. https://doi.org/10.1016/j.jclepro.2019. 118501 7. Sajeena BB, Madhu G, Sahoo DK (2015) Performance and kinetic study of semi-dry thermophilic anaerobic digestion of organic fraction of municipal solid waste. Waste Manag 36:93–97. https://doi.org/10.1016/j.wasman.2014.09.024 8. Bag S, Dubey R, Mondal N (2015) Solid waste to energy status in India: a short review. Discovery 39(177):75–81 9. Annual report 2019–2020, The ministry of new and renewable energy, Government of India 10. Dasgupta BV, Mondal MK (2012) Bio energy conversion of organic fraction of varanasi’s municipal solid waste. Energy Procedia 14:1931–1938. https://doi.org/10.1016/j.egypro. 2011.12.1190 11. Elango D, Pulikesi M, Baskaralingam P, Ramamurthi V, Sivanesan S (2007) Production of biogas from municipal solid waste with domestic sewage. J Hazard Mater 141:301–304. https://doi.org/10.1016/j.jhazmat.2006.07.003 12. Forster-Carneiro T, Perez M, Romero LI, Sales D (2007) Dry-thermophilic anaerobic digestion of organic fraction of the municipal solid waste: focusing on the inoculum sources. Biores Technol 98:3195–3203. https://doi.org/10.1016/j.biortech.2006.07.008 13. Hilkiah IA, Ayotamuno MJ, Eze CL, Ogaji SOT, Probert SD (2008) Designs of anaerobic digesters for producing biogas from municipal solid—waste. Appl Energy 85:430–438. https://doi.org/10.1016/j.apenergy.2007.07.013 14. Imu NJ, Samuel DM (2014) Biogas production potential from municipal organic wastes in Dhaka city, Bangladesh. Int J Res Eng Technol 3(1):453–460 15. Usman MA, Olanipekun OO, Kareem OM (2012) Biogas generation from domestic solid wastes in mesophilic anaerobic digestion. Int J Res Chem Environ 2(1):200–205 16. Carotenuto C, Guarino G, Morrone B, Minale M (2016) Temperature and pH effect on methane production from buffalo manure anaerobic digestion. Int J Heat Technol 34(2):425– 429 17. Ali JMA, Mohan S, Velayutham T, Sankaran S (2016) Comparative study of biogas production from municipal solid waste using different inoculum concentration on batch anaerobic digestion. Asian J Eng Technol 4(4):59–65 18. Tufaner F, Avsar Y (2016) Effects of co-substrate on biogas production from cattle manure: a review. Int J Environ Sci Technol 13:2303–2312. https://doi.org/10.1007/s13762-016-1069-1

Chapter 39

Plant Micronutrient Relationship with Water and Soil in Backdrop of Global Food Security Issue Sritama Chatterjee , Malabika Biswas Roy, Arunabha Majumder, and Pankaj Kumar Roy

Introduction Agriculture supports rural livelihood, generates employment among youth and serves the issue of food security which is a global concern. United Nations’ Committee on World Food Security defines food security as all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their food preferences and dietary needs for an active and healthy life which is accepted worldwide. This definition implies: availability of adequate nutritious food [1] to individual and its utilisation. The global food security issue posses a great threat to the developing countries in immediate future [2]. The ability of agriculture to support growing population is a global concern over generations [3]. Agricultural products of good quality are essential in spite of the fact that agriculture is governed by several constraints such as climatic condition, availability of skilled labourers, technical inputs and most importantly availability of sufficient water resources. Proper management of available water resources and to make safe water available easily to its consumer is a big challenge in front of world’s renowned organisations, both governmental and private as well as various non-governmental organisations. Water is a global concern for agriculture. Availability of adequate quantity and appropriate water quality for irrigation of crops and livestock management is essential. Depletion of groundwater level, uncertain rainfall and drying up of surface water bodies in tropical countries such as S. Chatterjee (&)
A. Majumder
P. K. Roy School of Water Resources Engineering, Jadavpur University, Kolkata, India e-mail: [email protected] P. K. Roy e-mail: [email protected] M. B. Roy Department of Geography, Women’s College Calcutta, Kolkata, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7_39

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India possess a great threat to the agricultural industry which raises the global food security concern. Wastewater resource reutilisation in this regard may be considered as a temporary solution for irrigation of crops to sustain agriculture in freshwater deprived areas and in areas where freshwater sources conservation is a challenging issue. The wastewater quality in this case forms a serious concern. WHO recommends utilisation of wastewater after treatment for agriculture and pisciculture purpose to make the wastewater free from bacteria, protozoa and other micro-organisms so that they do not affect the livestock [4]. In developing countries where treatment of supply water is still inadequate and population is increasing day by day, treatment of wastewater is a questionable factor in front of government as well as private organisations. This study has been executed in East Kolkata Wetlands area which has been declared as a Ramsar Site under article 8 of Ramsar Bureau of Convention on Wetlands in year 2002, [5–7]. The study area is a compendium of several man-made wetland bodies situated in the eastern part of adjoining metropolitan city of Kolkata, joining the north and south 24 Parganas districts of West Bengal, India. The rural part of this area provides the local stakeholders with opportunities of agriculture and pisciculture as primary occupation. The periphery of this area comprises of urbanised centres where tertiary sector is well developed and they depend on their food from the agricultural and pisciculture production generated from rural part of EKW. The wastewater generated from adjoining Kolkata metropolitan city flows through Dry Weather Flow Canal. The 50% of this wastewater is generated from domestic purposes and 30 per cent from commercial regions [8]. This area has a history of resource-reutilisation practice from several aspects. This study focuses on the wastewater as resource-reutilisation practice for growth of vegetable crops in certain parts of this region compared to freshwater irrigation practice in cultivation of vegetable crops in other parts of EKW region.

Sampling In EKW certain points have been identified and selected where wastewater irrigation practices are prevalent as shown in Fig. 39.1a marked as A, B, C and D. The wastewater samples have been collected from the nearby wastewater canals which is used for irrigation. Water samples have been also collected from control region where freshwater irrigation is in practice. The crop samples and soil samples have been collected from all four regions as well as from control region, detail provided in Table 39.1 for analysis in the laboratory. The crop samples so collected from the five sampling points as shown in Fig. 39.1b were of three types—root crops, leafy vegetables and vegetable plant. Four wastewater samples were collected at intervals of 6 h for each location and mixed homogeneously by shaking. Wastewater samples for general wastewater quality parameter analysis were stored at 4 °C for 24 h. Separate container was used

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a LEGEND EKW BOUNDARY WATER & WASTEWATER SAMPLING POINT SOIL SAMPLING POINT

Fig. 39.1 (a) Sampling points of water and wastewater irrigated land and soil sampling points in EKW area (b) Root crop, leafy vegetable and vegetable plant crop sampling points in EKW Area

for collection of samples for COD and heavy metal analysis where samples were pre-fixed with acid to maintain the pH < 2. Four number of soil samples were collected 2 to 4 cm below surface layer from three different points of crop field and mixed homogeneously before analysis. Crop samples were collected directly from the field from four different regions, washed, dried and grinded before analysis. In case of wastewater, soil and crops four heavy metals were analysed—zinc (Zn), copper (Cu), chromium (Cr) and nickel (Ni). A set of control was considered for the crops grown in EKW region but cultivated using freshwater. Soil samples and water samples were also collected from this point by composite grab sampling method. Water and soil samples collected from this point were analysed for the same general parameters analysed for wastewater and wastewater irrigated soil. Heavy metals zinc, copper, chromium and nickel were analysed for the water sample, soil sample and crops grown in control region.

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b

LEGEND CROP SAMPLING POINT A CROP SAMPLING POINT B CROP SAMPLING POINT C CROP SAMPLING POINT D CROP SAMPLING CONTROL REGION EKW BOUNDARY

Fig. 39.1 (continued)

Table 39.1 Wastewater, water, soil and food crop sampling locations within EKW S. no.

Sample type

Sampling regions

Latitude

Longitude

1 2 3 4 5 6 7 8 9 10 11

Wastewater

A* B# C** D## CONTROL*** A* B# C** D## CONTROL*** Root crop—Raphanus raphinistrium sativus Leafy vegetables—Spinacia oleracea Vegetable plant—Brassica oleracia botrytis Root crop—Raphanus raphinistrium sativus Leafy vegetables—Spinacia oleracea Vegetable plant—Brassica oleracia botrytis

22°31′34″N 22°31′42″N 22°31′35″N 22°30′30″N 22°30′29″N 22°32′11″N 22°31′43″N 22°31ʹ33ʺN 22°30ʹ44ʺN 22°30′29″N 22°32′11″N

88°25′56″E 88°25′16″E 88°26′35″E 88°30′08″E 88°32′10″E 88°25′58″E 88°25′16″E 88°26ʹ26ʺE 88°30ʹ51ʺE 88°32′10″E 88°25′58″E

22°32′21″N

88°25′43″E

22°32′17″N

88°25′48″E

22°31′43″N

88°25′16″E

22°31′45″N

88°25′15″E

22°31′47″N

88°25′06″E

Water Soil

Soil Crops from sampling point A

12 13 14 15 16

Crops from sampling point B

(continued)

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Table 39.1 (continued) S. no.

Sample type

17

Crops from sampling point C

Sampling regions

Latitude

Longitude

Root crop—Raphanus 22°31ʹ33ʺN 88°26ʹ26ʺE raphinistrium sativus 18 Leafy vegetables—Spinacia 22°31ʹ34ʺN 88°26ʹ29ʺE oleracea 19 Vegetable plant—Brassica 22°31ʹ36ʺN 88°26ʹ22ʺE oleracia botrytis 20 Crops from sampling Root crop—Raphanus 22°30ʹ44ʺN 88°30ʹ51ʺE point D raphinistrium sativus 21 Leafy vegetables—Spinacia 22°30ʹ44ʺN 88°30ʹ46ʺE oleracea 22 Vegetable plant—Brassica 22°30′43″N 88°31′40″E oleracia botrytis 23 Crops grown as Root crop—Raphanus 22°30′29″N 88°32′10″E control raphinistrium sativus 24 Leafy vegetables—Spinacia 22°30′20″N 88°32′33″E oleracea 25 Vegetable Plant—Brassica 22°30′18″N 88°32′41″E oleracia botrytis A*—Dhapa Region, B#—Chowbaga Region, C**—Bantala Region, D##—Karaidanga Region, CONTROL***—Bhojerhat Region

Methodology The wastewater samples were collected from four different locations of EKW. One region where wastewater is not used for irrigation was considered as control. The water sample collected from control region and wastewater samples collected from selected four regions by composite grab sampling method were analysed for eleven general parameters as well as four heavy metals content. pH meter with glass electrode probe was used for analysis of pH, TDS meter for analysis of Total Dissolved Solids (TDS), Turbidity meter for analysis of turbidity in water and wastewater samples. Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) were analysed manually by titrimetric method [9]. Total coliform (TC) and Faecal coliform (FC) bacteria for water and wastewater were analysed by most probable number (mpn) method in the laboratory [9]. Ammoniacal nitrogen, nitrate and phosphate were analysed by spectrophotometric method using UV-Vis spectrophotometer [9] and metals were analysed using Atomic Absorption Spectrophotometer. Soil samples were also collected from 2 to 4 cm below surface soil strata in composite grab sampling method for three different types of crops from sampling point A, B, C, D as well as region selected as control. The soil texture was analysed by manual sieving method. For analysis of pH, TDS, Turbidity, Electrical Conductivity (EC), soil samples were dissolved in distilled water and pH meter,

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TDS meter, Turbidity meter and Conductivity meter were used respectively for detection. Analysis was also performed for ammoniacal nitrogen, nitrate, phosphate, available sodium and available potassium by conventional methods along with estimation of four heavy metals—zinc, copper, chromium, nickel using Perkin Elmer AAnalyst200 Atomic Absorption Spectrophotometer for all the soil samples. 10 crop samples of each type i.e. root crop—radish (Raphanus raphinistrium sativus), leafy vegetable—spinach (Spinacia oleracea) and vegetable plant crop— Cauliflower (Brasicca oleracea botrytis) were collected from points A, B, C, D and control. These crops were segreggated, washed, dried and grinded before digestion to capture the inorganic heavy metal ions in the solution. Heavy metals analysis was performed using Atomic Absorption Spectrophotometer. The AAS was operated in flame mode with air acetylene flame for analysis of copper, zinc and nickel and nitrous oxide-air-acetylene flame for analysis of chromium.

Results and Discussions The results of wastewater and freshwater quality analyses for the water samples collected from EKW are summarised in Table 39.2. The general parameters determining wastewater quality and water used for control experiments show almost same pH value but markable difference in Total Dissolved Solids, ammoniacal nitrogen, Biochemical Oxygen Demand, Chemical Oxygen Demand, Total Coliform and Faecal Coliform values as well as in heavy metal concentrations. However, concentration of Total Dissolved Solids (TDS), Total Suspended Solids (TSS), ammoniacal nitrogen, nitrate, phosphate, Total Coliform (TC) and Faecal Coliform (FC) have same range of values for wastewater samples collected from the four different sampling points i.e. A, B, C and D. TC and FC values were found to be absent in water sample collected from control region where freshwater is used as source of irrigation instead of wastewater. But all other parameters including heavy metals though less in quantity compared to wastewater quality, were found to be present even in freshwater used for irrigation in control region. Figure 39.2 shows the heavy metal content in mg/L for water sample collected from control region and wastewater samples collected from sampling points A, B, C and D. The concentration of zinc recorded highest in water collected from control region. The concentration of nickel shows huge variation from first sampling point A compared to other sampling points and control. Highest concentration of Nickel was found in sampling point A with a value of 0.905 mg/L. Concentration of copper in sampling point A recorded the highest with 0.484 mg/L followed by sampling point D, C and B. Concentration of copper and chromium were least in the control region whereas concentration of Nickel was minimum in sampling point C.

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Table 39.2 Water quality analysis for different water and wastewater samples collected from EKW S. no.

Water quality parameters

1 2 3 4 5 6 7

pH TDS (ppm) TSS (ppm) Turbidity (NTU) Nitrate (ppm) Phosphate (ppm) Ammoniacal Nitrogen (ppm) Biochemical Oxygen demand (ppm) Chemical Oxygen demand (ppm) Total Coliform (mpn/100 mL) Faecal Coliform (mpn/100 mL) Copper (ppm) Chromium (ppm) Nickel (ppm) Zinc (ppm)

8 9 10 11 12 13 14 15

Average wastewater quality in EKW

A 7.6 1028 298 16 41.5 13 41.5

B 7.78 1600 496 3.96 21.6 12 21.6

C 6.67 1896 556 4.32 3.51 13 3.51

D 6.53 1035 400 1.79 1.3 13 1.30

Freshwater quality in EKW Control 6.45 1359 356 16.90 0.3 13 0.30

44

46

20

45

13

638.76

642.82

799.00

650

114.28

1.44  104

14  104

19  104

12  104

–

3.7  103

52  103

88  103

37  103

–

0.484 0.014 0.905 0.096

0.072 0.049 0.024 0.252

0.112 0.081 0.011 0.366

0.120 0.069 0.014 0.351

0.020 0.012 0.015 0.536

Fig. 39.2 Heavy metal concentration in wastewater collected from Sampling points A, B, C, D and water sample collected from Control region

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The order of concentration of selected heavy metals in soil showed Zn > Cu > Cr > Ni as relevant from Fig. 39.3. Concentration of zinc in soil was recorded highest in the region selected as control for the experiment accounting for 66.542 mg/Kg. Concentration of chromium in soil was recorded highest in sampling point D whereas the concentration of nickel and copper were recorded highest in sampling point A. The above Fig. 39.4 represents the heavy metal concentration in three types of food crops—Brassica oleracea botrytis (Cauliflower), Spinacia oleracea (Spinach) and Raphanus raphinistrium sativus (Radish) cultivated in the four sampling points A, B, C and D using wastewater for irrigation. In all cases Spinacia oleracea showed high accumulation rate for all heavy metals except for sampling point A where chromium accumulation rate in plant parts was minimum compared to other vegetable crops. Brassica oleracea botrytis showed minimum accumulation rate for all the heavy metals in each case among the three types of vegetable crops except for sampling point A where Brassica oleracea botrytis showed high accumulation rate for chromium. However, in sampling point B as shown in Fig. 39.4b. the concentration of chromium and nickel were higher in brassica oleracea botrytis compared to the accumulation in Raphanus raphinistrium sativus.

Fig. 39.3 Heavy metal concentration in soil sample collected from Sampling point A, B, C, D and Control region

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Fig. 39.4 (a) Heavy metal concentration in three types of crops in sampling point A (b) Heavy metal concentration in three types of crops in sampling point B (c) Heavy metal concentration in three types of crops in sampling point C (d) Heavy metal concentration in three types of crops in sampling point D

Figure 39.5 shows the distribution of heavy metals in three different crops grown in Control region without wastewater irrigation. The concentration of heavy metals Cr, Ni, Cu and Zn were highest in leafy vegetable crop i.e. spinacia oleracea or spinach. All the four heavy metal presence were also detected in root crop i.e. Raphanus raphinistrium sativus or radish and vegetable crop i.e. Brassica oleracea botrytis or cauliflower but the concentration level for Cr and Ni were much lower compared to concentration of Cu and Zn.

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Fig. 39.5 Heavy metal concentration in three types of crops grown in Control region

Conclusion The general parameters determining wastewater quality for selected four regions and water quality for control experiments show almost same pH value but markable difference in Total Dissolved Solids, ammoniacal nitrogen, Biochemical Oxygen Demand, Chemical Oxygen Demand, Total Coliform and Faecal Coliform values as well as in heavy metal concentrations indicating different source of water for control region from other sampling points. The water from control region has total and faecal coliform absent which confirms no open defaecation is practised in this region and the source is not a wastewater. The BOD and COD values are far lesser than those observed at other four sampling points confirming lesser organic and inorganic load. However, turbidity value is the highest indicating presence of suspended load and dissolved load in the freshwater sample collected from control region. Heavy metal concentration in the water collected from control sampling point was much lesser compared to the heavy metal concentration in wastewater of sampling points A, B, C and D. High concentration of chromium in sampling region B, C and D affirms source of chromium pollution in vicinity of these regions indicating presence of industry/industries generating chromium concentrated effluent which gets discharged into the nearby wastewater source utilised for irrigation of crops grown in this region. Similarly, high concentration of nickel in

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wastewater of sampling region A substantiate mixing of nickel concentrated effluent discharged directly or after partial treatment from industry/industries located nearby this sampling point into the wastewater utilised for irrigation of crops. Concentration of zinc and copper are high in both wastewater in sampling points A, B, C, D as well as water in control region used for irrigation of crops. Concentration of Total Dissolved Solids (TDS), Total Suspended Solids (TSS), ammoniacal nitrogen, nitrate, phosphate, Total Coliform (TC) and Faecal Coliform (FC) have same range of values for wastewater samples collected from the four different sampling points i.e. A, B, C and D implying wastewater source is common for these four different sampling points. TC and FC values are quite high indicating domestic wastewater as a source and practise of open defaecation in this region. The BOD load is high with similar values for wastewater sampling points indicates presence of huge quantity of organic load. The organic load may be from domestic wastewater sources as well as decomposition of organic matter such as plant parts and decomposition of microbes. COD load is high for sampling points A, B, C and D which confirms incoming of untreated or partially treated chemical effluent in wastewater from nearby regions. Heavy metals were found to be present in all soil samples with zinc having the highest concentration compared to copper, chromium and nickel. The concentration of zinc in soil is reasonably higher for both wastewater irrigated sampling points and freshwater irrigated control region. The next higher concentration of heavy metal followed by zinc is copper. Copper is a naturally occurring element in soil and essential for plants metabolism. Nickel and chromium are found in trace amount in soil. However, concentration of nickel and chromium were higher in the soil of control region. The four different types of crop i.e. root crop, leafy crop and plant crop shows presence of heavy metals in them in different concentrations. The accumulation of heavy metals was highest in Spinacia oleraca (spinach) i.e. leafy crops followed by Raphanus raphinistrium sativus (radish) i.e. root crop and least in florets of Brassica oleracea botrytis (cauliflower). The distribution of heavy metals showed a trend for high accumulation of zinc followed by copper. This suggests that accumulation rate of zinc is highest among the four selected heavy metals followed by copper in plants. The result also suggests that availability of zinc in suitable form is present in soil which can be readily absorbed by plants leading to highest concentration of zinc in plants. Zinc is an essential plant micro-nutrient which helps in synthesis of proteins by enzyme activation [10]. Copper is another essential micro-nutrient which acts as cofactor for many enzymes present in plant [11, 12]. It also helps in electron transfer to different parts of a cell in plants. Chromium and nickel showed trace level accumulation in plant parts with variable results. Concentration of chromium was observed high in sampling point B, C and D which substantiate accumulation rate of chromium is higher in wastewater irrigated region compared to crops irrigated in control region. The availability of micro-nutrients to plants is from soil and irrigated water. This is why there is no synchronisation between higher heavy metal concentration in water or wastewater used for irrigation with high accumulation rate of heavy metals

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in plant parts. The distribution pattern of heavy metal concentration in soil will not indicate the same pattern of distribution of heavy metals in the plants grown in that soil. The form of metal which has higher capacity to accumulate in plant parts is important. Heavy metals which also act as essential micro-nutrients for plants and is essential for human consumption to sustain their life processes is also dependent on accumulation rate of specific metals at specific sites of the plants. The concentration of heavy metals in plant parts suitable for human consumption depends on quantity ingested and quantity required for consumption by the body over time but its concentration in plant parts is auto-regulated by plant metabolism. In present context of food security, availability of food rich in sufficient micro-nutrients for consumption is an important issue. This demand for micro-nutrient rich food will increase in future with a decreasing trend of total food consumption. So, irrigation with wastewater rich in micro-nutrients or heavy metals should be supported provided the accumulation rate of heavy metals in plants is not toxic and the vegetable consumed by humans over time does not exceed allowable intake capacity based on their body weight. The contamination of chromium in wastewater may be attributed to partially treated or untreated effluent discharge by tanneries located near the site. Similarly, the contamination of nickel in wastewater may be attributed to partially treated or untreated effluent discharge by battery industries in locality. The available form of chromium for plants is readily available in sampling region B, C and D which resulted in high accumulation by plant parts. Similarly, the available form of nickel for plants is easily available in sampling region A accounting for high accumulation rate of nickel. Acknowledgements I would like to express my gratitude to School of Water Resources Engineering for supplying me with laboratory support and to my co-authors for their continuous involvement during my experimental work. I extend my thanks to University Grants Commission for their financial support and also the Director, School of Water Resources Engineering for providing me guidance during my work.

References 1. FAO(2003) Trade Reforms and Food Security: Conceptualizing the Linkages (Food and Agriculture Organization of the United Nations) 2. Rosenzweig C, Parry ML (1994) Potential impact of climate change on world food supply. Nature 367:133–138. https://doi.org/10.1038/367133a0 3. Rosengrant Mark W, Cline Sarah A (2003) Global food security: challenges and policies. Tragedy Commons, Sci 302:1917–1919. https://doi.org/10.1126/science.1092958 4. WHO guidelines for the safe use of wastewater, excreta and greywater (2006) vol-1, World Health Organization, France, 2006. p 35 5. Roy PK, Majumder A, Mazumdar A et al (2011) Impact of enhanced flow on the flow system and wastewater characteristics of sewage-fed fisheries in India. J Environ Sci Technol 5 (7):512–521 6. Kundu N, Pal M, Saha S (2008) East Kolkata Wetlands: A Resource Recovery System through Productive Activities. In: Sengupta M, Dalwani R (eds) Proceedings of Taal 2007:

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Conserving Lakes and Wetlands for Future. The 12th World Lake Conference, Jaipur, October–November 2007, pp 868–881 Roy MB, Roy R, Roy PK et al (2015) Education is a tool for conservation of East Kolkata Wetland in West Bengal: a case study. J Chem Pharm Res 7(2):95–101 Chatterjee S, Roy PK, Majumder A, et al (2020) Water to Wastewater—Use, Reuse and Management from Perspective of Water Quality. In: Prof. Himadri Chattopadhyay, Prof. Sudipta De (eds) ICESD’2020: topics in Water Conservation and Management. The 1st International Conference on Energy and Sustainable Development, Kolkata, February 2020, p 385 Rice EW, Baird RB, Eaton AD (eds) (2012) Standard Methods for Water and Wastewater Analysis, APHA, 22nd edn. APHA, AWWA and WEF, Washington,DC Brown PH, Cakmak I, Zhang Q (1993) Form and Function of Zinc Plants, In: Robson A.D. (eds) Zinc in Soils and Plants. Developments in Plant and Soil Sciences, vol 55. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0878-2_7 Yruela I (2009) Copper in plants: acquisition, transport and interactions. Funct Plant Biol 36 (5):409–430 Pilon Marinus, Abdel Salah E, Ghany Christopher Cohu et al (2006) Copper cofactor delivery in plant cells. Curr Opin Plant Biol 9(3):256–263

Index

A Acid digestion, 102, 103 Activated alumina, 140, 175, 277, 278 Adsorbent dose vs. removal of hexavalent Cr., 287–294 Adsorption, 99, 100, 102, 104–107, 129–134, 136, 139, 140, 170, 171, 174, 175, 180–182, 277, 278, 287–296 Agarose gel electrophoresis, QnrA gene, 123, 125 Aggregate gradation for bituminous concrete preparation, 261 Aggregate Impact Value (AIV), 270 Amdanga study area, 85–89, 91, 94, 220 Amount (%) of major land uses, 373 Angular momentum principle, 11, 13, 16 Anionic resin, 139, 140, 143 ANOVA, 92, 173, 338, 342 Antibiotic-Resistant Genes (ARGs), 124–126 Antibiotic resistant P. rustigianii and P. Aeruginosa, 126 Areal aspects, Kuya River, 441, 442, 445, 447–450, 453 Argentometric method, 142, 153, 220 AR Model, 234–238, 240 AR Model, rain days; period 1951-2004, 236 AR Model, rain spells during 1951-2004, 235 Arsenic and Iron Removal Plants, 169, 171, 172 Arsenic in tube-well, 221, 224 Arsenic removal on alum coagulant and BP dose, 281 Arsenic removal on coagulants and BP dose for initial (high level) As cconc., 283

Arsenic removal on coagulants and BP dose for initial (low level) As conc., 282 Arsenic removal on coagulants and BP dose for initial (medium level) As cconc., 283 Arsenic removal on ferric chloride coagulant and BP dose, 282 Arsenic removal on ferrous sulphate coagulant and BP dose, 282 Arsenopyrites, 139 Articles on River Ganga and River Nile, 319–326, 328 Author’s role, 33 Average compositions of mixed C&D waste, 464 B Baduria block, 172 Basic Properties of bentonite, 101 Basic Properties of Fly Ash, 101 Basic Properties of Laterite Soil, 100 Basin boundary, soil loss, Google earth image, TOPSIS rank, 435 Bay of Bengal (BoB), 233, 389, 403, 460 Bench-scale model for biogas generation & details, 525, 526 Bibliometric study, 41 Biogas collection by various methods, 527 Biogas generation per day, 525, 529 Biogas production from destruction of VSS and COD, 530 Biomass Calculation of Major species, 507 Block diagram of the solar park, 516 Blue flame of methane, 528 BOD & COD of in different seasons, 498

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Roy et al. (eds.), Advances in Water Resources Management for Sustainable Use, Lecture Notes in Civil Engineering 131, https://doi.org/10.1007/978-981-33-6412-7

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548 Borough, 149–151, 152, 156, 158, 159, 165 Break Specific Fuel Consumption (BSFC), 476 Buffer area of Gumti Wetland, 335 C Calculation for surface run-off, 505 Calorific value for gasoline, 487 Canal embankment, 3–5, 7–9 Carbon di oxide generation (%), 530 Category and amount of soil loss, 430 Cationic resin, 139–141 Causes of degradation in Gumti wetland, 337 C & D Waste generation, 461 Central Pollution Control Board, 190, 459, 467 Chandipur-Erashal Urban Region: its buffer and hinterland, 364, 367, 369–371 Chemical Attributes of Soil of Murti, 508 Chemical Oxygen Demand (COD), 501, 528, 529, 537, 538 Chemical Runoff and Erosion from Agricultural Management System (CREAMS), 414 Chlorophyll concentration for the post-monsoon season of 2019, 397 Chlorophyll concentration for the pre-monsoon season of 2019, 396 Citation per Publication, 39, 42, 43 Citations of River Ganga and River Nile articles, 323 Coarse Natural Aggregate (CNA), 269, 270 Coarse Natural Stone Aggregate (CNSA), 269 Coarse Recycled Concrete Aggregate (CRCA), 269, 270 Coefficients of WPI components, 357 Comparison of Additional income from crops, 519 Comparison of BSFC vs Load for different biodiesel blends, 477 Comparison of Energy Export, 519 Comparison of Radiation Level at Ground Level, 519 Comparison of TSR of plastics, 83 Composition of Construction & Demolition Waste, 460 Compound values, morphometric ranks, 406 Computation of best & worst value, TOPSIS, 435 Constraints of farmers, 307 Contact time effect on removal of hexavalent Cr., 289, 292, 293 Correlation of As and Fe in groundwater at Habra Block-II, 225

Index Crop management factor (C factor), 417, 423, 424, 428, 429, 433 Crumb Rubber Powder (CR), 261 Cumulative biogas generation, 529 D Date of acquisition and file name of the Landsat 8, 391 Depth vs. As and Fe concentration, 224 Detecting limit (DL) of heavy metals, 315 Deviations of Heavy Rain days in trend lines, 236 Deviations of Moderate Rain days in trend lines, 237 Diagram of C & D Waste management, 466 Dissection Index of Kuya River, 450 Dissolved Oxygen, 150, 192, 194, 204, 207 Dredging, 57–64, 67–70, 337 DS and PDS from trend lines, 239 Dumping of C&D wastes, 462 E Econometric model on water management institutions, 305 Effect of disinfectants & biocide on Gram-negative bacteria, 124 Effects of heavy metal exposure on WBC, 314 Environmental Information System, 188 Escape velocity, 15, 17, 20 Estimation of soil loss, 433, 434 Estimation removal at higher Cr (VI) conc, 295 Eutrophication, 204, 343, 413 Existence of Chandipur-Erashal urban region, 369 Experimental setup of test engine, 475 F Fecal coliform, 213, 499, 501 Flood plain Health, 52 Flow Accumulation & Direction, Slop, LS-factor map, 422 Flow chart in processing the data, 392 Flow chart of methodology, 367 Flow chart showing flood mapping, 115 Flow Diagram C&D management, 465 G Generalized diagram, porous asphalt pavement, 77 Geographical Information System (GIS), 25, 413, 417, 433 Geogrid, 3–10

Index Geotechnical criteria of Mixed Soil, 102 Ghatal block, 109, 110, 112, 115, 416 Groundwater recharge, 226, 425 Ground water well level data (2018), 251 Gully and rill formation within Shilabati basin, 417 H Heavy metal concentration in crops, 538–540 Heavy metal concentration in crops grown in control region, 535 Heavy metal concentration in soil sample, 540 Heavy metal concentration in wastewater, 539, 542 Heavy metal removal efficiency, 101, 104 Hydrological cycle, 59 Hyperpigmentation, 139 I Illustrative variables in the model, 341 Indian and Egyptian top 3 institution on Ganga research, 328 Indian standard, 139, 170, 174, 190, 210, 217, 219, 347, 466 Indirect Tensile Strength (ITS), 79, 259, 262 Inductively Coupled Plasma Mass Spectrometry (ICP-MS), 311 Initial adsorbate, 129, 134–136 Inter-granular distance, 11, 12, 20 Iron in tube-well, 224 Isotherm plots for Cadmium, Nickel, and Zinc, 105 Isotherm study, 294, 295 ITS and TSR reports, 262, 263 K Kriging, 159 Kruskal-Wallis, 178 Kurtosis, 189, 191–193, 196, 198, 199 Kuya River Stream Order, 444, 445 L Landsat 8 OLI and TIRS, 367 Land Surface Temperature (LST) in 2018, 252 Land use and land cover classification - C factor, 423, 428, 436, 441 Land use and land cover map, 423 Land use land cover, 405, 498 Leukocyte carcinoma or leukemia, 311 Linear aspects of Kuya River, 447 Linear regression Gumti wetland, 333, 338, 340, 341

549 LISS-III image of wetland area, post-monsoon, 336 LISS-III image of wetland area, pre-monsoon, 336 Log survival of isolates against disinfectants, biocide, 125 Longitude and cross-section profiles, urbanization, 374 Los Angeles Abrasion (LAA), 260 Low-Density Polyethylene (LDPE), 270, 271 LS and SS from trend lines, 239 M Main effects plot percentage arsenic removal using alum coagulant, 284 Main effects plot percentage arsenic removal using ferric chloride coagulant, 284 Main effects plot percentage arsenic removal using ferrous sulphate coagulant, 284 Major responsible factors for growth centre, 375 Mann Kendall, 26 Mann-Kendall trends in rain spell; period 1951-2004, 238 Map of the study area of wetlands, 334 Marshall Parameters for control mix, 273 Marshall Parameters for OBBA mix, 264 Marshall Parameters for RCA mix, 274 Marshall set up, 263 Marshall test, 81, 82 Mechanisms of chlorine disinfectants & biocide resistance, 124 Mesh diagram, 6 Methane generation (%), 530 Methodological flow chart, 367 Methodological flow diagram of the study, 368 Micro watershed, 402, 403, 405–409, 430 Mileage for the test vehicles, 491 Minor Irrigation Department (MID), 301, 302, 304 Morphometry of Kuya River, 442 Morphometry of Shilabati basin, 415, 425, 428 Mouzas with its area and population, 369 Multi Criteria Decision Making approach (MCDM), 415 Multiple Linear Regression (RWH), 87, 92, 93 N NAPA gradation for preparing mix, 81 NDVI vs. NDMI, Correlation, 249 NDWI vs. NDMI, Correlation, 250 NDWI vs. NDVI, Correlation, 251

550 Normalized Difference Moisture Index (NDWI), 246–253 Normalized Difference Vegetation Index (NDVI), 249, 250 Normalized Difference Water Index (NDWI), 246–253 Numerical modelling, 4, 5 O Oil bath temperature vs. Thermocouple temperature, 476 Operational Land Imagers (OLI), 367, 390, 418 Optimum Binder Content (OBC), 75, 82, 83, 262, 269, 272 Optimum Polymer Content (OPC), 269, 272, 273 Outcome of flood simulation, 116 Over Burnt Brick Aggregate properties (OBBA), 259–262 Overland flow, Kuya River, 445 P Particle arrangement, 11, 13, 14 Particle escape velocity, 11, 12, 15, 17, 20 Pathogen, 121, 122, 126, 127, 209, 213 Paver block casting for recycling C&D waste, 466 Pearson Product Moment Correlation, 155 Per capita energy consumption and vehicle mileage, 492 Per capita energy consumption of test vehicles, 491 Per capita resource index, 354 Percentage Yield of Methyl esters, 473 Performance studies on power plants, 517 Peripheral blood smear showing leukaemia in WBC, 314 Peripheral blood smear showing normal WBC, 314 Permeability test results, 82 Permeability test set up, 79, 80 Permeability values for different plastic, 83 PH effect on removal of hexavalent Cr., 291–294 PH, Turbidity, TDS & TSS of Neora in Seasons, 500–502 Physical water use efficiency in institution regimes, 307 Physicochemical properties of methyl ester from waste oil, 474 Platykurtic, 192, 194, 196, 199 Plaxis 3D, 3–6, 9 Polyethylene Terephthalate (PET), 270, 271 Polymers, 269–273, 275

Index Polypropylene, 3, 172, 259 Poor and interrupted drainage in the study area and map, 379 Population density (census 2011), 355 Pre-monsoon, post-monsoon distribution, CDOM and turbidity of 2019, 394, 395 Prioritized micro watershed: morphometric analysis, 408 Prioritizing sub-basin via TOPSIS, 431, 433, 436 Process Parameters of feedstock, 472 Properties of bitumen, 81 Properties of course NA and CRCA, 270 Properties of fine NA and filler, 271 Properties of normal aggregate and plastic coated aggregate, 77 Properties of plastic, 78 Properties of Super-Bond A-99, 79 Properties of VG30 binder, 261, 272 Proposed managemental ways of respondent’s perception, 382 Q Quality of life index, 357 Quantitative Polymerase Chain Reaction (qPCR), 313 R Rainfall Characteristics of the Shilabati river, 1998–2018, 419 Rainfall distribution, 419 Rainfall Erosivity factor, Shilabati River, 417, 419, 420, 429 Rainfall profile of Amdanga, 89 Rainwater Harvesting (RWH), 85 Range and levels of experimental factors, 281 Range of compound parameter, 404, 407, 408 Raw water quality, 279, 280 Real and the simulated floods, 118 Regression coefficients, 99, 357 Regressions, Coefficient (R2), Correlation (r) of Arsenic vs Iron, 225 Relationship of basin & sediment delivery, 430 Relationship of basin relief ratio & sediment delivery ratio, 430 Relative fold changes in expression of efflux pump (ARGs), 125 Relative Relief, Kuya River, 448 Relief aspects of Kuya River, 431, 448 Residential cum CBD congested zone and maps, 378 Revised Universal Soil Loss Equation (RUSLE), 413, 414, 417 Rhodamine B, 134, 136

Index River Ganga, river Nile related publication, world publication, 322, 323 River health, 25, 39–45, 47–53 Rooftop Rainwater Harvesting (RRWH), 86 Root Crop, Leafy Vegetable & crop sampling in EKW Area, 534–539 Rotovator Cutter Suction, 66 Route map for simultaneous operation of GPS devices, 488 Ruggedness Index Kuya River, 441, 448–450, 453 RUSLE factors, 428, 429 S Scanning electron microscopy, 132 Sediment Assessment Tool for Effective Examine Control (SATEEC), 414 Service area of tube wells, 353 Shape and Linear factor, 409 Shilabati River basin, 417, 419, 427, 431 Signal-to-Noise Ratio (SNR), 390 Silver diethyldithiocarbamate, 153 Simple Cubic Model (SCM), 11, 12 Slope Kuya river, 449 Soil Erodibility Factor (K), 417, 420, 427, 429 Soil map of Lataguri, 508 Spatio-temporal change in area and population, 368 Spatio-temporal change of settlement and transport, 372 Spatio-temporal change of water bodies and vegetation, 372 Spatio-temporal existence of study area and LULC survey, 371 Specifications of test Engine, 475 Specific Gravity (SG), 78, 81, 259–261, 272 Spectral bands and central wavelength of Landsat-8, 391 Speed-time variations, 489 Sprawling of the growth centre, 384 Stability of slopes, 4 Stability versus Bitumen content, 82 Standard deviation, 90, 92, 94, 169, 173, 176, 178, 189, 191, 192, 196, 248, 313, 339, 341, 344, 484, 498 Standard Precipitation Index, 248, 252 Status of Arsenic in Amdanga, 89 Steps curve, 9 Stream order, 403, 444–447 Stress-strain curve, 7 Study area, Sagar Island, 401, 402 Study area (tube well), 279 Study Routes in Kolkata, 485

551 Surfer 10 software, 155 SWOC analysis of the study area, 363, 382 T Tank management institutions, 304 Temperature effect on removal of hexavalent Cr., 292 Tensile Strength Ratio (TSR), 79, 259, 262 Test Vehicle specifications, 484, 485 Thermal gravimetric analysis, 130 Thermal Infrared sensor (TIRS), 367, 390 Top 10 subject categories, 324 Top 5 countries in Ganga and Nile research, 327 Top 5 journals for article on Ganga river, 325 Top 5 journals for article on Nile River, 325 Topographic Complexity Kuya River, 441, 442, 453, 454 Topographic factor, 421 TOPSIS model, 415, 426, 432 Total alkalinity and acidity of Neora in different seasons, 502 Total Biomass vs. Total Exchange cations, 507 Total coliform and fecal coliform of Neora in different seasons, 501 Total suspended matter for the pre-monsoon of 2019, 395 Traffic characteristics of routes, 490 Transportation of C&D waste, 463 Transport Factor (nT), 69 Transport network, of Chandipur-Erashal, 367, 369 Trend analysis, 31, 32, 39, 43, 50–52 Truncated Pyramid Model (TPM), 11, 12 U United State’s Environmental Protection Agency, 170 Universal Soil Loss Equation (ULSE), 414, 417, 438 V Variation in impact value of CRCA with polymer content, 271 Variation of BSFC vs. Load for WRBME, 477 Variation of BSFC vs. Load for WSOME, 476 Variation of BSFC vs. Load for WSUME, 477 Variation of flow value with CR, 265 Variation of flow value with polymer content, 275 Variation of fuel density, 487 Variation of radiation level and AV performance, 518

552 Variation of stability value with CR, 262, 264 Variation of stability value with polymer, 274 Variation of Temperature vs. load for WRBME, 478 Variation of Temperature vs. load for WSOME, 478 Variation of Temperature vs. load for WSUME, 478 Vertical column experimental setup, 102, 103 Vertical column test and Hydrus, 106 VG30 binder properties, 260–262, 272 Volatile Suspended Solid (VSS), 528 VOSviewer, 39, 42, 44, 45, 47, 48, 53 W Waste Camellia sinensis, 129, 130 Waste generation, 459–463, 467, 523 Wastewater, water, soil & crop sampling, EKW, 536

Index Water budget, 503, 509 Water poverty index, 347–350, 352, 358 Water poverty index of villages under Patharghata, 352 Water quality analysis, EKW, 539 Water Quality Index (WQI), 153, 156 Water Quality Parameters (WQP), 25, 94, 141, 142, 149, 150, 153–156, 160, 166, 188, 191, 220, 277, 278, 280, 389, 390, 398, 498, 499, 539 Water resources research, 28–30 Watershed modelling, 24 Watershed research, 29, 30 Water User’s Association (WUA), 308 Water velocity in different catchments, 506 Willingness to Pay (WTP), 334, 339, 340 World Health Organization (WHO), 481 WUAs’ on net returns per ha., 304