Urban Environment and Smart Cities in Asian Countries: Insights for Social, Ecological, and Technological Sustainability 3031259130, 9783031259135

Table of contents :
Foreword
Preface
Acknowledgments
Contents
Editors and Contributors
Part I Introduction
1 Urban Sustainability: The Way Forward
1.1 Introduction
1.2 Aims and Objectives
1.3 Research Design
1.4 Analysis, Discussion and the Findings
1.4.1 Urban Sustainability: Issues and Challenges
1.4.2 Consequences of Unsustainable Urbanization
1.4.3 Urban Sustainability: The Way Forward
1.5 Conclusion
References
Part II Urban Land Use Dynamics and Changing Biodiversity
2 Impact of Rapid Urbanization and Changing Face of Wetland: A Case Study of Berhampore Municipality, Murshidabad, West Bengal (India)
2.1 Introduction
2.2 Objectives
2.3 Study Area
2.4 Materials and Methodology
2.4.1 Supervised Image Classification
2.4.2 Accuracy Assessment
2.4.3 Normalized Difference Water Index (NDWI)
2.4.4 Normalized Difference Built-Up Index (NDBI)
2.4.5 Modified Normalized Difference Water Index (MNDWI)
2.4.6 Normalized Difference Pond Index (NDPI)
2.4.7 Normalized Difference Turbidity Index (NDTI)
2.4.8 Statistical Measures
2.5 Results and Discussion
2.5.1 Population Concentration
2.5.2 Landuse and Land Cover Change
2.5.3 NDWI
2.5.4 MNDWI
2.5.5 NDBI
2.5.6 NDPI
2.5.7 NDTI
2.6 Conclusion
References
3 Assessing the Impact of Urban Growth and Green Space Dynamics on Microclimate in Urban Areas: A Case Study of Siliguri City, West Bengal, India
3.1 Introduction
3.2 Study Area
3.3 Materials and Methods
3.4 Results and Discussion
3.4.1 Land Use/Land Cover Changes in Siliguri City
3.4.2 Spatial Analysis of BI
3.4.3 Spatial Analysis of NDVI
3.4.4 Analysis of Land Surface Temperature
3.4.5 Relationship Between LST, NDVI and BI
3.5 Conclusion
References
4 The Pattern of Urbanization and Fluctuations in the Urban Hierarchy of Haryana, India
4.1 Introduction
4.2 Urbanization: Universal and Indian Perspective
4.3 Objectives of the Study
4.4 Materials and Methods
4.5 Study Area
4.6 Results and Discussion
4.6.1 Comparative Urban Scenario of Nation and State: 1901–2011
4.6.2 Urbanization in Haryana from Early Formation to the Continuing Census (1971–2011)
4.6.3 The Concentration of Urban Population in Haryana
4.6.4 Sequential Vacillation in Numbers and Populations in the Urban Class Structure of Haryana
4.7 Association Between Urbanization and Some Related Urban Phenomenon
4.8 Conclusion
4.9 Suggestions/Recommendations
References
5 Assessment of Land Use-Land-Cover Maps for Detecting Change of Selected Quality of Urban Life in Purulia Municipality Area: A Case Study
5.1 Introduction
5.2 Materials and Methods
5.2.1 Study Area
5.2.2 Methodology
5.3 Results and Discussions
5.3.1 Analysis of Changing Scenario by LULC Maps: Residential Areas
5.3.2 Analysis of Changing Scenario by LULC Maps: Wetlands
5.3.3 Analysis of Changing Scenario by LULC Maps: Market Areas
5.3.4 Analysis of Changing Scenario by LULC Maps: Overall Change of LULC During 36 years
5.3.5 Analysis of QoUL with the Changing LULC: Impact on Social, Physical and Psychological Health
5.4 Conclusion and Recommendation
References
6 Assessing the Recent Trends of Land Use Pattern with Contemporary Issues by the Use of RS and GIS Techniques: A Case Study on Moyna Block, Purba Medinipur, West Bengal (India)
6.1 Introduction
6.2 Study Area
6.3 Materials and Methodology
6.3.1 Data Sources
6.3.2 Methodology
6.4 Result and Discussion
6.4.1 Accuracy Assessment
6.4.2 Normalized Difference Vegetation Index (NDVI)
6.4.3 LULC Status Analysis
6.4.4 Land Use Land Cover Change Detection (Error) Matrix
6.4.5 Gain and Loss of LULC (NET Change)
6.5 Conclusion
References
Part III Climate Change and Urban and Smart Cities Health and Waste Management
7 A State-of-the-Art Report on Solid Waste Management of Guwahati City, Assam, India
7.1 Introduction
7.2 Methodology
7.3 Solid Waste Generation and Characterization
7.4 Present Situation of the MSW Management in Guwahati
7.4.1 Storage of MSW
7.4.2 Collection and Transportation of MSW
7.4.3 Waste Segregation
7.4.4 Processing of Waste
7.5 COVID-19 and Its Effect on Biomedical Waste Generation
7.5.1 Rules and Regulations of Biomedical Waste Management
7.6 Conclusion
References
8 Solid, Biomedical and COVID-19 Waste Management in Bankura Municipality, Bankura, West Bengal—A Gap Analysis Between Policies and Practices
8.1 Introduction
8.2 Rationale of the Study
8.3 Materials and Methods
8.4 Results and Discussion
8.4.1 Solid Waste Management
8.4.2 Biomedical Waste Management
8.4.3 COVID Waste Management
8.5 Limitations of the Study
8.6 Existing Gap and Suggestions
8.7 Conclusion
References
9 Assessment of Land Surface Temperature Using Landsat Images: A Case Study on Durgapur Municipal Corporation, West Bengal, India
9.1 Introduction
9.2 Objectives
9.3 Study Area
9.4 Database and Methodology
9.4.1 Normalized Difference Vegetation Index (NDVI)
9.4.2 Built Up Index (BUI)
9.4.3 Land Surface Temperature (LST)
9.4.4 Statistical Measures
9.5 Result and Discussion
9.5.1 Analysis of NDVI
9.5.2 Analysis of NDBI
9.5.3 Analysis of Built Up Index (BUI)
9.6 Conclusion
References
10 Impact of COVID-19 Lockdowns on Air Quality Trend in Trichy District of Tamil Nadu, India
10.1 Introduction
10.2 Rationale of the Study
10.3 Materials and Methods
10.3.1 Study Area
10.3.2 Data
10.3.3 Study Periods
10.3.4 AQI Categories
10.3.5 Analysis of Individual Air Pollutant Concentration
10.4 Results
10.5 Discussions
10.6 Conclusion
References
11 Open Landfill Site and Threat to the Proximity Resident’s: Addressing Perceived Consequences of Unscientific Solid Waste Dumping Using GIS Techniques
11.1 Introduction
11.2 Location of the Study Area
11.3 Materials and Methodology
11.3.1 Participants and Sampling Technique
11.3.2 Gridding Based GIS Approach
11.3.3 Formulating Questionnaire Design and Data Collection Strategy
11.3.4 Landfill Satisfaction Index (LSI)
11.4 Results
11.4.1 Demographic Characteristics
11.4.2 Spatial Analysis of Perceived Impacts Due to Open Landfill
11.4.3 Impact of Proximity on Perceived Impact of Landfill
11.5 Discussion and Conclusion
References
12 Smart Cities and Associated Solid Waste, Biomedical Waste, E-Waste Issues, and Management
12.1 Introduction
12.2 Development of the Smart Cities
12.2.1 Global Scenario
12.2.2 Indian Scenario
12.2.3 Waste Management Issues
12.3 Solid Waste Generation and Handling Trends Associated with Smart Cities in India
12.3.1 E-waste Generation and Management
12.3.2 Biomedical Waste Generation and Management
12.3.3 Introduction of Smart Technologies
12.4 Current Challenges and Future Perspectives
References
Part IV Advances in Urbanism and Smart Cities Socio-environmental Perspectives
13 Teenagers, Cyberbullying and Cyber Security Measures: An Insight in Urban India
13.1 Introduction
13.2 Methods and Materials
13.2.1 Sampling Procedure
13.2.2 Instrument, Measures and Data Analysis
13.3 Results and Discussion
13.3.1 Frequency of Sharing Password
13.3.2 Personal Information Disclosed on Social Networks
13.3.3 Information Disclosed to Strangers on Social Networks
13.3.4 Accepting Invitation from Strangers on Social Networks to Meet
13.3.5 Posting of Private Digital Materials
13.3.6 Making Fun of Friends on Social Network
13.3.7 Frequency of Bullying Activity
13.3.8 Quantity of Safety Measures Adopted Online
13.4 Conclusion
References
14 Exploring the Transformation of Agrarian Villages into Global Cultural Urban Centres Through GIS and Reaffirmation Method: An Enquiry on ISKCON, Mayapur of West Bengal (India)
14.1 Introduction
14.2 Rationale of the Study
14.3 Objectives
14.4 Materials and Methods
14.4.1 Study Area
14.4.2 Database
14.4.3 Respondents’ Categorization
14.4.4 Image Processing
14.4.5 Statistical Techniques
14.5 Results and Discussions
14.5.1 Historical Background of ISKCON-Related Tourism in Mayapur
14.5.2 Agrarian Identity Versus Cultural Modernization of the Study Villages
14.6 Limitations of the Study
14.7 Conclusions and Recommendations
Appendices
Appendix 1
Appendix 2
Appendix 3
Appendix 4
Appendix 5
Appendix 6
References
15 Art and the City: An Exploration of Creatives’ Lived Realities in the Context of Cebu’s Place Branding Initiative
15.1 Introduction
15.2 Rationale of the Study
15.3 Materials and Methods
15.4 Results and Discussion
15.4.1 Official Narratives of the City
15.4.2 Independent Artists and Community Discourse
15.4.3 Negotiation in City Planning and Everyday Creative Planning
15.5 Recommendations
15.6 Conclusions
References
16 Strengthening Climate Change Governance & Smart City Through Smart Education in Bandung Indonesia
16.1 Introduction
16.2 Methodology and Analysis
16.3 Discussions and Results
16.3.1 Smart City as a Resilience for the Bandung City in Facing Vulnerability
16.3.2 Education Solution for Vulnerability and Resilience in Smart City Bandung
16.4 Conclusion
References
Part V Urban Environmental Management, Solutions, and Sustainable Future Smart Cities
17 Site Suitability Analysis for New Urban Development Using Fuzzy-AHP and DEMATEL Method: A Case of Mixed Agricultural-Urbanized Landscape of Burdwan Town, West Bengal, (India)
17.1 Introduction
17.2 About Study Area
17.3 Materials and Methods
17.3.1 Data Collection and Preparation
17.3.2 Integrated Fuzzy AHP and DEMATEL Methods
17.3.3 Fuzzy Analytic Hierarchy Process (FAHP)
17.3.4 DEMATEL Method
17.3.5 Factors Influencing New Urban Development
17.3.6 Standardization of Criteria
17.3.7 Determination of Weights
17.3.8 Criteria Restriction Map
17.3.9 New Urban Suitability Map (NUSM)
17.3.10 Validation of New Urban Development Model
17.4 Results
17.4.1 Site Suitability Analysis for New Urban Development
17.4.2 New Urban Suitability Map
17.5 Discussion
17.6 Conclusion
References
18 Inconveniences and Mobility Issues of Elders on Road: The Case of Kolkata Municipal Corporation, West Bengal, India
18.1 Introduction
18.2 Objectives
18.3 Materials and Methods
18.3.1 Study Area
18.4 Results and Discussion
18.4.1 Socio-demographic Characteristics of the Respondents
18.4.2 The Growth of Vehicles and Elderly Population in Kolkata Municipal Corporation
18.4.3 Inconveniences Faced by the Elders
18.5 Findings
18.6 Recommendation
18.7 Conclusion
Appendix
References
19 Assessing Housing Condition and Quality of Life in Midnapore Town, West Bengal, India: Analysis of 2011 Census
19.1 Introduction
19.2 Theoretical Orientation of the Research
19.3 Study Area
19.4 Materials and Methods
19.5 Result
19.5.1 Inequality in the Housing Condition
19.5.2 Development Indicators and Housing Conditions
19.6 Discussion
19.7 Conclusion and Recommendations
Appendix
References
20 Transport as a Driver of Sustainable Urban Growth: Evidence from Ankara, Turkey and Kolkata, India
20.1 Introduction
20.2 Background Information on the Two Cities
20.3 Urban Transport Systems of Two Cities
20.3.1 Road Infrastructure
20.3.2 Public Transport
20.3.3 Freight Movements and Logistics
20.3.4 Non-motorised Transport
20.3.5 New Technologies and Solutions
20.4 Master Plans for Sustainable Urban Transport
20.5 Evaluation of the Sustainability of Transport Systems of Ankara and Kolkata
20.6 Conclusion
References
21 Neighbourhood Level Geospatial Heterogeneity of WASH Performance in Indian Two Metropolitan Cities: Kolkata and Chennai
21.1 Introduction
21.2 Study Area
21.3 Methodology
21.3.1 Database
21.3.2 Measures of the WASH Performance Index
21.4 Results
21.4.1 Geospatial Inequality and Pattern in the Distribution of WASH Facilities
21.4.2 Spatial Heterogeneity and Local Pattern of WASH Facilities
21.4.3 Spatial Dependence and Pattern of WASH Performance
21.5 Discussion
21.6 Conclusion
Appendix 1: Location and administrative setup of Kolkata and Chennai
Appendix 2
References
22 Spatial Variation of Overall Infrastructural Development Index (OIDI) in Census Towns: A Study of Indo-Gangetic Plain Region, India
22.1 Introduction
22.1.1 Relevance of the Included Agenda: ‘Census Town’
22.2 Materials and Methods
22.3 Study Area
22.4 Result and Discussion
22.4.1 Physical Infrastructural Index (PII)
22.4.2 Social Infrastructural Index (SII)
22.4.3 Economic Infrastructural Index (EII)
22.4.4 Overall Infrastructural Development Index (OIDI)
22.4.5 State-Wise Distributional Scenario of Major Indicators
22.5 Policy Proposal: Rational Development of Infrastructure
22.6 Conclusion
References
23 Spatio-Temporal Urban Sprawl of Sargodha City, Punjab, Pakistan
23.1 Introduction
23.2 The Study Area
23.3 Research Methodology
23.3.1 Data Sources
23.3.2 Remotely Sense Data
23.3.3 Secondary Data
23.3.4 Census Data
23.3.5 Selection of Bands
23.3.6 Monitoring of Urban Sprawl
23.3.7 Accuracy Assessment
23.4 Results and Discussion
23.4.1 Change in Population of the City
23.4.2 Urban Sprawl in Sargodha City
23.4.3 Variation in Land Use Pattern
23.4.4 Accuracy Assessment of Classified Images
23.4.5 Change Detection of Urban Land Use During 1987–2017
23.4.6 Increase Population and Urban Sprawl
23.4.7 Dynamic Urban Sprawl Monitoring
23.4.8 Urban Sprawl Impact Land Surface Temperature
23.5 Conclusion
References
24 Greenswales: A Nature-Based Solution to Have High-Performing Urban Water Systems
24.1 Background
24.1.1 Hypothesis—Concept of Greenswales
24.2 Method
24.3 Results and Discussion
24.3.1 Benefits of Natural Channels
24.3.2 Benefits of Greenspaces
24.3.3 Characteristics of Greenswales
24.4 Conclusion
24.4.1 Limitations and Scope for Future Research
References
25 Impact Analysis of Deep Static Bluespace on Urban Heat Island: Case of Chandigarh
25.1 Introduction
25.2 Materials and Method
25.2.1 Study Overview
25.2.2 Study Area
25.2.3 Data
25.2.4 Land Surface Temperature Retrieval
25.2.5 Brightness Temperature (T10)
25.2.6 Effective Mean Atmospheric Temperature (Ta)
25.2.7 Atmospheric Transmittance (τ10)
25.2.8 Waterbody Extraction
25.2.9 Local Climate Zone Classification
25.2.10 Validation and Accuracy Assessment
25.2.11 Waterbody Thermal Impact Calculation
25.2.12 Statistical Analysis
25.3 Results
25.4 Discussion
25.4.1 Waterbody Impact on Land Surface Temperature
25.4.2 Influence of LCZ on Waterbody Impact
25.4.3 Implication of Waterbody Thermal Impact on Urban Planning
25.4.4 Limitation and Scope of Future Research
25.5 Conclusion
References

Citation preview

Human Dynamics in Smart Cities Series Editors: Shih‐Lung Shaw · Daniel Sui

Uday Chatterjee · Anzhelika Antipova · Shovan Ghosh · Sushobhan Majumdar · Martiwi Diah Setiawati   Editors

Urban Environment and Smart Cities in Asian Countries Insights for Social, Ecological, and Technological Sustainability

Human Dynamics in Smart Cities Series Editors Shih-Lung Shaw, Department of Geography, University of Tennessee, Knoxville, TN, USA Daniel Sui, Research & Innovation, 340 Burruss Hall, Virginia Polytechnic Institute & State University, Blacksburg, VA, USA

This series covers advances in information and communication technology (ICT), mobile technology, and location-aware technology and ways in which they have fundamentally changed how social, political, economic and transportation systems work in today’s globally connected world. These changes have raised many exciting research questions related to human dynamics at both disaggregate and aggregate levels that have attracted attentions of researchers from a wide range of disciplines. This book series captures this emerging dynamic interdisciplinary field of research as a one-stop depository of our cumulative knowledge on this topic that has profound implications for future human life in general and urban life in particular. Covering topics from theoretical perspectives, space-time analytics, modeling human dynamics, urban analytics, social media and big data, travel dynamics, to privacy issues, development of smart cities, and problems and prospects of human dynamics research, the series includes contributions from various disciplines with research interests related to human dynamics. The series invites contributions of theoretical, technical, or application aspects of human dynamics research for a global and interdisciplinary audience.

Uday Chatterjee · Anzhelika Antipova · Shovan Ghosh · Sushobhan Majumdar · Martiwi Diah Setiawati Editors

Urban Environment and Smart Cities in Asian Countries Insights for Social, Ecological, and Technological Sustainability

Editors Uday Chatterjee Department of Geography Bhatter College, Dantan (Affiliated to Vidyasagar University) Paschim Medinipore, West Bengal, India Shovan Ghosh Department of Geography Diamond Harbour Women’s University Sarisha, West Bengal, India

Anzhelika Antipova Department of Earth Sciences University of Memphis Memphis, TN, USA Sushobhan Majumdar Department of Geography Jadavpur University Kolkata, West Bengal, India

Martiwi Diah Setiawati Research Center for Oceanography National Research and Innovation Agency Ancol Timur, Jakarta, Indonesia

ISSN 2523-7780 ISSN 2523-7799 (electronic) Human Dynamics in Smart Cities ISBN 978-3-031-25913-5 ISBN 978-3-031-25914-2 (eBook) https://doi.org/10.1007/978-3-031-25914-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Disclaimer: The authors of individual chapters are solely responsible for the ideas, views, data, figures, and geographical boundaries presented in the respective chapters of this book, and these have not been endorsed, in any form, by the publisher, the editor, and the authors of forewords, preambles, or other chapters. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Dedicated to Young Scholars in the Field of Urban Studies, Urban Planning, City Planners and Smart City Policy Makers

Foreword

I consider it a great honour to contribute to the foreword for the book entitled Urban Environment and Smart Cities in Asian Countries, edited by Uday Chatterjee, Anzhelika Antipova, Martiwi Diah Setiawati, Shovan Ghosh, and Sushobhan Majumdar to be published by of the Springer. Smart cities strive to excel in governance, economy, transport, environment, resources, and people’s services, such as healthcare, education, housing, and living, in order to better the lives of their residents and make urban living sustainable. Smart cities genuinely represent the idea of an ecosystem, where each component is interconnected to create a unified, appealing whole. As they coexist with urban facilities, smart cities bring a variety of new services that have an impact on urban policymaking and planning. The smart city framework also offers numerous options for city planners to contribute to urban life and achieve the aforementioned objectives. The book analyses the interactions between urban environmental management, the influence of climate change on smart cities’ health and waste management from socio-environmental perspectives, management, solutions, and sustainability based on six parts and 25 chapters. The first part elaborates on the major principles involved and the development of sustainability in terms of the interplay between people and nature. In five chapters, the second part covers urban land use dynamics and changing biodiversity. This part demonstrates how India’s rapid urbanization process affects

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Foreword

quality of life, loss of green space, and develops urban microclimate zones. The third part also deals with five chapters that demonstrate the effects of climate change on waste management and urban and smart city health. Numerous socio-environmental problems cause stress to the local population as urbanization develops. The fourth part is divided into four chapters that discuss cyberbullying, art and cities, and the transformation of rural communities into vibrant urban centres using examples from both domestic and foreign contexts. The final part discusses the challenges with urban environmental management, remedies, and future sustainable smart cities. This book contains a wide range of distinctive and in-depth case studies from both national and global viewpoints. The book is not only comprehensive in terms of the range of themes it covers, but it also offers in-depth discussions that will be useful to academics and students studying urban and social geography. I want to thank and applaud the editors for taking the heroic step of publishing this priceless book with current relevance. The literature on smart cities, urbanism, social issues, human welfare, the environment in the context of climate change, and sustainable development challenges will greatly benefit from the contribution of this work. I hope that the geographers, urban planners, social scientists, development managers, practitioners, and decision-makers involved will highly praise it.

Prof. Suman Paul Department of Geography Sidho-Kanho-Birsha University Purulia, West Bengal, India

Preface

Recently, the urban sector in the world has undertaken a most important challenge following the country’s evolution for a market-based economy and the essence of subsidiarity expressed in the Constitution. Municipal bodies are now establishing a third tier to the national policy of the nation. Considering this present and future development, most of the countries of worldwide have introduced a few programmes to strengthen and upgrade the municipal services. The ‘Smart City’ idea is a techsavvy way to focus on issues of unplanned urbanization for better citizen services and sustainable practices. Smart city ‘is suitable in the way it manages and tackles problems of technical, social, and economic development, needs certain platforming features to work at a high level of efficiencies, such as culture and physical infrastructure designed to facilitate competitiveness and cooperation, facilitate technological innovations and acts, and laid the groundwork for new enterprise formation.’ Geospatial technology is arrayed at every step of planning, modelling, managing an expansion of smart cities across the full spectrum of functionalities. This brief calls on worldwide national government and policy makers to formulate a broad policy for planned specific growth of urban/towns to ensure the sustainable development of the country’s urban areas. This book offers a thorough description of the challenges posed by increasing global urbanization. Unplanned urbanization gave rise to change in urban land use patterns, slums, inadequate housing, sanitation hazards, rampant pollution, and rise of global epidemics. It focuses in particular on strategies and services to solve the difficulties and concerns raised by increasing urbanization. This is primarily due to transferring residents from rural to urban areas searching for jobs and improved living conditions. In addition, comprehensive perspectives are offered into how the contemporary urban challenges of our time are tackled by existing designers, architects, urban planners, and landscape architects: climate change, migration, resilience, politics, and environmental degradation. In addition, the contributors will take insights from environmental design, geography, strategic planning, application of science and technology, and engineering design to go beyond the jargon of technical innovation and expose the political, social, and physical effects of digitalizing the world in smart cities. This book will ix

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also address how to extant the state of the art and practice to develop smart city in worldwide with proper illustration for the urban innovation, sustainable solutions, and sustainable growth of future smart cities in worldwide. This book will focus on the framework of the application of geospatial technology of smart cities—including system design for basic services, real-time control, and the Internet of Things. This book will highlight the planning of land use, strategic development, and ecosystembased knowledge to enhance economic growth and healthy urban environment and smart city management, the contradictory aspects of smart city studies, and provide useful insights into the creation and execution of policies to strengthen decisionmaking processes in smart cities. In addition, the book leads the reader to a greater understanding of smart city growth, both theoretical and realistic. This book explores the latest advances in geographical and demographical tools and techniques for improvement of the quality of research theme. Our primary concern is to provide readers impactful research on the current theme. Each and every day, urban areas are being crowded. 50% of the world’s current population currently lives in cities and produces 75% of carbon emissions, and climate change issues are now challenging as demand for resource extraction continues to increase. The "Smart City" idea emerged as a development plan to fix the emerging urbanization issues with the main purpose of making communities more prosperous. Developing self-sustaining communities tends to be an alternative approach that generates balanced economic growth and high quality of life by transcending several core fields to take advantage of emerging possibilities in smart city innovation (e.g., environment, economy, mobility, governance, people and living conditions). The smart city facilitates automation and automated urban real-time tracking and control through a cellular network, sensors, cameras and data centres. Advanced and Geospatial technology manages vast volumes of data to assist all operating operations of municipality, city, jurisdiction, police, traffic management and infrastructure evaluation and to help us understand what every element of functionalities is where and how cities can monitor and track. The key areas of the proposed book focused on the emergence of Urban environment morphology building contemporary cities, societies, and economies and analysing sustainability. This book is divided into five major parts. The first part describes the dynamic approach concept of the urban environment and human interrelation and its future scope and sustainability. Part II will deal with the urban land use dynamic and loss of biodiversity, urban sprawl, rapid industrialization, contamination and its effects on urban environmental settings, basic services, infrastructure, quality of life in urban areas. Part III will explore the urban increases in land surface temperature due to climate change, air pollution, water scarcity, in leading developed and developing countries. Part IV, urban socio-environmental perspectives, how massive population growth as a result of urban expansion, this part also provides practical knowledge about the artificial intelligence on social hazard zonation mapping, energy conservation, and geospatial solutions. Part V describes about the various urban development and activities and ecosystem services, urban planning and management and regional disparity of worldwide urban areas for sustainable

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development of urban areas, sustainable development, and planning, Geocomputation tool, spatial modelling technique for smart city development. Also, this chapter will be focused on the worldwide issues, challenges, and future strategies for smart city development. It is hoped that the book as a whole will provide a timely synthesis of a rapidly growing and important field of study but will also bring forward new and stimulating ideas that will shape a coherent and fruitful vision for future work for the community of Undergraduate, Postgraduates, and Researchers in the fields of environmental sciences and humanistic and social sciences, Geography, Ph.D. Scholar, Policies makers, Environmentalist, NGOs, Corporate sectors, Social scientist, Government Organizations will find this book to be of great value. Midnapore, West Bengal, India Memphis, USA Jakarta, Indonesia South 24 Parganas, West Bengal, India Kolkata, India

Uday Chatterjee Anzhelika Antipova Martiwi Diah Setiawati Shovan Ghosh Sushobhan Majumdar

Acknowledgments

This book has been inspired by the tremendous hard work that has been put by urban planner, social scientist, urban policy makers who are trying their level best to curtail urban environment on socio-ecological and technological perspectives. We indebted our heartfelt gratitude to the researchers who have made this book a reality through their contributions. We thank the anonymous reviewers for their constructive reviews, which have helped to improve the quality of the book. We appreciate the incredible support that our colleagues, students, parents, family members, teachers, and collaborators while editing this book, such that it may create value and contribute positively towards the knowledge of sustainable development. Last, but not the least we would like to acknowledge the continuous assistance provided by our publisher and its publishing editor, Springer.

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Contents

Part I 1

Urban Sustainability: The Way Forward . . . . . . . . . . . . . . . . . . . . . . . . Vinita Prasad

Part II 2

3

4

Introduction 3

Urban Land Use Dynamics and Changing Biodiversity

Impact of Rapid Urbanization and Changing Face of Wetland: A Case Study of Berhampore Municipality, Murshidabad, West Bengal (India) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Subham Kumar Roy and Chumki Mondal Assessing the Impact of Urban Growth and Green Space Dynamics on Microclimate in Urban Areas: A Case Study of Siliguri City, West Bengal, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chandana Sarkar and Bimal Kumar Kar The Pattern of Urbanization and Fluctuations in the Urban Hierarchy of Haryana, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manju Sharma and Sandeep Kumar

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Assessment of Land Use-Land-Cover Maps for Detecting Change of Selected Quality of Urban Life in Purulia Municipality Area: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Shovan Ghosh and Krishna Mallick

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Assessing the Recent Trends of Land Use Pattern with Contemporary Issues by the Use of RS and GIS Techniques: A Case Study on Moyna Block, Purba Medinipur, West Bengal (India) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Subhajit Barman, Arpita Routh, and Avishek Bhunia

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Contents

Part III Climate Change and Urban and Smart Cities Health and Waste Management 7

A State-of-the-Art Report on Solid Waste Management of Guwahati City, Assam, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Arunav Chakraborty

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Solid, Biomedical and COVID-19 Waste Management in Bankura Municipality, Bankura, West Bengal—A Gap Analysis Between Policies and Practices . . . . . . . . . . . . . . . . . . . . . . . . . 177 Nabanita Mukhopadhyay and Paramita De

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Assessment of Land Surface Temperature Using Landsat Images: A Case Study on Durgapur Municipal Corporation, West Bengal, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Sandip Tah, Subham Kumar Roy, and Chumki Mondal

10 Impact of COVID-19 Lockdowns on Air Quality Trend in Trichy District of Tamil Nadu, India . . . . . . . . . . . . . . . . . . . . . . . . . . 219 T. Sankar, N. Kowshika, Mahesh Haroli, G. Amith, and G. Rajthilak 11 Open Landfill Site and Threat to the Proximity Resident’s: Addressing Perceived Consequences of Unscientific Solid Waste Dumping Using GIS Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Subham Roy, Arghadeep Bose, Debanjan Basak, and Indrajit Roy Chowdhury 12 Smart Cities and Associated Solid Waste, Biomedical Waste, E-Waste Issues, and Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Subhadeep Biswas, Ankurita Nath, and Anjali Pal Part IV Advances in Urbanism and Smart Cities Socio-environmental Perspectives 13 Teenagers, Cyberbullying and Cyber Security Measures: An Insight in Urban India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Sucharita Pramanick and Shovan Ghosh 14 Exploring the Transformation of Agrarian Villages into Global Cultural Urban Centres Through GIS and Reaffirmation Method: An Enquiry on ISKCON, Mayapur of West Bengal (India) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Tanmoy Basu, Biraj Kanti Mondal, and Mainak Ghosh 15 Art and the City: An Exploration of Creatives’ Lived Realities in the Context of Cebu’s Place Branding Initiative . . . . . . . . . . . . . . . . 347 Reynancia Yasmin Neo

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16 Strengthening Climate Change Governance & Smart City Through Smart Education in Bandung Indonesia . . . . . . . . . . . . . . . . 369 Dewi Kurniasih, Andi Luhur Prianto, Abdillah Abdillah, Umar Congge, and Erwin Akib Part V

Urban Environmental Management, Solutions, and Sustainable Future Smart Cities

17 Site Suitability Analysis for New Urban Development Using Fuzzy-AHP and DEMATEL Method: A Case of Mixed Agricultural-Urbanized Landscape of Burdwan Town, West Bengal, (India) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 Sanu Dolui and Sumana Sarkar 18 Inconveniences and Mobility Issues of Elders on Road: The Case of Kolkata Municipal Corporation, West Bengal, India . . . . . . 425 Shovan Ghosh and Sramana Maiti 19 Assessing Housing Condition and Quality of Life in Midnapore Town, West Bengal, India: Analysis of 2011 Census . . . . . . . . . . . . . . . 449 Avishek Bhunia and Amalendu Sahoo 20 Transport as a Driver of Sustainable Urban Growth: Evidence from Ankara, Turkey and Kolkata, India . . . . . . . . . . . . . . . . . . . . . . . . 477 Hülya Zeybek, Stabak Roy, and Saptarshi Mitra 21 Neighbourhood Level Geospatial Heterogeneity of WASH Performance in Indian Two Metropolitan Cities: Kolkata and Chennai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 Pritam Ghosh, Moslem Hossain, Jiarul Alam, and Asraful Alam 22 Spatial Variation of Overall Infrastructural Development Index (OIDI) in Census Towns: A Study of Indo-Gangetic Plain Region, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525 Sanjoy Saha, Somenath Halder, and Subhankar Singha 23 Spatio-Temporal Urban Sprawl of Sargodha City, Punjab, Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Humayun Ashraf, Ghani Rahman, Mehtab Ahmad Khan, Muhammad Farhan Ul Moazzam, and Muhammad Miandad 24 Greenswales: A Nature-Based Solution to Have High-Performing Urban Water Systems . . . . . . . . . . . . . . . . . . . . . . . . . 561 Ankita Sood and Arindam Biswas 25 Impact Analysis of Deep Static Bluespace on Urban Heat Island: Case of Chandigarh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Aditya Rahul and Mahua Mukherjee

Editors and Contributors

About the Editors Dr. Uday Chatterjee is an Assistant Professor at the Department of Geography, Bhatter College, Dantan, Paschim Medinipur, West Bengal, India and Applied Geographer with a Post-Graduate in Applied Geography at Utkal University and Doctoral Degrees in Applied Geography at Ravenshaw University, Cuttack, Odisha, India. He has contributed various research papers published in various reputed national and international journals and edited book volumes. He has authored jointly edited books entitled ‘Harmony with nature: Illusions and elusions from Geographer’s perspective in the 21st Century’, and ‘Land Reclamation and Restoration Strategies for Sustainable Development’ (November 2021, Edition: 1st, Publisher: Elsevier, Editor: Dr. Gouri Sankar Bhunia, Dr. Uday Chatterjee, Dr. Anil Kashyap, Dr. Pravat Kumar Shit· ISBN: 978-0-12-823895-0 (https://www.elsevier.com/ books/land-reclamation-and-restoration-strategies-forsustainable-development/bhunia/978-0-12-823895-0). He has also conducted (Convener) one Faculty Development Programme on ‘Modern methods of teaching and advanced research methods’ sponsored by Indian Council of Social Science Research (ICSSR), Govertment of India. His areas of research interest cover Urban Planning, Social and Human geography, Applied Geomorphology, Hazards and Disasters, Environmental Issues, Land Use and Rural Development. His research work has been funded by the West Bengal Pollution

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Control Board (WBPCB) Government of West Bengal, India. He has served as a reviewer for many International journals. Currently Dr. Uday Chatterjee is the lead editor of Special Issue (S.I) of Urbanism, Smart Cities and Modelling, GeoJournal, Springer. Dr. Anzhelika Antipova is Associate Professor in the Department of Earth Sciences, University of Memphis, Memphis, Tennessee, United States. Her areas of research interest cover Urban Planning, urban and economic geography, medical/health geography, transportation studies. She has contributed to various research papers published in various reputed national and international journals and edited journal special issues and book volumes. She has served as a reviewer for many International journals and books.

Dr. Shovan Ghosh an Associate Professor in the department of Geography, Diamond Harbour Women’s University, South 24 Pargana, West Bengal, has completed his M.A. and Ph.D. in Geography from the University of Burdwan, West Bengal. He has secured first position in both under graduation and postgraduation levels. He served as an Assistant Professor for more than ten years at Ramananda Centenary College, Purulia, West Bengal. His area of interests lies in Human Geography, Environmental Issues, Education and Geography, Regional planning, Backward Region Development, Urban Planning. His research topic for doctoral thesis was ‘Spatiality, Dichotomy and Problems of High School Education in Hooghly District, West Bengal—Geographical Appraisal’. He has completed UGC research project on ‘The Physico-Societal Barriers of Access and Success of School Education with Reference to Puncha Block of Purulia District—One of the Backward Districts of West Bengal’. He has more than 30 national and international papers, written five chapters in different books and had attended fortyfive national and international conferences and seminars. He has keen interest on ‘Multivariate analysis using SPSS, AMOS & R’ and got training on that in several workshops. He is associated with several Professional bodies like Indian Geographical Foundation,

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(IGF) Kolkata, Indian Institute of Geographers, (IIG) University of Pune, Maharashtra, Association of Population Geographers of India, (APGI) Punjab University, Punjab, National Association of Geographers India (NAGI), Department of Geography, Delhi School of Economics, University of Delhi, Delhi, etc. He was awarded Best Paper Presenter in international conference on ‘Urban development’, organized by Institute for Social development and Research, Ranchi in 2018, India. He performed his duty as Coordinator, NAAC Steering Committee, Ramananda Centenary College, Purulia and Nodal Officer, for AISHE from 2012 to 2017. Currently, he is supervising 5 research scholars on issues in Human Geography like problems to access to health care facilities for women; urbanization, technology, and quality of life; cyberspace and cyberculture; language dynamics. Dr. Sushobhan Majumdar is worked as an independent researcher after being awarded Doctoral degree from Jadavpur University in 2018. He received post graduates, i.e., M.Sc. degree from Presidency University and Jadavpur University in Geography and Geo-informatics. He was also engaged in a project focusing on population profile of Kolkata city under the Government of West Bengal. He has published on issues of sprawl, transportation, health issues, and microclimate of urban areas under national and international repute. He is currently expanding his research on the relationship between land use and surface temperature with its spatial modelling. Dr. Martiwi Diah Setiawati is currently a research fellow at the Research Center for Oceanography, National Research and Innovation Agency (BRIN)previously known as Indonesia Institute of Sciences (LIPI). She obtained her Bachelor’s degree in marine science and technology from IPB University, Indonesia, in 2009. In 2012, she got a Master of Science in Udayana University, Indonesia, and a Master of Engineering in Yamaguchi University, Japan, under a double degree programme. She received her Doctoral Degree in Environmental Science and Engineering from Yamaguchi University-Japan in 2015. From 2016 to March 2021, she joined the Integrated Research System for Sustainability Sciences (IR3S) (now known as Institute for

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Future Initiatives-IFI), The University of Tokyo as a project researcher under the Climate Change Adaptation Initiative Project in Indonesia. This project collaborates between the Ministry of Environment Japan and the Ministry of National Development Planning of the Republic of Indonesia (BAPPENAS) to mainstream climate change adaptation into a local development plan. As an environmental scientist, she is interested in remote sensing and GIS application to multiple environmental conditions, including habitat studies, disaster mitigation, climate change impact assessment, and adaptation. Her previous research projects cover the integrated climate assessment—risks, uncertainties, and society and developing models to predict future health risks posed by changes in climate, land use, and population. She has published nearly 20 papers in various international and local journals and proceedings.

Contributors Abdillah Abdillah Department of Government Studies, Universitas Muhammadiyah Makassar, Makassar, Indonesia Erwin Akib Department of English Education, Universitas Muhammadiyah Makassar, Makassar, Indonesia Asraful Alam Department of Geography, Serampore Girls’ College, University of Calcutta, Kolkata, West Bengal, India Jiarul Alam Department of Geography and Applied Geography, University of North Bengal, Darjeeling, West Bengal, India G. Amith Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, Tamilnadu, India Humayun Ashraf Department of Geography, University of Gujrat, Gujrat, Pakistan Subhajit Barman Department of Geography, K.D. College of Commerce and General Studies, Paschim Medinipur, Midnapore, West Bengal, India Debanjan Basak Department of Geography and Applied Geography, University of North Bengal, Siliguri, West Bengal, India Tanmoy Basu Department of Geography, Katwa College, Purba Barddhaman, West Bengal, India

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Avishek Bhunia Department of Geography, K.D. College of Commerce and General Studies, Midnapore, West Bengal, India Arindam Biswas Department of Architecture and Planning, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Subhadeep Biswas Civil Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India Arghadeep Bose Department of Geography and Applied Geography, University of North Bengal, Siliguri, West Bengal, India Arunav Chakraborty Department of Civil Engineering, Tezpur University, Assam, India Indrajit Roy Chowdhury Department of Geography and Applied Geography, University of North Bengal, Siliguri, West Bengal, India Umar Congge Department of Public Administration, Universitas Muhammadiyah Sinjai, Sinjai, Indonesia Paramita De Bankura University, Bankura, India Sanu Dolui Department of Geography, The University of Burdwan, Burdwan, W.B), India Mainak Ghosh Department of Architecture, Jadavpur University, Kolkata, India Pritam Ghosh Department of Geography, University of Calcutta, Kolkata, West Bengal, India; Department of Geography, Ramsaday College, Amta, West Bengal, India Shovan Ghosh Department of Geography, Diamond Harbour Women’s University, Sarisha, West Bengal, India; Department of Geography, Diamond Harbour Women’s University, Calcutta, West Bengal, India Somenath Halder Department of Geography, Kaliachak College, Malda, West Bengal, India Mahesh Haroli Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, Tamilnadu, India Moslem Hossain Department of Geography, School of Earth Sciences, Central University of Karnataka, Gulbarga, India Bimal Kumar Kar Department of Geography, Gauhati University, Guwahati, Assam, India Mehtab Ahmad Khan Department of Geography, University of Gujrat, Gujrat, Pakistan N. Kowshika Tamil Nadu Agricultural University, Coimbatore, Tamilnadu, India

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Sandeep Kumar Department of Geography, Institute of Integrated and Honors Studies, Kurukshetra University, Kurukshetra, India Dewi Kurniasih Department of Government Studies, Universitas Komputer Indonesia, Bandung, Indonesia Sramana Maiti Department of Geography, Diamond Harbour Women’s University, Sarisha, West Bengal, India Krishna Mallick Department of Geography, Balarampur College, Purulia, West Bengal, India Muhammad Miandad Department of Geography, University of Gujrat, Gujrat, Pakistan Saptarshi Mitra Department of Geography and Disaster Management, Tripura University, Tripura, India Biraj Kanti Mondal Department of Geography, Netaji Subhas Open University, Kolkata, India Chumki Mondal Department of Geography, Khandra College, Paschim Bardhaman, West Bengal, India Mahua Mukherjee Department of Architecture and Planning, Indian Institute of Technology Roorkee, Roorkee, India Nabanita Mukhopadhyay Bankura University, Bankura, India Ankurita Nath School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, India Reynancia Yasmin Neo Graduate School of International Development, Nagoya University Alumni Association, Nagoya, Japan Anjali Pal Civil Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India Sucharita Pramanick Department of Geography, Surendranath College for Women, Kolkata, West Bengal, India Vinita Prasad Department of Geography, A. N. College (Patliputra University), Patna, Bihar, India Andi Luhur Prianto Department of Government Studies, Universitas Muhammadiyah Makassar, Makassar, Indonesia Ghani Rahman Department of Geography, University of Gujrat, Gujrat, Pakistan Aditya Rahul Climate Change Consultant, Ernst & Young, Gurugram, Haryana, India G. Rajthilak National Agro Foundation, Kancheepuram, Tamilnadu, India

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Arpita Routh Department of Geography and Environmental Management, Vidyasagar University, Midnapore, India Stabak Roy Department of Geography and Disaster Management, Tripura University, Tripura, India Subham Roy Department of Geography and Applied Geography, University of North Bengal, Siliguri, West Bengal, India Subham Kumar Roy Department of Geography, Prof. Syed Nurul Hasan College, Murshidabad, West Bengal, India Sanjoy Saha Department of Geography, Kaliachak College, Malda, West Bengal, India Amalendu Sahoo Department of Geography, Tamluk, West Bengal, India T. Sankar Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, Tamilnadu, India Chandana Sarkar Department of Geography, Gauhati University, Guwahati, Assam, India Sumana Sarkar Department of Geography, The University of Burdwan, Burdwan, W.B), India Manju Sharma Department of Geography, Dayanand College, Hisar, India; Guru Jambheshwar University of Science and Technology, Hisar, India Subhankar Singha Independent Researcher, Ex-student, Department of Geography, Gour Banga University, Malda, West Bengal, India Ankita Sood Department of Architecture and Planning, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Sandip Tah Department of Geography, Khandra College, Paschim Bardhaman, West Bengal, India Muhammad Farhan Ul Moazzam Department of Civil Engineering, College of Ocean Sciences, Jeju National University, Jeju, Republic of Korea Hülya Zeybek Vocational School of Transportation, Eskisehir Technical University, Eski¸sehir, Turkey

Part I

Introduction

Chapter 1

Urban Sustainability: The Way Forward Vinita Prasad

1.1 Introduction The growth of large urban centers initially began in Europe and North America and was followed by even larger megacities in Latin America, Asia and Africa. Currently, more than half of the world’s population lives in cities and this figure is projected to reach two-third of the world’s population by 2050 (Vardoulakis & Kinney, 2019). As per United Nation’s report (World Urbanization Prospect: The 2018 Revisions), in 1950 about 30% of the global population was urban, which rose to 56% in 2020, that is in the span of 70 years (1950–2020), the world’s urban population registered a growth of 26%. The expansion of urban areas is inevitable thus, are bound to raise issues related to poverty, hunger, resource consumption and biodiversity loss which requires to be addressed prudentially adhering to sustainable means. It is important to understand the meaning and implications of the term ‘sustainability’ which was first used in German forestry circle by Hans Carl von Carlowitz in Sylvicultura Oeconomica in 1713 (Pisani, 2006). Sustainable development, is a concept that emerged in the context of growing awareness of an imminent ecological crisis at the end of the 20th century. In 1987, the World Commission on Environment and Development (WCED), published a report entitled ‘Our common future’, also known as the Brundtland report. The Brundtland Report stated that critical global environmental problems were primarily the result of the enormous poverty in the South and the non-sustainable patterns of consumption and production in the North. The report defined sustainability as, “meeting the needs of the present without compromising the ability of the future generations to meet their own needs (brundtland-report.html), and made the term ‘sustainability’, a buzzword. However, the United Nations Conference on Environment and Development (UNCED), also V. Prasad (B) Department of Geography, A. N. College (Patliputra University), North S K Puri, 800013 Patna, Bihar, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_1

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known as the ‘Earth Summit’, held in 1992 in Rio de Janeiro, Brazil, produced a broad agenda (Agenda 21), based on Brundtland report, a blueprint for international action on environmental and development issues, that would help guide international co-operation and development policy in the twenty-first century (www.un. org/). Further, in the year 2015, the United Nations adopted the Sustainable Development Goals (SDGs), identifying 17 integrated global goals to achieve a better and more sustainable future for all including sustainable cities and communities (UN SDG 11). Besides, at the Global Conference on Urban future (URBAN 21) in 2000, at Berlin, the Berlin declaration on the urban future was adopted (link.springer.com). Likewise, in 2018 at Kuala Lumpur, Malaysia, on the occasion of the ninth session of World Urban Forum, a new Urban Sustainability Framework (USF) was launched collectively by the World Bank and the Global Environment Facility (GEF) in an effort to support cities to achieve a greener future and sustainability (www.worldb ank.org/). It is a matter of great concern that, in spite the consolidated effort on the part of international forum, the present urban society is inherently unsustainable. The anthropogenic pursuits and human interventions have brought enormous transformation in the bio-physical sphere of the urban ecosystem and made unsustainability an inevitable emergent property of the systemic interaction between urban-industrial society and the ecosphere (whatcom.wsu.edu). The growing social inequality, hyper-segregation, poverty, homelessness (Yacobi, 2021), slum, political instability, communal disharmony, gender inequality, injustice, violence, crime and terror attacks all raise a question to the sustainability of urban places. Cities on the one hand, have potential for economic development and social wellbeing and on the other hand have negative consequences too in the form of air, water pollution, ecological foot prints, resource inequity and poverty. Many economically strong cities neglect environmental and social factors. However, for the cities to thrive today and into the future, a focus on prosperity beyond profit is needed, as an economy can’t thrive without its people and people cannot thrive without a hospitable natural environment, in which to live and work (The Arcadis Sustainable Cities Index, 2022). It is imperative to understand that the cities are centers of economic development, but at the same time, are complex biophysical entities, intricately inter-woven where an alteration in one component has repercussion on the other, that is, ruthless anthropogenic activities in the urban areas are creating disorder or entropy in the system and transforming cities to a dissipative structure. It is like the metabolic mechanism in living organisms, which converts food, water and oxygen into energy (input) and waste products (output) which enables them to sustain life. Similarly, the cities need energy, materials, water, and nutrients to provide sustenance and shelter to its citizens, to produce goods and services (input), to grow and to eliminate waste and pollution (output) (Kennedy et al., 2007). The present cities have transformed into intense energy and material consumption and waste production centers, creating dissipative structures (http://whatcom.wsu.edu). The intense anthropogenic pursuits, in the modern urban metabolic cycle, drives environmental change on a local-to-global scale, affecting land-use and cover, biodiversity, hydro-systems, bio-geo-chemical

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cycles and climate (Grimm et al., 2008), leaving colossal ecological foot prints in and around the cities. Paradoxically, the cities only account for less than 2% of the Earth’s surface, but they consume 78% of the world’s energy and produce more than 60% of greenhouse gas emissions (UN Habitat estimates, 2021). Despite growth in manufactured capital stocks, catabolism (degradation and dissipation) exceeds anabolism (production) in urban areas (http://whatcom.wsu.edu). The present urban ecosystem has been transformed in a concrete jungle, accelerating urban heat island effect, surface runoff and impeding groundwater recharge. Further, the unplanned urban expansion, has culminated in inadequate drainage networks, poor solid waste management practices, habitat fragmentation and destruction, loss of urban greens, contributing to higher concentrations of particulate matter and pollutants in the air causing several health hazards. Overall, the exposure to outdoor and indoor air pollution, accounts for about 1 in 9 deaths worldwide every year (World Health Organization, 2016). Outdoor particulate air pollution, causes around 4.2 million premature deaths annually, across the world, and India and China, account for 50% of such mortality (Vardoulakis & Kinney, 2019). The urban heat island, removal of green cover and emissions, have collectively created micro level anthropogenic climate change (Mitchell et al., 2016), accelerating disasters as heat waves, urban flood and rainfall anomalies in the urban milieu. A new study has found that the greenhouse gases played a predominant role in the heatwave and was, one of the triggering agents of recent heat waves in Europe (Advances in Atmospheric Sciences, 2022). Similarly, the September 2022 torrential rain that hit the silicon capital Bengaluru, in India, inundating a large area (www.ndtv.com, 2022), was a consequence of unsustainable urbanization. As per a study, between 1973 and 2017 in a span of 45 years, there has been a growth of 1028% in urban areas in Greater Bangaluru (ENVIS Technical Report, 2017). In fact, the urban area energy and resource consumption, has exceeded beyond their means and seeks to restore, symbioses between anthropogenic activities and the bio-physical environment which requires, long-term, viable and self-sustaining planning to ensure urban sustainability. As a matter of fact, urban sustainability is the way to manage the growing urban impact across the world, as cities cannot be sustainable over longer term if their economic growth impairs the environment, on which they depend upon for clean air, fresh water, food supplies, and other ecosystem services (https://cssh.northeastern.edu). However, it is extremely complicated to delineate universal matrix of sustainability, as the cities are of various magnitudes in terms of morphology, size, demographic attributes, social fabric, economic structure, urban governance, and environmental characteristics. According to some accounts, there are over 200 different definitions of sustainable development (Parkin 2000). Besides, each country has their own criteria of defining urban centers, giving way to temporal and spatial data gaps, making the task more tedious. More so, it is very difficult to measure sustainability, as all the three pillars of sustainability (Environmental,

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economic, and social sustainability), require different sets of indicators and measurement method and also involves a large variety of professionals from scientists to engineers to accountants to social workers (https://diversity.social/urban-sustainability/). The term sustainability, needs to be universally applicable, permitting an adaptable set of indicators to accommodate urban areas in different development stages in all parts of the world (https://sustainabledevelopment.un.org/).

1.2 Aims and Objectives The present chapter aims to analyse, the issues related to urban unsustainability and ponders, on the ways and means to achieve urban sustainability. To achieve this, the chapter focuses on three objectives: firstly, to identify and evaluate the drivers of urban unsustainability; secondly, examines the impact of unsustainable urbanization, on the environment, economy and society, and thirdly, it discusses the viability and relevance of sustainable urbanization to resolve issues of urban unsustainability. The concluding chapter observes the need of formulating eco-centric urban plans to make urban places sustainable.

1.3 Research Design This chapter, is an outcome of a qualitative research. It is descriptive in nature and is based on self-observation and in-depth literature review. Relevant information and materials were collected from various sources incorporating books, journals, records, conference proceedings, reports, newspaper articles and web-based sources. Hence, this paper is entirely dependent on secondary published information and facts, over time and space. Here, various dimensions of urban sustainability have been explored and discussed which observes a need of radical paradigmatic shifts in urban planning.

1.4 Analysis, Discussion and the Findings 1.4.1 Urban Sustainability: Issues and Challenges The world is witnessing an explosive growth of urban population, accommodating about six billion planet’s population of which, one out of four is living below the poverty line (https://link.springer.com) and are exposed to several contagious, and infectious diseases. The urban areas in the developing countries are passing through a phase of hyper growth and unsustainable urbanization, which is, that they are not driven by economic opportunity, but by high birth rates and a mass influx of

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rural people seeking to escape hunger, poverty, and insecurity (FAO, 2020). On the other extreme, the urban areas in the developed nations possess condemnable education, health infrastructure, women empowerment, low and failing birth rates and high proportion of ageing population. The unsustainable use of energy and resource cause trash generation and urban decay. As a matter of fact, the present status of urbanization is globally unsustainable, but still the urban areas are expanding horizontally as well as vertically at a greater pace. The intense urban growth and urbanization owes to employment, and commercial opportunities, value added job prospect, access to various socio-economic services, urban amenities and infrastructure facilities, efficient transport and communication, open and liberal attitude and comfortable life in urban areas. In fact, a host of socio-economic, cultural and technological factors, collectively attract people to urban areas, making urban places over crowded. The population concentration keeps increasing and ultimately surpasses the carrying capacity of the urban centers thus make the city unsustainable. Likewise, with the expansion of urban areas, anthropogenic pursuits become all the more complex and disturbs the bio-physical equilibrium, triggering environmental degradation and unsustainability. Among the drivers of unsustainability, rural–urban migration and consequent overcrowding is most potent. Overcrowding is a state where population increases beyond the carrying capacity of the city. According to United Nations, the urban population of the world has grown from 751 million in 1950 to 4.2 billion in 2018. The congestion of people in limited spaces reduces the quality of air, contaminates water, (https://www.geeksforgeeks.org) and puts extra ordinary pressure on existing urban infrastructure. The movement of people from rural to urban centers occur mainly due to increased population pressure and limited resources available for the large population in the rural areas (https://www.grin.com). The rural people move to urban centers in search of jobs and better living standards which urban areas offer (Clark, 2003). The overcrowding leads to space crunch and makes housing expensive, culminating into rise of slums and makes the city all the more unsustainable. The urban development is intricately interwoven with good governance, which requires community participation, accountability, transparency and accessibility on the part of governing institutions. In fact, the urban governance is the software that enables the urban hardware to function (UN-Habitat, 2017). The urban administration and civic bodies are often nor competent, neither equipped enough to deal with emerging unsustainable issues in the urban milieu. The urban civic bodies depend on previous data and maps which are often stored in formats and at scales so diverse that they cannot be compared or easily updated (https://www.wiley.com). Besides, the old records and documents lack description of entire section of cities, peri-urban areas and urban sprawl hindering the process of urban planning. The local governing bodies often maintain their own data base and they rarely share data (nap.nationalacademies.org), depriving the planners and policy makers of various relevant information. The governing bodies often yield under political and bureaucratic pressure and are forced to sanction unsustainable urban projects. The change of governing officials, authorities or governments often leads to change of policies, creating ambiguity and confusion, making polices irrelevant and unviable. The

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administration of urban affairs is managed by multiple agencies which usually lacks co-ordination and synchronization in the formulation, and implementation of urban plans. Besides, the non-government stakeholders are rarely consulted in the process of formulation and execution of urban policies, jeopardizing the very essence of urban governance. Good governance requires to ensure representation of every section of the population, not just the elite or powerful in the civil society (Singh, 2008), which is not the case and has direct bearings on the aspects of environment, urban poverty, infrastructure services, equity and capacity building (Jain, 2014). Inaccessibility of urban services by the masses, lack of transparency in urban governance and nonenthusiasm in vesting of powers and authorities in favor of urban local bodies have enhanced the problem of urban governance manifold (https://ijcrt.org/papers). The inadequate, ambiguous and time-consuming legal provisions, meant to address and redress urban issues, prove to be fatal to urban sustainability. The urban centres are the melting point of diverse communities, culture, social norms, values, ideology, thought, custom and traditions. Though it is natural to have difference of opinion among stakeholders, but at times it ends in disharmony, acrimony and even violence, which erodes mutual trust, cooperation and puts social capital at stake. Social capital is a set of shared values, that allows individuals to work together in a group to effectively achieve a common purpose (https://www. investopedia.com) and is one of the pillars of urban sustainability. Lack of vision and commitment on the part of politicians, make them view urban folks as mere vote bank and the political parties, along with several regional organizations and cults with vested interest, ignites disharmony among various stakeholders, putting people’s security at stake and making things all the more unsustainable. The capitalistic and profit oriented urban outlook has given rise to goods production and consumerism, accelerating environmental degradation and trash generation. The world bank estimates say that, in 2020, about 2.24 billion tons of solid waste was generated across the globe and is expected to increase to 3.88 billion tons (73%) by the year 2050. The quantum and unscientific management of waste adds to unsustainability. The high consumerism leads to introduction of non-biodegradable substances such as plastic, fibre, electronic waste and harmful chemicals, in urban ecosystem deteriorating the quality of air, water and soil making them unsustainable. The urban civilization is fully dependent on energy consumption and according to UN Habitat, cities consume 78% of the world’s energy and produce more than 60% of greenhouse gas emissions which has serious repercussion on urban sustainability. The unsustainable infrastructure, such as improper drainage, unscientific methods of waste disposal, inefficient public transportation system, fossil fuel driven mode of transportation, coupled with unsustainable use of resources further aggravates the situation. Most urban bodies do not generate the revenues needed to renew infrastructure, nor do they have the creditworthiness to access capital markets for funds (https://www.worldbank.org) (Fig. 1.1). The unchecked expansion of urban areas has engulfed green cover, natural and man-made waterbodies, adjoining agricultural land and has resulted in immense loss of biodiversity in urban areas across the world. The urban vegetation has been mercilessly mopped to create space and infrastructure to accommodate growing

1 Urban Sustainability: The Way Forward Fig. 1.1 Drivers of urban unsustainability

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Drivers of Urban Unsustainability Social Drivers Poor Governance Legal Lacuna

Ecoinomical Drivers Captilistic outlook High consumerism

Overcrowding

Weak Finance

Rural urban migration

High energy consumption

Dishamony Inequality

Industries

Ecoclogical Drivers Pollution Trash Health hazard Disasters Emmisions

Policy, planning, and regulation deficiencies High material consumption

population and facilitate complex urban pursuits. The green cover left is in highly unsustainable state and contains mostly decorative and exotic plants of selected species which are detrimental to floral diversity. The removal of green cover has resulted in habitat loss and fragmentation which has proved fatal for various lifeforms, dwindling faunal diversity. The loss of urban flora and fauna is detrimental to urban sustainability. The dwindling water bodies also have repercussion on genetic diversity of aquatic flora and fauna. Loss of biodiversity is also detrimental to human health as exposure to the microbiome of biodiverse environments provides immunoregulatory health benefits to human beings (Rook et al., 2003). The vegetation cover acts as a buffering zone against noise and air pollution shielding urban population against air borne diseases and noise related ailments and is also linked to lower levels of depression, anxiety and stress (Beyer et al., 2014). The environmental degradation aggravates symptoms of climate change and its direct consequences such as floods, droughts and heatwaves have adverse repercussions on physical infrastructures, transport systems, water and energy supply, the provision of ecosystems goods and services (https://www.ihs.nl/) and human health which are detrimental to urban sustainability. Massive construction, renovation, demolition and landscaping has converted urban areas into concrete jungle creating heterogeneous skyline, artificial ecosystem and cemented impervious surface. The concrete buildings, impervious surface and coloured buildings have given way to high temperature absorption, as high walls, cemented courtyards and coloured roofs retains higher proportion of heat (Andrew: 1990) and creates urban heat island further deteriorating urbanites quality of life (Gomez et al., 2004). Likewise, the heterogeneous skyline exerts powerful frictional pool on the moving air causing micro turbulence (bidi.). The temperature rise in turn has affected evaporation rate, soil and air moisture content at micro-level, causing unsustainable urban setting.

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1.4.2 Consequences of Unsustainable Urbanization The unsustainable urbanization has various socio-economic and bio-physical implications. The socio-economic implications incorporate overcrowding, housing problem, origin of slums, unemployment, poverty, injustice, crime, drug abuse, violence, vulnerability to disasters, epidemics and terrorism, tremendous pressure on educational, health, transportation, energy and administrative infrastructure. On the other hand, the bio-physical issues have culminated in deterioration in the quality of ubiquitous resources, pollution, loss of green, blue and brown infrastructure, dwindling biodiversity, depletion of underground water table, urban flood, temperature anomaly creating heat island, pocket rain, and health hazard. These highlight the issues and challenges arising due to urban unsustainability. The perennial exodus of rural migrants makes urban areas overcrowded giving way to space crunch and housing problem. The high housing cost compel people to live in substandard houses and leads to formation of slums. At present, 50% of urban population in Asia lives in slums (scholar.google.co.in). There are one billion people living in the slums across the world (https://sustainabledevelopment.un.org/) deepening the socio-economic inequality in urban milieu. The slums accommodate huge under privileged and resource less folks in highly unsustainable way. Almost 40% of urban dwellers have no access to safely managed sanitation services and many lack access to adequate drinking water (https://www.who.int/). The huge urban crowd creates job crunch and the unemployed people are often unable to meet their basic needs, diverting them easily towards crimes such as snatching, robbery, trafficking, prostitution and drug abuse (Fig. 1.2). The people residing in urban areas are highly vulnerable to disasters, epidemics and terrorism. Globally, the intensity and frequency of disasters are escalating in Social Issues • Overcrowding

• • • • • • • • • •

Slum Inadequate housing provisions Injustice Crime Violence Terrorism Durg abuse Diseases and epidemics Illiteracy and ignorance Gender discrimination

Economic Issues

Ecological Issues

• Unemployment • Poverty • Expensive housing and real estate • Critical infrastructure shortage • Crucial service deficiencies • High energy cost • Inadequate transport infrastructure • Ambiguous policies and absence of transparency • Absence of micro-finance schemes

• Air Pollution • Noise Pollution • Light Pollution • Loss of biodiversity • Water scarcity and Pollution • Loss of green, blue and brown cover • Ground and portable water Contamination • Emmisions • Reccuring Disasters • Climate change • Urban flood • Temperature anomaly and heat wave • Pocket rain • Resource Depletion • Ecological foot prints • Health hazards • Problem of solid waste • Acuafer depletion • Sewage treatment problem • Electronic waste

Fig. 1.2 Consequences of urban unsustainability

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urban areas (https://www.sciencedirect.com/). The urban areas are vulnerable to both natural and manmade disasters. The natural disasters such as flood, cyclone, earthquake, tsunami, heatwaves, and epidemics have great impact on urban dwellers. The compact urban areas, high population and lack of preparedness leads to loss of life, property and cause infrastructure damage. Likewise, the manmade disasters including industrial accidents, fire, infrastructure failure, urban flood, stampede, food and water contamination too takes a high toll. The urban dwellers are vulnerable to the non-communicable diseases, infectious diseases and psychological issues such as depression and anxiety. Infectious diseases like the COVID-19, tuberculosis, dengue and diarrhea thrive in poor and overcrowded environments and are closely related to unhealthy housing, poor sanitation and waste management (https://www.who.int/news-room/fact-sheets/detail/ urban-health). Along with this, the non-communicable diseases like heart disease, asthma, cancer and diabetes are worsened by unhealthy living and working conditions, inadequate green space, pollution such as noise, light, air, water and soil contamination, urban heat islands and a lack of space for walking, cycling and active living (bidi.). The urban areas being center of political, economic and social activities are soft target of terrorist attacks and has far reaching repercussion. Urban acts of terror destroy the infrastructural development and divert resources away from investment, causing cities to regress (https://gsdrc.org/). Along with this, the urban areas often witness acrimony and violence. The population pressure in urban areas severely effects the educational, health, transportation, energy and administrative infrastructure. Everyday about 1.93 lakh new city dwellers are added to urban population across the world and it is estimated that the urban population in the developing world will rise to 5.3 billion by the year 2050 (https://www.researchgate.net/). To cater the basic requirements of growing population, major portion of the urban budgetary allocation is spent on infrastructure consolidation, neglecting the other development goals. The unsustainable anthropogenic activities have greatly depleted and deteriorated the quality of ubiquitous resources like air, water and soil, rendering them unsuitable for human use. The use, misuse and abuse of ubiquitous resources has severely polluted them. The household combustion devices, motor vehicles, industrial facilities and forest fires are common sources of air pollution introducing unwanted particulate matter and harmful gases such as carbon monoxide, ozone, nitrogen dioxide and sulphur dioxide in the atmosphere modifying its natural characteristics (https://www. who.int/). Cities consume over two-thirds of the world’s energy and are responsible for over 60% of greenhouse gas emissions causing 91% of urban dwellers to breath polluted air (bidi.). The main source of water pollution is concentration of lethal chemicals, trash, bacteria and parasites in water bodies rendering it unsuitable for consumption and other activities. The enormous volume of sewage discharge makes the sewage water treatment plants unsustainable further adding to the woos. The unscientific solid waste disposal is the major cause of urban land pollution which further accelerates water contamination and air pollution due to sub-soil leaching

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and release of methane gas following decomposition of organic solid waste respectively. The unmindful disposal of electronic waste too has devastating impacts. The E-waste contains toxic materials such as lead, zinc, nickel, flame retardants, barium, and chromium and when warmed up, releases toxic chemicals in the air damaging the atmosphere (https://www.iberdrola.com). It has often got mixed with municipal solid waste and found its away in landfills culminating in seepage of toxic materials in groundwater, affecting both land and sea animals (ibid.). The noise and light pollution further deteriorate environmental quality. Noise pollution can cause health problems for people and wildlife (https://education.nation algeographic.org). The loud or inescapable sounds from vehicles, aircraft, industrial machines, loudspeakers, crackers, home appliances etc. can cause hearing loss, stress, and high blood pressure. Likewise, the light pollution caused by high voltage halogen night lampposts disrupt metabolism, growth and behavior (Dunlap, 1999) of various lifeforms and threatens biodiversity. The depletion of green, blue and brown infrastructure is another side effect of unsustainable urbanization. The removal of green cover, reclamation of water bodies, depletion of underground water table, aquafer depletion and conversion of soil surface into cemented one are some of the devastating impacts of uncontrolled urban expansion. The loss of vegetation cover depletes and destroys biodiversity, contributing to extinction of various flora and fauna. The pollution and lowering of environmental qualities put human health at greater risk. The cities have become epicenters of disease transmission, making people struggle with morbidity and mortality. Every cloud has silver lining so is the urbanization which is associated with economic development of the country and social and cultural integration of heterogeneous population. The rise in urbanization accelerates the growth of commercial activities in the cities and towns (Clark, 2003). However, there is a long way to go to combat negative impacts of unsustainable urbanization and make it sustainable.

1.4.3 Urban Sustainability: The Way Forward The urban areas are passing through a critical phase and are in a dire need to strike the balance between the anthropogenic activities and biophysical attributes. The overall urban metabolism is totally unsustainable and it may worsen if no immediate action is taken (https://www.un.org). At present, urban sustainability seems to be the only option left to regulate and revive the urban infrastructure. The urban sustainability depends on environmental, economic and social sustainability. The environmental sustainability is measured in terms of ecological footprints. The economic sustainability reflects pattern and efficacy of resource utilization and resultant economic return, whereas, social sustainability calls for social equity, integration, well-being and inclusive development. It is evident that urban sustainability requires synchronization between the natural and cultural heritage. There is a need to focus on

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financially and environmentally sustainable services rather than mere adding physical infrastructure. The realization of following goals may help to achieve urban sustainability. Realization of sustainable urban development goals is a difficult task and requires effective strategic framework for planning. It requires political support and commitment, bureaucratic transparency, institutional cooperation, accessible legal provisions, and cooperation and mutual trust among various stakeholders. At the same time, it is essential to enhance the efficacy of urban governance which requires to entrust local civic body sole authority to look after urban governance in a gradual manner. This will eliminate confusion and ambiguity at policy formation and execution front and bring homogeneity, transparency and enable smooth functioning of municipality. Competent human resource is a pre-requisite for urban sustainability and requires efficient, educated, trained and committed workforce, having safe, healthy and cordial work environment. Apart from periodic professional training and workshops, aesthetic and ethical workshops are essential for character building of workforce. Gender equality is essential at workplace and in society as a whole. The work infrastructure needs to support especially abled workers. Stringent and just legal provision are required to check corruption and nepotism and bring social justice and equality. Urban energy solutions require full life cycle analysis, taking into account the full spectrum of positive and unintended negative impacts of transition to renewable energy (Scovronick et al., 2019). Harnessing eco-friendly local renewable sources of energy, micro grid technology, energy efficient city space, effective machinery to control greenhouse emissions and energy saving techniques is one of the essential attributes of urban sustainability. Construction of sustainable buildings equipped to tap solar, wind and biomass energy will be of great help. Good ventilation and light quotient of buildings, use of energy saving technologies and energy saving practices such as habit of switching off lights and electrical appliances when not in use will reduce energy demand. Well-designed and maintained urban vegetation and high reflectance roofs can help reduce the Urban Heat Island effect and building overheating (Salmond et al., 2016). Likewise, plantation around the buildings and vertical and roof top garden at the building will keep the building cool reducing energy demand. The energy sustainability also depends on the cost incurred during the process of energy generation and grey energy consumption, lower the value higher the sustainability. The grey energy consumption is the hidden energy associated with the product’s life cycle from its production to its disposal (http://www.educapole s.org). Cities are only truly sustainable if they are built on planet-friendly foundations (arcade2022). Resourceful, cost effective, fuel efficient and integrated public transport system is crucial for urban sustainability. Better integration should result in more demand for public transport and should see people switching from private car to public modes of transport, which should be more sustainable (https://www.coolgeography.co.uk). The transport system requires to integrate provisions to take care of the special needs of disabled and elderly population. This apart, the manufacturing of hybrid solar energy based battery driven vehicles will substantially reduce carbon footprint. The strict

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traffic rules and its adherence is also important for smooth traffic flow and to prevent road accident resultant causality. Construction of cycling tracks, cycle parking lot and cycle banks too is an important component of sustainable urban setup as cycling takes care of both carbon foot print and people’s fitness. Cycling in the Netherlands is the most popular form of daily transport, having more than 25 million cycles in the country (https://www.iamexpat.nl/) and it has the distinction of being the safest country in the world, with only 3.8 road deaths per 100.000 inhabitants (https://aboutt henetherlands.com/). Use of local products will help to ease up transport emissions. The water management and conservation are essential part of sustainable urban planning, as only 1% of water is available for human consumption. The water management practices include preservation, conservation and development of the surface and groundwater water resources, storm and waste water management. The innovative practices such as regulated use of water, recycling of water, water harvesting, purification of waste water at domestic level can go a long way in water management. Prevention of water pollution is also an essential component of water conservation. The installation of water saving devices such as push and pressure taps, aerated shower heads, duel flush toilets (https://www.coolgeography.co.uk), vacuumed flush used in aeroplanes, can help a lot in preventing wasteful use of water. Rationing of per capita water consumption and maintenance of plumbing devices and leakage are equally important, after all every drop counts. The waste disposal and management is a biggest challenge in urban planning. It requires participation of all the stakeholders right from generation, segregation, recycling and disposal of waste. Practices such as segregation at disposal source, community composting of organic waste, generation of biomass energy from organic waste and night soil slurry and incineration of hazardous waste can substantially handle the issue of waste management. A strict protocol on production, marketing, consumption and disposal of hazardous products may also help. It is important to regulate the consumerism behavior and one should buy the things one needs rather than one likes. The production of durable items is necessary to stop the use and throw culture. Adequate provision for disposal and recycling of electronic waste is of utmost importance to prevent contamination and health complication aroused due to radiation emitted from the electronic thrash. In Britain, 25% of disposed electronic and electrical waste are reusable after repair (https://www.coolgeography. co.uk), It is important to revive and adhere to traditional prudence and practice of waste management. The urban land use requirement has left cities with meagre green cover which need to be revived. Apart from aesthetic and recreational importance, the green cover has ethical dimensions too and need to be developed and maintained. The green public space in urban area provide space to people to rest, walk and enjoy and also allow other life forms to thrive. The green cover is closely associated with the biodiversity and creation of nesting space for birds, insects and flies. Enclosed animal and biodiversity corridors in urban regions are desirable.

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Fig. 1.3 Components of Urban Sustainability

Resource Efficiency

Sustainable Modes Of Tranportation

Efficient Green, Brown & Blue Infrastructure

Efficient Energy Sources

Urban Sustainability

Social Coherence

Sustainable Landuse & Economic Growth

Good Governance

The effective urban land use planning is essential to restore sustainability which requires to redevelop brown fields, development of artificial water bodies and safeguard ecological sensitive zones. The green cover can be restored through initiative such as plantation, vertical and rooftop farming and habitat conservation. Besides, the Japanese Miyawaki forestation method (https://www.sei.org/) can be of immense help in creating green space in urban centers. The Fig. 1.3 above represents components of urban sustainability. The urban areas are vulnerable to disasters and requires in-depth strategy to cope with any eventualities. The climate change too ignites disasters hence it is essential to frame mitigation and adaptation strategy. Disaster Risk Reduction (DRD) is an important component of sustainable urbanization. In developed and developing countries urban planning should play a key role in enhancing urban safety by taking on issues of disaster preparedness, post disaster and post conflict reconstruction and rehabilitation as well as urban crime and violence (https://www.researchgate.net). The affordable housing, slum upgradation, poverty eradication, creating avenues for sustainable employment is essential to empower under privileged poor citizens. The sustainable livelihood approach can go a long way in achieving the same. It is a participatory approach based on the recognition that all people have abilities and asset that can be developed to help improve their lives (https://policy-practice.oxf am.org). Livelihood is sustainable when it can cope up with and recover from stresses and shocks and maintain or enhance its capabilities and asset both now and in the future, while not undermining the natural resource base (Carney, 1998). Urban Sustainability also calls for the strengthening of peri-urban and adjutant rural infrastructure, minimizing food wastage, adoption of precautionary and preventive measures to preserve bio-physical characteristics, public friendly and efficient legal institution to impart fair and quick justice, financial institutions to mobilize

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investment and credit to citizens and organizations for the development of sustainable technology, citizen’s security and control on crime and violence.

1.5 Conclusion The process of urbanization is quite complex and despite the unprecedented rate of urbanization throughout the world, human society is still facing the challenge of coordinating urban socioeconomic development and ecological conservation (https://www.mdpi.com/). It is evident that there is no “cookie-cutter” approach to urban sustainability (http://nap.edu/23551) and requires patient consolidated effort on the part of all stakeholders to evolve an institutional framework for developing and implementing conservational measures at the micro level. There is a need to breaking out of the ‘silo mentality’ and restore transparency and mutual trust among the stakeholders, academicians, planners and practitioners involved in urban development. Universal consensus on basic definition of sustainability is imperative to evolve effective policies to attain urban sustainability. Besides, it is also essential to formulate strategy for rural development and extension of allied economic activities which will evolve newer employment opportunities for the rural folks and in turn, will arrest rural urban migration and ease up pressure in urban areas. The basic understanding of environmental, social and urban ecology is needed to be inculcated among urban folks, so that one can recognize the intricate linkage between human and natural systems and perceive the earth’s bio-physical limits. The concept of circular economy, needs to be adopted which aims to revive the balance between nature and anthropogenic activities. Social justice and equality are one of the prerequisites for the sustainable urbanization and calls for eliminating or at least reducing socio-economic, political, and class inequalities. In fact, just urban sustainability is not enough as the resource demand, repercussion of anthropogenic pursuits has far reaching impact and are not confined to urban areas only. The urban areas largely depend on hinterland to meet their various needs and act as waste sinks (Rees, 2012) for the trash and pollutants generated in the cities. This calls for a harmonious symbiosis between urban and their rural hinterland which is equally essential to attain urban sustainability. It is obvious that the urban areas play an important role in social and economic development and are the center of cultural, intellectual, educational, technological achievements and innovations (http://www.eea.europa.eu). The urban centers accounts for 55, 73 and 85% Gross National Product in the least developed, middleincome and developed countries respectively (http://www.unep.org/urban_enviro nment). It is obvious that the urban areas are critical for society’s wellbeing and the sustainable urban development is the most appropriate option to regulate urban growth by integrating the socio-economic and ecological dimensions of sustainability. A well-conceived urban sustainable development plan holds the potential to ensure viable and ample livelihood avenues; accelerate economic growth; improve social liaison; reduce poverty, injustice and inequality; protect environment and

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reduce ecological foot prints. In other words, Urban sustainability helps revitalization and tradition of urban areas and cities to improve livability. Promoting innovation reduces environmental impacts while maximizing economic and social co-benefits (https://www.eea.europa.eu). As per the United Nations estimates by 2030, 6 out of every 10 people will live in urban area, hence it is high time to work hard to realise the sustainable development goal which calls for making cities and human settlements inclusive, safe, resilient and sustainable by 2030 (https://unfoundation.org/).

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Part II

Urban Land Use Dynamics and Changing Biodiversity

Chapter 2

Impact of Rapid Urbanization and Changing Face of Wetland: A Case Study of Berhampore Municipality, Murshidabad, West Bengal (India) Subham Kumar Roy

and Chumki Mondal

2.1 Introduction Wetlands are permanently or seasonally water-saturated hydraulic landscape where perennial water lies on shallow or deep trench. According to US Fish and Wildlife Service (1979)—Wetlands are transitional zones between terrestrial and aquatic ecosystem where the water level is usually at or near the surface. They not only maintain the ecological balance also control floods, increase ground water recharge, flourish aquaculture, develop as good recreational sectors and also crucial natural resource on the earth surface which may help to support rich species diversity. ‘Wetland’ is a generic term of water bodies which includes diverse hydrological entities such as marshes, swamps, bogs, wet meadows, potholes, and river overflow lands (Tiner, 1999). Because of these characteristics wetlands are described as “the kidneys of the landscape” (De & Jana, 1997). Recently wetlands have been analyzed as “biological supermarkets” for their extensive food webs and rich biodiversity support (Mitsch & Gosselink, 1993). Wetlands have diversities on the basis of their geographical location, nature, dominated flora and fauna species, soil and sediment properties (Space Application Centre, 2010). Schuyt and Brander (2004) describe four functions of wetlands: (i)

Regulation functions: Wetlands regulate ecological processes that contribute to a healthy environment. (ii) Carrier functions: Wetlands provide space for cultivation, energy production and habitat for animals.

S. K. Roy (B) Department of Geography, Prof. Syed Nurul Hasan College, Murshidabad, West Bengal, India e-mail: [email protected] C. Mondal Department of Geography, Khandra College, Paschim Bardhaman, West Bengal, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_2

23

24

S. K. Roy and C. Mondal

Table 2.1 Classification of wetland category in study area Types Inland wetland Natural

Example Lake, pond, ox bow lake, cut off meander, water logged, river etc.

Manmade Reservoir, tank, pond, recreational spot etc.

(iii) Production functions: Provide resources like food, water, raw materials etc. to sustain human life. (iv) Information functions: Wetlands control mental stability by providing scientific and spiritual values. It can be classified inland wetlands into two major types in our study area (Table 2.1). In Berhampore town wetlands constitute a special ecosystem by nurturing a large variety of flora and fauna and it is used for multiple purposes. Households of peripheral zones directly and indirectly utilize the wetlands. Local habitats which are close to wetlands are classified into two categories according to their uses; such as (a) Bed village and (b) Belt village. Bed villages are located at the immediate vicinity around the wetlands and intimately linked with the wetland functions. On the other hand Belt villages are located away from locality and exploit wetlands especially for major commercial purpose only (Seshavatharam, 1992). Green–blue infrastructure (GBI) is presented as a strategy to deal with climate change in urban areas. The following table (Table 2.2) is the summarization of all literature: After reviewing many national and international open access research papers, it has established that many works have been done on wetland and basis of the qualitative and quantitative functionality of remote sensing and GIS. In India we can access many journals on wetlands but they are all upon metropolitan cities like Hyderabad, Bangalore and Kolkata etc. All of the journals have giving their importance on Ramsar wetlands. But local wetlands are not under any environmental consideration and conservation. So, this chapter emphasizes on Berhampore municipality of Murshidabad district of West Bengal and how dynamic urbanization brings changes on wetland ecosystem of this municipality has to be analyzed by LULC and GIS methods.

2.2 Objectives i. To evaluate consequences of unplanned urbanization process on wetlands through spatio-temporal changes of land use pattern. ii. To analysis the major problems and probable solutions for wetland conservation in study area.

Data source

SPOT-5 Image

LANDSAT 5 TM & LANDSAT 7 ETM+Image

LANDSAT 4 MSS Image

LANDSAT 5 TM & SPOT IMAGE

LANDSAT ETM+, IKONOS & TOPO MAP

Study location

Rift valley, Sanegal

Fujian Province, SE China

Western Nebraska, U.S.A

Nanjing City, China

Olympic park, Beijing, China

Table 2.2 Main theme of previous studies Main theme, statistical analysis & Variables

Improve the NDBI by using a semiautomatic segmentation approach and accuracy assessment

Accurate mapping of rapid urban area and showing the relationship between NDVI and NDBI

Delineation of open water features with the help of NIR band and NDWI

Using the NDWI and MNDWI for enhance and extract water information for a water region

Assessment of spatio-temporal evolution of ponds by NDPI & NDTI and showing the relationship among ponds dynamics, vegetation cover, and turbidity associated with mosquitoes, production and abundance

References

He et al. (2010)

(continued)

Zha et al. (2003)

McFeeters (1996)

Xu (2006)

Lacaux et al. (2007)

2 Impact of Rapid Urbanization and Changing Face of Wetland: A Case … 25

Improving the accuracy of water bodies and current status of water bodies has been detected by NDWI, MNDWI, NDPI and AWEI

It mainly focuses on status, threats and future Xu et al. (2019) protection of Ramsar wetlands all over the world

LANDSAT 5 TM, LANDSAT 7 ETM+, SPOT 5 & QUICK BIRD IMAGES

Ramsar official website

Shanghai, China

All over the world

(continued)

Xie et al. (2014)

McFeeters (2013)

QUICKBIRD Image

California, USA

This study mainly focuses on the presence of surface water and then incorporates vector-based data layers within a GIS to identify residential land parcels with detectable water

The first objective is to improving the general Ghermandi et al. (2008) understanding of both natural and human-made wetland values by conducting a meta-analysis by meta regression models with explanatory variables and to show the anthropogenic pressure exercises on the wetlands. Second one is to explore the variation in valuation of the interactions between wetland types and ecosystem services

Published data on various research papers, Govt. and Non Govt. sources

References

Main theme, statistical analysis & Variables

Data source

Study location

All over the world

Table 2.2 (continued)

26 S. K. Roy and C. Mondal

Firstly, identify the urban fabric and Rojas et al. (2022) modelling the urban variables of density, distance, roads, and green areas surrounding an inland wetland. Secondly, evaluate the accessibility of wetland and its effect on plant composition using a biodiversity indicator Distribution of wetland according to broad physiographic division, values of wetlands, threats of wetland, needs of conservation and implementation of models to manage wetlands

Published data on various research papers and Govt. Non Govt. sources

Published data on various research papers and Govt. Non Govt. sources

Published data on various research papers and IRS LISS III images

All over the world

Chile, South America)

India

Dumax and Rozan (2021)

(continued)

Prasad et al. (2002)

Investigate the role of wetlands in urban areas Alikhani et al. (2021) by answering how urban wetlands contribute to the urban environment

Water Framework Directive on artificial wetlands, Adapted Habitat evaluation procedure, Habitat Suitability Index of ecosystem services and wetland restoration on Libellule Zone

Published data on various research papers and Govt. Non Govt. sources

Saint-Just, France

Lettoof et al. (2020)

The study insights into the influence of urbanization and climate change on host-parasite interactions of snakes and highlight the importance of museum specimens to assessing spatial and temporal changes in urban ecology

Published data on various research papers, Govt. and Non Govt. sources

References

Main theme, statistical analysis & Variables

Data source

Study location

Australia

Table 2.2 (continued)

2 Impact of Rapid Urbanization and Changing Face of Wetland: A Case … 27

Flood plain wetland resource identification, Sarkar and Borah (2017) functionality and impact of climate change on wetlands The dynamics of urbanization and its impact Das and Das (2019) on the ecosystem services; and to assess the impact of the land use and land cover changes on the ecosystem services from 1990 to 2017

Published data on various research papers

LANDSAT 5 TM & LANDSAT 8 OLI IMAGES

LANDSAT 5 TM & LANDSAT 8 OLI IMAGES

Literature study, field survey and published data on various research papers and Govt. Non Govt. sources

India

Malda Municipality, India

Bangalore, India

Patna, Bihar

Identify the direct and indirect impacts of urbanization on wetlands

Barman et al. (2020)

The present study attempts to contextualize Brinkmann et al. (2020) wetland dynamics with other landscapes, indicators of urbanization process and giving a special focus on land cover changes within the potential wetland areas from 1965 to 2018

Understanding the relationship among Singh et al. (2014) visible, NIR and SWIR bands with the help of NDWI and MNDWI

Multispectral IRS-P6 satellite images

References

Main theme, statistical analysis & Variables

Data source

Study location

Sri Muktsar Sahib district, Punjab

Table 2.2 (continued)

28 S. K. Roy and C. Mondal

2 Impact of Rapid Urbanization and Changing Face of Wetland: A Case …

29

2.3 Study Area At the earlier stage, few Bramhan households laid out great effort to establish the village Bramhapur. It was a historical place which was famous for silk production, jute production, ivory craft and kasha pitals item; those were the full factors of European merchants (Armenian 1665, French, Dutch & British) to establish their port. After the construction of their port due to pronunciation problem Bramhapur converted into Berhampore. In the latter half of Eighteenth century for observation of Nawabi activity a military camp was formed in Berhampore, which was the first footmark of urbanization. After the Battle of Palashi (1757) and Mir Qashim (1763) the construction of cantonment was started at Berhampore mouza (Present near square field). According to Hunter, that cantonment was completed in 1767 and the chief architect was A. Campbell. But after the Sipoy Mutiny in 1857, the cantonment was demolished nearly in 1870 and the Berhampore town was formed around this cantonment in 1876. Berhampore Municipality is under Berhampore block of Murshidabad is one of the oldest municipalities in West Bengal. In the year 1884, according to “Bengal Municipal Act 1876”, finally the Administration and management of Berhampore Municipality was handed over to 14 elected and 5 governments nominated members. The first Chairman of Berhampore Municipality (1884) was Ray Bahadur Baikunthanath Sen, who was a reputed Advocate and the President of District Bar Association as well. At that time the area of Berhampore Municipality was divided into 6 wards i.e. (1) Gorabazar (2) Cantonment (3) Berhampore (4) Khagra (5) Saidabad and (6) Cossimbazar. The latitudinal extension of the area is 24º04' 39'' N to 24º07' 48'' N and longitudinal extension is from 88º14' 57'' E to 88º15' 50'' E (Fig. 2.1) with an area around 31.42 km2 . This municipality has 25 Wards. The Bhagirathi River flows throughout the entire western part of the city with a direction from north to south. The N.H. 34 divides the city into two parts and the Easter Railway running through eastern side. Berhampore is the administrative, nodal, and district headquarters of the Murshidabad district. According to census 2011 of India it was categorized as class-1 municipality town and 7th largest city in West Bengal. It is perhaps the only municipality in West Bengal which is the member of Indian Heritage Cities Network (Sharma, 2012). It’s a diversified functional unit with great urban services all over the urban territory and its surroundings.

2.4 Materials and Methodology This work has been done on the basis of secondary data, which are collected from many official and non-governmental sources. Raw Landsat images of 1991, 2001, 2011, and 2021 (Table 2.3) have been collected from USGS Earth Explorer and cloudfree photos are used to achieve the goals without errors. Ground truth verification was carried out in order to improve the accuracy. Information related to growth and development of the town has been gathered from various office of Berhampore

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S. K. Roy and C. Mondal

Fig. 2.1 Location map of study area

Table 2.3 Details of used Landsat data Sl. no

Date acquired

Satellite sensor

Path/Row

Datum

Map projection

1

1991-01-24

Landsat 5/TM

139/43

WGS 84

UTM

2

2001-01-19

Landsat 5/TM

139/43

WGS 84

UTM

3

2011-01-31

Landsat 5/TM

139/43

WGS 84

UTM

4

2021-02-11

Landsat 8/OLI-TIRS

139/43

WGS 84

UTM

Municipality. Remote sensing techniques, Arc GIS 10.2 and MS Excel have been used for mapping and cartographic representation.

2.4.1 Supervised Image Classification Supervised image classification is a user-guided method which involves in selecting training sites as category references (Campbell, 1996; Jensen, 1996). This classification technique is used to classify the pre-processed images to recognize spectrally comparable areas on satellite images by assigning training sites of known targets and then extracting those spectral fingerprints to other areas of unknown targets.

2 Impact of Rapid Urbanization and Changing Face of Wetland: A Case …

31

Table 2.4 Land use and Land cover classes of study area LULC classification Description Waterbodies

Natural and manmade lake, ox-bow and ponds

Vegetation areas

Deciduous forest lands, gardens, mixed forest lands, roadside or riverside vegetation areas etc.

Open spaces

Stadium, play ground, park, recreational spot and Project area under construction

Builtup areas

Residential, commercial and services lands

It has three-stages those are training, classification and output. The analyst selects training sites to reflect known areas during the training stage. Analyst identifies links between different types of land and their spectral frequency of multiple wave lengths at this stage. The second step in the supervised classification involves categorizing a large number of spectral bands into precise land use and land cover categories (Table 2.4). Maximum likelihood is the most extensively used classification algorithm. The output stage is the last step in this process. The results are presented, visualized and interpreted by the output products. Finally, the analyst compresses the classified data into a specified group of classes and presents in digital, graphical and tabular form. The statistical parameters, accuracy assessment table and other supporting information are included the end product (Khorram et al., 2013).

2.4.2 Accuracy Assessment The term ‘accuracy’ is often used to indicate the measure of correctness of a map which can be tested by using an error-matrix. Remote sensing studies have been focusing on accuracy assessment as a key component. 150 samples of the study area were collected by using GPS and verified by Google Earth software for accuracy assessment. The Kappa coefficient (K) is another indicator which may use to determine the relevance of the study. The values of Kappa coefficient range from 0 to 1 (Das & Sahu, 2020; Rwanga & Ndambuki, 2017). Where 0 is for less significance and 1 is for highly significance of the research. The equation of kappa coefficient (K) is: (K) =

(Totalsample − Totalcorrectedsample) − ∑(ColumnTotal × RowTotal) × 100 (Totalsample)2 − ∑(ColumnTotal × RowTotal)

On the basis of accuracy assessment of the selected study area, the value ranges from 0.74 to 0.92 (Table 2.6). According to Rwanga and Ndambuki (2017) the values fall under “good to very good” range (Table 2.5). Which indicates the maps are accurate and relevance for the study.

32 Table 2.5 Kappa statistics (Rwanga and Ndambuki, 2017)

S. K. Roy and C. Mondal Sl no

Value of K

Status

1

0.01 (Very high)

7.98

−0.16– −0.07 (Medium)

−0.07- 0.01 (High)

3.18

24.37

40.1

25.39

10.12

6.58

13.68

8.17

2.99 8.99

2.4

20.94

5.48

45.53 14.55

26

9.51

17.41

46.3

28.61

7.63

5.04

14.88

9.45

2.05

%

−3.53

16.04 −3.43

2.6 0.77

0.61

−1.88

%

5.43

47.35

30.07

6.52 −0.7

Area (Km2 ) %

−1.37

1.05

1.46

−1.11

%

−8.33

7.25

4.68

3.6

%

Area (Km2 ) %

Area (Km2 ) %

Area (Km2 ) %

Area Change 1991–2001 2001–2011 2011–2021 1991–2021

2021

1991

2011

2001

Area

0.01 (Very high)

1.94

4.88

30.8

9.68

4.8

−0.07 − 0.01 (High)

14.89

45.89 19.8

4.68

15.27

4

6.17

15.53 1.46

4.54

63.01 21.42

3.45

4.64

14.44 1.02

3.73

68.17 23.22

12.73

0.38

−1.53

−2.23 3.24

−1.4

−2.57

5.73

−1.75

−2.54 5.16

%

%

−1.09

17.12

%

11.87 −15.27

73.9

10.98

Area (Km2 ) %

−5.16

−18.93

28.01

−3.91

%

1991–2001 2001–2011 2011–2021 1991–2021

2021

Area (Km2 ) %

Area (Km2 ) %

Area (Km2 ) %

2001

1991

2011

Area change

Area

−0.15 − −0.07 14.42 (Medium)

0.1 (Very High)

3.84

−0.05 − 0.05 (Medium)

0.05 − 0.1 (High)

3.02

4.94

3.68

28.51

8.1

49.64 14.7

12.21

9.61 5.41

5.01

25.77

7.46

46.78 13.54

15.72

11.71 5.88

6.2

23.74

7.01

43.09 12.33

17.21

15.94

−2.03

22.31 −2.74

1.49

4.23

%

−3.69

3.51

2.1

%

39.24 −2.86

18.71

19.73

Area (Km2 ) %

−1.43

−3.85

1.5

3.79

%

−6.2

−10.4

6.5

10.12

%

Area (Km2 ) %

Area (Km2 ) %

Area (Km2 ) %

Area change 2021

1991–2001 2001–2011 2011–2021 1991–2021

2011

2001

Area

1991

0.03 (Very High)

Area

5.47

36.6

54.67

3.24

1.65

10.88

16.95

1.94

Area

5.25

34.62

53.94

6.17

1.52

10.53

16.42

2.95

Area

4.83

33.51

52.25

9.38

1.4

10.48

16.3

3.24

Area

%

4.45

33.35

51.87

10.31

−1.11 −0.42

−0.22

−1.69

3.21

%

2001–2011

−1.98

−0.73

2.93

%

1991–2001

(Km2 )

2021

Area change 2011

1991

2001

Area

μ + 2 × σ

UHI CT = Tcore − Tperiphery Where Lλ

Spectral radiance

QCAL

Calibrated DN

LMAX

Spectral radiance scaled to QCALMAX

LMIN

Spectral radiance scaled to QCALMIN

QCALMIN

Minimum calibrated DN QCALMAX

Maximum calibrated DN

T

Effective satellite temperature

K2

Constant 2

K1

Constant 1

ML

Radiance multiplicative band

AL

Radiance add band

O

Correction value for Band 10

PV

Proportion of vegetation

μ

Mean LST

∑

Standard deviation of LST

CT = Comparative Temperature

has been done in this perspective. ∑ (xi − x)(yi − y) r=√ (xi − x)2 (yi − y)2 where, ‘r’ is the Pearson’s product moment correlation coefficient, ‘x i ’ is values of x variable in a sample, ‘x i ’ is mean value of x variable, yi is the value of y variable in a sample and ‘y i ’ is mean value of y variable. Linear regression is used for the measurement of variable’s positive or negative character beyond the mean. y = β0 + x1 β1+... x p β p + ε where, ‘β’ reflects how much x effects on y and ‘1’ is the error term.

9.5 Result and Discussion 9.5.1 Analysis of NDVI The pattern and distribution of vegetation cover throughout the year 1990 to 2020 with a 15 years gap, have been explained by Normalized Difference Vegetation Index

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map (Fig. 9.2). The map has been classified into three categories (Table 9.6), such as low (less than 0 value), medium (0–0.2 value) and high (more than 0.2 value). In the year 1990, 2005 & 2020 the highest NDVI values are respectively 4.88%, 2.60% and 2.54%. NDVI map showing the spatio temporal variation of vegetation cover of Durgapur city. In 1990, the highest values of NDVI are mostly concentrated in the north eastern part of the city, nearly at Steel Plant Airport; in 2005 the higher values are constantly found at Nachan and Birja villages which are located in the north eastern part of the city. But in the year 2020, the highest values are in the eastern part of the city, under IQ city road & B-zone areas. So there is a negative declining trend of NDVI values from 1990 to 2020, which fall under nearly 2.34 percentages of total area. Above Table 9.7 showing summary of statistical values of NDVI map. In the year 1990 the NDVI value ranges between 0.39 to −0.1 and in the year 2020 ranges from 0.34 to −0.07. It basically indicates the proportion of vegetation in the city area has declining from 0.145 in 1990 to 0.135 in 2020. This is a clear indicating that according to time vegetation covers decrease from urban core to fringe.

Fig. 9.2 Normalized difference vegetation index (NDVI) from 1990 to 2020

9 Assessment of Land Surface Temperature Using Landsat Images …

207

Table 9.6 Area under different NDVI in DMC from 1990 to 2020 NDVI

1990 Area

0.2 High

2005 %

5.15

3.33

141.51

91.77

7.54

4.88

Area 6.48 143.7 4.02

2020 % 4.2 93.19 2.6

% Area change

Area

%

4.08 146.2

1990–2005

2005–2020 −1.56

2.64

0.87

94.81

1.42

1.62

2.54

−2.28

−0.06

3.92

Table 9.7 Dispersion of NDVI in DMC from 1990 to 2020 Year

Max

Min

Mean

SD

1990

0.39

−0.1

0.145

0.346

2005

0.36

−0.08

0.14

0.311

2020

0.34

−0.07

0.135

0.289

9.5.2 Analysis of NDBI The nature and pattern of artificial urban structure of the Durgapur city has been explained by Normalized Difference Built up Index (NDBI) map (Fig. 9.3) from the year 1990 to 2020 with a 15 years gap. The NDBI map has been classified into three categories (Table 9.8) such as low (less than 0 value), medium (0–0.30 value) and high zone (more than 0.30 value). The very high NDBI value of 1990, 2005 and 2020 are respectively 8.7, 9.22 and 10.07 percentages. NDBI values continuously increase over time, almost 1.37 percentages. In 1990, higher values of NDBI were mostly concentrated in CBD, A zone, Benachity and some patches of surroundings of industrial sector. But in 2020, the built up area are highly flourished by rapid urbanization in some selected places, such as Netaji colony, Bidhanagar and B zone etc. But in the extreme western and northern western part of city such as Bansola, Raghunathpur and Parulia is not much developed due to distance from city center. Table 9.9 shows mean and standard deviation of NDBI. In 1990, the NDBI value is between 0.63 to −0.32 and in 2020 it ranges from 0.75 to −0.37. The proportion of built up area in the urban sector has gradually increased from 0.15 in 1990 to 0.19 in 2020. It’s highly correlated with the nature of urban expansion accordingly.

9.5.3 Analysis of Built Up Index (BUI) Built up Index is used to show the urban outgrowth of a particular city region. According to Zha et al. (2003) positive value of BUI basically shows the artificial structure on earth surface with the help of band ratioing technique. Figure 9.4 and Table 9.10 are presenting the variation and dispersion of BUI of Durgapur city of the year 1990, 2005 and 2020. In the year 1990, the NDBI value

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Fig. 9.3 Normalized difference built up index (NDBI) from 1990 to 2020

Table 9.8 Area under different NDBI in DMC from 1990 to 2020 NDBI

1990 Area

0.3 High

6.68 134.1 13.42

2005 % 4.33 86.96 8.7

Area 5.48 134.5 14.22

2020 %

Area

% area change %

1990–2005

2005–2020 −0.9

3.55

4.1

2.65

−0.78

87.22

134.6

87.28

0.26

0.06

9.22

15.5

10.07

0.52

0.85

Table 9.9 Dispersion of NDBI in DMC from 1990 to 2020 Year

Max

Min

Mean

SD

1990

0.63

−0.32

0.15

0.67

2005

0.81

−0.45

0.18

0.89

2020

0.75

−0.37

0.19

0.79

is from max 0.61 to min −0.69, but in 2020 it changes maximum 0.91 to minimum −0.44. So it is clear that in 1990 higher values of BUI were mostly concentrated only in CBD, A-zone, Benachity and its surroundings areas of the city. After that in 2005, the built up areas had highly flourished and expanded all over the region within a short period and the highest value of BUI reached up to 0.95. But when we look into the year 2020, rather previously established urban sector, many new places such as Netaji colony, Bidhanagar and B-zone areas are under the process of rapid urbanization. Presently highest value of BUI mainly found in the North eastern fringe area of city which was previously under forest area or barren land. Due to maximum distance from core region, the north eastern part of the city remains static before 2005. So it is highly reflected that the study area has an outward growth process with dynamicity during last two decades.

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Fig. 9.4 Built up index (NDBI) from 1990 to 2020

Table 9.10 Dispersion of BUI in DMC from 1990 to 2020 Year

Max

Min

Mean

SD

1990

0.61

−0.69

−0.04

0.91

2005

0.95

−0.85

0.05

1.27

2020

0.91

−0.44

0.235

0.95

Figure 9.5 shows the urban expansion of Durgapur city from the year 1990 to 2020. Within this time period nearly 13.56 km2 area expanded under urbanization, and this area experiences very high temperatures for several years.

9.5.3.1

Analysis of Land Surface Temperature (LST)

Changing pattern of Land Surface Temperature (LST) has been studied of mainly winter season or pre monsoon season from the year 1990 to 2020 (Fig. 9.6). The maximum temperature of the image of the year 1990 is 27 °C and minimum temperature distribution is 15 °C. On the other hand from the map of the year 2020 we can get the highest temperature 33 °C and minimum temperature 13 °C. So, the mean temperature within the 30 years of the study area has been increased by approximately 3 °C. LST maps of the study area showing the highest temperate areas by dark red shade and bluish shade represents lowest temperate zones. The south western & eastern part of the city being Industrial zone represents dark red shade. North, North east, North West and Southern part (Mayabazar, Angadpur, City center, Benachity, A-zone, B zone and Bidhannagar) of the city basically shows the commercial and residential zone, represents as yellowish color. Eastern (Haribazar) & northern (Kamalpur) part of city showing the vegetation cover area or urban fringe, shaded as bluish color. In 1990, Kamalpur region was covered by vegetation, so land surface temperature

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Fig. 9.5 Urban expansion of 1990 and 2020

was low and shown in the map by bluish color but next image of that vegetated land shown as dark red because of conversion of vegetated land into urban land. So, it’s clear that land surface temperature is increased by the process of rapid unplanned urbanization. Table 9.11 shows the distribution of LST from 1990 to 2020 in the study area. LST values are classified into three categories, as Low (less than 16 °C), medium

9 Assessment of Land Surface Temperature Using Landsat Images …

Fig. 9.6 Land surface temperature from 1990 to 2020

211

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Table 9.11 Changes of land surface temperature from 1990 to 2020 1990

LST

Area 19 High

134.7 5.36

2005 %

Area

2020 %

Area

% area change %

1990–2005

2005–2020

9.17

9.97

6.5

6.1

3.95

−2.67

−2.55

87.35

133.78

87

133

86.25

−0.35

−0.78

3.47

10.45

7

15.1

9.79

3.53

2.79

(16–19 °C) and high (more than 19 °C). It has been found that in 1990 almost 87.35% of study area falls under medium zone and only 3.47% falls under high zone of LST category. But this condition has dramatically changed with a very short time and the highest value of LST was recorded 7% and 9.79% of area under this category. Nearly 9.74 sq. km (8.18%) areas had increased within last thirty years, basically remarkable for urban heat island. To identify Comparative temperature (CT) value, 50 samples were collected over the study area. In 1990 the average core temperature was 22 °C in core region and 17 °C in peripheral region; 27 and 20 °C in 2005 and 31 and 20.5 °C in 2020. It’s a clear indication of distribution of urban heat land dynamics and the changes in CT values from 1990 to 2020. A cross sectional study (Fig. 9.7) (A–A' for 1990; B–B' for 2005 and C–C' for 2020) shows the spatial distribution of LST from industrial zone to urban fringe zone. The highest temperature has been recorded in the industrial zone nearly 25 °C in 1990, 29 °C in 2005 and 32 °C in 2020 respectively and the lowest temperature has been recorded in the urban fringe nearly 18 °C to 21 °C from 1990 to 2020. So, the temperature is rising very rapidly which may create lots of problems for city dwellers. Figure 9.8 shows the interrelationship between urban expansion and distribution of LST from 1990 to 2020 in the study area. The city’s industrial zone is located in the southern, south western and south eastern parts. Because of this, the land’s surface temperature is rising very quickly. Figures 9.5 and 9.8 make it abundantly evident that the commercial and residential zones are mostly found in the city’s north, north eastern, and north western part. It is a clear indication of urban out growth basically found in those parts of the city and as a result, land surface temperature also increased very rapidly. However, an unpredictable low temperature has been recorded in the eastern section of the city, which is the vegetation covered region or urban fringe.

9.5.3.2

Statistical Analysis

Accuracy assessment is most important stage of the research to validate our work with actual earth and Kappa coefficient analysis is most useful technique to identify the accuracy assessment of study. For the accuracy assessment a random sampling method of 120 samples was chosen. Collected samples were compared by Google Earth image and Landsat images. Table 9.12 represents the kappa value which expresses the level of accuracy of our research (Kaimaris & Patias, 2016).

9 Assessment of Land Surface Temperature Using Landsat Images …

Fig. 9.7 Land surface temperature cross section profile from 1990 to 2020

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Fig. 9.8 Direction wise mean land surface temperature from 1990 to 2020

30 25 20 15 10 5 0

NORTH WEST

WEST

NORTH EAST

LEGEND

1990℃

EAST

2005℃ SOUTH EAST

SOUTH WEST

2020℃

SOUTH

Table 9.12 Summary of kappa coefficient

Year

Index

Overall accuracy

Kappa

1990

NDVI

90

0.8

2005

2020

NDBI

88

0.79

BUI

92

0.82

LST

95

0.9

NDVI

92.9

0.83

NDBI

89

0.81

BUI

94

0.84

LST

90

0.8

NDVI

91

0.81

NDBI

92

0.82

BUI

92

0.82

LST

96

0.92

After analysis of NDVI, NDBI and LST maps, it has been clearly excavated that there is an inverse relationship between NDVI and NDBI. From 1990 to 2020, artificial structures vastly increased and as a result Land Surface Temperature values also increased dynamically. Higher vegetation cover has lower LST value and vice versa. So, a negative relationship is there. After the NDBI map analysis, it is clearly notified that there is a positive relationship between NDBI and LST. Table 9.13 and Fig. 9.9 are showing the relationship between NDVI and LST and all the r 2 values are highly competent with our research objectives.

9.6 Conclusion Multi-temporal Landsat satellite images have been used in this study to analysis the impact of urban landscape on land surface temperature in Durgapur Municipal Corporation from the year 1990 to 2020. Cities are typically warmer with higher

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Table 9.13 Correlation among LST, NDVI & NDBI Year

Index

LST

NDVI

NDBI

1990

LST

1

**

**

NDVI

−0.71

1

**

NDBI

0.72

−0.64

1

LST

1

**

**

NDVI

−0.78

1

**

NDBI

0.79

−0.722

1

LST

1

**

**

NDVI

−0.809

1

**

NDBI

0.822

−0.8

1

2005

2020

Note:

**

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

-0.1

30

20 10

20 10

0

0

0.1 0.2 NDVI

A

0.3

0.4

-0.4

-0.2

R² = 0.6184

40 LST

LST 0

0.1 0.2 NDVI

0.3

-0.4

0.4

-0.2

R² = 0.6558

40

0.6

R² = 0.6376

0 NDBI

0.2

0.4

R² = 0.6787

30

20

LST

LST

0

40

30 10

C

0.4

10

B

0

0.2 NDBI

20

10

-0.1

0

30

20 0

0

40

30

-0.1

R² = 0.5229

40

R² = 0.506 LST

LST

30

20 10

0

0.1 0.2 NDVI

0.3

0.4 -0.4

-0.2

0

0 NDBI

0.2

0.4

Fig. 9.9 A Correlation between NDVI-LST & NDBI-LST of the year 1990; B correlation between NDVI-LST & NDBI-LST of the year 2005 and C correlation between NDVI-LST & NDBI-LST of the year 2020

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temperature compared to their adjacent fringe area. The temperature difference is known as Urban Heat Island (UHI). Metropolitan cities becoming UHI and it is mainly noticeable greater at night than in the day time. Besides, use of low impermeable material, thermal mass buildings which store a lot heat during day time, lack of vegetation cover, trafficking & human gathering are main causes of increasing land surface temperature of the selected city. Highly dense vegetation cover of the study area has been rapidly changing into built up area. Within this study time 2.34% of vegetation cover washed out and almost 1.3% area converted into built up area. The correlation analysis among LST, NDVI & NDBI has been done to analysis the strong relationship among the parameters, which shows how urbanization process affects in increasing land surface temperature. But we can see a negative relationship between NDVI and LST. Because vegetation cover can reduce the heat of urban places. By cross sectional analysis, it is clearly described that commercial or industrial zone of the town has gained maximum temperature compare to the fringe or vegetation cover land. The change in mean temperature has been recorded in built up areas or the core zone is about 4 °C from the year 1990 to 2020, but the peripheral zone or far from the industrial region received only 2.5 °C mean temperature throughout these years. Urban heat should be controlled by taking some measures like using light colored concrete instead of low albedo material, planning green parks in cities, gardening at individual residency, proper urban planning with green belt and various heat reduction policies. This research work mainly giving minor solution to this problem, but proper scientific policies can reduce the urban heat in a better way.

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Chapter 10

Impact of COVID-19 Lockdowns on Air Quality Trend in Trichy District of Tamil Nadu, India T. Sankar , N. Kowshika , Mahesh Haroli , G. Amith, and G. Rajthilak

10.1 Introduction Air pollution is one of the major concerns in our country. Every year there had been reports of intense Air pollution during the winter months in New Delhi where the entire country takes a look on the other metro cities also in comparison with the national capital. Though rapid industrialization and urbanization of the last century forced drastic effect on the environment, all these had a sudden pause due to Covid19 pandemic lockdown, where people and industries had to restrict themselves from functioning. In this aspect of inactiveness there had been a lot of improvement in environmental health during and post lockdown which is discussed in this paper. Among the districts, Tiruchirappalli is the 4th largest city in Tamil Nadu, known for its educational and industrial sectors apart from agriculture and horticulture. Tamil Nadu Pollution Control Board is operating Continuous Ambient Air Quality Monitoring Station (CAAQMS) at five locations in and around Trichy district located at Thennur, Main Guard Gate, Bishop Heber College, Golden Rock, and Central Bus Stand. Complex air quality data of various pollutants were converted into a single number (index value) called Air Quality Index (AQI), and colour which fall in one of the six AQI categories, namely Good, Satisfactory, moderately polluted, Poor, Very Poor, and Severe with their associated health impacts. COVID 19 started its spread from Wuhan, Hubei Province, China in December 2019 which laid siege over 70 countries within the next three months itself. The T. Sankar (B) · M. Haroli · G. Amith Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, Tamilnadu, India e-mail: [email protected] N. Kowshika Tamil Nadu Agricultural University, Coimbatore, Tamilnadu, India G. Rajthilak National Agro Foundation, Kancheepuram, Tamilnadu, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_10

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virus had its entry in India during January (WHO Situation Report 101) followed by imparting a national level lockdown since March 2019. India being a country with diversified sectors this lockdown seizure had a great toll on our economy, while people remained in their residences (Paital et al., 2020). When the movement of people had reduced it did not necessitate the intense transportation which along with closure of production sectors gave positive impacts environmental pollution across the country due to reduced emissions (Sengupta & Jha, 2020). In order to break the chain of spread several lockdowns were imparted and transportation through any means like road, air and rail services were suspended with exception for transport of essential items like groceries, vegetables, milk services (Pandey et al., 2021). As a result, entire range of developmental, service and transportation activities were put to halt. (Gouda et al., 2021; Yashvardhini et al., 2021). Tiruchirappalli or Trichy district is the 4th largest city in Tamil Nadu being predominantly agrarian (Kavitha & Aruchamy, 2013) is also a hub for manufacturing and centre for education and information technology (TNRTP, 2020). Bharat Heavy Electricals Limited (BHEL), the Golden Rock Railway Workshop, the Ordnance Factory, and Heavy Alloy Penetrator Project are some of the heavy industries that have a presence in the city (The Economic Times, 2010). The present study produces an assessment of air quality index (AQI) in the industrial and urbanizing fast growing Trichy district during the COVID-19 lockdown period. The study is felt essential to understand the air quality index (AQI) and it’s influence on climate change and human health. Though COVID-19 pandemic became out of control worldwide, it did leave some positive environmental footprints also. Hence, this present investigation was made with an aim of analysing most optimistic scenarios in environment especially with enhanced air quality over Tiruchirappalli district of Tamilnadu.

10.2 Rationale of the Study As a result of stringent travel restrictions and curbing of inter-state and intra-district movement of people, all types of vehicular movement got reduced considerably during lockdown periods. Therefore, it was Impact of COVID-19 lockdown on Air Quality of Trichy district anticipated that the environmental quality, particularly in urban areas would show considerable improvement. To understand the impact of COVID-19 lockdowns on the air quality of Trichy district in both urban and rural areas, the study was undertaken with the following objectives. 1. To determine the influence of lockdowns on the Air Quality Index (AQI) and the presence of different air pollutants in the ambient air in and around Trichy CAAQM stations by analysing and comparing the data on Air Quality Index and different pollutant parameters between pre-lockdown, 1st, 2nd and 3rd lockdown periods and post-lockdown period of the year 2020 and 2021.

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2. To assess the air quality of Trichy during 2020 and 2021 lockdown regimes in comparison with air quality in the months of 2019 as a base period.

10.3 Materials and Methods 10.3.1 Study Area Tiruchirappalli district is the central part of Tamilnadu located at latitude 10.8155°N and longitude 78.69651°E with Cauvery River running in the mid of city making it a perfect Delta centre. Trichy is famous for Rice and Banana crops where both TNAU Rice research centre and National Research Centre for Banana are in the district. Apart from the major crops, vegetables are grown in the outskirt villages.

10.3.2 Data Tamil Nadu Pollution Control Board is operating Continuous Ambient Air Quality Monitoring Station (CAAQMS) at five locations in and around Trichy district. The places are located at Thennur, Main Guard Gate, Bishop Heber College, Golden Rock, and Central Bus Stand. The present study depicts the Air Quality Index (AQI) data received from the CAAQM stations located in and around Trichy district, which were obtained from their official website and used. The position of the CAAQM stations of TNPCB in Trichy district are presented in the below given Fig. 10.1.

10.3.3 Study Periods Manual Monitoring Stations or Continuous Ambient Air Quality Monitoring (CAAQM) stations are recording air quality in Trichy district, being maintained by Tamil Nadu Pollution Control Board (TNPCB). Real time AQI data is provided by the web based CAAQM system, designed as an automated system that captures data from continuous monitoring stations without human intervention, and displays AQI based on running average values. Janta curfew was announced by Indian government on 22nd March 2020 (Prelockdown) and thereafter complete lockdown was announced from the midnight of 24th March till 14th April over the nationwide (1st Lockdown). Another spell was subsequently announced and extended for up to 3rd May (2nd Lockdown). Third version of the lockdown was continued till 17th May 2020 with considerable relaxations (3rd Lockdown). Finally, there were more relaxations with some restrictions continued till 14th August 2020 (post-lockdown). The classified study period of

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Fig. 10.1 Continuous ambient air quality monitoring station (CAAQMS) at five locations in and around Trichy district

COVID-19 for studying AQI has been split into five spells during 2020 which is presented in below Table 10.1. During the second wave of COVID-19, Tamil Nadu government announced lockdown in the state till 31st March 2021 due to spread in novel coronavirus cases and extended till 9th May 2021 (Pre-lockdown). Thereafter government has announced Table 10.1 Classification of study period during 2020 Lockdown status

Period

Duration (Days)

Remarks

Pre-lockdown

01−03−2020 to 23−03−2020

23

Normal activities: Janta curfew on 22 March 2020 not separately considered

1st Lockdown

24−03−2020 to 14−04−2020

22

Complete lockdown

2nd Lockdown

15−04−2020 to 03−05−2020

19

Complete lockdown

3rd Lockdown

04−05−2020 to 17−05−2020

14

Lockdown with certain relaxations

Post-lockdown

18−05−2020 to 14−08−2020

89

Lockdown with certain restrictions

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Table 10.2 Classification of study period during 2021 Lockdown status

Period

Duration (Days)

Remarks

Pre-lockdown

20−03−2021 to 09−05−2021

51

Higher educational institutions under the control of higher education department closed and conducted Online Classes

4th Lockdown

10−05−2021 to 31−05−2021

22

Complete lockdown

5th Lockdown

01−06−2021 to 05−07−2021

35

Lockdown with certain relaxations

6th Lockdown

06−07−2021 to 31−07−2021

26

Lockdown with certain relaxations

Post-lockdown

01−08−2021 to 15−09−2021

46

Lockdown with certain restrictions

(Source: https://www.tn.gov.in/go_view/dept/26).

a complete Covid-19 lockdown from May 10 to May 31 (4th Lockdown), as health experts anticipated the coronavirus peak in the State during the speculated time. To enable people, purchase essential requirements, the State government has put off restrictions on May 22 and May 23 to allow shops to open till 9 pm. The new relaxations were given till end of July 2021 by following the recommendations of the medical experts committee and authorities team to curb the spread of Covid-19 in the state (5th Lockdown and 6th Lockdown). The Covid-19 lockdown with some restrictions declared till November 15 with further relaxations (post-lockdown). The classified study period of COVID-19 for studying AQI has been split into five spells during 2021 which is presented in below Table 10.2.

10.3.4 AQI Categories The Air Quality Index was obtained from Tamil Nadu Pollution Control Board’s official web portal. Data on sampling dates were converted into COVID-19 lockdown period average and monthly averages was estimated using the data of all sampling stations. Trend analysis of Air Quality Index was done to understand the air quality before and after COVID-19 lockdown in Trichy district. Complex air quality data of various pollutants were converted into a single number (index value) called Air Quality Index (AQI), and colour which fall in one of the six AQI categories as given in Table 10.3 was used to classify the Air Quality status of Trichy district.

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Table 10.3 Air quality index classification AQI Classification Possible health impacts 0-50

Good

Minimal impact

51-100

Satisfactory

Minor breathing discomfort to sensitive people

101-200

Moderate

Breathing discomfort to the people with lungs, asthma and heart diseases

201-300

Poor

Breathing discomfort to most people on prolonged exposure

301-400

Very poor

Respiratory illness on prolonged exposure

401-500

Severe

Affects healthy people and seriously impacts those with existing diseases

Source: Central Pollution Control Board

10.3.5 Analysis of Individual Air Pollutant Concentration Individual air pollutants, namely PM10 , Ozone and SO2 data collected from Trichy Urban and Trichy rural TNPCB stations were only collected from secondary sources and analysed for this study. The PM10 data is collected for both Trichy Urban and Trichy rural area and Ozone and SO2 data is collected in Trichy Urban and Trichy rural respectively. The obtained parameter for the AQI were analysed during different COVID-19 lockdown regimes of 2021 period.

10.4 Results (a) Impact of Lockdowns on Air quality in and around Trichy district The mean values of Air Quality Index (AQI) extracted from the daily AQI report in and around five CAAQM stations of Trichy during 2020 lockdowns and 2021 lockdowns for different COVID-19 lockdown regime periods are presented in Fig. 10.2 and Fig. 10.3. The results reveal that the values of air quality during the lockdown periods of both 2020 and 2021 years brought a significant improvement of Trichy air quality in all its recorded stations when compared to pre-lockdown period of respective years. The AQI of 2020 lockdown periods in the different stations recorded values ranging from 61 to 42 during the pre-lockdown period, the similar ranged from 52 to 33, 26 to 8 and 42 to 21 during the 1st lockdown, 2nd lockdown and 3rd lockdown periods respectively. During post-lockdown period of 2020 lockdown, the AQI values ranged from 50 to 28. The average value of all five stations showed the ranged from 51.4 to 20.3 AQI. Among all five stations, huge improvement was noticed in Bishop Heber College during the 2020 COVID-19 lockdown, the values range from

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Fig. 10.2 Average air quality index (AQI) in five CAAQM stations in Trichy during 2020 lockdown

Fig. 10.3 Average air quality index (AQI) in five CAAQM stations in Trichy during 2021 lockdown

48 to 8 in pre-lockdown to lockdown 2.0 period respectively. The mean value of AQI computed for each of the five stations of Trichy district are presented in Fig. 10.2. The mean values for all the five CAAQM stations of Trichy district during 2021 revealed that the AQI fell sharply from 50.8 to 16.1. The same recorded AQI values ranged from 65 to 33 during pre-lockdown period. The AQI fell gradually from 24.3 to 7.5, 40 to 22 and 51.3 to 32.7, respectively for the three periods of 4th lockdown, 5th lockdown and 6th lockdown periods. Among all five stations, huge improvement was noticed in Thennur during the 2021 COVID-19 lockdown, the values range from

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54 to 7.5 in pre-lockdown to lockdown period respectively. The improvement in the air quality in all five stations of Trichy district during different lockdown regimes are represented in Fig. 10.3. (b) Impact of Lockdowns on Annual Air quality of Trichy district Analysis of the data revealed that the complete 1st, 2nd, 4th and 5th lockdown periods in 2020 and 2021 years brought a significant improvement of Trichy air quality in all of its five CAAQM stations, as revealed from the values of Air Quality Index. The below illustration reveals that in the pre-lockdown time the average quality of air was in (“Satisfactory category”) and which was brought to “Good status category” due to complete lockdowns and post-lockdown regimes of COVID-19 2020 and 2021 years. The values of Annual Air Quality Index ranges from 51.4 to 36.8 and 50.8 to 43.8 during pre-lockdown and post-lockdown periods. With many relaxations in industrial, commercial and trade activities brought into force during the postlockdown period, the mean AQI values were increased (Fig. 10.4). (c) Examining AQI determinants during different lockdown regimes over Trichy district Air pollutants assessment have done for determining AQI of different 2021 lockdown regimes for Trichy Urban and Trichy Rural CAAAQM stations, which was analysed. It is noticed that during most of the days in the pre-lockdown, during and postlockdown periods in Trichy TNPCB stations, PM10 were determining the AQI over Trichy Rural areas and Ozone over Trichy Urban areas, accounting for 40.7% and 26% of data points respectively. PM10 served as the deciding pollutant for AQI during pre-lockdown period over Trichy Urban, accounting 39.6% of the total data points. It is observed that AQI determinants concentration was less during 2021 6th Lockdown period in all kinds of air pollutants in and around Trichy district. Number of data

Fig. 10.4 Annual average air quality index (AQI) in five CAAQM stations in Trichy during Covid19 lockdown periods

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Table 10.4 AQI determinants of Trichy during 2021 lockdown Prelockdown

Lockdown 4.0

Lockdown 5.0

Lockdown 6.0

Postlockdown

Total Data points

Urban

72

ND

63

36

42

67

Rural

53

ND

45

35

37

114

Ozone

Urban

57

ND

40

38

40

74

SO2

Rural

ND

ND

45

33

33

25

Period/Station

PM10

a ND–No

Data

points for each pollutant deciding the AQI were computed and depicted in Table 10.4. (d) Comparison of Air quality in Trichy CAAQM stations during 2020 and 2021 different lockdown regimes with that of 2019 Second objective of the present study is to assess the air quality of Trichy CAAQM stations during 2020 and 2021 lockdown regimes in comparison with air quality in 2019 to understand the impact of lockdown restrictions on air quality of Trichy district and its surroundings. Average of daily values of AQI in the five monitoring stations during the pre-lockdown, during lockdown and post-lockdown months in the years 2019, 2020 and 2021 have been arrived at and furnished in Fig. 10.5 to Fig. 10.9. It is noticed that in each of the five sites, quality of air was significantly increased during 2020 and 2021 COVID-19 lockdown periods as compared to that of 2019. Results revealed that average air quality of all the five CAAQM stations in Trichy was increased during complete lockdown months of April and May in both 2020 and 2021 respectively with respect to base period of 2019. The air quality was transferred from “Satisfactory” condition to “Good” condition in during and postlockdown periods when compared to pre-lockdown periods. Among the five Trichy CAAQM stations, highest air quality was observed in Thennur during May 2021 lockdown followed by April 2020 in Bishop Heber College. In Thennur CAAQM station, air quality status increased drastically during May 2021 complete lockdown (AQI of 7) when compared to all other lockdown regimes. Thennur mean monthly AQI cut down from 94 (“Satisfactory”) in 2019 to 33 in 2020 lockdown and 37 in 2021 lockdown (“Good”) respectively (Fig. 10.5). Data of Main Guard Gate CAAQM station revealed that the air quality was highest in the month of April 2020 (AQI of 26) when compared to all other months of lockdown spells. Mean monthly air quality status showed that the air quality status improved from 88 (“Satisfactory”) in 2019 to 46 in 2020 and 50 in 2021 lockdown regimes, classified “Good” category (Fig. 10.6). Air Quality Index data of Bishop Heber College CAAQM station revealed that the mean air quality status jumped from “Satisfactory” state (AQI of 67) in base study period 2019 to “Good” state (AQI of 29 and 32) in 2020 and 2021 lockdown years

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Fig. 10.5 Status of average air quality of thennur CAAQM station in Trichy during Covid19 months

Fig. 10.6 Status of average air quality of main guard gate CAAQM station in Trichy during Covid19 months

respectively. Due to complete 1st lockdown of 2020 study period (AQI of April–8 and May−21) the air quality status increased drastically then all other pre-lockdown, during and post-lockdown months (Fig. 10.7). Air Quality Index data of Golden Rock CAAQM station revealed that the mean air quality status increased from “Satisfactory” state (AQI of 74) in base study period 2019 to “Good” state (AQI of 29 and 33) in 2020 and 2021 lockdown years respectively. Lockdown 1.0 of 2020 study period (AQI of April -21) and 2021 (AQI of May−22) the air quality status increased drastically then all other pre-lockdown, during and post-lockdown months during the years (Fig. 10.8).

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Fig. 10.7 Status of average air quality of bishop heber college CAAQM station in Trichy during Covid-19 months

Fig. 10.8 Status of average air quality of golden rock CAAQM station in Trichy during Covid19 months

The Central Bus Stand CAAQM station air quality data revealed that the air quality is increased during lockdown 1.0 and lockdown 2.0 of 2020 and 2021 years respectively with respect to pre-lockdown period. The average value of air quality in Central Bus Stand CAAQM station was 34 and 52 during 2020 and 2021 years with respect to base period of 2019 year (AQI of 86), which is depicted in Fig. 10.9.

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Fig. 10.9 Status of average air quality of central bus stand CAAQM station in Trichy during Covid-19 months

10.5 Discussions (a) Reduction of AQI during lockdown periods in Trichy district TNPCB is predominantly accounting the land use pattern while locating air monitoring stations in different places of any City. The predominant different land use zones recognized in the State are: Primary residential, Mixed residential, Industrial, Commercial, Educational, Public and Semi-Public, Agriculture use zones. For locating the CAAQM stations in and around Trichy district, representative land use zones are Thennur (Residential zone−R), Main Guard Gate (Traffic intersection−TI), Bishop Heber College (Sensitive zone−St), Golden Rock (Residential zone−R) and Central Bus Stand (Traffic intersection−TI). The air quality during the COVID-19 2020 pre-lockdown period mostly in the state of “Satisfactory category”, except “Good status category” in Bishop Heber College and Golden Rock. Mean AQI values in all the five stations of Trichy district during 2020 lockdown moved to “Good category” during the 1st Lockdown and 2nd Lockdown regimes. The same “Good category” trend was observed during 3rd Lockdown and post-lockdown periods except Main Guard Gate, were “Satisfactory category” was noticed. Overall, the AQI in Trichy district during 2020 lockdown, all the stations recorded “Good category” air quality status during and after COVID19 lockdown when compared to “Satisfactory category” in pre-lockdown period. The colour coding of all the five CAAQM stations AQI during 2020 lockdown is presented in Table 10.5. The result was supported by Gupta et al., (2020), the authors expressed their views on COVID-19 lockdown as the environment is self-restoring and mentioned that such a continuous lockdown might be a great chance for the environmental list and research to make a fruitful strategy for the near future (Bhat

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Table 10.5 Colour coding of AQI in different stations of Trichy during 2020 lockdown R TI St R TI Period/Station Mean Main Guard Bishop Heber Golden Central Thennur Gate College Rock Bus Stand Pre-lockdown S S G G S S 1st Lockdown

G

G

G

G

G

G

2nd Lockdown

G

G

G

G

G

G

G G

S S

G G

G G

G G

G G

rd

3 Lockdown Post-lockdown

et al., 2021; Kumar et al., 2020). The similar comparative study result was obtained in Sanathnagar area of Hyderabad City, India. ILO by Hemavani & Rao, (2020). Average values of AQI for 2021 lockdown regimes in all the five stations showed a marked reduction of AQI during different lockdown periods (“Good category”) with reference to pre-lockdown (“Satisfactory category”). Analysis of the data revealed that the lockdown regimes brought in significant improvement of Trichy air quality in most of its recorded stations during the COVID-19 lockdown 2021. When compared to pre-lockdown period of Main Guard Gate and Central Bus Stand (“Satisfactory category”), rest of the stations are in (“Good category”) only but the AQI value is reduced drastically. The improvement in the air quality among various stations during different 2021 lockdown periods are represented in terms of their standard colour coding as prescribed by the TNPCB in Table 10.6. Pronounced improvement in air quality over these large densely populated metropolitan footprints of India appeared during the lockdown where US embassies are located, but the lives of hundreds of millions of Indian people have been disrupted due to the lockdown in response to the COVID-19 pandemic (Singh et al., 2020). Our results showed a pronounced decline in air pollutants during lockdown especially in Delhi and Kolkata; these two cities are known to be highly polluted cities in India and in the world (Singh & Chauhan, 2020). Table 10.6 Colour coding of AQI in different stations of Trichy during 2021 lockdown R TI St R TI Period/Station Mean Main Guard Bishop Heber Golden Central Thennur Gate College Rock Bus Stand G S G G S S Pre-lockdown

*

4th Lockdown

G

S

G

G

G

G

5th Lockdown

G

G

G

G

G

G

6th Lockdown

G

G

G

G

G

G

Post-lockdown

G

G

G

G

G

G

TI-Traffic Intersection; R-Residential; St-Sensitive

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(b) Impact of COVID-19 lockdown on individual pollutant parameters in Trichy district The individual pollutant values for Trichy Urban and Trichy Rural stations of TNPCB were compared for their concentration during different lockdown periods taken up for the study. The Indian government has formed a regulatory body to monitor the air pollutants and striving hard for controlling air pollution. The major pollutants are Particulate Matter (PM), NO, SO, Ozone, CO and O (Muhammad et al., 2020). (i) PM 10 impact on AQI of Trichy Urban and Trichy Rural Daily recorded values of PM10 in Trichy Urban and Trichy Rural CAAQM stations are depicted in Fig. 10.10 and Fig. 10.11. Data revealed that the average PM10 values of Trichy Urban area ranged from 100 to 33, 85 to 40, 55 to 28 and 85 to 30 during prelockdown, 5th lockdown, 6th lockdown and post-lockdown of 2021 year respectively. Data revealed that the highest reduction in PM10 was noticed in 5th lockdown and 6th lockdown of Trichy Urban station when compared to the pre-lockdown values and post-lockdown values over the study period, indicating substantial reduction in industrial and transport activities in the urban area during these periods (Fig. 10.10). According to the Central Pollution Control Board (CPCB) data, COVID-19 induced lockdown significantly controlled the air pollutants level in Indian megacities. If the COVID-19 pandemic is a curse to the people, then in terms of air pollution, it is “Blessings” (Kumari & Toshniwal, 2020). The particle matter (PM) drastically got reduced in the Indian cities during COVID-19 lockdown which enables the clear blue sky visual from the polluted cities. Sikarwan and Rani (2020) observed and studied the concentration of air pollutants (PM and NO) in the big city Delhi. They reported that the air pollution level was beyond the range set by WHO before the lockdown period and the air pollutants came down to the acceptable range after the COVID-19 lockdown periods. The highest drop of ranges from 50 to 28 in average PM10 was observed during 6th lockdown over the Trichy Rural area. The highest value of PM10 in Trichy Rural

Fig. 10.10 Mean value of PM10 in Urban area of Trichy during 2021 Covid-19 lockdown periods

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Fig. 10.11 Mean value of PM10 in Rural area of Trichy during 2021 Covid-19 lockdown periods

area was observed during pre-lockdown period and thereafter it was reduced in 5th lockdown and 6th lockdown. After the relaxation in the COVID-19 lockdown, the AQI of the Trichy Rural area increased slightly during post-lockdown period due to increase in the PM10 value from 50 (6th lockdown) to 61 which is depicted in Fig. 10.11. Mahato et al., (2020) studied the air pollution level in the Indian megacity called ‘Delhi’ during pre-lockdown and post- lockdown phases due to the COVID-19 crisis. The authors have employed the National Air Quality Index (NAQI) to observe data from Central, Southern, Northern, Eastern and Western parts of Delhi. The air quality factors considered for this study are PM, NO, SO, CO, NH and O. They revealed that there is 45% improvement in the air quality when compared with the data of the year 2019. (ii) Ozone impact on AQI of Trichy Urban Changes in station wise mean values of Ozone concentration over Trichy Urban area during pre-lockdown and different lockdown periods are presented in Fig. 10.12. Data revealed that maximum improvement in air quality in terms of Ozone in Trichy Urban is recorded during 6th lockdown period. Average Ozone values at the four lockdown periods were from 73 to 36, from 49 to 31, from 47 to 27 and from 50 to 33 during the pre-lockdown, 5th lockdown, 6th lockdown and post-lockdown periods respectively. Srivastava et al., (2020) studied the air pollutants (PM, NO, SO and CO) data of the Indian major cities such as Delhi and Lucknow in the first phase of COVID-19 lockdown (21 days). From the author results, it is found that comparing with the former years, there was a considerable decline in the major air pollutants in study locations during the lockdown periods. Kumari and Toshniwal, (2020) studied the air quality in the Indian cities namely Delhi, Mumbai and Singrauli in the pre-lockdown and post-lockdown phases. A notable reduction in the particulate matters and NO2 were observed in Delhi and Mumbai and a significant improvement in Ozone level also observed.

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Fig. 10.12 Mean value of Ozone in Trichy (Urban area) during 2021 Covid-19 lockdown periods

10.6 Conclusion Air Quality analysis on Trichy district during the pandemic period between 2020 and 2021 revealed that during the complete lockdown periods, there had been a significant promising improvement in Air Quality compared with the pre-lockdown, announcements of relaxations in the ending phase of lockdown and post-lockdown events. Reduced movement of people, restricted or complete shutdown of industries, reduced transportation resulting in less vehicular fuel combustion would have been reasons towards improvement in air quality. Though the period of hardships in the Covid 19 lockdown have imparted a dip in Indian economy, it has helped in rejuvenation of environment through natural ways. This is an eye opener on the level of anthropogenic interruption into natural systems, causing damage to environment and its associated living organisms. Removing human causes of Air pollution for just few days from the environment has given higher levels of positive impacts on Air quality which needs to be taken into consideration by Policy makers on decision making towards greener future and it is the prime responsibility of every citizen towards building a society with cleaner air.

References Bhat, S. A., Bashir, O., Bilal, M., Ishaq, A., Dar, M. U. D., Kumar, R., & Sher, F. (2021). Impact of COVID-related lockdowns on environmental and climate change scenarios. Environmental Research, 195, 110839. Gouda, K. C., Singh, P., Benke, M., Kumari, R., Agnihotri, G., & Hungund, K. M. (2021). Assessment of air pollution status during COVID-19 lockdown over bangalore city in India. Environmental Monitoring and Assessment, 193(7), 1–13.

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Gupta, N., Tomar, A., & Kumar, V. (2020). The effect of COVID-19 lockdown on the air environment in India. Global Journal of Environmental Science and Management, 6 (Special Issue (Covid-19)), 31–40 Hemavani, B., & Rao, G. S. (2020). A comparative study on seasonal variations of air quality index (AQI) in sanathnagar area of hyderabad city, india. ILO, 7(01) Kavitha, D., & Aruchamy, S. (2013). Development of dynamic thematics for cropping pattern using GIS-A case study of tiruchirappalli district, Tamilnadu, India. International Journal of Current Research and Academic Review, ISSN, 2347–3215 Kumar, A., Malla, M. A., & Dubey, A. (2020). With corona outbreak: Nature started hitting the reset button globally. Frontiers in Public Health, 8, 569353. Kumari, P., & Toshniwal, D. (2020). Impact of lockdown measures during COVID-19 on air quality: A case study of India. Intl. Journal of Environmental Health Research, 1–8. Mahato, S., Pal, S., & Ghosh, K. G. (2020). Effect of lockdown amid COVID-19 pandemic on air quality of the megacity delhi. Science of the Total Environment. https://doi.org/10.1016/j.scitot env.2020.139086 Muhammad, S., Long, X., & Salman, M. (2020). COVID-19 pandemic and environmental pollution: A blessing in disguise? Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2020. 138820 Paital, B., Das, K., & Parida, S. K. (2020). Internation social lockdown versus medical care against COVID-19, a mild environmental insight with special reference to India. Science of the Total Environment.https://doi.org/10.1016/j.scitotenv.2020.138914. Pandey, M., George, M. P., Gupta, R. K., Gusain, D., & Dwivedi, A. (2021). Impact of COVID-19 induced lockdown and unlock down phases on the ambient air quality of Delhi, capital city of India. Urban Climate, 39, 100945. Sengupta, S., & Jha, M. K. (2020). Social policy, COVID-19 and impoverished migrants: Challenges and prospects in locked down India. The International Journal of Community and Social Development, 2(2), 152–172. Sharma, S., Zhang, M., Gao, J., Zhang, H., & Kota, S. H. (2020). Effect of restricted emissions during COVID-19 on air quality in India. Science of the Total Environment. https://doi.org/10. 1016/j.scitotenv.2020.138878 Sikarwar, A., & Rani, R. (2020). Assessing the immediate effect of COVID-19 lockdown on air quality: A case study of Delhi, India. https://doi.org/10.21203/rs.3.rs-31822/v1. Singh, R. P., & Chauhan, A. (2020). Impact of lockdown on air quality in India during COVID-19 pandemic. Air Quality, Atmosphere & Health, 13(8), 921–928. Sridevi, T., Lather, R., Vandana & Gurnam Singh. (2021). Impact study of environment on health during Covid-19 Lock down: A Review Article. International Journal of Current Microbiology and Applied Sciences. 10(02):1863–1878. doi: https://doi.org/10.20546/ijcmas.2021.1002.22 Srivastava, S., Kumar, A., Bauddh, K., Gautam, A. S., & Kumar, S. (2020). 21-Day lockdown in India dramatically reduced air pollution indices in Lucknow and New Delhi, India. Bulletin of Environmental Contamination and Toxicology. Tamilnadu Rural Transformation Project (TNRTP). District Diagnostic Report (DDR) Tiruchirappalli. Government of Tamilnadu. Available at: https://tnrtp.org/wp-content/uploads/2020/07/TRI CHY-FINAL-1.pdf The Economic Times. (2010). Trichy: IT infrastructure to pep up property prices. Available at: https://economictimes.indiatimes.com/property/trichy-it-infrastructure-to-pep-up-pro perty-prices/articleshow/5429292.cms WHO Situation Report 101. (2020). In: Timeline of the COVID-19 pandemic-Wikipedia https://en. wikipedia.org Yashvardhini, N., Kumar, A., Gaurav, M., Sayrav, K., & Jha, D. K. (2021). Positive impact of COVID-19 induced lockdown on the environment of India’s national capital, Delhi. Spatial Information Research, 1–11

Chapter 11

Open Landfill Site and Threat to the Proximity Resident’s: Addressing Perceived Consequences of Unscientific Solid Waste Dumping Using GIS Techniques Subham Roy , Arghadeep Bose , Debanjan Basak , and Indrajit Roy Chowdhury

11.1 Introduction Urbanization, lifestyle changes, and growing population are primarily responsible for the increasing amount and diversity of solid waste generated in developing nations (Alam & Ahmade, 2013). However, the existing Municipal Solid Waste (MSW) management procedures, notably collection, processing, and disposal, are deemed inefficient. Consequently, environmental and health issues are becoming more prevalent in these nations (Srivastava et al., 2015). It has become increasingly necessary to find new ways to dispose of municipal solid waste (MSW) because of the lack of landfill space (Blumberg & Gottlieb, 1989; Purcell & Magette, 2010). Even though landfills efficiently remove vast amounts of MSW, but the public objection to these facilities seems to be rising (Marshall & Farahbakhsh, 2013) as communities have been forced to dump solid wastes in open fields in an unscientific manner, roadsides as well as river banks and perform open burning, causing colossal damage to air and water due to absence of well-established MSW management systems (Ejaz et al., 2010; Tyagi et al., 2014). For public health and environmental-related concerns, open dumping is a serious issue in urban areas (Boadi & Kuitunen, 2005; Sankoh et al., 2013). In most developing nations, including India, open dumping is the most typical technique of managing urban garbage that is neither scientific nor planned (Henry et al., 2006; S. Roy (B) · A. Bose · D. Basak · I. R. Chowdhury Department of Geography and Applied Geography, University of North Bengal, Siliguri, West Bengal 734013, India e-mail: [email protected] I. R. Chowdhury e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_11

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Tyagi et al., 2014). Toxic gas emissions from open burning may be caused by uncontrolled and poorly managed landfills of trashes (Boadi & Kuitunen, 2005; Srivastava et al., 2015). Decomposing organic waste in landfills releases greenhouse gases and pollutes the surrounding soil and water bodies (Lim et al., 2016). MSW clogs the drains in urban areas, resulting in stagnant water suitable for breeding insects and overflows during the rainy season (Ejaz et al., 2010). Subsequently, Various diseases have been linked to the illegal disposal of municipal solid waste (MSW) (Reinhart, 1993; Sharholy et al., 2008). People who live in a garbage-strewn neighborhood are more likely to have malaria, diarrhea, and respiratory diseases (Zohoori & Ghani, 2017). Individuals may also be exposed to a variety of diseases and other toxins if they use contaminated MSW water for drinking, bathing, and irrigation purposes (Alam & Ahmade, 2013). People who live near garbage disposal facilities are more likely to suffer from respiratory symptoms, skin, nose, and eye irritation, gastrointestinal issues, fatigue, headaches, and psychological issues, as well as allergies (Abul, 2010; Gouveia & Prado, 2010). Municipal trash often includes significant quantities of various organic and inorganic components that are readily decomposed by a variety of microbes of different species (Alam & Ahmade, 2013; Semrau, 2011). Byproducts of municipal garbage, such as volatile organic compounds, ammonia, organic sulfur compounds as well as hydrogen sulfide, which are produced under anaerobic circumstances, have a significant impact on the health of the community’s inhabitants (Bobeck, 2010; Mataloni et al., 2016). As a result of uncontrolled municipal solid waste incineration, a large number of pollutant particles, such as Particulate Matter (PM), SO2 , CO2 , CO, furans, and dioxins, are released into the environment. Exposure to these pollutants may have a significant negative impact on the physical as well as mental health of residents in the vicinity (Roberts & Chen, 2006; Silverman & Ito, 2010). Children, the elderly, and those with chronic diseases like asthma or pre-existing cardiovascular disease tend to be particularly vulnerable to these effects (Zanobetti et al., 2000; Rushton, 2003). Chronic exposure to low concentrations of these toxic pollutants emitted from MSW has been connected with chronic health impacts such as impaired lung function, higher rates of bronchitis, shorter life span, heightened rates of respiratory complaints, and lung cancer (He et al., 2015). Ambient air pollution due to landfill is connected with unfavorable birth outcomes, such as premature birth, low birth weight, births at a short gestational age, as well as premature deaths (Kumari et al., 2019; Porta et al., 2009). The public’s reaction to environmental exposure is heavily influenced by risk perception (Slovic, 2000). The findings on people’s perceptions of the effect of poor MSW management methods are varied. According to some research, the public believes that insufficient solid waste management contributes to illness causation and pollution, while others claim that the public does not correlate municipal garbage with negative health impacts (Addo et al., 2015; Babs-Shomoye & Kabir, 2016). There is a lack of efficiency in India’s MSW management system (Asnani and Zurbrugg, 2007; Ojha, 2011). Open dumping, open burning, and unscientific sanitary landfills have become the norm across large swaths of the nation (De & Debnath, 2016; Joshi & Ahmed, 2016). Located in the state of West Bengal, Siliguri is not

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an exception. Don Bosco Colony, which includes Don Bosco School and Silesian College, has a dumping area where all of the city’s solid garbage is collected and deposited without any sorting. In order to reduce trash volume, it is fairly common to light it on fire (Vaverková, 2019). The pollution of water, land, and air in the vicinity of the Siliguri dumping ground has been caused by uncontrolled open dumping and open incineration (Roy & Mandal, 2019). However, little attempt has been made to investigate the people’s comprehension, experience, and attitude to the open dumpsite. Therefore, the present study adopted residents’ perception due to unscientific landfill as a critical way to understand the effects of garbage exposure and design effective treatments. The findings of this study shed light on the general public’s perspective of environmental concerns and the individuals living near a Siliguri dumping area. Using GIS, researchers were able to establish the allowable distance between homes and dumping grounds and the relationship between distance and dumping site issues (Balram & Dragi´cevi´c, 2005; Higgs & Langford, 2009). In order to improve the environmental quality, life of the local inhabitants as well as the efficiency of facility evaluation and design, this information would be beneficial to planners and governments alike. Furthermore, the study’s results might serve as a call to action for the town’s governing authority to include citizens living near the town’s open dumpsite in its efforts to provide a better quality of life for the whole community (Rahardyan et al., 2004). A better knowledge of the viewpoints of the inhabitants near the open dumpsite is envisaged to motivate the town and municipality to actively participate in the development, implementation, and enforcement of a successful MSW management system for Siliguri.

11.2 Location of the Study Area Siliguri is one of the fast-growing cities in West Bengal, India, which is situated in a small corridor that links Nepal in the northwest, Bangladesh in the south, and Bhutan in the north, making it the “gateway to north-eastern India” (District Census Handbook, 2011; Roy & Singha, 2020). This favorable location is responsible for the city’s tremendous economic and demographic expansion. According to the 2011 census, within 41.9 sq.km, Siliguri had a population of 5.13 lakhs, with a booming density of 11,274 people per sq. km distributed over 47 wards; however, the city has withstood an almost five-fold increase in its population density since 1931 (CDP report Siliguri, 2015; Roy et al., 2021). Therefore, such a tremendous increase in population result in excessive generation of solid waste per day. According to the study of Roy and Mandal (2019), the daily waste generation of the Siliguri city comprises 350−400 tons per day which rises to 500−600 tons/day during the festive seasons. As a result of such excessive amount of waste generation, the local administrative bodies failed to manage it appropriately, and as a result, unscientific landfill was taken place.

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Fig. 11.1 Location map of the study area (1000 m buffer at an interval of 200 m around the Siliguri landfill concerning municipal boundary). The map also showing DEM of Siliguri municipal corporation, rivers and wad-wise boundary. Total 115 grids (150 m) was identified around landfill site among which 10 grids are uninhabited

The Siliguri landfill site is located in the north-eastern corner of the city, namely at Dabgram 1, between the Don Bosco School and the Eastern Bye-Pass road. It is situated in between ward number 41 and 42 (Fig. 11.1). The area covered by the Siliguri landfill is about 28 acres, but due to excessive population burden and encroachment, the actual size remains 22 acres (8.9 ha) (CDP report Siliguri, 2015). Residents living near the Siliguri landfill are concerned about contamination of ground and surface water and the nuisance consequences of dust, smell, and visual intrusion. The daily garbage that arrived at the landfill site accumulated, and the adjoining area quickly became an open waste dumping area. As a result, it impacts people’s everyday lives, resulting in dissatisfaction and opposition (Roy et al., 2023).

11.3 Materials and Methodology The Siliguri dumping yard is situated less than 100 m from residential areas (Roy et al., 2022). As a result, the purpose of this study was to learn about residents’ impressions of the landfill’s influence on their health and way of life. In order to begin,

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Fig. 11.2 Methodological flowchart of the present study integrated using questionnaire and GIS based approach

a reconnaissance study was conducted surrounding the landfill site to determine the dwellings and other functional institutions in the vicinity. The present study involves completing various activities, including literature research and field surveys, as well as GIS-based procedures like gridding-based approach, result visualization, and quantitative techniques (Fig. 11.2). The present study has been applied around 1000 m of landfill area of Siliguri Municipality.

11.3.1 Participants and Sampling Technique Before the research, a preliminary survey was undertaken to determine the most affected residents due to unscientific landfills. In order to examine the activities from the dumping site, an initial visit was performed through a perimeter walk nearby the landfill area. It is done to provide the researcher with first-hand knowledge of the facility’s operation and gain a situational viewpoint from community members. During the tour, the researcher engaged the inhabitants in an unstructured interview to establish the nature of the landfill’s impact on the neighborhood. However, according to the previous studies, people residing within the proximity of 500 m−2 km from the landfill sites are severely affected (De & Debnath, 2016; Ganesan, 2017; Singh et al., 2021; Urme et al., 2021). As a result, two techniques, namely purposive and criteria sampling procedures, were used to collect the data for the present study. Purposive sampling was chosen because, in this approach, a certain group of people, events, or activities are purposely chosen to offer knowledge that is not available from other sources since only a small group of people can supply the most helpful information (Moser & Korstjensc, 2018). Therefore, Purposive sampling can meet the research’s goal because the present study emphasis primarily on the ‘cases of exposed population’ to the landfill (Singh et al., 2021).

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Subsequently, the criterion sampling technique was another consideration for the present study, which includes the cases that only meet the requirements. However, according to Patton (2002), Criterion sampling entails choosing cases that fulfill some predetermined criterion of relevance. Therefore, for the present study, only those participants are selected those who meet the following criteria (i) Exposed household population to the landfill sites within 1000 m (1 km) radius concerning municipality jurisdiction (which is divided at an interval of 200 m buffer), (ii) at least one year of residence near the landfill sites (Etea et al., 2021; Ganesan, 2017; Njoku et al., 2019; Singh et al., 2021). However, a total of 400 questionnaires were distributed among the residents near the landfill sites (within 1 km), and among them, 384 questionnaires were collected with a 96% response rate.

11.3.2 Gridding Based GIS Approach According to the study of Che et al., (2013), the gridding-based approach using GIS techniques can be utilized to assess the impact of a landfill site on the residents who live within the proximity. Thus the present study adopted an integrated GIS-based gridding approach with questionnaire-based perception surveys to conduct a more efficient and scientific investigation of the study region from a holistic viewpoint. However, for the present study, each cell size of 150 m was considered, and there are 115 cells matrix in total, including ten cells with vacant sites and 105 cells with inhabited locations. In the next step, it is identified that one major college and school were situated within less than 500 m from the landfill sites, and thus perception about the impact of unscientific dumping was also considered from both institutions in the present study (Fig. 11.1). However, these 115 inhabited cells were finally categorized into five divisions at an interval of 200 m buffer to evaluate the perceived consequences.

11.3.3 Formulating Questionnaire Design and Data Collection Strategy At first pilot survey and group discussion were conducted to identify the issues related to a locality near the landfill sites in order to design a questionnaire that appropriately reflects and describes the difficulties faced by local inhabitants (Srangsriwong, 2019). Subsequently, the final interviews were done using a semi-structured questionnaire (includes both open and close-ended questions), which allowed respondents to express a wide range of opinions on how the dumpsite has impacted their lives. Besides, quantitative techniques like site observation, focus group discussion, and note on perceptions were also adopted to understand better their real-life scenarios and adjustments near the landfill sites (De & Debnath, 2016; Weldeyohanis et al,

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2020; Urme et al., 2021; Etea et al., 2021). However, all these data were collected with the help of some enumerators after proper instruction and preparation. The final questionnaires are easy yet comprehensive, which help to gather information on issues related to the public perceptions of the research area. The questionnaire was divided into two sections: socio-demographic information like age, sex, gender, education, occupation, and duration of residence included in the first section. While the second section includes the risk perceptions of the people residing near the landfill sites, which consequences their health and environment (at an interval of 200 m buffer which covers up to 1 km). This second section contains five-point Likert scale questions, and each respondent was given a list of six landfill impacts questions, e.g., Due to nearby landfills, how do you suffer from odour annoyance? Moreover, the responses vary from a ‘very serious problem’ (indicating 5) to a ‘not at all problem’ (indicating 1). Besides, one question was included at the top of the questionnaire, which includes the coordinates of the respondent residence, which help to determine their distance from landfill sites and also necessary for the gridding based approach using GIS technology which helps to visualize the impact of landfill over the people (Che et al., 2013).

11.3.4 Landfill Satisfaction Index (LSI) Landfill satisfaction index (LSI) is an aggregated tally of each answer received from the participants related to the perceived impact of landfill sites. Furuseth and Johnson, (1988) first developed the LSI tool in their research to analyze the perceptions of the peoples residing within 4800 m of the Harrisburg landfill site (at an interval of 800 m). Subsequently, the study of Okeke and Armour, (2000) applied the LSI tool to evaluate the respondent attitude towards nearby landfill sites within the proximity of 1600 m of Halton city. Therefore, for the present study, LSI was adopted to understand the perceptions of people residing in proximity to 1000 m in the Siliguri landfill site. For the construction of the LSI tool, scores for every six questions related to the landfill impacts were summed. Since the 5-point Likert scale questionnaire was used, the LSI for Siliguri ranged from 6 to 30, indicating 6 with a very low problem (1 score for all response) and 30 reflecting very serious problems (5 score for all response).

11.4 Results 11.4.1 Demographic Characteristics The socio-economic condition of respondents living near the municipal dumping area in Siliguri Municipal Corporation (SMC) is shown in Table 11.1 For the perception survey, a total of 384 people were considered, including 224 men and 160 women,

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with 66.1% of the sample population being married, indicating that the male-tofemale ratio was acceptable and that the bulk of the sample population is middle-aged and older (58.6% older than 30 years). Besides, Students (18.5%) and housewives (10.9%) were also included in the sample survey and the government or private work or business, which accounted for 70.6% of the respondent’s occupation. The sample population considered had a literacy rate of 97.9%. (Minimum of primary education). According to the sample, 31% of the respondent stay for 10−15 years within the proximity of landfill, followed by 22.9% stay for 15−20 years, 21.1% 5−10 years, 13.5% for 1−5 years, and only 10.7% of respondent stay for more than 20 years. However, Fig. 11.3 reveals about the perception of the respondent concerning landfill site. Table 11.1 Demographic characteristics of the study location

Characteristics

Classification

Number

%

Gender

M

224

58.3

F

160

41.7

51

13.3

18−30

108

28.1

30−45

69

18

45−60

98

25.5

>60

58

15.1

Married

254

66.1

Unmarried

112

29.2

Separated

18

4.7

Educated

376

97.9

Age group

Marital status

Education

20 years

41

10.7

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Fig. 11.3 Tree map showing perception of the respondent concerning landfill impacts a Pungent odour, b Perceived water contamination c Breathing issues d Issues due to burning, e Impact due to flies, mosquitoes & rodents, f Health related issues

11.4.2 Spatial Analysis of Perceived Impacts Due to Open Landfill Pungent odour is one of the major problems faced by any people residing near the open landfill, causing discomfort and annoyance (Aatamila et al., 2010; De & Debnath, 2016). Some landfill gas (LFG) components, like hydrogen sulfide, methane are essential contributors to pungent smell originating from a dumpsite (Vrijheid, 2000) which is mainly due to poor landfill management, such as improper compression of garbage dumped in the landfill and a lack of collection and use of LFG emissions (Njoku et al., 2019). Similarly, in the case of Siliguri landfill, an unpleasant odour emanating from landfills is a significant issue faced by the nearby residents and dump employees. The odour released by the waste has made people’s life difficult. It pollutes the air and has a detrimental impact on individuals walking or driving along the roads adjacent to the landfill sites. According to the study, 28.6% of the respondents report severe problems due to pungent odour followed by 25% high, 19.5% moderate, 16.1% minor, and 10.7% not at all problem, respectively (Table 11.2). The respondent further reported that the frequency of noxious smell occurrence was higher in summer compared to other seasons. Besides, Siliguri experience extreme temperature, high humidity with low pressure between the month of June to August (Roy et al., 2021), and during these months, the condition near the dumping ground was unbearable, according to the study. This study found a strong link between odour discomfort and distance from the landfill, with the majority of the respondents residing within 600 m (24 cells) reporting very high odour discomfort (Fig. 11.4). However, the aggravation lessened as the distance from the landfill increased. Subsequently, the respondent who resides within 800−1000 m reported very low odour annoyance. Another finding suggests that the presence of odours nuisance was linked to the direction of the wind. The Fig. 11.4 reveals that the odour was concentrated in the north and north-eastern part of the landfill site, which might be explained by the wind

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Table 11.2 Perceived impact by the respondent due to open landfill Landfill impacts

Very serious problem

Serious problem

Moderate problem

Minor problem

Not at all a problem

Pungent odour

28.6

25.0

19.5

16.1

10.7

Water contamination

26.3

22.1

27.3

12.5

11.7

Breathing problems

24.0

24.2

23.2

15.9

12.8

Issues due to burning

25.5

5.7

5.2

29.9

33.6

Impact of flies, mosquitoes & rodents

36.2

39.1

19.8

2.6

2.3

Health related issues

24.2

17.4

13.8

20.6

24.0

Fig. 11.4 Perceived issue due to pungent odour. The figure also showing annual wind direction and wind speed

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direction, and mostly blow in the northern direction throughout the year, especially in the summer. However, some cells in the southwest and southeast part contradict the pattern, possibly due to the barren land (5 uninhabited cells) in the southern part of the landfill site; as a result, the odour is not restricted within the vicinity and spreads throughout the southern part. On the other hand, a very serious problem due to water contamination is mainly reported by the people within proximity of 600 m (Fig. 11.5), besides some of the grids (10 cells) within 600 m having serious problems. However, 22.1% of the respondents report a serious water pollution problem distributed mainly in the northwestern and southern parts of the landfill site. According to the perception of the respondent’s water contamination is a seasonal phenomenon that mainly increases in the monsoon period (July–September) because rainfall has a significant impact on leachates formation, which percolates through the soil, causing water contamination (Vaverková, 2019). According to studies, landfills contaminate groundwater because leachate percolates into groundwater through cracks in membranes and contaminates it due to high bacterial content (Okeke and Armour, 2000; Maqbool et al., 2011; Edokpayi et al., 2018). Although no laboratory investigation of groundwater in the region was performed for this study, however according to the perception of respondents, those who reside near the 400−600 m of landfill site experience salty and bitter taste of water due to leaching. According to the previous studies, some of the major toxic compounds that change water taste near landfills include chloride, lead, and mercury (Akinbile and Yusoff, 2011; Ololade et al., 2019; ICCDI Africa, 2020). Subsequently, those who reside within 800−1000 m of the Siliguri landfill report almost no problem. Breathing issues are another common problem people face near landfills; about 24% of the respondents face severe breathing issues, followed by 24.2% of serious issues. According to the Fig. 11.6 respondent, those who reside in the western part of the dumping ground facing various issues related to breathing problems. According to previous studies, residents who live near an open dumping site are more likely to get respiratory ailments, supported by the present study (Njoku et al., 2019; Palmiotto et al., 2014; Sankoh et al., 2013). Bioaerosols and biological substances emitted from garbage sites can induce respiratory disorders like asthma, chronic obstructive pulmonary disease (COPD), and breathing disorder (Heldal et al., 2003; Kret et al., 2018). Aside from biological agents and volatile organic compounds generated by landfills, emissions from automobiles, trucks, and bulldozers utilized in the landfill site can also contribute to excessive emissions (Vimercati et al., 2016). Such emissions have harmed human health (Njoku et al., 2019). Therefore, it is not unexpected that respondents who lived up to 600−800 m from the dumpsite of Siliguri also reported moderate to minor breathing issues (Fig. 11.6). Besides, Landfill fire is a widespread and frequent phenomenon in Siliguri dumping sites that mainly occur during dry seasons of summer and winter (Siliguri Times, 2021). According to the findings, residents within 400−600 m are the most affected in terms of burning issues (Fig. 11.7). On the other hand, people away from the landfill (600 m away) report minor and not at all problems. Landfill fires may cause serious environmental damage by releasing poisons into the atmosphere, land, and

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Fig. 11.5 Perceived issue due to water contamination

water. The type of garbage, the location of the dump, and the sort of fire all influence risk factors (Toro and Morales, 2018). These flames often occur at low temperatures and in anoxic environments. In such settings, hydrocarbons, chlorinated materials, and pesticides create a range of hazardous gases, including dioxins/furans (EscobarArnanz et al., 2018), respirable particles (PM), and heavy metals (HM) (Rovira et al., 2018), as well as other damaging chemicals (Nadal et al., 2016). Smoke from the landfill fire might contain poisonous gases, including CO, H2S, and CH4, as well as carcinogenic chemicals like dioxins. According to the respondent, the waste dump catches fire throughout the summer and winter seasons, either due to spontaneous combustion as a result of chemical reactions and methane generation or due to people’s negligence, or due to the act of miscreants. Residents faced severe trouble in the surrounding area due to smoke and harmful gases resulting from waste burning in the landfill causes foul odour and also cause congestion in respiratory tracts.

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Fig. 11.6 Perceived breathing issues

Open landfill areas are susceptible to the incubation and multiplication of flies, mosquitoes, rodents, and cockroaches; as a result, it becomes a source of pollution (Singh et al., 2021); breeding of such insects are common around the landfill, which consequences in spreading of vector-borne diseases causing threat to the nearby residents (Urme et al., 2021). Besides, the problems due to flies, mosquitoes, and rodents have been studied for many years (Dhillon & Challet, 1985; Howard, 2001; Lole, 2005), but there has been a scarcity of work related to the perceived impact of such insects using GIS technology. However, the present study reveals that the entire study area (within 1000 m of landfill) is highly affected by the menace of flies, mosquitoes, and rodents (Fig. 11.8). About 36.2% of the respondent who resides within the 800 m distance (except few cells) face a very serious problem, followed by 39.1% of respondents who are susceptible to a serious threat of such insects. Except for a few grids (six cells) in the extreme northern and southern parts, the entire study area faces the issues of flies, mosquitoes, and rodents. According to the respondents,

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Fig. 11.7 Perceived issues due to the frequent burning near landfill

the impact of flies and mosquitoes became very high, mainly during the summer and monsoon seasons; as a result, some people nearby the landfill always shut their doors and windows daily to keep insects rodents out. Previous studies also support that during summer and monsoon, their adaptive capacity and incubation ability increase (Howard, 2001; The conversion, 2018; Ahmed et al., 2019). Therefore, landfills act as a breeding place for such insects, especially during the monsoon season, and hence diseases like dengue fever, malaria, and diarrhea are prevalent within the proximity area. According to the previous study, residents who live closer to landfill sites are more likely to suffer from medical issues such as asthma, infections, diarrhea, stomach ache, recurrent flu, cholera, malaria, cough, skin irritation, cholera, diarrhea, and TB than those who live further away (Vrijheid, 2000; Bridges et al., 2000; Brender et al., 2011; Njoku et al., 2019). Similarly, the present study reveals that those who reside mainly within the 400−600 m report severe health-related problems (Fig. 11.9).

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Fig. 11.8 Perceived issues due to the impact of flies, mosquitoes and rodents

According to the study, the major problems reported by the people near the Siliguri landfill are stomach-related problems, skin rashes, wounds, frequent fever, and diarrhea. Besides, some of the respondents also report respiratory problems like asthma and cough, mainly due to the bioaerosol exposure linked to a variety of respiratory disorders, including inflammation of the airways (Heldal et al., 2003). According to the study’s findings, local inhabitants are aware of the many ailments that may arise from the exposure of particular hazardous waste and living near a dumping ground as 24.2% of respondents report very serious health issues followed by 17.4% serious problems. However, in the present study, whenever respondents report two or more than two ailments like frequent stomach problems and skin irritation are considered ‘very serious’ health issues.

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Fig. 11.9 Perceived health-related issues near landfill

11.4.3 Impact of Proximity on Perceived Impact of Landfill The factors affecting the perceived impact of the landfill are not only determined by the nature of the landfill but also by the variables such as resident’s susceptibility toward the landfill, his or her geographic location concerning the landfill, and the perception of the specific problem concerning the landfill (Eyles et al., 1993; Okeke & Armour, 2000). Therefore, the findings of LSI reported in table 11.3 appear to endorse the application of a geographical externality related to the public concerns around the Siliguri landfill. It is evident from the result that the landfill act as a spatial externality which includes severe consequences like releasing greenhouse and methane gases from the breakdown of organic wastes. There is also the possibility of consequences from the leaching of hazardous metals and chemicals into the surrounding, contaminating the water. Similarly, externalities include the impact of smells and burning issues on neighborhood amenities and the influence of air pollution. Thus, the impact

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Table 11.3 Impact of proximity over the composite landfill evaluation score (%) Respondent distances from Siliguri landfill (in m) Scale

N

0.01). For a detail analysis line graph has been drawn based on the data collected from whole of Kolkata (Fig. 13.4). Though, the teens of Kolkata are mostly aware and most of them do not share password, but still in case of the involved teens

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incidence of sharing password appears to be more than the not involved ones. Bullyvictim are the ones who are more prone to sharing passwords, followed by victim and bully. Not involved teens are least prone to disclosing their password to other persons. The casual approach of the involved teens has made them vulnerable to this online bullying, while the conscious attitude of the not involved teens protected them from any future harassment. Similar scenario is found in the middle-class zone (F = 10.231, p > 0.01) and minority dominated zone (F = 11.276, p > 0.01), where the relation holds statistical significance. Posh zone and immigrant dominated zone has shown difference from that of Kolkata. In middle class families and minority communities, the teens and the children are often made aware and conscious about the cyber safety and security measures. The aware teens are less involved. The easy-going attitude of the posh area teens and the inexperience and lack of awareness of the immigrant dominated zone teens may be the reason of absence of any association between the concerned variables. Table 13.3 Frequency of sharing password (Source Computed by authors, 2022) Sum of squares Between groups

Kolkata

Within groups Middle class zone

Within groups

Posh zone

Between Groups

Between Groups

Within groups Minority dominated zone

Between Groups

Immigrant dominated zone

Between Groups

Within groups Within groups

32.112 807.809 20.294 472.114 3.318 114.806 17.636 107.393 3.725 96.621

df

Mean square

F

Sig

3

10.704

17.491

0.000

10.231

0.000

2.216

0.087

11.276

0.000

2.030

0.112

1320

0.612

3

6.765

714

0.661

3

1.106

230

0.499

3

5.879

206

0.521

3

1.242

158

0.612

90

Percentage of Respondent

80 70 60 50

Victim

40 Bully

30

BullyVictim Not Involved

20 10 0 Never

Rarely

Sometimes

Frequently

Fig. 13.4 Frequency of sharing password by teenagers in Kolkata

Always

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13.3.2 Personal Information Disclosed on Social Networks The cyber bullies often use sensitive personal information shared by the teenagers on social networking sites as a weapon to bully them. The teenagers, due to their immaturity and inexperience believe the bully to be their friend and share personal, sensitive and sometimes even socially embarrassing information about themselves and put themselves at risk. The one-way anova result for the whole of Kolkata is statistically not significant (Table 13.4). The anova test results for posh and immigrant dominated areas are also not statistically significant. The test results for the middle class (F = 7.693, p > 0.01) and minority dominated zone (F = 14.577, p > 0.01) are significant. Hence in these two zones this variable possess relation with the kind of involvement in cyberbullying. In these two zones, it is observed that with the increase in the quantity and frequency of sharing personal information on social networks the occurrence of becoming victim and bully victim has increased (Fig. 13.5). The bully or the not involved teens shared less personal information in compare to the previously mentioned two categories. Even if the minority and the middle-class zone is compared it is observed that the problem is more acute for the middle-class zone. They share maximum information online and become victim. Though in the posh area and the immigrant dominated area, the result might not be statistically significant, but they show a very similar pattern. It is the bully victim and the victim, who are sharing more information online which is a risky activity. The other variables might be more important in these two zones and so in spite of close similarity the anova results are not significant. Table 13.4 Personal Information disclosed on social networks (Source Computed by authors, 2022) Sum of squares Kolkata

Between groups Within groups

Middle class zone

Within groups

Posh zone

Between groups

Between groups

Within groups Minority dominated zone

Between groups

Immigrant dominated zone

Between groups

Within groups Within groups

24.863 1422.046 24.863 1422.046 8.524 337.937 9.753 45.942 5.795 150.403

df

Mean square

F

Sig

3

8.288

2.638

0.084

1320

1.077 7.693

0.000

1.934

0.125

14.577

0.000

2.029

0.112

3

8.288

1320

1.077

3

2.841

230

1.469

3

3.251

206

0.223

3

1.932

158

0.952

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70

70

Percentage of Respondent

Percentage of Respondent

MINORITY DOMINATED ZONE 80 60 50 40 30 20 10

60 50 40 30 20 10 0

0 0

1

2

3

4

5

Number of personal informations disclosed Victim Bully-Victim

Bully Not Involved

0

1

2

3

4

5

Number of personal informations disclosed Victim Bully-Victim

Bully Not Involved

Fig. 13.5 Personal Information disclosed on social networks by teenagers in minority dominated zone and middle-class zones

13.3.3 Information Disclosed to Strangers on Social Networks Teenagers are often advised and suggested not to share personal information in public online space like social networking sites and especially, to strangers whom they have met online. But many a times, either the teenagers ignore such warning advices or they have an ignorant attitude towards it, which exposes them to dangerous situations like cyberbullying. In this case they are violating two important safety measures of the cyber world. First, they are sharing personal information and secondly, that to a stranger. Both these two activities done together, increases their vulnerability to cyber bullies. The one-way anova test result for the whole of Kolkata, clearly indicated that this variable do have a strong association with online bullying (F = 32.460, p > 0.01) (Table 13.5). The same is true for all the four zones considered in the study. Almost all the teens who are not involved in this bullying, do not share any persona data or information to strangers. The involved ones, especially the victim and bully-victims also do not share such information in large scale generally (Fig. 13.6).

13.3.4 Accepting Invitation from Strangers on Social Networks to Meet The teens in this age group often wants to explore the world and make their own social identity. They even look for probable future partners. They often accept friend request on social networks not only from friends of friends, people with little acquaintance but also from complete strangers. They also get engaged in active communication and other interactions with them. Sometimes, they also agree to meet these strangers

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Table 13.5 Information disclosed to strangers on social networks (Source Computed by authors, 2022) Sum of squares Kolkata

Between groups Within groups

47.978 650.356

Between groups

Middle class zone

Within groups

Posh zone

Between groups

15.723 347.599 5.121

Within groups Minority dominated zone Immigrant dominated zone

80.196

Between groups Within groups

32.694 155.573

Between groups Within groups

6.194 47.583

df

Mean square

F

Sig

32.460

0.000

10.765

0.000

4.895

0.003

14.431

0.000

6.856

0.000

3

15.993

1320

0.493

3

5.241

714

0.487

3

1.707

230

0.349

3

10.898

206

0.755

3

2.065

158

0.301

Percentage of Respondent

60 50 40 30 20 10 0 Victim

Bully Never

Rarely

Sometimes

Bully-Victim

Not Involved

Frequently Always

Fig. 13.6 Information disclosed to strangers on social networks in Kolkata

in person, whom they met online. This often instigate cyber harassments like online bullying by strangers. For this variable, one-way anova test has been conducted for the whole of Kolkata. The statistically significant test result (F = 42.120, p < 0.01) suggests to reject the Null Hypothesis (H0 ) and accept the Alternative Hypothesis (H1 ). Accepting invitation of strangers online and meeting them in reality, is related to cyberbullying through social networks. The four zones of middle class (F = 23.324, p < 0.01), posh (F = 11.646, p < 0.01), minority (F = 9.857, p < 0.01) and immigrant dominated (F = 2.956, p < 0.05) also have showed statistically significant anova results (Table 13.6). All the zones have similar pattern and nature of that of Kolkata (Fig. 13.7). Though, the teens of Kolkata are not very comfortable with meeting the strangers, may it be any of the four categories, still it does exist and itsvery true for the involved teens. Almost

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Table 13.6 Accepting invitation from strangers on social networks to meet (Source Computed by authors, 2022) Sum of squares Kolkata

Between groups Within groups Between groups

Middle class zone

Within groups

Posh zone

Between groups

20.289 207.027

Within groups

16.216 106.746

Minority dominated zone

Between groups

Immigrant dominated zone

Between groups

Within groups Within groups

48.877 510.588

14.624 101.871 4.711 83.931

Never 100 80 60 40 20 0

Frequently Always

df

Mean square

F

Sig

3

16.292

42.120

0.000

1320

0.387

3

6.763

23.324

0.000

714

0.290 11.646

0.000

9.857

0.000

2.956

0.034

3

5.405

230

0.464

3

4.875

206

0.495

3

1.570

158

0.531

Rarely Victim Bully Bully-Victim Not Involved Sometimes

Fig. 13.7 Accepting invitation to meet from strangers on social networks by teenagers in Kolkata

all of the not involved teens, never accepted such invitation. A good proportion of the rest of the group of teens of bully, victim and bully-victim also avoid meeting strangers. Still, they show a little more inclination towards meeting the unknown strangers. This risky action increases their possibility of getting involved. It is the bully-victims who showed a little higher tendency than rest of the teens to meet strangers after interacting with them online.

13.3.5 Posting of Private Digital Materials In many cases of cyberbullying, private pictures or videos of the victim has been used as a weapon against them. If the bully gets access to any of the private videos and pictures, they can manipulate it or modify it, upload it and share it to humiliate,

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Table 13.7 Posting of private digital materials (Source Computed by authors, 2022) Sum of squares Kolkata

Between groups

Middle class zone

Between groups

Posh zone

Between groups

Within groups Within groups Within groups Minority dominated zone

Between groups

Immigrant dominated zone

Between groups

Within groups Within groups

24.028 1545.243 17.930 948.694 15.135 201.810 22.028 162.353 8.279 151.178

df

Mean square

F

Sig

3

8.009

6.842

0.000

4.498

0.004

5.750

0.001

9.316

0.000

2.884

0.038

1320

1.171

3

5.977

714

1.329

3

5.045

230

0.877

3

7.343

206

0.788

3

2.760

158

0.957

harass or defame or simply make fun of the victim. The teenagers because of their ignorance, lack of knowledge about cyber safety, often make such mistakes. This variable is evaluated by the help of five one-way anova tests (Table 13.7). The statistical result of the anova tests appear to be significant for the whole of Kolkata (F = 6.824, p < 0.01). Similar kind of results have been obtained for middle class (F = 4.498, p < 0.01), posh (F = 5.75, p < 0.01), immigrant dominated (F = 2.884, p < 0.01) and minority (F = 9.316, p < 0.01) dominated zones. For a detail and insightful analysis, bar graphs have been used (Fig. 13.8). The not involved teens are the ones who stays away from posting such materials online, which are private or which may be used for their humiliation. They are interestingly followed by the bullies. The bullies are the offenders and so they have the knowledge and idea regarding posting of which digital materials can be harmful for them. The victims and the bully-victims often showed tendency or inclination towards posting private photographs and other digital materials, which can be used by the bullies to harass them in the future. They might post some photographs and videos out of excitement, fun or just to seek attention, which have been used against them later.

13.3.6 Making Fun of Friends on Social Network While considering the online behavior of the teens, which might have triggered or poked the occurrence of cyberbullying, making fun of friends on social network platforms has been taken as an important variable. A total of five one -way anova, followed by a line graph has been conducted to get an insight view of this variable (Table 13.8). The mentioned anova tests for the whole of Kolkata (F = 124.245, p < 0.01) and as well as for the middle-class zone (F = 74.415, p < 0.01), posh zone, minority dominated (F = 21.462, p < 0.01) and immigrant dominated zone (F = 10.279, p
0.1) prioritizes five principal components (Table 14.3, Appendix 2). The figures (Figs. 14.17 and 14.18) show the scree plot and component plot of factor analysis in the three-dimensional quadrants. Based on the analysis the first principal, second, third, fourth, and fifth component eigenvalues cumulatively explain the factors up to 94.76%. The first principal component has the highest percentage of variance (43.91). The significant socio-economic factors have been extracted by this method are age less than forty years, male and female respondents, general caste, Muslim communities, well and moderately literate respondents, respondents who have family members less than four, permanent residents, respondents who have kuccha and mixed houses, monthly income of the respondents-less than rupees five thousand, five to ten thousand and greater than ten thousand. These factors are explained by components 1 to 4 or 5. The actual scenario of the study villages shows that the respondents who shifted their occupation from agriculture to ISKCON are 5% below forty years of age, 17% male population, 6% female population, 22% general caste population, 0% of the Muslim population, 6% of well and moderately literates, 11% with family members less than four, 11% permanent residents, 0% of kuccha and mixed houses, 0% have their monthly income less than rupees five thousand and greater than five thousand, 5% have their monthly income between rupees 5000 to 10,000. It is explained that in the study area mostly the younger population who have family members less than four shifted from agriculture to ISKCON for acquiring more monthly income. Mostly Hindu and general caste communities who are permanent residents have changed their occupation from agriculture to ISKCON. The group of the respondents having a monthly income, of not less than rupees 5000 and more than rupees 10,000 have shifted from agriculture towards ISKCON-oriented occupational activities. The overall development of the villages has been represented in Figs. 14.19, 14.20, and 14.21. The five Mouzas are surrounded by the ISKCON at Mayapur. Based on the composite standardized scores high developmental changes have been observed in Bamanpukur which is the main concentration zone of ISKCON-oriented culture and tourism among the three selected Mouza (Table 14.4).

14.5.2.4

Perception of the Changing Nature of the Socio-Economic Personality of the Study Villages

Perception of the changing nature of the socio-economic personality of the study villages pointed out that, i. Influence of ISKON: The census data (Census of India, 1971 to 2011) showed a large number of populations had engaged in agriculture. After the establishment of ISKCON, they sold their land to ISKCON and shifted from agriculture to other

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Fig. 14.17 Scree plot of the factor analysis

Fig. 14.18 Component plot in three-dimensional quadrant (respondents’ category-II)

14 Exploring the Transformation of Agrarian Villages into Global Cultural …

323

Fig. 14.19 Variation of development in the selected villages of ISKCON at Mayapur and surroundings

activities. The actual scenario is that the residences of Mayapur are strongly influenced either directly or indirectly by the ISKCON. Some of the respondents completely shifted their occupation from agriculture and engaged with ISKCON and some of them were engaged in both agriculture and ISKCON. Based on the study, 50% of the male and 10% female of the total respondents were engaged in ISKCON. Among them 6% of the respondents were directly engaged in agriculture, 27% of the respondents were directly engaged with ISKCON and 6% of the respondents were engaged with both agriculture and ISKCON. Among the respondents, 14% shifted their occupation from agriculture to ISKCON. About 23% of them sold their land to the ISKCON authority and 27% did not sell their land till now. Presently, 18% of the respondents are directly engaged with ISKCON, 14% of respondents are indirectly engaged with ISKCON and the rest of the respondents are not directly or indirectly involved in ISKCON and related activities. Figure 14.28 shows the different occupational categories of the respondents engaged with ISKCON and related activities in the study area. ii. Nature of occupational shifting: Twenty variables have been selected to show the relationship among the variables associated with the socio-economic factors which influence respondents’ identities and the nature of shifting of their occupation. Table 14.5 (Appendix 4) shows the correlation matrix of the selected criteria of the selected respondents. Priority has been given to the variables as a number of the respondents directly engaged with ISKCON, indirectly engaged with ISKCON, and not engaged with ISKCON. In this case, five values of correlation are significant only.

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Fig. 14.20 Concentration of tourist spots and temples

The relationships of the number of respondents directly engaged with ISKCON with the number of respondents having monthly income between rupees 5000 to 10,000 and the number of respondents having monthly income between rupees 5000 to 8000 from ISKCON is 0.884 (p < 0.05) and 0.968 (p < 0.01). Besides, the relationship between the number of respondents not engaged with ISKCON and the number of respondents who sold their 5 to 10 bigha land to ISKCON is 0.969 (p < 0.01). The strongly positive correlation between the respondents directly engaged with ISKCON and respondents having monthly income of rupees 5000 to 10,000 in general and rupees 5000 to 8000 from ISKCON signifies that people directly involved in ISKCON-related activities are paid rupees 5000 to 8000 per month which is the main income source of the respondents. Some of the respondents sold their land to ISKCON for financial requirements does not involve ISKCON. iii. Variability of the occupational shifting and related responses: The perception of the villagers of different occupational statuses, involved in agriculture or engaged with ISKCON has been discussed in the study. All those are conferred accordingly under the consideration of changing villages from agrarian to global cultural hubs. The pre-scheduled perception of the respondents has been recorded based on the number of respondents and their positive, negative, or neutral responses. The villagers have focused on traditional rural agriculture, which is transforming now due to the globalization processes of tourism. In this aspect, Table 14.6 (Appendix 5)

14 Exploring the Transformation of Agrarian Villages into Global Cultural …

325

Fig. 14.21 Tourism places near Mayapur and its surroundings

Fig. 14.22 Occupational categories of the respondents engaged with ISKCON-related activities

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shows the mean, standard deviation, and variances of the responses of the respondents (shifting occupation from agriculture to ISKCON, shifting occupation from agriculture to other occupational activities; not shifting or not changing occupation, involved only in agriculture, engaged only with ISKCON and engaged with both agriculture and ISKCON). Relatively high variability (≥1.00) has been observed in the positive responses about positive changes in the villages, supporting the changes, willing g to fully engaged with ISKCON, notable changes in the villages, potentiality of ISKCONrelated tourism, better financial opportunities in tourism activities than agriculture, tourism improves the image of the villages but deteriorate the rate of crime and other social problems; negative responses about involvement in agriculture, involvement in both agriculture and tourism, willing to sell the land to temple community, willing to go back to agriculture; both positive and negative responses about negative changes of the villages, shifting of occupation from agriculture, involvement with ISKCON, willing to fully engage with agriculture, psychologically attached with land, shifting from agriculture to tourism and facing any problem compared to early living. Relatively low variability (0.1) prioritizes five principal components (Table 14.3). Table 14.3 Component Score Coefficient Matrix of the respondents’ details (Category-II) Component Matrixa Component

Details of the respondents

1 Age (years)

< 40

Caste

3

4

0.569

0.632 −0.316

5

−0.194

0.908

−0.177

60 300-m distance from water bodies (Table 16.4). Distance from the river(C3): To safeguard that the flood does not endanger the structures, it is important to identify the rivers and their boundaries while planning the usage of the property. Also, contamination of surface water due to urban activities should be considered (Chen, 2017). The main river is the Damodar flowing through south of the study area, Banka and DVC canal intersected middle of the city. River map is prepared through Digitization from Toposheet and Verified by Google Earth. Higher weights are given to those places which are away from river because river was mainly utilized for agricultural purposes only. A buffer distance map was prepared for this purpose and divided into four classes, viz 0–300, 300–600,600–900, 900–5400 m (Table 17.4). Change in Built-up area (C4): Changes in built-up area are more frequent along the major state highway that follows the road network in the area. After generating the EBBI index for the years 1990, 2000, 2010, and 2020, a change detection approach was used to determine where the most changes occurred, and given higher weightage is given to positive values in change detection methods (Table 17.4).

408

S. Dolui and S. Sarkar

Distance From settlement patches(C5): Distances from settlement patches are separated into four categories based on their proximity to settlement patches: 0– 200 m, 200–400 m, 400–600 m, and 600–3050 m. As it observed from the distance mapping the first two categories have high probability of conversion to settlement patches. Nighttime Light Images(C6): Researchers are able to use it to estimate urban expansion and track the spatial extent of the development of peri urban areas surrounding large cities. In contrast to the peri-urban zones, which typically have multiple smaller patches within a 1 to 2 km radius of small towns or villages, the core urban area is defined by a large continuous concentration of settlements. Based on reflectance value of Luojia1-01 images, study areas are classified into four classes 89–300, 300– 600, 600–900, 900–1876 radiance value. As it was expected viewing from night light images most of high radiance values are clustered in city centre and some of the residential apartments outside the city (Table 17.4). Agricultural Lands(C7): Map of agricultural land were prepared from google earth images then distance is calculated, more weights were given those area which are away from agricultural land. The classes are separated into four appropriate divisions, based on observation that in most of villages people lived near their agricultural land for cultivation, 0.00–300 m (unsuitable),300–600 m (moderately suitable), 600–900 (less suitable), 900–6000 m (highly suitable) (Table 17.4). Distance from bare soil surface(C8): In this study bare soil surface were identified with bare soil index than distance was calculated. Bare soil surface map divided into four classes 0–400 m, 400–800,800-1200 m, 1200–5800 m based on distance from bare soil areas. (Table 17.4). Distance from higher educational institution(C9): This town has a cluster of degree colleges, engineering and polytechnic colleges, transforming it into an educational city and has expedited the urbanization trend. To facilitate student, teacher, academic staffs a lot of hostels, fats and residential apartment has established in this town. Education is the basic human needs therefore, future growth of urban should be reasonably close and well connected to existing educational institutions. Distance From educational Institution are calculated and divided into four categories 0–1500,1500–3000,3000–5000 and 5000–21,000 m. Distance From educational Institution are calculated and divided into four categories such as less than1500,1500–3000,3000–5000 and more than 5000 m (Table 17.4). Distance from railway track (C10): Barddhaman Junction is an important station in the Eastern Railway zone this railway line used by daily commuters railway routes make an significant contribution for not only to movement of passenger but also carried mineral resources from surrounding coal field of Paschim Burdwan District. Distance from railway stations were calculated and divided into Four regions, categories as: 0–1000,1000–2000,200–3000 and 3000–12,000 m. High weights were assigned to the 0–1000-m proximity class because the area has a higher likelihood of

17 Site Suitability Analysis for New Urban Development Using …

409

future development than others, and low weights were assigned to the > 3000 m proximity class because it has the lowest likelihood of development due to its remoteness from the railway network (Table 17.4).

17.4.2 New Urban Suitability Map This integrated FAHP and DEMATEL MCDM methods have the potential to improve the accountability and logical rationality of land use choices while also lowering costs. Different parameters that have been derived have varying degrees of influence on the selection of new urban sites. While ranking the most influential parameters in the combined methods of FAHP and DEMATEL, Proximity to water bodies (17.10%), distance from present settlement patches (16.40%) and railway line (16.18%), Road density (13.60%), night time reflectance (10.95%) and higher educational institution (6th, 10.73%) secured first, second, third, fourth, fifth and sixth rank respectively (Tables 17.6, 17.7, and 17.8). After all of the criteria maps had been combined and layered, the final site suitability map could be created. The outcomes of the assessment were depicted as the final site suitability map consisting of four distinct levels of suitability. The final suitability map of new urban development shows that: 13.54% (60.67 km2 ) area is Highly suitable, 22.69% (101.76 km2 ) areas is moderately suitable while 28.33% (127.85 km2 ) area is less suitable and 34.44% (158.95 km2 ) are categorized as unsuitable (Fig. 17.5, Table 17.9). Further the Consistency Ratio value for FAHP is calculated, the resulted C.R value 0.07 under prescribed limits of Satty ( 0) include water bodies (C2), railway (C10), higher educational institution(9), night light reflectance (C6) signifies that those factors influence the others factors, on the hand Effects domain group, criteria (RI-CI < 0) including Bare soil, agricultural land distance from river areas demonstrated that this criterion is highly affected by other criteria (Table 17.7, Fig. 17.4). CI value = 0.1075, RI = 1.49, CR value = 0.07 Evaluate Consistency of comparison matrix by Consistency index (C.I) consistency index (CI) = CI =

maxλ − n n−1

10.9676 − 10 = 0.1075 10 − 1

0.167

3.000

0.111

0.125

Agricultural land L

L

0.167

2.000

8.017

11.9190

Railway Line

Total

Geometric Mean

0.142

1.000

Bare Soil Surface

Educational Institution

0.200

1.000

0.167

Night Light Reflectance

Agricultural Land

0.330

12.339

3.000

2.000

1.000

3.000

0.250

2.000

Change in Built-up Area

0.142

Settlement Patches

1.000

0.125

Water Body

2.000

M

0.500

0.333

Distance from River

Road Density

5.7175

Night light reflectance

U

17.117

4.000

3.000

0.2

0.25

1.000

4.000

0.500

0.167

3.000

1.000

39.0194

31.333

4.000

3.000

0.333

1.000

4.000

7.000

2.000

1.000

5.000

4.000

39.500

5.000

4.000

0.500

1.000

5.000

8.000

3.000

2.000

6.000

5.000

M

5.619

8.4640

3.894

0.500

0.330

Geometric Mean

12.983

0.333

0.250

0.500 0.167

Criteria

8.784

3.000

0.333 0.142

0.330 0.500

1.000

1.000

0.200

0.250

0.250 0.333

1.000 0.167

5.317

2.000

0.333

3.000

1.000 0.142

2.000

Railway Line

0.500

1.000

0.200

1.000

M

Total

0.142

0.333

Bare Soil Surface

Educational Institution

2.000

0.200

1.000

0.167

Night Light Reflectance

Agricultural Land

0.250

2.000

0.200

1.000

Change in Built-up Area

Settlement Patches

0.500

0.167

0.333

0.142

Water Body

1.000

1.000

1.000

Water body

U

L

M

Road density

L

Distance from River

Road Density

Criteria

Step 1: Fuzzified Pair-Wise Comparison Matrix

Table 17.6 Calculation steps for Fuzzy AHP matrix

48.000

6.000

5.000

1.000

1.000

6.000

9.000

4.000

3.000

7.000

6.000

U

8.542

1.000

0.500

0.142

0.200

1.000

1.000

0.500

0.200

1.000

3.000

U

40.000

6.000

7.000

0.500

0.500

7.000

4.000

2.000

1.000

6.000

6.000

53.4953

45.000

7.000

7.000

1.000

1.000

5.000

6.000

5.000

1.000

7.000

5.000

L

54.000

8.000

8.000

1.000

2.000

6.000

7.000

6.000

2.000

8.000

6.000

M

Bare soil surface

39.7313

32.666

5.000

6.000

0.333

0.333

6.000

3.000

1.000

1.000

5.000

5.000

M

Distance from river L

63.000

9.000

9.000

1.000

3.000

7.000

8.000

7.000

3.000

9.000

7.000

U

48.000

7.000

8.000

1.000

1.000

8.000

5.000

3.000

1.000

7.000

7.000

U

22.997

6.000

3.000

0.167

0.330

3.000

2.000

1.000

0.500

3.000

4.000

10.8791

7.019

1.000

1.000

0.111

0.200

0.333

1.000

0.250

0.125

2.000

1.000

L

11.347

2.000

1.000

0.125

0.250

0.5

2.000

0.330

0.142

3.000

2.000

M

Educational Institution

22.5215

16.725

5.000

2.000

0.142

0.250

2.000

1.000

1.000

0.333

2.000

3.000

M

Change in Built-up area L

16.167

3.000

1.000

0.167

0.333

1.000

3.000

0.500

0.167

4.000

3.000

U

29.700

7.000

4.000

0.200

0.500

4.000

3.000

1.000

1.000

4.000

5.000

U

5.847

0.500

0.500

0.142

0.125

0.330

1.000

0.500

0.250

2.000

0.500

10.8791

4.478

1.000

0.333

0.111

0.167

0.25

1.000

0.142

0.142

1.000

0.333

L

6.989

1.000

0.500

0.125

0.200

0.330

2.000

0.167

0.167

2.000

0.500

M

Railway line

5.9886

4.018

0.333

0.333

0.125

0.111

0.250

1.000

0.333

0.200

1.000

0.333

M

Settlement Patches L

U

(continued)

10.317

1.000

1.000

0.167

0.250

0.500

3.000

0.200

0.200

3.000

1.000

U

9.142

1.000

1.000

0.167

0.142

0.500

1.000

1.000

0.333

3.000

1.000

410 S. Dolui and S. Sarkar

0.2802

0.1877

1.0881

Agricultural land

Bare soil surface

Educational institution

Railway line

A7

A8

A9

A10

Sources Computed by researcher

13.5279

9.5958

0.0536

Total

Reciprocal fuzzy number

0.0739

2.3097

1.4941

0.2243

0.3265

1.4241

2.3607

0.6969

0.3691

2.5210

1.8015

Middle

1.6562

0.2563

1.5573

1.0522

Settlement patches

Night light reflectance

A5

0.5211

A6

Distance from river

Change in built-up area

A3

1.7249

Water body

A2

A4

1.2719

Lower

Road density

A1

Fuzzy geometric means (Ri) value

Criteria

Serial number

0.1042

18.6401

3.1469

2.1550

0.2966

0.4311

1.9969

3.2113

0.9649

0.4814

3.3728

2.5832

Upper

0.0888

0.0584

0.0101

0.0137

0.0564

0.0835

0.0280

0.0150

0.0925

0.0682

Lower

Fuzzy weight (Wi)

Step-2,3,4 - calculation of fuzzy geometric mean value (ri), defuzzification, normalized weight and rank

Table 17.6 (continued)

0.1707

0.1104

0.0166

0.0241

0.1053

0.1745

0.0515

0.0273

0.1864

0.1332

Middle

0.3279

0.2246

0.0309

0.0449

0.2081

0.3347

0.1006

0.0502

0.3515

0.2692

Upper

1.1524

0.1958

0.1311

0.0192

0.0276

0.1233

0.1976

0.0600

0.0308

0.2101

0.1569

Defuzzification

1.00000

0.1699

0.1138

0.0167

0.0240

0.1070

0.1714

0.0521

0.0268

0.1823

0.1361

Normalized weight

100

16.99%

11.38%

1.67%

2.40%

10.70%

17.14%

5.21%

2.68%

18.23%

13.61%

In percentages

3

5

10

9

6

2

7

8

1

4

Rank

17 Site Suitability Analysis for New Urban Development Using … 411

1.000

Bare soil surface

Educational institution

Railway line

A8

A9

A10

Water body

A2

0.050

0.100

Agricultural land

Bare soil surface

Educational institution

Railway line

A7

A8

A9

A10

0.150

0.150

0.050

0.150

Settlement patches

Night light reflectance

A5

A6

0.050

0.150

Distance from river

Change in built-up area

A3

A4

0.100

A1

0.000

Criteria

Road density

0.050

0.000

0.000

0.050

0.000

0.200

0.100

0.000

0.000

0.050

A2

1.000

3.000

Serial number

0.000

0.000

1.000

0.000

4.000

2.000

0.000

0.000

1.000

A2

2.000

A1

Step-2: The normalized direct relation matrix

1.000

Agricultural land

A7

3.000

3.000

Settlement patches

Night light reflectance

A5

A6

1.000

3.000

Distance from river

Change in built-up area

2.000

A3

Water body

A2

A1

0.000

A4

Criteria

Road density

Serial number

A1

Step 1: Initial direct relationship for all matrix of responses

Table 17.7 Calculation steps for DEMATEL methods

0.000

0.000

0.050

0.100

0.000

0.100

0.050

0.000

0.050

0.100

A3

0.000

0.000

1.000

2.000

0.000

2.000

1.000

0.000

1.000

2.000

A3

3.000

1.000

1.000

1.000

3.000

3.000

0.000

2.000

4.000

3.000

A4

0.150

0.050

0.050

0.050

0.150

0.150

0.000

0.100

0.200

0.150

A4

4.000

3.000

1.000

2.000

4.000

0.000

2.000

2.000

4.000

3.000

A5

0.200

0.150

0.050

0.100

0.200

0.000

0.100

0.100

0.200

0.150

A5

2.000

1.000

0.000

0.000

0.000

3.000

2.000

0.000

1.000

4.000

A6

0.100

0.050

0.000

0.000

0.000

0.150

0.100

0.000

0.050

0.200

A6

1.000

1.000

1.000

0.000

0.000

2.000

0.000

2.000

2.000

1.000

A7

0.050

0.050

0.050

0.000

0.000

0.100

0.000

0.100

0.100

0.050

A7

1.000

1.000

0.000

0.000

0.000

2.000

2.000

1.000

1.000

0.000

A8

0.050

0.050

0.000

0.000

0.000

0.100

0.100

0.050

0.050

0.000

A8

3.000

0.000

0.000

0.000

1.000

0.000

0.000

0.000

2.000

1.000

A9

0.150

0.000

0.000

0.000

0.050

0.000

0.000

0.000

0.100

0.050

A9

0.000

2.000

1.000

0.000

3.000

1.000

0.000

0.000

2.000

3.000

A10

(continued)

0.000

0.100

0.050

0.000

0.150

0.050

0.000

0.000

0.100

0.150

A10

18.000

11.000

6.000

7.000

14.000

20.000

12.000

8.000

19.000

18.000

Sum

412 S. Dolui and S. Sarkar

0.114

3.266

3.434 2.806 1.264

Road density

Water body

Distance from river

Change in built-up area

Settlement patches

Night light reflectance

Agricultural land

A1

A2

A3

A4

A5

A6

A7

2.216

1.307

3.328

Ri

1.725

0.176

Criteria

1.992

0.263

0.106

0.168

0.151

0.276

0.169

0.079

0.227

0.257

A3

Serial number

Step 4: Criteria weights and Ranking

3.393

Ci

0.142

0.314

0.485

A9

A10

0.133

0.068

0.174

0.152

A7

A8

0.389

0.197

0.487

0.444

A5

A6

0.092

0.231

0.178

0.221

0.364

0.442

A2

0.256

A2

A3

0.353

A4

A1

Serial no

A1

Step-3: Total Relation Matrix

Table 17.7 (continued)

3.678

0.504

0.284

0.161

0.192

0.461

0.528

0.258

0.231

0.549

0.510

A4

1.689

2.676

4.075

3.678

1.725

1.992

3.393

Ci

4.075

0.583

0.392

0.170

0.241

0.529

0.427

0.365

0.239

0.582

0.547

A5

2.953

5.482

7.509

5.894

3.032

5.320

6.659

Ri + Ci

2.676

0.383

0.234

0.088

0.106

0.264

0.417

0.284

0.108

0.332

0.460

A6

1.264

3.434

−0.641 −0.424

2.216

−1.462

2.806

1.307

−0.419

0.130

3.328

3.266

−0.128 1.335

Average

1.525

0.204

0.146

0.049

0.064

0.140

0.248

0.194

0.111

0.211

0.157

A8

Ri–Ci

1.689

0.210

0.153

0.102

0.072

0.141

0.266

0.116

0.161

0.261

0.207

A7

1.169

0.253

0.076

0.035

0.039

0.154

0.125

0.079

0.034

0.206

0.169

A9

0.053

0.117

0.143

0.092

0.054

0.138

0.136

Scales

2.107

0.221

0.231

0.107

0.075

0.325

0.271

0.156

0.073

0.298

0.350

A10

9

5

1

6

8

2

4

Rank

(continued)

3.284

2.086

1.039

1.264

2.806

3.434

2.216

1.307

3.328

3.266

Ri

17 Site Suitability Analysis for New Urban Development Using … 413

3.284

Railway line

A10

6.66

5.48 2.95

Road density

Water body

Distance from river

Change in built-up area

Settlement patches

Night light reflectance

Agricultural land

Bare soil surface

Educational institution

Railway line

A2

A3

A4

A5

A6

A7

A8

A9

A10 5.39

3.25

2.56

7.51

5.89

3.03

5.32

Ri + Ci

Criteria

5.391

3.255

2.563

Ri + Ci

A1

2.107

1.169

1.525

Ci

Serial number

Step 5: Cause and effect relationship

2.086

Educational institution

A9

1.039

Bare soil surface

A8

Ri

Criteria

Serial number

Step 4: Criteria weights and Ranking

Table 17.7 (continued)

1.178

1.18

Cause

Cause

Effect

0.92

Effect

−0.49

Effect Cause

Effect

−0.64 −0.42

Effect

−1.46 0.13

Cause

−0.42

Effect

1.34

Identify

−0.13

0.137

0.087

0.043

Scales

Ri–Ci

3.284

2.086

1.039

−0.486 0.918

Average

Ri–Ci

3

7

10

Rank

414 S. Dolui and S. Sarkar

17 Site Suitability Analysis for New Urban Development Using …

415

Table 17.8 Criteria weights and final ranking Serial number

Criteria

DEMATEL

FAHP

Average

Percentage (%)

A1

Road Density

0.136

0.136

0.136

13.61

4

A2

Water Body

0.138

0.182

0.160

17.14

1

A3

Distance from River

0.054

0.027

0.041

3.37

8

A4

Change in Built-up Area

0.092

0.052

0.072

6.21

7

A5

Settlement Patches

0.143

0.171

0.157

16.43

2

A6

Night Light Reflectance

0.117

0.107

0.112

10.94

5

A7

Agricultural Land

0.053

0.024

0.038

3.11

9

Rank

A8

Bare Soil Surface

0.043

0.017

0.030

2.33

10

A9

Educational Institution

0.087

0.114

0.100

10.70

6

A10

Railway Line

0.137

0.170

0.153

16.16

3

Sources Computed by researcher

Table 17.9 Area according to Suitability scale

Suitability scale

Area in Km2

Unsuitable

158.95

35.44%

Less suitable

127.05

28.33%

Moderately suitable

101.76

22.69%

60.71

13.54%

448.47

100.00%

Highly suitable Total

Percentage

Causal Diagram Showing Criteria Relationship 1.5 Water Body, 5.32, 1.34

1

Relationship Indicators (RI-CI)

Causal Domain

Educational Institution, 3.25, 0.92

Railway Line, 5.39, 1.18

0.5 Night Light Reflectance, 5.48, 0.13 Road Density, 6.66, -0.13

0 0

1

2

3

4

5

6

7

8

Agricultural Land, 2.95, -0.42

-0.5

Distance from River, 3.03, -0.42

Bare Soil Surface, 2.56, -0.49

Settlement Patches, 7.51, -0.64

-1 Change in Built-up Area, 5.89, 1.46

-1.5 Effect Domain -2

Significance Indicators ( RI+ CI)

Fig. 17.4 Causal diagram showing the relationship among different criteria

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Fig. 17.5 Site suitability map of new urban development for Burdwan Town Comparison Between Weights of FAHP & DEMATEL

Railway Line Educational Institution Bare Soil Surface

0.170

0.137 0.114

0.087 0.017 0.043 0.024

Agricultural Land

0.053 0.107 0.117

Night Light Reflectance Settlement Patches

0.143

0.171

0.052 Change in Built-Up Area

0.092 0.027

Distance from River

0.054

Water Body

0.136 0.136

Road Density

0.000

0.182

0.138

0.020

0.040

0.060

FAHP

0.080

0.100

0.120

0.140

0.160

DEMATEL

Fig. 17.6 Comparison of Criteria weights among FAHP & DEMATEL methods

0.180

0.200

17 Site Suitability Analysis for New Urban Development Using …

Saaty (2008) defined the consistency ratio (CR) as =

417

CI CR

= 0.1075/1.49 = 0.07216

17.5 Discussion A GIS-Assisted multicriteria assessments that includes FAHP + DEMATEL was developed in order to find the piece of land that would be the most appropriate for ecologically responsible urban development. Road Density: The cost of constructing a new road is prohibitively exorbitant, as a consequence of this, more people are choosing to live near highways and other major thoroughfares because of the convenience of surrounding transportation options and quick access to urban centers. Site assessments for residential neighborhoods almost uniformly conclude that Locations that are further away from roadways are less desirable than those that are situated closer to them. Water Bodies: potable sources of water such as ponds and lakes are very important in urban development as they serve as drinking water supplies, recharge groundwater, regulate floods and give employment possibilities for a number of people. Anthropogenic disturbances and Unplanned urban growth and land alteration can increase sensitivity and vulnerability aquatic bodies. Distance from River Area: To ensure that the flood does not threaten the buildings, it is vital to establish the rivers and their boundaries in land use planning. As water resources in this area primarily used for agricultural reasons areas located further away from a river receive greater weights. Change in Built-up areas: The built-up area already has established infrastructure and municipal amenities; to take advantage of these amenities, new urban settlements are sprouting up around the present alteration specifically around fringe areas next to the municipal limit, has altered dramatically. Distance from Settlement area: It’s common practice for new developments to be located close to established ones because of existing infrastructure, and this proximity has advantages over those located further away. Night Light Image: As observed from night light images, the majority of high radiance values are concentrated in the city centre and some of the residential apartments outside the city. Aside from the main city, those city lights shine brightly along the roadway. Agriculture Land: Soils with high fertility underpin agricultural land, and these soils should be protected for agricultural use. Settlements within and near agricultural lands are considered less acceptable in this study, which aims to safeguard agricultural land. Distance from bare soil surface: Although the bare soil surface is devoid of fertility, the badland terrain and high proportion of sand impede agricultural activities, it may be appropriate for urban and industrial development. Railway Line: The Bardhaman railway and its sub-urban station were primarily used by daily commuters for the movement of products and vegetables, but it also carried people to cities for a variety

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of socio-economic and educational need, as well which contributes significantly to the expansion of Burdwan town. Higher Educational Institution: This town has a cluster of degree institutions, engineering and polytechnic colleges, which has transformed it into an educational hub for the surrounding areas and has accelerated the urbanization trend. Highly suitable areas account for 13.54%, These places are either already urbanized or, as a result of all the infrastructure, are likely to become urbanized in the near future. Cities’ main urban areas, census towns, and large villages all fall into this category. Moderately suitable lands, which account for 22.69% of the total, can be exploited as prospective candidate regions for future sustainable urban growth since they are adjacent to settlement patches. This area has direct access to transportation services, as these areas already have developed infrastructure and are ready to be urbanized. Despite the fact that these places are primarily located in peri-urban parts of the city, where the majority of agricultural land is under serious threat. Less suitable areas account for 28.33% of the total; essentially, such areas are likewise agricultural land, but they are located not far from existing settlement patches; nonetheless, due to a weak communication system, there are only a few settlements distributed over agricultural land. The suitability map revealed that 35.44% of the area is inappropriate for habitation (unsuitable), since this area primarily agricultural land and preservation of agricultural lands was one of the main objectives in this study that why this agricultural land taken as constraint variables making them unsuitable for future settlement expansion. Direction of urban expansion depicted from suitability maps that villages including Bajepratappur, Bahir Sarbamangala, Mirzapur, Alisha, Bamchandaipur, and Gangpur, Goda, Nababhat, Talit have been sprung up along highway and others communication facilities. Northern, western, southern parts of the burdwan towns are suitable for urban development, whereas the eastern portion are predominantly agricultural land were not appropriate for urban settlement. According to this study and earlier research, the integrated Multicriteria and GIS technique can serve as a powerful appropriateness analysis and evaluation tool for preserving natural resources and sustainable development, despite the fact that criteria used in each study may vary. In order to ensure that the resulting map is both reasonable and realistic a field survey for ground truth verification need to be performed. The ROC curve was utilized in order to evaluate the proposed suitability map in terms of its validity as well as its practicability. The overall accuracy of the model is shown in Fig. 8 after completing Monte Carlo simulation the Area under the ROC (AUC) value was 0.94 at 95% confidence interval (0.875–0.981). Hence, the values indicating the accuracy of prediction was excellent. The outcome shows that the implementation of MCDM approaches and GIS techniques in the development of future plans for sustainable development can contribute significantly toward the protection of the environment (Fig. 17.7).

17 Site Suitability Analysis for New Urban Development Using …

419

Fig. 17.7 Validation map for Burdwan Town

17.6 Conclusion This research improves the effectiveness of utilizing geospatial technology, particularly GIS-based MCDM analysis in complicated situations which can affect citizen safety, sustainability, and overall quality of life. However, MCDM sometimes produced biased results in determine criteria weights by the researcher but in this study, biasedness can be eliminated by improving performance of model through averaging both criteria weights (FAHP + DEMATEL) and given criteria weights based on the opinions of specialists and a review of relevant literature. The assessment of urban settlement suitability using FAHP + DEMATEL is proved to be unbiased, accurate, powerful and time saving with minimum human involvements. The evaluation criteria used in this research are restricted in their scope because of diversity of evaluation criteria and the intricacy of urban redevelopment process. In spite of this, it was strongly recommended for future study that additional environmental or social-economic data needs to be taken into consideration, in particular when researching peri-urban environment. Theoretical methodology that was utilized in this model will assists local authorities and urban planner in the process of improving the policies outlined in the City Development Plan (CDP) in locating most feasible for urban development in this area. Decision-makers may find this study to be useful in preventing future unplanned growth in periphery of comparable rapidly expanding cities in developing nations.

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Chapter 18

Inconveniences and Mobility Issues of Elders on Road: The Case of Kolkata Municipal Corporation, West Bengal, India Shovan Ghosh

and Sramana Maiti

18.1 Introduction Today, people wish to live longer and indeed, lives their life to the sixties and beyond. On the contrary, the newer generation wishes for a small family indicating decreasing fertility rate. As a consequence, the graph of the elderly population is going upward. The WHO report (2010) on global health and ageing shows that the proportion of people aged 65 or older is expected to grow from an estimated 524 million in 2010 to nearly 1.5 billion in 2050. India is soon to be facing a hike in the elder population graph. A report published by Economic times (2018) claimed the ageing population is rising more rapidly than previously thought and may have a 20% population of 60 years and above by 2050. In the case of West Bengal, as per the report of UNFPA (2014), along with the lower birth rate and death rates, the expected lifespan of people in West Bengal is higher than the national average as per the projection. In the case of Kolkata, the grey hair of the city has been seen. The city not only experiencing decreasing fertility and outmigration of young generations. Kolkata is having the lowest younger generation of 20–24 ages and the highest 60 plus years of people (Times of India, 2015). Making up a growing share of the population, the implications of elderly people can be seen in all sectors of Kolkata city and society. The existing policies should be revisited by the policymakers. Because the resource is limited and within limited resources society needs to adopt a sustainable elder-friendly approach. Towards making an elder-friendly approach, this chapter is aimed to address the issues faced by the elders on road. Be it economical or recreational, a physically fit elderly person has to avail the road even for a little time of the day. But, with increasing population pressure, the traffic has also increased and needless to say, the S. Ghosh · S. Maiti (B) Department of Geography, Diamond Harbour Women’s University, Sarisha, West Bengal, India e-mail: [email protected] S. Ghosh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_18

425

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S. Ghosh and S. Maiti

issues related to traffic are also growing. Against this backdrop, it is crucial to study the issues and inconveniences of elderly people’s mobility on road. Chakrabartty and Gupta (2014) have studied the traffic congestion of the metropolitan city of Kolkata and concluded that with high population density, high demand for mobility combined with limited road space results in high traffic congestion in Kolkata. Roy Chowdhury (2015) has studied traffic congestion in light of the environmental quality assessment of Kolkata City. The categorization of crossings based on congestion type helps to understand the intensity of traffic congestion. The author has concluded emission of automobile pollution is positively correlated with traffic congestion. To investigate the pedestrian risk factor in Kolkata City Roads, Priyadarshini and Mitra (2018) have found that high pedestrian volume and wider minor roads are associated with higher pedestrian fatalities. On the contrary, higher traffic volume, and higher postencroachment-time wider crosswalks have shown decreasing influence on pedestrian fatalities. This implies that sufficient footpath space can reduce unwanted accidents. Singh and Kaur (2018) have attempted to find out the factors affecting the congestion and encroachments in Ludhiana city and concluded that encroachment and traffic congestion are related to each other and the congestion issue is emerging because of the haphazard growth of hawkers. Dey (2016) has studied deeply the traffic congestion situation of Park Circus seven-point crossing of Kolkata city and suggested for integrated transport policy to prevent the congestion. George (2017) has represented the elder-friendly approach by proposing age-friendly street designs. The author has identified the inconveniences faced by the elders and categorized them in access and transportation, physical factors and psychological factors. Pulvirenti et al. (2020) have attempted to identify the elderly perception of critical traffic issues and found that cluster for irregular parking dissatisfaction, most of the respondents are men as among the sample male drivers were predominant. When the sidewalk problem, pedestrian crossings and driver behaviours are concerned, no particular characteristic among respondents has been noticed. As these problems are being faced by everyone. To analyse the quality and quantity of the footpath and identify the significant obstacle, Mitra et al. (2020) surveyed 260 numbers of urban commuters. The authors have identified Hawkers-Vendors, unwanted banners, damaged footpaths and potholes as the main obstacle to the footpath and also responsible for degrading the footpath quality. Murman (2015) has drawn attention towards the influence of age on cognition. The cognitive power decreases with age and cognitive tasks that require one to quickly process and transform information to make a quick decision may hamper it. On a busy road, one needs to be quick and smart. But concerning elderly people, the diminishing cognitive power may influence them during road crossing, walking, following signalling lights etc. Discussions on issues and problems of elderly people on road are a never-ending debate. The solutions must find out. Lin and Cui (2021) are finding solutions and suggested modifications in land use planning for housing provision. With providing strong support towards elderly drivers, balanced and flexible public transport arrangements should be adopted for elderly road users too to ensure the safety of the elderly pedestrians.

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427

After assessing the previous works, the mobility issues in an urban metropolitan set-up like Kolkata are aggravated by the high-speed cars, flyovers, crossings, escalators, signals and under path etc. As it is restricting older people from wandering safely in a metropolis. This makes them confined to their houses and aggregates their depression and stress. The risk of falls is a prevalent factor in activity restriction by older people in a variety of settings (Gorman et al., 2019). Likewise, the area under the core urban is a busy traffic zone, and there is no sufficient sidewalk for the elderly. They feel unsafe and anxious when they go outside their house. To achieve sustainability goals in terms of social, ecological and technological sustainability, the existing resources need to be allocated carefully. In the case of human resources, though the elderly population (60 years and above) is part of the population census, in most cases, the unemployed elderly people are not considered resources. On the other hand, the share of the elder population in the total population is ever-increasing. In this scenario, the smart city’s purpose can be served by adopting an elder-friendly approach, where elders can live without feeling deprived. Against this backdrop, it is essential to investigate the inconveniences and mobility issues of the elders in Kolkata Municipal Corporation.

18.2 Objectives The objectives of the present study are to find out the inconvenience faced by the elderly population on road and how these issues are influencing the elderly behaviour. It is also necessary to justify the present study and how it is important to make an elder-friendly road policy.

18.3 Materials and Methods Materials and Methods The study is essentially based on a field survey conducted on different road crossings across the Kolkata Municipal Corporation (KMC) area. The sample sites have been randomly chosen from South Kolkata to North Kolkata and they are considered important nodes of the city. These nodes are Rashbehari Avenue, Gariahat Crossing, Jadavpur 8B Crossing, Park Circus 7 points crossing, Esplanade, Mahatma Gandhi Road and Chittaranjan Avenue (Fig. 18.2). The samples have been collected through the Simple Random Sampling technique with the help of a questionnaire schedule survey. The target group of our study is the elderly population i.e. above 60 years old people (as per Census of India) including commuters as the study is aimed to observe the inconveniences of elders on road. To collect the primary data, an extensive field survey has been carried out and 135 samples have been collected so far from selected nodes (Table 18.1).

428 Table 18.1 Sample collection

S. Ghosh and S. Maiti The number of samples collected

Sampling site

24

1. Rashbehari avenue 2. Gariahat crossing

15

3. Jadavpur 8B crossing

22

4. Park circus 7 point crossing

20

5. Esplanade

26

6. Chittaranjan avenue

15

7. Mahatma Gandhi road crossing

13

Total

135

To assess the satisfaction level and derive the inconveniences of elders on road, some variables related to city roads have been selected. These variables are as follows: Variables

Explanation of the variables

Elders’_mobility

Respondents’ perception of free movement of elderly people

Crowd

Different kinds of vehicles on road and also roadside vendors, other pedestrians etc. occupy the road

road_system

Inclusive perception of traffic, traffic management, signalling lights, road crossings, footpath quality, vehicle availability etc.

Number of questions asked

A total of 50 questions were asked during the interview including their driving_culture Perception on the identification behaviours of information. The drivers during interview process driving a vehicle. lasted from 10 to E.g. Rash driving, cautious driving etc. 15 min for each respondent Pedestrian_crossing_Satisfaction Perception on whether they are satisfied regarding pedestrian crossing time

Participation type The interview procedure had been done with Simple Random Sampling through a questionnaire schedule survey. The participants voluntarily took part in this process

(continued)

18 Inconveniences and Mobility Issues of Elders on Road: The Case …

429

(continued) Variables

Explanation of the variables

Stress_road_cross

Respondents’ feelings while crossing the road

Available_foot path side

A road contains two sides of the footpath generally and if the respondents can use both sides of the footpath

Traffic_cacophony

Formation of the noisy situation due to horns of vehicles, unnecessary mic announcements, public hollering etc.

Traffic_congestion

Vehicles, passengers and pedestrians create congestion due to the mismanagement of traffic flow

safe_feeling

Perception of safety of the respondents

footpath_width

Perception of the width of the footpath

Public_transport_satisfaction

Perception of availability and frequency of public transportation

Vehicle_hire

Perception of vehicle hire from a convenient place or not

Satisfied_signal

Perception of the signalling system and management and signalling time

Signalling_Light_satisfaction

The visibility of signalling lights

Number of questions asked

Participation type

The perceptions of the respondents have been converted to data to derive the satisfaction index. The satisfaction Index developed by Hall et al. (1975), has been employed for this chapter, following this formula: Is = (fs − fd)/N

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S. Ghosh and S. Maiti

where, Is: Satisfaction Index; Fs: No. of Satisfied Respondents; Fd: No. of Dissatisfied Respondents. The values range from −1 to +1 and having a positive value indicates the highest satisfaction level and vice versa. To understand the weightage of the Satisfaction Index, the variables’ scores have been ranked according to their score. After deriving the satisfaction index, the chi-Square test has been done to evaluate the significant association between different variables. Some tools of descriptive statistics have been used to assess the basic sociodemographic profile of each respondent. In the case of secondary data, the District Statistical Handbook (2014) was published by the Department of Statistics and Programme Implementation, Government of West Bengal and the report of the City Disaster Management Plan of Kolkata was published by Kolkata Municipal Corporation (2018).

18.3.1 Study Area The administrative area under Kolkata Municipal Corporation (Fig. 18.1) is selected as the study area. It is located at 22°30, North latitude and 88°30, East longitude. It is the main port entry in North-Eastern India. The area under Kolkata Municipal Corporation (KMC) is 286.08 km2 . There are 10 boroughs and 144 wards. Around 4.5 million population live in this age-old metro city. Kolkata- the city with culture, emotion and lots more. The city has unified the cultures of the eastern and western over the years. The population density is 24,252 km2 . The population growth rate is −1.67% compared to the 2001 census (3.93%). As per the 2011 census, the total population of KMC was 4,496,694 out of which 52.41% are male and 47.58% are female. The interesting fact is that the male population gets lowered in number that 2001 and on the other side, the female population has increased since 2001. Female literacy is at 84.06% which is quite impressive as per the census 2011. To assess the inconvenience of elders on road, some nodes and crossings have been selected randomly from Kolkata Municipal Corporation. In this phase, previous pieces of literature have helped the researcher to study the type of congestion faced by these nodes. The nodes selected for the study have been shown in Table 18.2 and the location of the major roads are being represented through a road map (Fig. 18.2).

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Fig. 18.1 Location of the study area Table 18.2 Selected nodes/crossings and type of traffic congestion Nodes/crossings

Type of congestion

1. Rashbehari avenue

Multiple interactions on homogenous roads, congestion due to network morphology

2. Gariahat crossing

Trigger neck congestion, network control congestion, congestion due to network morphology

3. Jadavpur 8B crossing

Bottle neck, trigger neck congestion

4. Park circus 7 point crossing

Congestion due to network morphology, network control congestion

5. Esplanade

Multiple interactions on homogeneous roads

6. Chittaranjan avenue

Simple interaction homogeneous roads, network control congestion

7. Mahatma Gandhi road crossing

Simple interaction homogeneous roads, network control congestion, trigger neck congestion

Source Roy Chowdhury (2015). Type of Traffic Congestion in the city of Kolkata. IJHSSI

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S. Ghosh and S. Maiti

Fig. 18.2 Map showing major roads and selected nodes/crossings of Kolkata Municipal Corporation

18.4 Results and Discussion 18.4.1 Socio-demographic Characteristics of the Respondents The socio-demographic profile of the respondents has been revealed and it can be said that most of the respondents are from two categories of age i.e. 60 years to

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433

65 years and 66 to 70 years of age category. The most spontaneous responses came from the male respondents. In the case of marital status and family type, married respondents from the nuclear family are the main participants. Interestingly, most of the respondents are connected with either business or they are pension holders. The ownership of different modes of vehicle is not as varied. Socio-demographic characteristics of the respondents N

Minimum

Maximum

Mean

Std. deviation

age_cat

135

1.00

5.00

1.5037

0.91320

Gender

135

1.00

2.00

1.2222

0.41729

Marital_status

135

1.00

4.00

1.8963

0.87481

Family_type

135

1.00

5.00

1.8444

0.47105

Family_Members

135

1.00

9.00

2.5852

1.13549

Source_Income

135

1.00

4.00

2.9481

0.92493

Bicycle

135

1.00

2.00

1.0667

0.25037

Bike_scooter

135

1.00

2.00

1.0444

0.20685

Car

135

1.00

2.00

1.0519

0.22255

Valid N (listwise)

135

Source Field Survey, 2022

The survey has been done randomly which means those who have availed the roads, got a chance to be interviewed. It is quite natural that the age category of 75 and above is not a frequent road user. Besides, the businessman and in some cases, the employed respondents mostly belong between 60 and 70 years of age. The gender disparity is very eye-catching. From the researcher’s viewpoint, the women are more likely to avoid the interview.

18.4.2 The Growth of Vehicles and Elderly Population in Kolkata Municipal Corporation Kolkata is one of the oldest urban areas of modern India. Historically, Kolkata (formerly Calcutta) port was built as a trading port and commercial capital of India and was the administrative capital for British Indian Empire for a long time. Being an unplanned city, the erstwhile Calcutta is striving with challenging issues like traffic transportation, congested roads, the presence of large-scale informal activities along the major road junctions, the growing pressure of population, growth of slums etc. (City Disaster Management Plan of Kolkata, 2018). The city, now governed by Kolkata Municipal Corporation (under Calcutta Municipal Act, 1980), has witnessed many transportation revolutions though. The first metro railway system in India was introduced in Kolkata (then Calcutta). This city also holds its pride in having an exclusive Tram transportation network not only in India but also in the continent. No

434

S. Ghosh and S. Maiti

wonder the city is bearing a long road and transportation history. According to the report provided by Kolkata Municipal Corporation (2018), the number of registered vehicles is having an upward growth comparing 2011–12 years. Considering 2011–12 as the base year the growth of registered goods vehicles, motor cars and jeeps and motorcycles are having 5.8, 4.5 and 6.53% growth at the end year i.e. 2016–17. The taxi is showing a greater hike of growth than any other vehicle i.e. 20.91% in the year 2016–17. Interestingly, the growth of Taxis is showing a 20.91% growth rate the biggest growth of other vehicles. Another significant growth (13.19%) has been observed in the growth of Buses. The number of vehicles has increased over 5 years. So does the population of the Kolkata Municipal Corporation. It is pretty convincing that to support the increasing demand along with the population pressure, new vehicles are on road. The growth rate of the population is in a continuous upward graph (Fig. 18.3). As a result, the increasing population and increasing numbers of vehicles together make the road quite congested. As it is quite impossible to change the width of the road so frequently. Besides the crowd, the road also makes itself overfilled with hawkers, trees, dump yards and other stuff. Elderly people also use the road like others. Kolkata Municipal Corporation is now facing the growth of the elderly population (Fig. 18.4). Comparing the 2011 census there is around a 2% increase in the elderly population. It should also be mentioned that Kolkata is currently having the highest ageing index i.e. 62.383 than any surrounding district. So, it is necessary to study the issues faced by the elderly population in society. The issues have been broadly studied before and it has been revealed that the elderly drivers are more prone to lose control of physical strength such as steering or brake stimulus, looking aside or backside etc. In the case of walking speed, it has been studied that elders’ walking speed is less than the standard intersection crossing speed (Gonawala et al., 2013). It should also be mentioned that elders are prone to lessen cognitive ability with age (Murman, 2015) and due to the reduction of cognitive abilities, cognitive impairment concerning perception-reaction time is increased by 20–30% (Gonawala et al., 2013). In this scenario, the inconveniences for elders on road will rise naturally. . Fig. 18.3 Decadal growth of population of Kolkata Municipal Corporation. Source District Statistical Handbook, 2011

Population growth of kolkata municipal corporation 600 500 400 300 200 100 0 1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001 2011

18 Inconveniences and Mobility Issues of Elders on Road: The Case … Fig. 18.4 Decadal elderly population growth of Kolkata Municipal Corporation. Source District Statistical Handbook, 2011

435

Growth of Elderly population in Kolkata Municipal Corporation

6 4 2 0 60-64

65-69

70-74

75-79

2001

2011

80+

18.4.3 Inconveniences Faced by the Elders In this part of the chapter, the inconveniences of the road network have been identified based on the perception of elderly people. Based on the satisfaction index, interestingly, around 84.4% of elders in our sample, the elders of our society are unified to state that the normal mobility of the elders has been restricted. But, here a question may arise. Why does the elderly population feel that their mobility is restricted? The satisfaction index (Table 18.3) displays the reasons. The reasons can be divided into two categories. The first category includes the reasons which are directly caused by human behaviour (Driving Culture, Crowd, Traffic Volume, hiring vehicles, elders’ mobility, Safe feeling, Stress during Road Crossing), and another category includes human behaviour along with inhuman systems like traffic administrations, road engineering (Pedestrian Crossing, Road System, Available Footpath side, Overall traffic, Footpath Width, Public Transport, Traffic Signal, Signalling Light). The driving culture of the drivers ranks a very low satisfaction score. The collective mindset of drivers creates a problem to a great extent. Every driver wants to be in the front and also they want to take every opportunity to fill the gap and not let another driver in. Deliberate overtaking of each other’s vehicle makes the road dangerous. The tendency to break the traffic rule is another matter of concern. According to some respondents, state bus drivers are more reckless. Apart from this, auto-rickshaw drivers and Bike Riders create congestion all the time. From the researcher’s experience, auto-rickshaw drivers often try to fit their vehicles even if there’s a small place left. In the case of Bike Riders, people often get horrified because of their speed and engine noise. Near about 58% and 23% of respondents blames the auto-rickshaw and bike riders respectively for making the road inconvenient (Fig. 18.5). Till 31st March 2018 there were 342,119 numbers of registered scooters, mopeds and Motorbikes on the road of Kolkata (Road Transport Year Book 2017–18 and 2018–19, 2021). During the Field Survey, the respondents often stated that the young generation and bikes are dangerous for them. Most of the youngsters (age group 15–24 years, UN reports) do not obey the traffic rules properly and often try to show off by boosting the bike’s speed. Another aspect is the perspective of elderly drivers should consider

436

S. Ghosh and S. Maiti

here. The elderly drivers, though very few respondents, stated that congested traffic (35%) and rash driving (30%) of other drivers makes them anxious while driving (Table 18.10). Here, driving stress plays an important role. According to Jhao and Yamamoto (2021), driving stress escalates with age and in some cases driving stress remains the same throughout the lifespan. Here, driving stress includes traffic jams, long-distance driving etc. (Table 18.4). The dissatisfaction regarding Pedestrian Crossing and the road system is another issue faced by elders along with stress during road crossing. Talking about pedestrian crossing, generally, pedestrian crossing time fluctuates between 5 and 15 s at major crossings. In Kolkata, the major road crossings are having 60 ft or even 80 ft road crossing width. Ironically, for a normal elder adult without any morbidity, the normal gait speed is 0.6 m/s (Gunasekaran et al., 2016). So a quick mathematical test can clear the scenario about pedestrian crossing dissatisfaction. For example, a 60 ft road (1ft = 0.3048 m) will be crossed with a gait speed of 0.6 m/s within 30.48 s. And it has already been known that the pedestrian crossing time is allotted for roughly 5–15 s. According to the former chief traffic and transportation engineer of the transport department, the transport planning is carried out with only motorists in mind and pedestrians are ignored (Times of India Report, 2020). Not only the normal pedestrians but the elderly pedestrians have been ignored. The pedestrian crossing time and road crossing are interrelated. Road crossing has also disappointed elderly adults. Here, cognitive ability and slow walking are playing the main role. Slow walking, in this case, refers to the perception of the elderly respondents. They have been asked whether they feel a slow walker i.e. the walking speed has been reduced or not. With increasing age and decreasing cognitive ability, road crossing gets dangerous for elders they cross the road while estimating the vehicle’s speed and overestimating their walking speed (Zito et al., 2015). In the case of slow walking, out of the total participant of this study, around 78.5% have identified themselves as a slow walker. Finding out the reason, almost half of the elderly people have considered that old age is the main reason for slow walking. Heavy traffic (14.2%) and disease (27.4%) are other major reasons for slow walking, according to the respondents. Though insignificant other reasons, around 4% of elderly respondents place their blame on psychological reasons. These reasons can only make a fragmented picture. Apart from the Disease, the heavy traffic, feeling of being an old person creates mental pressure to some extent and as an ultimate result, the elders often hesitate to cross the road. Even if they start to cross it, they do it under stress. The statement made by the researcher has been echoed in the hypothesis testing, shown in Table 18.5. The significant value reveals that around 30% and 45.3% (Table 18.11) slow walker respondents strongly agreed and agreed respectively to the statement ‘I feel stressed while crossing the road’. The next segment of dissatisfaction of respondents includes Crowd, Traffic cacophony and Traffic Satisfaction. Based on the satisfaction score, the crowded road bothers elders pretty much. The traffic cacophony is aggravating the discontent and thus, ultimately affecting overall traffic satisfaction. Crowded roads or congested roads influences negatively not only the elderly population but also other than them. It wastes time and energy, causing pollution and stress and leading to a decrease in productivity. Chakrabartty

18 23 20 31

117

97

90

Pedestrian_crossing_Satisfaction

Crowd

road_system

111

42

39

9

12

Public_transport_satisfaction

Vehicle_hire

Satisfied_signal

Signalling_Light_ satisfaction

Source Field Survey, 2022

89 96

46

footpath_width

121

85

48 73

62

44

Traffic_congestion

40

77

Traffic_cacaphony

safe_feeling

47

84

88

Stress_road_cross

Available_foot path side

9

10

114

113

driving_culture

Satisfied (fs)

Elders_mobility

Unsatisfied (fd)

Table 18.3 Satisfaction index of different variables

2

15

0

4

4

18

25

18

0

20

25

15

0

13

11

Undecided

135

135

135

135

135

135

135

135

135

135

135

135

135

135

135

Total respondents (N)

9

−0.10

0.81

0.76

0.42

0.35

0.29

15

14

13

12

11

10

8

−0.27 0.21

6 7

−0.39 −0.30

4 5

−0.55

3

−0.73 −0.52

0.5 0.5

−0.77 −0.77

Rank

Satisfaction index (Is)

18 Inconveniences and Mobility Issues of Elders on Road: The Case … 437

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S. Ghosh and S. Maiti

Table 18.4 Relationship between driving status and reason for anxiety while driving Influencing variable

Influenced variable

Chi-square value

Significant level

Reason for anxiety while driving

Driving status

89.814

p < 0.01

Source Field Survey, 2022

Problematic Vehicles as per the Respondents

Fig. 18.5 Problem creating vehicles on road. Source Field Survey, 2022

AutoRickshaw Bike

11% 4% 4%

Bus 58%

23%

Taxi-Ola-Uber Others

Table 18.5 Relationship between slow walking and stress while road crossing

Influencing variable

Influenced variable

Chi-square value

Significant level

Stress during Road crossing

Slow-walking

24.272

p < 0.01

Source Field Survey, 2022

and Gupta (2014) have divided the causes of congestion into micro and macro categories. The micro category includes accidents, vehicle breakdowns, poorly timed traffic signals, special events like mass gatherings, political rallies etc. whether macro-level factors include land-use patterns, employment patterns, income levels, car ownership trends, infrastructure investment and regional economic dynamics. The authors have identified micro-level factors as triggering elements for congestion and macro-level factors as driving elements. In this chapter, the researchers attempted to find out the influence of crowns on elderly behaviour. The study reveals that the behaviour of going outside is significantly related (Table 18.6) to the traffic crowd. Here crowd implies traffic congestion and hurried people. The most preferable traffic window, chosen by the elders, is early morning and morning time (Table 18.12). The traffic rush has several segments. From 9:00 a.m. to 11:00 a.m., the city faces the office and student rush. From 3:00 p.m. to 4:30 p.m., the school dismissal time and 6:00 p.m. and 9:00 p.m. is the evening rush of office dismissal and people often get out for refreshments. No wonder, the elders are more likely to choose that part of the day to go outside. Around 40% of respondents have chosen early morning and 30% of respondents opted for the morning to go out and avoid the crowd. The traffic cacophony comes side by side with the traffic crowd. The vehicle noise is normal for a metro city but that does not imply that it should be normalized. Also, not only the

18 Inconveniences and Mobility Issues of Elders on Road: The Case … Table 18.6 Relationship between traffic crowd and travel window

439

Influencing variable

Influenced variable

Chi-square value

Significant level

Traffic crowd

Travel window

35.000

p > 0.05

Source Field Survey, 2022

Table 18.7 Relationship between traffic cacophony and frequency of going outside

Influencing variable

Influenced variable

Chi-square value

Significant level

Traffic cacophony

Frequency of going outside

38.926

p > 0.05

Source Field Survey, 2022

vehicle noise and horn are concerned but the unnecessary loud music, political rallies and mic announcements, and tire bursting are included in this. Road traffic noise also influences the quality of life of elders. Limited exposure to traffic cacophony is positively correlated with a higher quality of life (Domhnaill et al., 2022). In the case of the field study, it has been revealed that the more the frequency of going outside, the more agitation can be increased among elder adults. Even those who don’t go outside for a while, s/he also feel agitated because of traffic noise. Of those who go outside regularly, 56% of elders feel flustered due to traffic cacophony (Table 18.13). Interestingly, during the direct interaction of respondents, the researcher has found that the people who are in constant noise like those living on the main road do not feel different at all anymore. They said that they have been habituated to the sound as there is no way out from the noise (Table 18.7). There is another issue that is responsible for increasing elder annoyance. Considering the footpath-related issues, the satisfaction index reveals that the width of the footpath and the available side of the footpath are on completely different edges. In the case of footpath width, people are quite satisfied. But when it comes to the availability of footpaths the picture is quite different. Most of the respondents have agreed that they can avail the footpath either only on one side or they use it anyway despite obstacles. During the survey, the participants were given some options to choose footpath obstacles. The interview revealed that the hawkers and vendors are the prime actor (68.15%) who are making the footpath tough to use and identified as an obstacle (Fig. 18.6). This particular obstacle is so dominant that other things like temples, trees or cars etc. have been shadowed. Following the hawkers and vendors, irregular pavements are the next identified obstacles. The researcher has also experienced the same as the respondents. At Rashbehari Avenue, Temple, Hawkers-Vendors have encroached the footpath completely. The squeezed footpath makes it difficult to walk together side by side. In the case of Gariahat Crossing, the area is noted as a famous shopping spot. Naturally, hawkers and vendors have grabbed the footpath without any hustle. The photos were taken by the researcher, reveal that the width of the footpath is satisfied most of the time; but trees, car parking

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TYPE OF FOOTPATH OBSTACLES ON ROAD 68.15 6.67

10.37

13.33

1.48

Source: Field Survey, 2022 Fig. 18.6 Different types of footpath obstacles. Source Field Survey, 2022

and under construction footpath makes it inconvincible. In a nutshell, the footpath of Kolkata Municipal Corporation is getting robbed by several things. The minimum width for the footpath, as per Kolkata Municipal Corporation norms, is 5 ft. This width can be varied from place to place. But in reality, in most cases, the effective width of the footpath has encroached day by day. The Times of India Report (2004) reported about footpaths shrinking even 18 years ago. According to this report, 50– 70% of the footpath network, which is 60 km long altogether, has been trimmed to widen roads. It can be easily assumed that what is the magnitude of footpath issue now. Das Mahapatra et al. (2021) have drawn attention towards several types of encroachment among 32 surveyed stretches. Chaotic informal vendor distribution has been observed for 46.9% of the surveyed area. But interestingly, the vendors are not the main player in encroachment as they are away from the line of pedestrian flow. Rather, places of religious interest (68.8%) are playing a more active role in the encroachment. A communal open bath is also a concern for footpath encroachment. For checking the perception of walkability, the cross-tabulation between slow walking and footpath availability has been performed. The significant relationship (Table 18.8) says that near about 82.8% of respondents have agreed that they walk slowly because of encroachment (Table 18.14). In the case of unwanted hoarding and car parking, 60% and 88.1% of respondents choose slow walking respectively. Contextually, to find out the pedestrian risk factor Priyadarshini and Mitra (2018) studied the traffic volume as well as pedestrian volume and concluded that these two variables are strongly correlated with other variables like road width and footpath width encroachments. The authors have found that high pedestrian volume with minor road widths caused higher pedestrian fatalities and higher traffic volume, higher post-encroachment—time and wider crosswalks are showing a diminishing trend towards pedestrian fatalities. Looking into the positive side of the picture, elder people are satisfied with feeling safe on the road, footpath width, availability of public transport, hiring vehicles from preferable places, Signalling satisfaction and signalling light satisfaction. The elderly

18 Inconveniences and Mobility Issues of Elders on Road: The Case … Table 18.8 Relationship between slow walking and footpath availability

Influencing variable

Influenced variable

Chi-square value

Footpath availability

Slow walking 13.353

441 Significant level p > 0.05

Source Field Survey, 2022

respondents of our society are almost satisfied with the signalling system including signalling light and time. The status of these variables is representing that the Kolkata Municipal Corporation is a safer place for elders according to the respondents and the traffic signalling system is quite impressive. Here, the public transport availability and hiring vehicles are showing positive responses but from a researcher’s perspective and traffic administration’s point of view, this does not seem right. During the interview, the interviewer asked the elderly respondents whether they can hire a vehicle wherever they want. Interestingly, they responded positively and they said it is convenient for them. The personal experience of the researcher also supports their statement. Whenever someone wants to hire an auto-rickshaw or bus irrespective of gender and age, all have to do is wave a hand at the roadside. It seems a passenger-friendly event but in reality, it causes traffic congestion most of the time. Though the traffic authority has placed a waiting area or signboard saying “No Buses will stop here” the vehicles disobey these directions frequently to gain more passengers. Das Mahapatra et al. (2021) have resolved in their study that public buses stop informally during various times of the day. According to the field survey, 31.1% of respondents have opted for Bus as the preferable transport followed by auto-Rickshaw (23%). Replying why they choose this particular vehicle, near about 45.93% of respondents said that it is convenient for them. To find out the relationship between the variables, a crosstabulation has been run through the system. The Significant relationship (Table 18.9) and the cross-tabulation say 43.5% of respondents have chosen auto just for it is convenient (Table 18.15). But why? The Auto-Rickshaws are small in size and can pass through even for little space is left on the congested road. Secondly, the autorickshaw is easy to hire and can drop the passenger at his or her destination very quickly. In the case of Buses, also anybody can avail of the buses wherever they want besides it is cost-effective. The minimum cost for travelling in a public bus in Kolkata is 9 rupees, whereas by auto it is 10 rupees. Exceptional cases happen if the traffic policies are posted near the traffic signal or if two buses of similar route race with each other. Table 18.9 Relationship between preferable transport and reasons

Influencing variable

Influenced variable

Chi-square value

Significant level

Reason

Preferable transport

109.688

p > 0.05

Source Field Survey, 2022

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18.5 Findings From the field survey and with the help of quantitative and statistical tools, the Elder’s mobility scenario of the Kolkata Municipal Corporation is getting a clear vision. The elders are not satisfied with their overall mobility and the factors like driving culture, pedestrian crossing, crowd, road system and crossing the road, available footpaths, traffic cacophony and traffic congestion are responsible for this dissatisfaction. The interrelation of these variables is not significant all the time. But these variables can influence the elder’s behaviour. Considering the driving culture, elderly drivers are influenced by the unorganised driving culture. They feel stressed and anxious while driving. The researcher and respondent’s experiences are the same in this matter. Public bus drivers and auto-rickshaw drivers often operate their vehicles recklessly. According to the participants, the state bus (West Bengal Transport Corporation) drivers are quite incautious. Adding to this, young bikers with trending motorbikes are another problem on road. This driving culture causes rough traffic. The elders are forced to choose a particular travel window because of chaotic traffic. They avoid the rush hour of traffic for smooth use. Most of the elders are prone to choose the early morning or afternoon to go out. The elders, who opted for the morning, mainly to avail of daily services like grocery and vegetable shopping, banking etc. The traffic cacophony is also negatively controlling the travel window of elders. The unnecessary honking, unwanted mic announcements and accidental tyre bursting hurt the eardrums of everyone including elders. Besides, the sound of particular vehicle engines makes uncomfortable noises like trucks, motorbikes etc. Apart from the traffic-related variables, footpath availability is the foremost issue for elders. In a short, the footpath is simply not suitable for elders. The encroachment and the condition of the pavements make it inconvenient. The unavailable footpath makes the elders slow walkers i.e. they cannot walk profusely on the footpath. In this case, they have to choose the roadside and that may increase the rate of fatalities. Even the respondents prefer roadside walking more than the footpath. On the other hand, the elders are quite satisfied with the security, footpath width, public transport availability, hiring vehicles, traffic signalling and Signalling Light system. Regarding security, a few elders have stated their dissatisfaction towards it. Most of the dissatisfied elders have faced bullying or pickpocketing on the road. The footpath width is another variable that has made them contended. Interestingly, those respondents expressed their discontent towards this, they also stated that it is quite impossible to increase the width of the footpath in a busy city like Kolkata Municipal Corporation. In the case of hiring vehicles, elderly people are satisfied with it as they can hire the vehicle wherever they want. In the course of the interview, they have been asked ‘does the bus/ auto stop wherever you need, even if it is not a stoppage?’ It is needless to say that the respondents strongly agreed with this question. This facility makes auto and bus more preferable transport mode than any other. But, stopping anywhere despite having an allotted spot, makes the road congested and chaotic. Nevertheless, elderly people, most of the time faces an orthopaedic problem or physical mobility issues. For them hiring a vehicle from their preferred spot seems

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elder friendly; also breaking traffic rules or unconsciousness of drivers may cause fatalities to all pedestrians including elders. It is like horns of a dilemma for elders and this issue needs serious addressing. The signalling system and signalling light system are impressive and whenever they have been asked if a separate signalling system is needed for elders or not; they spontaneously answered that may be it needed for us but it is not possible. Kolkata is already a busy city with a huge traffic load. A separate signalling system or expanding signalling time may slow the traffic and cause traffic congestion more awfully. It is quite interesting that the issues, addressed in this chapter have not been influenced by gender. Both male and female participants agreed to the same.

Photos taken from different points of the study area, showing the conditions of footpaths in Kolkata Municipal Corporation, 2022; from the clockwise direction: Hawkers encroachment, electric post, trees on footpath limiting the effective footpath width, shops and bench on footpath, car parking

18.6 Recommendation The road system is essential for urban centres and a well-built road network can be a blessing for the urban transport system. From the study and observation, several issues have come forward like a different driving culture of drivers irrespective of their vehicle type, Psychological stress during pedestrian crossing time, crowd, traffic

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congestion, traffic cacophony footpath availability etc. The suggestive measures can mitigate the problems to some extent. Road Traffic System: The traffic system was planned for all commuters, and pedestrians irrespective of their age and gender. Naturally, chaotic traffic is a mere hustle for a 25-year-old man or woman but it will be considered an issue and sometimes a severe problem for a 65 years old man or woman. It is needed to mitigate the issues or eradicate them, if possible. In the case of driving culture, strict police monitoring is needed though it is fully dependent on the drivers’ morale. Pedestrian road crossing is an important issue. Here, gait speed plays a vital role. But, to improve one’s gait speed, his or her physical ability is concerned. The probable solution could be a pedestrian footbridge or pede-bridge. The footbridge will solely be dedicated to the elders which will make it crowd free. The steps should be gently sloped. The footbridge should have a handrail on both sides and a stair walker on one side of the staircase. This will act like a stair climber while using staircases but with minimum effort. In case of traffic noise, continuous monitoring and installing sound-sensitive sensors could help. The ‘No Horn’ signboard could prevent unnecessary honking. Besides, political processions should be restricted on major roads and also the timing should be particular and regular. Preferably, not more than one procession can take place in a day. Footpath Availability: In the case of footpath availability, the hawker’s encroachment is a severe issue. The immediate reallocation of the hawkers to a convenient place is important. In places, where reallocation is not possible because of tradition and nostalgia (Like Gariahat), the vendors can take only one portion of the footpath and the distance between two stalls should preferably be 5 m so that the crowd pressure can be reduced. In case of irregular pavements, car parking, trees, and electric post- the active role of the Government is necessary.

18.7 Conclusion Kolkata is one of the prime metro cities in India. It is a vital river port and hinterland. It is also the commercial capital of Eastern India. Kolkata is also a point joining the eastern arm of the Golden Quadrilateral. Having 6,000,000 floating population per day, Kolkata faces huge population pressure regularly. Against this backdrop, the road network and mode of transport are expected to be good enough. To find out the inconveniences on road, faced by the elderly population, traffic congestion, and crowding are the typical ones. The footpath availability and quality of the footpath pavement need to be taken care of. The psychological pressure while crossing the road may create permanent trauma for them. Like the traffic crowd can influence their decision to go outside. It should also consider that Elder people are also the stakeholder in this city and society. It is necessary to protect their interest too. In the process of achieving smart city goals, these inconveniences can make a major drawback. The introduction of newer technology and the emphatic approach of drivers and

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Table 18.10 Cross-tabulation of the Chi-square between driving status and reason for anxiety while driving Reason_fear NA (%)

Driving_status

Never

Rash drive (%)

Congested traffic (%)

Chaotic traffic (%)

Total (%)

100.0

0.0

0.0

0.0

100.0

Sometimes

30.0

30.0

35.0

5.0

100.0

Always

89.6

4.4

5.2

0.7

100.0

Source Field Survey, 2022

Table 18.11 Cross-tabulation of the Chi-square between slow walking and stress while road crossing feel_streesed while road crossing Disagree (%) Neutral (%) Agree (%) Strongly agree Disagree (%) (%) Slow_Walker No

37.9

34.5

3.4

24.1

100.0

Yes 18.9

9.4

45.3

26.4

100.0

Source Field Survey, 2022

other pedestrians could make a better space for elders. Regular checking on potholes, garbage places and pavements can improve the footpath quality and increase walkability. The encroachment of the footpath is a serious issue which is needed to be taken care of. Eradication of these issues can make the roads of Kolkata a better place for elders and more convenient. Eventually, the other stakeholders than the elderly generation of the road transport system should be more cautious and responsible to make the road better for elders of our society.

Appendix See Tables 18.10, 18.11, 18.12, 18.13, 18.14 and 18.15.

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Table 18.12 Cross-tabulation of the Chi-square between traffic crowd and travel window Crowd

Travel window

Strongly disagree (%)

Disagree (%)

Neutral (%)

Agree (%)

Strongly agree (%)

Early morning

1.9

11.5

11.5

65.4

9.6

Morning

2.4

22.0

4.9

63.4

7.3

Afternoon

0.0

14.3

28.6

50.0

7.1

Evening

0.0

7.7

15.4

30.8

46.2

Night

0.0

0.0

0.0

0.0

100.0

As needed

0.0

21.4

7.1

71.4

0.0

Source Field Survey, 2022

Table 18.13 Cross-tabulation of the Chi-square between traffic cacophony and frequency of going outside Traffic cacophony Strongly disagree (%) Frequency of going outside

Disagree (%)

Neutral (%)

Agree (%)

Strongly agree (%)

Always

1.3

26.9

15.4

53.8

2.6

Sometimes

0.0

34.7

10.2

55.1

0.0

Never

0.0

0.0

16.7

83.3

0.0

Source Field Survey, 2022

Table 18.14 Cross-tabulation of the Chi-square between slow walking and footpath availability footpath availability

Slow_Walker

NA (%)

Encroachment (%)

Irregular pavements (%)

Hoarding (%)

Car parking (%)

Market (%)

No

55.2

17.2

0.0

6.9

17.2

3.4

Yes

31.1

22.6

8.5

2.8

34.9

0.0

Source Field Survey, 2022

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Table 18.15 Cross-tabulation of the Chi-square between preferred transport and reason to prefer

Preferred transport

Cost effective (%)

Safe (%)

Bus

69.0

31.0

0.0

0.0

Train

16.0

0.0

64.0

20.0

Metro

0.0

0.0

100.0

0.0

Auto

0.0

12.9

87.1

0.0

Taxi-Ola-Uber

0.0

54.5

45.5

0.0

20.0

30.0

40.0

10.0

Walking

Convenient (%)

Others (%)

Source Field Survey, 2022

References Basic Road Statistics of India 2017–2018. (2021). Transport research wing. Ministry of Road Transport and Highways. Government of India. Banerjee, A., Roy, S. K., & Maurya, A. (2015). Study of pedestrian movement on functionally different types of sidewalks in Kolkata. In 3rd CTRG. Census Report. (2011). Office of the Registrar General & Census Commissioner, India. Ministry of Home Affairs. Government of India. Chakrabartty, A., & Gupta, S. (2014). Traffic congestion in the metropolitan city of Kolkata. Journal of Infrastructure Development, 6(1), 43–59. Domhnaill, C. M., Douglas, O., Lyons, S., Murphy, E., & Nolan, A. (2022). Road traffic noise, quality of life, and mental distress among older adults: Evidence from Ireland. Cities & Health. https://doi.org/10.1080/23748834.2022.2084806 Das Mahapatra, G., Mori, S., & Nomura, R. (2021). Assessing accessibility of footpath-level walkability in old core cities of India for promoting universal mobility through architectural planning research: Case study of Central Kolkata, India. Preprints 2021, 2021120412. https://doi.org/10. 20944/preprints202112.0412.v1 Dey, T. (2016). An assessment of road traffic congestion at park circus crossing, Kolkata. Indian Journal of Spatial Science, 7.0(2), 49–58. George, A. (2017). Design considerations in designing age-friendly streets. Forensic Science & Addiction Research, 1(5). FSAR.000525. https://doi.org/10.31031/FSAR.2017.01.00052 Global Health and Ageing. (2010). World Health Organization, United Nations Gonawala, R., Badami, N., Electicwala, F., & Kumar, R. (2013). Impact of elderly road users characteristics at intersection. Procedia - Social and Behavioral Sciences., 104, 1088–1094. https://doi.org/10.1016/j.sbspro.2013.11.204 Gorman, M., Jones, S., & Turner, J. (2019). Older people, mobility and transport in low- and middle-income countries: A review of the research. Sustainability, 11(21), 6157. Gunasekaran, et al. (2016). Normal gait speed, grip strength and thirty seconds chair stand test among older Indians. Archives of Gerontology and Geriatrics, 67(November–December), 171–178. Hall, J., Yen, S., & Tan, S. L. (1975). Satisfaction of loving condition. In S. H. Yen (Ed.), Public housing in Singapore. Singapore University Press, Singapore (pp. 157–311). Lin, D., & Cui, J. (2021). Transport and mobility needs for an ageing society from a policy perspective: Review and implications. International Journal of Environmental Research and Public Health, 18, 11802. Mitra, S., Debbarma, D., & Roy, S. (2020). Determinants of urban footpaths and impact on quality and mobility mapping: A study in Agartala city. Geographical Review of India, 82(4), 348–367.

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Murman, D. (2015). The impact of age on cognition. Seminars in Hearing, 36(03), 111–121. https:// doi.org/10.1055/s-0035-1555115 Priyadarshini, P., & Mitra, S. (2018). Investigating pedestrian risk factors leading to pedestrian fatalities in Kolkata city roads. Transportation in Developing Economies, 4, 1. https://doi.org/10. 1007/s40890-017-0054-9 Pulvirenti, G., Distefano, N., & Leonardi, S. (2020). Elderly perception of critical issues of pedestrian paths. Civil Engineering and Architecture, 8(1), 26–37. https://doi.org/10.13189/cea.2020. 080104 Road Transport Year Book 2017–18 and 2018–19. (2021). Ministry of Road Transport & Highways. Transport Research Wing. New Delhi. Roy Chowdhury, I. (2015). Traffic congestion and environmental quality: A case study of Kolkata city. International Journal of Humanities and Social Science Invention, 4(7), 20–28. Singh, H., & Kaur, G. (2018). Factors affecting the congestion & encroachments on urban roads (case study-Ludhiana city). International Journal for Research in Engineering Application & Management (IJREAM), 04(08), 49–61. https://doi.org/10.18231/2454-9150.2018.1053 The status of the elderly in West Bengal, 2011. (2014). UNFPA. Zito, et al. (2015). Street crossing behaviour in younger and older pedestrians: an eye- and headtracking study. BMC Geriatrics, 15, 176. https://doi.org/10.1186/s12877-015-0175-0

Selected Web References Retrieved July 5, 2021, from https://www.who.int/ageing/publications/global_health.pdf Retrieved July 11, 2021, from https://timesofindia.indiatimes.com/city/kolkata/kolkata-is-ageingfaster-than-other-metros/articleshow/49546289.cms Retrieved June 4, 2021, from https://economictimes.indiatimes.com/news/politics-and-nation/dem ographic-time-bomb-young-india-ageing-much-faster-than-expected/articleshow/65382889. cms?from=mdr Retrieved June 7, 2021, from https://www.un.org/esa/socdev/documents/ageing/MIPAA/politicaldeclaration-en.pdf Retrieved July 11, 2021, from https://www.who.int/ageing/publications/global_health.pdf

Chapter 19

Assessing Housing Condition and Quality of Life in Midnapore Town, West Bengal, India: Analysis of 2011 Census Avishek Bhunia

and Amalendu Sahoo

19.1 Introduction The idiom “bread, clothing, and a roof over your head” is used to represent the basic need of people throughout the history of civilization. The house is one of them, serving as a place of safety and refuge. It is also regarded as a basic process of development of how the built environment is produced, utilised, and preserved for economic, social, and physical well-being (Lawrence, 2004). Every home’s occupants are constantly seeking a higher standard of living. It is a major worry, especially in less developed nations where it is viewed as nothing more than the fulfilment of bare minimal requirements. However, in practice, it goes well beyond the determination of minimum requirements like the presence of a physical structure alone. It covers how the residential setting satisfies a variety of needs, determines the wellbeing of the households, and is therefore essential to their quality of life (Jiboye & Ogunshakin, 2010; Sharma & Singh, 2017). It comprises many housing indicators such as ownership status of the houses, condition of the census houses, number of dwelling rooms, materials used for wall, floor and roof of the houses etc. Urbanisation is viewed as a sign of economic growth, it is clear that quality of life is closely linked to the processes of urbanisation and development (Bhunia & Rana, 2017; Daspattanayak, 2000). Economic development involves more than just meeting people’s material needs; it also involves qualitative changes in social and cultural levels of living, the provision of essential services and infrastructure, and the reduction of inequality and poverty. The general well-being of people and communities is referred to by the term “Quality of Life” (hence abbreviated as QoL). Therefore, A. Bhunia (B) Department of Geography, K.D. College of Commerce & General Studies, Midnapore, West Bengal 721101, India e-mail: [email protected] A. Sahoo Department of Geography, Tamralipta Mahavidyalaya, Tamluk, West Bengal 721636, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_19

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a person’s quality of life is determined by how well they are able to make use of their significant opportunities. The future prospects for the region’s socioeconomic change depend greatly on the state of the condition of housing, the presence of essential infrastructure, and the accessibility of social amenities. For a nation’s citizens to live in dignity and with physical, psychological, social, and economic security, adequate and affordable housing is essential. Since the 1948 adoption of the Universal Declaration of Human Rights, the United Nations and organisations like the World Bank, Asian Development Bank, and others have repeatedly emphasised its significance. It is crucial to comprehend the needs and dynamics of the demand side of the situation in developing countries like India where an increasing proportion of the population lives in urban areas and faces various institutional, social, and financial challenges to obtain adequate housing. Only then can the supply side be planned for and implemented in an efficient manner. Although the goal of “housing for all” has been long envisioned and pursued, a significant portion of urban households who are socially and economically disadvantaged are excluded, which has disastrous effects on the nation’s development (Kumar, 2015a, 2015b). The quality of life in urban areas is reflected in the housing indicators’ levels. Ironically, though, the quality of living in urban India is far from ideal. The environment, infrastructure, and basic facilities in the country’s urban centres are among the worst in the world. After independence, there was a period of rapid population growth that was not matched by adequate expenditures in urban development, which resulted in a severe shortage of housing indicators in the country’s towns and cities. According to the latest census, little more than 30% of households in West Bengal resides in urban areas and the levels of housing indicators are more discouraging as about one-fourth of urban households in West Bengal do not have their own houses. Over 40% households are yet to be covered by the good condition of houses and nearly 33% of the households still do not have cement-floored houses. Around 30% urban households in this state do not have brick-built houses. More than half of urban residents in the state are still found to be residing in homes without concrete roofs. These are some of the primary causes of not just widespread poor health and high rates of morbidity, but also of low economic productivity and poor quality of life in urban centres. In addition to the housing indices’ shortcomings, the local government also has difficulties with the operation and restoration of the infrastructure (Hassan & Daspattanayak, 2008). In many of the state’s urban centres, the living conditions are not only subpar overall but particularly inadequate, which dehumanises residents. Large cities and metropolitan areas might also be seen to lack access to standard housing conditions. In addition to the natural population rise, many places have experienced rapid rural immigration, which is driven by the destabilisation of the agrarian economy or by a simple desire for better possibilities (World Bank Report, 2003). Urban centres are experiencing fast demographic change as a result of urbanisation, which has serious “demand and supply” implications. Many newly industrialised and expanding urban centres have local governments that are overstretched. As a result, developing plans and programmes for the comprehensive development of urban centres in a region becomes more challenging. In the case of Midnapore Town, the figure is identical. Evidence also points to a notable variance in housing quality across Midnapore

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Town’s wards. The general socio-cultural context, economic viability, and expanding urban population all contribute to this severe scarcity. The quality of life in Midnapore Town has declined as a result of rapid urbanisation.

19.2 Theoretical Orientation of the Research To satisfy their basic requirements, each person requires a variety of assets and services, which can then be improved by giving things like decent housing, enough building supplies, clean drinking water, sanitation facilities, banking services, drainage services, etc. (Das et al., 2021a, 2021b). Housing is generally the process of creating a residential environment that includes amenities like shelter, infrastructure, and services, but to some people, it has greater significance because it is one of the best measures of one’s standard of living and social standing (Nubi, 2008). When discussing housing, it’s important to note that the neighbourhood and infrastructure are included in addition to the actual physical structures and buildings intended for human habitation. It also involves the process of acquiring land, labour and finance (Turner, 1972). Many nations still struggle with housing issues today. Even though several housing policies have been developed and put into practice in the past, there is still a significant lack of appropriate and cheap housing for the poor, who make up a large portion of the metropolitan population. All communities strive to provide high-quality housing because it shows how well they can meet their residents’ needs while also allowing for growth and economic development. Additionally, it is considered that the region won’t be able to achieve sustainable growth if the current trend continues (Yoade et al., 2015). The needs of the user population vary depending on income level; for instance, the low-income group seeks housing close to the city centre and the places where they work. Moderate and high-income earners understand the importance of such necessities as identity and security. Users’ requirements are influenced by their socio-economic situations, cultural backgrounds and worldviews, and the political and economic situation of the nation as a whole (Olotuah, 2009). Numerous scholars have also looked into the significance of housing projects for low-income households as well as their many challenges (Aribigbola, 2008; Hassan, 1980; Turner, 1972). In his study, Sandhu (2000) sought to comprehend the character, scope, and causes of housing poverty. Indicators of housing poverty include housing stock, new household formation, homelessness, kind of structure, number of rooms and households, slums and squatter settlements, housing investment, housing affordability, ownership occupancy, water connection, and toilets. Rather than a shortage of funding, the problem is a lack of political will. Strong political will is required to fully grasp phenomena and improve human capabilities through participation in society and support from a democratic government (Kumar, 2014). Assessment and measurement of people’s quality of life has emerged as key area of study in both academic and policy discourse domains (Greyling & Tregenna, 2017). Every nation must assess development and poverty on a variety of scales, including the national, regional, and local levels, in order to better understand how households are faring

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(Greco et al., 2016). The assessment of housing and living conditions of households has become a challenging issue, especially in emerging nations like India where a high percentage of the population lives below the poverty line and inequalities in the distribution of resources are common (Mundetia et al., 2018). This country also has persistent poverty and poor human development performance (Aiyar, 2016). Numerous studies have been conducted to evaluate the trajectory of inequality and the standard of living in Indian states (Chaudhuri & Gupta, 2009). Regional poverty has decreased, according to a number of studies (Bhanumurthy & Mitra, 2004; Sundaram & Tendulkar, 2003), while economic disparity has risen significantly over the past two decades (Das et al., 2021a, 2021b; Deaton & Dreze, 2002; Krishna, 2004; Sen & Himanshu, 2004). There are many research projects that demonstrated the wide disparities in poverty and inequality throughout India (Deaton, 2003; Jain et al., 1988; Chauhan et al., 2015; Das et al., 2021a, 2021b). In order to identify regional issues and execute efficient planning and policies, it is important to examine the housing and living conditions of households (Antony & Rao, 2007; Chotia & Rao, 2015; Das et al., 2021a, 2021b; Erenstein et al., 2010; Kumar et al., 2016; Mohanty, 2009). However, there is more demand for urban commodities due to the rapid growth in population in urban areas, living expenses have gone up. Urban land is scarce and expensive, and housing is also expensive and frequently out of the financial grasp of the bulk of urban people (Olayiwola & Adeleye, 2006). A sizable population of individuals living on low wages and dealing with erratic work fills the urban centres. This section of the urban population (sometimes migrants) is indeed impoverished and is restricted to small, inadequate, crowded, filthy, and chilly shelters as well as a generally deteriorated environment (Mabogunje, 1992; Olotuah, 2006; Onokerhoraye, 1995; Das et al., 2019; Mahadevia et al., 2012; Das & Das 2019a; 2019b). These are the urban poor, whose lives are marked by unstable nutrition and health conditions, scant or inadequate material belongings (UNHS, 2008; World Bank, 1995). The poor physical condition of the structures is mostly to blame for the inadequate quality of the majority of urban dwellings. The investigations also demonstrate that the structures frequently lack security and safety and do not adequately protect occupants from the weather (Yoade et al., 2015). The world has recently experienced the largest surge of urban population growth (Cohen et al., 2006; Hove et al., 2013). According to a World Health Organization research from 2015, 54% of people worldwide live in urban areas, and this number is rising. It is crucial to emphasise that due to the scarcity of essential urban services and housing, the hyper-urbanization of developing countries’ cities puts a lot of strain on their ability to maintain ecological and environmental sustainability (Adeyinka & Olugbamila, 2015; Kumar, 2015a, 2015b). Quality of life is greatly impacted by social disorders, housing shortages, and urban residents’ general living conditions (Gardner & Oswald, 2007; Rappaport, 2008; Wheeler & Hendon, 2004). Additionally, a strong connection between the delivery of urban services and the shifting standards of living and aspirations of city people was recognised (Knox et al., 2007; North Shore City Council, 2005). Urban planners and policymakers place a strong focus on comprehending the values of housing among the poor, as well as the deployment of appropriate development techniques and improvement of the quality of life of urban people, in this context

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(UNFPA, 2007; Lee, 2008; Tesfazghi et al., 2010; Ayoola & Amole, 2014). The assessment of the quality of life of urban residents has been the subject of numerous research studies in a variety of disciplines, including geography, sociology, and the economy (Das, 2008; Grasso & Canova, 2008; Dunning et al., 2008; Epley & Menon, 2008; Rossouw and Naude, 2008; Chen & Davey, 2008; Marans & Stimson, 2011). In order to better understand and improve quality of life through various family and socially-oriented schemes and activities, it is vital to examine the housing condition of the HHs. Due to socio-economic disparities in the area, the quality of life has recently emerged as one of the top concerns for social science academics. Enhancing quality of life is important, especially for underdeveloped regions, and should be the main objective of public initiatives. Unlike gross domestic product (GDP), quality of life is influenced by a variety of factors, including education, access to essential services and facilities, housing quality, etc. As a result, a comprehensive strategy that incorporates all the factors that affect HHs’ quality of life has been adopted. Prior research investigations have provided well-documented detailed assessments of living standards and indicators (Das et al., 2021a, 2021b; Hagerty et al., 2001). Instead of focusing on analysis at the household level, earlier research investigations addressed the living conditions of people across districts. This is the unique aspect of this research study since it examines the housing conditions of households and quality of life across wards of Midnapore Town. The town will be easier to visualise from this point of view, and the scenario of the housing conditions of the households will be better understood. The current chapter aims to assess the levels of housing conditions and quality of life in Midnapore Town in light of the aforementioned debate. In addition, the current article looks at regional disparities in the levels of a few housing indicators throughout the town’s wards in 2011.

19.3 Study Area Midnapore Town is the district headquarters of Paschim Medinipur District in West Bengal (India). The government in the town is handled by the municipal body. According to population size, the Census of India designated it as a class I town (i.e., with more than 1,00,000 population), and after Kharagpur Town, it has the second-largest population overall. Midnapore Town served as the district’s administrative centre before Midnapur district was carved out on January 1, 2002. The undivided Midnapur district was once the biggest district in India overall, not only in West Bengal. According to the most recent census, there are 1,69,264 people living in Midnapore Municipality, including 84,977 males and 84,287 females. The average literacy rate in Midnapore Town is 88.99. In this municipality, the percentages of SC and ST residents are 7.63 and 2.72%, respectively. Between the Census periods of 2001 and 2011, there was an overall rise of 13.02 in the urban population. Agriculture was the mainstay of the undivided Midnapore’s economy. Due to its status as a district town, Midnapore served as the administrative and judicial hub for the rural

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Fig. 19.1 Study area

district. As a result, this function was the centre of many enterprises and services, which naturally had an adverse influence on the district’s division. In comparison to other towns in the Purba or Paschim Medinipur District, Midnapore still serves this function and has the highest concentration of physicians, lawyers, teachers, banks, and administrative offices. Poorer segments in this semi-rural society work in transportation, basic agriculture, small shops, and physical labour in the construction industry, among other things. Figure 19.1 is representing the location of the study area.

19.4 Materials and Methods The chapter is based primarily on secondary sources of data, which were gathered from the Primary Census Abstract, Census of West Bengal, 2011 and Houselisting & Housing Census, Census of West Bengal, 2011 (all in electronic format). For the present purpose, six important housing indicators such as ownership status of the houses, condition of census houses, number of dwelling rooms in the houses, materials of floor, wall and roof of the houses are taken into consideration. All of the aforementioned indicators are self-explanatory. Two of these indicators, as can be seen, require more explanation. The first is on the ‘condition of census houses’. Census of

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India classified ‘households with condition of census house’ as ‘good’, ‘livable’ and ‘dilapidated’. The ‘good condition of the census house’ is taken into consideration as the standard indicator for the current investigation. Likewise, The Census of India also divided up the ‘number of dwelling rooms in the households’ into categories like ‘no exclusive room’, ‘one room’, ‘two rooms’, ‘three rooms’, ‘four rooms’, ‘five rooms’, and ‘six rooms and above’. With the current discussion taking into account the existing couple in the household, ‘three dwelling rooms’ is also treated as a crucial influence in this instance (i.e. husband and wife, father and mother, unmarried children or son and his wife). Several statistical methods have been used for the analysis. The next paragraphs provide more detail about this. When a group of indicators are combined in a standardized way that provides a useful statistical measure of overall performance or development, it can be called as a Composite Index. Here Composite Index has been applied to calculate the Quality of Housing Index (QHI). In order to construct Composite Index of QHI, the following formula has been used. CI J =

n  

 X i j − X i /S Di

i=1

where, ‘CIJ’ is the Composite Index of QHI of ‘ jth’ observation, ‘n’ is the total number of observations, ‘X i j ’ is the value of ‘ith’ indicator in the ‘ jth’ observation, ‘X i ’ is the mean of the ‘ith’ indicator and ‘S Di ’ is the Standard deviation of the ‘ith’ indicator. In order to work out dispersion among different select housing indicators across the wards of Midnapore Town in terms of percentage share of households that have each of the select housing indicators, Coefficient of Variation has been applied. Additionally, a Deprivation Index has been developed using the following formula in order to examine the disparities in housing indicator levels across wards in the Municipality: Depri vation I ndex, D I =

M X i − Oi j M X i − Mn i

where, ‘Mxi ’ and ‘Mni ’ are the indicator’s largest and smallest values across all the urban centres, and ‘Oi j ’ is the value of ith indicator in jth urban centre (i.e. the urban centre for which the index is intended to be worked out). This merely represents a relative level of deprivation in relation to a certain ward’s housing indicator. The highest and lowest values in the town serve as the basis for the measurement, in other words. The calculation clearly shows that the index’s value will range from 0 to 1.0. The urban centre with the highest performance in terms of a certain service or amenity will report an index of perfect ‘zero’, while the one with the lowest coverage would record an index of perfect ‘one’. Therefore, the greater the deprivation concerning a given housing indicator, the further an observation is from ‘zero’ (Hassan & Daspattanayak, 2008). The correlation analysis technique has been used to examine the link between various urban development indicators throughout the wards of Midnapore

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Town and individual housing indicators. The results are presented in the form of a Correlation Matrix based on Karl Pearson’s Product Moment Correlation method (Mahmood, 2008). Cross-tabulation, cartographic methods and some descriptive statistics are used to represent the data sets well. All statistical and cartographic works are performed with the help of MS-Excel, SPSS-17 and Arc GIS-10.5 software respectively.

19.5 Result It is evident that the relationship between quality of life and urbanisation and development is very strong. The housing condition, facilities offered, infrastructure, employment prospects, income level, and migration volume are all factors that affect a region’s quality of life. (Graves, 1983; Krumm, 1980; Smith, 1983). Many people go to metropolitan areas in quest of work/employment opportunities and basic facilities because rural areas lack the essential amenities and services needed for a comfortable existence. However, the majority of them choose to work in the informal sectors of the economy instead of the formal ones, which do not provide them with social or economic security. The majority of migrants choose to live in squatter settlements devoid of adequate basic public services, which contributes to urban poverty and puts further strain on the city’s already-strained housing and civic services (Singh, 2013). Thus, the combinations of socio-economic, political, and environmental elements in the host place, the quantities and types of public services and the quality of housing that are accessible to a certain set of people are the overall result. Housing conditions have a direct impact on a household’s financial situation as well as providing a clear indication of its income level; households with higher incomes are more likely to have larger houses, single-family dwellings, and decent housing conditions (Vijaya & Krishnan, 2014). Different aspects of a household’s living conditions are influenced by its overall housing state. The importance of access to basic necessities for life, such as shelter, drinking water, and sanitation facilities, among others, has been amply proven by MEA (2005). Numerous housing indicators are important in raising the quality of life for households. The welfare of the families was evaluated using these variables in numerous earlier research investigations (Haq & Zia, 2008; Rahman et al., 2012; Zorondo-Rodríguez et al., 2012; Das et al., 2021a, 2021b). It is noted from the Census table that in terms of standard housing indicators the share of the study area exceeds the share of the state as well as the share of the district as a whole. But the share of Midnapore Town as a whole with regards to housing indicators is not satisfactory. It is remarkable to note that more than one-tenth households in this municipality reside in rented houses. Similarly, around 10% of households are living in dilapidated houses with life-risk anxiety and nearly 30% of households operate their daily activities in a single room. The living room is used both for living and cooking purposes and this condition is not visible only for the poor people rather for the Govt. employees those who have been allotted a single room. Nowadays human health and indoor air quality are linked issues. In the

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subject of housing, especially for women and children, the quality of indoor air and its impact on human health is a major concern (Daspattanayak & Bhunia, 2016). Though Midnapore is a class I town, about one-fifth of households of this municipality are staying in mud-floored houses and the corresponding figure for the mud build wall is around 17%. More than 17% of households reside in metal/GI/asbestos roofed houses instead of concrete. Consequently, it may be said that the housing condition of Midnapore Town is not up to mark level and the households are sustaining with sub-standard quality of life in comparison to other class I towns. The housing conditions of the households are not satisfactory in the study region, which contributes to their relatively poor quality of living. According to Table 19.1, on average, 59% of census homes are in good condition. Ward No. 13 has the lowest figure (41.20%) and Ward No. 23 has the highest percentage (89.50%). Most of the HHs in this area do not have ‘three dwelling rooms’ to live in. The average percentage of households with three bedrooms is barely 17.40%, with Ward No. 22 having the highest percentage (27.50%) and Ward No. 19 having the lowest percentage of households with three bedrooms (10.20%). However, the majority of HHs in the research area resided in their ‘own house’. Approximately 85% of HHs in this area are having their own houses. This percentage is highest in Ward No. 11 (93.20%) and lowest in Ward No. 23 (73.60%). The percentage of HHs with ‘wall materials with burn bricks’ over the entire municipal area is 74.03%. It is highest in Ward No. 5 (96.50%) and lowest in Ward No. 17 (42.40%). The percentage of houses with ‘concrete roofs’ is highest in Ward No. 23 (92.80%) and lowest in Ward No. 21 (39.70%); the average for the study area is 65.40%. The average percentage of HHs who lives in ‘cement floored houses’ is just above 75%. The bulk of HHs (91%) in Ward No.17 resides in ‘cement floored houses’, but the proportion is negligible in Ward No.19 (59.60%). Ward-wise variability of each of the housing indicators is presented in Fig. 19.2. Additionally, the available housing indicators across the wards of Midnapore Municipality are also presented in Fig. 19.3a–f. From the data analysis, it is clear that the housing condition in this host location is below par. Since 1961, India has conducted two distinct phases of its population census: ‘Houselisting and Housing Census’ and ‘Population Enumeration’. The purpose of the house listing and housing census was to create the master plan for the population count that would take place when this phase has been in place for one year. However, when several programmes to enhance the household quality of life were started, demand for house listing data increased among policymakers as well. The Quality of Housing Index (QHI) covers a wide range of aspects and a number of carefully chosen housing indicators. Based on publicly available 2011 Census data, the study aims to analyse Midnapore Town’s wards in terms of QHI (Das & Mistri, 2013). The percentage of households in the wards of the Midnapore Municipality that chose housing indicators during the most recent census is shown in Table 19.1, along with regional variations. Attempts have been made to incorporate a range of housing variables through the formulation of a composite index in order to evaluate the interward disparity and inequality in Midnapore Town. Ward No. 5 in Midnapore Town has the highest Quality of Housing Index (QHI) score of all the study area’s wards (6.43), followed by Ward No. 23 (5.00), Ward No. 22 (4.87), Ward No. 24 (4.08), and Ward No. 14 (3.78). A 2011 assessment of the inferior Quality of Housing

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Table 19.1 Quality of housing index variation, ward-wise, Midnapore Town, 2011 Area name Percentage of households with

Quality of housing index (QHI)

Own house

Good condition of census house

Three dwelling rooms

Wall of the house made by burnt brick

Roof of the house made by concrete

Floor of the house made by cement

Ward No. 1

88.80

53.70

15.40

66.60

53.60

65.80

−2.88

Ward No. 2

86.60

48.00

15.80

68.60

57.40

70.20

−2.53

Ward No. 3

87.20

66.00

17.90

74.10

67.60

78.10

1.41

Ward No. 4

56.00

75.90

19.00

91.70

80.20

86.90

1.65

Ward No. 5

78.20

78.90

22.00

96.50

88.80

88.00

6.43

Ward No. 6

90.00

66.40

24.90

77.70

71.50

74.40

3.36

Ward No. 7

81.60

33.00

12.40

59.80

48.90

68.80

−6.39

Ward No. 8

86.40

55.50

12.10

86.00

65.30

77.00

−0.14

Ward No. 9

87.10

63.50

18.60

89.60

73.20

79.20

3.01

Ward No. 10

87.70

54.00

13.90

69.90

63.20

76.80

−1.03

Ward No. 11

93.20

51.10

15.00

75.60

62.00

71.20

−0.68

Ward No. 12

91.60

47.30

17.20

73.70

70.00

78.80

0.69

Ward No. 13

88.30

41.20

13.00

72.50

50.70

71.70

−3.42

Ward No. 14

92.30

57.30

20.60

84.60

73.80

82.80

3.78

Ward No. 15

86.30

57.60

14.90

57.80

51.50

75.60

−2.59

Ward No. 16

89.90

57.90

13.50

64.50

63.30

74.90

−1.16

Ward No. 17

89.90

55.70

16.60

42.40

68.20

91.00

0.09

Ward No. 18

88.30

68.00

17.40

72.10

72.80

85.60

2.75

Ward No. 19

92.60

45.00

10.20

69.70

48.30

59.60

−5.06 (continued)

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Table 19.1 (continued) Area name Percentage of households with

Quality of housing index (QHI)

Own house

Good condition of census house

Three dwelling rooms

Wall of the house made by burnt brick

Roof of the house made by concrete

Floor of the house made by cement

Ward No. 20

82.20

43.90

14.20

68.60

45.50

71.10

−4.47

Ward No. 21

82.00

53.30

14.80

49.90

39.70

60.30

−6.77

Ward No. 22

81.30

76.70

27.50

92.50

82.20

70.20

4.87

Ward No. 23

73.60

89.50

24.70

96.30

92.80

68.10

5.00

Ward No. 24

82.10

77.50

26.00

76.10

79.10

76.40

4.08

Source Computed by the author from Houselisting and Housing Census, West Bengal, 2011

24 100.00 23 22

1 2 3

80.00

4

60.00

21

Own House 5

40.00 20

6

20.00 0.00

19

7 8

18 9

17

Good condition of census house Three dwelling rooms Wall of the house made by Burnt brick Roof of the house made by Concrete Floor of the house made by Cement

10

16

15

11 14

12 13

Fig. 19.2 Ward-wise variation of housing indicator

Index (QHI) places Ward No.21 at the top (−6.77), followed by Ward No.7 (−6.39), Ward No.19 (−5.06), and Ward No.20 (−4.47). It is evident from Fig. 19.3g that the Quality of Housing Index (QHI) is relatively low in central and peripheral areas of the study area adjacent to the rural areas and comparatively high in the middle and in the eastern direction. The remaining wards near high or low QHI wards have a moderate QHI.

Fig. 19.3 Housing indicators across the wards and QHI

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19.5.1 Inequality in the Housing Condition Regardless of socio-economic status or geographic location, everyone has the same right to a decent life. But the issue of inequality persists in people’s daily lives, particularly those who reside in emerging nations like India (Venkatanarayana, 2013). These inequalities are also visible in large cities and metropolitan regions. The percentage of households in the wards of the Midnapore Municipality that chose housing indicators during the most recent census is shown in Table 19.2. The table also includes the Deprivation Indices for housing indicators based on the equation indicated above. The values of the Coefficient of Variation with regard to each housing indicator are also provided in the table’s last row. Table 19.2 shows that the levels of housing indicators are very variable amongst wards. It is also interesting to note that there is a difference between various housing indicators within a ward. In Midnapore Town, there is inequality in the levels of housing indices across the wards. To detect the variances of each indicator, six key housing indicators are discussed here. However, the discrepancy between wards is assessed using the Coefficient of Variation, which changes depending on the indicator. It is discovered that there aren’t many variations between houses with ‘good condition census houses’, households with ‘houses with concrete roofs’, and households with ‘wall made of brunt bricks’. On the other hand, there is relatively little regional variation in the proportion of households that ‘own houses’ throughout the wards. This is especially true for those who live in ‘cemented floor houses’. In terms of households with ‘three dwelling rooms’, the value of Coefficient of Variation across the wards reveals the greatest disparity. The size of the range of variance in the percentage of households that ‘own houses’ across the wards is substantially lower. Therefore, it can be claimed that out of six chosen housing indicators, households with their ‘own houses’ are more consistent than other households in the research area, as shown by the line graph in Fig. 19.4. Additionally, Table 19.2 makes clear that while a significant portion of households in few wards exceed those in the entire town and enjoy access to a variety of housing indicators, this opportunity is not equally spread across all wards in Midnapore Town. The majority of households in some wards are chasing subpar quality of life while living beyond this opportunity. Additionally, it shows that the share of housing indicators varies considerably between the research area’s wards. Based on the Deprivation Index, the regional variation is evaluated. The inequality of housing conditions is easily represented in Fig. 19.5. The study conveys that out of 24 wards, the average deprivation index is found maximum in Ward No. 21 followed by 7, 19 and 20. But the average deprivation index is much lesser in Ward No. 5 followed by Ward No. 23, 22 and 24. It is astonishing to realise the fact that the western wards (i.e. Ward No. 20 and 21) report a very low level of housing indicators in the study areas. This scenario is identical in the central part (i.e. Ward No. 7). The comparable scenario is also depicted in Ward No. 19, which is situated in the southeast corner. However, due to a number of socioeconomic factors, the availability of these housing indicators is better in Wards No. 5, 23, 22, and 24.

88.80

86.60

87.20

56.00

78.20

90.00

81.60

86.40

87.10

Ward No. 2

Ward No. 3

Ward No. 4

Ward No. 5

Ward No. 6

Ward No. 7

Ward No. 8

Ward No. 9

63.50

55.50

33.00

66.40

78.90

75.90

66.00

48.00

53.70

18.60

12.10

12.40

24.90

22.00

19.00

17.90

15.80

15.40

Own Good Three house condition dwelling of census rooms house

Percentage of households with

Ward No. 1

Area name

89.60

86.00

59.80

77.70

96.50

91.70

74.10

68.60

66.60

Wall of the house made by Burnt brick

73.20

65.30

48.90

71.50

88.80

80.20

67.60

57.40

53.60

Roof of the house made by concrete

79.20

77.00

68.80

74.40

88.00

86.90

78.10

70.20

65.80

0.16

0.18

0.31

0.09

0.40

1.00

0.16

0.18

0.12

Floor of Own the house house made by cement

0.46

0.60

1.00

0.41

0.19

0.24

0.42

0.73

0.63

0.51

0.89

0.87

0.15

0.32

0.49

0.55

0.68

0.70

Good Three condition dwelling of census room house

Deprivation index

Table 19.2 Deprivation index of housing indicators, ward-wise, Midnapore Town, 2011

0.13

0.19

0.68

0.35

0.00

0.09

0.41

0.52

0.55

Wall of the house made by Burnt brick

0.37

0.52

0.83

0.40

0.08

0.24

0.47

0.67

0.74

Roof of the house made by concrete

0.38

0.45

0.71

0.53

0.10

0.13

0.41

0.66

0.80

2.01

2.83

4.40

1.92

1.08

2.19

2.43

3.43

3.54

(continued)

0.34

0.47

0.73

0.32

0.18

0.36

0.41

0.57

0.59

Floor of Aggregate Average the house made by cement

462 A. Bhunia and A. Sahoo

54.00

51.10

47.30

41.20

57.30

57.60

57.90

55.70

Ward 93.20 No. 11

Ward 91.60 No. 12

Ward 88.30 No. 13

Ward 92.30 No. 14

Ward 86.30 No. 15

Ward 89.90 No. 16

Ward 89.90 No. 17

16.60

13.50

14.90

20.60

13.00

17.20

15.00

13.90

Own Good Three house condition dwelling of census rooms house

Percentage of households with

Ward 87.70 No. 10

Area name

Table 19.2 (continued)

42.40

64.50

57.80

84.60

72.50

73.70

75.60

69.90

Wall of the house made by Burnt brick

68.20

63.30

51.50

73.80

50.70

70.00

62.00

63.20

Roof of the house made by concrete

91.00

74.90

75.60

82.80

71.70

78.80

71.20

76.80

0.09

0.09

0.19

0.02

0.13

0.04

0.00

0.15

0.60

0.56

0.56

0.57

0.85

0.75

0.68

0.63

0.63

0.81

0.73

0.40

0.84

0.60

0.72

0.79

Good Three condition dwelling of census room house

Deprivation index Floor of Own the house house made by cement

1.00

0.59

0.72

0.22

0.44

0.42

0.39

0.49

Wall of the house made by Burnt brick

0.46

0.56

0.78

0.36

0.79

0.43

0.58

0.56

Roof of the house made by concrete

0.00

0.51

0.49

0.26

0.61

0.39

0.63

0.45

2.78

3.12

3.46

1.83

3.68

2.62

3.00

3.06

(continued)

0.46

0.52

0.58

0.31

0.61

0.44

0.50

0.51

Floor of Aggregate Average the house made by cement

19 Assessing Housing Condition and Quality of Life in Midnapore Town, … 463

45.00

43.90

53.30

76.70

89.50

77.50

23.23

Ward 92.60 No. 19

Ward 82.20 No. 20

Ward 82.00 No. 21

Ward 81.30 No. 22

Ward 73.60 No. 23

Ward 82.10 No. 24

CV

27.04

26.00

24.70

27.50

14.80

14.20

10.20

17.40

18.83

76.10

96.30

92.50

49.90

68.60

69.70

72.10

Wall of the house made by Burnt brick

21.39

79.10

92.80

82.20

39.70

45.50

48.30

72.80

Roof of the house made by concrete

10.76

76.40

68.10

70.20

60.30

71.10

59.60

85.60

–

0.30

0.53

0.32

0.30

0.30

0.02

0.13

–

0.21

0.00

0.23

0.64

0.81

0.79

0.38

0.38 –

–

0.00

0.07

0.86

0.52

0.50

0.45

Wall of the house made by Burnt brick

0.09

0.16

0.00

0.73

0.77

1.00

0.58

Good Three condition dwelling of census room house

Deprivation index Floor of Own the house house made by cement

Source Computed by the author from Houselisting and Housing Census, West Bengal, 2011

9.23

68.00

Own Good Three house condition dwelling of census rooms house

Percentage of households with

Ward 88.30 No. 18

Area name

Table 19.2 (continued)

–

0.26

0.00

0.20

1.00

0.89

0.84

0.38

Roof of the house made by concrete

–

0.46

0.73

0.66

0.98

0.63

1.00

0.17

–

1.70

1.42

1.48

4.51

3.91

4.14

2.10

–

0.28

0.24

0.25

0.75

0.65

0.69

0.35

Floor of Aggregate Average the house made by cement

464 A. Bhunia and A. Sahoo

19 Assessing Housing Condition and Quality of Life in Midnapore Town, …

465

Coefficient of Variation (CV)

30.00 25.00 20.00 15.00 10.00 5.00 0.00 Own house Series1

9.23

Good condition of census house 23.23

Three dwelling rooms 27.04

Wall of the house made by Burnt brick 18.83

Roof of the house made by Concrete

Floor of the house made by Cement

21.39

10.76

Fig. 19.4 Variability of housing indicator

Fig. 19.5 Inequality of housing condition

19.5.2 Development Indicators and Housing Conditions After the above observation one can examine the relationship between development indicators and the housing condition of Midnapore Town. Table 19.3 shows the inter-correlation among select variables representing the levels of development and

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housing indicators in the form of correlation coefficient (i.e. ‘r’). The matrix table displays the correlation of different development indicators, on the one hand, and levels of housing conditions, on the other. For the levels of development of the wards, indicators like population density, growth in urban population, work participation rate, percentage of the scheduled population, literacy rate, and percentage of slum population have been taken into account performed in 2011. The study reveals that the diagonal of the correlation matrix (i.e., the numbers that go from the upper left corner to the lower right corner) always consists of ones (1). That is because these are the correlations between each variable and itself (and a variable is always perfectly correlated with itself). The row and column intersection for two variables define the correlation value for that particular pair of variables. From the Correlation Coefficient Matrix, it is evident that in most of the cases individual urban development indicators are not significantly correlated with the levels of housing indicators. On the other hand, it is also remarkable to note that in most of the cases Table 19.3 reveals the negative relationship between individual urban development indicators and levels of housing indicators. It is noted from the Table 19.3 that growth in urban population of Midnapore Town is negative and significantly correlated with the roof of the houses made of concrete and the floor of the houses made of cement. Referring back to the Table 19.3 and verifying correlation value, it is also noted and confirmed that out of all urban development indicators, only literacy rate is positively and significantly correlated with the good condition of census houses, houses having three dwelling rooms, wall of the houses made by burnt brick, roof of the houses made by concrete and floor of the houses made by cement etc. It is also noted from the table that the percentage of scheduled population [i.e. combination of Schedule Cast (SC) and Schedule Tribe (ST)] and percentage of slum population of the study area are negatively correlated with the housing condition representing a positive side. Similarly, the study reveals that different urban development indicators viz. population density, growth in urban population, and work participation rate of Midnapore Municipality are not having a significant bearing on the levels of housing indicators or the condition of houses.

19.6 Discussion When it comes to active government involvement in the levels of policy and programme formation, housing concerns are now equated with housing ignorance. Additionally, they result from issues brought on by uncontrolled development, untamed urban sprawl, income inequality, poverty, illiteracy, and unemployment. The difficulties with finding, providing, and using housing have gotten much worse in the research area due to urbanisation and rising economic inequality. Concerns concerning affordable housing, homelessness, poor housing quality, and mismatches in demand and supply of housing stock can be understood within the context of the current economic, political, and policy environment. It is also widely recognised that the quality of housing has a direct impact on a nation’s level of public health and

0.267

X3

−0.134

1

1

0.233

0.055

−0.153

0.219

X4

X3

0.263 0.198

0.856b 0.790b 0.737b

−0.404 0.623b 0.558b

−0.096 −0.316 0.813b 1

1

−0.108

0.011

1

1

−0.062

0.487a

1

−0.368

−0.209

0.718b

1

0.573b

0.355

−0.172

−0.221

0.616b

−0.290

−0.505a

−0.119 −0.384

−0.033

1

0.499a

−0.323

0.616b

−0.187

−0.240

1

−0.267

−0.116

0.087

−0.506a

−0.297

X 12

−0.326

−0.118

−0.529b

−0.180

X 11

0.230

−0.093

−0.297

0.079

X 10

−0.036

−0.102

−0.190

−0.080

X9

0.089

−0.130

−0.165

X8

0.068

0.130

X7

−0.327

0.238 0.195

−0.389

X6

−0.436a

X5

Notes Number of observations (Wards) = 24, X 1 -Population density, X 2 -Growth in urban population, X 3 -Work participation rate, X 4 -Percentage of scheduled population, X 5 -Literacy rate, X 6 -Percentage of slum population, X 7 -Own house, X 8 -Good condition of census house, X 9 -Three dwelling rooms, X 10 -Wall of the house made by burnt brick, X 11 -Roof of the house made by concrete, X 12 -Floor of the house made by cement Source Computed by the author from Houselisting and Housing Census, West Bengal, 2011 a Correlation is significant at the 0.05 level (2-tailed) b Correlation is significant at the 0.01 level (2-tailed)

X 12

X 11

X 10

X9

X8

X7

X6

X5

X4

1

1

X1

X2

X2

X1

Variables

Table 19.3 Correlation matrix, ward-wise, Midnapore town, 2011

19 Assessing Housing Condition and Quality of Life in Midnapore Town, … 467

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A. Bhunia and A. Sahoo

quality of life, as well as a secondary impact on labour productivity via worker morale. In order to ensure that everyone has access to housing, the circumstances must take advantage of the extensive architecture of agencies, policies, and market frameworks for housing (Lee, 1988; Nissar & Nuzhat, 2016; Tiwari & Rao, 2016). Basic urban amenities like access to power, clean drinking water, toilets, and housing quality are crucial indications of how effectively urban residents are able to maintain their standard of living. One of the key factors affecting the metropolitan quality of life is the accessibility of affordable housing. This is why it is important to quantify the housing indicator for urban residents. Therefore, evaluation of the housing condition of the population in Midnapore Town would be crucial for the future development of all the wards’ infrastructure as well as for closing the gap between the wards. According to the findings of the study, there are differences in the housing indicator among the different Midnapore Town wards. In certain cases, the housing condition in the study area is substantially better than that of the towns in other regions of the state. There are differences in the housing conditions of urban residents depending on whether western wards or wards from other directions are compared. The proportion of people living in cities is growing quickly, yet infrastructure improvements in towns are unable to keep up with this growth. Midnapore Town is one of the most urbanised towns in West Bengal, and this urbanisation is mostly due to the town’s expansion into its surrounding areas and the presence of numerous vital facilities (such as educational, medical, banking, and entertainment facilities). Although the town has grown, the housing conditions for city people are also subpar. In this study’s context, there is also an inter-wards inequality in terms of housing quality (Das et al., 2021a, 2021b). Additionally, it should be emphasised that due to high per capita income and high levels of investments, only a few wards display a higher percentage of residents under these housing indicators than the town’s average. On the other side, a sizeable population continues to live in the less developed wards in utterly dehumanised conditions. The main commercial, cultural, and educational hubs, as well as close proximity to private sector businesses, support the developed wards. However, due to economic sluggishness, low per-capita income, low employment rates, migration, minorities, low literacy rates, and a lack of awareness of the advantages of desired housing conditions that are related to quality of life, the situation in some of the wards is incredibly miserable.

19.7 Conclusion and Recommendations Urban regions cannot be sustained without having access to adequate infrastructure and essential services at reasonable costs, as has been noted. The quality of urban life is improved as well as the economic productivity of an urban centre when there is adequate infrastructure and essential services. They finally produce an effective rural–urban connectivity and support the expansion of the rural economy. In

19 Assessing Housing Condition and Quality of Life in Midnapore Town, …

469

other words, a region’s socio-economic development is greatly influenced by the availability of basic utilities and other infrastructure facilities. In order to assess the quality of life, this study looked at housing conditions and regional differences. It is evident from the analysis that some wards’ living conditions are subpar. Therefore, a variety of programmes and schemes will be adopted by the state and Municipal authority by assuring a better delivery of urban housing and services in order to establish the uniform development and overall improvement in quality of life of urban people across the area. Considering the significance of housing and shelter in ensuring people’s physical, psychological, social, and economic security as well as the need for planned urbanisation and development of existing urban areas, the Indian government and numerous other national, sub-national, and state agencies have developed and sparked many policy initiatives over the past few decades. However, there are still issues with urban housing, including severe housing shortages, a lack of affordable housing, an increase in vacant homes, inaccessible rental housing, inadequate housing services, slums, regional imbalances, exclusionary urbanisation, the empowerment of local bodies, and the implementation of second-generation reforms, that must be addressed as a matter of urgency. There is no doubting the fact that, ever since the First Five-Year Plan, when social housing schemes for EWS-LIG-MIGs were developed, various programmes and policies have been implemented from time to time to improve the levels of urban housing, sanitation, and basic services. However, later initiatives have focused exclusively on the underprivileged. These included Integrated Subsidized Housing Scheme for Industrial Workers and economically weaker sections (1952), Low Income Group Housing Scheme (1956), Slum improvement/Clearance Scheme (initiated in 1956 and discontinued in 1972 at national level). After that Environmental Improvement of Urban Slums (1972), National Slum Development Programme (1996), Scheme for Housing and Shelter Upgradation (SHASHU as part of Nehru Rozgar Yojna, introduced in 1989 and discontinued in 1997), the Shelter Upgradation Scheme under PMIUPEP (Prime Minister’s Urban Poverty and Employment Program had even a shorter life span 1996–97), Night shelter (1988– 89). Two Million Housing Programs, VAMBAY (Valmiki Ambedkar Awas Yojna (launched in 2001–02), after that JNNURM-2005 (Jawaharlal Nehru National Urban Renewal Mission). In order to achieve the aim of ‘Affordable Housing for All’, the National Urban Housing and Habitat Policy (NUHHP-2007) promotes various forms of public–private partnerships, with a focus on the urban poor in particular. The Rajiv Awas Yojana Project (RAY-2011), a Model State Affordable Housing Policy, seeks to eradicate slums in India (2015). The purpose of the Draft National Urban Rental Housing Policy (NURHP-2015), which calls on the States to develop State Urban Housing and Habitat Policies as well as State Urban Housing and Habitat Action Plans, is to “establish a vibrant, sustainable, and inclusive rental housing market in India”. Currently being introduced is ‘Housing for All 2022’ (HFA-2015). According to the President’s Speech, the HFA strategy calls for giving “every family a pucca house with water connection, toilet facilities, 24 × 7 energy supply and access”. Through States/UTs, the Pradhan Mantri Awas Yojana (PMY), which is being implemented from 2015 to 2022, offers government assistance to Urban Local Bodies (ULBs) and other implementing agencies. In addition, various ministries have

470

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had their own programs targeted towards their constituencies. It is obvious that while aspects of household quality have advanced significantly over the past ten years, the development is still far from sufficient. Various policies, programmes, and interventions have been launched periodically during the Five-Year Plans, occasionally with a holistic development perspective that sometimes with a long-term view that later became time-bound and target-focused, and other times with a short-term one. In addition, some programmes involved the general public directly or were wholly run by the government. However, owing to a variety of factors, including corruption, a lack of sufficient understanding among the local populace, natural hindrances, an improper use of natural resources, etc., the policies and scheme do not achieve their intended goals. In this shift, Public–Private Partnerships (PPPs) have been a crucial effort in the last ten years to improve public infrastructure. The Central Government’s contribution to infrastructure development decreased from 40 to 34% during the Eleventh Five-Year Plan, while the State Government’s contribution fell from 35 to 30%. At the same time, however, private investment increased by an unprecedented amount. In the PPP model, industries including telecommunications, power, ports, and roadways have displayed brisk expansion. Additionally, inclusive development should be prioritised to prevent the interests of the wealthy and powerful from dominating at the expense of the weak and the poor becoming destitute. In order to achieve the goal of faster, more sustainable, and more inclusive growth, measures should be taken to specifically and purposefully include economically and socially disadvantaged groups in urban housing. In the end, it can be said that continual evaluation and monitoring of the implemented policies are necessary rather than simply implementing planning and policies in order to improve the housing conditions and quality of life of urban people. Therefore, it is vital to re-evaluate corrective actions for the advancement of service delivery and urban facilities as well. It has been established that increasing investment and allocating funding for infrastructure and amenity development should be based on regional demand or priority. Additionally, it is clear that the housing crisis won’t be solved overnight; it will take public support as well as strong social and political will. As elsewhere in the world, Urban Local Bodies (ULBs) have to take some responsibility to ensure standard housing in urban areas.

Appendix See Table 19.4.

19 Assessing Housing Condition and Quality of Life in Midnapore Town, …

471

Table 19.4 Development indicators, West Bengal, 2011 Wards

Development indicators Population density

Growth in urban population

Work participation rate

Percentage of scheduled population (SC + ST)

Literacy rate

Percentage of slum population

Ward No.1

3753.47

17.44

36.10

6.46

89.37

45.32

Ward No.2

3534.58

6.25

33.31

13.01

87.81

34.18

Ward No.3

13,655.00

6.26

35.29

10.79

93.59

17.86

Ward No.4

11,714.67

5.73

37.56

16.61

93.59

53.16

Ward No.5

6227.63

2.58

35.03

5.60

96.86

4.80

Ward No.6

14,712.00

17.28

36.04

11.60

92.83

20.72

Ward No.7

8266.67

−4.30

36.85

40.24

85.05

16.35

Ward No.8

6336.36

−1.60

37.54

11.36

88.05

17.89

Ward No.9

22,275.00

12.94

34.64

7.42

93.88

23.97

Ward No.10

8296.72

17.89

32.52

0.85

87.86

16.95

Ward No.11

19,188.64

8.16

32.65

2.01

84.57

29.00

Ward No.12

10,824.00

1.71

38.16

2.35

88.61

35.78

Ward No.13

31,354.17

16.32

35.39

0.61

85.88

35.82

Ward No.14

6206.17

−3.42

36.07

1.89

93.71

44.54

Ward No.15

4681.48

39.95

40.84

11.62

90.33

30.09

Ward No.16

9497.44

18.78

36.15

10.64

86.69

12.42

Ward No.17

9246.34

−7.33

32.13

6.94

94.10

24.44

Ward No.18

7298.75

7.99

35.88

7.67

93.81

57.54

Ward No.19

33,087.50

22.68

36.24

10.35

84.53

64.46 (continued)

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A. Bhunia and A. Sahoo

Table 19.4 (continued) Wards

Development indicators Population density

Growth in urban population

Work participation rate

Percentage of scheduled population (SC + ST)

Literacy rate

Percentage of slum population

Ward No.20

20,453.13

30.73

35.03

13.69

83.95

28.45

Ward No.21

9998.18

32.59

34.16

23.70

81.87

42.39

Ward No.22

14,074.55

13.16

36.78

11.02

92.59

45.02

Ward No.23

8966.67

3.14

33.35

3.46

96.32

19.13

Ward No.24

23,233.33

14.26

33.47

11.09

81.82

26.73

Source Computed by the author from Primary Census Abstract (PCA) and Houselisting and Housing Census, West Bengal, 2011

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Chapter 20

Transport as a Driver of Sustainable Urban Growth: Evidence from Ankara, Turkey and Kolkata, India Hülya Zeybek , Stabak Roy , and Saptarshi Mitra

20.1 Introduction Transport impacts the spatial and economic development of cities and regions. The attractiveness of particular locations depends partly on the relative accessibility depending on the quality and quantity of the transport infrastructure (Banister and Lichfield, 1995). The percentage of people living in cities has increased significantly over the past few decades and will do so going forward (United Nations, 2019). According to predictions, by 2050, two-thirds of the world’s population will live in urban areas (World Bank, 2021). However, the patterns of urbanisation are highly heterogeneous, both within and across countries. Cities produce 80% of the global domestic product overall (McKinsey Global Institute, 2011). In OECD countries, urban areas are classified as: large metropolitan areas if they have a population of 1.5 million or more; metropolitan areas if their population is between 500 000 and 1.5 million; medium-size urban areas if their population is between 200 000 and 500 000; and small urban areas if their population is between 50 000 and 200 000 (OECD, 2022). Cities of over 10 million people are defined by the United Nations (UN) as megacities, primarily a phenomenon in the developing world (Bugliarello, 1999). According to these definitions, Kolkata is a mega-city, while Ankara is a large metropolitan area. The main prerequisite for growing cities is sustainable transport: clean, safe, reliable, and affordable systems for delivering goods and moving people. A sustainable H. Zeybek (B) Vocational School of Transportation, Eskisehir Technical University, Eski¸sehir, Turkey e-mail: [email protected] S. Roy · S. Mitra Department of Geography and Disaster Management, Tripura University, Tripura, India e-mail: [email protected] S. Mitra e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_20

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transport system requires a dynamic balance between the main pillars of sustainable development, i.e., economic, social, and environmental issues. The combination of social, ecological, and economic aspects is to be considered to create a fully sustainable transport system. Thus, the urban transport system should integrate all relevant urban development policies effectively. However, no single strategy is sufficient to promote more sustainable urban transport. Moreover, different measures may be more appropriate for smaller and mediumsized developing cities than megacities (Pojani and Stead, 2015). To meet the challenges of rapid urbanisation, transport in smaller and medium-sized or big cities has become a top priority issue on the agenda of developing countries. Many developing countries invest in sustainable inter-city and rural-urban linkages and such largescale public transport systems as underground or overland metro systems to improve their urban transport systems. Specifically, dense and compact small and mediumsize developing cities have great potential to develop sustainable transport systems (Pojani and Stead, 2015). Adequate urban transport infrastructure is crucial for cities to function. Transport infrastructures such as railways, roads, highways, airports, bridges, waterways, and terminals play a vital role in the flow of goods and people during urban agglomeration and sprawl (Durango-Cohen and Sarutipand, 2007). On the other hand, transport infrastructures can enhance the sustainability performance of a city and its wider region by managing urban growth, alleviating traffic congestion, and reducing carbon emissions (Banister and Berechman, 2001). As transport infrastructure is a network infrastructure that constitutes the link between nodes and regions, this promotes the spatial transfer of production factors and mobility of goods (Wang et al., 2018). In addition, effective routes and connections can improve the management of urban flows, provide better accessibility and enhance the quality of urban centres (Ferbrache and Knowles, 2016). Transport infrastructure is a critical element in economic development at all income levels, supporting personal well-being and economic growth (OECD, 2013). However, the irrational planning of transport infrastructure leads to unfavourable outcomes like ecological deterioration, a rise in traffic accidents, climate change, CO2 emissions, and decreased transport effectiveness. Accordingly, the prior requirements for an urban transport system are; a) to ensure accessibility to all inhabitants, commuters, tourists, and businesses and b) to reduce the negative impact of the transport system on the health, safety, and security of the citizens, c) to reduce air pollution and noise emissions, greenhouse gas emissions, and energy consumption, d) to improve the efficiency and cost-effectiveness of the transport of people and goods e) contribute to the quality of the urban environment (Lozzi et al., 2020). Therefore, it is necessary to identify multiple impacts of transport infrastructure from existing studies. Recently, the impact of transport infrastructure on sustainable urban growth has been receiving more attention and debate because of the pursuit of the economic progress of both regions and sectors. Elbert and Rentschler (2021) conducted a comprehensive investigation of freight on public transport using a systematic literature review. They find that freight on urban public transportation is a highly dynamic

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and diverse research field. Alpkökin et al. (2016) assess the LRT and street tram policies and their implementation in 8 Turkish cities by reviewing the railway and the competition between rail and bus-based systems. Babalik-Sutcliffe (2002) investigates the parameters behind a successful urban rail investment and summarises opinions on factors leading to incorrect urban rail system investments. The empirical analysis of Pradhan et al. (2021) provides clear evidence for the strong relationships between urbanisation, transportation infrastructure, ICT infrastructure, and economic growth for the G-20 countries. Wang et al. (2018) present a scientometric review to identify multiple impacts of transportation infrastructure and show emerging trends and challenges. Aljoufie et al. (2013) explore the reciprocal spatial-temporal effects of transport infrastructure and urban growth of Jeddah city between 1980 and 2007 and show that transport infrastructure is a constant and strong spatial influencing factor of urban growth in the polycentric urban structure. Otuoze et al. (2020) explain urban growth dynamics of transport space over the past decades as a basis for predicting future space demands in Kano, Nigeria. The authors processed, classified, and analysed satellite images of 1984, 2013, and 2019 and find out spatial classifications of land-use land-cover (LULC) with transport space. Rode et al. (2017) identify the association between transport and urban morphology based on global evidence highlighting the direct and indirect costs of choices made. It then presents the tipping points that can allow proceeding from sprawling urban development and conventional motorised transport to more compact cities characterised by innovative mobility choices shaped around shared and public transport. Lindkvist and Melander (2022) review the literature on Mobility-as-aService (MaaS) and Urban Consolidation Centers (UCC), and they argue that new transport services promise to deliver both social and environmental sustainability. This chapter aims to compare two cities selected as examples of different regions, sizes, and physical and socio-economic characteristics of the urban areas, to evaluate urban transport patterns and their sustainability and contribute to the more general literature on sustainable urban transport patterns. We focus on sustainable urban growth in Ankara, the second-biggest city in Turkey, and Kolkata, the seventh-biggest city in India. We examine how the chosen cities practice and justify urban transport infrastructure planning and development concerning the three dimensions of sustainability and whether there is any progress achieved. In line with the study objective, previous literature, government publications, population data, and academic articles have been reviewed and analyzed to understand and examine the cases.

20.2 Background Information on the Two Cities In Turkey, cities and their hinterland are growing fast due to industrialisation and urban population growth. The discontinuous and disjointed settlements outside the boundaries of urban settlements lead to a new growth model in the metropolitan, namely the urban sprawl (Sezgin and Varol, 2012). Ankara is the capital and the

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second-largest city in Turkey. It is located in the central Anatolia region at the crossroads of trade and forms a major junction in the transport network (Fig. 20.1). Highways, state roads, and conventional and high-speed railways connect Ankara with other provinces. Moreover, Ankara is Turkey’s second most important industrial city after Istanbul. Esenboga International Airport is available for international and domestic flights. Industrial warehouses are mainly in Ankara Logistics Base and Saray, Kazan, Gölba¸sı, and Akyurt regions.

Fig. 20.1 Location map of ankara. (Source Prepared by the authors, 2022)

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Demography, particularly population size and growth, has an impact on demand (Kin et al., 2017). The population of Ankara is steadily increasing and reached 5,7 million inhabitants in 2020, of which 38% are under 30 years old. 88% of the population lives in the city center. The population nearly doubled in the 20 years between 1980 and 2000 (Fig. 20.2). Car ownership reached 280 per 1,000 persons in 2020, which was 264 in 2019 (see Table 20.1). Ankara ranks second in the number of private cars after Istanbul and ranks first in car ownership per 1,000 persons in Turkey. Increased motorisation is also a result of favourable economic conditions. The climbing population and vehicle ownership trend in Ankara have resulted in rapid urbanisation. Compared to its total population, Ankara has the highest number of higher education graduates. With a foreign trade volume of 19,6 billion dollars, Ankara realised approximately 5% of Turkey’s overall foreign trade volume as of 2019. This reality increases travel demand and add pressure to transport infrastructure and services. Mega city Kolkata is the seventh most populated city in India after Mumbai, Delhi, Bangaluru, Hyderabad, Ahmedabad, and Chennai, located on the left bank of river Hooghly. The total geographical area of the city is 1851.41 km2 which lays in between 22°0' 19'' N to 23°0' 01'' N Latitudes and 88°0' 04'' E to 88°0' 33'' E Longitudes (Maity et al., 2022). About 206.08 km2 administratively falls under Kolkata Municipal Corporation (KMC) (Fig 20.3). The city’s urban population increased daily with an 7.0

Population (million)

Fig. 20.2 Population growth of ankara

6.0 5.0 4.0 3.0 2.0 1.0 0.0 1920

1940

1960

1980

2000

2020

2040

Years

Table 20.1 Background information on two cities

City profiles

Ankaraa

Kolkata

City population (million)(2021)

5,7

14,8

Per Capita GDP (USD)(2020)

12508

6,685b

Area

(km2 )

25437

1851.1

Cars per 1000 people (2020)

280

225

Cars(‘000)

1607

651

2290

800

Motor Vehicles (’000) a

b

Source TUIK,2022 https://metroverse.cid.harvard.edu/city/

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Fig. 20.3 Location map of kolkata. (Source Prepared by the authors, 2022)

annual population growth rate of 1.8 per cent between 2001 and 2011 (Census of India, 2011). Kolkata has a diverse array of transport options, including ferries, metro rail, hand-driven rickshaws, trams, buses, and trains. Howrah and Sealdha are two major railway stations in this area (Gupta and Dutta, 2016). Howrah station is the largest railway station in India with 23 platforms (Kumar et al., 2018). The city has two ports, i.e., Kolkata port and Budge Budge port. The Kolkata port alone is situated on the bank of the Hooghly river at a distance of about 200 km from the Bay of Bengal. An area of about 77 km2 is covered by the port, including the protective zone, with a depth of 400 meters around it (Das et al., 2000). The Budge Budge port is situated about 50 km down the river Hooghly. The Kolkata International Airport area covers about 664 hectares. Daily, on average, 310 aircraft, both national and international. Kolkata metro railway is one of the oldest metro transport systems in the country. Kolkata became the first million cities in India in 1901 and experienced fast growth until 1971; afterward growth rate plunged. As per the census 2011, the decadal growth rate of the Kolkata urban agglomeration was just 6.87%, below the natural growth rate of West Bengal and India (Yadav and Bhagat, 2014). The largest sector in Kolkata consists of the trade and transport industries, accounting for 35.43% of employees in the city (Fig. 20.4). Greenhouse gas (GHG) emissions per capita generated in Ankara are below 10 tCO2 e per capita. The trends for SO2 differ by the city but are getting worse

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Fig. 20.4 Estimated regional greenhouse gas emissions per capita in turkey. Tons CO2 equivalent (tCO2 e), large regions (TL2), 2018 (OECD, 2021)

in all Indian cities, including Kolkata. India stands third among the global greenhouse gas emitter after China and the United States of America (The Carbon Brief Profile, 2020). Around 70% of Kolkata’s inhabitants suffer from respiratory problems caused by pollution from the city’s chaotic transport system (Sarkar and Tagore, 2011). Kolkata averaged a PM 2.5 concentration of 85.4 micrograms per cubic metre (µg/m3 ) of air in 2018, more than eight times the recommended limit of 10 µg/m3 .

20.3 Urban Transport Systems of Two Cities The role of transport in developing urban areas has increased its importance in urban life. The potential role and impact of urban transport in Ankara and Kolkata on sustainable urban growth will be reviewed on: (a) road infrastructure (b) public transport (c) freight movements and logistics (d) non-motorised transport systems (e) new technologies and solutions.

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20.3.1 Road Infrastructure The most common sort of infrastructure is a road in both cities. The road infrastructure has such benefits as shorter travel times, reduced transport costs, and improved road safety (Merlo et al., 2021). It also has a positive impact on the growth of some regions. In accordance with the government policy towards highway priority investments as a solution to urban transport problems since 1994, the construction of limited-access, multi-lane divided roads increased in Ankara. Moreover, the multilevel highway intersections built on ring roads, prime corridors, and even in the city centre (Öncü, 2009), deepening segregation by cutting walkable networks. Highway infrastructure works have been conducted on Eski¸sehir Road, Konya Road, Esenbo˘ga Road, and Istanbul Road, which constitute the external connections of the city. 90 km/h speed limit on divided roads makes it impossible to make room for soft modes on these roads. The mayor of the time declared that from 1994 to the third quarter of 2017, they built 146 new boulevards, 312 bridges, and 139 pedestrian overpasses to relieve congestion, reduce travel times, and even decrease emissions (DHA, 2017). Despite the negative consequences of car dependence, investments in urban roads are increasing. Of the 1,832 km long road network in Ankara, 976 km (53 per cent) are divided roads (UAB, 2020a). The modal share of private cars in daily urban transport is 39 per cent (2020) (Table 20.2). In Kolkata, an under-river road tunnel could well add pace to cargo movement between the city-dock at Kolkata port and the national highway across the Hooghly river, which connects to states in the north and south of the country.In this way, urban highways sacrifice livable neighbourhoods to facilitate car traffic and cause the induced demand phenomenon, which encourages more people to drive. A few years after a highway opens, congestion, pollution, and other harmful driving impacts become worse, and the anticipated benefits do not materialise. The detrimental effects of urban highways are so clear that most cities in high-income countries have stopped building them. Cities like Seoul, Paris, New York City, San Francisco, Utrecht, and Milwaukee have begun demolishing urban highways (ITDP, 2021). While Kolkata provides many affordable public transport options, including buses, yellow taxis, suburban railways, metro, tram, auto-rickshaws, sharing transport modes (Bike-taxis, OLA/Uber) and ferries, there has been an increase in personal motorised modes of transport. The preference for owning personal vehicles is largely based on the economic capability of an individual. Kolkata has an extensive bus network, covering every part of the city. Despite its status as a megacity, Kolkata has an unusual combination of high population density, low vehicular ownership, and low road length–constraints that could be harnessed to create more adaptable, environmentally sustainable transportation alternatives (CPR, 2021).

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Table 20.2 Urban transit systems Ankara

Kolkata

Modal share of private cars in daily urban transport per cent (2020)

39

12

Modal share of public transport in daily urban transport per cent (2020)

56.3

80

Modal share of rail (metro, light 14.3 metro, suburban) in daily public urban transport per cent (2020) Urban rail system(s) and route lengths as of 2021

Metro (M1): 14.7 km

Metro (M1): 31.36 km

Metro (M2): 16.6 km

(Dakshineswar to Kavi Subhash)

Metro (M3): 15.4 km

Metro (M2): 7.205 km

Metro (M4): 9.2 km

Salt Lake Sector V to Phoolbagan

Light Metro(A1): 8.5 km Suburban 36 km Total urban rail network length

100.4 km

Urban rail under construction

4.1 km

Planned urban rail

53.2 km

Number of railway stations

54+28(suburban)=82

38.565 km

35

Source UAB, 2020a; Ankara Kalkınma Ajansı, 2021; Shahin and Yeti¸skul (2021)

20.3.2 Public Transport Public transport uses vehicles for group travel, such as buses and trains, to convey people throughout urban regions. A reliable and flexible public transport system is essential to a sustainable transport system (Banister, 2006). The primary characteristic of urban transport is that it transports many people in a single vehicle (e.g., buses) or a collection of connected vehicles (trains). Rail transit systems are more sustainable, safe, healthy, efficient, and productive than urban highways. Bearing in mind that transport solution, sustainable from an environmental point of view, does not guarantee economic feasibility, trams and light rail systems are surging in many European cities (Lozzi et al., 2020). Population and disposable income growth increase the transport demand in daily life. Compared to low-income households, high-income households frequently have easier access to cars and travel more often. Additionally, poorer people typically reside in more polluted locations than wealthier. As a result, equalities in society are a component of sustainable transportation (Toth-Szabo et al., 2011). Slum population in Kolkata is around 31 per cent, while their presence is concentrated in central and north-eastern parts of the city (Haque, 2019).

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Promoting and supporting the expansion of public transport and its new technologies ensures accessibility and reduces negative environmental impacts. Ankara currently benefits from public transport system that includes a bus network, a metro network, and limited suburban rail infrastructure dating back to the 1950s. Ankara is the second city in Turkey that operates metro systems after ˙Istanbul. More than half of the daily trips in Ankara are by public transport. Rail share is around 14 per cent (see Table 20.2). A bus is the most used mode. Bus services in Ankara are managed by Ankara Electricity, Gas and Bus Operations Organization (EGO General Directorate). EGO is a state-owned company affiliated with Ankara Metropolitan Municipality. The use of minibuses is also prevalent. Since 1997, the inauguration of the first metro line, the metro line has reached 55.9 km, and in addition, there is an 8.5 km light metro line, all operated by Ankara Metropolitan Municipality. 36 km suburban line put into service after renovation of lines and stations in 2018. In addition to the existing 100.4 km rail transit system, 57.3 km is under construction and planned (Table 20.2). However, metro system capital and fixed costs are high, and construction time is extended (Fig. 20.5). It is important to highlight that the COVID-19 pandemic has reduced the attractiveness of the public transport system. The covid-19 pandemic significantly negatively impacts public transit ridership and mobility behavior in Ankara between March 2020 and March 2021. The increase in car ownership in Ankara was more than expected in 2020, implying the requirement for additional measures to encourage public transport use during normalisation or recovery periods afterwards (Shahin and Yeti¸skul, 2021).

Fig. 20.5 Ankara rail-based public transport map

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Kolkata has an extensive network of public transport system consisting of railways, metros, trams, ferries and buses. Auto-Rickshaw, Cycle Rickshaw and HandRickshaw serve the last mile connectivity in the city. There are 11,621 public buses operating on 925 routes in Kolkata. In India, Kolkata is the only city where the tram is still now operating. The practicability of extending and advancing the metro and suburban railway networks for the city’s transport becomes significant. The metro, with its underground and elevated channels, is essentially a space-saving medium of the transport network. The metro and suburban railway networks have special significance as they constitute quick and bulk transport links (Sarkar and Dentinho, 2021). Thus, the metro’s new extensions may undoubtedly be referred to as a catalyst for initiating the urban development process of Kolkata and its surroundings—by altering the location and design of spatial development through the process of integrated transport links and changes in land use (Das and Gupta, 2016). The impact of transport development in new Kolkata developments is predicted to act through the growth of new urban centres and sub-centres, together with the expansion of new industrial hubs within the Kolkata Metropolitan Development Authority (KMDA) region. Likewise, the completed construction of the East-West metro corridor would integrate the two urban hubs, Kolkata and Howrah, bringing about manifold transformations in Kolkata and the more significant urban agglomeration while renewing the existing city area further. This has also been a constant catch for realtors and investors willing to develop the property at the newly constructed city spreads of New Town, Rajarhat, Barasat, Joka, and Howrah for good business and regular rent earnings (Sarkar and Dentinho, 2021) (Fig. 20.6) The construction of metro rails is thus expected to hike real estate prices all along the passage and buffer zone, i.e. the ‘influence zone’. The development of the Urban Rail Based Transit Systems is perceived as a catalyst to improve the standard of living of a considerable section of the urban population, simultaneously ensuring sustainable urban development (Shah, 2015). Kolkata has the largest share (around 80 percent) of public transport trips among the major cities of India (DownToEarth, 2022).

20.3.3 Freight Movements and Logistics In urban areas, passenger and goods transport share the same infrastructure. Good cooperation in and between cities and surrounding regions is needed for efficient traffic flows in urban nodes and for liveability and accessibility. Urban freight traffic is growing fast. Goods are carried from logistical facilities, such as warehouses and terminals, either directly to the receiver or through intermediary transshipment locations (Kin et al., 2017). E-commerce has facilitated the rapid growth of the small package delivery business. Due to the challenges of the fast-evolving logistics and supply chains, such as just-in-time deliveries, and asymmetrical trade patterns, many vehicles are operating empty or below capacity.

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Fig. 20.6 Kolkata rail-based public transport map

A sustainable urban freight policy seeks to reduce purposeless truck movements through neighborhoods and congested urban centers and improve urban freight operations’ environmental and economic efficiency. Centralized urban distribution and logistics centers provide local cooperative delivery of goods, often using moto-bikes. Transport planning in urban nodes impacts flows along the corridors (on modal distribution) and the other way around.

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In Ankara, access to certain parts of the town is restricted to freight vehicles based on their weight/size at certain day hours. Besides, the number of home deliveries is growing impressively. In order to meet these demand requirements, bundling in consolidation centres, the use of locker boxes and more tailored transport modes (e.g., motor-bikes, cargo bikes) are emerging. Most home deliveries are requested online. The number of e-shoppers varies from 17.7% (Turkey) to Ankara 9% (Ministry of Trade, 2021). The COVID-19 pandemic has made the situation worse for e-commerce and public transport providers. Within the borders of Ankara, 823 km of railway, including 363 km of highspeed (YHT) and 460 km of conventional, is available. 7.091.302 passengers travel from Ankara annually, and 1,226,283 tons of cargo are transported. International routes, such as Middle Corridor, pass through Ankara. The concept of urban logistics continued to develop as e-commerce increased its market share with the change in habits. Companies that offer home service aim to increase the number of warehouses in the city due to their commitment to providing fast service to their customers besides their warehouses. In addition, the need for large logistics areas to support logistics for the city is increasing. The ASO I, Ivedik, and OSTIM OIZs, three of Ankara’s ten organised industrial zones with production areas, are situated in the city’s western region. Northwest of the city is also home to small industrial sites, industrial businesses, and the Ankara Logistics Base. Bus rapid transit (BRT), suburban commuter trains, EGO buses, special public buses, and minibuses are the public transport options serving the region. According to the Ankara Master Plan, housing developments, industrial areas, and warehouses have been planned in the city’s northwest. As a result, there will be increased investments in the years to come (Yıldırım, 2013). In Kolkata, on some of the tram routes, goods are also transported by local vendors (India Smart Grid, 2017).

20.3.4 Non-motorised Transport Walking, cycling and public transit are more sustainable, safe, healthy, efficient, and productive than urban highways. The environment for pedestrians and cyclists across Ankara is currently poor, with prioritisation of road traffic and thus poor road safety. Cycling might be the best for urban areas for a 5 km trip. Even in the future, it is possible to see the cycle-rickshaw as a connecting means of transport. The cycle-rickshaws function should be considered in small and medium-sized cities. The cycle-rickshaw is a basic mode of transport, even in metropolitan areas with underdeveloped mass transit systems. The auto-rickshaws are very noisy and create noise pollution in residential areas (Sarkar and Tagore, 2011). One significant problem is the absence of social interaction in open public spaces and the severely neglected pedestrian circulation. In residential neighbourhoods,

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narrow sidewalks frequently used by cars offer little possibility for pedestrian movement or the emergence of vibrant, diverse street life. Ankara needs to address air pollution, sufficient accessibility, and connection in the city due to the quantity and quality of open public areas (UrbanAgenda Platform, 2020).

20.3.5 New Technologies and Solutions Alternative-fuel vehicles and intelligent transportation systems(ITS) are the two key technological innovations that cities worldwide are actively pursuing concerning urban mobility (Pojani and Stead, 2015). Battery-powered vehicles and private cars are very rare both in Ankara and Kolkata. Ankara is one of the cities in Turkey where Intelligent Transportation System (ITS) applications such as Vehicle Tracking System, Bus Lines Information System (in EGO Cep), Traffic Density Map (ABB Traffic), City Cameras, Smart Card System (AnkaraKart), Smart Stops, In-Bus Information System, In-Bus Security System are in use (Kesgin and Aydemir, 2017). A single card payment system is available in Ankara for public transport system. Recognising the impact of poor air quality and the global effects of carbon emissions, the city is developing a greener future by investing in its public transport system and working on a comprehensive strategy to improve its environmental performance. When considering investments in public transport in cities of developing countries, a key priority should be to improve existing bus systems. There are many projects toward the digitalisation and greening of public transport. Ankara became the 44th member of the European Bank for Reconstruction and Development (EBRD) Green Cities, the European Bank for Reconstruction and Development’s flagship urban sustainability program. With the EBRD loan, the city’s public transport company, EGO will replace polluting diesel buses with 254 compressed natural gas (CNG) buses and install a CNG filling station(EGO, 2021). However, significant funding is needed to implement high-level ITS on a broad scale. In Ankara, a comprehensive traffic management concept is made possible by traffic data generation systems, information systems, central junction management, and monitoring systems. The daily, weekly, and monthly data on traffic density are gathered through counting sensors in major boulevards. To reduce traffic congestion, these data are processed and shared with citizens via mobile apps (UAB, 2020b). The Ankara Metropolitan Municipality focus on the needs of people and the environment through the “Public Transport Route and Business Optimization Project” carried out by EGO General Directorate to assure sustainable mobility, lessen city traffic, decrease carbon emissions, and provide more comfortable transport service. The project will be financed by the grant of the American Trade and Development Agency and SAS (UITP, 2020). Ankara Metropolitan Municipality realized the production of Turkey’s first 100% electric bus converted from diesel (Temiz Enerji, 2021) (Fig. 20.7). https://temizenerji.org/2021/02/26/ankara-buyuksehir-belediyesi-dizelden-don usturulmus-elektrikli-otobus-uretti.

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Fig. 20.7 Electric bus in use in Ankara and Kolkata

In Kolkata, as of 2019, 80 electric buses have been introduced, with another 100 planned for 2020. These 180 electric buses will lead to an annual reduction of 14,086 tonnes of CO2 emissions. Kolkata invests in and improves its public transport options. The city plans to deploy 5,000 electric buses and fully electrify the ferries on the Ganges River by 2030.

20.4 Master Plans for Sustainable Urban Transport In 2015, the Ankara Transport Master Plan (AUAP) was designed. The plan’s primary goal is to achieve sustainability through compact, concentrated, and urban development strategies are prioritising public transport, particularly rail systems like the metro and commuter trains. The compact and concentrated strategy aimed to promote sustainability by reducing energy consumption and emission rates, shortening car trips, and promoting the effective use of urban space. Adopting a strategy that prioritises public transport aims to concentrate urban residents near locations with easy access. However, it is evident that the AUAP of Ankara, which has already entered sprawl from the compact structure, will have difficulty adopting a compact and concentrated approach (Kocaku¸sak, 2021). The AUAP strongly emphasises the crucial connection between sustainable mobility and urban design and development. New construction in the key nodes is intended to enhance sustainable transportation. Public transport is considered as a key driving factor for reaching a more sustainable city. There is also an ongoing EU-funded Sustainable Urban Mobility Project in order to adopt a SUMP (Sustainable Urban Mobility Plan) for Ankara in line with the EU best practices and establish a new SBS (Smart Bicycle System) infrastructure (UAB, 2020b). In India, the national government promulgated National Urban Transport Policy in 2006 to guide the states to cope with the urban mobility crisis because of the dual effect of growth in urban population and growth in motorised transport, which is

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attributed to economic growth. However, the West Bengal state government has not been able to present any urban transport policies in line with the demand for sustainable urban transport in the greater Kolkata area. Traditional road-centric strategies continue to overshadow the need to move toward the implementation of sustainable urban transport policy in the absence of an appropriate urban transport policy (Bhattacharya, 2013).

20.5 Evaluation of the Sustainability of Transport Systems of Ankara and Kolkata It is good to identify the current position of the urban transport systems and areas to be focused on for improvement of sustainability of cities and urban areas considering all dimensions of sustainability, i.e., economic growth, environmental protection, and social equity, for the deployment of new transport services. We evaluate the sustainability of transport systems of two cities with a set of indicators developed based on the previous literature and studies under broad categories of sustainable transport (Table 20.3).

20.6 Conclusion Sustainable urban transport initiatives aim to create a transport network that is less harmful to the environment, offers adequate mobility and access, and safeguards the livability of its users. Regarding urban transport from a larger perspective, accessibility and offering more transport options are equally as important as mobility. The use of public transport is a primary driver for creating more sustainable cities. Public transport uses vehicles for group travel, such as buses and trains, to convey people throughout urban regions. Ankara and Kolkata, the cities reviewed, have an extensive network of public transport systems consisting of railways, metros, and buses. Differently, Kolkata also has tram and rickshaw systems. Increased motorisation is also a result of favourable economic conditions. Depending on the per capita income, the number of private vehicles and their share in urban transport is higher in Ankara than in Kolkata. Accordingly, the use of public transport is high in Kolkata and reaches 80%. Ankara and Kolkata have an extensive public bus network, covering every part of the city, and thus bus is the most used public urban transport system in both cities. The situation in Ankara serves as an example of the scope of the difficulties facing efforts to change road-building, automotive dependence, and expansive development patterns. It also demonstrates the potential power of strategies that support wiser growth and more sustainable mobility, as well as the possibility for strong advocacy efforts by business and public interest groups to promote meaningful change. The

20 Transport as a Driver of Sustainable Urban Growth: Evidence … Table 20.3 Overview of major sustainability indicators for ankara and kolkata

Indicators Number of different public transport modes Public Transport (PT) Infrastructure expansion Integration in a smart card Priority lanes for PT Transfer stations Parking charge

Ankara

Kolkata

AnkaraKart

West Bengal Transport Card

Low-emission public Use of CNG and electric buses transport vehicleselectric and CNG

Use of LPG and electric buses

Deploying electric vehicles Limit the entrance of HGVs in city center Congestion charging Promotion of nonmotorised transport Sufficient sidewalk Reducing the road capacity

Cycling network

Mobility of freight

Use of locker boxes, motorbikes, cargo bikes are emerging

Cycle-rickshaw in use

Bundling in consolidation centres Intelligent transportation systems

In use

Planned

Not Planned

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public’s awareness of Ankara’s urban transport, sprawl, and air quality issues has grown significantly, which has started to encourage smart growth policies and investments, even if there are still considerable barriers to further transformation. Urban roads encourage expansion that is unsustainable from an ecological and financial standpoint. Highways promote driving, which worsens air pollution and climate change. Investments in urban roads are rising despite the drawbacks of car dependence. The metro rail has been extremely helpful to urban sustainability and will further promote the safety, convenience, and comfort of urban passenger movement while reducing urban pollution. On the other hand, developing a sustainable transportation system for Kolkata may appear like a challenging task, particularly for the city’s older, densely populated, heavily settled, and congested districts. However, by changing a few of those polluting transport methods, some transformation is always achievable. The metro rail is highlighted once more because it has been extremely helpful to the process and is anticipated to improve mass passenger movement’s safety, convenience, and comfort while decreasing urban pollution. However, considering the high cost of the metro system, the municipalities in both cities are investing in low-emission LPG, CNG, and buses for the short-run solutions to sustainable urban transport.

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Chapter 21

Neighbourhood Level Geospatial Heterogeneity of WASH Performance in Indian Two Metropolitan Cities: Kolkata and Chennai Pritam Ghosh , Moslem Hossain , Jiarul Alam , and Asraful Alam

21.1 Introduction Water, sanitation, and hygiene (WASH) practices are fundamental for overall development, and access to reliable drinking water, and adequate sanitation are considered basic human rights (UN-HRC, 2010). The UN Millennium Development Goals (MDGs) considered drinking water and sanitation as priority development issues from 2000 to 2015. Different nations are also trying to ensure drinking water and adequate sanitation for all (Sustainable Development Goal 06) during the SDG era. Besides, ensuring adequate, safe, and affordable housing with basic services including water, and sanitation facilities and upgrading the slums within the cities are the most influential initiatives to make sustainable cities and communities (SDG 11). Un-Habitat has indicated the incorporation of proper water, waste, and hygiene management for a building design for enhancing environmental sustainability. Some researchers (Das & Mistri, 2013; Fiadzo et al., 2001; Haque et al., 2020; Ilesanmi, 2012; Meng & Hall, 2006; Mondal, 2020) highlighted the accessibility to drinking P. Ghosh (B) Department of Geography, University of Calcutta, 35, Ballygunge Circular Road, Kolkata, West Bengal 700019, India e-mail: [email protected] Department of Geography, Ramsaday College, Amta, West Bengal 711401, India M. Hossain Department of Geography, School of Earth Sciences, Central University of Karnataka, Gulbarga, India J. Alam Department of Geography and Applied Geography, University of North Bengal, Darjeeling, West Bengal, India A. Alam Department of Geography, Serampore Girls’ College, University of Calcutta, Kolkata, West Bengal, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_21

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water, sanitation, waste management, and hygiene management as basic amenities necessary for adequate housing or housing quality. Another side, this WASH infrastructure in urban areas has enough potential to reduce poverty or urban deprivation (SDG 01). As a result, identifying spatial units with a higher level of WASH poverty is critical to implementing adequate measures to meet the aforementioned Sustainable Development Goals. WHO and UNICEF (2015) have already emphasized the proper management system of water, sanitation, waste, and hygiene to prevent COVID-19. WASH management has been identified as an important factor in controlling COVID spread in various urban areas around the world by some studies (Parikh et al., 2020; Liu, 2020; Wilkinson et al., 2020a, 2020b; Wang & Liu, 2021; Feizizadeh et al., 2021; Anaafo et al., 2021). Several researchers (Tampe, 2020; Wilkinson et al., 2020a, 2020b) highlighted the complex chronic conditions and health crises in urban slums of lower-middle-income countries during the pandemic. Inadequate WASH facilities have also been identified as a contributing factor to the spread of COVID in Indian large and megacities (Mishra et al., 2020; Das et al., 2020a, 2020b, 2020c; Corburn et al., 2020). More than half of the COVID-19 incidents have been found to occur in metropolitan cities in India due to comparatively high population density in association with a huge contact rate for functional transmissions to other regions. Therefore, proper management of household drinking water, wastewater, drainage systems, accessibility to in-house drinking water, adequate hygienic space for quarantine living, and accessibility of safe latrines in Indian cities are essential. Against this backdrop, the identification of spatial units considering the aforesaid facilities is important. Poor access to safe drinking water, improved sanitation, and hygiene, on the other hand, has been identified as a major risk factor for diarrhoea, trachoma, helminthic infections, maternal health, and mortality (Fewtrell et al., 2005; Hunter et al., 2010; Walker et al., 2013; PrüssÜstün et al., 2014; WHO, 2014; Okullo et al., 2017). Yan Previous research has shown that poor supply and management of WASH facilities in India increases the risk of infectious diseases such as cholera, diarrhoea, typhoid, and dengue fever (Hathi et al., 2017; Mallik et al., 2020; Patel et al., 2019; Shukla et al., 2020; Vijayan & Ramanathan, 2020). Regarding this context, the estimation of household WASH performance status and the spatial pattern of these WASH facilities in old, internationally connected metropolitan cities are very significant during this pandemic situation. Presently, 96 and 82% of the global urban population access improved drinking water sources and improved sanitation, respectively (UNICEF/WHO, 2015). The scenario in India, in this case, is slightly different. Out of the total Indian population, more than 31% live in urban cities (Census of India, 2011). Most Indian cities are characterized by urban poverty and slum populations (Kundu, 2014). Nearly 65% of urban dwellers have no water facilities at their household premises, and a huge population passes their lives without basic sanitation facilities (India’s Smart Cities Mission, 2018). However, some studies have outlined inter-city spatial inequality in WASH facilities (Shaban and Sharma, 2007; Saroj et al., 2020) and intra-city

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waterborne disease vulnerability (Ajmal et al., 2022) in India. But there is a gap in the literature explaining the intra-city spatial pattern of WASH performance. The Government of India (GoI) and the local urban government have already implemented several initiatives to improve cities’ WASH conditions. The Jawaharlal Nehru National Urban Renewal Mission (JNNURM) was launched in 2005 to develop urban water supply, drainage, sewerage, and urban transport systems. This was replaced in 2014−15 by (i) Atal Mission for Rejuvenation and Urban Transformation (AMRUT), (ii) Smart City Mission, and (iii) Swachh Bharat Mission−Urban (SBM-U) for the improvement of water supply and sewerage systems, smart solutions for selected urban areas, and the improvement of sanitation and waste management in urban areas, respectively. Moreover, the budget allocation for the urban area (SBM-M) under this scheme has already increased in the last few years to enhance urban sanitation sustainability, focusing on the maintenance of sanitation facilities, management of wastewater, solid waste, and faecal sludge. As we used the Census of India, 2011 data, this particular study has not considered the effect of different projects launched after 2011 for the improvement of WASH conditions. With a greater concern for the aforesaid facts, the present study focuses on developing a WASH performance index for two old Indian metropolises, Kolkata and Chennai. In this context, we have extracted all possible aspects of drinking water, sanitation, and hygiene facilities from the latest census database (2011) and combined all indicators through the TOPSIS-MCDM method. Secondly, the research has also estimated the ward level geospatial inequality in the distribution of different WASH facilities for these two cities. Besides this, the spatial dependence, heterogeneity, and pattern in the neighbourhood-level distribution of several WASH facilities and the overall WASH performance status have also been outlined in the study.

21.2 Study Area We have selected two old metropolises, Kolkata and Chennai, from different parts of India. The Kolkata metropolitan region is the largest urban center in eastern India, and due to its strategic location, it is known as the “Gateway of Eastern India.“ On the other side, Chennai is the largest industrial hub in southern India and is also highlighted as the “Gateway to South India” (U.S. International Trade Commission, 2007; see also Krishnamurthy & Desouza, 2015). During their establishment, these two eastern and southern Indian cities had British colonial history. Chennai began to grow in 1653, shortly after the East India Company completed St. George Fort on the Coromandel Coast. On the other side, Calcutta was established in 1698, and fortification started this year after the competition of Fort William. On the other hand, different political and administrative structures existed in these two states. West Bengal was under a communist government for more than three decades (Das, 2020). Tamil Nadu often transformed its ruling party into one of its two strong regional political alternatives that followed a political ideology similar to social liberalism (Wyatt, 2013). Different political environments contribute to different

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administrations and governance structures (Biswas et al., 2019). Presently (Census of India, 2011), Kolkata and Chennai cover about 209 and 197 km2 areas, 141 and 155 spatial units (municipality wards), respectively, with 4.50 and 4.68 million people. The share of the slum population was similar in these cities (31% in Kolkata and 27% in Chennai). In this context, we have tried to examine the local status and the spatial pattern of WASH facilities in these two cities. The location and administrative setup of both cities have been represented in Appendix 1.

21.3 Methodology 21.3.1 Database The present research relied on the 2011 house listing and housing tables released by the Census of India. The table provided statistics related to housing conditions, household assets, and the basic amenities in the possession of households. In this study, the ward-wise data of Kolkata Metropolitan City (KMC) and Chennai Metropolitan City (CMC) were extracted.

21.3.2 Measures of the WASH Performance Index We have selected eight indicators from the domain of drinking water, sanitation, and hygiene to construct the WASH performance index considering the previous research, local situation, and availability of the 2011 Census of India housing and house listing data (Table 21.1). Earlier, Tsesmelis et al., (2020) considered the type and distance of water source, quantity, quality, and availability of drinking water to measure the condition of drinking water, kinds of latrines, Sewerage, drainage system, the capacity of garbage bins to estimate sanitation index and pest control activities, frequency of cleaning, hygiene promotion, understanding hygiene level. Several studies (Chaudhuri et al., 2020; Soraj et al., 2020; Ghosh et al., 2021a; Ghosh et al., 2022) estimated the level of WASH condition by taking into account the accessibility and availability of drinking water sources, toilets, separate kitchen and house conditions, fuel type, and so on. Therefore, we also considered the location of the drinking water source and the quality of the drinking water. Here, the treated source of drinking water indicates a better quality of drinking water. To estimate the sanitation condition, we have considered the location or availability of latrines, bathrooms within the household premises, and wastewater connections to closed drainage. Cooking within the house with solid fuel creates carbon monoxide, hydrocarbons, particulate matter (PM 2.5, PM 10), sulfur dioxide, nitrous oxide, etc. and causes indoor air pollution, which negatively influences child, adult, and maternal health (Epstein, 2013; WHO, 2014; Yan et al., 2016; Liao et al., 2016; Matawle et al.,

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Table 21.1 Selected indicators, their descriptions and assigned weight for TOPSIS MCDM Domain

Dimension

Indicators

Drinking-water

Availability or location of drinking water source

Households availing drinking water source within premises

Quality of drinking water

Household accessing tap water from treated source

Availability or location of latrine

Household having latrine within premises

Availability or location of the bathroom

Household having bathroom within premises

Waste disposal facility

Household holding wastewater outlet connected to a closed drainage

Hygienic condition of the house

Good residential condition of the house

Hygienic measure of fuel use

Household accessing LPG/PNG for cooking

Indoor, outdoor pollution control

Household having separate kitchen facilities inside or outside the premises

Sanitation

Hygiene

2016; Ali et al., 2021). On the other hand, cooking outside the home with soil fuel causes local environmental pollution (WHO, 2014). Besides, the use of liquid fuel (LPG/PNG) does not create indoor air pollution like solid fuel. Similarly, dilapidated house conditions indicate the unhygienic situation of the household (Ghosh et al., 2021a; Mishra et al., 2020). Therefore, we have selected a residential house in good condition, the use of LPG or PNG as cooking fuel, and the availability of a separate kitchen to represent the household’s good hygiene condition.

21.3.2.1

Determination of WASH Performance Index

We employed one of the most important, extensively used Multi-Criteria Decision Making (MCDM) techniques, the TOPSIS method, to determine the WASH performance index of the selected metropolitan cities. The method was initially represented by Yoon and Hwang (1981) and later Lai et al. (1994). After that, several researchers have used this technique to estimate water quality (Li et al., 2013), road safety (Rosic et al., 2017), etc. We determined the index using this method through several steps. Firstly, we normalized the values of every indicator (x ij ) by the following formula and calculated a normalized decision matrix (Xij). aij xij = /∑ m k=1

akj2

W here, I = 1, 2 . . . , m. j = 1, 2, . . . n.

(21.1)

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Here x ij are the normalized value of any selected indicators, m implies the absolute number of observations, and n is the total number of indicators considered for the study. In the next step weighted normalized matrix was prepared. The following formula determined the weighted normalized value Vij = xij ∗ wj

(21.2)

where ∑n Vij the weighted normalized value, wj is the weight of jth indicators and j=1 wj = 1. In this present study, we considered more weights for the availability and quality of drinking water, as these also help to maintain sanitation and hygiene activity in the household and to fix the equal weights to three dimensions of the WASH performance Index. After that, the ideal best (positive value) and ideal bad (negative value) were adopted for each indicator and based on these ideal positive and negative reference point distance of each observation was determined. These distances were calculated based on the following formula: Γ |∑ )2 | m ( ∗ vij − vj∗ , j = 1, 2, 3, . . . , m S1 = √

(21.3)

j=1

Γ |∑ )2 | m ( − vij − vj− , j = 1, 2, 3, . . . , m S1 = √

(21.4)

j=1

Lastly, the relative closeness or the closeness coefficient was determined as follows: CC =

S− S∗ + S−

(21.5)

Greater values of relative closeness indicate the better performance of WASH, and lesser values show the opposite.

21.3.2.2

Assessment of Spatial Inequality and Pattern of WASH Performance

We estimated the intra-city geospatial inequality in the coverage of WASH facilities through the Gini coefficient measure (Gini, 2005). Besides, we represented the ward-wise situation of different indicators of WASH condition through quintile maps. We also used different spatial statistics techniques to outline the spatial interactions and patterns of all selected indicators of WASH condition and overall WASH performance level. We adopted Global Moran’s Index (Moran, 1948) to understand

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the neighbourhood level spatial dependence or interaction of the different dimensions of WASH condition and overall WASH performance of both selected municipality corporations. Therefore, The Global Moran’s I index represents the mean situation of spatial autocorrelation of the selected dimension of the WASH condition. In this context, we have created the geographical weight of the indicators of the WASH condition based on K-nearest neighbour weight matrices. Therefore, we have generated the spatial weights based on k-3 nearest neighbour matrices for both cities. Besides, the local spatial pattern of these aspects is also outlined through Local Indicators of Spatial Association (LISA) mapping (Anselin, 1995, 1996). The LISA maps have demarcated significantly the clusters of municipal wards comprising good and bad WASH performance (High−High and Low−Low zone). Furthermore, the spatial outliers of this situation (low−high and high−low zones) have been identified. We also used local Gi* statistics (Getis & Ord, 1992) to detect local spatial clusters of various WASH indicators as well as overall WASH performance status. We used Moran’s I statistics to presume the spatial interaction and spatial pattern of the WASH facilities in this study. Traditional measures of inequality fail to capture the spatial pattern of socioeconomic issues as well as the relationship between inequality and space. Researchers often neglected space’s function in inequality for a long time due to their unsustained attention to space. The spatial autocorrelation, or relationship between neighboring spatial units, has a significant impact on the distribution of any socioeconomic aspect (Panzera and Postiglione, 2020). The spatial dependence denotes the similarity among the neighbouring geographical locations and spatial units. In this context, spatial dependence significantly influences or shapes the spatial ordination or geographical distribution of different socio-economic issues. The geographical location and spatial scale play an important role in deriving spatial dependence or spatial autocorrelation (Cartone & Postiglione, 2020). Despite this, spatial heterogeneity indicates the unequal distribution of different aspects linked to spatial autocorrelation. Therefore, the local spatial heterogeneity mapping significantly demarcates the spatial clusters with specific characteristics. Numerous studies on social, economic, and demographic issues have recently highlighted the influence of spatial effects (spatial dependence and spatial heterogeneity) in social science. These have demonstrated the impact of spatial dependence on the distributional pattern of various socioeconomic and demographic issues, as well as spatial epidemiology (Ghosh & Cartone, 2020; Haque et al., 2020; Mondal, 2020; Panzera & Postiglione, 2020; Taiwo & Ahmed, 2015; Wang & Chi, 2017) and the WASH condition (Chaudhuri & Roy, 2017; Pradhan & Mondal, 2020). The application of spatial statistics is significant for this present study to demarcate the intra-city clusters of spatial units comprising different WASH characteristics. Cartographic diagrams, and maps were prepared using Microsoft Excel 2013, QGIS 2.14, and GeoDa 1.18.0 software. A detailed explanation of inequality measures, Global and Local Moran’s I statistics, and Local G* statistics has been represented in Appendix 2.

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21.4 Results 21.4.1 Geospatial Inequality and Pattern in the Distribution of WASH Facilities Figure 21.1 depicts the overall distribution of various WASH facilities in the municipal corporations of Kolkata and Chennai. Besides, Chennai showed a relatively better situation than Kolkata in accessing selected WASH facilities, except for the accessibility of tap water from the treated source. There were about 15.6, 17.6, and 21.4% gaps between Kolkata and Chennai in accessing good residential conditions, LPG or PNG for cooking, and separate kitchens, respectively. In the case of household’s bathroom coverage and closed drainage facilities, there were 13.8 and 14.7% differences, respectively, between the two metropolises. On the other hand, two different scenarios were found in terms of geospatial inequality in the ward-wise distribution of WASH facilities in two metropolises (Fig. 21.2). In Kolkata, geographical inequality was found to be maximum in the case of the use of LPG/PNG for cooking fuel, following the distribution of closed drainage systems and the availability of drinking water sources within the premises compared to the distribution of other WASH facilities. In the case of Chennai, neighbourhoodlevel geospatial inequality in accessing drinking water sources within the household premises is relatively higher than the other wash facilities. Except for the in-house drinking water source and latrine facilities, geospatial inequality in the distribution of other WASH facilities was comparatively higher in Kolkata than in Chennai. However, the difference in geospatial inequality in accessing in-house drinking water and latrine facilities between the two metropolises was very low.

Fig. 21.1 Drinking water, sanitation and hygiene condition in Kolkata and Chennai

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Fig. 21.2 Gini index of inequality among wards of Kolkata and Chennai in drinking water, sanitation and hygiene facilities

21.4.2 Spatial Heterogeneity and Local Pattern of WASH Facilities The aforesaid traditional methods of measuring inequality cannot explain the spatial orientation of any indicators of any socio-economic aspects. In this context, spatial dependence has a significant effect on the geospatial distribution of any aspect. The spatial dependence of the ward-wise distribution of WASH facilities was also determined through Global Moran’s I Index significantly (P < 0.05) based on the geospatial weight calculated from k−3 and k−4 nearest neighbour matrices (Table 21.2). The spatial dependence of clustering was more evident in the distribution of all aspects of WASH facilities in Kolkata compared to Chennai. In the case of Kolkata, the spatial dependence was moderately high in the distribution of the household’s accessibility to LPG/PNG for cooking, closed drainage, and separate kitchen facilities. Besides, the location of the drinking water source, accessibility to reliable drinking water, bathroom facilities, and good residential conditions represented moderate spatial dependence. On the other hand, very low spatial dependence was determined in the distribution of almost every aspect of WASH facilities in Chennai. In the case of the distribution of all sanitation facilities, the accessibility of treated drinking water, and LPG/PNG as cooking fuel, the global Moran’s I index was near the “0” value, which indicates the random distribution of the mentioned matters. We performed quintile maps of the ward level distribution of different WASH facilities in Kolkata and Chennai Municipality Corporations (Fig. 21.3). Besides, LISA maps (Fig. 21.4) have also significantly outlined the local spatial pattern of

26.5

61.9

42.6

23.3

30.6

12.1

42.4

Tap water from treated source

Latrine within premises

Bathroom within premises

Wastewater connected to closed drainage

Good residential condition

Use LPG/PNG for cooking

Separate kitchen

Maximum

96.6

93.3

88.1

99.8

98.4

99.8

99.5

97.3

Geometric mean

71.20

61.08

63.48

81.30

81.77

94.73

86.35

74.02

Moran’s I (K = 3)

0.530

0.678

0.484

0.666

0.489

0.265

0.440

0.484

58.1

39.7

52.8

61.3

54

42.8

45.6

23.2

Minimum

Chennai Maximum

99.7

97

93.1

100

100

100

94.4

98.8

Geometric mean

93.54

81.14

78.64

95.47

94.44

94.13

77.90

73.01

Moran’s I (K = 3)

0.360

0.098

0.216

0.026

0.098

0.082

0.036

0.240

Global Moran’s I have been determined based on k-nearest neighbor (k = 3) weight metrics. The significance levels are based on 999 times of permutations, p-value < 0.05

33.7

Minimum

Kolkata

Drinking water within premises

Indicators

Table 21.2 Descriptive statistics and spatial dependence of the neighbourhood level WASH facilities in Kolkata and Chennai

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Fig. 21.3 Neighborhood level geospatial distribution of WASH facilities. Proportion of households accessing a & b drinking water within the premises, c & d tap water from treated source, e & f latrine, g & h bathroom within the premises i & j waste water connection to closed drainage, k & l good house condition, m & n LPG/PNF for cooking, o & p separate kitchen facilities in Kolkata and Chennai respectively

these distributions. In the case of availability and quality of drinking water and connection of wastewater to closed drainage, north, and central Kolkata showed an improved situation. In contrast, the wards situated along the marginal parts of eastern, southern, and western Kolkata showed the opposite situation. Accessibility to closed drainage facilities was also poor in some wards of western and southwestern Kolkata. LISA Maps significantly (P < 0.5) identified 10, 12, and 15 wards situated in the extreme west and south-western parts of the city, which show poor

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Fig. 21.4 Neighborhood level geospatial heterogeneity of WASH facilities. Proportion of households accessing a & b drinking water within the premises, c & d tap water from treated source, e & f latrine, g & h bathroom within the premises i & j waste water connection to closed drainage, k & l good house condition, m & n LPG/PNF for cooking, o & p separate kitchen facilities in Kolkata and Chennai respectively

water availability within premises and accessibility of tap water from the treated source and drainage facilities, respectively. The wards situated in southern Kolkata showed a significantly higher coverage of households with latrines and bathroom facilities. In contrast, some wards in the southern-central and western regions had significantly lower coverage of latrine and bathroom facilities. The proportion of good residential houses, households using liquid fuel for cooking, and separate households’ kitchen availability were comparatively higher in southern Kolkata. The share of good

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residential houses was significantly lower in some wards of north Kolkata. On the other hand, 10 to 12 wards of western Kolkata showed a poor situation in accessing hygienic cooking fuel and separate kitchen facilities. In the case of Chennai, a different scenario was observed in the spatial pattern of WASH facilities. A few wards situated western and central part showed good condition of drinking water sanitation and hygiene facilities. Some scattered wards in the northern and eastern parts of the city represented the poor state of various WASH facilities.

21.4.3 Spatial Dependence and Pattern of WASH Performance Inequality in the WAH performance index’s word-wise distribution in Kolkata is represented through histograms (Fig. 21.5a). A negatively skewed distribution of WASH performance was noticed. The ward situated along the eastern, southern, and western boundaries showed a lower performance of WASH conditions (Fig. 21.5b). The Global Moran’s Index denoted the moderate spatial autocorrelation in the distribution of the WASH performance index in Kolkata. Univariate LISA identified (P 0.05) two clusters of wards (a total of 14 wards) in the extreme western and southwestern parts of the city (Ward nos. 80, 123, 124, 125, 126, 127, 133, 134, 135, 137, 138, 139, 140, 141) with lower WASH performance. On the other side, a cluster of wards in the south-eastern part and some other wards are situated randomly in the central and northern parts (Ward no. 16, 26, 52, 69, 71, 94, 95, 96, 98, 102, 104, 105, and 109) showed a significantly better status of WASH performance. A few wards situated randomly in the central and northern parts indicated a relatively better condition of WASH facilities. The hotspot maps of Getis and Ord using the local Gi* value indicated similar neighbourhood-level clustering in this case (Fig. 21.5e). A negatively skewed distribution has been found in the WASH performance index (Fig. 21.6) in Chennai. The eastern coastal part of the municipality showed a comparatively lower level of WASH performance. The Global Moran’s I index represented a very low spatial dependence in the ward-wise distribution of WASH facilities. Univariate LISA identified a cluster of wards situated at the northern central part (Ward no 4, 11, 32, 40, 41, 42, 45, 109) that showed a lower level of WASH condition in the municipality. Wards 19, 23, 54, 60, 65, 77, 88, 89, 118, 120, and 125 (distributed throughout the central part) demonstrate the WASH condition’s good performance. The Getis and Ord cluster maps determined by the local Gi* value indicated similar results. These Getis and Ord cluster maps identified the good performance of WASH conditions in wards 44, 127 and the opposite situation in wards 6 and 30.

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Fig. 21.5 a Geospatial inequality and b spatial pattern of ward level WASH performance Index, c spatial dependence, d LISA cluster map and e Local Gi* cluster map of ward level WASH performance in Kolkata

21.5 Discussion We have estimated the geospatial inequality in the distribution of different WASH facilities based on the Gini index of inequality (Previously used geospatial inequality of housing and housing amenities in India, see also Chaudhuri & Roy, 2017; Ghosh et al., 2021b, 2022). In addition to that, we have adopted the spatial dependence of the coverage of WASH facilities in both metropolises at the neighbourhood level. The local spatial pattern of this ordination has also been outlined by LISA mapping and Local Gi* Mapping (Getis & Ord, 1992, 1996). This has significantly demarcated the neighbourhood with relatively higher and less coverage of WASH facilities considering spatial effects through Moran’s I statistics. However, the

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Fig. 21.6 a Geospatial inequality and b spatial pattern of ward level WASH performance Index, c spatial dependence, d LISA cluster map and e Local Gi* cluster map of ward level WASH performance in Chennai

neighbourhood-level spatial assessment of WASH facilities is limited in the case of Indian cities. We have outlined the spatial dimension of WASH performance in Indian two old metropolises, Kolkata and Chennai. In this context, we have performed the level of WASH performance by combining all possible water, sanitation, and hygiene aspects available in the most reliable census database through one of the most significant Multi-Criterion Decision Making approaches, Order Preference by Similarity Ideal Solution (TOPSIS). We observed comparatively better coverage of WASH facilities in Chennai than in Kolkata. Both cities were under the JNNURM Project, which included the improvement of housing, water supply, and sanitation facilities. According to the Census of India (2011), these two cities represented different results in the improvement of

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these urban facilities. It may occur due to the difference in local urban governance, and local economic conditions of the households between the two cities. Besides, our study has outlined some significant findings regarding spatial heterogeneity in the distribution of WASH facilities in two historical cities in India. Spatial inequality and dependence are much higher in Kolkata than in Chennai in almost every dimension of the WASH facility. The Global Moran’s I index showed the neighbourhood level spatial autocorrelations in WASH performance were moderate in the case of Kolkata, whereas Chennai showed it very low. Besides this, a significant spatial pattern in the distribution of WASH facilities has been found in Kolkata, whereas the geospatial pattern of WASH distribution in Chennai was random or near-random. The present existence of intra-city spatial inequality and the neighbourhood level spatial autocorrelation is rooted in unequal land use development during the British colonial era. Still, there is a significant distinctive socio-spatial inequality between Black Town, which is presently known as Old Kolkata, mainly indicates the northern part of present-day Kolkata, and White Town, the southern and central part of present-day Kolkata. It has been stated that the legacy of segregation has remained from the colonial period into the post-colonial city concerning socio-economic status, religion, and caste (Chakroborty, 2005). Clark and Landes (2010) discovered high spatial concentrations of caste groups among Brahmins in specific city areas. Another side, the north-central part, is the oldest part of the city. This is the most congested and depressed area of the city. Therefore, the scarcity of WASH facilities has been noticed in the northern part. During the last few decades, the city expanded towards the south. The local urban government distributed very well the WASH facilities in different wards in the newly developed part of the city. Therefore, we observed a lower degree of inequality and spatial clustering in Chennai as compared to Kolkata. We observed relatively poor conditions of access to drinking water sources within household premises, treated drinking water, and wastewater connection to closed drainage in the southern and southwestern parts of Kolkata. The northern part showed a comparatively better situation in these matters. Despite having water connections, rich people living in the multi-story apartments pump their water. However, an alarming situation is that Chakraborty et al. (2017) previously highlighted the higher level of arsenic contamination in the southern and western parts of Kolkata (specifically, the level of arsenic >50 µg/l in wards 139, 140, and 141 of western Kolkata). Another team, Ali et al. (2020), have also mapped the poor quality of groundwater in the eastern part of Kolkata. Against this backdrop, our study suggests the arrangement of in-house treated drinking water sources in the southern and western parts of Kolkata. Besides, brick sewer systems were previously developed in many Indian cities like Kolkata, Delhi, Mumbai, and Chennai. During colonial time (as early as 1868), the man-entry brick sewer system was constructed in the old core city of Kolkata, which is the northern part of present-day Kolkata. However, after 140 years of continuous

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deterioration, a lack of timely proper maintenance, and increasing population pressure, the system collapsed. Later in 2008, Kolkata Municipal Corporation (KMC) took the initiative to rehabilitate these old underground brick sewer systems (Basu et al., 2013a, b). Consequently, a good state of wastewater connection to closed drainage was observed in north Kolkata. Another side, southern Kolkata, is the extended part of the city, which is the habitat of elite, educated people. Urban poverty and the slum population are relatively lower in this part (Mishra, 2020; Haque et al., 2020). In correlation with these findings, our study also outlined good housing conditions, including basic WASH amenities like accessibility to the latrine and bathroom, liquid fuel, and a separate kitchen facility in the southern part of the city. Besides, the household’s accessibility to a bathroom, a separate kitchen, and LPG/PNG as cooking fuel is very poor in the extreme western part of the city. The concentration of the slum population, a relatively backward society comprising less female education may influence this situation in this region. Some wards in north Kolkata (the old city or where the city began) had a very small proportion of those with decent housing. Consequently, many houses in that part of the city are still very old and are in a dilapidated state. Alternatively, due to the increase in new build-up areas, the southern part of the city exhibited good housing conditions. We observed lower performance of WASH facilities in some wards in the western and south-western parts of Kolkata. It may be due to the appearance of slum housing, a higher proportion of poor residents, uneducated people, and a relatively higher level of urban deprivation in these wards (Mishra, 2018; Haque et al., 2020). On the other side, the southeastern part of Kolkata showed a relatively good WASH performance. Some researchers (World Bank, 2001; Dasgupta et al., 2013; Ali & Ahmed, 2018, 2020) outlined the risks of monsoonal food, flood vulnerability, water logging problems, the risk of dengue, a water-borne disease outbreak, and poor water quality in the western and southwestern parts of Kolkata. In agreement with these previous findings, our investigation also confirmed the poor WASH performance in these southwestern and western parts. We identified a cluster of neighborhoods in Chennai’s northern central region that have poor WASH performance. The southwestern part shows the better condition in this case. Baud et al., (2009) outlined a higher degree of urban deprivation in social, human, financial, and physical capital among the wards of the northern central part and a lower degree in the southwestern part. Besides this, they have also confirmed the more concentration of slum population in the northern central part of the city. Later, Krishnamurthy and Desouza (2015) mapped the higher and moderate concentration of slum population in the north-central part and along the southeastern coastal boundary of the city, respectively. Corroborating with previous findings our research also outlined the poor coverage of all selected drinking water, sanitation, and hygiene facilities and a lower degree of overall WASH performance over the northern central part. The wards situated in the southwestern part comprising a comparatively less

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share of the slum population showed good WASH performance. Several researchers (De, 2020; Krishnamurthy & Desouza, 2015; Satapathy, 2014) previously stated that there was less coverage of tap water connections, accessibility to clean water, and sanitation in slum houses than in their non-slum counterparts. Our inquiry confirms the higher level of scarcity of WASH facilities in the slum areas of the city. In addition to that, this uneven distribution of slum population and a higher level of urban deprivation within the city may have an association forming this pattern of WASH performance within Chennai. Besides, the northern part of the city is also characterized by large-scale and small-scale industries that were established from 1900 to 1940. This zone created employment and low-income residences that are linked with the higher share of the slum population in this region and its surroundings (Baud et al., 2009). The land use pattern showed that Chennai expanded over the northern part during the 1990s decade and then expanded over the southern side (Sikarwar & Chattopadhyay, 2020). Therefore, corroborating these findings, our study also observed a comparatively higher share of dilapidated housing in the northern part of the city, as this part of the city is comparatively older than the newly emerging southern part. The earlier investigation suggests high malaria prevalence in the northeastern and south-eastern parts of the city (Kumar et al., 2014). Based on our findings, it can be assumed that poor wash performance in association with people’s poor socioeconomic status, and unhygienic living conditions, may have increased the risk of malaria prevalence over this portion of the city.

21.6 Conclusion In this study, we proposed a WASH performance index combining possible indicators through the TOPSIS-MCDM method based on the availability of the most reliable 2011 Indian census data. We highlighted the better situation of different WASH facilities in Chennai than in Kolkata. The level of spatial inequality and dependence in Kolkata was higher than in Chennai. We also identified the spatial clusters of the neighbourhood with poor WASH performance. Therefore, the observation suggests more initiative and attention to improving household WASH infrastructure in the identified wards with poor WASH performance. These activities may also help to build up sustainable cities and communities (SDG 11), and reduce spatial inequality (SDG 10) and urban poverty (SDG 01) along with SDG 06. The Government of India has already launched several projects for WASH development in the last few decades. However, as we have used Census of India 2011 data, the study has failed to estimate the effect of the recently launched (after 2011) WASH improvement project. These initiatives to improve WASH facilities will also generate local employment, which may hasten the society’s economic sustainability, particularly in deprived

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urban areas. The development of a household’s WASH amenities indicates social security and enhances the social status and dignity of the household, which are linked with social sustainability and sustainability communities.

Appendix 1: Location and administrative setup of Kolkata and Chennai

Source: World Map and India Map: GADM database of Global Administrative Area. Kolkata Municipal Corporation Ward Map: District Census Handbook, Census of India. Chennai Municipal Corporation Ward Map: District Census Handbook, Census of India

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Appendix 2 Gini Coefficient can be explained by the following equation: Gini =

n n | 1 ∑ ∑ || Yj − Yi | 2 2n u j=1 i=1

(21.6)

| | where, n = Number of Wards, u = mean the selected variables, | Yj − Yi | = absolute inequality among Municipality Wards. The Gini coefficient value varies from “0” (perfect equality) to “1” (perfect inequality) (Wagstaff et al., 1991; Cowell, 1998). Global Moran’s I Index The following equations can explain the global Moran’s I index: ∑ ∑

n Ix = ∑ ∑ i

i j

wij (xj − x)(xi − x) ∑ 2 i (x2 − x)

j

wij

(21.7)

where n = number of wards considered for the study, x is the value of the indicators of the districts i, x = mean value of the selected indicators and wij are the geographical weights of the spatial units indicating spatial proximity between i and j. Moran’s I value varies from −1 to +1, where the +1 value indicates high positive spatial autocorrelation (Clustering) and the −1 value denote the high negative spatial autocorrelation (dispersion). In this case, the “0” value indicates no spatial autocorrelation in the spatial distribution. Local Indicators of Spatial Association (LISA) Statistics The Local Moran’s statistics which is known as LISA statistics is the decomposition of the global Moran’s I statistics, which can be expressed as the following: n(xi − x) I (i)x = ∑

∑ j

i (xi

wij (xj − x)

− x)2

, j /= i

(21.8)

where n = number of the municipal wards considered for the analysis, xi is the variable under study, x= mean value of the selected indicator xi , and wij are the generated spatial weights. Local Gi* Statistics We have used local Gi* statistics (Getis & Ord, 1992) that can be explained as follows: ∑n ∑n j=1 Wij j=1 Wij Xj − X ∗ (21.9) Gi = / [ ] s

n

∑n

j=1

)2 (∑ n Wij2 − j=1 Wij n−1

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519

where X is the value of the indicators, W ij is the spatial proximity between i and j, and n denotes the total number of wards considered for the study. Here S can be represented as: /∑ S= ∑n

Here the value above lone.

j=1

n

Xj

n j=1

n

Xj2

( )2 − X

(21.10)

indicate the high zone and below this level indicate low

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Chapter 22

Spatial Variation of Overall Infrastructural Development Index (OIDI) in Census Towns: A Study of Indo-Gangetic Plain Region, India Sanjoy Saha , Somenath Halder , and Subhankar Singha

22.1 Introduction An essential feature of urbanization in developing countries like India is that low and unplanned urban infrastructure growth leads to the crisis of urban civic amenities and environmental threats in urban areas (Jain, 2018; Park & Yoo, 2022; Kutor et al., 2022). In developing nations, the large urban centers are the spheres of pulling rural or countryside labor force seeking better employment (Liu et al., 2022; Sheng et al., 2022). Consequently, the large urban centers in these nations with growing economies are overburdened by rural immigrants or job-seekers and making nonestimated requirements for housing, sanitation facilities, power consumption, and transportation (Mitra, 2022). So, the urban administrative bodies of the large cities are being faced with a critical situation for making a rational strategy to enhance the urban infrastructure to support the surplus migrated rural labor forces pretend as new citizens (Eze, 2021; Sultana et al., 2022). In this context, it can be stated that metropolitan cities have been characterized by slums as the problematic areas of the major functioning cities for managing sustainable urban infrastructural development. Therefore, it is a new challenge for the urban development authorities of the cities to manage the unexpected pressure on the urban center imposed by the migrated rural force (Kamath & Tiwari, 2022). One of the significant reasons for the increasing urban population in India is the migration of the rural workforce to urban areas. Even the infrastructural development in some developed villages in India has reached such a level that they should be considered the urban center (Glaeser, 2022; Hashim, 2022). Based on the statistical dimension, a rural center S. Saha · S. Halder (B) Department of Geography, Kaliachak College, Malda, West Bengal, India e-mail: [email protected] S. Singha Independent Researcher, Ex-student, Department of Geography, Gour Banga University, Malda 732101, West Bengal, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_22

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having at least 5,000 population sharing with more than 75% of the male population performing non-agricultural activities and arithmetic population density is 400 persons/km2 . Still, it is not recognized administratively as an urban center by the Registrar General of India (RGI) and is defined as a ‘census town’ by the Census of India. Based on the administrative dimension, the Government of India has two types of settlements that are recognized, i.e., Statutory Towns administrated by local urban administrations and Villages administrated by Gram Panchayats. These two types of administration function as per rules mentioned in the 73rd and 74th Constitutional Amendment Acts, enacted in 1992. In modern times, the census town is categorized as a village settlement and governance by Panchayats or grass-root rural governments even though they have the characteristics of urban settlements (Chatterjee, 2014). Therefore, census towns, the typical rural settlements in India, require a particular policy for optimum functioning as urban units to continue their development without hampering environmental aspects (Coutinho-Rodrigues et al., 2011; Sahoo et al., 2022).

22.1.1 Relevance of the Included Agenda: ‘Census Town’ The most noticeable facts about the flourishing of census towns (regarded as nonstatuary urban centers) in India are that the number and population of the census towns have increased drastically compared to the statutory towns. The share of urban population to the total urban population of the census towns rose from 7.6% in 2001 to 14.5% in 2011 (Onda et al., 2019; Pradhan, 2017). The number of census towns has increased about three times compared to 2001 in 2011. The Census of India reveals that 2001 the number of census towns was 1362 in 2001 while it became 3894 in 2011. Such a drastic growth in the number of census towns might be encouraged by ‘artificially induced infrastructural’ development rather than selfinduced development (Kundu, 2011; Patil et al., 2022). Due to remarkable changes in the demographic and workforce characteristics in the rural settlement in India, many rural settlements were identified as census towns in the 2011 Census. Consequently, the numbers of census towns show a massive increment in 2011 compared to the 2001 Census (Bhagat & Mohanty, 2009; Mukhopadhyay et al., 2016). As per the Census of 2001, about 90 million urban population increased from 1991 to 2001. The reason for such a noticeable increase in the urban population during the said decade was about 44% natural growth of urban population, 21% urban population growth for rural to urban migration, and the rest for re-classification of rural settlements as census towns. Urban-level infrastructural development in some rural settlements uplifted them into ‘census towns.‘ The infrastructural development in an urban area incorporates three types of infrastructural aspects, i.e., physical, social, and economic infrastructure, that are the basic parameters for assessing the level of development of the urban area (Bibri & Krogstie, 2020). Therefore, to determine the growth of the census towns in India, it is necessary to analyze the mentioned aspects of the infrastructure intensively. Despite this,

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infrastructural status assessment is a vital task in the search for a good strategy for sustainable urban development (Hellström et al., 2000; Ramaswami et al., 2012). A notable spatial variation in the growth of census towns among the Indian provinces is found due to variations in the demographic and socio-economic conditions among the rural settlers. Additionally, a significant variation in the infrastructural growth in the census towns of different states is observed in the current decades. A study on the nature of census towns in India revealed that the highest concentration of census towns is found in Kerala and West Bengal (Pradhan, 2017). In 2016, The Ministry of Urban Development (Government of India) recommended to all provinces of India to recognize the actual census towns of the respective states, and the said ministry also decided to convert some of the census towns to statutory towns to make a strategy to ensure the planned infrastructural development by the statutory ‘Local Urban Body’ (Chandiramani & Patil, 2018). Especially the haphazard infrastructural development in the census towns in India creates problems for managing the gradually increasing demands of the civic amenities by the rural dwellers of the census towns. On the other hand, unplanned infrastructural development in these areas creates a notable crisis for the sustainability of the urban environment (Haque & Patel, 2018; Arif & Gupta, 2020). Therefore, careful studies are needed to manage the census towns in India for better tackling methods and overall management (Barbhuiya et al., 2022; Maqbool & Jowett, 2022). Our study aimed to investigate the spatial variation in the infrastructural development among the census towns developed in the provinces of the Indo-Gangetic Plain. Analyzing spatial variation in the census towns’ infrastructural development can assist in reframing a rational policy for the optimal or desired development of the census towns.

22.2 Materials and Methods Detailed data about the infrastructural development requires analyzing the growth scenario of the census towns of the study area. Our study is solely based on secondary data about the Indian census towns. Therefore, we have collected the census data regarding the ‘census towns’ of the concerned study area from the Census of India, 2011. The indices of the infrastructural development in the census towns are categorized into the sub-indices, i.e., physical, social, and economic infrastructure. At first, all the infrastructural indicators are averaged at the district level of each state. To reduce the dimension of the components and systematic analysis, Principal Component Analysis (PCA) has been applied. The PCA aftermath of the Kaiser–Meyer– Olkin (KMO) measure shows that the sampling adequacy is 0.654, which reflects good fitness. Simply put, the result demonstrates the adequacy of the dimension reduction method through PCA. Hence, the Principal Component Analysis (PCA) method determined each sub-index of the infrastructure indicators. The indicators are normalized in the following equation (Table 22.1). Here, normalized indicators are changed into weighted indicators. The determining indicators of the Physical Infrastructural Index (PII) are the availability of

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Table 22.1 Equations used for computation Equations number Description

Equations Nri =

PI I =

SI I =

EII =

Ii Xi

(1)

Where, Ni = Normalized indicator; Ii = Individual indicator; X i = Mean of the indicators



Pwi Ni

(2)

Where, P I I = Physical Infrastructural Index;  Pwi = Weighted physical indicators; Ni =Number of physical indicator

Swi Ni

(3)

Where, S I I = Social Infrastructural Index;  Swi = Weighted social indicators; Ni =Number of social indicator

(4)

Where, E I I = Economic Infrastructural Index;  E wi = Weighted economic indicators; Ni =Number of economic indicator

(5)

Where, O I D I = Overall Infrastructural Development Index;  (P I I + S I I + E I I )= Sum-up index value of physical, social, and economic indicators; Ni =Number of indicator





O I DI =

E wi Ni



(P I I +S I I +E I I ) Ni

drainage facilities in the census towns, distances of the health facility center from the census towns, and the provision of electricity. In the case of drainage facilities, we considered all types of drainage system avail in the Census of India; those are as follows drainage systems (type 1: pit system; type 2: flush/pour flush/waterborne; type 3: service; and type 4: others). Whereas the health indicator includes the facilities like hospitals (allopathic & others, distance in km.), family welfare centers (distance in km.), maternity and child welfare centers (distance in km.), T.B. hospital/clinic (distance in km.), nursing homes (distance in km.), veterinary hospital (distance in km.), mobile health clinic (distance in km.) and charitable hospital/ nursing home (numbers) availed by local dwellers. The provision of electricity (as an indicator) includes the availability of domestic, industrial, and commercial electricity, road lighting points, and other electricity-related facilities. The Social Infrastructural Index (SII) incorporates the number of educational centers, including primary, secondary, and higher secondary schools and colleges. Only two determining indicators, i.e., the number of banks and agricultural credit societies, are derived through PCA for Economic Infrastructural Index (EII). Finally, the Overall Infrastructural Development Index (OIDI) measures the all-around infrastructural development in the census towns in the focused study area. The normalized indicators are multiplied by the weightage as the percent of variance derived from PCA Initial Eigenvalues.

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The weighted scores of each individually predetermined index value of three major indicators (i.e., PII, SII, and EII) have been extracted to measure the status of each category of infrastructure in the census towns. Then, the Overall Infrastructural Development Index (OIDI) was calculated as the mean of the Physical Infrastructural Index (PII), Social Infrastructural Index (SII), and Economic Infrastructural Index (EII). The above-said statistical analysis has been pursued using Excel-2019 and SPSS-22 software. The mapping of each index (i.e., major indexes and summarized index) is prepared to visualize the infrastructural development scenario of the census towns in the study area by ArcGIS-10.30. From the derived results, a detailed discussion has been made to explain the situation of the infrastructural development of the census towns under the Indo-Gangetic Plain region. Furthermore, an attempt also has been made to give some recommendations for framing a sustainable plan for the infrastructural development in the census towns.

22.3 Study Area This chapter aims to assess the infrastructural development in the census towns developed in India. Therefore, we need detailed information on the selected regions where the census towns have grown remarkably. Indo-Gangetic Plain of India is an important geo-climatic region that significantly favors agriculture (Kashyap & Agarwal, 2021; Sati, 2014). The Indo-Gangetic Plain of West Bengal is located in the extreme eastern part having a total geographical area of about 88,752 sq. km. In West Bengal, there were 19 districts in 2011, but it has 23 districts at present. As per the 2011 Census, the state has about 32% of the urban population to its total population. Metropolitan city Kolkata in West Bengal is the third largest city in India and functions as a significant key role player in the economy of north-east India (Basu Roy & Saha, 2011; Paul & Patra, 2021; Das, 2018). Kolkata is the most important pulling center of the rural migrating workforce, not only from West Bengal but also from other northeastern neighboring states. The rural areas of West Bengal are also growing their infrastructure rapidly. Another important state in Indo-Gangetic Plain is Bihar, which has a common state boundary with West Bengal in the western part. The total geographical area covers Bihar, about 94,163 sq. km., and had 38 districts in 2011. As per Census (2011), the state of Bihar shares only about 11% urban population to its total population. Patna, the capital city of Bihar, functions as the dominating polling center for rural migrants. Uttar Pradesh is the largest state in the Indo-Gangetic Plain and covers a total geographical area of about 240,928 sq. km. As per Census (2011), Uttar Pradesh has 71 districts, and sharing of the urban population is around 22%. The state of Uttar Pradesh has a significant industrial and agricultural background for its economic functions (Upreti & Singh, 2017). Varanasi, Kanpur, Agra, Lucknow, and Meerut are the significant prime cities of Uttar Pradesh that dominate India’s industrial economy. Haryana state has a common boundary with Uttar Pradesh in the eastern part. Haryana covers about 44,212 sq. km. total geographical area, and it has 21 districts as per Census (2011). The same state, Haryana, had an urban population

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share of about 15% of the total population in 2011. Faridabad, Rohtak, and Panipat are significant cities in Haryana function as the industrial belt of the state. The fifth state, Punjab, is situated in the extreme western part of the Indo-Gangetic Plain, and it covers about 50,362 sq. km. of total geographical area. Punjab shares about 37% urban population with its total population. On the other side, Chandigarh, Amritsar, Patiala, and Jalandhar are important cities in Punjab. In this great plain, many agrarian-economy-based rural settlements flourish primarily. It has been observed that there are many villages under the targeted regions, which are developed (along infrastructural parameters) to such a level that they are considered ‘census towns.’ Thus, we selected the provinces of this plain for spatial analysis. These provinces are West Bengal and Bihar, Uttar Pradesh, Haryana, and Punjab (Fig. 22.1). There are 1,135 census towns in the study area as per Census (2011). The highest numbers (759) of census towns are identified in West Bengal. At the same time, the lowest number of census towns (35) are found in Bihar. In the states of Uttar Pradesh (240), Punjab (53), and Haryana (48), the number of census towns is remarkably low as compared to West Bengal. Thus, a clear picture of the difference in the growth of census towns among the provinces of the Indo-Gangetic Plain is an important matter for analysis for the researchers and planners. In the provinces of the foresaid region, rural areas are densely populated. Hence, there is enormous pressure on agricultural land and a crisis in employment generation. Consequently, many labor forces are migrating to the major cities to seek employment. The major cities in the study area face the

Fig. 22.1 Location map of the study area

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supply of optimum accommodations for rural migrants. Rural-to-urban migration is urgently required to combat the problem (Roy & Thangaraj, 2021). In this context, the employment opportunity in the census towns of the study area may be enhanced by the infrastructural development in a well-planned manner. Therefore, the current study area is a significant region for research persuasion, linked with census towns.

22.4 Result and Discussion The results of incorporated sub-indices and the Overall Infrastructural Development Index (OIDI) of the census towns at the district level in all selected Indo-Gangetic Plain extracted from the weighted index analysis also provide a distinct picture of the infrastructural scenario amongst census towns. A vivid spatial disparity of infrastructural development among the census towns of each selected state is presented by sub-indicator-based indexed scores and major indicator-based indexed scores.

22.4.1 Physical Infrastructural Index (PII) The Physical Infrastructural Index (PII) shows a significant variation in the availability of the physical infrastructural indicators, such as the provision of drainage systems, health service-related infrastructure, and electricity in the census towns of each selected state in the study area (Table 22.2). The highest score of PII is 3.79, and the lowest is 0.23; both are observed in Buddha Nagar district and Kabir Nagar district of Uttar Pradesh, respectively. Thus, there is a remarkable variation in the physical infrastructural growth in the census towns and inter-districts variation of the same state. The result of the physical infrastructural index also shows a state-wise variation in physical infrastructure. For displaying the degree of variation of an indexed score of PII, mainly five categories have been chosen, such very low, low, moderate, high, and very high (Fig. 22.2). From the mapping of PII, it is observed that a very few numbers of districts in the selected five states have a relatively high indexed score. On the other side, comparatively ‘very low’ indexed score has been seen in a large number of districts in all four states, except West Bengal. Thus, the scenario of infrastructural development in West Bengal is merely good compared to the other states. Contrastingly, most of the districts of the states (like Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal) display low to moderate physical infrastructural status in the census towns. The spatial variation in the Physical Infrastructural Index among the states and the districts within a state is for their differential economic strength and adopted development strategy by the local administrative bodies. Moreover, the attitudes of dwellers of census towns toward building up the physical infrastructure in their houses, like the sewage system and sanitation-related physical aspects (Jain et al., 2021).

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Table 22.2 Indo-Gangetic Plain (India): district-wise diversification of PII Magnitude of physical infrastructure diversification

Index value

Name of states and districts

Very low

0.00–0.76

West Bengal (Dakshin Dinajpur, Bankura, Maldah, Murshidabad, Burdwan, Purba Medinipur, Darjeeling, South 24 Parganas, Howrah, Jalpaiguri, Nadia, and Cooch Behar) Bihar (Bhagalpur, Sitamarhi, Jamui, Muzaffarpur, and Samastipur) Uttar Pradesh (Kabir Nagar, Siddharth Nagar, Kaushambi, Lalitpur, Ravidas Nagar Bhadohi, Mirzapur, Gorakhpur, Chandauli, Mahamaya Nagar, Dehat, Lucknow, Pratapgarh, Farrukhabad, Faizabad, Bareilly, Kheri, Aligarh, Auraiya, Ambedkar Nagar, Mathura, Azamgarh, Jhansi, Rampur, Sultanpur, Mau, Jaunpur, Allahabad, Moradabad, Balrampur, Agra, Pilibhit, and Muzaffarnagar) Punjab (Bathinda, Firozpur, Rupnagar, Patiala, Amritsar, Jalandhar, Panchkula, Ajit Singh Nagar, Bhagat Singh Nagar, and Ludhiana) Haryana (Mewat)

Low

0.77–1.52

West Bengal (North 24 Parganas, Hooghly, Birbhum, Uttar Dinajpur, and Paschim Medinipur) Bihar (Patna and Rohtas) Uttar Pradesh (Ghaziabad, Bijnor, Unnao, Ballia, Sonbhadra, Gonda, Bulanshahr, Bara Banki, and Firozabad) Punjab (Hoshiarpur, Tarn Taran, Kapurthala) Haryana (Sonipat, Ambala, Gurgaon, Yamunanagar, and Panipat)

Moderate

1.53–2.27

Uttar Pradesh (Varanasi)

High

2.38–3.03

Uttar Pradesh (Kanpur Nagar)

Very high

3.04–3.79

Uttar Pradesh (Meerut and Buddha Nagar)

Source Census, 2011 (Computed by authors)

22.4.2 Social Infrastructural Index (SII) Across the selected census towns of the Indo-Gangetic Plain, the social infrastructural factor is considered a major influencing parameter for the overall infrastructural status. This selected indicator is composed of the factors like the number of educational centers serving to achieve education by the dwellers. Though detailed information on this indicator is given in the earlier paragraph. In this context, the Social Infrastructural Index (SII) indicates a notable spatial variability among the census towns in the focused region. The highest SII index score (3.15) is observed in the Jhansi district (Uttar Pradesh). At the same time, the lowest index score (0.07) is found in the Bathinda district (Punjab). A small number of districts in the study area have comparatively high indexed scores (Table 22.3), while about 57% have moderate indexed scores in educational infrastructure. Among the districts of West Bengal, the

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Fig. 22.2 Spatial distribution of PII in Indo-Gangetic Plain region

educational infrastructure of census towns is observed as good compared to the other four states (Punjab, Haryana, Uttar Pradesh, and Bihar). In contrast, Punjab stands in a poor position compared to the other states (Fig. 22.3). In the case of the Social Infrastructural Index in the concerned study, we have chosen the only number of educational institutions in the census towns as only this indicator is available in the Census of India (2011). The number of educational institutions in a census town depends on the government’s policy and the requirement of the educational institution as demanded by the dwellers according to their motivation towards education (Chen & Huang, 2021). Besides government educational institutions, few private educational institutions flourish in an area to promote education if the dwellers of the area are highly interested in achieving education as a priority of their socio-cultural needs (Malin & Tan, 2022). Thus, the sum-up inter-state spatial differentiation of the Social Infrastructural Index (SII) is not only due to the differentiation of implemented policies by several administrative bodies but also to the dwellers’ attitude and education progress.

22.4.3 Economic Infrastructural Index (EII) Here, the consulting indicator majorly indicates the institutions or organizations that perform a vital role in financial transactions in the census towns under the IndoGangetic Plain region. A perusal of Table 22.4 signals an unexpectedly low indexed

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Table 22.3 Indo-Gangetic Plain (India): district-wise diversification of SII Magnitude of social infrastructure diversification

Index value

Name of states and districts

Very low

0.00–0.63

West Bengal (Murshidabad, Uttar Dinajpur, Nadia, Hooghly, South 24 Parganas, Burdwan, Howrah, Paschim Medinipur, North 24 Parganas, Maldah, Jalpaiguri, Birbhum, Purba Medinipur, Darjeeling, Cooch Behar, Dakshin Dinajpur, and Bankura) Bihar (Sitamarhi, Jamui, Muzaffarpur, Bhagalpur, Lucknow, and Samastipur) Uttar Pradesh (Rampur, Pratapgarh, Moradabad, Chandauli, Buddha Nagar, Faizabad, Ambedkar Nagar, Gonda, Ballia, Sultanpur, Unnao, Mathura, Dehat, Varanasi, Farrukhabad, Azamgarh, Gorakhpur, Aligarh, Agra, Kaushambi, Bareilly, Jaunpur, Mirzapur, Bara Banki, Muzaffarnagar, Ravidas Nagar Bhadohi, Kanpur Nagar, Mahamaya Nagar, Mau, Allahabad, Bijnor, Sonbhadra, Auraiya, Firozabad, Pilibhit, Bulanshahr, Kheri, and Kabir Nagar) Punjab (Bathinda, Kapurthala, Amritsar, Firozpur, Ludhiana, Patiala, Ajit Singh Nagar, Tarn Taran, and Rupnagar) Haryana (Sonipat, Panipat, Ambala, Yamunanagar, Mewat, and Gurgaon)

Low

0.64–1.26

Bihar (Rohtas and Patna) Uttar Pradesh (Meerut, Siddharth Nagar, Ghaziabad, Balrampur, and Lalitpur) Punjab (Jalandhar, Bhagat Singh Nagar, and Hoshiarpur) Haryana (Panchkula)

Moderate

1.27–1.89

–

High

1.90–2.52

–

Very high

2.53–3.15

Uttar Pradesh (Jhansi)

Source Census, 2011 (Computed by authors)

score among the census towns, which promptly shows poor economic infrastructure status and the urgency of the need for reframed policy. Again, a remarkable spatial variation of the indexed score of EII has also been noticed among the census towns located in the selected districts of respective states (Fig. 22.4). For instance, Jamunanagar district of Haryana has the highest EII score (4.55). While the lowest EII index score (0.00) is observed in Uttar Dinajpur of West Bengal, Jami and Sitamarhi districts of Bihar, and Bijnor, Moradabad, Meerut, Aligarh, Mahamaya Nagar, and Mirzapur of Uttar Pradesh. The round-up economic infrastructural index status in census towns of West Bengal is comparatively better than in other states. The EII is comparatively the lowest across the census towns of Punjab compared to the other four states. The spatial variation in the Economic Infrastructural Index (EII) generally happens due to the disparity in economic activities performed by the dwellers in the concerned area (Iqbal et al., 2022). Additionally, sometimes the economic strength

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Fig. 22.3 Spatial distribution of SII in Indo-Gangetic Plain region

of the dwellers is functionally responsible for such remarkable spatial variation. Other than these, financial organizations like nationalized or private banks are also interested in establishing their branches in areas with good potential for financial transactions. In the study area, such condition is insufficient in all census towns.

22.4.4 Overall Infrastructural Development Index (OIDI) The customized overall infrastructural development index status of the census towns of the current study area is extracted from the analysis of the Overall Infrastructural Development Index (OIDI). A distinct inequality in the overall infrastructural development in census towns is revealed from the study of OIDI (Table 22.5). The highest OIDI (2.05) is observed at Yamunanagar (Haryana). On the other hand, the lowest indexed score (0.21) is found at Sitamarhi (Bihar). While a few numbers of census towns have a high OIDI. Contrastingly, many census towns have low OIDI, reflecting the necessity of infrastructural development in this region. In short, the status of overall infrastructure in the census towns is not at the expected level (Fig. 22.5). Even though the census town of Haryana had the highest OIDI, the status of the infrastructural profile could be better. Simultaneously, in eastern states like West Bengal, the overall infrastructural development is in a better position than in the rest of the states (i.e., Punjab, Haryana, Uttar Pradesh, and Bihar). Relation

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Table 22.4 Indo-Gangetic Plain (India): district-wise diversification of EII Magnitude of economic infrastructure diversification

Index value

Name of states and districts

Very low

0.00–0.91

West Bengal (Uttar Dinajpur, Darjeeling, South 24 Parganas, Paschim Medinipur, Burdwan, North 24 Parganas, Jalpaiguri, Cooch Behar, Bankura, Hooghly, Nadia, Howrah, Maldah, Birbhum, Murshidabad, Purba Medinipur, and Dakshin Dinajpur) Bihar (Jamui, Patna, Sitamarhi, Muzaffarpur, Bhagalpur, Rohtas, and Samastipur) Uttar Pradesh (Bijnor, Moradabad, Meerut, Aligarh, Mahamaya Nagar, Mirzapur, Faizabad, Pilibhit, Ravidas Nagar Bhadohi, Chandauli, Bulanshahr, Mathura, Kaushambi, Agra, Varanasi, Firozabad, Kabir Nagar, Gonda, Kanpur Nagar, Jhansi, Gorakhpur, Farrukhabad, Sultanpur, Azamgarh, Mau, Siddharth Nagar, Allahabad, Ballia, Bara Banki, Sonbhadra, Rampur, Lalitpur, Ghaziabad, Jaunpur, Muzaffarnagar, Ambedkar Nagar, Unnao, Lucknow, Bareilly, and Auraiya) Punjab (Bathinda, Patiala, Firozpur, Kapurthala, Jalandhar, Rupnagar, Ludhiana, Bhagat Singh Nagar, and Amritsar) Haryana (Ambala and Mewat)

Low

0.92–1.82

Uttar Pradesh (Pratapgarh, Dehat, Buddha Nagar, Balrampur, and Kheri) Punjab (Ajit Singh Nagar and Hoshiarpur) Haryana (Sonipat, Panipat, and Panchkula)

Moderate

1.83–2.73

Punjab (Tarn Taran) Haryana (Gurgaon)

High

2.74–3.64

–

Very high

3.65–4.55

Haryana (Yamunanagar)

Source Census, 2011 (Computed by authors)

among the indicators is found in the comprehensive study; the economic condition of any area is the most dominating factor, which helps enhance the development of other infrastructures (Hu et al., 2022). In the study area, a significant variation in the overall infrastructural index is found due to a differential rate of development in all infrastructural indices.

22.4.5 State-Wise Distributional Scenario of Major Indicators A close perusal of Table 22.6 shows a (comparatively) higher disparity among the selected major indicators and across five pre-selected administrative regions (State: West Bengal, Bihar, Uttar Pradesh, Haryana, and Punjab) under the

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Fig. 22.4 Spatial distribution of EII in the Indo-Gangetic Plain region

Indo-Gangetic Plain. Figure 22.6 represents a triangular diagram that primarily highlights the scenario where Haryana stands with the highest EII indexed score (1.62), followed by Punjab and other remaining states (Table 22.6). In the case of the physical infrastructural index (PII) again, Haryana gains the highest score (0.88), followed by Uttar Pradesh (0.80) and other selected states. On the other side, Uttar Pradesh scored with a greater magnitude (0.50) in connection with SII, followed by Haryana (0.47) and other states. In short, it can be said that relatively Haryana and Punjab are in a better position in terms of the infrastructural condition of census towns.

22.5 Policy Proposal: Rational Development of Infrastructure From the precise analysis of the infrastructural development scenario of the census towns in the Indo-Gangetic Plain region, it can be argued that there is an urgent need for a good policy to enhance the infrastructural development in the census towns. From this point of view, the physical infrastructure in the census towns is to be enhanced to pull the commercial activities in the concerned area. In this way, the economic and social infrastructures would be improved as usual to fulfill the requirement of dwellers associated with commercial activities. The physical infrastructure’s development must be planned to maintain the sustainable development strategy. In

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Table 22.5 Indo-Gangetic Plain (India): district-wise diversification of OIDI Magnitude of overall infrastructure diversification

Index value

Name of states and districts

Very low

0.00–0.41

West Bengal (South 24 Parganas, Burdwan, Darjeeling, Murshidabad, and Maldah) Bihar (Sitamarhi, Bhagalpur, Jamui, and Muzaffarpur) Uttar Pradesh ( Mirzapur, Chandauli, Moradabad, Faizabad, Aligarh, Mahamaya Nagar, Ravidas Nagar Bhadohi, Kaushambi, Mathura, Kabir Nagar, Gorakhpur, and Farrukhabad) Punjab ( Bathinda, Firozpur, and Patiala)

Low

0.42–0.82

West Bengal (North 24 Parganas, Howrah, Bankura, Jalpaiguri, Nadia, Hooghly, Uttar Dinajpur, Cooch Behar, Birbhum, Dakshin Dinajpur, Purba Medinipur, and Paschim Medinipur) Bihar ( Samastipur, Rohtas, and Patna) Uttar Pradesh (Sultanpur, Rampur, Azamgarh, Bijnor, Agra, Siddharth Nagar, Pilibhit, Mau, Allahabad, Ambedkar Nagar, Pratapgarh, Lucknow, Gonda, Jaunpur, Ballia, Bareilly, Muzaffarnagar, Lalitpur, Bulanshahr, Unnao, Auraiya, Dehat, Firozabad, Sonbhadra, Bara Banki, and Ghaziabad) Punjab (Amritsar, Rupnagar, Ludhiana, Kapurthala, Jalandhar, Ajit Singh Nagar, and Bhagat Singh Nagar) Haryana (Ambala, Sonipat, and Mewat)

Moderate

0.83–1.23

Uttar Pradesh (Varanasi, Kheri, Balrampur, and Kanpur Nagar) Punjab (Hoshiarpur and Tarn Taran) Haryana (Panchkula, Panipat, and Gurgaon)

High

1.24–1.64

Uttar Pradesh (Meerut and Jhansi)

Very high

1.65–2.05

Uttar Pradesh (Buddha Nagar) Haryana (Yamunanagar)

Source Census, 2011 (Computed by authors)

view of framing the policy for development in the census towns, it should be kept in mind that employment opportunity will attract the migrated rural workforce from the large urban centers, which will minimize the pressure of job-market and helps develop the urban economy in the long run.

22.6 Conclusion From the detailed analysis of the infrastructural development scenario of the IndoGangetic Plain region, it would be inferred that as per Census 2011 about the census towns in the said region, despite a hike increase in the number of this category of towns, the infrastructural status is insufficient to meet the needs of civic amenities by the dwellers. More elaborately, in most of the districts having census towns located under the Indo-Gangetic Pain region, there is an urgent need to upgrade basic

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Fig. 22.5 Spatial distribution of OIDI in Indo-Gangetic Plain region

Table 22.6 Indo-Gangetic Plain (India): state-wise variation of major indicators State

West Bengal

Index value Physical Infrastructural Index (PII)

Social Infrastructural Index (SII)

Economic Infrastructural Index (EII)

Overall Infrastructural Development Index (OIDI)

0.74

0.32

0.33

0.46

Bihar

0.65

0.46

0.07

0.39

Uttar Pradesh

0.80

0.50

0.46

0.59

Haryana

0.88

0.47

1.62

0.99

Punjab

0.65

0.43

0.79

0.62

Source Census, 2011 (Computed by authors)

infrastructural improvement. Apart from this, this chapter shows a significant correlation in the case of major indicators selection (e.g., physical infrastructure, social infrastructure, and economic infrastructure). The study shows that the economic factor is the most dominating factor influencing the other two indicators to a large extent. The spatial difference in infrastructural development among the studied census towns is due to their diverse level of socio-economic setup. This chapter also underlines that West Bengal is positioned as a state where a large number of census towns recently emerged, indicating a trend of rapid urbanization compared to the

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Fig. 22.6 Triangular diagram showing the major indicator-wise variation of the summarized index score

other four states. The occupational mobility of the rural dwellers would be directly responsible for changing the nature of settlement or so-called rapid urbanization (un-notified rural settlement to notified settlement/census towns). There is a partial scarcity of transport and communication-related data about the census towns in the Census of India 2011. So, it remains challenging to explore the connectivity of the census towns to the large urban center of the concerned states. However, better development of the census towns requires a good transport facility. As per the motive of the Ministry of Urban Development (Government of India), it can be argued that it is an essential requirement to change the census towns to statutory towns to uplift sizeable infrastructural development. Such steps would help minimize the migration of rural dwellers to the nearest large urban centers. Moreover, it should be noted that, during policy reframing for infrastructural development of the census towns, neither the green environment nor the sustainable land use should be hampered. For this purpose, a rational and sustainable plan is necessary to upgrade the infrastructural development in the census towns, which further creates the base of a (dream) smart city. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of Interests The corresponding author declares no potential conflicts of interest on behalf of all authors.

References Arif, M., & Gupta, K. (2020). Spatial development planning in peri-urban space of Burdwan City, West Bengal, India: Statutory infrastructure as mediating factors. SN Applied Sciences, 2(11), 1–19. Barbhuiya, M. R., Bhardwaj, M., Shukla, S., Kibret, A. T., & Panda, G. (2022). IoT technologybased urban water management strategies using Indian traditional knowledge system. IoT and IoE Driven Smart Cities (pp. 275–291). Springer.

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Chapter 23

Spatio-Temporal Urban Sprawl of Sargodha City, Punjab, Pakistan Humayun Ashraf, Ghani Rahman , Mehtab Ahmad Khan , Muhammad Farhan Ul Moazzam , and Muhammad Miandad

23.1 Introduction One of the most dynamic phenomena on the surface of the earth that consumes thousands of acres of arable land each year is the Urban Sprawl (Mehdi et al., 2021). Globally urban areas are expanding due to increase in Population and also migration from rural areas (Lyu et al., 2019; Moazzam et al., 2022). In the beginning of 20th Century, there were only 16 cities globally having population more than one million but in just 100 years, this figure reaches to 417 with population of more than one million (Lyu et al., 2019; Rydin et al., 2012). According to an estimate, in 2030 about 60% of the world’s population will live in urban areas (Ashraf et al., 2022; Knorr et al., 2018; Seto & Fragkias, 2005). The urban expansion on one hand shows the rate of development of a country but on the other side, unplanned expansion deteriorating the life of inhabitants in these cities, in terms of non-availability of fresh air, clean drinking water, green spaces, and increase in noise pollution as well as traffic congestion problems (Rahman et al., 2011b). Forces behind urban sprawls are different in developing and developed countries. The urban sprawl in developed countries is H. Ashraf · G. Rahman (B) · M. A. Khan · M. Miandad Department of Geography, University of Gujrat, Gujrat, Pakistan e-mail: [email protected] H. Ashraf e-mail: [email protected] M. A. Khan e-mail: [email protected] M. Miandad e-mail: [email protected] M. F. Ul Moazzam Department of Civil Engineering, College of Ocean Sciences, Jeju National University, 102 Jejudaehakro, Jeju 63243, Republic of Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_23

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the result of economic and social disparities while, in developing countries, urban sprawl is often related to population increase (Miandad et al., 2020; Tendaupenyu et al., 2016). The use of Geographic Information System (GIS) techniques and Remotely Sense (RS) data are playing vital role in studying the urban sprawl in the modern era (Mehdi et al., 2021; Rahman et al., 2011a). Most of the developed as well as in developing countries urban sprawl is monitored through high resolution satellite images applying different techniques in GIS. In developed and developing countries studies of urban sprawl also carried out by using statistical techniques along with the use of GIS Techniques and RS data (Farid et al., 2022; Gomez-Chova et al., 2006; Jat et al., 2008). Dimension and rate of urban expansion has direct relationship with population growth, and it also affects the regional economic development. Population increase and urban sprawl results into increase in land values, cause social stratification and economic inequalities in the same city (Mehdi et al., 2021; Rahman, 2016). Population explosion is posing severe threats to environmental sustainability of the cities in developing countries (Ojima & Hogan, 2009; Tariq et al., 2022). Urbanization is generally regarded as a positive index for economic development of a country. Although urbanization puts population pressure due to increase in migration in urban centers but at the same time migrants are the source of economic and social development as it is the source of cheap labor (Turok & McGranahan, 2013). In world the boom of urbanization has been shift from developed to developing countries. Industrialization was a key driving force of urbanization in the developed countries but the same driving force for urbanization is not valid for developing countries (Adaku, 2014). The most important push factor for urbanization in developing countries is rural urban migration (Yousafzai et al., 2022). Such unplanned and haphazard migration results in urban poverty and environmental degradation rather than modernization and industrialization. Urban sprawl is a global issue as it is not only affecting the cities of developing countries but also becoming an urban hazard in the cities of developed countries as well. The relatively small towns offer better facilities of health, education, and other social services as compared to the rural areas, because of this these small towns are the first stopping points of rural migrants rather than migrants travel to large metropolitan cities. There is certain evidence from Pakistan which shows that Secondary towns have been able to fascinate and grab substantial numbers of rural migrants (Jamal & Ashraf, 2004). Sargodha is one of the secondary cities in Pakistan. Sargodha has depicted the rapid urban sprawl in terms of spatial and demographic extent like most of the cities of Pakistan. In fact, the Sargodha City has expanded on greater rate as compared to the other secondary cities of Pakistan. Pakistan is one of the developing countries where the rate of urbanization is increasing day by day (Malik & Wahid, 2014; Miandad et al., 2020). In the past various studies have been conducted on urban sprawl in Pakistan in mega and small cities (Ashraf et al., 2022; Butt et al., 2012; Ghaffar, 2015; Mehmood et al., 2016; Yousafzai et al., 2022). This study is based on the monitoring urban sprawl in the Secondary city i.e., Sargodha. Urban sprawl rate of Pakistani cities is on higher side as compared to the other Asian Countries. There is serious deficiency of social and

23 Spatio-Temporal Urban Sprawl of Sargodha City, Punjab, Pakistan

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economic facilities in these secondary cities of Pakistan. This study will be helpful in determining the rate of urban sprawl and the course of future urban extension in the study areas. It will also be helpful for policy makers to determine the future of the secondary cities in Punjab Province. This study will be helpful for policy makers to develop the city plan for future extension.

23.2 The Study Area Sargodha city and Sargodha district is part of Punjab province. The Punjab province is an agricultural hub of Pakistan and is known as food basket of the country (Rahman et al., 2017). Sargodha city is an administrative center of the Sargodha division as well as Sargodha district located in Punjab province, Pakistan (Fig. 23.1). It is the 12th largest city in term of population in the country. Latitudinal and longitudinal extent of the city is 32.08 North latitude, 72.66 East longitude. It is the home of largest airbase in the country that is why Sargodha is also called as “City of Eagles”. The town of Sargodha was established in 1903 by the British colonist. It was developed in the form of well-planned residential sectors and civil lines area. The civil line area was not only the home of administrative setup, but the residences of Government servants were also established here. The town was divided into two parts by the railway line running through the city. During the early stages of growth, the town continued to grow on its original planned pattern. At the time of partition of the sub-continent, the arrival of refugees across the border-initiated growth of the city at much faster rate as compared to the past. As a result the private colonies and satellite town were established. All the extension of the city was on planned pattern (Punjab, 2012). According to the population census report of 1951 the total population of the city was 78,000, in 1961 it was 129,000, in 1971 it was 200,000, in 1981 it was 291,000, while in 1998 this figure reached to 458,000 and in 2017 the total population of the city was 605,905 (GoP, 1981, 1998, 2017).

23.3 Research Methodology The current research work focused on urban sprawl of Sargodha city of Punajb Provinve. The methodological frame work of the current research is presented below in Fig. 23.2.

23.3.1 Data Sources Authentication of the reasearch depend upon the sources of the data collection. Data collection is the soul of any reasecrh work. The current research is primarily based

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Fig. 23.1 Location of the study area Urban Growth

Landsat MSS (1987)

Landsat ETM+ (1997, 2007)

Sentinel-2 (2018)

Image Correction

Maps of Urban Growth (1987, 1997, 2007, 2017)

Accuracy Assessment

Kappa Coefficient

Fig. 23.2 Methodological framework

23 Spatio-Temporal Urban Sprawl of Sargodha City, Punjab, Pakistan

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Table 23.1 Characteristics of remotely sense data Sensor

Time

Path & Row

Spatial resolution (m)

Source

Sargodha City LANDSAT

1987-97-2007

150/38

30

GLCF/USGS

SENTINEL-2

22-03-2017

–

10

ESA

upon the remotley sensed data of Landsat images acquired from Global Land Cover Facility and USGS websites. At present remotely sensed data is the cost effiecient, less time consuming and most widely used data source to measure the urban sprawl in all over the world.

23.3.2 Remotely Sense Data The spatial extent of urban sprawl was identified using remotly sensed data of various years ranging from 1987 to 2017. The remotely sense data of Sargodha city was accquired from Global Land Cover Facility (GLCF), United State of Geological Survey (USGS), and European Space Agaency (ESA) from 1987 to 2017. The accquired images were of 1987, 1997, 2007 and 2017 of same months to avoid the seasonal varition on the accquired satellite imagery (Table 23.1). The images were subjected to layer stacking, image enahancement, geometric and radiometric correction before performing supervised classification for land use and land cover analysis.

23.3.3 Secondary Data Secondary data of the Sargodha city was acquired from government reports, population census and published official sources. The urban sprawl of the city over the three decades were compared with the population increase in these decades. This comparison was based on the secondary data.

23.3.4 Census Data Census data is a sub source of secondary data. Census reports give comprehensive accounts of population over the various periods. In Pakistan so far six population censuses have been conducted. The first census was conducted in 1951 and the most recent was in 2017.

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Table 23.2 Band combination for false color composite

Sr. No

Satellites

Bands combination

01

Landsat 5

Band 4, Band 3, Band 2

02

Landsat 5

Band 4, Band 3, Band 2

03

Landsat 5

Band 4, Band 3, Band 2

04

Sentinel-2

Band 7, Band 4, Band 2

23.3.5 Selection of Bands Bands of different combination were used to monitor the urban area expansion in Sargodha city. The details of band combination used in this study is given below in Table 23.2. After selecting the suitable bands for image classification different land use classes were made after applying supervised image classification algorithm. The formation of land use classes was based on ground knowledge. These land use classes mainly include Built-up land (residential and commercial), barren land, vegetation cover and water bodies.

23.3.6 Monitoring of Urban Sprawl The follwoing mathematical equation is used to calculate the urban sprawl at an interval of 10 years spaning between 1987 and 2017. DU =

1 D Br 2 − D Br 1 × × 100 D Br 1 T2 − T1

(23.1)

where DU is the dynamic change rate of urban sprawl for sepecific periods D B r 1 is the total built-up area in T1 D B r 2 is the total built-up area in T2 T1 and T2 are the specific years to monitor the rate of urban sprawl.

23.3.7 Accuracy Assessment Accuracy of the classified images were calcualted using Kappa Index of Coefficient. It was applied to calculate the accuracy of the classified images. It is most popular and widely used method to determine the accuracy of the classified images. It was first introduce in 1982 to assess the accuracy of ground refrence data and maps prepared as a result of land use classification (Galton, 1892).

23 Spatio-Temporal Urban Sprawl of Sargodha City, Punjab, Pakistan

K

∧

=

N

k i=1 (xi i ) − i=1 (xi + ×x k 2 N − i=1 (xi + ×x + i )

k

+ i)

549

(23.2)

23.4 Results and Discussion Land use and land cover is one of the dynamic phenomena on earth for the utilization of earth for different purposes. With the creation of human being on surface of earth, they started dwellings and utilized earth for their requirements. The development of the civilization give raise to proper city and urban areas development that ultimately resulted the phenomena of urban sprawl and urban centers in the world. Man has restless nature and urge of better standard of living always pushing humans to migrate for having more facilities in life. Sometimes, this migration is in a planned manner and brings positive changes not only in the standard of living of the migrants but also add skilled labor in the economic sector which is the backbone of the economy of the country, but in most of the cases particularly in cities of developing countries rural to urban migration is haphazard. There is no check and balance on migrants from the Government. This migration puts a lot of pressure on land availability for shelter and business as well as on other facilities of life. Thus, city starts to expand in leapfrog manner. The fertile agricultural land on the periphery of the city is extensively invaded for expansion of the built-up areas. Built-up land increased due to increase in population because of the immigration in city and a natural increase. Carrying capacity of the city is damaged and environment is degraded if the expansion is unplanned. Pakistan is amongst developing countries of the world. Punjab province is densely populated province in Pakistan. Lahore and Faisalabad are the most populous cities of Pakistan and ranked 1st and 2nd in Punjab province while Rawalpindi and Gujranwala ranked 3rd and 4th largest cities of Pakistan. These cities are densely populated and supporting more population than their carrying capacities. Intermediate cities of Pakistan are also growing on a very rapid pace due to haphazard urbanization. The trend of migration of population has been shifted towards intermediate cities due to the availability of facilities of life that were once only available in the large metropolitan cities. Among these intermediate cities of Pakistan particularly in Punjab province, Sargodha is one of the major centers of urbanization after Lahore, Faisalabad, Rawalpindi, and Gujranwala. According to population census of Pakistan, which was held in 2017, Sargodha is ranked 12th position according to population wise cities ranking. It is expected that in near future the population will cross the landmark of one million thus bringing serious changes in land use pattern of Sargodha. The haphazard population increase and urban expansion has resulted a number of environmental and urban issues in intermediate cities like solid waste (Khan et al., 2018), that arising other problems like urban flooding (Ijaz et al., 2021), and helping in spread of infectious diseases like tuberculosis and hepatitis (Miandad et al., 2019).

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Growth Rate %

10

8 6 4 2 0 1921

1931

1941

1951

1961

1972

1981

1998

2017

Fig. 23.3 Inter census growth rate in Sargodha

23.4.1 Change in Population of the City Inter census growth rate in Sargodha is shown in Fig. 23.3. It can be noticed that there had been very high increase in population during period of 1941–51. This is since after partition many refugees came from India settled in Sargodha City. The growth rate decreased during the decade 1951–61. The rate of growth again increased abnormally in the period of 1961–72 as shown in Fig. 23.3. This increase can be attributed to the establishment of the Divisional Headquarter at Sargodha, which increase the importance of the town, and people migrated here in search of employment, education and health facilities. During this decade, another cause of the city expansion and population increase was the expansion of Airbase and the establishment of cantonment in Sargodha. During 1972–1998 the growth rate decreased, and this decline was due to the decline in migration. In this way the connectivity of Sargodha was increased with the major cities of Pakistan. The Population of Sargodha has doubled in less than 20 years during 1921–1941 and again the population was more than doubled in just 10 years in 1951 census report. The high increase in population during 1941–1951 is attributed with the migrants came during partition in this city. From 1972 to 2017 the population of Sargodha city has increased with an alarming rate (Fig. 23.4). This increase in population may be attributed with different factors among which is the rural urban migration is the prominent cause. The people of rural areas of Punjab province migrated here to seek better opportunities of job and better facilities of life available in secondary city.

23.4.2 Urban Sprawl in Sargodha City The spatio-temporal changes in LULC is shown in Fig. 23.5. A significant variation in the patterns of land use Spatio-temporally is overserved in past 30 years. The urban LULC of the city has been changed to great extent. The change in built-up area of

23 Spatio-Temporal Urban Sprawl of Sargodha City, Punjab, Pakistan

551

900

800

Population (000)

700 600 500 400

300 200 100 0 1921

1931

1941

1951

1961

1972

1981

1998

2017

Fig. 23.4 Historical growth of population in Sargodha

the city is attributed to the change in population. Earlier the population was confined to the city center in 1987 which expanded gradually with increase in population and demand for more houses. Thus the boundaries of the city got expanded. It is obvious from the LULC map of 1987, that there were a number of scattered and cluster settlements but most of the built-up area was confined to the center of Sargodha city. The built-up area increased in 1997 while significant decrease was noted in water bodies and vegetation covered area (Fig. 23.6). The high proportion of water bodies in the east and southeast in 1987 image was due to sumps in the area which were later covered with dumping soil but still the area is did not get any development (Fig. 23.5). In 1997–98 the Sargodha city population was 0.4 million which indicated that more area was required for full filing the requirement of the increasing population. From the LULC map of 1997, an increase in barren land was noted which was the result of dumping of swampy area as the water in swamps remained in soil and it was not suitable for growing crops as well as for any construction purpose as well this is why in the image it seems that the increase in barren land. In the period of 1997 to 2007, there was almost 100% increase in built-up area. In this period built-up land engulfed the city vegetation and barren land. The north-eastern and south- eastern sides of the city faced the urban expansion in this time span of 10 years. The increase in built-up area in these directions was engulfing the fertile agricultural land. As a result of this invasion the agricultural and barren land were converted into residential and other infrastructure in the city. To examine the variation in the land use from the period of 2007 to 2017. For the LULC change assessment of 2017, the Sentinel-2 image was used. The results of the Sentine-2 showed more visible expansion in urban area. The 2017 image classification also showed an increase in barren land which was due more swampy area dumping in the city. The LULC of 2017 give better direction to the urban sprawl in the city (Figs. 23.5 and 23.6). The 30 years spanning from 1987–2017 are the evident of significant changes in land

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Fig. 23.5 The land use land cover change in Sargodha city during 1987–2017

use pattern in the study area. In these 30 years demographic profile of the city also changed which forced these variation in the built-up area of the city. The overall morphology of the city has been changed with the increase in population of the city as well.

23.4.3 Variation in Land Use Pattern Figure 23.7 reveals the spatio-temporal variation in LULC pattern in the study area. The vegetal cover was the most dominant land use category in 1987 which covered almost 114 Sq.km area in the study area. After the vegetal cover 2nd most was builtup land. In 1987 the area under built-up was 09 Sq.km. Collectively water body and

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Fig. 23.6 Satellite image of Sargodha city

barren land and cover approximately 11 Sq.km, out of 11 Sq.km. barren land covered the area of 07 Sq.km. while 04 Sq.km. was under the cover of water body in 1987. From 1987 to 1997, the built-up area increased to almost double of the area in 1987. The built-up area expanded over the fertile agricultural land and during this period the area covered by water bodies also declined. As the built-up land was increased there was significant decrease in the land use of barren land and water body. But the story of urban expansion doesn’t end here. Significant increase in built-up area noted from 1997 to 2007. In 1997, the built-up area was 18 Sq. Km. which increases to 24 Sq. Km. in 2007. This increase in urban expansion continued and the built-up area increased to 31 Sq. Km. in 2017, while the vegetation covered area reduced from 114 Sq. Km. in 1987 to 95 Sq.km in 2017 due to urban expansion.

23.4.4 Accuracy Assessment of Classified Images Accuracy assessment was performed using Kappa coefficient and error matrix for all the LULC maps (Table 23.3). The accuracy of the LULC maps of 1987, 1997, 2007, and 2017 was 0.86, 0.87, 0.89, and 0.88 respectively. The level of accuracy achieved in this study was more than the Anderson’s standard accuracy (Anderson, 1976).

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Built-up Vegetation

Area in Sq.Km

100

Water Bodies Barren Land

80 60 40 20

0 1987

1997

2007

2017

Fig. 23.7 Land use of Sargodha city (1987–2017)

Table 23.3 Accuracy of classified images Land cover types

PA (%) UA (%) PA (%) UA (%) PA (%) UA (%) PA (%) UA (%) 1987

1997

2007

2017

Built-up land

89

85

81

86

90

83

88

100

Vegetation

94

89

87

83

81

86

77

93

Water bodies

100

82

82

85

81

81

100

100

Barren land

75

95

91

86

92

87

100

79

Overall accuracy (%)

86

87

89

88

Kappa coefficient (%)

81

83

81

84

Note PA = Producer’s Accuracy UA = User’s Accuracy

23.4.5 Change Detection of Urban Land Use During 1987–2017 Change detection is one of the key aspects in urban sprawl studies. This gives a comprehensive idea about change in various land use in the study area over desired period. From change detection of urban land use, it can easily be concluded that which land use has been increased and which one has been decreased. Figure 23.8 is showing the results of change detection of major four land use classes in the study area. From the figure it is obvious that about 16.42% built-up land was increased in Sargodha City from 1987–2017. This increase in built-up land was on the cost of degradation in vegetal cover and water bodies (that was sumps area) in the city. The figure below show that the vegetal cover has been reduced to 14.18% while water bodies reduced to 2.24% on the other hand there was an increase of 04% barren land as compared to the past. This increase is due to its conversion from sumps to barren land.

23 Spatio-Temporal Urban Sprawl of Sargodha City, Punjab, Pakistan 20.00

16.42

Built-up

Water

Vegetation

Barren Land

555

15.00 Change in Area %

10.00 4.00

5.00 0.00 -2.24

-5.00

-10.00 -15.00

-14.18

-20.00

Fig. 23.8 Variation in urban land use of Sargodha city

35

Area in Sq.Km

30

700000 Urban Sprawl

Population

600000

25

500000

20

400000

15

300000

10

200000

5

100000

0

0

1987

1997

2007

2017

Fig. 23.9 Urban sprawl and increase in population

23.4.6 Increase Population and Urban Sprawl Figure 23.9 is an attempt to draw a comparison between Spatio-temporal variation in the population and urban sprawl of Sargodha City. The Fig. 23.9 showing that as population increases from 1987 to 2017 the built-up land was also increased for the same time period. In 1987, population of 235,000 was occupying 09 Sq.km built-up lands while in 2017 population of 769,000 covering the 31 Sq.km of built-up land in Sargodha City.

23.4.7 Dynamic Urban Sprawl Monitoring To examine the trend of urban sprawl of Sargodha city for last three decades, the process of change detection was carried out. Change detection was identified by overlapping satellite imagery from 1987 to 2017. This overlapping of time series data gives rates and trend of land use land cover with high accuracy. First images

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Table 23.4 Rate of urban sprawl in selected cities (1987–2017) Built-up

1987

1997

2007

2017

Built-up area (km2 )

9

18

24

31

9

6

7

3.3

2.9

Net Area

(km2 )

10

The dynamic rate (%)

Table 23.5 Indicators of urban sprawl Sr. No

Spatio-temporal indicators of urban sprawl

01

Land use indicators

Change in Built-up land, agricultural land, Barren land, Commercial land, forest land, and water bodies

02

Density indicators

Population density, built-up area density, industrial density, agricultural density, and economic density

03

Landscape pattern

Isolated, fragmented, distance from the economic corridor, distance from metropolitan cities

04

Social indicators

Job opportunities, health facilities, education facilities, economic viability, and quality of life

were joined by using different bands like 4, 3 and 2 to monitor the change. After combing bands classification was performed and various land use categories were formulated. Individually land use and land cover of each year was calculated. After that all the images were overlapped to examine the difference between built-up and other land use categories using Eq. 23.1. From Table 23.4 it is clear that the rate of urban sprawl in Sargodha city was higher during 1987–1997 and has showed 10% change in the built-up land then this rate decreases during 1997–2007 as there was only 3.3% change in the built-up area and finally fall down to 2.9% from 2007 to 2017 (Table 23.4).

23.4.8 Urban Sprawl Impact Land Surface Temperature The impact of urban sprawl and land surface temperature was assessed using the thermal bands of each year Landsat imageries. The minimum and maximum temperature showed temporal and spatial increase in the study area (Figs. 23.10 and 23.11). The minimum temperature in 1987 was 15.92 °C which increased to 25.11 °C in 2007 and again decreased to 23.53 °C in 2007. Whereas the maximum temperature in 1987 was 25.12 °C and increased to 32.71 °C in 2017.

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Fig. 23.10 Spatial distribution of land surface temperature in the study area during 1987–2017 35 30.78

Temperature in C

30 25

25.12

32.27

32.71

25.11 23.53

20 15

20.06 15.92

10 5 0 1987

1997 Minimum Temp

2007 Maximum Temp

2017

Fig. 23.11 Temporal land surface temperature in the study area during 1987–2017

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23.5 Conclusion An attempt has been made to conduct and monitor urban sprawl in Sargodha city of Punjab Province, Pakistan and link it to the population growth of the city. According to the population census 2017 of Pakistan based on Population Sargodha city is ranked on 12th position in terms of population. This city has shown significant increase in population over the period of three decades. Land use land cover changes are associated with the change in population of an area. Population pressure due to natural increase and rural urban migration are the key driving forces to alter the morphology of the city thus bringing the change in land use land cover pattern and ultimately triggering the process of urban sprawl. Once the process of urban sprawl triggers it is almost impossible to control. Urban sprawl often results in haphazard growth of the city engulfing the fertile agricultural land on the periphery of the city. Demographic profiles of the city show that population of the Sargodha city has been increased significantly as compared to the past. In 1987 the built-up land was 09 Sq.km quite enough to serve the population at that time. But as the population grew demand of land for residential purposes also increases thus in 1997 the built-up land in Sargodha City was 18 Sq.km and continue to increase up-to 24 and 31 Sq.km in 2007 and 2017 respectively. So, it can be concluded that urban sprawl has been takes place in Sargodha in every decade of the study period, however the trend and the rate of urban sprawl varies at the same time span. Apart from accuracy assessment of all classified images were calculated. Almost all classified images are according to the standards of accuracy assessment. Leapfrog and scattered development were observed in Sargodha City.

References Adaku, E. (2014). Urban sprawl: A view from developing and developed countries. African Journal of Geography and Regional Planning, 1(6), 193–207. Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data. US Government Printing Office, 964. Ashraf, H., Mobeen, M., Miandad, M., Khan, M. A., Rahman, G., & Munawar, S. (2022). Assessment of urban sprawl in Sargodha city using remotely sense data. Ecological Questions, 33(4), 1–16. Butt, M. J., Waqas, A., Iqbal, M. F., Muhammad, G., & Lodhi, M. (2012). Assessment of urban sprawl of Islamabad metropolitan area using multi-sensor and multi-temporal satellite data. Arabian Journal for Science and Engineering, 37(1), 101–114. Farid, N., Moazzam, M., Ahmad, S., Coluzzi, R., & Lanfredi, M. (2022). Monitoring the impact of rapid urbanization on land surface temperature and assessment of surface urban heat island using landsat in megacity (Lahore) of Pakistan. Frontiers in Remote Sensing, 3, 897397. https:// doi.org/10.3389/frsen Galton, F. (1892). Finger prints. Macmillan and Company. Ghaffar, A. (2015). Use of geospatial techniques in monitoring urban expansion and land use change analysis: A case of Lahore, Pakistan. Journal of Basic and Applied Sciences, 11, 265–273.

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Gomez-Chova, L., Fernández-Prieto, D., Calpe, J., Soria, E., Vila, J., & Camps-Valls, G. (2006). Urban monitoring using multi-temporal SAR and multi-spectral data. Pattern Recognition Letters, 27(4), 234–243. https://doi.org/10.1016/j.patrec.2005.08.004 GoP. (1981,1998, 2017). Population census report. Islamabad. Ijaz, S., Miandad, M., Mehdi, S., Anwar, M. M., & Rahman, G. (2021). Solid waste management as a response to urban flood risk in Gujrat city, Pakistan. Malaysian Journal of Society and Space, 17(1), 1–13. Jamal, Z., & Ashraf, M. (2004). Development of intermediate-size towns: An alternative form of urbanization. Science Vision, 9(1), 1–8. Jat, M. K., Garg, P. K., & Khare, D. (2008). Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India). International Journal of Remote Sensing, 29, 543–567. https://doi.org/10.1080/01431160701280983 Khan, M. A., Nawaz, A., Khalid, M., Muahammad, M. A., Chandio, N. H., Miandad, M., Ashraf, H., Rahman, G., & Zafar, U. (2018). Solid waste as an environmental hazard; a case study of Bahawalpur City, Pakistan. Journal of Biodiversity and Environmental Sciences, 12(3), 355–360. Knorr, D., Khoo, C. S. H., & Augustin, M. A. (2018). Food for an urban planet: Challenges and research opportunities. Frontiers in Nutrition, 4, 73. Lyu, H., Dong, Z., Roobavannan, M., Kandasamy, J., & Pande, S. (2019). Rural unemployment pushes migrants to urban areas in Jiangsu Province, China. Palgrave Communications, 5(1), 1–12. Malik, S., & Wahid, J. (2014). Rapid urbanization: Problems and challenges for adequate housing in Pakistan. Journal of Sociology and Social Work, 2, 87–110. Mehdi, S. S., Miandad, M., Anwar, M., Rahman, G., & Ashraf, H. (2021). Temporal variation in land use and land cover in Gujrat (Pakistan) from 1985 to 2015. Geography and Natural Resources, 42(4), 386–394. Mehmood, R., Mehmood, S. A., Butt, M. A., Younas, I., & Adrees, M. (2016). Spatiotemporal analysis of urban sprawl and its contributions to climate and environment of Peshawar using remote sensing and GIS techniques. Journal of Geographic Information System, 8(02), 137. Miandad, M., Anwar, M. M., Ahmed, S., Rahman, G., & Khan, M. A. (2019). Assessment of risk factors associated with spread of tuberculosis in Gujrat city Pakistan. Co˘grafya Dergisi, 39, 41–60. Miandad, M., Gondal, S., Aamir, A., Malik, S. M., Rahman, G., Ashraf, H., & Zafar, U. (2020). Spatio-temporal residential land-use changes in DistrictAbbottabad from 1990 to 2018. Journal of Biodiversity and Environmental Sciences (JBES), 16(2), 25–38. Moazzam, M. F. U., Doh, Y. H., & Lee, B. G. (2022). Impact of urbanization on land surface temperature and surface urban heat Island using optical remote sensing data: A case study of Jeju Island. Republic of Korea. Building and Environment, 222, 109368. Ojima, R., & Hogan, D. J. (2009). Mobility, urban sprawl and environmental risks in Brazilian urban agglomerations: challenges for urban sustainability. Urban population-environment dynamics in the developing world: Case studies and lessons learned (pp. 281–316). Paris: Committee for international cooperation in national research in demography (CICRED). Punjab, G.o. (2012). Sargodha city profile, Punjab cities improvement investment program. Rahman, A., Aggarwal, S. P., Netzband, M., & Fazal, S. (2011a). Monitoring urban sprawl using remote sensing and GIS techniques of a fast growing urban centre, India. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(1), 56–64. Rahman, A., Kumar, Y., Fazal, S., & Bhaskaran, S. (2011b). Urbanization and quality of urban environment using remote sensing and GIS techniques in East Delhi-India. Journal of Geographic Information System, 3, 62–84. Rahman, G., Anwar, M. M., Ahmed, M., Ashraf, H., & Zafar, U. (2017). Socio-economic damages caused by the 2014 flood in Punjab Province, Pakistan. Proceedings of the Pakistan Academy of Sciences: b. Life and Environmental Sciences, 54(4), 365–374. Rahman, M. T. (2016). Detection of Land use/land cover changes and urban sprawl in Al Khobar, Saudi Arabia: An analysis of multi temporal remote sensing data. International Journal of Geoinformation, 5(15), 1–16. https://doi.org/10.3390/ijgi5020015

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Rydin, Y., Bleahu, A., Davies, M., Dávila, J. D., Friel, S., De Grandis, G., Groce, N., Hallal, P. C., Hamilton, I., Howden-Chapman, P., Lai, K.-M., Lim, C. J., Martins, J., Osrin, D., Ridley, I., Scott, I., Taylor, M., Wilkinson, P., & Wilson, J. (2012). Shaping cities for health: Complexity and the planning of urban environments in the 21st century. Lancet (london, England), 379(9831), 2079–2108. https://doi.org/10.1016/S0140-6736(12)60435-8 Seto, K. C., & Fragkias, M. (2005). Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landscape Ecology, 20, 871–888. Tariq, A., Mumtaz, F., Zeng, X., Baloch, M. Y. J., & Moazzam, M. F. U. (2022). Spatio-temporal variation of seasonal heat islands mapping of Pakistan during 2000–2019, using day-time and night-time land surface temperatures MODIS and meteorological stations data. Remote Sensing Applications: Society and Environment, 27, 100779. Tendaupenyu, P., Hilary, C., Magadza, D., & Murwira, A. (2016). Changes in landuse/landcover patterns and human population growth in the Lake Chivero catchment, Zimbabwe. Geocarto International, 32(7), 797–811. https://doi.org/10.1080/10106049.2016.1178815 Turok, I., & McGranahan, G. (2013). Urbanization and economic growth: The arguments and evidence for Africa and Asia. Environment and Urbanization, 25(2), 465–482. https://doi.org/ 10.1177/0956247813490908 Yousafzai, S., Saeed, R., Rahman, G., & Farish, S. (2022). Spatio-temporal assessment of land use dynamics and urbanization: Linking with environmental aspects and DPSIR framework approach. Environmental Science and Pollution Research, 29, 1–14. https://doi.org/10.1007/s11356-02221393-6

Chapter 24

Greenswales: A Nature-Based Solution to Have High-Performing Urban Water Systems Ankita Sood

and Arindam Biswas

24.1 Background “Urban water refers to all water that occurs in the urban environment and includes consideration of natural surface water and groundwater, water provided for potable use, sewage and other ‘waste’ waters, stormwater, flood services, recycling of water (third pipe, stormwater harvesting, sewer mining, managed aquifer recharge, etc.), techniques to improve water use efficiency and reduce demands, water sensitive urban design techniques, living streams, environmental water and protection of natural wetlands, waterways and estuaries in urban landscapes” (Government of Western Australia, n.d.). In the current century, urban water systems are overly stressed with polluted surface water, unmanageable surface runoff, and depleting groundwater issues. The primary causes are over-dependence upon piped drainage systems, extensive surface sealing, and climate change. Piped drainage systems are notorious for draining off the urban pollutants accumulated on urban catchments to the surface water sources. This pollutant-laden water harms the aquatic ecosystem, affects the quality of the drinking water supply, and makes the recreational areas unsafe and unpleasant (EPA, 2010a; Gavri´c et al., 2019; Yang & Zhang, 2011). Urban pollutants include (i) trace metals like lead, zinc, and copper; (ii) nutrients such as nitrogen and phosphorus; (iii) traffic-associated hydrocarbons like oils and grease; (iv) viruses and bacteria from pet wastes. Surface sealing in urban areas is due to buildings, roads and other infrastructural constructions, which strips the city of its soft cover. Surface sealing takes away the infiltration and evapotranspiration opportunities (Pauleit & Duhme, 2000), resulting A. Sood (B) · A. Biswas Department of Architecture and Planning, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India e-mail: [email protected] A. Biswas e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_24

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in increased runoff volume (Khaladkar et al., 2009; Leopold, 1968; Walsh et al., 2012; Yao et al., 2015). Some urban areas are found to have generated five times more runoff than a forest of the same size (EPA, 2010a). In addition to rising runoff volumes, scant infiltration due to surface sealing limits groundwater replenishment. Climate change has emerged as the biggest challenge of the twenty-first century affecting various sectors, including stormwater management in cities. Climate change induced increased rainfall intensity is proven to increase stormwater generation (Seneviratne et al., 2012). Rising runoff volume, if unmanaged, can potentially cause pluvial flood situations in cities (Hammond et al., 2015). Evidently, cities require actions for the improvement and enhancement of urban water systems. Except for climate change, cities can tackle surface sealing and overdependence upon piped drainage systems through solutions at the local level. The required holistic solution must reduce surface sealing and supplement the piped drainage system. Cost-effective nature-based solutions (NBS) are proven to solve urban water issues sustainably. For example, bioswales, green roofs, rain gardens, and vertical gardens; all of which are an amalgamation of vegetation and water. The provision of vegetation with water provides pollution control, rainwater interception, evapotranspiration and groundwater recharge functions. Taking cues from these NBS, the holistic urban water system should have the dual benefits of vegetation and water.

24.1.1 Hypothesis—Concept of Greenswales This study focuses on providing a holistic NBS that will add to the inventory of current nature-based solutions that facilitate sustainable, resilient and high-performing urban water systems. Authors have hypothesised developing greenspaces over seasonal channels as a holistic NBS. The term greenswale is used in this chapter to convey the greenspace overlapping seasonal channels. Concept of greenswales: Greenspaces + seasonal natural channels = Greenswales Both, seasonal channels and greenspaces are challenged with existential crises due to fast-paced urbanisation. Ephemeral and intermittent natural channels are seasonal channels as they have non-permanent streamflow and lose their function during dry months. It proves detrimental to their existence. They are not seen as an asset in many cases but as a future nuisance and are done away with during the land reclamation process (Sood & Biswas, 2021). If not lost during the land reclamation process, natural channels fall prey to garbage dumping after settlements come into being (Satterthwaite et al., 2007). Similarly, greenspaces are also lost during the expansion of cities because buildings and road construction get precedence. The concept is a symbiosis of greenspaces and natural channels—greenspaces preserve endangered channels; in return, greenspaces get space and irrigation to flourish.

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Fig. 24.1 Leisure valley (green belt) laid over the ephemeral channel in Chandigarh city, India (Left); Google Earth image showing a section of the natural channel meandering through various sectors in Chandigarh city (Right) (Source Authors)

Fig. 24.2 Bridges in leisure valley to facilitate streamflow of N-choe during rains (Source Chandigarh tourism website)

The concept of greenswales is inspired by the case of Leisure Valley in Chandigarh, India. Leisure valley is an 8 km long greenspace that runs from one end of the city to the other and is laid over an eroded natural channel, namely N Choe (Chandigarh Administration, n.d.) (Fig. 24.1). Choe is the word for stream in the local language. The perceived image of this seasonal channel as a greenspace does not affect its hydrological function of stormwater conveyance during the rainy season (Sood & Biswas, 2022). Figure 24.2 shows various images of bridges in leisure valley that facilitate streamflow during rains. Akin to the superimposition of Leisure valley over the N Choe, greenswales are greenspaces laid judiciously over the natural channels. The concept has the potential to bring about a paradigm shift in the planning of neo-developments.

24.2 Method It is theoretical and exploratory research. The study’s primary aim is to support the hypothesis presented in this research through state-of-the-art literature. A literature

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review is a well-established research methodology for exploratory researches and involves reading, analysing, evaluating, and summarizing scholarly literature about the desired topic. In this study, the literature review is based on research journals and scientific reports, majorly in the domains of sustainable urban water management and urban greening. The greenswales should have the dual benefits of greenspaces and natural channels; which are identified from the relevant literature with a focus on hydrological benefits. The critical analysis of relevant literature has also led to the formulation of the characteristics of an ideal greenswale.

24.3 Results and Discussion 24.3.1 Benefits of Natural Channels 1. The natural channels follow the topography contours, thus, the water from the urban catchment can easily flow to the greenswale with little or no intervention. 2. All artificial stormwater drainage systems are designed to fall into streams and rivers ultimately. The seasonal channels being part of a vast natural drainage system, are already connected to perennial channels. This connection with higherorder channels eliminates the need for designing an outfall. 3. The concave-shaped cross-section of natural channels facilitates water detention during heavy rainfall. Many cities are now resorting to developing floodable public places that get submerged in water during heavy rains but retain their function in the dry seasons e.g. Water square in Rotterdam (Hampshire & Sipes, 2019), and Yanweizhou wetland in China (la Loggia et al., 2020). Floodability is a good strategy to mitigate pluvial floods and enhance the resilience of the area (la Loggia et al., 2020). Thus in the planning of neo-townships, the provision of greenswales can reduce a lot of load from grey infrastructure, enhancing flood resilience. 4. The vast network of natural channels is an opportunity to develop an integrated network of greenspaces by developing networked greenswales. It has been proven that networked green infrastructure has more benefits than segregated ones (Benedict & McMahon, 2000).

24.3.2 Benefits of Greenspaces 1. The imperviousness in urban areas does not let the stormwater seep into the ground. Therefore, the pollutants accumulate on the catchment surface in dry weather and. In wet weather, the stormwater runoff carries pollutants to the streams, harming the aquatic ecosystem and affecting the quality of the drinking

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3. 4. 5.

6.

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water supply (EPA, 2010b; Gavri´c et al., 2019). Green covers filter the surface runoff and reduce water pollution (Purvis et al., 2018). In urban catchments, stormwater is directed to streets, gutters, and channels that rapidly convey the water downstream (Cronshey et al., 1985). Obstruction from vegetation decreases overland flow speed and prevents flooding in downstream areas (Cronshey et al., 1985; McDaniel & O’Donnell, 2019). Vegetation also intercepts rainfall, reducing net stormwater (Xiao et al., 2000). Transpiration in plants reduces soil moisture, thereby increasing subsurface water storage capacity (Bartens et al., 2008). Vegetation increases porosity of soil by generating macropores through root holes and by stabilising interpedal spaces between large soil aggregates through organic matter (Jarrett & Hoover, 1985). This facilitates groundwater recharge. Apart from the above hydrological benefits, grenspaces have recreational, social, environmental and health benefits as well.

24.3.3 Characteristics of Greenswales A greenswale is multi-functional, attributed to its four fundamental characteristics: streamflow, network, large size, and vegetative cover. In addition to enhancing urban water systems, these characteristics open the way for multiple possibilities when integrated judiciously into urban planning and design. The characteristics, associated benefits and possibilities are summarised in Fig. 24.3 and discussed in the following subsections. Streamflow Streamflow in ephemeral and intermittent natural channels is limited to a few months but imparts many functions and infrastructure possibilities. A greenswale is primarily a conduit for surface runoff conveyance during and after precipitation events. Upon integration with an artificial drainage system, stormwater pipes may find their outfall in the greenswale, thus downsizing piping. The measure allows for keeping water in the city for groundwater recharge and other functions instead of taking it away immediately, unlike the conventional approach of managing stormwater (Cronshey et al., 1985). In addition, greenswales can aid in building resilience against pluvial floods. With their inherent ability to collect water from surroundings, natural channels can take up surplus runoff volumes and prevent pluvial floods. Provisions of bluespaces, ditches and mounds can provide water retention and detention functions, thereby facilitating water storage and enhanced groundwater recharge along with recreational and aesthetic functions. Bluespaces also render thermal comfort through evapotranspiration during the summer months when the grass dries out because of its shallow root system (Gill et al., 2007). Therefore, streamflow has the possibility and potential to impart flood resilience, thermal comfort, water storage and groundwater replenishment.

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Fig. 24.3 Summary of the characteristics of greenswales and their corresponding functions/ benefits along with design possibilities (Source Authors)

Vegetative cover Overlapping greenspaces in greenswales impart and enhance hydrological functions. Riparian vegetation improves water quality by direct chemical uptake or indirect mechanisms (Dosskey et al., 2010). Vegetation increases the channel bed roughness and decreases the flow velocity (Cronshey et al., 1985; McDaniel & O’Donnell, 2019; Velísková et al., 2017), thereby reducing flood risk of downstream areas and increasing in-situ infiltration. Greenspaces simultaneously provide shading, evaporative cooling, rainwater interception, storage and infiltration functions while reducing pollution and sequestering carbon (Rosenzweig et al., 2015). Greenspaces improve physical and perceived thermal comfort through evapotranspiration (Klemm et al., 2015). Vegetation increases infiltration by increasing macroporosity of soil through root holes, decomposing organic matter and increasing the population of the earth organisms (Jarrett & Hoover, 1985; Fischer et al., 2015; Spehn et al., 2000). Designing floodable greenspaces that are allowed to get submerged and detain water during heavy rains may provide floodability and enhance flood resilience. Floodability is the ability of a system to withstand floods that occur in a part of or in the whole system while still maintaining a sufficient level of operation (la Loggia et al., 2020). Large Size Greenswales have the advantage of large size, which allows more functions than conventional fragmented greenspaces combined. The large-sized greenspaces have high effectiveness in heavy rainfall events (Artmann, 2014). Greenswales’ large size

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allows vegetation diversity and urban forestry, which helps restore ecosystem balance through facilitating fauna. Vegetation diversity solely enhances soil hydraulics by enhancing infiltration irrespective of soil texture (Fischer et al., 2015). Large greenspaces have tall trees (Aram et al., 2019) that provide additional functions compared to other flora. Trees have higher interception and evapotranspiration rates because of their higher LAI and more extensive root system (Zölch et al., 2017). The carbon absorption of cities is enhanced through the trees (Whitford et al., 2001). Trees can reduce temperatures in cities up to 8 °C, and consequently lower the use of air conditioning and related emissions by up to 40% (UNEP, 2019). Integration of diverse landscaping elements like ponds and lakes; and recreational facilities are also made possible by the large size of greenswales. Network Greenswales can form a network of varying greenspaces developed over interconnected natural channels. Individual elements linked together to form a network have more benefits than the local ones (Davies et al., 2006). The basis of greenswales is the network of natural channels underneath that can serve as an additional stormwater management network in the city. The possibility lies in developing regional greenswales where the greenspace gradually becomes a wetland and, ultimately, a water body. The network holds the possibilities for developing cycleways and walkways. A shift to non-motorised vehicles can reduce greenhouse gases in cities (Sioufi, 2010). Studies on cycling have found evidence that riders prefer dedicated cycling facilities separated from high-speed and high-volume traffic (Aldred et al., 2017; Lusk et al., 2020). Cyclists tend to take attractive and green routes over the shortest routes (Krenn et al., 2014). Greenswales as walkways have enormous potential as humans exhibit biophilia, which tends to seek connections with nature and other life forms (Gullone, 2002). Greenswales’ network can also facilitate the rewilding activities in cities, serve as an animal corridor and compensate for the lost biodiversity.

24.4 Conclusion Urban water systems are facing extreme issues of pollution, groundwater depletion and pluvial flooding. At the same time, the critical seasonal channels are lost during the city development phase, which can be used as a nature-based solution to tackle the urban water system crises. The concept of greenswales is introduced in this study which is the judicious overlapping of greenspaces over seasonal channels. Greenswales present the solution to preserve seasonal channels and utilise them as a mechanism to restore urban water systems by quality improvement, flood mitigation and groundwater replenishment. The symbiosis of natural channels and greenspaces renders various benefits and possibilities to cities. More benefits of multi-functional infrastructure over mono-functional ones form the basis of the greenswales concept. Through overlapping, cities can overcome land

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scarcity to provide more greenspaces as multi-functionality uses limited spaces more efficiently by combining various functions (Ahern, 2011). Theoretically, greenswales can enhance the water quality, sustain streamflow, prevent pluvial floods and facilitate groundwater recharge altogether. Moreover, greenswales can help in restoring bio-diversity by making provisions for varied vegetation plantations and facilitating animal corridors. The networked greenswales can accommodate shaded and aesthetical walkways and cycleways. The growing desire for a walkable lifestyle and biking makes greenswales a perfect fit in new urban environments. Thus, greenswales are a valuable contribution to the inventory of nature-based solutions.

24.4.1 Limitations and Scope for Future Research The analytical and conclusive literature review has helped to theoretically justify the hypothesis presented in this chapter which is—developing greenspaces over seasonal channels is a holistic NBS to have high-performing urban water systems. However, the concept needs to be validated by incorporating it into new development as a pilot project. The applicability of greenswales is limited to new developments only as the natural hydrology in existing cities cannot be restored. However, the urban expansion of Indian cities is an excellent opportunity to test this hypothesis. Conclusively, the study limitations should not deter but encourage the further exploration of the greenswales concept. Acknowledgements The authors would like to sincerely thank the Indian Institute of Technology Roorkee, India, for supporting this research.

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Chapter 25

Impact Analysis of Deep Static Bluespace on Urban Heat Island: Case of Chandigarh Aditya Rahul and Mahua Mukherjee

25.1 Introduction Urban waterbody plays a multidimensional role in the city’s healthy well-being by catering to its economical, ecological, and socio-cultural needs. They were an integral feature of the urban landscape of most Indian cities, but over the past few decades, unprecedented and unplanned urbanisation has led to a steady decline in urban waterbody areas in India (Bindu & Mohamed, 2016). This decline in the waterbody area coincides with the rise in the temperature of urban areas. It has been established that urban areas have a higher temperature than surrounding rural areas (Oke, 1987). In regions with very hot summers, any reduction in temperature is a welcome relief. This study investigates the potential of waterbody to provide cooling in their surroundings. Previous studies have shown that a waterbody has the ability to influence the thermal environment of its nearby surroundings (Hathway & Sharples, 2012; Wang et al., 2019). Although various studies report that the impact of waterbody on the thermal environment is weak (Jacobs et al., 2020; Peng et al., 2018), results of some other studies report substantial negative relationship between waterbody and thermal environment of its surroundings (Song et al., 2014). Waterbody impacts the thermal environment by generating latent heat through evaporation and the transfer of sensible heat between its surface and the urban air (Webb & Zhang, 1997). Waterbody can provide a cooling effect, sometimes referred to as water cooling island (Coutts et al., 2012; Gunawardena et al., 2017; Sun & Chen, 2012), and in some cases, it has also been observed that it can contribute to thermal stress by providing warmth in some A. Rahul (B) Climate Change Consultant, Ernst & Young, Gurugram, Haryana, India e-mail: [email protected] M. Mukherjee Department of Architecture and Planning, Indian Institute of Technology Roorkee, Roorkee, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Chatterjee et al. (eds.), Urban Environment and Smart Cities in Asian Countries, Human Dynamics in Smart Cities, https://doi.org/10.1007/978-3-031-25914-2_25

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climate zones (Moyer & Hawkins, 2017). In a study conducted in the warm and temperate climate of Sheffield, UK, through air temperature measurement, it was observed that a 22 m wide river could provide a cooling impact of 1.5 °C over a distance of 30 m (Hathway & Sharples, 2012). In Japan, cooling from the Ota River was noted to occur close to 300 m away from the 270 m wide river (Murakawa et al., 1991). In humid subtropical climate of Pennsylvania, US, it was observed that a small urban river provides a warming effect in its surroundings. For every 1000 m increase in distance from the river, the temperature decreases by 0.6 °C in summer, 0.5 °C in fall and spring, and 0.3 °C in winter (Moyer & Hawkins, 2017). Waterbodies can be classified into two categories (i) Dynamic (rivers, streams) and (ii) Static (lakes, ponds). Due to their difference in fluid flow characteristics, sensible thermal impact of these two differs from each other (Gunawardena et al., 2017). While there has been a substantial amount of work on the thermal impact of dynamic waterbodies (Hathway & Sharples, 2012; Moyer & Hawkins, 2017), the impact of static waterbodies is still comparatively less explored. Traditionally thermal investigation is carried out through on-site temperature measurement. A study carried out in the warm tropical climate of Guangzhou, China, through on-site measurement found that cooling by an urban lake can be as high as 2.2 °C (Chen et al., 2009). Another study conducted through air temperature measurement in Seoul, South Korea, observed that a river could provide a cooling effect to a distance of 37.2 m by 0.46 °C during summer (Park et al., 2019). While the on-site temperature measurement method is the most accurate, it provides only point data for investigation and is highly resource intensive. Recent developments in remotely sensed imagery offer a viable alternative to studying the thermal environment by providing high-resolution thermal data, which can be further used to retrieve the land surface temperature owing to its continuity in spatial resolution (Cristóbal et al., 2018; Wang et al., 2015). MODIS data was employed for the study conducted in Chongqing, China, where it was observed that during night river provides warming by 1 °C to a distance of 400 m, whereas it cools by 3.82 °C till a distance of 460 m during the day (Cheng et al., 2019). Landsat-8 TIRS data with a spatial resolution of 30 m was used to investigate the thermal impact of urban wetlands in northeast China. It was observed that urban wetlands have the potential to provide a cooling effect to 800 m by 2.74 °C (Xue et al., 2019). The novelty of this study lies in observing and analyzing the thermal impact of waterbody with respect to their surrounding areas. It is common knowledge that the urban surroundings influence the behaviour of urban microclimate. Thus, it is imperative to analyze the impact of waterbody on microclimate with respect to different surrounding areas. Local climate zone (LCZ) classification scheme is employed to classify the study area to facilitate this analysis. This approach enables comparing study results irrespective of site location, as the micro-climate behaviour in LCZs remains the same. In previous studies of similar nature (Gupta et al., 2019; Hou & Estoque, 2020; Sun et al., 2020), the results are site-specific and cannot be inferred to make design decisions in other parts of the world. This study investigates the thermal impact of a deep static waterbody (Sukhna Lake) on its nearby surroundings in the Subtropical Humid climate of Chandigarh, India. Land Surface Temperature

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(LST) derived from LANDSAT-8 TIRS data is employed to quantitatively assess (i) thermal impact range, (ii) maximum amplitude of impact, and (iii) thermal impact gradient of Sukhna Lake in the city of Chandigarh, India. The lake surroundings are classified in various local climate zones (LCZ) (Stewart & Oke, 2012a), to facilitate analysis of waterbody impact with respect to change in the surrounding area. It will help in establishing the role of waterbodies in influencing the microclimate of their nearby surroundings, thus motivating the concerned authorities to undertake steps for its conservation. It will also assist urban planners and designers in better planning of built environment around waterbody.

25.2 Materials and Method 25.2.1 Study Overview The study presented in this paper consists of following four major tasks: 1. 2. 3. 4.

Land Surface Temperature calculation Local Climate Zone classification Thermal impact of waterbody calculation Statistical analysis

Land surface temperature (LST) is calculated for the summer and winter seasons of the study area by calculating and averaging various LST’s obtained during months representative of the aforementioned seasons. The second step involves classifying the study area in local climate zones to facilitate the study of impact of different urban forms on temperature variation. Subsequently, the thermal impact of waterbody on its surroundings is calculated in the form of its impact intensity and impact distance.

25.2.2 Study Area Chandigarh (30.74°N, 76.79°E) is a union territory and serves as the capital of two states, namely Punjab and Haryana in India. It is located at an altitude of 321 m and covers a total area of about 114 km2 . The city of Chandigarh has come into existence in the 1950s as part of post-independence India. Architect Le Corbusier planned the main city, but many suburban developments have come up in recent times, making it one of India’s most rapidly developing cities with an average population growth of 4% per annum. This study will provide a pathway for urban planning bodies to suitably develop new areas around the waterbody for better heat attenuation (Fig. 25.1). Chandigarh lies on the foothills of the Shivalik ranges of the lesser Himalayas. According to Koppen climate classification, it has a humid subtropical climate (Cwa) characterized by very hot summers, mild winters, unreliable rainfall, and

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Fig. 25.1 Location of Sukhna lake in Chandigarh

great temperature variation. January is the coldest month with an average minimum temperature of 6.1 °C and maximum of 20.4 °C, whereas June is the hottest month with an average maximum temperature of 38.6 °C and minimum temperature of 25.4 °C. Chandigarh experiences monsoon, usually during July–September, with an annual average rainfall of 1110.7 mm. August experiences the highest average precipitation of 307.5 mm. In contrast, April is the driest month, with an average rainfall of 8.5 mm. The study area also offers different urban typologies around the waterbody to facilitate this study. The area on the southwest side of the waterbody was planned by architect Le Corbusier, whereas the surrounding area in the southwest of the waterbody is an unplanned development, and the area on the northern side is vegetated land. Thus the study area offers varied surroundings to facilitate better analysis of the thermal impact of waterbody. Sukhna Lake is an artificial lake located in Chandigarh. Designed by Le Corbusier, it serves as a recreational and rejuvenation spot for residents and tourists. It has a total surface area of 3 km2 and a maximum depth of 4.9 m, with average depth being 2.4 m. It is fed by Sukhna Choe, a seasonal stream.

25.2.3 Data Remotely sensed data from LANDSAT 8 OLI/TIRS is employed to conduct this study. Two sets of imagery representative of two different seasons, i.e., summer and winter, are obtained. Summer and winter are the only two seasons that exhibit extreme temperature in Chandigarh. Summers are too hot with maximum temperature going

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upto 45 °C and winters are too cold with minimum temperature dipping upto 2 °C. As the main aim of this study is to analyse the thermal behaviour of waterbody to facilitate comfortable thermal regime in the surrounding area only these two seasons are analysed. Summer is represented by four images acquired during April 19, 2019, to June 22, 2019, and winter by four images acquired during November 10, 2018, to January 13, 2019. The images selected for the study have cloud cover less than 4% (Table 25.1). A methodology flow chart is presented in Fig. 25.2. Table 25.1 Details of data used in investigation Season

Image ID

Date Cloud Time (DD/MM/YYYY) cover (Indian (%) standard time)

Summers LC08_L1TP_147039_20190622_20190704_01_T1 22-06-2019 (April, LC08_L1TP_147039_20190521_20190604_01_T1 21-05-2019 May and LC08_L1TP_147039_20190505_20190520_01_T1 05-05-2019 June) LC08_L1TP_147039_20190419_20190423_01_T1 19-04-2019

0.64

10:54

3.41

10:54

0.03

10:53

0.15

10:53

Winters (Nov, Dec, and January)

LC08_L1TP_147039_20190113_20190131_01_T1 13-01-2019

2.4

10:54

LC08_L1TP_147039_20181228_20190129_01_T1 28-12-2018

0.19

10:54

LC08_L1TP_147039_20181126_20181210_01_T1 26-11-2018

0.31

10:54

LC08_L1TP_147039_20181110_20181127_01_T1 10-11-2018

0.09

10:54

Fig. 25.2 Flow chart of methodology used in this study

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25.2.4 Land Surface Temperature Retrieval Data from Landsat 8 satellite has been employed in this study to retrieve land surface temperature. LANDSAT 8 satellite has two thermal bands, band 10 and band 11. However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. There are three methods available for LST retrieval from LANDSAT imagery, namely: (a) Mono window algorithm, (b) Single Channel Algorithm, and (c) Split window algorithm. The first two uses only a single thermal band (Band 10), whereas the third, i.e., the Split window algorithm, uses two thermal bands (Band 10 and Band 11). Thus Split window algorithm is eliminated from this study as it employs both bands. In a study conducted in Yangtze River Basin, a comparison of the Mono Window algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the MW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm (Wang et al., 2015). So mono window algorithm is employed in this study to retrieve land surface temperature. The following equation is employed for the same: T s = [a10 (1 − C10 − D10 ) + (b10 (1 − C10 − D10 ) + C10 + D10 )T10 − D10 Ta ]/C10

(25.1)

T s is the LST retrieved from Landsat 8 TIRS band 10; Ta stands for effective mean atmospheric temperature; T10 is the brightness temperature of band 10; a10 and b10 are the constants of the algorithm with values of −62.7182 and 0.4339, respectively. C10 and D10 are the internal parameters derived by: C10 = τ10 ε10

(25.2)

D10 = (1 − τ10 )[1 + (1 − ε10 )τ10 ]

(25.3)

where τ10 stands for atmospheric transmittance of Landsat 8 TIRS Band 10 and ε10 is ground emissivity.

25.2.5 Brightness Temperature (T10 ) Top of Atmosphere thermal radiance (L) is calculated using Digital numbers (Q 10 ) and Landsat 8 calibration coefficients. M10 is the multiplicative rescaling factor for Landsat 8 TIRS band 10 and A10 is the additive rescaling factor with values of 0.0003342 and 0.1, respectively.

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L = M10 Q 10 + A10

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(25.4)

Planks radiance function was employed to convert thermal radiance (L) into brightness temperature (T10 ) employing the following equation: T10 = 

K2  log10 1 +

K1 L

 − 273.15

(25.5)

K 1 and K 2 are band specific thermal constants, which are 774.8853 and 1321.0789, respectively. T10 is the at sensor brightness temperature (◦ C).

25.2.6 Effective Mean Atmospheric Temperature (Ta ) Linear relation proposed by Qin et al. (2001) for the approximation of effective mean atmospheric temperature (Ta ) from near surface air temperature (T0 ) is used. For mid latitude summer Ta = 16.0110 + 0.9262 T0 . Temperature data from NOAA, (National Centers for Environmental Information) is used to obtain T0 for each day.

25.2.7 Atmospheric Transmittance (τ10 ) Atmospheric transmittance is derived according to the linear relation mentioned by Wang et al. (2015). For Mid-latitude summer with water vapour content of 1.6–4.4 g cm−2 atmospheric transmittance is given by: τ10 = 1.0163 − 0.1330w

(25.6)

Water vapour content (w) can be considered as 2.5 g cm−2 for the clear sky in mid-latitude summer. Thus τ10 is 0.6838.

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Ground Emissivity (ε10 )

Ground emissivity is estimated through the NDVI threshold method (Yu et al., 2014). Ground emissivity (ε10 )

NDVI

0.973–0.047ρ 4

NDVI < 0.2

0.9863ρ v + 0.9668 (1 − ρ v ) + C i

0.2 ≤ NDVI ≤ 0.5

0.9863 + C i

NDVI > 0.5

C i is the surface roughness coefficient; pv is the fractional vegetation cover derived from NDVI by: 

NDVI − NDVImin pv = NDVImax − NDVImin

2 (25.7)

and NDVI =

ρ5 − ρ4 ρ5 + ρ4

(25.8)

where ρ5 and ρ4 are land surface reflectance of the near-infrared band and red band, respectively. The emissivity of waterbody is considered as 0.995 (Lin et al., 2015). Figures 25.3 and 25.4 represent the land surface temperature of Chandigarh during winter and summer. It has been ensured that the images selected for LST retrieval have cloud cover less than 4%. Days leading to January 13 too didn’t have any cloud cover, which led to maximum intake of incident solar radiation. Lack of wind on the day allowed the earth’s surface to remain warm, resulting in increased LST for January 13. In case of June 22, Chandigarh experienced some pre-monsoon showers in the days leading to June 22. Thus cooling the earth’s surface and making the LST of June 22 lowest.

25.2.8 Waterbody Extraction Remote sensed data provides a viable alternative to extract waterbody shape and area. Different approaches are available for waterbody extraction through both spectral analysis and image classification methods. Waterbody indices such as NDWI, MNDWI, and AWI based on spectral analysis method are sometimes unable to extract the waterbody area accurately from satellite imagery due to its inability to segregate various urban structures (e.g., Roads, dark built-up areas, shadows, etc.) (Huang et al., 2015). Sukhna lake has vegetation adjacent to it in the northwest direction, casting shadow on the waterbody. These dark pixels render the use of indices based on

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Fig. 25.3 Land surface temperature of winter calculated as mean of land surface temperature of four different days

Fig. 25.4 Land surface temperature of summer calculated as mean of land surface temperature of four different days

spectral analysis insufficient for classification. In environments with low spectral reflectance where non-water dark surfaces are present, spectral indices cannot accurately distinguish water pixels from non-water pixels. (Sarp & Ozcelik, 2017). It has been found that techniques based on the image classification method offer a better alternative for waterbody shape and area extraction where dark pixels are present. (Huang et al., 2015). This investigation employs Support Vector Machine (SVM) algorithm with RBF kernel for waterbody extraction. The Support Vector Machine (SVM) algorithm is based on the statistical learning theory and structural risk minimization principle (Chapelle et al., 1999). It has a high generalization performance and is suited for the classification of large feature space (Huang et al., 2015).

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25.2.9 Local Climate Zone Classification It has been observed that climatic behaviour in urban areas changes with change in surroundings (Oke, 1987). Past studies have established that the microclimate of an area depends upon the various site metadata such as landcover, the height of built masses and density of built masses, etc. To accurately document and analyze the thermal environment pertaining to a neighbourhood, we need to define the site metadata comprehensively. Researchers for urban climate studies have proposed various classification schemes for comparability across different regions and climates. From Chandler’s classification of Greater London in four parts in 1965 to Oke’s “Urban Climate Zones” in 2004, the classification schemes have evolved to incorporate more just parameters. This study adopts the Local Climate Zone classification (Stewart & Oke, 2012b) to classify the designated urban area. This classification scheme has a provision to classify any urban area into a total of 17 categories based on the surface cover, structure, material, and human activity (Stewart & Oke, 2012a). In this study, the Local Climate Zone classification is carried out through the Satellite image-based method (Bechtel et al., 2015). Training areas representative of typical LCZ is identified using google earth. Random Forest classifier in SAGA-GIS is employed using the defined training area polygons to classify different LCZ around the waterbody.

25.2.10 Validation and Accuracy Assessment Accuracy assessment was conducted to evaluate the accuracy of LCZ classification of the city of Chandigarh. 545 reference data points were collected randomly from the whole region for assessment. A confusion matrix was developed, subsequently summarizing the degree of confusion between the resultant classification and the ground truth data by comparing the generated LCZ classes with the reference data. The confusion matrix (Table 25.2) presents the user and producer accuracy of each LCZ with the overall accuracy and the Kappa coefficient. The overall accuracy of the LCZ map of Chandigarh is 70.09%, and the Kappa coefficient is 0.672. The map exhibits acceptable accuracy and can be used for further studies. For further climatic analysis, the following four Local Climate Zones are identified around the waterbody in the city of Chandigarh Fig. 25.5: 1. LCZ 6 (Open Low rise): LU 1 2. LCZ 3 (Compact Low rise): LU 3 3. LCZ A (Dense Trees): LU 2 and LU 4 (Fig. 25.6).

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0.672

Overall accuracy (%)

Kappa

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6

0

0

3

0

0

0

2

0

0

0

1

0

1

0

0

0

0

0

0

0

0

0

B

0

3

0

1

0

1

1

0

0

2

0

8

59 1

24 0

2

2

1

0

2

0

4

1

0

10 A

0

0

0

2

3

0

0

0

0

0

0

0

0

0

0

0

0

C

20 14 39 73 13 5

0

0

4

0

0

0

0

0

3

11 0

0

1

0

0

1

0

0

8

0

0

0

0

0

2

0

0

0

0

1

0

0

E

0

0

2

84 6

2

6

0

59 1

3

0

0

4

0

1

0

6

2

0

0

1

0

D

4

0

2

0

1

0

0

1

0

0

0

0

0

0

0

0

0

0

F

15

6

68

8

11

70

38

15

22

15

85

59

0

74

15

0

55 545

93

13

33

87

38

73

84

63

53

50

7

75

73

69

40

No. of classified pixels User accuracy (%)

41 44

0

0

1

2

0

4

1

0

0

2

3

0

0

0

1

0

G

69 75 50 55 57 62 81 62 60 70 33 50 75

43 74

0

1

0

0

0

0

3

0

2

0

1

64 0

0

0

0

0

0

7

Producer accuracy (%)

0 0

0 0

0 0

0 1

0 0

0 0

0 0

0 1

0 0

0 3

0 2

0 4

0 43 9

0 0

3

2

0

6

14 69 0 62 85 2

0

0

0

0

0

0

2

4

0

3

4

0

2

0

51 0 7

3

0

3

No. of ground truth pixels 0

0

0

0

0

2

0

0

2

1

0

0

0

1

2

1

LCZ

Table 25.2 Confusion matrix of LCZ map of Chandigarh

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Fig. 25.5 LCZ classification of Chandigarh, India

Fig. 25.6 LCZ’s identified for analysis

25.2.11 Waterbody Thermal Impact Calculation A total of 100 buffers at a distance of 30 m from the waterbody edge was created, spanning 3000 m. Subsequently, the intersection of these buffers with the identified

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Fig. 25.7 100 buffer at 30 m distance around the waterbody

local climate zones was extracted. Further, the mean LST of each intercept extracted in the previous step was calculated by overlay analysis of the intercept and the LST for different seasons (Fig. 25.7).

Waterbody thermal impact is calculated using the third order polynomial method proposed by Lin et al. (2015). Mean LST of the intercept as calculated in the previous step is plotted against the distance from waterbody edge. The graph’s trend line fits a third order polynomial where the Y-axis represents the LST of the intercept buffer and the X-axis the distance from the waterbody edge (Fg. 25.8). f (x) = ax 3 + bx 2 + cx + d The amplitude of thermal impact is defined as the temperature difference between the waterbody’s mean surface temperature and the LST of the particular intercept buffer. As Sun and Chen (2012) proposed, the first turning point of the curve represents the maximum impact distance (ID) of the waterbody, and the maximum impact

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Fig. 25.8 Identified LCZ’s, waterbody buffer and their intercepts

intensity amplitude (IA) is the difference in LST of the inflection point and mean LST of the waterbody. ID = x =

−2b −

√

4b2 − 12ac 6a

I A = f (I D) − d The average slope of the curves over different LCZ’s is calculated to represent the impact gradient (Xue et al., 2019).

25.2.12 Statistical Analysis Pearson correlational analysis was undertaken between Impact distance and impact amplitude in different LCZ’s over two seasons, i.e., summer and winter, to establish and quantify the waterbody’s thermal impact behavior.

25.3 Results The four land cover zones representing three different LCZ’s have shown different trends in land surface temperature change while the distance from the waterbody

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edge varies. The summer and winter trends reveal that the change in season affects the LST differently for the same LCZ. In the city of Chandigarh, which has a humid subtropical climate, waterbody’s presence provides a cooling effect in summer and varied impact in winter months in the zones considered for the study (LCZ 6, LCZ 3, and LCZ A). However, the trends for each LCZ have different magnitudes, which are discussed below in detail. In summers, the distance and temperature difference of first inflection point in the curve of surface temperature with respect to edge of waterbody is; (i) 120 m, and 1.25 °C for LU 1 (LCZ 6); (ii) 240 m, and 1.41 °C for LU 2 (LCZ A); (iii) 270 m, and 3.47 °C for LU 3 (LCZ 3); (iv) 330 m, and 3.52 °C for LU 4 (LCZ A); respectively (Fig. 25.9). In winters, the distance and temperature difference of the first inflection point in the curve of surface temperature with respect to the edge of waterbody is; (i) 120 m, and 0.52 °C for LU 1 (LCZ 6); (ii) 300 m, and 0.20 °C for LU 2 (LCZ A); (iii) 150 m, and −0.72 °C for LU 3 (LCZ 3); (iv) 180 m, and 0.57 °C for LU 4 (LCZ A); respectively (Fig. 25.10). All four cases have shown a fair value of Pearson correlation Table 25.3.

Fig. 25.9 Graph representing LST with respect to distance from waterbody edge with inflection point in summer

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Fig. 25.10 Graph representing LST with respect to distance from waterbody edge with inflection point in winter

Table 25.3 Waterbody cooling impact Season

LCZ

In summers

LU 1 (LCZ 6) LU 2 (LCZ A) LU 3 (LCZ 3)

In winters

Impact amplitude (°C)

Impact range (M)

Impact gradient (°C/M)

Pearson correlation between LST and distance from waterbody

1.25

120

0.01

0.90

1.41

240

0.0058

0.99

3.47

270

0.0128

0.99

LU 4 (LCZ A)

3.52

330

0.01

0.94

LU 1 (LCZ 6)

0.52

120

0.0043

0.93

LU 2 (LCZ A)

0.20

300

0.0006

0.90

LU 3 (LCZ 3)

−0.72

150

0.0048

−0.98

LU 4 (LCZ A)

0.57

180

0.0031

0.94

25.4 Discussion 25.4.1 Waterbody Impact on Land Surface Temperature This investigation shows that waterbody has a cooling effect during summer and has a varied thermal effect during winter in Humid Subtropical climate on its surroundings.

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The lake’s cooling effect during summer has a maximum amplitude of 3.52 °C, and the impact goes to a maximum distance of 330 m. This behaviour agrees with another study conducted in Wuhan, China, which concluded that maximum local cool island intensity could exceed 3 °C in summer (Wu et al., 2019). This effect can be due to the lower surface temperature of the waterbody owing to its higher heat capacity, forcing it to warm at a lower rate than the landmass. This result also indicates that waterbody has the lowest temperature when compared to its nearby surroundings during summer. It is also evident that the cooling effect decreases with an increase in distance from the waterbody, which is observed in various previous researches (Chen et al., 2009; Saaroni & Ziv, 2003). During winter, the thermal behaviour of the waterbody is different. It does provide a cooling effect in LCZ 6 and LCZ A, albeit with low intensity. However, in LCZ 3, it shows a low-intensity warming effect. This result is similar to another study conducted in Hokkaido, Japan, where it was found that wetlands can provide cooling during the warm months and warming during the winter (Shudo et al., 1997). The warming effect in LCZ 3 can be attributed to the higher temperature of the waterbody. This higher temperature of waterbody has resulted from two factors. Firstly, the waterbody is exposed to direct solar radiation, but the direct solar radiation is very limited in compact and low rise LCZ due to mutual shading by the built mass. Secondly, the lake’s higher heat capacity doesn’t allow it to cool as rapidly as the cooling of the landmass. A similar investigation carried out by Moyer and Hawkins (2017) in the Harrisburg area, which has a Humid and Subtropical climate, found that river has a heating impact on its nearby surroundings (Moyer & Hawkins, 2017). Hills are a prominent microclimate influencing landform. The hills present in the study area are located at about 2.7 km distance on the North-East side from the waterbody. In various previously published studies, it has been found that the maximum thermal impact range of waterbody is up to double its width (Hathway & Sharples, 2012). The maximum width of the waterbody in the hill’s direction is 900 m. Thus any direct impact from the hill range will be negligible in this study. Moreover, the hills are not on the wind path (which has a prevailing direction of north west to south east), thus further limiting its impact on the region’s microclimate.

25.4.2 Influence of LCZ on Waterbody Impact This study reflects that the impact of waterbody on land surface temperature differs in different surroundings, as observed by various researchers previously (Hathway & Sharples, 2012; Manteghi, 2015; Sun & Chen, 2012). The range of waterbody impact during summer is maximum in LU 4 (LCZ A), which can be owing to the fact that LCZ A itself promotes cooling through evapotranspiration and offers comparatively fewer obstacles for airflow than other LCZs considered here. It is to be noted that while LU 2 and LU 4 both are LCZ A but the impact range is substantially different. LU 4, located in the north of the waterbody, lies directly in the wind path, which is

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predominantly South East to North West. Thus carrying the cooling effect to a much larger distance in comparison to LU 2. The highest waterbody impact gradient of 0.0128 °C/M is observed during summer in LU 3 (LCZ 3). Previous studies also found that LST in LCZ 3 is most sensitive to urban waterbodies (Wang et al., 2019). Cheng and fellow researchers found that impervious surfaces are the most susceptible to the thermal effect of the nearby water bodies (Cheng et al., 2019). This thermal behaviour can be attributed to the smaller building envelope and narrow streets. Smaller building envelope absorbs less radiation, and narrow streets facilitate mutual shading thus keeping the surroundings cooler. Narrow streets also exhibit higher wind velocity (Blocken et al., 2007), allowing for more significant cooling. During winter, the impact range is maximum in LU 2 (LCZ A) as the wind is negligible and dense trees facilitate the cooling. The maximum thermal impact amplitude and impact gradient are observed in LU 3 (LCZ 3) at −0.72 °C and 0.0048. The lake provides a warming effect during winter in compact low rise (LU 3) area. LU 3 (LCZ 3) has a higher impervious surface percentage than the other land use. These impervious surfaces cool much more rapidly, making the LU 3 the region with the lowest land surface temperature during winter. In comparison, the waterbody exhibits higher temperature due to more extensive exposure to solar radiation and high specific heat. This temperature regime difference allows the waterbody to provide a warming effect in LU 3 (LCZ 3). This investigation shows a definitive trend that the thermal impact of waterbody is significantly less in winter when compared to summer.

25.4.3 Implication of Waterbody Thermal Impact on Urban Planning In cities, the urban heat island effect is leading to high thermal stress for residents. This also increases the need for air cooling devices, which are expensive and energyintensive, hence not sustainable. This makes the need for nature-based solutions to mitigate heat in urban areas a prime focus for contemporary research. This study provides a solution in the form of a waterbody to attenuate urban heat. Although it is challenging to plan a new waterbody in an existing urban area, this study will assist urban planners in making an informed decision while designing new townships around the waterbody to avail its maximum cooling potential.

25.4.4 Limitation and Scope of Future Research This investigation has its own set of limitations. The thermal bands of Landsat-8 TIRS satellite resolution is 100 m, but it is resampled made available for public

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usage at 30 m. The LST retrieved from the improved mono window algorithm has an average error of −0.05 K (Wang et al., 2015). The spatial and temporal resolution of the remotely sensed data (LANDSAT-8 TIRS) is too coarse and can be further improved by using data with better temporal and spatial resolution. The satellite data employed here is captured at 10:54 a.m.; thus, this result is unidimensional in the time of the study. It fails to capture the mid-day temperature or the night-time temperature. The thermal impact of waterbody can also be explored in various other LCZ’s as this case provided the opportunity to explore just some of them. This study analyses the impact from just a single waterbody, thus there is a need to explore this phenomenon with different waterbodies in different settings so that a relationship can be established between shape, size, and depth of waterbody to its impact on heat stress. Future research should also investigate this relationship using on-site measured data, as the LST generated from satellite imagery has some limitations.

25.5 Conclusion This study analyzed the thermal impact of waterbody (Sukhna Lake) on its surrounding microclimate in Chandigarh, India. Remote sensed data from LANDSAT-8 TIRS was used for this investigation. The results indicate that a deep static waterbody provides a cooling effect during summer and the thermal impact is varied from cooling to warming in winter in Subtropical Humid climate of Chandigarh. The cooling impact can go as high as 3.52 °C in summer in an area of dense trees, and it has the potential to provide warmth up to 0.72 °C in area of compact low rise structures during winter. The maximum impact range is observed to be 300 m in winter and 330 m in summer in the area of dense trees. Maximum impact gradient of 0.0128 °C/M in summer and 0.0048 °C/M in winter is achieved in an area that is compactly built low-rise structure. These results are representative of a single case of Chandigarh and employ a single methodology. There is a need to evaluate this behaviour through various other methods in different cases so it can help urban designers and urban planners in making a better-informed decision regarding planning built environment beside a waterbody or vice-versa for better heat stress attenuation. Acknowledgements The author would like to sincerely thank the Indian Institute of Technology Roorkee, India, for supporting this research.

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