Urban Health and Wellbeing: Indian Case Studies [1st ed. 2020] 978-981-13-6670-3, 978-981-13-6671-0

Table of contents :
Front Matter ....Pages i-xxxiv
Urban Health and Wellbeing: Emerging Trans-disciplinary Stream (Aakriti Grover, R. B. Singh)....Pages 1-32
Research Background (Aakriti Grover, R. B. Singh)....Pages 33-61
Geographical Background: Delhi and Mumbai (Aakriti Grover, R. B. Singh)....Pages 63-101
Changing Urban Environment in Megacities (Aakriti Grover, R. B. Singh)....Pages 103-149
Urban Microclimates (Aakriti Grover, R. B. Singh)....Pages 151-177
Urban Health Risk Analysis (Aakriti Grover, R. B. Singh)....Pages 179-217
Strategic Plan for Urban Health and Wellbeing for the Indian Megacities (Aakriti Grover, R. B. Singh)....Pages 219-249
Health Policy, Programmes and Initiatives (Aakriti Grover, R. B. Singh)....Pages 251-266
Back Matter ....Pages 267-273

Citation preview

Advances in Geographical and Environmental Sciences

Aakriti Grover R. B. Singh

Urban Health and Wellbeing Indian Case Studies

Advances in Geographical and Environmental Sciences Series Editor R. B. Singh, University of Delhi, Delhi, India

Advances in Geographical and Environmental Sciences synthesizes series diagnostigation and prognostication of earth environment, incorporating challenging interactive areas within ecological envelope of geosphere, biosphere, hydrosphere, atmosphere and cryosphere. It deals with land use land cover change (LUCC), urbanization, energy flux, land-ocean fluxes, climate, food security, ecohydrology, biodiversity, natural hazards and disasters, human health and their mutual interaction and feedback mechanism in order to contribute towards sustainable future. The geosciences methods range from traditional field techniques and conventional data collection, use of remote sensing and geographical information system, computer aided technique to advance geostatistical and dynamic modeling. The series integrate past, present and future of geospheric attributes incorporating biophysical and human dimensions in spatio-temporal perspectives. The geosciences, encompassing land-ocean-atmosphere interaction is considered as a vital component in the context of environmental issues, especially in observation and prediction of air and water pollution, global warming and urban heat islands. It is important to communicate the advances in geosciences to increase resilience of society through capacity building for mitigating the impact of natural hazards and disasters. Sustainability of human society depends strongly on the earth environment, and thus the development of geosciences is critical for a better understanding of our living environment, and its sustainable development. Geoscience also has the responsibility to not confine itself to addressing current problems but it is also developing a framework to address future issues. In order to build a ‘Future Earth Model’ for understanding and predicting the functioning of the whole climatic system, collaboration of experts in the traditional earth disciplines as well as in ecology, information technology, instrumentation and complex system is essential, through initiatives from human geoscientists. Thus human geosceince is emerging as key policy science for contributing towards sustainability/survivality science together with future earth initiative. Advances in Geographical and Environmental Sciences series publishes books that contain novel approaches in tackling issues of human geoscience in its broadest sense—books in the series should focus on true progress in a particular area or region. The series includes monographs and edited volumes without any limitations in the page numbers.

More information about this series at http://www.springer.com/series/13113

Aakriti Grover R. B. Singh •

Urban Health and Wellbeing Indian Case Studies

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Aakriti Grover Department of Geography School of Earth Sciences Central University of Tamil Nadu Thiruvarur, Tamil Nadu, India

R. B. Singh Department of Geography Delhi School of Economics University of Delhi Delhi, India

ISSN 2198-3542 ISSN 2198-3550 (electronic) Advances in Geographical and Environmental Sciences ISBN 978-981-13-6670-3 ISBN 978-981-13-6671-0 (eBook) https://doi.org/10.1007/978-981-13-6671-0 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Human health is an outcome of complete physical, mental and social wellbeing; it is not just the absence of disease. Therefore, health is a multi-dimensional concept, which involves the physical, mental and social sphere of human life. Urban environment has distinct natural, built and institutional elements that determine the physical, mental and social health and wellbeing of people, living in cities, towns and urban areas. Urban health, therefore, refers to the study of health of urban population and its underlined basic causes including physical and environmental and social factors. Wellbeing is a broader concept that helps meeting the basic needs and further leads to satisfaction level of individuals and communities in society, and an important determinant of wellbeing is good physical health together with mental and social health. Urban environment encompasses both physical and social environment in urban areas. The urban environment has direct and indirect bearing on human health. The direct influences are ones that influence human health regardless of behaviour, e.g. the impact of environmental pollution on human health. The indirect influences include the choices one makes, personal behaviour like lifestyle and eating habits. In the present research, the focus is on the impact of physical environment on health and finally wellbeing in urban areas. In the present analysis, land use and land cover (LULC) and air quality changes have been taken as a proxy to understand changing urban environment. LULC change is the modifications in existing LULC, wherein there is transformation of natural environment into built environment. As a result, the nature and composition of surface characteristics change leading to microclimatic changes. The pervious soil and vegetation surfaces (grass, thatch roofs, dry soil and sand) are substituted by impervious urban built up materials like concrete, metal, steel, stone, tiles and asphalt. The expanding impervious surfaces, dense urban geometry, increased greenhouse gases’ (GHGs) emissions intensify the temperature of urban areas. The comparative increase of land surface temperature (LST) in core of urban area to the periphery is called Urban Heat Island (UHI). The creation and intensity of UHI are dependent on various natural and human factors like geographical location, climate, local weather, time, city geometry, air pollution, energy, LULC and city size. v

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The comparative study between Delhi and Mumbai on changing health and wellbeing in response to changing urban environment has showcased the paradox of urban development in the two megacities of India. The deterministic approach that environment is supreme cannot be totally neglected for the fact remains that there exists intrinsic relationship between human beings and environment. However, the degree of environmental influence is often mediated by technological growth. The present research findings assert this undeniable fact that changing urban environment influences human health and wellbeing, and therefore, innovative pathways should be created for sustainable and healthy urban growth. The study is broadly organized into nine chapters. Chapter 1 presents the evolution of concept of health, medical geography and geography of health. It also briefs about the measures, indicators and agencies promoting health. Further, an overview of health in India is discussed. Chapter 2 conceptualizes the research problem, linking changes in urban environment and human health. All major concepts related to study have been properly defined. Further, it deals with brief description of study area, detailed literature review about LULC, air pollution and health, LST, urban microclimate (UMC), research questions, objectives, brief description of methodology of each objective. Chapter 3 deals with detailed description of study area, i.e. megacities of Delhi and Mumbai. It discusses the geographical locations of cities, early history of cultural evolution, physiography including elevation and slope, drainage and water resources, climatic conditions, status of forests and vegetation cover, population trends, density, literacy and sex ratio, age–sex composition, poverty, transport network and vehicles growth, population health status, growth of industries, brief description of land use and environment, natural and human-made disasters and hazards, slums, etc. Chapter 4 discusses in detail the LULC and population change as factors of urban environment of Delhi and Mumbai. LULC change and air quality change are considered as the indicators of urban environmental change in Mumbai and Delhi. LULC change has been studied using Landsat satellite images, while the air quality change has been studied based on the data collected from CPCB and MPCB. Extensive fieldwork has also been done in order to cross-verify the results. Chapter 5 deals with the assessment of land surface temperature, UHI and UMC using Landsat satellite data. NDVI and NDBI have also been assessed as the predictor and factor of urban microclimate. NDVI indicates vegetation health, and NDBI indicates built up density (concrete surface). Chapter 6 focuses on urban health risk in Delhi and Mumbai. Impact of air pollution on human health has been particularly dealt with empirical evidences. Data related to deaths caused by different disease including circulatory and respiratory system has been collected from various governmental sources. The extensive fieldwork was also done for understanding the disease pattern across pollution strata (occupation, gender, age, income, etc.). Further, the incomewise analysis of mortality caused by different disease related to air pollution was studied. Also, agewise analysis of deaths has been presented for Delhi. Chapter 7 deals with reviews of governmental plans and policies regarding the human health, environment including air pollution, transport and land use, etc. Further, it

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presents strategic plan for urban health and wellbeing for Indian megacities, i.e. Delhi and Mumbai, which can be further applied to other cities for sustain urban development of Indian cities. Chapter 8 summarizes the plans, policies, programmes, legislations, laws and international efforts for promotion of good health and wellbeing, and the last chapter compiles the appendices. Thiruvarur, India Delhi, India

Aakriti Grover R. B. Singh

Acknowledgements

It gives us immense pleasure to express our gratitude to all those who contributed in their own ways for the successful completion of this book. We are thankful to each soul that has come across all through the journey. We are obliged to the Central University of Tamil Nadu (CUTN) fraternity for their kind support. Our sincere thanks to Vice Chancellor Prof. A. P. Dash, Registrar Dr. S. Bhuvaneshwari, Dean of School of Earth Sciences— Prof. Sulochana Shekher and colleagues of CUTN. We are also grateful to all the faculty members of the Department of Geography, Delhi School of Economics, who have been a source of inspiration and motivation, especially Prof. H. Ramachandran, Prof. S. C. Rai, Dr. Anindita Datta, Dr. Subhash Anand, Dr. B. W. Pandey, Dr. Pankaj Kumar and Dr. N. Sahu. We appreciate valuable suggestions of Prof. S. K. Agarwal, Dr. B. Khan, Dr. Ashis Saha, Dr. Aparajita De, Dr. Kiran Bhairannavar, Dr. Anjan Sen and Dr. Praveen K. Pathak. The learned authors’ contributions are the guiding light and the temples of learning, i.e. libraries connect the ecosystems of learning from all across the world. We are deeply grateful to the library staff of Central University of Tamil Nadu; Dr. Lokesh Sharma (Retired Librarian) and his team at the Ratan Tata Library for creating conducive spaces for authors. The library staffs of Indian Institute of Technology—Bombay, International Institute of Population Science, Central Pollution Control Board and Teenmurti Bhawan Library have been generous in sharing the resources for the present research. We are grateful to each one of them. The foundation and base of the research is sound dataset. We are obliged to Dr. D. D. Basu, Retired Scientist, CSE; Dr. Padmaja S. Keskar and Mrs. Pranita M. Tipre, Health Department, Brihanmumbai Municipal Corporation; Dr. Sanjeev Agarwal and Dr. Sangeeta, Central Pollution Control Board; Prof. D. Parthasarathy, Indian Institute of Technology—Bombay; and all the interviewees for their kind assistance and patience. The research group led by Prof. R. B. Singh has been supportive throughout. We are indebted to Dr. Ajay Kumar for their guidance and timely assistance for the technical support. We are

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thankful to Dr. Chintan Chaudhary, Consultant at Safdarjung Hospital, and Dr. P. Ram, Jaslok Hospital, for fruitful discussions. This piece of work could not have been possible without our family, and we are particularly indebted to all.

Contents

1 Urban Health and Wellbeing: Emerging Trans-disciplinary Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 The Value of Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Definition and Concept of Health . . . . . . . . . . . . . . . . . . 1.3.1 Traditional Medical/Biostatistical Concept . . . . . . . 1.3.2 The Concept of Health Given by WHO . . . . . . . . 1.3.3 The Ecological Concept of Health . . . . . . . . . . . . 1.3.4 The Holistic or Normative Concept of Health . . . . 1.3.5 Potential Alternative Universal Concepts of Health 1.4 Approaches to Geography of Health . . . . . . . . . . . . . . . . 1.5 From Medical to Health Geography . . . . . . . . . . . . . . . . . 1.5.1 Medical Geography . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3 Health Geography . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Measures and Indicators of Health . . . . . . . . . . . . . . . . . . 1.7 Contribution of Other Disciplines in Geography of Health and Medical Geography . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 National and International Institutional Mechanisms . . . . . 1.9 Overview of Health in India . . . . . . . . . . . . . . . . . . . . . . 1.9.1 Effect of Air Pollution on Health in India . . . . . . . 1.10 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Research Background . . . . . 2.1 Introduction . . . . . . . . 2.2 Conceptual Framework 2.3 Literature Review . . . .

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2.3.1 Urban Environmental Change . . . . . . . . . . . . . . . . 2.3.2 Urban Heat Island . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Impact of Changing Urban Environment on Urban Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Urban Environment of Delhi . . . . . . . . . . . . . . . . 2.4.2 Urban Environment of Mumbai . . . . . . . . . . . . . . 2.5 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Data Collection and Methodology . . . . . . . . . . . . . . . . . . 2.8 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4 Changing Urban Environment in Megacities . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Driving Forces of Urban Environmental Change . . . . . 4.2.1 LULC Change and Population Change . . . . . . 4.2.2 Vehicular Growth . . . . . . . . . . . . . . . . . . . . . 4.3 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Datasets Used in LULC Classification . . . . . . . 4.3.2 Data Sources of Air Pollution . . . . . . . . . . . . . 4.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Pre-processing of Images for Land Use/Cover Classification . . . . . . . . . . . . . . . . . . . . . . . . .

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3 Geographical Background: Delhi and Mumbai . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 3.2 Geographical Location . . . . . . . . . . . . . . . 3.3 Early History and Cultural Evolution . . . . . 3.4 Physiography . . . . . . . . . . . . . . . . . . . . . . 3.5 Drainage and Water Resources . . . . . . . . . 3.6 Climate . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Natural Resources . . . . . . . . . . . . . . . . . . . 3.7.1 Forest and Tree Cover . . . . . . . . . . 3.7.2 Energy Resources . . . . . . . . . . . . . 3.8 Demography . . . . . . . . . . . . . . . . . . . . . . . 3.9 Transport Network and Vehicular Traffic . . 3.10 Health . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.11 Industrial Growth . . . . . . . . . . . . . . . . . . . 3.12 Air Quality . . . . . . . . . . . . . . . . . . . . . . . . 3.13 Hazards and Disasters . . . . . . . . . . . . . . . . 3.14 Squatter Settlements and Slums . . . . . . . . . 3.15 Concluding Remarks . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4.4.2 Land Use/Cover Classification, Mapping and Change Detection . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Post-classification Processing . . . . . . . . . . . . . . . . . 4.4.4 Analysis and Quantification of Differences in LULC 4.4.5 Estimation of Trends of Air Pollution . . . . . . . . . . . 4.5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Land Use/Cover Change in Delhi and Mumbai . . . . 4.5.2 Status of Air Quality Change in Delhi and Mumbai . 4.5.3 Status of Air Quality Change in Mumbai . . . . . . . . 4.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Urban Microclimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Urban Environment . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Urban Heat Island . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 Factors Affecting UHI and LST . . . . . . . . . . . . . . 5.1.4 Inter-Relationship Between LST, NDVI and NDBI 5.1.5 UHI Studies in India . . . . . . . . . . . . . . . . . . . . . . 5.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Image Pre-processing . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Relative Radiometric Correction (RRC) . . . . . . . . . 5.3.3 Estimation of NDVI . . . . . . . . . . . . . . . . . . . . . . . 5.3.4 Estimation of NDBI . . . . . . . . . . . . . . . . . . . . . . . 5.3.5 Estimation of LST . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Spatial Patterns and Trends of LST and UHI in Delhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Spatial Patterns and Trends of LST, NDVI and NDBI in Delhi . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Relationship Between LULC, LST, NDVI and NDBI in Delhi . . . . . . . . . . . . . . . . . . . . . . . 5.4.4 Spatial Patterns and Trends of LST and UHI in Mumbai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.5 Spatial Patterns and Trends of LST, NDVI and NDBI in Mumbai . . . . . . . . . . . . . . . . . . . . . 5.4.6 Relationship Between LULC, LST, NDVI and NDBI in Mumbai . . . . . . . . . . . . . . . . . . . . . 5.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6 Urban Health Risk Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Data Sources and Methodology . . . . . . . . . . . . . . . . . . . 6.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Impact of Air Pollution on Mortality in India . . . 6.3.2 Temporal Analysis of Mortality from Circulatory and Respiratory System in Delhi . . . . . . . . . . . . . 6.3.3 People’s Perception on Urban Environment and Health of Delhi and Mumbai . . . . . . . . . . . . 6.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 Strategic Plan for Urban Health and Wellbeing for the Indian Megacities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Existing Plans and Policy for Health and Wellbeing in Changing Urban Environment . . . . . . . . . . . . . . . . . . . . 7.2.1 International Level . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 National Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3 Delhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.4 Mumbai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Lacuna in Existing Policies and Plans . . . . . . . . . . . . . . . . 7.3.1 Land Use/Cover . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Air Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Urban Heat Island . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.4 Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Systems Approach and Sustainable Urban Environment . . . 7.4.1 Air Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Land Use/Cover and Urban Heat Island . . . . . . . . . 7.4.3 Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Strategic Planning for Delhi and Mumbai . . . . . . . . . . . . . . 7.5.1 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Health Policy, Programmes and Initiatives . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Health Sector in India—Structure, Roles and Functions . . 8.2.1 Role of Government of India in Preservation and Promotion of Public Health: Health Missions, Five Year Plans and National Health Policies . . . . 8.2.2 Historical Evolution of Health Policies, Plans and Programmes in India . . . . . . . . . . . . . . . . . . .

. . . . 219 . . . . 219 . . . . . . . . . . . . . . . . .

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220 220 222 223 226 227 227 227 229 230 232 233 238 240 242 244 247

. . . . . 251 . . . . . 251 . . . . . 253

. . . . . 254 . . . . . 255

Contents

8.3 8.4

Constitutional Provisions: Acts and Statues in India . . . . Role of Judiciary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Some Important Legislation Related to Health . . . 8.5 Ministries Related to Improving Health . . . . . . . . . . . . . 8.6 International Treaties and Conventions Ratified by India . 8.7 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xv

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257 261 261 262 263 264 265

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267

Abbreviations

AMRIT AOI APPCDC ASTER AUHI AusAID AVHRR AYUSH BEST BKC BLUHI BMC BMR BOD BPL CARE CBD CCS CDRI CGWB CH4 Ckt.km CLUHI CMNND CNG CO CO2 COP COPD CPCB

Affordable Medicines and Reliable Implants for Treatment Area of interest Asia-Pacific Partnership on Clean Development and Climate (AP6) Advanced Spaceborne Thermal Emission and Reflection Radiometer Atmospheric Urban Heat Island Australian Agency for International Development Advanced Very-High-Resolution Radiometer Ayurveda, Yoga and Naturopathy, Unani, Siddha and Homoeopathy Brihanmumbai Electric Supply and Transport Bandra Kurla Complex Boundary Layer Urban Heat Island Brihanmumbai Municipal Corporation Bombay Metropolitan Region Biochemical Oxygen Demand Below Poverty Line Cooperative for Assistance and Relief Everywhere Central business district Country Cooperation Strategy Climate Disaster Resilience Index Central Ground Water Board Methane Circuit kilometre Canopy Layer Urban Heat Island Communicable, maternal, neonatal and nutritional diseases Compressed Natural Gas Carbon monoxide Carbon dioxide Conference of Parties Chronic Obstructive Pulmonary Disease Central Pollution Control Board

xvii

xviii

CR DALY DCB DDA DDAP DFID DFLE DMRC DN DPCB DRR DTC DVAT ENT EHE EPA ETM+ EU EWS FCC g/m3 GCP GDEM GDP GHG GOES GPS HALE HC HDI IAP ICDS ICMR ICPD ICSU IDSP IISBE IMR IPCC IR ISBT ISC IT ITO

Abbreviations

Central Railway Disability-Adjusted Life Year Delhi Cantonment Board Delhi Development Authority Drug De-addiction Programme Department for International Development Disability-free life expectancy Delhi Metro Rail Corporation Limited Digital Numbers Delhi Pollution Control Board Disaster risk reduction Delhi Transport Corporation Delhi Value Added Tax Ears, Nose and Throat Extreme Heat Events Environmental Protection Agency Enhanced Thematic Mapper Plus European Union Economically weaker section False Colour Composite Grams per cubic metre Ground control point Global Digital Elevation Model Gross Domestic Product Greenhouse gas Geostationary Operational Environmental Satellite Global Positioning System Healthy Life Expectancy Hydrocarbon Human Development Index InterAcademy Panel Integrated Child Development Services Indian Council for Medical Research International Conference on Population and Development International Council for Scientific Union/International Science Council Integrated Disease Surveillance Projects International Initiative for a Sustainable Built Environment Infant mortality rate Intergovernmental Panel on Climate Change Infrared Inter State Bus Terminus (also called Maharana Pratap ISBT) International Science Council Information Technology Income Tax Office

Abbreviations

IYGU JSSK km km2 KV LISS LPG LRD LRS LRT LST LULC m MCD MCGM MDM MGDs micro g/m3 MIR MMR MMRDA MODIS MoHFW MOP MPCB MPT MRTS msl MSS MW NA NAAQ NAAQM NACP NAPCC NCCP NCD NCPTOD NCR NCRP NCT NDBI NDMC NDVI

xix

International Year of Global Understanding Janani Shishu Suraksha Karyakram Kilometre Square kilometre (sq.km) Kilo volt Linear Imaging Self-Scanning Sensor Liquefied Petroleum Gas Lower Respiratory Diseases Lower Respiratory Symptoms Lower Respiratory Tract Land Surface Temperature Land use/Land cover Metre Municipal Corporation of Delhi Municipal Corporation of Greater Mumbai Mid-day meal Millennium Development Goals Micro grams per cubic metre (µg/m3) Mid-infrared Mumbai Metropolitan Region Mumbai Metropolitan Region Development Authority Moderate-resolution Imaging Spectroradiometer Ministry of Health and Family Welfare Meeting of Parties Mumbai Pollution Control Board Mumbai Port Trust Mass rapid transport system Mean sea level Multi-spectral scanner Megawatt Not available National Ambient Air Quality National Ambient Air Quality Monitoring Programme National AIDS Control Programme National Action Plan on Climate Change National Cancer Control Programme Non-communicable diseases National Programme for Control and Treatment of Occupational Diseases National Capital Region National Cancer Registry Programme National Capital Territory Normalized Difference Built up Index New Delhi Municipal Council Normalized Difference Vegetation Index

xx

NEERI NFCP NGCP NGEP NH NHP NIDDCP NIDM NIR NLEP NMHP NNAPP NO2 NOAA NOIDA NOx NPCB NPHCE NPPCC NPPCD NPPCF NRHM NSS NTC NTCP NUHM NVBDCP O3 OECD OLI OOP ORD OXFAM PAH Pb PCB PHC PM PMJAY PMSSY ppm PVC PYLL QALY RGNDWM

Abbreviations

National Environmental Engineering Research Institute National Filaria Control Programme National Goitre Control Programme National Guinea Worm Eradication Programme National Highway National Health Policy National Iodine Deficiency Disorder Control Programme National Institute of Disaster Management Near-infrared National Leprosy Eradication Programme National Mental Health Programme National Nutritional Anaemia Prophylaxis Programme Nitrogen dioxide National Oceanic and Atmospheric Administration New Okhla Industrial Development Authority Nitrogen oxide National Programme for Control of Blindness National Programme for Health Care in Elderly National Programme for Prevention and Control of Cancer National Programme for Prevention and Control of Deafness National Programme for Prevention and Control of Fluorosis National Rural Health Mission National Sample Survey National TB Control Programme National Tobacco Control Programme National Urban Health Mission National Vector Borne Disease Control Programme Ozone Organisation for Economic Co-operation and Development Operational Land Imager Out-of-pocket expenditure Other Respiratory System Diseases Oxford Committee for Famine Relief Polycyclic aromatic hydrocarbon Lead Pollution Control Board Primary Health Centre Particulate Matter Pradhan Mantri Jan Arogya Yojana Pradhan Mantri Swasthya Suraksha Yojana Parts per million Polyvinyl chloride Potential Year of Life Lost Quality Adjusted Life Years Rajiv Gandhi National Drinking Water Mission

Abbreviations

RNTCP ROB RRC RSPM RTO RUBs SAR SARS SGDs SGNP SIDO SME SO2 SPM SPOT SUHI TB TIRS TM TSP UA UHI UIP UMC UNAIDS UNCHS UNDP UNEP UNFCCC UNFPA UNICEF UNISDR URD URS URT USAID USGS UV VBDP VOC w.e.f. WCO WHO WR YLD

xxi

Revised National TB Control Programme Road over Bridge Relative radiometric correction Respirable Suspended Particulate Matter (PM10) Regional Transport Office Road under Bridges Synthetic-aperture radar imager Severe acute respiratory syndrome Sustainable Development Goals Sanjay Gandhi National Park Small Industries Development Organisation Small and Medium Enterprise Sulphur dioxide Suspended Particulate Matter (PM2.5) Satellite Pour I’Observation de la Terre Surface Urban Heat Island Tuberculosis Thermal infrared sensor Thematic Mapper Total Suspended Particulate Urban Agglomeration Urban Heat Island Universal Immunization Programme Urban Micro Climate United Nations Programme on HIV and AIDS United Nations Centre for Human Settlement United Nations Development Programme United National Environment Programme United Nation Framework Convention on Climate Change United Nations Population Fund United Nations International Children’s Fund United Nations International Strategy for Disaster Reduction Upper Respiratory Diseases Upper Respiratory Symptoms Upper Respiratory Tract United States Agency for International Development United States Geological Survey Ultraviolet Voluntary Blood Donation Programme Volatile organic compound With effect from WHO Country Office World Health Organization Western Railway Years Lived with Disability

xxii

YLL YUVA lg/m3 lm

Abbreviations

Years of Life Lost Youth for Unity and Voluntary Action Microgramme per cubic metre Micrometer

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3

Fig. 1.4

Fig. 1.5

Fig. 1.6

Fig. 1.7

Fig. 2.1

Holistic view of ‘health’. Source Created by the Authors . . . . Inter-disciplinary nature of geography of health . . . . . . . . . . . Epidemiological transition ratios of the states of India in 1990 (left) and 2016 (right). Credit Reprinted from Report by Indian Council of Medical Research, Public Health Foundation of India and Institute of Health Metrics and Evaluation licensed under the CC BY 4.0 license . . . . . . . . . . . . . . . . . . . . . . . . . Contribution of various risk factors in DALY (in percentage), 2016. Credit Reprinted from Report by Indian Council of Medical Research, Public Health Foundation of India and Institute of Health Metrics and Evaluation licensed under the CC BY 4.0 license . . . . . . . . . . . . . . . . . . . . . . . . . Contribution of air pollution as a risk in DALY, 2016. Source Analyses based on data compiled from Anonymous (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contribution of ambient (left) and household (right) air pollution as a risk in DALY, 2016. Credit Reprinted from Report by Indian Council of Medical Research, Public Health Foundation of India and Institute of Health Metrics and Evaluation licensed under the CC BY 4.0 license. . . . . . . . . . Statewise distribution of contribution COPD (left) and lower respiratory infections (right) in DALY, India, 2016. Credit Reprinted from Report by Indian Council of Medical Research, Public Health Foundation of India and Institute of Health Metrics and Evaluation licensed under the CC BY 4.0 license . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework to conceptualize linkages between urban environment and level of physical health and wellbeing. Note *Direct impacts, **Indirect impacts . . . . . . . . . . . . . . . .

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4 16

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25

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26

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26

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27

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xxiii

xxiv

Fig. 2.2

Fig. 2.3 Fig. 3.1 Fig. 3.2 Fig. 3.3

Fig. 3.4 Fig. 3.5 Fig. 3.6 Fig. 3.7 Fig. 3.8 Fig. 3.9 Fig. 3.10 Fig. 3.11

Fig. 3.12 Fig. 3.13

Fig. 3.14

Fig. 3.15

Fig. 3.16 Fig. 3.17 Fig. 3.18 Fig. 3.19

List of Figures

Idealized model of Urban Heat Island. Source Based on Voogt and Oke (2003); United States Environmental Protection Agency (2008); Valsson and Bharat (2009) . . . . . . . . . . . . . . Methodological framework . . . . . . . . . . . . . . . . . . . . . . . . . . . Location of a Delhi and b Mumbai in India (Background images are Landsat TM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Statutory towns as representation of urban sub-divisions of Delhi, 2011 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Important locations in Greater Mumbai (Background image is Landsat TM). Source Based on locations taken from Google Earth (Background images are Landsat TM) . . . . . . . Elevation map of Delhi. Source Based on ASTER GDEM2 (https://lpdaac.usgs.gov/) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Slope map of Delhi. Source Based on ASTER GDEM2 (https://lpdaac.usgs.gov/) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elevation map of Mumbai. Source Based on ASTER GDEM2 (https://lpdaac.usgs.gov/) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Slope map of Mumbai. Source Based on ASTER GDEM2 (https://lpdaac.usgs.gov/) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . View of Powai Lake from Indian Institute of Technology, Bombay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Growth of forest and tree cover in Delhi (1993–2009). Source Planning Department of Delhi (2013) . . . . . . . . . . . . . Tree cover along the roads in Delhi . . . . . . . . . . . . . . . . . . . . Location of old city forests and other important places in Delhi (Background image is Landsat TM). Source Based on locations taken from Google Earth; Forest Survey of India 2001, 2011 (Background images are Landsat TM) . . . . . . . . . Mangrove trees along Thane Creek in Mumbai . . . . . . . . . . . Growth of total and urban population in Delhi (in millions). Source Based on data from Planning Department of Delhi (2006), Census of India (2011b) . . . . . . . . . . . . . . . . . . . . . . . Population growth of Mumbai (1901–2011) (in millions). Source Based on data from Ramachandra et al. (2014), Census of India (2011a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentage share of length of road under different agencies in Delhi. Source Based on data from Directorate of Economics and Statistics (2012a) . . . . . . . . . . . . . . . . . . . . a Railway tracks at Bandra Station. b Network map of local train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Bus services and b double-decker bus provided by BrihanMumbai Electric Supply and Transport . . . . . . . . . . . . Homeless population in Delhi . . . . . . . . . . . . . . . . . . . . . . . . . a and b Living conditions in slum of Mumbai (Parel) . . . . . .

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66

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68

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List of Figures

Fig. 4.1

Fig. 4.2

Fig. 4.3

Fig. 4.4 Fig. 4.5

Fig. 4.6

Fig. 4.7

Fig. 4.8

Fig. 4.9 Fig. 4.10 Fig. 4.11

Fig. 4.12

Fig. 4.13

Trend of population growth in Delhi and Mumbai (city and suburban), 1901–2011 (in millions). Source Compiled from Planning Department of Delhi (2006), Census of India (2011a, b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Growth rate of population in Delhi and Mumbai (city and suburban), 1901–2011 (in per cent). Source Compiled from Planning Department of Delhi (2000, 2006), Census of India (2011a, b) . . . . . . . . . . . . . . . . . . . . . . . . . . . Trend of density of population in Delhi and Mumbai (city and suburban), 1901–2011 (in persons per km2). Source Compiled from Planning Department of Delhi (2000, 2006), Census of India (2011a, b) . . . . . . . . . . . . . . . . . . . . . . . . . . . Growth in number of villages in Delhi (1961–2001). Source Based on data from Planning Department of Delhi (2001) . . . Source states for in-migration to Delhi during 1991–2001 (in per cent). Source Planning Department of Delhi (2009), Department of Environment and Forests (2010) . . . . . . . . . . . Actual and projected population (*) growth in largest urban agglomerations of the world, 1975–2025 (in millions). Source Based on data from United Nations 2011 . . . . . . . . . . Decadal variation of population in Mumbai (in per cent). Source Compiled from Census of India (2011a, b), Planning Department of Delhi (2013). Note Since Mumbai suburban district was constituted after 1951, the data prior to it is same as of Mumbai city district . . . . . . . . . . . . . . . . . . . . . . . . . . . . Actual and projected growth of vehicles in Delhi (1991–2025). Source Compiled from Firdaus and Ahmed (2011), Government of NCT of Delhi (2005) . . . . . . . . . . . . . . . . . . . Growth of vehicles in Mumbai (1980–2005). Source Based on data from Motor Vehicles Department (2011) . . . . . . . . . . Vehicular traffic at Vikhroli, Mumbai . . . . . . . . . . . . . . . . . . . Rate of change in number of vehicles in Mumbai (1981–2005) (in per cent). Source Based on data from Motor Vehicles Department (2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Location and nature of air quality monitoring stations in a Delhi and b Mumbai (Background images are Landsat TM). Source CPCB (2012); www.mpcb.gov.in. Note R—residential, I—industrial, TJ—traffic junction. Bandra–Worli station 1990–1999 the station was at Bandra and post 2000 was shifted to Worli; air quality station data used in Delhi are monitored by NAMP/CPCB and in Mumbai by NAMP/NEERI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodological framework . . . . . . . . . . . . . . . . . . . . . . . . . . .

xxv

. . 104

. . 104

. . 105 . . 105

. . 106

. . 107

. . 107

. . 109 . . 110 . . 110

. . 111

. . 115 . . 118

xxvi

List of Figures

Fig. 4.14 Fig. Fig. Fig. Fig. Fig. Fig. Fig.

4.15 4.16 4.17 4.18 4.19 4.20 4.21

Fig. Fig. Fig. Fig.

4.22 4.23 4.24 4.25

Fig. 4.26 Fig. 4.27 Fig. 4.28 Fig. 4.29 Fig. 4.30

Fig. 4.31

Fig. 4.32

Fig. 4.33

Fig. 4.34

Fig. 4.35

Transformation of agricultural land to built up land use in East Delhi along the River Yamuna . . . . . . . . . . . . . . . . . . . . . . . . Land use/cover of Delhi in 1993 . . . . . . . . . . . . . . . . . . . . . . Land use/cover of Delhi in 2000 . . . . . . . . . . . . . . . . . . . . . . Land use/cover of Delhi in 2010 . . . . . . . . . . . . . . . . . . . . . . East Delhi housing colonies . . . . . . . . . . . . . . . . . . . . . . . . . . Land use/cover change in Delhi (1993–2000) . . . . . . . . . . . . . Land use/cover change in Delhi (2000–2010) . . . . . . . . . . . . . Bird’s eye view of CBD, Nariman Point and other important locations in Mumbai . . . . . . . . . . . . . . . . . . . . . . . . Hiranandani Complex in Mumbai suburban district . . . . . . . . Land use/cover of Mumbai in 1991 . . . . . . . . . . . . . . . . . . . . Land use/cover of Mumbai in 2003 . . . . . . . . . . . . . . . . . . . . Clearance of vegetated hill areas for urban development in Mumbai suburban district . . . . . . . . . . . . . . . . . . . . . . . . . . Land use/cover of Mumbai in 2010 . . . . . . . . . . . . . . . . . . . . Shrinking of Powai Lake located in Mumbai suburban district . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Land use/cover change in Mumbai (1991–2003) . . . . . . . . . . Land use/cover change in Mumbai (2003–2010) . . . . . . . . . . a Annual trend of SO2 in residential areas in Delhi (in µg/m3). Source Based on data from CPCB 1990–2011. b Annual trend of SO2 in industrial areas in Delhi (in µg/m3). Source Based on data from CPCB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Annual trend of NO2 in residential areas in Delhi (in µg/m3). Source Based on data from CPCB. b Annual trend of NO2 in industrial areas in Delhi (in µg/m3). Source Based on data from CPCB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Annual trend of SPM in residential areas in Delhi (in µg/m3). Source Based on data from CPCB. b Annual trend of SPM in industrial areas in Delhi (in µg/m3). Source Based on data from CPCB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Annual trend of RSPM in industrial areas in Delhi (in µg/m3). Source Based on data from CPCB. b Annual trend of RSPM in residential areas Delhi (in µg/m3). Source Based on data from CPCB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Monthly average of SO2 and NO2; b SPM and RSPM in Delhi (1990–2011) (in µg/m3). Source Based on data from CPCBRSPM is one of the most dangerous pollutant components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monthly average of a SO2, b NO2, c RSPM and d SPM for residential and industrial areas in Delhi (1990–2011) (in µg/m3). Source Based on data from CPCB. Note The x-axis shows months, and y-axis represents pollutant concentration .

. . . . . . .

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122 123 124 125 125 126 127

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. . . .

128 129 130 131

. . 131 . . 132 . . 132 . . 133 . . 134

. . 136

. . 138

. . 139

. . 140

. . 140

. . 141

List of Figures

Fig. 4.36

Fig. 4.37

Fig. 4.38

Fig. 5.1

Fig. 5.2

Fig. 5.3 Fig. 5.4 Fig. 5.5 Fig. 5.6 Fig. 5.7 Fig. 5.8

Fig. 5.9 Fig. 5.10

Fig. 5.11 Fig. 6.1 Fig. 6.2

Monthwise probable causes of high and low pollutant levels in Delhi. Source Compiled by the authors based on CPCB data. Note Red colour represents high pollutant level, and green represents low pollutant level . . . . . . . . . . . . . . . . . Annual trend of a SO2, b NO2, c SPM and d RSPM in Mumbai (1992–2011) (in µg/m3). Source Based on data from www.mpcb.gov.in . . . . . . . . . . . . . . . . . . . . . . . Monthly average of a SO2 and NO2, b SPM and RSPM in Mumbai (1990–2011) (in µg/m3). Source Based on data from www.mpcb.gov.in . . . . . . . . . . . . . . . . . . . . . . . Factors affect creation and intensity of Urban Heat Island (continuous line boxes indicate natural factors and dash line boxes indicate human factors). Source Compiled by the authors from Voogt and Oke (2003); Lo and Quattrochi (2003); Giridharan et al. (2004); Xiao and Weng (2007); Zhang et al. (2012) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of urban geometry in trapping heat and intensifying Urban Heat Island. Source Adopted from United States Environmental Protection Agency (2008) . . . . . . . . . . . . . . . . Location of profile lines (north-south and west-east) for comparison of LST, NDVI and NDBI in Delhi . . . . . . . . Location of profile lines (west-east) for comparison of LST, NDVI and NDBI in Mumbai . . . . . . . . . . . . . . . . . . . . . . . . . Methodological framework . . . . . . . . . . . . . . . . . . . . . . . . . . . Low building height in Delhi . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distribution of LST, NDVI and NDBI in Delhi (2000 and 2010) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial relationship of a LST, c NDVI and e NDBI in west-east profile and b LST, d NDVI and f NDBI in north-south profile in Delhi (2000–2010) . . . . . . . . . . . . . . Spatial distribution of LST, NDVI and NDBI in Mumbai (1991–2010) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial relationship of LST (a1, b1, c1), NDVI (a2, b2, c2) and NDBI (c1, c2, c3) for A–a west-east profile (a1, a2, a3), B–b west-east profile (b1, b2, b3), C–c west-east profile (c1, c2, c3) (1991–2010). Note See Fig. 5.4 for reference of A–a, B–b and C–c. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dense high rise buildings in Mumbai city . . . . . . . . . . . . . . . Inter-relationship between temperature, air quality and human health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodological framework. Note The analysis is *only for Delhi, ^Delhi and Mumbai . . . . . . . . . . . . . . . . . . . . . . . .

xxvii

. . 142

. . 143

. . 144

. . 155

. . 157 . . 163 . . 164 . . 165 . . 166 . . 167

. . 169 . . 171

. . 172 . . 174 . . 180 . . 185

xxviii

Fig. 6.3

Fig. 6.4

Fig. 6.5

Fig. 6.6

Fig. 6.7

Fig. 6.8

Fig. 6.9

Fig. 6.10

Fig. 6.11

List of Figures

Percentage of deaths from diseases of circulatory system in India (1990–2009). Source Based on data from Office of the Registrar General, India 2009 . . . . . . . . . . . . . . . . . . . . Percentage of deaths from diseases of respiratory system in India (1990–2009). Source Based on data from Government of India (2010) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proportion of deaths due to respiratory and circulatory illness in Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b) . . . . . . . . . . . . . . . . . . . . . . . . . Deaths from a diseases of URT, b diseases of LRT and c ORD in Delhi (2001–2011) and Mumbai (2007–2011). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b); Municipal Corporation of Greater Mumbai (2007– 2011). Note The dotted line is for Delhi and continuous line for Mumbai, excludes mortality from pneumonia in Mumbai, data not available for 2002 and 2003 . . . . . . . . . . . . . . . . . . . Deaths due to major respiratory diseases from a pneumonia (Institutional and hospital deaths in Delhi only), b influenza, c bronchitis, broncholitis, asthma and unspecified emphysema and d whooping cough in Delhi and Mumbai. Source Based on data from Government of NCT of Delhi (2001a, 2004a, 2005a, 2006a, 2007a, 2008a, 2009a, 2010a, 2011a); Municipal Corporation of Greater Mumbai (2007–2011) . . . . . . . . . . . . . Deaths from respiratory TB, Delhi and Mumbai. Source Based on data from Government of NCT of Delhi (2001a, 2004a, 2005a, 2006a, 2007a, 2008a, 2009a, 2010a, 2011a); Municipal Corporation of Greater Mumbai (2007–2011) . . . . . . . . . . . . . Deaths due to cancer of respiratory and intra-thoracic organs in Delhi and Mumbai. Source Based on data from Government of NCT of Delhi (2001a, 2004a, 2005a, 2006a, 2007a, 2008a, 2009a, 2010a, 2011a); Municipal Corporation of Greater Mumbai (2007–2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deaths from different kinds of neoplasm in Mumbai (2007–2011). Source Based on data from Government of NCT of Delhi (2001a, 2004a, 2005a, 2006a, 2007a, 2008a, 2009a, 2010a, 2011a); Municipal Corporation of Greater Mumbai (2007–2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Direct and indirect causes of death in statutory towns and rural areas in Delhi (in percent) in a MCD, b NDMC, c DCB and d rural areas (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b,

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List of Figures

Fig. 6.12

Fig. 6.13

Fig. 6.14

Fig. 6.15

Fig. 6.16

Fig. 6.17

Fig. 6.18

Fig. 6.19

2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003 . . . . . . . . . . . . . . . . . . . . Deaths from respiratory diseases in statutory towns and rural areas in Delhi (in percent) for a MCD, b NDMC, c DCB and d rural areas (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003 . . . . . . . . . . . . . . . . . . . . . . . Trend analysis of deaths from respiratory diseases in statutory towns and rural areas in Delhi for a MCD, b NDMC, c DCB and d rural areas (2001–2012); x-axis represents number of deaths and y-axis represent year. Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003 . . . . . . . . . . . . . . . . . . . . . . . Trend of mortality due to tuberculosis in statutory towns and rural areas of Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003 . . . . . . . . . . . . . . . . . . . . . . . Trend of mortality due to heart disease and heart attack in statutory towns and rural areas of Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003 . . . . . Trend of mortality due to pneumonia in statutory towns and rural areas of Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003 . . . . . . . . . . . . . . . . . . . . . . . Trend of mortality due to influenza in statutory towns and rural areas of Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003 . . . . . . . . . . . . . . . . . . . . . . . Trend of mortality due to bronchitis and asthma in statutory towns and rural areas of Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003. . . . . . . . . . . . Agewise composition of deaths from diseases of respiratory system. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b) . . . . . . . . . . . . .

xxix

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Fig. 6.20

Fig. 6.21

Fig. 6.22

Fig. 6.23

Fig. 6.24

Fig. 6.25

Fig. 6.26

Fig. 6.27

Fig. 6.28

Fig. 6.29

Fig. 6.30 Fig. 6.31 Fig. 6.32

List of Figures

Agewise composition of deaths from diseases of circulatory system. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b) . . . . . . . . . . . . . Trend of mortality due to URD, LRD and ORD in (a) Children from 1 to 14 years and (b) elderly. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agewise composition of deaths due to major divisions of the respiratory systems, a URT, b LRT and c ORD. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agewise composition of deaths due to major respiratory diseases a acute bronchitis and broncholitis, b asthma, c influenza and d pneumonia where x-axis shows age groups and y-axis represents number of deaths. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agewise composition of deaths due to malignant neoplasm of respiratory and intra-thoracic organs. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Occupational composition of respondents in Delhi and Mumbai. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016 . . . . . . . . . . . . . . . . . . . . . . People’s perception on state of urban environment in Delhi and Mumbai. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016 . . . . . . . . . . . . . . . . People’s perception on reason of foul smell in Delhi and Mumbai. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016 . . . . . . . . . . . . . . . . . . . . . . People’s perception on quality of environment around their place of living. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016 . . . . . . . . . . . . . . . . People’s response on lifestyle and habits of their family members. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016 . . . . . . . . . . . . . . . . . . . . . . People’s perception on health in Delhi. Source Primary survey conducted by the authors in Delhi 2013–2016 . . . . . . People’s perception on health in Mumbai. Source Primary survey conducted by the authors in Mumbai 2013–2016 . . . . Scatter plot representing the relationship between income groups and human health in Delhi and Mumbai. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Figures

Fig. 7.1

Fig. 7.2

Fig. 7.3

Fig. 7.4

Fig. 7.5 Fig. 7.6

Fig. 7.7 Fig. 7.8 Fig. 7.9 Fig. 7.10

Fig. 7.11 Fig. 8.1

Frequency of response by respondents on problems associated with public transport. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016 . . . . . . . . . . . . . . . . Frequency of response by respondents on problems associated with health facilities. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016 . . . . . . . . . . . . . . . . Response on implementation of policies. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Frequency of response by respondents on problems associated implementation of policies. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016 . . . . . . . . . . Networks in city system analysis. Source Adopted and modified after Silva et al. (2012) . . . . . . . . . . . . . . . . . . . . . . Response of people on suggestions and recommendations to improve air quality (in per cent). Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016 . . Outdoor green façade in Delhi . . . . . . . . . . . . . . . . . . . . . . . . Cab with green roof in Kolkata. Source The Telegraph, 17 May 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a River Yamuna in Delhi, b diminishing water along Mumbai coast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial inter-linkages between LST and pollution levels in Delhi. Note The LST in °C and pollutant average in micro g/m3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial linkages between LST and pollution levels in Mumbai. Note The LST in °C and pollutant average in micro g/m3 . . . Highlights of Sustainable Development Goal 3: good health and wellbeing. Source Adopted from United Nations Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Tables

Table 1.1

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 3.1 Table 3.2 Table 3.3 Table Table Table Table

3.4 3.5 4.1 4.2

Table Table Table Table Table

4.3 4.4 4.5 4.6 4.7

Table 4.8 Table 5.1 Table 5.2 Table 5.3 Table 5.4

Rank of lower respiratory infections and COPD as causes of death and disability for states and union territories of India, 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Important studies on air quality change . . . . . . . . . . . . . . . . . Important studies on land use/cover change . . . . . . . . . . . . . . Details of major satellite data used for UMC and UHI analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . UHI studies conducted in major cities of the world using Landsat thermal data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Administrative divisions of Greater Mumbai . . . . . . . . . . . . . Districtwise distribution of forest cover in Delhi (2011) . . . . . Trend of literacy rate (per cent) and sex ratio (females per thousand males) in Delhi (1901–2011) . . . . . . . . . . . . . . . . . . Decadal variation of population (1901–2011) (in percent) . . . Trend of sex ratio (1901–2011) . . . . . . . . . . . . . . . . . . . . . . . Details of satellite images of Delhi and Mumbai . . . . . . . . . . Characteristics of Landsat satellite image used in the present study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Permissible limits and sources for selected pollutants. . . . . . . Details of available data of air pollution for Delhi . . . . . . . . . Details of available data of air pollution for Mumbai . . . . . . . Land use/cover classification . . . . . . . . . . . . . . . . . . . . . . . . . Land use/cover change in Delhi in 1993, 2000 and 2010 (in km2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Land use/cover change in Mumbai in 1991, 2000 and 2010 (in km2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Major causes of UHI formation with explanation . . . . . . . . . . Albedo of selected surfaces and cover types used in urban areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prominent researches on different factors affecting UHI . . . . . Details of satellite images of Delhi and Mumbai . . . . . . . . . .

.. .. ..

28 38 41

..

42

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45 69 78

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. 85 . 87 . 89 . 112

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113 114 116 117 119

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xxxiv

Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5 Table 8.1 Table 8.2 Table 8.3 Table 8.4

List of Tables

Classification of causes of death due to air pollution . . . . . . . Sources and human health impacts of major air pollutants . . . Age distribution of deaths due to diseases of respiratory system in India (2009) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total deaths from disease of URT, LRT and ORD of respiratory system in Delhi and Mumbai . . . . . . . . . . . . . . . . Area, population and mortality details of statutory towns and rural areas in Delhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total deaths from 2001 to 2012 in Delhi . . . . . . . . . . . . . . . . Distribution of health institutions and beds in Delhi . . . . . . . Comparative permissible limits of key pollutants in residential areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measures suggested for strategic plan for Indian megacities . Regionwise suggestions to improve urban environment of Delhi and Mumbai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Feature/land use-wise recommendations to improve urban environment of Delhi and Mumbai . . . . . . . . . . . . . . . . . . . . National health missions in India . . . . . . . . . . . . . . . . . . . . . . National health policies/other related policies for promotion of health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . National health programmes: communicable diseases . . . . . . . National health programmes: non-communicable diseases, injury and trauma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 1

Urban Health and Wellbeing: Emerging Trans-disciplinary Stream

Abstract This chapter deals with the value, nature, concept, measures, indicators, institutions and health status in India. The concept of health includes the traditional biomedical, ecological, holistic and other universal concepts. There can be various approaches to study geography of health. These coincide with the paradigms in the evolution of health geography such as environmental deterministic, ecological, biomedical and systems approach. Further, the chapter presents a detailed account of evolution of medical geography to health geography. Following this, various measures and indicators of health are discussed. Health geography has broadened its horizons and has evolved to be trans-disciplinary. Detailed account of linkages between health geography and other science and social science disciplines is presented herewith. At national and international levels, many agencies are working in establishing good health. These include United Nations, multilateral development banks, foundations, NGOs, think tanks, human rights organizations, bilateral agencies and government agencies. The nature of work by them is listed. Additionally, a comprehensive overview of health status in India is presented. Keywords Evolution · Medical and health geography · Health status · India

1.1 Introduction Human health and environmental changes have complex causal linkages that are dependent on a number of factors. These linkages are usually indirect in nature and fluctuate with characteristics of space and with time. Often human activities induced changes in physical environment alter atmospheric, lithospheric, hydrospheric and biospheric compositions leading to disruption in functioning of the ecosystem. Urbanization is one strand of human induced environmental change that has led to irreparable damage to the physical environment on earth. Extreme events, microclimatic changes and increasing pollution levels in air, water and lands are byproducts of urbanization. These changes are manifested in the form of proliferation of human health illnesses and diseases. The upturn in physical illness is accompanied by adverse impact on mental health, consequently impairing human wellbeing (Tzoulas and James 2004; WHO 2005). © Springer Nature Singapore Pte Ltd. 2020 A. Grover and R. B. Singh, Urban Health and Wellbeing, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-13-6671-0_1

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1 Urban Health and Wellbeing: Emerging Trans-disciplinary Stream

Urban health exhibits greater vulnerability as globally larger area and population are becoming urban, but these are not necessarily ecologically sustainable. Urban health is of interest to scientists, academician and policy-makers and bears essential concern for two reasons (1) the large numbers of persons residing in urban area and (2) the fact that population density of the urban area changes the potential for both public health problems and public health solutions (ICSU 2011). In the twenty-first century, cities are so ubiquitous and their impact so pervasive that it is difficult to consider any aspect of health ignoring the role of cities. As growing proportion of world’s population lives in cities, the health of urban population contributes to global population health (Galea and Vlahvov 2005; Breslow 2002). Urbanization presents opportunities and risks, as well as enormous challenges for maintaining and improving human health and wellbeing. It is closely related to urban health that can vary across urban environments. On the positive side, urbanization can imply improved access to modern medical technologies, healthcare facilities and highly trained healthcare professionals. Cities generally provide better living conditions and improved healthcare facilities. On the negative side, living in urban areas often entails greater exposure to outdoor air pollution, increasing temperatures, polluted water supply in many urban areas and associated health problems (Bloom et al. 2008).

1.2 The Value of Health Being in a state of good health is considered as one of the basic value in the present times. The importance given to health, hygiene, personal care and promotion of health services is of prime concern in the modern times. It is long noticed that the doctors and medical practitioners are highly regarded and well paid since ancient times. The value of health can be determined by the understanding of crucial place the science and art of medicine holds in the modern times. The constant research on means to eradicate and prevent of diseases comes in light to build a healthy community (Nordenfelt 2007).

1.3 Definition and Concept of Health The word ‘health’ is defined by the Oxford Dictionary as ‘state of being well in body or mind’. Similar to this, the Webster dictionary mentions that ‘the condition of being sound in body, mind or spirit especially freedom from the physical disease and pain’ is health. The Perkins Dictionary, on the other hand, adds the word ‘equilibrium’. It states that health is a ‘state of relative equilibrium of body, form and function which results from its successful dynamic adjustment to forces tending to disturb it. It is not passive interplay between body substances and forces impinging upon it but an active response of body forces working towards readjustments’. Hence, the

1.3 Definition and Concept of Health

3

importance is given to the balance between mind and body, usually laying emphasis on being free of illness, pain, injury, disability, impairment or defect. As per Balog (1978), three major views on health have emerged in the recent times. These are the traditional medical concept, concept by WHO and the ecological concept. However, other scholars have added holistic, normative and psychological concepts of health also (Azmat and Razum 2014).

1.3.1 Traditional Medical/Biostatistical Concept The traditional medical concept was accepted in the first half of the twentieth century. This concept was based on the assumption that health and disease were objective and observable phenomena that are biological concepts. The absence of disease and illness is health, and there is nothing evaluative or subjective about health and disease. A disease, in this case, is a subnormal functioning of a bodily or mental part of the human being. Boorse’s biostatistical theory’s aim was to analyse the distinction between normal and pathological concept (Boorse 1997; Nordenfelt 2007). In a nutshell, a person is completely healthy, if and only if, all organs of the person are functioning normally. The concept is criticized on many grounds like the role of all attributes, including social, emotional, spiritual, is ignored. There is too much emphasis on the specific disease/s and parts of body and the individual is neglected. It also believes in the dichotomy of the disease and illness but in reality it may not be the case as to be healthy, it is not necessary to be in ‘absolute’ disease-free state (Boruchovitch and Mednick 2002; Paul and Roth 2002).

1.3.2 The Concept of Health Given by WHO The World Health Organization (WHO) conceptualized the definition of health as ‘a state of complete physical, mental and social wellbeing and not merely the absence of disease or infirmity’. WHO’s 1986 Ottawa Charter for health promotion further stated that health is not just a state, but also ‘a resource for everyday life, not just the objective of living. Health is a positive concept emphasizing social and personal resources, as well as physical capabilities’. The three basic components of this definition are the physical, mental and social aspects. The physical aspects relate to the normal functioning of the body and organs. It is physiological or the biological concept and is generally referred to as ‘soundness of body’. This is achieved when there is a state of perfect functioning of the body or when every cell and every organ is functioning at optimum capacity and in perfect harmony with the rest of the body. Herein there is absence of illness, disease, pain or any other kind of infirmity. This concept is closely related to the traditional biomedical concept of health.

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1 Urban Health and Wellbeing: Emerging Trans-disciplinary Stream

The social aspect includes individual, society and the relationship between them. Cordial and harmonious relations of individuals with other fellow beings are considered healthy. When a person feels satisfied, happy and nurtured in the respective social system, the social health improves. Herein, social interaction, interactive social network and connectivity to other members of the society are important. The ability to connect, interact and function in a society in a healthy manner promotes social health and wellbeing. This may also include the personal dimension of connecting the individual with the divinity, that is, spiritual dimension. The mental aspect is the psychological, emotional and mental status of the individual. Self-satisfaction, self-confidence, cheerful personality and absence of negative emotions make an individual mentally healthy. The presence of negative emotions, stress, emotional apathy, etc., represents illness. The definition by WHO is a broader view of health as it encompasses the psychological and social aspects along with the physiological health. This concept is more holistic and absolute having a utopian view (Fig. 1.1). By inclusion of the social and psychological criteria, WHO acknowledged that health is not determined by one single criterion, rather, it is multi-dimensional in nature. Also, human beings as a person and part of society were more important than the biological body. Though it is a holistic definition of health, it is not completely accepted and is criticized on various grounds. The term ‘wellbeing’ is not very clearly defined making the concept of health vague and unclear. It is also relegated as it lacks specificity and cannot be applied in practical situations (Lewis 1953; Boruchovitch and Mednick 2002). Besides this, the definition is so broad that it implies a state of perfect state of health that is practically not possible to achieve. Many scholars also noted that the definition is inappropriate for most people in the world. It is also considered unrealistic having unreachable goals and no defined means to achieve it. It is believed that the WHO’s definition cannot be used in context of the ordinary health care due to its ambiguousness that tries to conceptualize an ideal healthy human being which Fig. 1.1 Holistic view of ‘health’. Source Created by the Authors

Mental

Social HEALTH

Physical

1.3 Definition and Concept of Health

5

cannot be attained in the real world (Nordenfelt 2007). Awofeso (2012) in Amzat and Razum (2014) observed that the definition is inflexible and unrealistic and the inclusion of the word ‘complete’ in the definition makes it unlikely for anyone to be healthy for a reasonable period of time (Amzat and Razum 2014). The definition tries to project health in an absolute ideal situation by combining the three aspects of social, physical and mental health of human life. To gain complete contentment in all the three aspects is complex, unachievable though maybe considered possible. In addition, Saracci (1997) in Amzat and Razum (2014) suggested that WHO’s definitions should be reconsidered and revised. He argued that the WHO’s view links health with happiness. Only happiness is not an indicator of healthy individual as happiness may/may not be dependent on health and vice versa. Amzat and Razum (2014) mention Huber et al. (2011), on the other hand, observed that if presence of chronic diseases and disability (physical health) makes an individual unhealthy and ill, the value of coping with the situation and adaptability is relegated. They opined that capacity of human beings to cope up with physical illness and other challenges is possible and should be given due importance. In the given circumstances, despite several criticisms, the WHO has not revised the definition since long. There may be multiple reasons that can be cited like perhaps no alternative holistic, universal, conceptual definition is foreseen; or the objective is to give understanding of health and not to cater to a particular sector; or to keep is multi-dimensional and broad so that health can be interpreted and researched using trans-disciplinary approaches.

1.3.3 The Ecological Concept of Health Many ecological and relative notions of health emerged from 1960 to 1970s (Boruchovitch and Mednick 2002). These notions had ‘function-oriented perspective’ wherein every individual feels happy, contended and satisfied if they can successfully carry out their duties and responsibilities. It also brought in the concept of quality of living. This concept was much different from the earlier ones in the sense that it envisages health as a more relative concept that varies from person to person. Also, the emphasis on individual’s quality of life and surroundings was emphasized to ensure healthy beings. The individual’s capacity to adapt to the changing environment and society was central to the ecological concept of health. Health, as per the ecological concept, is equilibrium between human beings and their environment and as a result, the need to live in clearer surroundings was emphasized. In contradiction, Lewis (1953) questions the quality of environment in which individuals may adapt. In certain cases, one may adapt to unhealthy, disease provoking environment and therefore the distinction between healthy and unhealthy adaption needs to be made clear (Lewis 1953).

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1 Urban Health and Wellbeing: Emerging Trans-disciplinary Stream

1.3.4 The Holistic or Normative Concept of Health This concept encompasses biomedical, ecological and psychological concepts under one umbrella. This is a multi-dimensional concept that tries to understand the health of human beings in the context of environment. The definition of health, given by WHO, is closely related to the holistic concept implying that health is multi-causal and is affected by various sectors. This philosophy emphasizes that to achieve the desired goal/s a person should be healthy in body and mind and not in a particular organ. Hence, the goal is of the whole human being and thus health is holistic in nature. Any person is completely healthy if he/she is able to achieve his/her goal/s in the given bodily and mental state (Nordenfelt 2007; Boruchovitch and Mednick 2002; Paul and Roth 2002).

1.3.5 Potential Alternative Universal Concepts of Health There are many attempts made to conceptualize a wholesome, comprehensive, universal and valid definition of health. Using the ecological concept, Dunn (1959) used the term wellness for good health in the sense of people’s capacity to function in their respective environment and their ability to adjust to the environmental stress. Similar to this view, Dubos (1965) defined the state of health and disease as expressions of the success and failure experienced by the organism in its effort to respond adaptively to environmental changes (Dubos 1965). However, it should be remembered that the concept of adaptation socially and culturally constructed and may vary in spatio-temporal framework. Saracci (1997) proposed a definition of health as ‘a condition of wellbeing, free of disease or infirmity, and a basic and universal human right’. However, this definition lacks the holistic nature of health and ignores mental and social health components. There is too much emphasis on physical health. Additionally, it presents health as a basic human right contradicting the policy objectives of the insurance companies and others for whom health is a commodity. It was insisted and asserted by Balog (1978, 1981) to integrate the different views of health into one single unifying concept. Balog defined health as ‘a state of body and mind-well functioning which affords man the ability to strive towards his both functional objectives and culturally desired goals’ (Balog 1978). This definition integrates the WHO and ecological concepts to make the concept of health multidimensional and universal. Balog also stressed on union of biological functioning and individual’s mind and body functioning. In contradiction to Balog’s view, many scholars believe that it is not possible to have a universally acceptable concept of health since as per the context and usage, the term health differs. It essentially means absence of illness and is a multi-dimensional concept that can be interpreted and defined as per the context it is used in (Parsons 1958; Baumann 1961; Dolfman 1974; Balog 1978, 1981; Eberst 1984). Additionally,

1.3 Definition and Concept of Health

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some scholars gave multiple views and models of health like Smith (1981). Smith (1981) proposed clinical, role performance, adaptive and eudiamonistic models of health. The scope of clinical model is limited, whereas it broadens towards eudiamonistic model. Model-based approach as a combination of four models, namely medical, World Health Organization, wellness and environmental models was suggested by Larson (1999). Similar to Smith (1981), the perspective of models expands and widen from former to the latter. Medical model suggests absence of disease or disability; WHO’s model is multi-disciplinary with state of complete physical, mental and social wellbeing; wellness model integrated mind, body and spirit; and the environmental model focuses on adaptation to achieve balance. The use of modelbased approach may be more holistic but contradictions and multiplicity of models may create hindrance to universality of the concept. Bircher (2005) defines health as ‘a dynamic state of wellbeing characterized by a physical and mental potential, which satisfies the demands of life commensurate with age, culture, and personal responsibility’. This is a much complicated definition as the context and connotation of the words like age, culture and wellbeing is unclear. Further, sociological view of health was put forth by Parsons (1972) with definition of health as ‘the state of optimum capacity of an individual for the effective performance of the roles and tasks for which he/she has been socialized’. In this sense, health is fulfilment of expectations, self-actualization and having healthy relations in the society. Blaxter (1990, 2010) acknowledged the relativity and diversity of the concept of health and hence presented a descriptive analysis of health. Among this is the lay concept of health which is based on individual’s own assessment and judgement of whether he/she is healthy or not. The key indicator in lay concept is the presence or absence of symptoms. However, this is highly subjective and relative concept. Blaxter (1990) also identified the three ‘states’ of health, namely freedom from illness, ability to function and fitness. Blaxter (2010) argued that health is dynamic varying across one life span influenced by personal and structural factors (Azmat and Razum 2014). Recently, many scholars are focusing on adaptation and adaptive capacity to overcome physical, emotional and social health challenges. Among them are Huber et al. (2011) and Jadad and O’Grady (2008) (Azmat and Razum 2014). This is an attempt to proffer a more acceptable perspective in comparison to the criticism received by the ‘complete state’ as stressed by WHO’s definition. The capacity to adapt is considered more important now in light of the behavioural adjustments that are needed to overcome the chronic diseases. Adaptation as an integral part of health also reflects on the change in the nature of health problems from acute to chronic diseases. Even though numerous definitions of health can be found, there are gaps identified in each version and even till now no one single unified universally acceptable concept exists. Surprisingly, this lack of consensus on a universal health concept is not a major impediment in health-related research. There is agreement in only certain characteristics like absence of disease and health being a multi-dimensional and complex concept.

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1.4 Approaches to Geography of Health Broadly, there are two spheres of studying health: clinical health and public health. While the former focuses on individual, the latter focuses on generating benefit for wider population. Health geography overlaps with public health in the subjective sense. While mostly the quantitative data is utilized for health research, qualitative research is also being conducted. The qualitative researches focus on ‘how we feel’ and self-assessment is an important indicator of health assessment. Health geographers try to understand the variations of health parameters across space and time and the reasons to such patterns. Primary focus is on location, direction, social groups and temporal variations. Maps are important visual tools for exploring spatial patterns. Approaches essentially depend on the definition of health and disease. Traditionally, disease is associated with ill-karma and God. Correlating health with moral and religious versions has been long believed. However, Greek philosophers first proposed the relationship between health and environment using empirical observations. The ‘Hippocratic Corpus’ written by many authors in association with Hippocrates mentions influence of environment on health, ethics and holistic medicine. The reference to place is clear in its section called ‘On airs, waters and places’. The ecological approach followed environmental deterministic suggesting that health and behaviours are determined by environmental factors. This approach is criticized, and many scholars now believe that free will, medicine, behavioural changes and adaptation and important in changing the relationship between health and environment. Under the paradigm of ecology, human health is considered as a part of integrated disease cycle, rejecting the idea that humans and environment are separate entities. Human species are a part of the ecological system interacting with animals, plants and biogeo-chemical cycles. Herein, humans hold integral part of the ecological system along with infectious agents, disease vectors and environmental conditions. Adaptation is dependent on evolution and hereditary. In the nineteenth century, initiating improved medicinal and public health practices like, vaccination, chlorination, sewage treatment and availability of clean drinking water reduced the infectious diseases. The cause-and-effect relationship between causative agents and symptoms was extensively researched to improve health. This was the biomedical perspective of health that considered health in a very narrow sense ignoring the social and mental aspects. To overcome the limitations of the biomedical concept, many alternative approaches to health emerged encompassing mental, social and spiritual health. Many new healing methods were practised like yoga, meditation, hypnosis, counselling, Ayurveda, homeopathy, behavioural changes and others. The lack of scientific evidences of effectiveness of alternative medicines makes these controversial means of treatment. Presently, there is forward walk from biomedical concept to a more holistic perspective that insists that there are many factors that influence disease. Therefore, multi-disciplinary approach like systems approach should be considered. For

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instance, asthma is a disease related to air pollution but may be caused or aggravated as per working conditions, lifestyle, diet, housing conditions and government policies. Hence, biomedical approach is negated and wider view is suggested to understand the relationship between health and environment. The social and cultural theories have added another dimension to health geography. Inclusion of concepts like politics, gender, race, culture, ghettos and marginal population has been increasingly researched. Issues of equality, accessibility, availability to healthcare services in relation to these indicators have broadened the scope of health geography. They stress profoundly on psychology, behaviours and wellbeing. From geographer’s point of view, health is a place-based phenomenon, i.e. certain places may have healing effect while others may be hot spots of diseases. To understand health and disease from the point of view of location, environment and place is purview of health geographer. Health and disease can also be socially constructed wherein individuals consider certain landscapes healthy and unhealthy. Health can also be socially constructed, as per the post-modern approach. As per the biomedical approach, interpretation of disease is the simple interaction between causative agent and patient that limits health research. Healthcare and provision of structural infrastructure are also determined using geography. For instance, location to construct the hospital and clinics can be suggested using GIS technology. Geography is also exploring use of traditional medicinal plants and healing methods, accessibility and equality of health services and ethics of care. Recently, ICSU has proposed ‘systems approach’ to study health and wellbeing of a region. A system can be defined as a complex of living and non-living parts, interacting in networks, establishing networks of interacting components and behaving as coherently organized entity (Gatzweiler et al. 2017). System approach goes beyond the fact that health and wellbeing are affected by multiple factors. It includes feedbacks, interdependencies and interactions between individuals and environments over time. These relationships can be nonlinear, unexpected, complex having unanticipated effects on space or time. The systems approach incorporates inter-disciplinary and trans-disciplinary approaches. As per ICSU (2011), systems approach is uniquely appropriate approach as it is integrative; incorporates feedbacks, nonlinearities, inter-relationships; identifies constrains and incompatibilities; highlights unpredictability, dynamics; promotes comprehensive database and provides predictions even when data is sparse. The systems of health and wellbeing can have two types of problems, that is, complicated and complex. While the former requires linear causality and can be solved through hierarchical solutions using quantitative analysis; the latter has circular problems that are multi-dimensional and require qualitative information (Gatzweiler et al. 2017).

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1.5 From Medical to Health Geography 1.5.1 Medical Geography The traditional medical geographers were concerned about the disease aetiology, accessibility of treatments and models that can be used to generalize and predict (Meade and Emch 2010). The earliest records of medical geography can be traced back to 400 B.C. in Hippocrates book ‘Air, water and places’ (Meade and Emch 2010). Before 1960s, disease mapping and disease ecology dominated the subject. The quantitative techniques were successfully applied to concepts of disease. Most influential paper in this regard is by Gerald Pyle on analysis of cholera (Brown et al. 2010; Mayer 2010). Mayer (2010) asserts that medical geography is a misnomer because in true sense it is not related to medicine. Rather, it should be renamed as ‘epidemiologic geography’ or ‘geography of disease’ or any other possible variations of these words. Medical geography traces the inter-relationship and inter-dependence between environment and human health using the concepts and techniques of geography. For them, the focus is on location, place and space on which human activities occur. The influence of human behaviour and role of space in shaping health is ignored. Space and place are explored as merely locations in medical geography characterized by spatial patterning/locational analyses of disease, illness and medical care (Rosenberg and Wilson 2005). The research focused on geographical distribution of medical care, health facilities, diseases and access and utilization of healthcare services. Further, attention was given to spatial distribution and trends of health indicators as per health policy, medical insurance and medical coverage. In the early 1990s, research was conducted with the help of geographical information systems (GIS). The methodology was quantitative, and there was dominant use of statistical and mathematical frameworks (Rosenberg and Wilson 2005). Rosenberg and Wilson (2005) mention that the conventional medical geography tradition is exemplified by Cliff and Haggett (1988), Cliff et al. (2000), Gould (1993), Joseph and Phillips (1984), Shannon and Dever (1974), Thomas (1992), Rosenberg and Wilson (2005). Medical geography as a sub-discipline focuses on the involvement of spatial and ecological perspectives on disease and healthcare delivery. As per Askari and Gupta (2016), medical geography attempts to answer following major six questions: 1. Why is a phenomenon distributed in a particular way? 2. Why are facilities and businesses located where they are? Why are the offices of physicians, public clinics or research hospitals located in certain places and not in others? 3. Why do people move in certain directions for certain distances? 4. Why do innovations (including ideas and material goods) spread as they do? 5. Why do people vary in perception of the environment? 6. How do objects, ideas, processes and living beings interact to characterize and constitute places?

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1.5.2 Evolution From the literature review, it is clear that both medical and health geography use different methodology and concepts although both deal with the human health in relation to environment. Hence, both are different kinds of geographies. Though sometimes medical and health geography are used, alternatively, many scholars trace the emergence of health geography from the traditional medical geography (Brown et al. 2010). However, there are many viewpoints for the origin of health geography. De Angulo and Losada (2015) while discussing the health paradigm shifts mention that rather than revolution, evolution of medical geography in the twentieth century led to the growth of health geography. Kearn’s paper was the breaking point where he calls for ‘reformed medical geography’ and a ‘post-medical geography of health’ making it more holistic, socially relevant and placing it in context of justice (Kearns 1993; Mayer 2010). This was accompanied with the historical development of disease-specific paradigm of medicine towards application of systems theory making health more inclusive, comprehensive and holistic. The biomedical approach was replaced by the ecological approach to overcome the limitations of the older traditional medical geography to modern health geography (DeAngulo and Losada 2015). The former version was closer to the concerns of disease and accessibility to healthcare services, while the latter is in the favour of wellbeing, social models of health and health care. The accompanied academic contributions have been propelled towards the emergence of (new) geography of health (Kearns and Moon 2002). However, Rosenberg (1998) states that medical geography has grown increasingly eclectic to the point where majority of medical geographers now prefer alternative terms like health geographer, geography of health or health geography. A group of scholars believe that medical geography is essentially bifurcated into geography of disease and the geography of health care. The former describes disease frequency, illness occurrence, relationship between illness and associated environmental factors in respect of answering the three major questions of geography, i.e. who, why and where. The latter, on the other hand, describes the facility location, accessibility and utilization and patient behaviour patterns. Parr (2003) also classified medical geographical research similar dimensions of research work (1) on the spatial distribution of disease and death and geographical complexities surrounding the provision and (2) access to and (in) equality of health care (Askari and Gupta 2016). Mishra (2007) further expands the idea to include four perspectives of viewing medical geography. These are study of pattern of health and ill health on space; intensity and frequency of the health problem and factors influencing it; causes and risk factors of health and ill health by aetiological hypotheses testing and examination of spatial distribution of healthcare facilities, suggesting optimal location, policies and programmes keeping in mind the present needs (Askari and Gupta 2016). In the late 1980s, there were signals of change and one such example is the publication of Jones and Moon’s (1987) textbook, Health Disease and Society: A Critical Medical Geography and selected chapters in Wolch and Dear (1989) (Brown et al. 2010). Mayer (1986) and Meade et al. (1988) continue to argue for the importance of

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use of disease and cultural ecology although re-interpreted incorporating new viewpoints from geography of health and healthcare. Bentham et al. (1991: ix) noted that ‘… Medical geography is a lonely discipline’ (Kearns and Moon 2002; Bentham 1991). The very thin participation in the seminars and conferences became a push factor for medical geographers to look for new directions. Bentham (1991) noted that the dichotomy between the twin streams of medical geography became blurred having concerns inward looking and became more open to influences from the outside world. The inter-twining of these two streams has rearticulated medical geography as health geography. Del Casino and Dorn (1998) argue that such a renaming might too easily embody new and less than helpful sets of dualisms such as new/old, and traditional/contemporary. Curtis and Taket (1996) assert that the emphasis is on complementarity and not competition (Kearns and Moon 2002). The transition of medical to health geography can be charted with reference to publications in Progress in Human Geography. Alternatively, a small influential group of medical geographers argued for having more meaningful, policy-oriented and holistic understanding of health. They urged to shift focus to the reciprocal relationships between health and place that are dynamic and also the incorporation of cultural theories (Rosenberg and Wilson 2005). Kearns (1993) advocated post-medical geographies of health incorporating cultural and humanistic approaches that led to debates in the Professional Geographer with attention given to health and wellness (Kearns and Moon 2002). Kearns (1993) strongly urged the integration of place, identity and health into medical geography and placing it with social geography, thus, making it dynamic and contemporary (Kearns 1993; Rosenberg 1998). Similarly, Litva and Eyles (1995) noted that medical geography is old fashioned and should address new issues to tackle the concerns. Third group of commentaries come from the statements delivered at major conferences that assert that medical/health geography has changed in the recent past from traditional model to thematic concerns. The journal ‘Health and Place’ was launched in 1995 with central concern of ‘… where place matters with regard to health, health care and health policy’. The Dictionary of Human Geography (Johnston et al. 2000) also acknowledges that changes in both medical and health geography have taken place in the themes, theory and methodology (Mohan 2000a, b; Kearns and Moon 2002).

1.5.3 Health Geography The narrow and rigid purview of medical geography was replaced by the health geography in the late 1990s that encompassed wider approach to sub-discipline of geography of health. Geography of health embodies itself with the ecological approach to health thereby ingrained within the perspectives of WHO and UNDP. It is firmly based on the idea of health in contrast to disease. Health, in this context, is the result of individual, groups and communities, as well as the conventional host— agent characteristics (Mayer 2010). It has its foundation on the developments of

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social theory in geography and social environment in health, disease and illness. There was increased interest in justice, wellbeing and wellness (Kearns and Moon 2002; Meade and Emch 2010). Here emphasis was given to diverse groups, and individual people were considered as persons and not individuals or observations. The use of qualitative methods in the form of interview, observation, focus groups, and text analysis was highlighted. Apart from theoretical basis and methodological pluralism, three distinct themes of health geography are (1) emergence of place as a framework for understanding health, (2) theory building on the basis of social and cultural theories and (3) a quest to develop critical geographies of health. These themes as identified by Kearns and Moon (2002) make the sub-discipline unique and dissimilar from the old traditional medical geography. (1) Place: Instead of spatial analysis, place, its analysis and process, was given importance. It is not merely a container or stage where things are recorded; rather, it is seen as an operational ‘living’ constructs which ‘matters’. It is both active and dynamic. There are two way relationships between health and place. But defining ‘place’ is complicated due to the diversity of viewpoints from which it is interpreted. With regard to geography of health, the value of place is asserted in three ways—as localities, landscape and place awareness. • A locality specific study range from home, rural, urban or local area and studies the place-specific aspects of health indicators, community responses and threats to health. • The cultural notion of place is landscape. Therapeutic landscapes proposed by Gesler (1992) and landscapes of consumption (Gesler and Kearns 2002; Kerns and Moon 2002) have become central concepts through which health and healthcare situations are investigated. • Place awareness can be done using multivariate modelling enduring quantitative research tradition. (2) Theory: The theory building became more pluralistic, explicit and open to inputs from other disciplines. Theoretical orientation, hence, became more divergent. (3) Critical view: Instead of measuring inequalities, developing a critical view of health disparities along with their causes and consequences became the foundation of the discipline (De Angulo and Losada 2015; Kearns and Moon 2002; Cutchin 2007; Meade and Emch 2010). Needless to say, geography and health are intrinsically linked. Geography of health is evolving itself in ways that are directly relevant to health policy. Rosenberg (2015) mentions the new division between health geography as qualitative and quantitative health geographers. The quantitative health geographers remain focused on disease-specific research with assessments of health inequalities. The qualitative groups of health geographers are more interested in vulnerability of people to various health problems and mental health. Five strands of health geography can be identified as spatial patterning of disease and health, spatial patterning of service provision, humanistic approaches to ‘medical geography’, structuralist/materialist/critical

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approaches to ‘medical geography’ and cultural approaches to ‘medical geography’ (The University of British Columbia 2018).

1.6 Measures and Indicators of Health Common measures used to assess disease, illness or sickness are incidence rate, prevalence rate (period prevalence rate, point prevalence rate), attack rate, secondary attack rate, case fatality rate, case fatality rate, duration of illness or sickness, relative risk, attributable risk and odds ratio (Government of India 2015). The demographic measures of mortality and fertility are critical indicators of health status. Mortality rates are measured through crude death rate, age-specific death rates, sex-specific death rate, cause-specific death rate, infant mortality rate, neonatal mortality rate, post-neonatal mortality rate, perinatal mortality rate, foetal death rate, still birth, infant mortality rate, maternal mortality rate, proportional mortality rate (ratio), expectation of life at birth and survival rate. On the other hand, for fertility analysis crude birth rate, general fertility rate, age-specific fertility rate, total fertility rate, gross reproduction rate and net reproduction rate are used. An indicator is a key statistical measure selected to describe a situation, track progress and performance, and act as a guide to decision-making. An indicator is a measure that is used to demonstrate change in a situation, or the progress in, or results of, an activity, project or programme. Health indicators support authors with evidence and, therefore, can be qualitative like infant mortality rate, or quantitative like quality of life and perception studies depending upon the nature of research (Government of India 2015). Health indicators should be feasible to collect, valid statistically, reliable and objective. It is necessary that the health indicator can be applied internationally and facilitates comparisons. It is necessary that the indicator is able to track progress and performance of health indicators with time that would help in decision-making and amendments. The Global Burden of Disease by the World Bank and Harvard University have listed a number of indicators of health like Quality Adjusted Life Year, The Disability Adjusted Life Year, Healthy Life Expectancy, Years of Life Lost and disability-free life expectancy. • QALY: Quality Adjusted Life Year It is a measure in which health status between perfect health and death is weighted by the utility to the individual of time spent in each of these states. QALY refers to a time-based measure that includes life expectancy and non-fatal health outcomes where time spent with non-fatal outcomes is adjusted by a preference weight. QALY measures years of survival weighted for the quality of life, which people may be expected to have in the context of different states of illness (ICMR et al. 2017). • DALY: Disability Adjusted Life Year The Disability Adjusted Life Year (DALY) is a health gap measure that extends the concept of Potential Year of Life Lost (PYLL) due to premature death to include

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equivalent years of ‘healthy’ life lost by virtue of being in states of poor health or disability. In simple, DALY is calculated by adding Years of Life Lost (YLL) and the Years Lived with Disability (YLD). The YLL is determined using the West model life table to determine age–sex-specific life expectancies. Years Lived with Disability (YLD) is calculated on the basis of the incidence and duration of conditions resulting in non-fatal outcomes and are weighted according to the severity of the disability of the sequel. Similarly, another explicit value is attached to the time lived with a disability to make it comparable to time lost due to premature mortality (ICMR et al. 2017). • HALE: Healthy Life Expectancy To reducing the incidence, duration and severity of minor diseases that cause morbidity but not mortality and to reducing their impact on people’s lives, it is important to capture both fatal and non-fatal health outcome in a summary measure of average levels of population health. Healthy Life Expectancy (HALE) at birth is one of such indicator explains the expectation of life for different health status, adjusted for severity distribution making it sensitive to change over time or difference between countries. HALE is defined as average number of years that a person can expect to live in ‘full health’, by taking into account years lived in less than full health due to disease and/or injury (ICMR et al. 2017). • Years of Life Lost (YLL) Year of life are lost (YLL) take into account the age at which deaths occur by giving greater weight to deaths at younger age and lower weight to death at older age. The Years of Life Lost (percentage of total) indicator measured the YLL due to a cause as a proportion of the total YLL lost in the population due to premature mortality. YLL are calculated from the number of deaths multiplied by a standard life expectancy at the age at which death occurs (ICMR et al. 2017). • DFLE: Disability-free Life Expectancy The institutionalization rate (derived from census) and the prevalence of various states of functional disability (from disability survey) are incorporated with the years lived at various ages by the population of a life table. The period life expectancy for the modified table is calculated in the traditional manner yielding the value of Disabilityfree Life Expectancy (ICMR et al. 2017).

1.7 Contribution of Other Disciplines in Geography of Health and Medical Geography Medical geography became popular among various social science disciplines like historians, cultural anthropology, sociology, psychology and others. Many of the social scientists have relied on spatial analysis for understanding the dynamics of

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medical geography (Askari and Gupta 2016). Later, similarly, health geography also was accepted by all and at the same time it adopted and amalgamated concepts from other disciplines. Health and wellbeing are inter-disciplinary in nature. The purview of health and healthcare research overlaps with many science, social science and management disciplines. The ways in which these are linked are briefly discussed below (Fig. 1.2). History: Historical evolution of disease, changes in pattern of spread of disease, treatment modes and evolution of medical system. Political science: Evolution of policies and programmes for improving the health at local, regional, national and international levels, refugees and heath, role of public–private partnership and community in generating healthcare services, legislations and acts.

Fig. 1.2 Inter-disciplinary nature of geography of health

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Economics: Cost of health services, cost of ill health on economy, insurance and health care, cost-benefit analysis, economic behaviour, optimal locations, demands of health services in the overall marketplace (Pyle 1976). Sociology: Role of gender, race, age, caste, religion, special needs and other social indicators on health, availability and accessibility to healthcare services, equality, habits and lifestyle. Epidemiology: Cohort studies, therapeutic and clinical trials, epidemiology of communicable and non-communicable diseases. Chemistry: Reactions in water, air and soil that cause diseases. Demography: Calculation of birth and death rates, prediction of future population, IMR, life expectancy, total fertility rate, age–sex composition, family planning and population policy, migration and health. Urban, rural and regional planning: Land use planning, transport management, sewage management, housing and living conditions. Law: Laws for improvement of health and environment, distribution of healthcare services and infrastructure, polygamy laws, social laws, occupation-related laws. Statistics: Sampling, analysis and interpretation of data, tests of significance. Disaster management: Health care in emergency situations, first aid kit preparation, safe locations of hospitals. Media and Information technology: Health education and promotion, awareness generation, health training, experience sharing Psychology: Mental health and counselling Management: Managing the delivery of health care and medicines Environmental Science: Climate change and health, extreme events, ecological approach to health. Cultural anthropology: It tries to understand the problems of disease related to diet and nutrition (Pyle 1976). There are a number of factors that call for inter-disciplinary approach to study health. The solutions to multifaceted challenges faced by the world cannot ignore the need for global support. This support and co-operation should be received by different sectors and disciplines keeping in the health objectives and target. It may range from advocacy to technical support, or research and awareness and many others.

1.8 National and International Institutional Mechanisms With globalization and large-scale movement of people from one country to another, the risk of spread of disease is exceptionally high. Co-operation, if not adhered to, may lead to spreading of diseases at a faster rate than expected. To combat the situation, strict migration rules are formulated and international migrations are constantly under health scanner. International mechanisms are also needed for advocacy, technical assistance and to undertake research. Many developing countries depend

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on drugs and treatment methods on the developed countries. A range of international banks provide the developing countries with financial assistance to overcome health challenges, prevention of diseases and to spread awareness. Besides these functions, international organizations also help during emergency situations. It is therefore recognized that different actors need to work together to enhance global health. The various actors, broadly, can be classified under the United Nations, multilateral developmental banks, foundations, NGOs. Human rights organizations and bilateral development agencies (Skolnik 2016). a. United Nations: WHO, WHO India, UNDP, UNFPA, UNAIDS, UNICEF b. Multilateral development banks: African Developmental Bank, Asian Developmental Bank, The World Bank c. Foundations: The Aga Khan Foundation, The Bill and Melinda Gates Foundation, The Clinton Foundation, The Rockefeller Foundation d. NGOs: BRAC, CARE, OXFAM, Save the Children, Doctors without Borders, Catholic Relief Services e. Think tanks: Centre for Global development, Results for development institutes f. Human Rights Organizations: Amnesty International, Human Rights Watch, Physicians for Human Rights g. Bilateral Development Agencies: AUSAID, DANIDA, DFID, USAID h. Government organization in India: ICMR. a. United Nations World Health Organization (WHO): It is an inter-governmental organization that works with its member countries through respective Ministries of Health. WHO is the prime international organization that works on global health issues, research, awareness generation, setting up of norms and standards, monitoring and assessing global health trends. It plays critical role in joint action and engaging partnership for health research and providing technical support. WHO India has its headquarter in Delhi. India became a part of WHO in 1948, and presently Dr. Henk Bekedam is the WHO representative to India. With the joint efforts of Ministry of Health and Family Welfare, Government of India and WHO Country Office for India (WCO), Country Cooperation Strategy (CCS) 2012–2017 has been developed. The main aim of CSS is to improve health and equity in India. It aims to create a balance between the national goals of the government and global agenda of WHO. Three priorities are set up to contribute to CSS aims and objectives. Strategic priority 1 Supporting an improved role of the Government of India in global health Strategic priority 2 Promoting access to and utilization of affordable, efficiently networked and Strategic priority 3 Helping to confront the new epidemiological reality of India (https://www.who.int/countries/ind/en/). UNDP aims to reduce poverty, inequalities and exclusion in about 170 countries of the world. The goals of UNDP are in line with the 2030 Agenda for Sustainable

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Development, disaster risk reduction and climate change. The most important contribution of UNDP is annual publication of the Human Development Report that focuses on key developmental issues in the world (www.undp.org/content/undp/en/ home.html). United Nations Population Fund—UNFPA—focuses its attention to safe child birth, delivering healthy and productive lives for women in all parts of the world. UNFPA has expanded its role in spreading awareness about sexually transmitted diseases including HIV. Like UNDP, it is also aligned to the framework of the Sustainable Development Goals (https://www.unfpa.org/). It is linked with the Government of India since 1974 in assisting with the family planning programme, improving maternal health, advancing reproductive rights and improving opportunities for vulnerable women and girls. It is actively involved in many other national programmes like reducing gender-biased sex selection and gender empowerment. India is undergoing demographic change and as per the UNFPA projections; India will continue to have youngest population till 2030. In this scenario, it is imperative to focus on children and youth, especially the disadvantaged and marginalized groups. USAID is one of the lead agencies of the US government that works to end global poverty, promote economic development, improve global health, education, food security and protect human rights. It also aims to protect the environment from degrading and provides assistance in emergency situations (https://www.usaid.gov/). UNAIDS works in helping to achieve the eradication of AIDS. It generates AIDSrelated information, monitors the response and tries to bring equality among people. It works for securing the human rights of people living with HIV (www.unaids. org/en). UNICEF, established in 1946, is oriented to work for the welfare and wellbeing of children. It is headquartered at New York but has offices in 190 countries. UNICEF is deeply involved with family planning programmes, antenatal care, providing safe motherhood and in enhancing health of children. UNICEF focuses on a wide range of issues related to children such as nutrition, early child development, child protection, primary education, child rights, immunization and child survival, emergency relief care and HIV/AIDS. Individuals generate one-third of the total funding by selling products through campaigns such as ‘Check out for Children, Change for Good, and Trick or Treat’ (https://www.unicef.org/). b. Multilateral Development Banks: World Bank, Asian Developmental Bank The World Bank has played a catalytic role in education, health, infrastructure, agriculture, natural resource management and many other sectors by providing lowinterest loans and zero- to low-interest credits. The World Bank was established in 1944 to provide financial and technical assistance to the developing countries for development-related projects. Presently, it comprises 189 member countries. The bank is constantly working for sharpening the knowledge base and sharing it with participants (https://www.worldbank.org/). The Asian Development Bank was set up in 1960s to initiate economic growth and co-operation in the Asian region. It aims to foster growth through financial assistance

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by providing loans, technical assistance, grants and equity investment. Composed of 67 member countries, of which 48 are from Asia-Pacific region, it targets to improve quality of life, reduce poverty and promote equality (https://www.adb.org/). c. Foundations Many foundations are involved since almost a century that supports global health efforts. These include Ford, Hewlett-Packard and Soros foundation, The Rockefeller Foundation, The Aga Khan Foundation, The Bill and Melinda Gates Foundation and The Clinton Foundation. The Rockefeller foundation is one of the most active foundations on global health. The aim of the foundation is ‘to promote the wellbeing of humanity throughout the world’. For achieving this aim, it focuses on building healthier cities; promote universal health coverage and equitable health services. Most of its attention is attracted towards the poor and marginalized population of developing countries. The foundation has established strong public–private relationships to implement the health programmes worldwide. It also pays sincere attention to the issue of food security in Africa and in strengthening universal health coverage (https://www. rockefellerfoundation.org/). The Bill and Melinda Gates Foundation, based in Seattle, USA, is the largest charitable foundation that works on global health issues. To achieve global health, it focuses on health directly and the social determinants of health. The global development work of the foundation supports investments in seven areas: discovery and translational sciences, enteric and diarrhoeal diseases, HIV, malaria, neglected infectious diseases, pneumonia and tuberculosis in developing countries of the world (https://www.gatesfoundation.org/). The Wellcome Trust is the second largest charitable foundation in the world. Its vision is to improve human and animal health through research. The foundation has significantly contributed to the understanding of genes and its linkages with cancer and diabetes paving way for future researches. It actively supports research on infectious diseases of the tropical regions (https://wellcome.ac.uk/). d. NGOs International Science Council, ISC, was founded in 1931 having representation from 120 national members from 140 countries and 30 members from International scientific unions. The vision of the council is advancement of science for public good. The Council works for with a broad range of co-sponsored projects on issues like global sustainability, poverty, urban health and wellbeing and disaster risk reduction, to data, observing systems and science advice to governments (https://council. science/). CARE International Foundation started in France after the Second World War in 1946 and is spread across 90 countries now. With the acronym ‘Cooperative for Assistance and Relief Everywhere’, its mission was limited to distribution of food, relief and other material to affected areas. But post 1993, the scope of CARE widened including health, education, wellbeing, poverty and women empowerment

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(https://www.care-international.org/). CARE India, a part of CARE International Foundation is working in India since last 65 years focusing on alleviating poverty and social exclusion. It also works intensively for promotion of health programmes, education, livelihood and disaster response and preparedness. For health promotion and delivery, CARE India is trying to provide innovative solutions to health problems of India, especially, the marginalized groups. It promotes immunization, womenrelated healthcare services, means of reducing malnutrition and infant deaths. With 38 projects in 2017–18, it is directly affecting 24.1 million people and over 85 million indirectly (https://www.careindia.org/our-work/health/). OXFAM, Oxford Committee for Famine Relief, founded in Britain in 1942 focused on providing food supplies to starving women and children during the Second World War. Later it became an international confederation of 19 organizations (based in: Australia, Belgium, Brazil, Canada, Denmark, France, Germany, Great Britain, Hong Kong, Ireland, India, Italy, Mexico, The Netherlands, New Zealand, Quebec, South Africa, Spain and the USA) working together in more than 90 countries since 1995. Their aim is to reduce poverty and injustice. It is also active in working with vulnerable communities, combating climate change and other global issues. The Oxfam International Secretariat is based in Nairobi, Kenya (https://www. oxfam.org/). OXFAM India believes in ‘Right to life with dignity to all’. It is working in India since 1951. Keeping in line with the global objectives, it works with over 60 NGOs at grassroots levels to combat poverty in India (https://www.oxfamindia. org/). Save the Children is a global NGO founded in 1919 and presently is spread in 80 countries. Started in 2008, by 2017, it was working in 19 states of India. Registered as ‘Bal Raksha Bharat’, it has contributed immensely in improving the condition of children in the country. The NGO intensively works on delivering good lifestyle to boys and girls all over the world (https://www.savethechildren.in). Doctors without Borders was founded in 1971 with 300 volunteers including doctors, nurses and other staff to provide medical care to everyone regardless of their background (https://www.doctorswithoutborders.org/who-we-are/history/ founding). Since then has treated tens of millions of people since then. Young doctors visit emergency areas to provide aid. Many disaster and war victims received the humanitarian help from them. Since 1980, it has spread to 28 countries employing 30,000 people across the world. For their exemplary contribution, the organization was awarded the Nobel Peace Prize in 1999 (https://www.doctorswithoutborders. org/). Catholic Relief Service—The Catholic Bishops of the United States established Catholic Relief Services in 1943 to help war-torn Europe and its refugees. Since then the work area has expanded, and now their mission includes providing assistance to most in need without any discrimination. Their programmes include poverty eradication, agricultural initiatives, community banks, health, education and clean water projects (https://www.crs.org/).

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e. Think Tanks Centre for Global development, a non-profit think tank, is based in Washington DC and focuses on international development. It was formed in 2001 and has hosted over 200 public and private events since then. They focus on research on various topics including those that impact global poverty, aid effectiveness, education, globalization and global health, as well as the impact of trade and migration on development (https://www.cgdev.org). The think tank development institutes aim at creating selfsustaining systems that support healthy, educated people at global level. Their vision is ‘Healthy, educated people—the foundation of prosperous societies’. Established in 2008, this think tank tries to break the nexus between thinking and doing. With branches in 55 countries, it is untiringly working in the sphere of health, education, nutrition and other related issues (https://www.r4d.org/). f. Human Rights Organizations Amnesty International is a London-based NGO that focuses exclusively for human right protection. It was started in 1961, and now has over 7 million members and supporters. The organization was awarded the Nobel Peace Prize in 1977 for ‘defence of human dignity against torture’, and the United Nations Prize in the Field of Human Rights in 1978. Amnesty International India continues to work for minority and marginalized groups against the displacement, social conflicts and other human right issues (https://www.amnesty.org/en/). Human Rights Watch with its headquarters in New York was started in 1968 to safeguard the human rights of people. The group pressurizes the governments, policymakers and human rights abusers to respect human rights. They work for refugees, children, migrants and political prisoners. In 1981 America watch and in 1985, Asia watch was founded followed by Africa Watch in 1988 and Middle East Watch in 1989 (https://www.hrw.org). Physicians for Human Rights also has the headquarters in New York. It was founded in 1986 to advocate health professionals to prevent torture, mass atrocities and protect human rights. Strongly upholding the humanitarian principles, the organization has treated all those in need. The members actively rallied and demanded accountability for violent attacks in Syria, Yemen, Chile, former Yugoslavia and many other countries. They also advocate protection of health workers and upholding the professional ethics in providing treatment. In 1997, it was the co-recipient of the Nobel Peace Prize (https://phr.org/). g. Bilateral Development Agencies AusAID, Australian Agency for International Development, previously known as Australian Development Assistance Agency (1974) has its headquarters in Canberra, Australia. Apart from providing policy advice and aid, it has also engaged itself in subsidiary goals like improving health, education services, fighting corruption, improving security and improving the effectiveness of government organizations. It operates in five regions: Papua New Guinea, South Asia, East Asia, Pacific and Middle East (www.dif.mp.gov.in/ausaid.htm).

1.8 National and International Institutional Mechanisms

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Department for International Development (DFID) is UK-based department responsible for administering overseas aid. It aligns itself to the Millennium Development Goals and strongly promoted reduction of poverty. The DFID India promoted poverty eradication and focuses on economic development by supporting the poor and marginalized population (https://www.gov.uk/government/organisations/ department-for-international-development). USAID (United States Agency for International Development) is an independent agency of the United States federal government responsible for administering foreign aid for civilian and for developmental issues. Started in 1961, it has widely contributed for health, family planning, education, economic growth and other global concerns. Its mission is to promote democratic ideas worldwide and advance free, peaceful and prosperous world. The USAID in India has invested since the food aid in 1951. The organization has helped in establishing agricultural universities, engineering colleges, green buildings and many infrastructural projects. It has strengthened the national health programmes related to immunization, family planning, maternal and child health, HIV/AIDS, tuberculosis and polio (www.usaid.gov, https://www.usaid. gov/india/history). h. Government organization in India Indian Council of Medical Research (ICMR) is the apex body in India for the formulation, coordination and promotion of biomedical research. Established in 1911, it is one of the oldest bodies for health care in the world. ICMR is funded by the Government of India through the Department of Health Research, Ministry of Health and Family Welfare. ICMR along with 26 national institutes address research on specific health topics like tuberculosis, leprosy, cholera and diarrhoeal diseases, viral diseases including AIDS, malaria, kala-azar, vector control, nutrition, food and drug toxicology, reproduction, immuno-haematology, oncology, medical statistics, etc. Research on communicable and non-communicable diseases, maternal and occupational health is also its thrust areas. It has six regional medical research centres that also aim to strengthen or generate research capabilities in different geographical areas of the country. Through its centres, research, data collection, sensitization, awareness programmes, education and policy-making are initiated (https://icmr.nic.in/).

1.9 Overview of Health in India There are many measures to understand the health status of India, and one of them is life expectancy at birth. Overall, life expectancy is improving since 1953 from 36.6 years to 68.8 in 2018. The life expectancy of India has improved from 37.2 to 67.4 years for males and 36 to 70.5 years for females for the same period (http://worldpopulationreview.com/countries/india-population/ indicators/). The Crude death rate was 26.7 per thousand (1953) and sharply reduced to 7 per thousand (2018). But death rate is a crude method of assessment. The death rate does not take into account the amount of life lost when a person dies, that is,

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how much premature was the mortality. For this purpose, Years of Life Lost (YLLs) is used but a better measure is DALY that takes into account the years lived with disability and impact of diseases and injuries on premature mortality and disability. The recent state-level study on health status of India (1990–2016) conducted jointly by Indian Council of Medical Research, Public Health Foundation of India and Institute of Health Metrics and Evaluation (2017) uses DALY to estimate the disease burden (ICMR et al. 2017). DALY is measured as sum of the number of Years of Life Lost due to premature death and a weighted measure of the years lived with disability due to a disease or injury. Since DALY is a metric system, it allows easy comparisons and promote decision-making. It also helps in estimating the burden of premature death and disability. It is made up of two components, namely Years of Life Lost (YLLs) and Years of Life Lived with Disability (YLDs). YLLs measure all the time people lose when they die prematurely, before attaining their ideal life expectancy (i.e. based on the highest life expectancy for the person’s age group all over the world). YLDs, on the other hand, measure Years of Life Lived with any short- or long-term condition that prevents a person from living in full health. It is calculated by multiplying an amount of time (expressed in years) by a disability weight (a number that quantifies the severity of a disability). The analysis using DALY as a measure reveals that in the last 26 years (1990–2016) the disease pattern in India has shifted from decline in mortality due to communicable, maternal, neonatal and nutritional diseases (CMNNDs) but increase in the share of non-communicable diseases (NCDs) and injuries. It is to be noted that in 1990, India had 60.9% of the total DALY from CMNNDs, 30.5 from NCDs and 8.6 per cent from NCDs while this became 32.7, 55.4 and 11.9% (2016), respectively. Considering the deaths from major disease groups, 53.6% of all deaths were attributed to CMNNDs and 37.9% to NCDs (1990) while the trend almost reversed in 2016 with the share of CMNNDs being 27.5% and NCDs 61.8%. The share of deaths from injuries increased from 8.5 to 10.7% in the same period. Among the CMNNDs, maximum deaths took place due to diarrhoea, lower respiratory and other common infectious diseases (15.5%). The diseases responsible for large share of NCDs are cardiovascular diseases (28.1%) and chronic respiratory diseases (10.9%) (Anonymous 2017; ICMR et al. 2017). The top causes of death in India were identified as ischaemic heart disease, COPD, diarrhoea, lower respiratory infection and stroke. However, there are variations as per the socio-economic development in respective states and UTs. India is facing a dual challenge where the burden from CMNNDs and NCDs. Mortality from diarrhoea, lower respiratory infections, tuberculosis and neonatal disorders, though reducing, are still high contributors and at the same time, the contribution to health loss of non-communicable conditions such as heart disease, stroke and diabetes is on a rise. The epidemiological transition ratios range between 0.16 (Kerala) to 0.74 (Bihar) revealing that while the former has already progressed, the latter is undergoing acute burden of double diseases (Fig. 1.3) (Anonymous 2017). The risk factors are the drivers of diseases and injuries that cause pre mature death and disability. The top risk factors that are causing the disease burden in 2016 were malnutrition (14.6%), air pollution (9.8%), dietary risks (8.9%), high blood

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25

Fig. 1.3 Epidemiological transition ratios of the states of India in 1990 (left) and 2016 (right). Credit Reprinted from Report by Indian Council of Medical Research, Public Health Foundation of India and Institute of Health Metrics and Evaluation licensed under the CC BY 4.0 license

pressure (8.5%) and high plasma fasting glucose (6%) (2016). It is to be noted that air pollution as a risk factor was third cause with 11.1% in 1990 (ICMR et al. 2017) (Fig. 1.4).

1.9.1 Effect of Air Pollution on Health in India Air pollution is a major and growing risk factor for ill health in India (Haque and Singh 2017). It is estimated that approximately 1.8 million premature deaths and 49 million DALYs are lost due to air pollution in India (WHO 2016). As per the joint study of World Bank and the Institute of Health Metrics, 8.5% of India’s GDP was lost due to air pollution in terms of the welfare costs and the lost labour incomes in the year 2013 (WHO 2016). Changes in the air quality are predicted to be one of the leading causes for increasing rates of respiratory and allergenic diseases. The epidemiological researches over the past have demonstrated the effect of pollutants on respiratory and cardiovascular system. Among the air pollutants, most critical are the particulates and nitrogen dioxide (NO2 ). An insight to the role of air pollution as a risk factor in DALY in states and union territories of India reveals that Rajasthan, Bihar, West Bengal, Uttar Pradesh and Haryana have highest risk. Smaller states like Meghalaya, Goa, Nagaland, Mizoram and Arunachal Pradesh have least role of air pollution in DALY (Fig. 1.5).

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Fig. 1.4 Contribution of various risk factors in DALY (in percentage), 2016. Credit Reprinted from Report by Indian Council of Medical Research, Public Health Foundation of India and Institute of Health Metrics and Evaluation licensed under the CC BY 4.0 license

Fig. 1.5 Contribution of air pollution as a risk in DALY, 2016. Source Analyses based on data compiled from Anonymous (2017)

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Fig. 1.6 Contribution of ambient (left) and household (right) air pollution as a risk in DALY, 2016. Credit Reprinted from Report by Indian Council of Medical Research, Public Health Foundation of India and Institute of Health Metrics and Evaluation licensed under the CC BY 4.0 license

It is equally important to understand the exposure level of people to various health risks. The top three risk factors contributing to DALY that the people are exposed to are high systolic blood pressure (8.5%), short gestation for birth weight (6.5%) and ambient particulate matter pollution (6.4%) (2016). A closer look at air pollution reveals that exposure to indoor air pollution has dropped by 52%, while outdoor air pollution has increased by 17% since 1990 to 2016 (Anonymous 2017) (Fig. 1.6). The leading causes of DALY are lower respiratory infections, COPD (Chronic Obstructive Pulmonary Disease), ischaemic heart diseases, diarrhoeal diseases, tuberculosis, iron deficiency anaemia, road injuries, neonatal disorders, sense organ diseases, intestinal infection diseases, stroke, migraine, skin diseases, asthma, neonatal encephalopathy, meningitis, measles and tetanus. All these causes are ranked for 2016 to understand the leading causes and share of contribution to DALY (Anonymous 2017). Of these, lower respiratory infections and COPD are important causes of death and disability caused due to air pollution (Fig. 1.7). In 2016, Arunachal Pradesh, Meghalaya, Rajasthan and Sikkim recorded lower respiratory infections as the top most cause followed by Madhya Pradesh and Nagaland. On the contrary, COPD as the cause of DALY was highest in Mizoram and Uttar Pradesh followed by Andhra Pradesh, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Karnataka, Kerala, Maharashtra, Rajasthan, Telangana and Uttarakhand occupying the second rank (Table 1.1).

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Fig. 1.7 Statewise distribution of contribution COPD (left) and lower respiratory infections (right) in DALY, India, 2016. Credit Reprinted from Report by Indian Council of Medical Research, Public Health Foundation of India and Institute of Health Metrics and Evaluation licensed under the CC BY 4.0 license Table 1.1 Rank of lower respiratory infections and COPD as causes of death and disability for states and union territories of India, 2016 Rank

States and union territories of India Lower respiratory infections

No. of states

Chronic Obstructive Pulmonary Disease (COPD)

1

Arunachal Pradesh, Meghalaya, Rajasthan, Sikkim

4

Mizoram, Uttar Pradesh

2

2

Madhya Pradesh, Nagaland

2

Andhra Pradesh, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Karnataka, Kerala, Maharashtra, Rajasthan, Telangana, Uttarakhand

11

3

Assam, Bihar, Jharkhand, Mizoram Tripura, Uttarakhand

6

Delhi, Punjab, Sikkim, West Bengal

Source Analyses based on data adopted from Anonymous (2017)

No. of states

4

1.10 Concluding Remarks

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1.10 Concluding Remarks The geography of health, wellbeing and wellness are upcoming strands of human geography. With widespread environmental changes, disasters and epidemics, the role of health geographers is more pivotal in the present times. Newer concepts like therapeutic landscapes and geography of care, and geography of happiness are based on health geography. It is widely accepted that good health is essential for wellbeing and happiness. The trans-disciplinary nature of geography of health accommodates the solutions to multifaceted problems of the world.

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Web References Amnesty International (2018) https://www.amnesty.org/en/. Accessed 1 Dec 2018 Asia Development Bank (2018) https://www.adb.org/. Accessed 1 Dec 2018 Australian Development Assistance to India (2028) www.dif.mp.gov.in/ausaid.htm. Accessed 1 Dec 2018 Bill and Melinda Gates Foundation (2018) https://www.gatesfoundation.org/. Accessed 1 Dec 2018

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CARE (2018) https://www.care-international.org/. Accessed 1 Dec 2018 CARE India (2018) https://www.careindia.org/our-work/health/. Accessed 1 Dec 2018 Catholic Relief Services (2018) https://www.crs.org/. Accessed 1 Dec 2018 Centre for Global Development (2018) https://www.cgdev.org. Accessed 1 Dec 2018 Department for International Development (2018) https://www.gov.uk/government/organisations/ department-for-international-development. Accessed 1 Dec 2018 Human Rights Watch (2018) https://www.hrw.org. Accessed 1 Dec 2018 International Science Council (2018) https://council.science/. Accessed 1 Dec 2018 OXFAM (2018) https://www.oxfam.org/. Accessed 1 Dec 2018 OXFAM India (2018) https://www.oxfamindia.org/. Accessed 1 Dec 2018 Physicians for Human Rights (2018) https://phr.org/. Accessed 1 Dec 2018 Results for development (2018) https://www.r4d.org/. Accessed 1 Dec 2018 Rockfeller Foundation (2018) https://www.rockefellerfoundation.org/. Accessed 1 Dec 2018 Save the Children (2018) https://www.savethechildren.in. Accessed 1 Dec 2018 The University of British Columbia (2018) GIS and health geography: perspectives on health geography. http://ibis.geog.ubc.ca/courses/geob479/notes/Handouts/Lecture05.pdf. Accessed 15 Nov 2018 UNAIDS (2018) www.unaids.org/en. Accessed 1 Dec 2018 UNDP (2018) www.undp.org/content/undp/en/home.html. Accessed 1 Dec 2018 UNFPA (2018) https://www.unfpa.org/. Accessed 1 Dec 2018 UNICEF (2018) https://www.unicef.org/. Accessed 1 Dec 2018 USAID (2018) https://www.usaid.gov/. Accessed 1 Dec 2018 USAID India (2018) https://www.usaid.gov/india/history. Accessed 1 Dec 2018 Wellcome Trust (2018) https://wellcome.ac.uk/. Accessed 1 Dec 2018 WHO (2018) https://www.who.int/countries/ind/en/. Accessed 1 Dec 2018 World Bank (2018) https://www.worldbank.org/. Accessed 1 Dec 2018 World Population Review (2018) http://worldpopulationreview.com/countries/india-population/ indicators. Accessed 1 Dec 2018

Chapter 2

Research Background

Abstract This chapter provides a framework between linkages between urban environment and level of physical health and wellbeing. Using land use/land cover changes, air pollution increase as indicators, the human health and wellbeing are analysed. The chapter conceptualizes the research problem, linking changes in urban environment and human health. All major concepts related to study have been properly defined. Further, it deals with brief description of study area, detailed literature review about land use/land cover, air pollution and health, land surface temperature, urban microclimate, research questions, objectives and brief description of methodology of each objective. It also outlines the scope and limitations of the study. Keywords Urban heat island · Microclimate · Land use · Land cover

2.1 Introduction Health is a state of complete physical, mental and social wellbeing and not merely absence of disease or infirmity (WHO 1996). Hence, health is a multi-dimensional concept involving physical, mental and social components of human life. Urban environment has distinct natural, built and institutional elements that determine the physical, mental and social health and wellbeing of people, living in cities, towns and urban areas (ICSU 2011). Urban health, therefore, in its broadest sense refers to the study of health of urban population. Wellbeing is a broader concept encompassing good physical, social and mental health, security, personal relations and employment that help meeting the basic needs and leads to satisfaction level of individual (da Silva et al. 2012). Newton (2007) mentions that wellbeing, ‘is a positive physical, social and mental state; it is not just the absence of pain, discomfort and incapacity. It requires that basic needs are met, that individuals have a sense of purpose, that they feel able to achieve important personal goals and participate in society. It is enhanced by conditions that include supportive personal relationships, strong and inclusive communities, good health, financial and personal security, rewarding employment, and a healthy and attractive environment’. As per Rock (2006), Millennium Ecosystem Assessment, five dimensions can be used to assess wellbeing, of which health is an important © Springer Nature Singapore Pte Ltd. 2020 A. Grover and R. B. Singh, Urban Health and Wellbeing, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-13-6671-0_2

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2 Research Background

component. Health and wellbeing are closely integrated as economic, social, political, residential, psychological and behavioural circumstances having essential bearing on health consequences (WHO 2005). Physical health therefore is one of the basic determinants of wellbeing (WHO 2005). Da Silva et al. (2012) state that the purpose of urban system is to achieve wellbeing that includes basic survival needs, security, health, good social relations and freedom of choice and action. Therefore, the human wellbeing in broader sense relates to overall mental, physical, economic, social and health satisfaction. Urban environment encompasses both physical as well as social environment. The physical urban environment is represented by built and natural environment excluding the indoor environment (Sustainable Development Commission 2008). The urban environment has direct and indirect bearing on human health. The direct influences are ones that influence human health regardless of human behaviour like the impact of environmental pollution on human health. The indirect influences include the choices one makes, personal behaviour like lifestyle and eating habits.

2.2 Conceptual Framework In the present research, the focus is on the impact of physical environment on physical health and wellbeing in urban areas. The changing urban environment can be understood in context of air quality, water quality, soil pollution, solid waste management, noise, traffic, climate, composition of different land covers, safety and disasters, etc. But in the present analysis, LULC and air quality changes have been taken as a proxy to understand changing urban environment (Fig. 2.1). Cities grow due to immigration, both from rural and relatively smaller towns and therefore experience a constant influx of people from other cultures and climates. The challenges faced in an urban environment range from difficulties of organizing public transportation, increased pollution level, income inequality, poverty, high density of human populations and vehicles, lack of green and liveable space, creation of suburbs and many others. This leads to changing LULC composition, contributing to localized climate change, microclimates and health hazard (Kumar and Singh 2003; UN Habitat 2011a). The number of large cities and the size of the world’s largest cities are experiencing uncontrollable population growth. Nearly 45% of the developing world’s population and 30% of India’s total population lived in urban areas in 2010. India’s urban population is expected to increase to 39.7% in the next two decades (UN Habitat 2011b). Urbanization has led to expansion of residential, commercial, service-oriented and industrial development at the periphery, thus encroaching on the forested and/or agricultural hinterlands surrounding the city (Lo and Quattrochi 2003). The rapid population growth in the recent times is responsible for global environmental changes like greenhouse gas-induced warming, changes in air, water and soil quality, deforestation, habitat fragmentation, desertification and loss of biodiversity

2.2 Conceptual Framework

35 Changing Urban Environment (physical)

Air pollution

Land use/land cover change

Water pollution

Waste disposal

Disasters

Depletion of groundwater

Energy consumption

Noise pollution

Determinant: Growth of vehicles

Determinant: Growth of population

Delhi: 9 stations (6 residential & 3 industrial)

Landsat TM and ETM+ imagery

Mumbai: 3 stations (3 residential & 1 industrial)

Delhi: 1993, 2000, 2010

Urban Heat Island (UHI) Atmospheric UHI

Surface UHI

Spatial correlations and trends of SUHI, NDVI and NDBI for Delhi and Mumbai

Physical health and wellbeing

Aggravating factors: Urbanization, LULC, Climate, Natural environment, Air quality, Energy use

Mitigating factors: Land use planning, Regulations, Infrastructure, Transport, Accessible medical facilities

Level of exposure and vulnerability

Death *Diseases of Upper Respiratory Tract

Intensity/severity of impact

*Diseases of Lower Respiratory Tract *Other Respiratory Diseases and minor infections; **Heart disease and illness, Respiratory TB, Cancer, Whooping cough

Number of people affected

Health and wellbeing

Fig. 2.1 Framework to conceptualize linkages between urban environment and level of physical health and wellbeing. Note *Direct impacts, **Indirect impacts

36

2 Research Background

(Grimmond 2007). Urban growth and sprawl have drastically altered the biophysical environment (Lo and Quattrochi 2003). Land use change leading to changes in air quality affects the Urban Microclimate (UMC) (Singh 2001; Lo and Quattrochi 2003; Foley et al. 2005). It has replaced soil and vegetation cover with impervious urban materials, such as concrete, asphalt and buildings, which affects the albedo and run-off characteristics of the land surface, thus significantly impacting the local and regional land-atmosphere energy exchange processes (Lo and Quattrochi 2003). Urban areas are major sources of anthropogenic carbon dioxide emissions from burning of fossil fuels for heating and cooling; from industrial processes; transportation of people and goods and so forth. The clearing of vegetation cover for cities and roads and the demand for goods and resources by urban residents are the major drivers of regional LULC change and deforestation, which has reduced the magnitude of global carbon sinks (Grimmond 2007; Svirejeva-Hopkins et al. 2004). Increase in the anthropogenic heat discharge, decrease in surface evapotranspiration, changes in thermal characteristics, increasing traffic and air pollution have disrupted the radiation balance within the urban system as surfaces absorb longwave radiation and are unable to radiate it back, out to the atmosphere. Hence, urban areas generally record elevated temperature relative to the natural areas surrounding them (Lo and Quattrochi 2003; Sailor 1995; Hedquist 2005). As a result, temperatures in urban areas are usually higher than those of the surrounding hinterland area, which is commonly known as the ‘Urban Heat Island’ (UHI) effect (Stone et al. 2010; Oke 1982) (Fig. 2.2). The heat island effect is a phenomenon through which cities exhibit higher temperatures than the surrounding countryside. This temperature differential can exceed

Pervious surface

Impervious surface

Land surface temperature

Fig. 2.2 Idealized model of Urban Heat Island. Source Based on Voogt and Oke (2003); United States Environmental Protection Agency (2008); Valsson and Bharat (2009)

2.2 Conceptual Framework

37

10 °C. It results from several factors like, loss of vegetation accompanying loss of evapo-transpiration; darker surfaces with low albedo (i.e. surface reflectivity), which absorb and then reradiate heat; building configurations that trap heat; and the concentrated generation of heat from generators, vehicles, and other sources (Stone et al. 2010; Oke 1982; Giridharan et al. 2004) (Fig. 2.2). As there is transformation of the urban environment, urban health also experiences changes. The introduction of cars and new building technologies, industrialization, urban sprawl and carbon-based construction materials contribute to changing urban health. The higher land surface temperatures (LST) in the city brought about by UHI has adversely affected air quality through creation of ground-level ozone and other gases. Ground-level ozone is produced from photochemical smog mechanism wherein volatile organic compounds (VOCs) in the presence of nitrogen oxides and sunlight undergo complex set of chemical reactions (Cardelino and Chameides 1990; Lo and Quattrochi 2003). Various gases are emitted from motor vehicles, power plants and other sources of combustion involving fossil fuel that aggravate UHI. The impact of LULC, air pollution and UHI on health can be both; direct and indirect. The direct impacts include respiratory illness that maybe of upper respiratory tract (URT), lower respiratory tract (LRT) and other respiratory diseases (ORD). Additionally, the UHI is seen as a possible contributor for increased instances of human mortality during high heat events. Repeated exposure to pollution may cause permanent damage to the lungs and can worsen bronchitis, heart disease and asthma (Lo and Quattrochi 2003). High degree of heat and air pollution together can cause stress and heat stroke especially for people with cardiovascular and respiratory disorders (Piver et al. 1999). Exposure to extreme heat resulting into physiologic heat stress may also lead to death (Reid et al. 2009). The dynamics of microclimatic changes, hence, affects public health in urban areas. These can cause indirect effects, e.g. cardiovascular illness, respiratory tuberculosis (TB), cancer, malignant neoplasm of respiratory and intra-thoracic organs and whooping cough. The impacts, however, may vary with respect to age group, income group, gender, exposure and other factors (Piver et al. 1999; ICSU 2011). The present research tries to explore the changes in urban environment of Delhi and Mumbai and the factors that have contributed to the formation of UHI. Further, it tries to establish linkages between physical environmental changes with human health and wellbeing. The strategic plan encompassing effective policy implementations, LULC planning, accessible and affordable medical facilities and other proactive initiatives by the government, civil society and other agencies that can prove helpful in mitigation of impaired human health in urban areas are also discussed herewith.

38

2 Research Background

2.3 Literature Review 2.3.1 Urban Environmental Change The studies on air pollution as a precursor of environmental change in urban areas are substantial. The most commonly studied pollutants are nitrogen dioxide (NO2 ), sulphur dioxide (SO2 ) and particulate matter (PM). However, with growing awareness and data availability for other pollutants like carbon dioxide (CO2 ), carbon monoxide (CO), ozone (O3 ), methane (CH4 ), lead (Pb) and toxins [VOCs, hydro carbons (HC)] in increasing (Table 2.1). Shandilya et al. (2007) tried to compare Table 2.1 Important studies on air quality change Reference

Location

Pollutants analysed

Temperaturepollutant linkages

Piver et al. (1999)

Tokyo

NO2 , O3

Yes

Goyal and Sidharth (2003)

Delhi

CO, NO2 , SO2 , SPM, RSPM, Pb

No

Filleul, et al. (2006)

France (9 cities)

O3

Yes

Shandilya et al. (2007)

South Delhi, India

SPM, RSPM

No

Singh et al. (2007)

Ahmedabad, Chennai, Delhi, Indore, Kanpur, Kolkata, Pune

NO2 , SO2 , SPM, RSPM

No

McMichael et al. (2008)

Ljubljana, Bucharest, Sofia, Delhi, Monterrey, Mexico City, Chiang Mai, Bangkok, Salvador, Sao Paulo, Santiago, Cape Town

PM

Yes

Jain and Khare (2008)

Delhi, India

CO, NO2 , SO2 , SPM, RSPM, Pb

No

Stafoggia et al. (2008)

Italy

PM10

Yes

Ali et al. (2012)

Delhi, Pune

NO2 and O3

Yes

Guttikunda and Gurjar (2012)

Delhi

PM2.5

No

Tahir et al. (2002)

Cities of India

CO, CO2 , CH4 , HC, NO2 , SO2 , SPM, RSPM

No

Source Compiled by the authors

2.3 Literature Review

39

the SPM (suspended particulate matter/PM2.5 ), RSPM (respirable suspended particulate matter/PM10 ) and total suspended particulates (TSP) levels of Delhi with Satna, Madhya Pradesh. They state that climatic conditions in Delhi are conducive for increasing the secondary pollutants. Singh et al. (2007) presents a comparative analysis of SO2 , Nitrogen Oxide (NOx ) and SPM levels in Ahmedabad, Chennai, Delhi, Indore, Kanpur, Kolkata and Pune during 1995 to 2000. The total suspended particulate (TSP) was computed using the weights of Whatman papers. The results show high SO2 concentration for Pune and increasing NOx and SPM for Delhi. For Delhi, there are ample studies (Table 2.1) on the comparative analysis of preand post-CNG pollutant levels, but similar studies are missing for Mumbai, despite the fact that Mumbai too shifted to Compressed Natural Gas (CNG) same time as Delhi. Some important studies on role of CNG in mitigating pollution levels in Delhi are by Goyal and Sidharth (2003), Ravindra et al. (2006), Kumar and Foster (2007), Chelani and Devotta (2007) and Khillare et al. (2008). Jain and Khare (2008) monitored the vulnerability index for all Central Pollution Control Board (CPCB) air pollution monitoring stations in Delhi using the toxicity weighing factors for all pollutants. This analysis is presented for seven consecutive years from 1997 to 2004 and reveals that most critical locations are near the traffic intersections and roadways. It also states that efforts like introduction of CNG have helped in reducing pollution load at all stations. Nevertheless, Income Tax Office (ITO), Town Hall and Siri Fort show highest vulnerability index. Guttikunda and Gurjar (2012) attempted to understand the seasonal variations in pollution levels in Delhi. They interlink temperature, wind direction and other factors of climate with the changing levels of PM2.5 . As per the study, road dust is the major cause in summers, biomass burning in autumn, winters as well as spring. The third important contributor is diesel exhaust for all seasons. Stafoggia et al. (2008) and Ali et al. (2012) also established linkages between occurrence of pollutants and temperature changes. Nagdeve (2004) examines the trend of level of environmental pollution in Delhi. The emergence of Delhi as a megacity has manifold environmental effects. He analyses the trend of growth of pollution from vehicles, sewage and liquid wastes generated by human settlement and from industries with their possible health effects. According to him, with the increase in population, urbanization and industrialization, the transport demand has also increased consequently. He further states that Delhi is one of the most polluted cities in the world. It is hence necessary that along with environmental legislations made by the government, voluntary organisations and citizens to work together for environmental conservation. Karn et al. (2003) tries to establish link between socio-economic and environmental conditions with health of the urban poor. The study is based on primary data from a survey of 1070 households in four poor settlements in Mumbai, comprising of slum, pavement dwellers and squatters, on their living environment and health conditions. They use matrices of Pearson’s correlation coefficient and multiple correlation coefficients to identify the possible relationship with respect to income, literacy, basic amenities, sanitation and hygiene. The study concludes that there are intra-urban poor differences with respect to the economic status and their income levels. In this

40

2 Research Background

case, the health of slum people is better placed than the pavement dwellers as the latter are hit most hard by environmental pollution and lack of basic amenities in the concerned areas. Yedla (2003) presents the dynamics of environmental problems in Mumbai city. An evolution concept is applied to study the present environmental status of Mumbai. In order to study its dynamics, the entire process of environmental evolution is divided into four types, viz. poverty, industrialization and urbanization, rapid economic growth and wealthy lifestyle-related environmental issues. Dynamics of these indicators has been studied temporally and different types of environmental problems identified. It is found that Mumbai has prevalence of rapid economic developmentrelated environmental problems, while poverty-related environmental issues have low significance level. The LULC change analysis is widely studied aspect in urban areas. There exists plethora of research on changing LULC of various cities of the world like Indianapolis (Lu and Weng 2006), Mexico (Southworth 2004; Torres-Vera et al. 2009), Nairobi (Mundia and Aniya 2005) and Tokyo (Bagan and Yamagata 2012) to name a few. The availability of free satellite data, particularly, the Landsat data has helped in promotion of LULC studies across the globe. The LULC change for Delhi and Mumbai too has been studied using Landsat satellite imageries (Table 2.2). The 1972–2003 analysis for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image for Delhi is presented by Rahman et al. (2012), while LISS-III images were used to understand 1997 to 2008 LULC change by Mohan et al. (2011) and Sharma and Joshi (2012) utilized Landsat Thematic Mapper and Enhanced Thematic Mapper Plus (TM/ETM+) data to explore spatio-temporal LULC changes for two decades (1998–2011). Samant and Subramanyan (1998) and Kamini et al. (2006) examined LULC for Mumbai for 1994 and 1987–2005, respectively. Ramachandra et al. (2014) mapped LULC changes for Mumbai from 1973 to 2009. They also calculated accuracy and Kappa statistics for land use analysis. Further, Shannon’s entropy was calculated to understand the direction and compactness of urban growth. The study concludes that urban built up area has increased by 155% at the expanse of forest area and the urban growth is towards south-west and north-east directions.

2.3.2 Urban Heat Island 2.3.2.1

Data Sources

There are six major sensors systems that provide thermal data and mostly used by scientists (Table 2.3) for estimation of UMC, LST and UHI. Landsat 4 and 5 (TM), 7 (ETM+), 8 (TIRS 1 and 2), ASTER and Moderate-resolution Imaging Spectroradiometer (MODIS) are sun-synchronous satellite systems, while Advanced Very High Resolution Radiometer (AVHRR) is polar orbiting sensor. The Landsat and AVHRR are the oldest and largest thermal data sensors that have been providing the

Lu and Weng (2006)

Southworth (2004)

Mundia and Aniya (2005)

Kamini et al. (2006)

Torres-Vera et al. (2009)

Mohan et al. (2011)

Bagan and Yamagata (2012)

Rahman et al. (2012)

Sharma and Joshi (2012)

Ramachandra et al. (2014)

Atlanta, Georgia Metropolitan Area

Indianapolis, Indiana, USA

Ticul, Yucatan state, Mexico

Nairobi

Mumbai, India

Mexico

Delhi, India

Tokyo, Japan

NW Delhi, India

Delhi, India

Mumbai, India

1973–1992–1998–2009

1998–2011

1972–2003

1971–2011

1997–2000–2003–2004–2005–2008

1973–1992–2000

1987–2005

1976–1988

1995

2000

1973–1979–1983–1992–1997–1998

1925–1967–1994

Years

Landsat MSS, TM, ETM+

Landsat TM, ETM+

ASTER

Landsat MSS, TM, ETM+

LISS-III images of IRS

MSS NALC images, Landsat TM, ETM+

Landsat TM, CARTOSAT-I

Landsat MSS, TM, ETM+

Landsat TM

Landsat ETM+

Landsat TM

Landsat TM

Satellite/Sensors

Source Compiled by the authors; Singh and Grover (2014) Note TM Thematic Mapper, ETM+ Enhanced Thematic Mapper Plus, MSS Multi Spectral Scanner, LISS Linear Imaging Self Scanning Sensor

Samant and Subramanyan (1998)

Yang and Lo (2002)

Mumbai, India

Reference

Location

Table 2.2 Important studies on land use/cover change

2.3 Literature Review 41

16 July 1982–30 June 2001

1 March 1984–5 June 2013

15 April 1999–till date

11 February 2013

11 February 2013

18 December 1999 May 2002

18 December 1999

27 June 1979

Landsat 4 TM

Landsat 5 TM

Landsat 7 ETM+

Landsat 8 TIRS1

Landsat 8 TIRS2

Terra ASTER* Aqua ASTER

Terra MODISa Aqua MODIS

NOAA/AVHRR

14

10.95–11.65

4 5

10.3–11.3 11.3–12.5

32

13

10.25–10.95

11.77–12.27

12

8.925–9.275

31

11

8.457–8.825

10.78–11.28

10

11

10

6

6

6

Band

8.125–8.457

11.50–12.51

10.60–11.19

10.40–12.50

10.40–12.50

10.40–12.50

Wavelength (µm)

1100

1100

1000

1000

90

90

90

90

90

100

100

60

120

120

Spatial resolution (m)

–

–

8

8

16

16

16

16

16

16

16

16

16

16

Temporal resolution (days)

07:30 a.m.

07:30 a.m.

10:30/22:00

10:30/22:00

10:30 a.m.–12:00 p.m. 1:00 a.m.–3:00 p.m.*

10:00 a.m.

10:00 a.m.

10:00 a.m.

9:45 a.m.

9:45 a.m.

Equatorial crossing time

Source Compiled by the authors; Singh and Grover (2014) Note TM Thematic Mapper, ETM+ Enhanced Thematic Mapper Plus, TIRS Thermal Infrared Sensor, ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer, MODIS Moderate-resolution Imaging Spectroradiometer, NOAA/AVHRR National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer a Benali et al. (2012) (Local time)

Launch and decommission date

Satellite and sensors

Table 2.3 Details of major satellite data used for UMC and UHI analysis

42 2 Research Background

2.3 Literature Review

43

data since early 1980 s. ASTER and MODIS on the other hand have been recently launched (1999) but have been proved to be very effective for LST studies in urban areas. The Landsat thermal data is available since the launch of Landsat 4 on 16 July 1982. Since then, it has had a series of satellite missions (Landsat 5, 6 and 7), and recently, the Landsat 8 was launched. The 6th band of Landsat 4 (TM), 5 (TM) and 7 (ETM+) has been providing the thermal data in the wavelength of 10.40–12.50 µm. The Landsat 7 ETM+ acquires thermal data at two levels, which are often referred as band 6L and band 6H. The band 6L is acquired using low gain setting and is useful for temperature ranging from 130 to 350 K. The band 6H is acquired using high gain setting and is useful for temperature range of 240–320 K (Chander and Markham 2003; Chander et al. 2009). In the low gain and high gain setting, the minimum saturation levels are 17.21 and 12.78 watts/(metre squared*str*µm), respectively (NASA 2003). The Landsat 8 (TIRS—Thermal Infrared Sensor) acquires thermal data in two wavelengths, i.e. 10.6–11.19 µm and 11.5–12.51 µm, which are not identical to its predecessor sensors. The ASTER provides thermal data in five wavelengths ranging from 8.125 to 11.65 µm, while the MODIS and AVHRR provide thermal data in two wavelengths each. For all the sensors, the broad wavelength for thermal data ranges between 8.125 and 12.5 µm. The spatial resolution of Landsat 4 and 5 (TM) is 120 m, while Landsat 7 (ETM+) provides the finer resolution thermal data (60 m). The recent Landsat 8 (TIRS) provide thermal data at 100 m spatial resolution. Similarly, ASTER is also a fine resolution thermal data (90 m). Thus, their data are best suited for large-scale areas such as city level and results can be compared. The MODIS and AVHRR are coarse resolution images (1000 and 1100 m); therefore, they are best suited for small-scale areas such as continents or countries. The authors have also attempted to compare day and night time LST, UMC and UHI. The Landsat images are captured during the day time (around 10 am) (Ding and Shi 2013; Hung et al. 2006; Sharma and Joshi 2012); therefore, they are mostly used to compare the daytime temperatures. The ASTER and MODIS images are acquired both during day and night, so they are in addition used to compare day and night time LST and UHI. The AVHRR gives the aggregate surface conditions. Most of the thermal data are now provided free of cost via US Geological Survey (USGS) through many websites (NASA 2003). Most important and frequently used websites for downloading the satellite data are http://glovis.usgs.gov and http:// earthexplorer.usgs.gov.

2.3.2.2

Main Methodologies Used for UHI Studies Using Landsat Thermal Data

There are many methodologies used for estimating LST and further the UHI phenomena in different parts and cities of the world using Landsat thermal data, i.e. 6th band in Landsat TM and ETM+ sensors and 10th and 11th and in Landsat OLI-TIRS sensor. Majority of the studies have used following three steps for estimating the

44

2 Research Background

LST as suggested by Markham and Barker (1986) for TM4, Chander and Markham (2003) for TM5 and Chander et al. (2009) for entire series of Landsat thermal data including, TM4, TM5, ETM+7. Landsat 7 Science Data Users Handbook of NASA (2003) has particularly explained the following steps for ETM+7 thermal data. (1) Conversion of the Digital Number (DN) to at-sensor Spectral Radiance (L) (2) Conversion of Spectral Radiance to Temperature in Kelvin (3) Conversion of temperature in Kelvin to Celsius scale. The 6th band of the Landsat TM and ETM+ data has been widely used for mapping and estimating LST and UHI. Of the above-stated steps to convert the raw thermal band to temperature, first and second steps are essential and the third is optional. Many studies have restricted to Kelvin scale of temperature and many have further converted the Kelvin to degree Celsius. While the satellite images are obtained, they are in raw form and many corrections and pre-processing need to be applied on them. Initially, the raw satellite images (6th band in the case of thermal remote sensing) are converted to spectral radiance. Further, the spectral radiance image is essentially converted to surface temperature Kelvin scale and in some cases from Kelvin to degree Celsius (Chander et al. 2009). Other than satellite data, fixed and mobile meteorological stations are also used to understand the UHI. Borbora and Das (2014) use fixed stations to map UHI in Guwahati, India. Summertime Urban Canopy Layer (UCL) is mapped using temperature data from May to October 2009.

2.3.2.3

Global Distribution of Major UHI Studies and Various Facets Using Landsat

Worldwide, generous amount of research has been carried out with regard to UMC and UHI. Landsat thermal data has been extensively utilized for understanding the impact and extent of UHI for various cities in the world. Most researched country is China, wherein the fast-growing cities, e.g. Shanghai and Beijing have been extensively studied (Table 2.4). Apart from these, Guangzhou, Boluo, Dongguan, Panyu, Foshan, Gaoming, Huadu, Huizhou, Nanhai and Sanshui in Guangdong Province, Guizhou Province, Zhujiang Delta, Pearl River delta and Wuhan city are other areas, where similar researches have been carried out. Asian cities of Hong Kong, Singapore, Tokyo, Seoul, Pyongyang, Bangkok, Manila, Ho Chi Minh, Ahmedabad, Mumbai and Delhi have also been explored well. Twin Cities Metropolitan Area of Minnesota, Atlanta and Indianapolis in USA were examined with UHI intensity. Few researches can be seen in Mexico, Tabriz urban area, Iran and Israel—Egypt border. Despite high levels of urban growth, Europe, Australia and USA are poorly covered areas, whereas rapid urbanization growth in past two decades accompanied with high population in Asian cities has impelled and promoted UHI research in Asian cities. The understanding of UMC and UHIs is that the air temperatures of a city are often higher than its countryside was first conceptualized and proved by Howard (1833). Since then, this fact has been tested

2.3 Literature Review

45

Table 2.4 UHI studies conducted in major cities of the world using Landsat thermal data City, country

Satellite/sensors

Reference

Shanghai, China

Landsat TM and ETM+

Li et al. (2012)

Shanghai, China

Landsat ETM+

Yue et al. (2007)

Shanghai, China

Landsat ETM+

Li et al. (2011)

Beijing, China

Landsat TM

Zhang et al. (2010)

Beijing, China

Landsat TM and ETM+

Ding and Shi (2013)

Beijing, China

Landsat TM and ETM+

Jiang and Tian (2010)

Guangzhou, Boluo, Dongguan, Panyu, Foshan, Gaoming, Huadu, Huizhou, Nanhai and Sanshui, China

Landsat ETM+

Zhang and Wang (2008)

Pearl River Delta, China

Landsat TM and ETM+

Chen et al. (2006)

Guizhou Province, China

Landsat TM

Xiao and Weng (2007)

Guangzhou, China

Landsat TM

Weng and Yang (2004)

Zhujiang Delta, China

Landsat TM and ETM+

Qian et al. (2006)

Wuhan City, China

Landsat TM

Zhang et al. (2012)

Tuen Mun, Hong Kong

Landsat ETM +

Nichol (2005)

Hong Kong

Landsat TM

Liu and Zhang (2011)

Hong Kong

Landsat ETM +

Xipo et al. (2007)

Tokyo, Japan

Landsat TM

Kawashima (1994)

Tokyo, Japan

Landsat TM and ETM+

Bagan and Yamagata(2012)

Atlanta, Georgia

Landsat TM

Lo and Quattrochi (2003)

Twin Cities Metropolitan Area of Minnesota, USA

Landsat TM and ETM+

Yuan and Bauer (2007)

Indianapolis City, USA

Landsat ETM+

Weng et al. (2004)

Tabriz urban area, Iran

Landsat TM and ETM+

Amiri et al. (2009)

Ticul, Mexico

Landsat TM

Southworth (2004)

Singapore

Landsat ETM+

Jusuf et al. (2007)

Israel–Egypt border

Landsat TM

Qin et al. (2001)

Tokyo, Beijing, Shanghai, Seoul Pyongyang, Bangkok, Manila And Ho Chi Minh City

Landsat ETM +

Hung et al. (2006)

Ahmedabad, India

Landsat TM and ETM+

Raykar (2005)

Delhi, India

Landsat TM

Rahman et al. (2009)

Delhi, India

Landsat TM

Mallick et al. (2012)

Delhi, India

Landsat ETM +

Mallick et al. (2008)

Greater Bangalore, India

Landsat MSS, TM, ETM+

Ramachandra and Kumar (2010a, b)

Delhi, India

Landsat TM and ETM+

Sharma and Joshi (2012)

Mumbai, India

Landsat TM

Dwivedi et al. (2015)

Source Compiled by the authors; Singh and Grover (2014)

46

2 Research Background

for a range of cities in the world. Different components and aspects of UHI have been studied including its characteristics, causes and impacts. The basic characteristic features of UHI using Landsat thermal data have been researched by many including Li et al. (2012); Hung et al. (2006) and Nichol (2005). Using Landsat data, the process of LULC changes and associated thermal properties have been extensively examined for world leading cities. Jiang and Tian (2010), Ding and Shi (2013) presented their results for Beijing, China; Li et al. (2011) for Shanghai, whereas, Zhang and Wang (2008), Chen et al. (2006), Xiao and Weng (2007), Weng and Yang (2004) and Qian et al. (2006) on different provinces and cities of China. Other prominent researches are by Southworth (2004) on forest area of Yucatan, Mexico, Dwivedi et al. (2015) on Mumbai and on Delhi by Mallick et al. (2008, 2012). Ramachandra and Kumar (2010a, b) utilized Landsat, MODIS and LISS-III data to examine spatio-temporal changes in UHI in Greater Bangalore (1972–2007). Normalized Difference Vegetation Index (NDVI) is regarded as a one of the reliable indicators of UHI, as the forest and tree cover in a densely populated city act as cooling agents and heat sinks. The forested areas and vegetation cover have low surface temperatures as compared to the concrete zones, which have higher temperature. With respect to this understanding, abundant literature is available on validation of this fact. Some prominent researches that have used the Landsat thermal data are on Twin Cities Metropolitan Area of Minnesota by Yuan and Bauer (2007), Amiri et al. (2009) on Tabriz urban area, Iran, Weng et al. (2004) on Indianapolis City, USA, Kawashima (1994) on Tokyo, Japan, Liu and Zhang (2011)on Hong Kong, Yue et al. (2007), Zhang et al. (2010) and Zhang et al. (2012) on fast-growing cities of China. Zha et al. (2003) present a detailed analysis on UHI, NDVI and NDBI of Nanjing in eastern China. Rahman et al. (2009) tried to assess the various environmental issues related to UHI for the city of Delhi using Landsat data. Ramachandra and Uttam (2009) mapped the NDVI for Greater Bangalore from 1992 to 2007.

2.3.3 Impact of Changing Urban Environment on Urban Health The relationship between air pollution, temperature and health is a complicated one. The studies are either related to direct impacts of air pollution or temperature on human health; or indirectly whereby air pollution leads to increasing temperature and vice versa. The direct correlations are well researched, but work on the latter is scarce. The most studied pollutants are PM followed by NO2 , SO2 and O3 . In the recent past VOCs and impact of other toxins on human health are also being studied, but this depends on data availability. Piver et al. (1999) tried to establish the link between temperature and air pollution, as a prominent risk factors for heat stroke in Tokyo. There is prolonged exposure to high air temperatures during July and August besides the high concentration of

2.3 Literature Review

47

air pollutants. To assess the impacts of these combined exposures, daily numbers of heat stroke, emergency transport cases/million residents for Tokyo were stratified by gender and three age groups: 0–14, 15–64 and more than 65 years of age, for the months of July and August during 1980–1995. A regression model was constructed using daily maximum temperature (T max ) and daily average concentrations of NO2 and NO3 as model covariates. It was found that the numbers of heat stroke emergency cases were greater for males than females in the same age groups. The least vulnerable group was of females with 0–14 years of age, and most vulnerable was for males of more than 65 years of age. According to the study, age and gender are important determinants for estimating heat stroke risk factor. Kamat and Doshi (1987) present in-depth examination of impact of SO2 , NO2 and SPM in Mumbai for 1977–1982. Through primary survey and questionnaire method, they studied 22,272 persons from slum areas of the city and tried to understand the impact of pollutants. The results show that slum and rural areas had higher morbidity for common cold, cough, dyspnoea, chest pain and other related illnesses. Young children and old population was found to be most vulnerable to SO2 and SPM. The health impact due to PM10 in Mumbai based on Ostro’s approach is undertaken by Joseph et al. (2003). The premature mortality and total mortality from respiratory and heart-related diseases are examined. The research concludes that the mortality due to air pollution caused by increased construction activities has increased in Bandra, while Kalbadevi observed lowest mortality owing to fact that it is a residential area. The study also mentions that it is difficult to use ‘dose-response function’ method for improving health in developing countries. Since Delhi has attracted abundant research on air pollution levels, the studies on its impact are also well-explored. Nidhi and Jayaraman (2007) made an attempt to determine linkages between pollution and respiratory morbidity (1998–2004) through medical records collected from seven different hospitals. These were correlated with monthly averages of five pollutants, viz. SO2 , NO2 , SPM, RSPM and O3 . Further, through Poisson distribution the impact was assessed for different seasons and meanwhile the success or failure of CNG implementation analysed. The results revealed that SPM and RSPM had consistent effect on respiratory and circulatory organs especially in areas where construction works were going on (North and South Delhi). In western Delhi, SO2 impacts were predominant and in east Delhi, all pollutants except NO2 had high impact. Positive association was found in case of NO2 -related respiratory admission reduction due to government regulations and SO2 reduced from locations where industries were closed. Later, Jayaraman and Nidhi (2008) used ‘generalized additive Poisson regression model’ and demonstrated associations between daily patient visits and pollutants (O3 , NO2 and RSPM) for variations in respiratory morbidity. They found that O3 , NO2 and RSPM exert significant impact on health in Delhi. Siddique et al. (2011) focused on respiratory health of children and conclude that there exists significant positive association between PM10 level in Delhi’s air and the prevalence of LRT symptoms in children. They also compared the results with West Bengal and Uttaranchal (presently Uttarakhand). There are some recent studies that have tried to find association between pollutants on diseases other than that of the respiratory and circulatory system. Yorifuji et al.

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(2015) explored the role of pollution on low birth weight in Japan and found that air pollution exposure during pregnancy increases the risk of low birth weight. They also state that the non-smoking mothers are more susceptible to NO2 and SPM than smoking mothers, though the reasons are unclear. The impact of atmospheric pollution on Vitamin D levels in infants for Delhi is explored by Agarwal et al. (2002) and state that there is strong correlation between the two. The impacts of increasing temperatures on human health are less explored for Indian cities. However, there are ample studies at world level that prove strong relationship between temperature increase and effects on human health. Davis et al. (2003) studied heat related mortality in USA, Ramlow and Kuller (1990) for Allegheny County, Pittsburgh, Tan et al. (2010) for Shanghai and Williams et al. (2012) on Perth, Australia. Reid et al. (2009) outlined the community determinants of heat vulnerability and create cumulative heat vulnerability index. Heat waves can result in both, increased illness and deaths. The identified ten vulnerability factors for heat related morbidity/mortality were mapped and analysed for USA. These variables were: six demographic characteristics (age, poverty, education, living alone and race/ethnic) and two household air conditioning variables, land cover and diabetes prevalence. Factor analysis was performed to obtain a cumulative heat vulnerability index value. Four factors explained more than 75% of the total variance in the original ten vulnerability variables: (a) social/environmental vulnerability (combined education/poverty/race/green space), (b) social isolation, (c) air conditioning prevalence and (d) proportion elderly/diabetes. In urban areas, inner cities showed the highest vulnerability to heat. McGeehin and Mirabelli (2001) assess the potential impacts of climate variability and change on temperature-related morbidity and mortality in the USA. High temperature and heat waves are projected to increase in severity and frequency with increasing global mean temperatures. There is close association between increase in urban mortality and due to increase in temperature. Health effects associated with exposure to extreme and prolonged heat appear to be related to environmental temperatures. Models of weather–mortality relationships indicate that populations in north-eastern and mid-western USA cities are likely to experience the greatest number of illnesses and deaths in response to changes in summer temperature. Within heat sensitive regions, urban population, mainly elderly, young children, the poor are the most vulnerable to adverse heat related health outcomes. Rainham and Smoyer-Tomic (2003) examined the role of air pollution in increasing heat stress and thereby mortality for Toronto, Canada. The results prove that high air pollution has the potential to increase the temperature of the city that leads to spurt in cardiac-respiratory illness and mortality. Similarly, Filleul et al. (2006) correlated O3 with temperature for nine cities of France to explore elevated O3 concentrations and temperature effects on health. Using random effect approach and an empirical Bayes approach, they confirm that O3 levels have a non-negligible impact on public health. On the contrary, Stafoggia et al. (2008) explored the role of temperature in modifying air pollution and mortality levels in Italy. Considering the levels of PM10 and its

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variation across seasons, the distribution of deaths from cardiovascular and respiratory causes was analysed. The results revealed higher effect of PM10 during summer months. The study confirms that there is higher exposure to pollutants in warmer months and hence, elevated temperature aggravated health impacts. Similar results are indicated by Qian et al. (2008) that correlated PM10 with temperature change and its impact in Wuhan city, China. The linkages between air pollution and temperature or UHI are not deeply investigated in Indian context. In 2012 Ali et al. (2012) explored the prevalence of groundlevel ozone in Delhi and Pune. McMichael et al. (2008) related PM with heat- and cold-related mortality in 12 cities of the world including Delhi. Tahir et al. (2002) correlated CO, CO2 , CH4 , HC, NO2 , SO2 , SPM and RSPM with transportation lines and human health. Unlike the vast literature available on characteristics, intensity and causes of UHI, the works on its impact are scarce. UHI phenomenon not only indicates the changes in heat budget of an urban area but also is useful in understanding the spatial pattern of spread of urban diseases caused by increase in temperature and air pollution. The most prominent study in this regard is by Lo and Quattrochi (2003) on Atlanta metropolitan area, Georgia, USA. Lo and Quattrochi (2003) try to understand the UHI phenomenon with respect to LULC change and its implications on human health in Atlanta Metropolitan Area, Georgia. Changes in LULC were analysed by using Landsat MSS and TM images for 1973, 1979, 1983, 1987, 1992 and 1997. Dramatic changes were noted with loss of forest and cropland to urban built up use. The lowdensity urban land use was increased by over 119% between 1973 and 1997. These changes had altered the land surface characteristics. An analysis of Landsat images revealed an increase in surface temperature and decline in NDVI values from 1973 to 1997 leading to the development of UHI effect at both the urban canopy and urban boundary layers. An increase in ground-level ozone was observed. Using canonical correlation analysis, surface temperatures and NDVI, extracted from Landsat TM images, were found to correlate strongly with VOC and NOx emissions. These are also significant ingredients of cardiovascular and chronic lower respiratory diseases. Similar methodology has been adopted by Tomlinson et al. (2011) leading to the parallel research results. Stone et al. (2010) compare the vulnerability of sprawling cities and compact cities to climate change and Extreme Heat Events (EHEs). The increasing frequency of EHEs was observed in large cities of the USA that are responsible for a greater annual number of climate-related fatalities. The study examines the association between urban form at the level of the metropolitan region and the frequency of EHEs over a five-decade period using sprawl index. It was found that metropolitan LULC is important tool to understand the heat related health effects of climate change. Takano and Nakamura (2001) present various indicators of urban environments for healthy cities. They aim to identify and categorize the various city indicators that are related to health levels, demonstrate the extent of influence on health of these categorized health determinants. By using city statistics of study areas, the health index and health determinant indices were formulated. The extent of influence of health determinants on the health index was examined by regression analysis; the

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interrelations between the health determinants and the health index were examined by correlation analysis. Health determinants are considered to include such factors as income and social status, education, employment and working conditions, access to appropriate health services and physical environment.

2.4 Study Area The present research compares the two largest cities of India. The rationale behind the selection of Delhi and Mumbai as the study area owes to many similarities and dissimilarities. The similarities between both cities are that these two attract large-scale in-migrations from nearby states and daily commuters; high pace of urbanization; high density of population; spurt in vehicular growth; and consequently large-scale LULC changes are taking place in both the cities. Due to inherent impacts of urbanization, these cities are going through drastic environmental changes imposing threat to human health. There are certain dissimilarities between Delhi and Mumbai and foremost are the locational and climatological conditions. While Delhi is located in interior of the country, it experiences extreme continental climate and Mumbai is a coastal city, and hence, the effect of land and sea breeze modifies/moderates the environment. While the city size of Delhi is 1,483 km2 , Greater Mumbai is only 603 km2 , but the actual range of these cities is much higher. As a result, the situation becomes complex and multiple factors work in determining urban environment. Due to these similarities and dissimilarities, the comparative analysis for LULC, air quality, UHI and urban health is selected for the present study.

2.4.1 Urban Environment of Delhi Delhi is the national capital, located in the northern Indian plains between 28°24 17 and 28°53 00 North latitudes and 76°50 24 and 77°20 37 Eastern longitudes. The population of Delhi increased from 0.7 million in 1947 to 16.75 million in 2011 mainly due to in-migration. The population of Delhi is projected to nearly double to 27.9 million by 2026 (Urban Health Resource Centre 2007; Census of India 2011b). With about 97% of population living in urban areas, Delhi has the highest percentage of urban population among all the States and Union Territories of India. Spreading over an area of 1,483 km2 , Delhi has 11,297 persons per km2 population density. The Census of India (2001) estimated a slum population of about 1.85 million in Delhi, which was 18.7% of Delhi’s urban population (Urban Health Resource Centre 2007). The total vehicle population of Delhi is more than the other three metropolitans. There has been phenomenal growth in vehicular traffic in Delhi since 1981. As against eight cars per thousand population of India, Delhi possesses 85 cars per thousand populations (Department of Environment and Forests 2010). The total numbers of

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vehicles in Delhi are recorded to be 0.7 million in 2011–2012 (Directorate of Economics and Statistics 2012). High vehicular growth has inevitable consequences in terms of accidents, pollution, commuting time and wasteful energy consumption. The primary fuel used in vehicles in Delhi is diesel. The use of CNG has increased in the past few years. There exists an acute air pollution problem in the capital city (Department of Environment and Forests 2010). Delhi has 31 industrial estates spread over an area of 4,647 acres, having more than 25,000 industrial units, which is an important root cause of environmental degradation. The land resources of Delhi are under stress owing to the pressures of rapid urbanization. Population growth, unplanned urbanization and haphazard industrial growth have contributed to uncontrolled changes in LULC. The urban area of Delhi has grown from 182 km2 in the 1970s to 750 km2 in 2001 leading to environmental degradation (Department of Environment and Forests 2010).

2.4.2 Urban Environment of Mumbai Mumbai is located on the western coast, i.e. Konkan coast of Maharashtra, India, between 18°53 –19°19 Northern latitudes and 72°45 –73° Eastern longitudes. Mumbai, also referred as Greater Mumbai, covers two revenue districts of the state of Maharashtra, viz. Mumbai city and Mumbai suburb covering 157 and 446 km2 area, respectively (603.4 km2 ). It experiences tropical savannah climate. The impact of land and sea breeze makes the climate moderate. Mumbai city and suburban districts together make up Greater Mumbai that had total population of 12.4 million in 2011 (Census of India 2011a). The density of the Mumbai suburban district was 20,925 persons per km2 , whereas the Mumbai city had 20,038 persons per km2 density (Census of India 2011a). The Mumbai megacity also known as the Mumbai Metropolitan Region (MMR) is the largest megacity in India with total population of 18.39 million. It includes two districts of Greater Mumbai, Ambernath, Badlapur, Kalyan and Dombivali, Mira and Bhayander, Navi Mumbai, Thane and Ulhasnagar. There is large proportion of commuting population in Mumbai, and therefore, transportation system is of key importance. Railway network, especially Mumbai Local, is the lifeline of Mumbai’s transportation system. The system carries more than 7.24 million commuters daily (Gardas et al. 2013). If annual ridership (2.64 billion) were taken into account, the suburban rail would be the second busiest rapid transit system in the world (Gardas et al. 2013). It has the highest passenger density of any urban railway system in the world. As per Government of Maharashtra (2007), total number of passengers in Central Railway (CR) main line, CR harbour line and Western Railway (WR) are 1.31, 0.828 and 1.4 million daily, respectively. The daily trips by CR main line, CR harbour line and WR are 658, 414 and 923 trains, respectively (Government of Maharashtra 2007). The total number of daily passengers using the bus services is estimated as 4.12 million (Government of Maharashtra 2007). Private vehicles are increasing in Mumbai. Due to increasing air pollution load

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in the city, steps like use of CNG in public vehicles and shifting or closure of industries were undertaken by the government. According to Mumbai Pollution Control Board (MPCB), there are about 7,850 industries in Mumbai region. The number of factories in the region, however, is declining. Due to increasing air pollution, many textile factories were closed down by the state government in the 1980s–90s.

2.5 Research Questions The study includes following research questions: 1. Is there any change in urban environment of Delhi and Mumbai? 2. Has changing urban environment led to formation of urban microclimates, i.e. urban heat islands? 3. Is there any link between changing urban environment, heat islands and health?

2.6 Objectives The study has following objectives: 1. 2. 3. 4.

To understand the changing urban environment in megacities. To identify the urban heat island due to changing urban environment. To assess the urban health risk due to urban heat island. To prepare a strategic plan for urban health and wellbeing for the Indian megacities.

2.7 Data Collection and Methodology Objective 1 This objective is to assess changes in physical environment of cities of Delhi and Mumbai over the period of 1990–2010. Changes of LULC categories (built up, agricultural, forests lands, etc.) and air quality have been considered as indicators of physical environment. LULC mapping was done for three time periods (1993, 2000, 2010 for Delhi and 1991, 2003, 2010 for Mumbai) using Landsat TM and ETM+ satellite images. The satellite images were downloaded from the websites of US Geological Survey (USGS) http://earthexplorer.usgs.gov/. Necessary image corrections methods (geometric, radiometric corrections-Digital Number (DN) to reflectance conversion, etc.) were applied based on Chander et al. (2009), thereafter supervised classification method was applied for LULC mapping using Erdas

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9.3. Ground verification was also done with the help of Global Positioning System (GPS). Thereafter, cross-classification of three LULC maps was attempted in order to estimate the changes over the period of time. An attempt was made to assess potential driving forces (population growth) of LULC changes. Population growth is statistically represented through diagrams (Fig. 2.3). Air quality has been assessed on the basis of amount of SO2 , NO2 , SPM and RSPM present in the air. Therefore, records of SO2 , NO2 , SPM and RSPM were collected for all possible locations in Delhi and Mumbai so that these represent all types of LULC classes and for time periods akin to LULC change analysis. Records of air quality were collected from CPCB for Delhi (nine stations) and MPCB for Mumbai (three stations). Of the nine stations in Delhi, six (Pitampura, Sarojini Nagar, Town METHODOLOGICAL FRAMEWORK

Objective 1: To understand the changing urban environment

Land use/cover change detection

Land use/cover change detection (crossclassification)

Objective 2: Identify the Urban Heat Island

Air Quality change analysis

Plotted SO2, NOx, SPM, RSPM data for all stations (1990-2011)

Landsat TM and ETM+ satellite imagery collected for 1993, 2000, 2010 (Delhi and Mumbai)

Surface temperature mapping

NDVI and NDBI mapping

Objective 3: Assess urban health risk

Random household survey on diseases caused by air quality change and temperature increase

Correlated mortality data with pollutants

Correlate the surface temperature with NDBI and NDVI Population growth as

Vehicular growth

Regression applied

factor of land use/cover change

as factor of air quality change

between air pollution

Urban environment and urban health and wellbeing correlated

Fig. 2.3 Methodological framework

and diseases

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Hall, Nizamuddin, Janakpuri, Siri Fort) are located in residential areas and three (Shahdara, Shahzada Bagh, Mayapuri) in industrial areas. Besides, the data for ITO was not available, which is a traffic junction type observatory. Of the three stations found in Mumbai, two (Bandra–Worli and Kalbadevi) are residential and one (Parel) is located in industrial area, where as the data for traffic junctions (Sion and Mulund) was not available. As for the Bandra–Worli station, during 1990 to 1999 station was located at Bandra and post-2000 was shifted to Worli. Analysis of air quality was based on permissible limits set by CPCB, India. The limits have been set in view of human health, which are different in residential and industrial area. The study also attempts to analyse factors leading to changes in air quality such as number of vehicles. The number and growth of vehicles was collected from Regional Transport Offices (RTO) and websites of transport departments. Objective 2 The objective assesses the LST regime/patterns, usually known as UMC or UHI. The microclimates of Delhi and Mumbai were mapped using Erdas Imagine. The Landsat TM and ETM+ sensors (6th band) sense emitted energy from the earth surface that is used to map temperature/thermal properties of the surface. Also, the Landsat sensors cross equator at about 10 am, and thus, the thermal properties of surfaces extracted from images of TM and ETM+ sensors can be directly compared. The patterns and changes of surface temperature were mapped following Chander et al. (2009), Mallick et al. (2008), Landsat 7 Science Data Users Handbook of NASA (2003), Murayama and Lwin (2010) and Sahin et al. (2011). The LST estimation followed three steps, (1) Conversion of DNs (pixels) to spectral radiance, (2) Conversion of spectral radiance to temperature and (3) Conversion of temperature in Kelvin scale to Celsius scale. The detailed methodology is explained in Chap. 5. Patterns of surface temperatures are considered to be closely associated with LULC. Thus, an attempt was made to assess spatial correlations between LULC and surface temperatures. The NDVI that represents vegetation greenness, stress or density and NDBI that represents density of built up land (Xu 2007) was also mapped using Erdas Imagine. Further, the surface temperatures were spatially correlated in order to understand their relationships. The NDVI using Landsat image is calculated using following equation (Lo and Quattrochi 2003): (NIR − R)/(NIR + R) where, NIR = band 4 and R = band 3 NDBI using Landsat image is calculated as (Chen et al. 2006; Xu 2007) following: (Band 5 − Band 4)/(Band 5 + Band 4) where, MIR = band 5 and NIR = band 4. The images of NDVI, NDBI and surface temperature are of different spatial resolutions, e.g. 30 m for NDVI and NDBI and 60 and 120 m for images of surface temperature of ETM+ and TM, respectively. Thus, images of surface temperature

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were resampled to the spatial resolution of NDVI and NDBI, i.e. 30 m so that they can be spatially correlated with greater accuracy. Objective 3 This objective assesses urban health risk due to Urban Heat Island. Poor air quality is a health hazard that worsens at times of high intensity of UHI, i.e. during summers. It can cause chronic lower respiratory and cardiovascular illness and lead to permanent damage of lungs and can worsen bronchitis, heart disease, emphysema and asthma. To understand the linkages between changing environment and human health, pollutants and mortality were correlated in Delhi and Mumbai. For the objective, the death records and its various causes were collected for government departments and websites. The main health data collected includes deaths due to diseases of circulatory system, respiratory system, other diseases, e.g. respiratory TB, cancer and whooping cough. It was further analysed based on age and gender characteristics. The diseases of respiratory system are divided into three groups, viz. URT, LRT and ORD. Detailed classification of the diseases system is mentioned in Chap. 6. Further, household survey on socio-economic differences in pollution and temperature induced disease occurrence was conducted representing population from different income groups, age and gender. A total of 145 households were surveyed in both the cities (75 in Delhi and 70 in Mumbai). A total of three field visits to Delhi and three to Mumbai were made to conduct primary survey. Selection of households was based on random sampling. Equal consideration was given to gender, age, different types of households (income), etc. Questions were particularly asked about health, lifestyles, living conditions and means of transportation, exposure to environment, and access to health services, suggestion, recommendations and gaps in government policies. The results were represented through diagrams and maps. Doctors related with concerned disease were also consulted to understand as to from which area and income group they receive the patients. Objective 4 Cities require efficient and comprehensive strategic planning for their sustenance as healthy city. Thus, based on present study, a strategic plan was prepared. The plan deals with green spaces, built up land with special focus of suburb areas, mitigations of UHIs and disease. The objective primarily reviews the government plans, policies, environmental acts, their success and failures. Further, the suggestions and recommendations have been made for the strategic plan for sustainable city. The local people’s perceptions have also been considered and interpreted for the plan.

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2.8 Limitations The sound reliable data availability is a critical component of research work. In case of the present work, both the primary and secondary data has been used. The lacuna regarding air pollution was that pollution data for ITO was not available. Regarding the health data, morbidity data on possible health effects of air pollution on human health are not recorded by any agency, and hence, the strong correlations may be an understatement. For surface UHI identification, Landsat satellite images were used. However, image quality is a crucial element in selection of images. For Mumbai, there were not usable images from May to October due to heavy cloud cover. However, in spite of drawbacks, the research tries to overcome these. The means of conjoining urban economic growth, parity in health services and sustainable urban environment shall be the main aim of urban areas. The present state of urban environment and health status in Delhi and Mumbai is dealt herewith as part of the key findings.

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Chapter 3

Geographical Background: Delhi and Mumbai

Abstract The third chapter deals with detailed description of study area, i.e. megacities of Delhi and Mumbai. It discusses the geographical locations of cities, early history of cultural evolution, physiography including elevation and slope, drainage and water resources, climatic conditions, status of forests and tree cover, population trends, density, literacy and sex ratio, age-sex composition, poverty, transport network and vehicles growth, population health status, growth of industries, brief description of land use and environment, natural and human-made disasters and hazards, slums, etc. Keywords Physiography · Population · Transport · Disasters · Delhi · Mumbai

3.1 Introduction The study area comprises two most populous cities of India, viz. Mumbai and Delhi. These are ever-evolving centres of political, economic, social and cultural activities in India. New Delhi is the national capital of India while Mumbai is the financial and commercial capital. Delhi is also the political, judicial and administrative centre of the country. On the other hand, Mumbai harbours major domestic and international banks and the Bombay Stock Exchange (Pacione 2006). It is also the largest port of western India, hub of petrochemicals industries, Indian film industry and is also the capital of Maharashtra state. The two cities stand unique with respect to population characteristics. There is 11.03 million urban city population (97.5%) of the total 16.7 million population in NCT of Delhi (16.3 million is urban population of Delhi Metropolitan Region) (Census of India 2011b). With over 12 million population, Mumbai is 100% urban (18.39 million is the population of MMR) (Census of India 2011a). Fast pace of urbanization process has caused manifold impacts on the city structure. Mumbai, in true sense, is representative of the cosmopolitan city. It is a melting pot of many cultures and social phenomenon. Delhi has observed multiple layers of immigration from various parts of India during both pre- and post-independence era. However, the process of becoming a megacity has not been well-planned and hence the growth and development have led to straining of natural resources. The exceptional natural, © Springer Nature Singapore Pte Ltd. 2020 A. Grover and R. B. Singh, Urban Health and Wellbeing, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-13-6671-0_3

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physical and socio-economic alterations have jeopardized the sustainability of the city. Urban environments are likely to face multiple challenges associated with growth of slums and squatter settlements, food shortage, environmental degradation and lifestyle-related problems. These complex urban changes have strong bearing on the human health that is difficult to forecast. In light of the prevalent urban environments in the city, Delhi and Mumbai are apt and ideal locations for understanding the inter-linkages between changing urban environments and its impact on health and wellbeing.

3.2 Geographical Location The selected area for Delhi is the administrative city of Delhi (that includes Delhi Metropolitan Region) and Greater Mumbai (Mumbai city and suburban districts) (Fig. 3.1a, b). Delhi Delhi (National Capital Territory of Delhi) is located in the northern India between 28°24 17 and 28°53 00 North latitudes and 76°50 24 and 77°20 37 Eastern longitudes (Fig. 3.1a). It occupies an area of 1483 km2 and is bounded by the neighbouring states of Haryana and Uttar Pradesh. Its maximum length and width are 51.90 km and 48.48 km, respectively (NIDM 2014). For administrative purpose, Delhi is divided into 9 districts and 27 tehsils or sub-districts. It also enjoys the status of Union Territory of India. There are three types of statutory towns in Delhi, which are: Municipal Corporation of Delhi (MCD), New Delhi Municipal Council (NDMC) and Delhi Cantonment Board (DCB). As per 2011 Census, there are 112 villages in Delhi (Fig. 3.2). The Survey of India toposheets 53D and 53H represent Delhi, while the path and row of Landsat satellite image that covers Delhi are 146 and 40, respectively. In 1985, the Planning Commission with the objective of creating countermagnets around Delhi to reduce the pressure on the city identified the planning region named National Capital Region (NCR). The NCR having area of 46,208 km2 covers 9 districts of National Capital Territory (NCT) of Delhi, parts of Haryana, Uttar Pradesh and Rajasthan. There are 13 districts from Haryana, namely Bhiwani, Jind, Karnal, Mahendragarh, Palwal, Faridabad, Gurugram (formerly known as Gurgaon), Rohtak, Sonepat, Rewari, Jhajjar, Mewat and Panipat; Alwar and Bharatpur districts from Rajasthan and 7 districts from Uttar Pradesh, namely Hapur, Muzaffarnagar, Meerut, Ghaziabad, Gautam Budh Nagar, Bulandshahr and Bhagpat (NCR Planning Board 2013). Mumbai Mumbai is located on the western coast, i.e. Konkan coast of Maharashtra, India, between 18°53 and 19°19 Northern latitudes and 72°45 –73°00 Eastern longitudes

3.2 Geographical Location

Fig. 3.1 Location of a Delhi and b Mumbai in India (Background images are Landsat TM)

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Fig. 3.2 Statutory towns as representation of urban sub-divisions of Delhi, 2011

(Fig. 3.1b). The 1:50,000 scale toposheets numbered C 132/29, 30, 35 and 36 correspond to the region while 47 B and F on 1:250,000 scale. The Landsat 5 TM images of the study area bear the path and row of 148 and 47, respectively. Mumbai is approximately 11 m above the msl (Government of Maharashtra 2015; Kumar and Hingane 1988) surrounded by Arabian Sea on the south and west, Thane district and Vasai creek on the north, Thane creek on the east and south-east. The study area was primarily spread over seven islands. Successive reclamation of swampy and marshy lands lying between the islands has made it a larger island (Grover and Parthasarathy 2012). Present study area of Mumbai (Greater Mumbai) covers two of total 35 districts of the state of Maharashtra, called the Mumbai city and suburb covering 157 km2 and 446 km2 area, respectively (603.4 km2 ) (MCGM 2010a). Greater Mumbai has east-west extent of about 12 km and north-south of about 40 km (Government of Maharashtra 2007). Together the two revenue districts of Mumbai city and suburban

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67

district are called Greater Mumbai, which come under one civic body, Municipal Corporation of Greater Mumbai (MCGM). It accounted for 0.2% of total area, 12.36% of total population and 29.2% of total urban population of Maharashtra, as per 2001 census data (Municipal Corporation of Greater Mumbai 2010b). Prominenet places in Mumbai City Island are Colaba, Cuff Parade, Churchgate, Mumbai Central, Breach Candy, Byculla, Parel and Mazagaon. The Mumbai suburban district encompasses Dadar, Dharavi, Bandra, Andheri, Kurla, Chembur, Powai, Ghatkopar, Santa Cruz, Gorai and Mankhurd (Fig. 3.3). For administrative purposes, Greater Mumbai is divided into 7 zones and 24 wards named from A to T. The suburban district is divided into Eastern and Western suburb. In the present research, as in most texts, Greater Mumbai is synonymously referred as ‘Mumbai’ unless city and suburban districts are referred separately (Table 3.1). The extension of the city beyond Greater Mumbai refers to the regions from nearby districts known as Navi Mumbai (3917 km2 ) and collectively called the Mumbai Metropolitan Region (MMR). The total area covered by the MMR is 4355 km2 . The Navi Mumbai extends to Thane district comprising of Thane, Kalyan, Bhiwandi, Ulhanagar tehsils and part of Vasai tehsil. Some parts of Raigad district comprising of Uran tehsil, part of Panvel, Karjat, Khalapur, Pen and Alibaug tehsils also encompass the Navi Mumbai. Navi Mumbai consists of seven Municipal Corporations (Greater Mumbai, Kalyan-Dombivili, Navi Mumbai, Thane, Ulhasnagar, Bhiwandi and Mira-Bhayandar) and 13 Municipal Councils (Alibaug, Ambernath, Karjat, Khopoli, Kulgaon-Badlapur, Matheran, Nallsopara, Navghar-Manikpur, Panvel, Pen, Uran, Vasai, Vihar) (Indira Gandhi Institute of Development Research 2014).

3.3 Early History and Cultural Evolution Delhi The history of the city of Delhi is rooted in the Mahabharata times. As history records, Delhi was founded by the Pandavas that was named as Indraprastha. Later the Rajput Tomars named it Lal Kot followed by the conquest of Chauhans, when it came to be known as QuilaRaiPithora. Over the period, the city was destroyed and rebuilt by consequent waves of conquerors. This included ‘Siri’ built by Allaudin Khilji, ‘Tughlaqabad’ built by Ghiyasuddin Tughlaq, ‘Jahapanah’ built by Muhammad bin Tughlaq, ‘Kotla Firoz Shah’ by Firoz Shah Kotla, ‘Dinpanah’ by Humayun and finally ‘Shahjahanabad’ around the Red Fort by the Mughal Emperor, Shahjahan. The last city of Shahjahanabad still exists (Old Delhi) a reminder of the past of Delhi. The Britishers took over Delhi from the Mughals in 1857 and in 1911 shifted the capital from Calcutta (presently Kolkata) to Delhi. The modern city of Delhi was conceived and planned in the colonial period by Edwin Lutyens, presently known as New Delhi. The New Delhi became the capital of India in 1911 and was inaugurated in 1931 by the Britishers.

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Airport Creek Sanjay Gandhi National Park Important locations

Fig. 3.3 Important locations in Greater Mumbai (Background image is Landsat TM). Source Based on locations taken from Google Earth (Background images are Landsat TM)

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Table 3.1 Administrative divisions of Greater Mumbai Districts of Greater Mumbai/Mumbai

Area (km2 )

Zone number

Number of wards

Ward names

Mumbai city district

157

1

5

A

Fort

B

Dongri

C

Marine Lines

D

Grant Road

2

Mumbai suburban districta

446

3

4

5

6

7

4

3

3

3

3

3

E

Byculla

F/S

Parel

F/N

Matunga

G/S

Elphinston Road

G/N

Dadar

H/Eb

Khar-Santacruz

H/Wb

Bandra

K/Eb

Andheri East

K/Wb

Andheri West

P/Sb

Goregaon

P/Nb

Malad

Lc

Kurla

M/Ec

Chembur East

M/Wc

Chembur West

Nc

Ghatkopar

Sc

Bhandup

Tc

Mulund

R/Sb

Khandivili

R/Nb

Dahisar

R/Cb

Borivalli

Source Municipal Corporation of Greater Mumbai (2010b) (a Sub-divisions of Mumbai suburban district are: Western suburban wards b : H/E, H/W, K/E, K/W, P/N, P/S, R/C, R/N, R/S; Eastern suburban wards c : L, M/E, M/W, N, S, T)

The population of the city increased by more than 200% in three decades from 1911 to 1941 (Kumar 2013). Further, from 1941 to 1951, the population increased to more than double as a result of influx of refugee population from Pakistan to India. The sudden unexpected influx of people strained the physical infrastructure and resources. In terms of area, urban Delhi expanded from 43.25 km2 in 1901 to 624 km2 in 1961 and 792 km2 in 2001 (Kumar 2013). The spatial expansion of the city took place in almost all directions. Keeping in mind the haphazard developments, Delhi Development Authority (DDA) was created in 1957 and in 1962 the first master

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plan of Delhi was enforced. In 1997, the NCR was carved out as a planning region covering Delhi and parts of nearby states. Of the total area of the city, the MCD occupies 1397.29 km2 area and rest is shared between NDMC and DCB (Firdaus 2012). Mumbai In the mid-sixteenth century, the islands of Bombay were passed on to the Portuguese from the Muslim domination. Under the Portuguese rule, the island remained largely rural and undeveloped. The Bombay Island was a group of seven separate islands separated by tidal marshes (Pacione 2006). The southern-most long-narrow island was called Colaba (Fig. 3.3). To the north of Colaba was the Old Women’s Island (called ‘Al Omani’’ in Arabic meaning deep-sea fishermen). Further north was the largest island of the group called Mumbai (derived from Mumba Devi temple) that was called Bombaim by the Portuguese and Bombay by the British. The western part had two islands of Worli and Mahim while Mazagaon island and an island occupied by the Sewri (Wadala-Sion region) existed in the east. The total length of the island in the 1670s was estimated at 8 miles (12.87 km) and circumference of 20 miles (32.18 km) (Kosambi 1986). In 1661, as a matrimonial and military alliance, the island with all rights, profits, territories, income, revenue and appurtenances was gifted to the Britain’s Charles II (Kosambi 1986; Tindall 1982). In 1667, the commercially unproductive island was transferred to the East India Company that owes the indictment of its growth and prosperity. The British nurtured Bombay and, in 1684, East India Company’s western headquarters were shifted from Surat to Bombay. During the industrial revolution in Britain, raw cotton from Gujarat was exported from Bombay to the cotton mills in Manchester. Other than cotton, opium collected from Malwa region and Gujarat was exported to China. The regional politics led to repositioning of Bombay from periphery to centrality in the mainstream of Indian politics. In 1858, the East India Company’s role ended and its territorial possessions were passed to the British Crown hence, British Empire. This led to the formation of Bombay Presidency that consisted of Konkan, Deccan, Gujarat, Sind and Karnataka divisions. Massive growth took place in the Bombay Presidency during the nineteenth century. This was bestowed on the growing ship-building industry and booming cotton exports. In 1853, the first railway track was laid in the country from Bombay to Thane that was later extended to Pune. Soon an extensive railway network was built between Bombay and other cities of the sub-continent (Kosambi 1986). With the spurt in economic developments, the land reclamation process also gained momentum. The Bombay Municipal Corporation (presently BrihanMumbai Municipal Corporation—BMC) was created in the 1980s and made responsible for drinking water, public health, hygiene, safety, education, drainage, local transport and other amenities. The opening up of the Suez Canal in 1869 made Bombay closer to Europe, which enhanced its status and prospects. This made Bombay the ‘Gateway of India’ with respect to the overseas communication from the west. Mumbai grew as a port and as a consequence of trade by the British East India Company, the population grew from 10,000 in 1661 to 60,000 in 1675 (Pacione 2006). The population of

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71

Bombay was 644,405 in 1872 and was the largest city of the Indian sub-continent. It proudly hailed as the ‘urbs prima in Indis’ and was the second-largest city of the British Empire after London. This was followed by Calcutta and Madras. But by the twentieth century, it became the second-largest city as Calcutta took over as the largest city of India (Kosambi 1986). The primacy of Bombay in the Western India is unparallel since the establishment of the colonial rules in the first half of the nineteenth century. In 1872, Bombay was five times larger than both Ahmedabad and Pune, the next largest cities in the Bombay Presidency. Even after independence, the same pace of growth predominated and persisted. Mumbai still remains a major maritime, commercial, industrial and financial centre of national importance. Mumbai holds variety of government, private and international headquarters. It accommodates the Reserve Bank of India, Bombay Stock Exchange, many banks and insurance companies; Bollywood and many business tycoons, viz. the TATA and Reliance. It is also the largest port on the western coast handling passenger traffic and also trades in petrochemical. The prospering business of dubbawalas is well known in the world for their large fleet and meticulous management. The growing population is inevitably shifting north and eastward to the suburb. Later, the Bombay Metropolitan Region (BMR) was designated for regional planning taking a semi-circular shape from the mainland, i.e. planned as a countermagnet to the Bombay city duplicating its port and commercial–industrial functions (Kosambi 1986).

3.4 Physiography Delhi Physiographically, Delhi lies between Indo-Gangetic alluvial plains in the north and east, the Thar Desert in the west and the Aravalli Range in the south. It is generally a flat land except the Aravalli Range extension. On the basis of physiography, Delhi can be divided broadly into four broad categories. These are: Delhi (Quartzitic)ridge/Koli, older alluvium/Bhanger, younger alluvium/Khadar and alluvium deposits of Chattarpur enclosed basin/Daber or low-lying areas (Central Ground Water Board 2012; Firdaus 2012). The Delhi ridge or Aravalli ridge is a quartzitic ridge in nature that enters Delhi from the south-east, and extending up to western bank of River Yamuna near Wazirabad. The length of the ridge is nearly 35 km and the direction it follows is NNE–SSW (Central Ground Water Board 2012). The total area of the ridge is estimated to be about 86.9 km2 (Firdaus 2012). The area under the alluvial plain is almost flat. While the old alluvium (west of ridge) is found along the ridge, the new alluvium (east of ridge) is found along the flood plain of River Yamuna. Further, in the South Delhi, closed alluvium basin occupies region around Chattarpur. The Daber is the basin area in west Delhi between the South ridge and Bhanger in north.

72

3 Geographical Background: Delhi and Mumbai

This region is drier than the rest of area with evidences of wind erosion and deposition like deflation hollows and dunes (Central Ground Water Board 2012) (Figs. 3.4 and 3.5). The average height of the city ranges between 198 and 220 m above the msl (Rai 2011; Ali et al. 2012). Mumbai Mumbai is located on the Konkan coastal strip, which is one of the three major physical divisions of the State of Maharashtra. The other two physical divisions are Maharashtra Plateau and Sahyadri range. Konkan coast is a narrow strip of coastal land between the Sahyadri range and the Arabian Sea. There are four main creeks, i.e. Thane, Malad, Manori and Mahul Creek, which are characterized by mangroves and wetlands (Fig. 3.3). The Western Ghats range spreads north to south separating the coastal districts of Thane, Mumbai, Raigarh, Ratnagiri and Sindhudurg from rest of the State (Indira Gandhi Institute of Development Research 2014). Greater Mumbai is generally broad and flat except some peaks and hill ranges in the north (Fig. 3.6). The Mumbai Island is part of the Deccan Volcanic Province

Fig. 3.4 Elevation map of Delhi. Source Based on ASTER GDEM2 (https://lpdaac.usgs.gov/)

3.4 Physiography

73

Fig. 3.5 Slope map of Delhi. Source Based on ASTER GDEM2 (https://lpdaac.usgs.gov/)

and indicates past volcanic activities. The region represents the Deccan Basalts, volcanic tuffs, inter-trappean sediments, dykes, laterite and alluvium. The Eastern and Western ridges run in north-south direction on respective sides with lowlands between the ridges. While the Eastern ridge is characterized by basalt, pillow structures and red ash, the Western ridge consists of yellow-brownish ash and acid tuff (Rani et al. 2015). The small hills in Mumbai city are Malabar, Colaba, Worli and Pali. The Powai, Kanheri hills in Mumbai suburban district form the largest hilly terrain (Fig. 3.7) (Central Ground Water Board 2010). The coastal areas with low elevation and generally gentle slopes are characterized by mudflats or tidal flats, salt pans, sandy and rocky beaches. The tidal flats formed due to tidal flow are covered with salt water and are found along the Thane, Manori and Malad creeks. The prominent rocky beaches are Madh, Aska and Gorai. The sandy beaches on the western coast are Juhu, Gorai and Worli (Rani et al. 2015).

74

3 Geographical Background: Delhi and Mumbai

Fig. 3.6 Elevation map of Mumbai. Source Based on ASTER GDEM2 (https://lpdaac.usgs.gov/)

3.5 Drainage and Water Resources Delhi There are various water sources in Delhi, including river, lakes, traditional water resources (ponds) and groundwater. Surface Water The largest tributary of River Ganga is River Yamuna, which is the perennial river, flowing in southerly direction. The River Yamuna originates from Yamnotri glacier in Uttarakhand and enters Delhi from Palla. It flows through Wazirabad to reach Okhla. The total length of the river in the city is approximately 48 km (Firdaus 2012). During rainy season, large areas are covered with flood along the river course. The river is

3.5 Drainage and Water Resources

75

Fig. 3.7 Slope map of Mumbai. Source Based on ASTER GDEM2 (https://lpdaac.usgs.gov/)

not a very active eroding agent; however, it is gradually shifting eastwards and was about half a mile east along the Red Fort during Mughal times. The Aravalli ridge acts as a water divide between eastern and western Delhi (Rai 2011). Various researches on water quality of River Yamuna reflect the grim reality of over-pollution. The Najafgarh drain is the biggest drain and is estimated to contribute about 26% of total biochemical oxygen demand (BOD) load in the river (Central Pollution Control Board 2006). The Najafgarh drain was the freshwatercarrying tributary in Delhi, which has now become polluted and turned to be drain due to sewage water discharge. There are approximately 70 sub-drains, which join the Najafgarh drain adding to toxic pollutants in the River Yamuna. There are 30 sewage treatments plants in the city to treat the unclean water (Rai 2011). The primary water drainage basins are Najafgarh drain, Barapullah nala, Shahdara drainage area and Bawana drainage basin that directly drain into the River Yamuna.

76

3 Geographical Background: Delhi and Mumbai

The traditional water bodies also called kundi, baoli, hauz, step wells, bunds and ponds (johad) are also there in Delhi. In 1970, there were 807 water bodies spread over an area of 14.41 km2 that declined to 640 covering area of 8.51 km2 in 2008 (Singh et al. 2013). The spatial distribution of these water bodies (2008) reflects that North-West Delhi had maximum of 233 such water bodies followed by South-West Delhi with 214 water bodies. The richest source of water bodies was the North-West district (151) (Singh et al. 2013). Groundwater The Central Ground Water Board (CGWB) estimated the total groundwater potential to be 280 million cubic metres in 2008 as compared to 427.07 million cubic metres in 1983. This suggests that there has been sharp reduction in the groundwater resources in the recent past. The level of groundwater varies with changing geology, topography and consumption patterns. The main use of groundwater has been for drinking. Over the recent years, the quality of water has also deteriorated, resulting in the use of water for limited purpose only such as washing, livestock and agriculture. While along the Yamuna flood plain, the depth of freshwater is 35–45 m, the patches at Najafgarh and Alipur are shallow and highly polluted (Department of Environment and Forests 2010a, b). Mumbai Greater Mumbai is surrounded with the Arabian Sea making it rich in water resources. The city district has no natural drainage outlet making it susceptible to floods while the suburban district has four main streams named Mithi, Dahisar, Poisar and Oshiwara. Encroachments, siltation and reclamation have reduced the water-carrying capacity of these rivers. Mithi River with 17.9 km total length (11.8 km is MCGM and rest under Mumbai Metropolitan Region Development Authority—MMRDA) originates at the Sanjay Gandhi National Park (SGNP) and flows southward via Vihar and Powai lakes. It enters the Mahim Creek joining the Arabian Sea. The Dahisar River flows westward having total length of about 12 km originates at Tulsi Lake and joins Manori Creek. The Poisar River also originates from the SGNP near Krantinagar, Kandivali East. The total length of the river is 7 km with its mouth at Malad Creek. Oshiwara River too is nearly 7 km in length. It originates at Aarey Colony, Goregaon East and terminates into the Malad Creek (MCGM 2010a). There are numerous creeks around the city, e.g. Thane creek in the west, while Malad and Manori creek in the east. The water supply from six main sources, namely Vihar, Tulsi, Tansa, Modak Sagar, Upper Vaitarna and Bhatsa is 3950 million litres per day (www.bcpt.org.in). In 2021, the total water demand is expected to be 6382 million litres per day. There are three major lakes in Mumbai, viz. Powai, Tulsi and Vihar. The Vihar and Tulsi Lakes provide drinking water to many areas but the Powai Lake is polluted. Powai Lake is a human-made lake that was built in 1891 (Salaskar et al. 2008) (Fig. 3.8).

3.6 Climate

77

Fig. 3.8 View of Powai Lake from Indian Institute of Technology, Bombay

3.6 Climate Delhi Due to the inland location, Delhi experiences extreme continental climate. It has ‘semi-arid type’ climate, wherein extreme dryness and intense temperatures are recorded in summers and winters are severely cold. The year can be divided into four seasons: winter season (November to March), summer season (March to June), monsoon season (July to September) and post-monsoon season (October to November). The temperature ranges from a minimum of 4 °C in January and maximum of 41 °C in May–June (Roy et al. 2011). Delhi receives average annual rainfall of 714 mm. It receives rain from both southwestern monsoon branch in summers (Bay of Bengal branch and Arabian Sea branch) and north-western monsoon branch in winters, i.e. western disturbances. Over 80% of the annual rainfall takes place in the summer monsoon season (June–September). January is recorded to be the coldest month of year and June observes highest temperature. The humidity level is generally low during the greater part of year, except monsoon months of July, August and September. Highest humidity is recorded in monsoon season while April and May are the driest months. Winds help in redistribution and or moderating the temperatures in the city. It is necessary to allow larger movement of winds in order to maintain the city temperatures. The winter months experience light cold waves from the North, whereas, in summers, strong hot winds called ‘loo’ blow. Thunderstorms or dust storms may occur in the evening in city usually in the summers. Mumbai Mumbai, being a coastal city, experiences the ‘tropical savannah climate’ (Rani et al. 2015). Mumbai receives heavy rainfall from south-west monsoon winds during

78

3 Geographical Background: Delhi and Mumbai

summer months. The Maharashtra state is divided into nine agro-climatic zones on the basis of rainfall, soil type and vegetation. Mumbai falls in the North Konkan Coastal Zone having high rainfall, non-lateritic soil, 98% humidity in rainy season and 60% humidity in winter season. The relative humidity ranges between 47 and 86% while the average annual rainfall is recorded as 2607 mm. Soil is coarse and shallow, acidic, rich in nitrogen and poor in phosphorus and potash (NIDM 2016). The temperature ranges from 21.5 to 35 °C (daily or seasonally). The climate is moderate as a result of the influence of Arabian Sea and land–sea breeze. The wind direction is south/south-west in monsoons and north/north-east in winter season (Anonymous 2003).

3.7 Natural Resources 3.7.1 Forest and Tree Cover Delhi The Delhi is endowed with rich forests and tree cover, diversity of flora. As per the Forest Survey of India (2011), 176.20 km2 area is under forest cover, which is 11.88% of the total geographical area of Delhi (Table 3.2). The forest and tree cover in Delhi has seen steep rise since 1999 (Figs. 3.9 and 3.10). From merely 22 km2 in 1993, it has risen to over 299 km2 in 2009 (Planning Department of Delhi 2013). The density of vegetation in Delhi is low and therefore maximum area is under open forests (119.96 km2 ). Nearly 49 km2 area is under moderately dense forest while only 6.76 km2 is under very dense forest cover (Forest Survey of India 2011). The vegetation in Delhi is mainly tropical thorny type. The natural species comprises of Kikar, Soobabul, Khor, Plas, Shisham, Bur, Pipal, Gular, Tun, Siras and Palm trees. Table 3.2 Districtwise distribution of forest cover in Delhi (2011)

Districts

Forest cover (km2 )

Per cent of total geographical area

Central

05.05

East

02.99

04.67

North-East

04.10

06.83

North-West

16.49

03.75

New Delhi

16.31

46.60

North

04.82

08.15

South-West

41.80

09.93

South

78.32

31.33

West

06.33

04.91

Total

176.2

Source Forest Survey of India (2011)

20.20

11.88

3.7 Natural Resources

79

Fig. 3.9 Growth of forest and tree cover in Delhi (1993–2009). Source Planning Department of Delhi (2013)

Fig. 3.10 Tree cover along the roads in Delhi

Common trees found in Delhi are Amaltas, Kadamb, Peepal, Bargad, Jamun, Ashok, Neem, Babool and Gulmohar (Department of Forests and Wildlife 2013). In recent decades, new plant species have been introduced. Many imported varieties of trees are planted in the city for shade and ornamental purposes, e.g. pine planted in Raisina (Firdaus 2012). The popular medicinal plants grown in the city are Bale, Amaltas, Imli, Falsa, Neem, Sarphonk and Gilou (Firdaus 2012). The spatial distribution of forest cover suggests that South and South-West Delhi have good area under forest cover. Apart from forest and tree cover, there are about 15,000 parks of different sizes (Khera et al. 2009). The Forest Survey of India (2011) lists the 12 old city forests in Delhi, namely Sultanpur, Alipur, Nasirpur, Hauzrani, Mitraon, Ghoga, Garhi Mendu, Shahapur Garhi, Bawana, Jindpur, Mukhmel pur and Mamur pur (Fig. 3.11). The total area covered under these 12 old city forests is about 6.7 km2 . The report mentions that apart from these, there are 30 newly created forests in Delhi.

80

3 Geographical Background: Delhi and Mumbai

Old city forest Wildlife Sanctuary Important locations Airport

Fig. 3.11 Location of old city forests and other important places in Delhi (Background image is Landsat TM). Source Based on locations taken from Google Earth; Forest Survey of India 2001, 2011 (Background images are Landsat TM)

The Delhi ridge covers about 77.84 km2 of area and has been notified as Reserve Forest. There are five sections of Aravalli range in Delhi. These are Northern ridge, Central ridge, South Central ridge, Nanakpura South Central ridge and Southern ridge. The Southern ridge covers maximum area (Forest Survey of India 2011). The Asola wildlife, sanctuary established in 1992, lies in the south Delhi district and covers an area of about 68.00 km2 (Forest Survey of India 2011). The century lies on the Southern ridge and about 23 check dams have been constructed to store rainwater in Asola wildlife sanctuary. Mumbai As per the Forest Survey of India (2001), the total forest cover in Mumbai city and suburb was 0.64 and 18.39% of the total geographical area, respectively (Department of Environment and Forests 2001). This increased to 1.27% for the city and 26.91

3.7 Natural Resources

81

for Mumbai suburban district by 2011 (Forest Survey of India 2011). Mumbai has one national park, i.e. SGNP, which is located within the administrative boundary of the city. The SGNP is located in northern parts of the Mumbai suburban district, covering an area of 103 km2 (Zerah 2007). The SGNP was called Borivilli National Park prior to 1981 and earlier than 1974 known as Krishnagiri National Park. The park dates back to fourth century BC when the total area was 20 km2 , which later expanded to 103 km2 in 1969 (MGCM 2010a). The SGNP caters to environmental and historical value, as it encloses 104 Buddhist caves (including Kanheri caves) and 3 sacred groves that attract a half million visitors every year (Zerah 2007). The park plays an important role in maintaining the ecological balance of the city by acting as a major site for carbon sink. The park is rich in biodiversity containing more than 1000 species of plants, 59 mammals, 52 reptiles, 13 amphibians, 250 birds and 115 butterfly species (MGCM 2010a). Apart from this, there are 209 gardens, 293 playgrounds, 356 recreational grounds, 25 parks and 64 open spaces. Maximum number of green areas was found in Ward P/N, i.e. Chembur East (122) followed by R/C—Ghatkopar (104) while the minimum number was in H/E ward—Andheri East (17) and B ward—Dongri (20) (Municipal Corporation of Greater Mumbai 2010b). The city is also rich in wetlands and mangroves (Fig. 3.12). The mangroves can be mainly sited along the Vasai creek, Thane creek, Manori creek, Malad, Mahim-Bandra, Versova and Siwari. The mangroves have social, economical and environmental significances.

Fig. 3.12 Mangrove trees along Thane Creek in Mumbai

82

3 Geographical Background: Delhi and Mumbai

They are, however, threatened by encroachers and developers. At the same time, they are crucial for controlling coastal erosion, siltation and pollution, etc. It is estimated that 70% of Mumbai mangroves have been destroyed due to various developmental activities in recent decades. In 2001, merely 1 km2 area was under mangroves in the Mumbai city and 26 km2 in the suburban district as compared to 34 km2 in Raigad, and 47 km2 in Thane (Indira Gandhi Institute of Development Research 2014). Navi Mumbai still boasts of 50,000 m2 area under mangroves including mudflats.

3.7.2 Energy Resources Delhi The availability of energy is critical for economic growth. The installed capacity of electricity in Delhi has increased from 552 to 951 megawatts (MW) during 1991–92 to 2011–12 (Planning Department of Delhi 2013). The sale of energy in various sectors, viz. domestic, commercial, industrial, Delhi Metro Rail Corporation (DMRC)/railways/street lightning and other also observed significant rise from 15,984 million units in 2007–08 to 20,714 million units in 2010–11. Its annual growth rate was recorded as 8.5% in this period. The number of customers using electricity increased from 2.56 million to 4.3 million from 2002–03 to 2011–12 (Planning Department of Delhi 2013). The transmission networks in the city are mainly under Delhi Transco Limited. It consists of three 400 kV stations and twenty-nine 22 kV sub-stations. By 2012, the three main stations installed in Delhi were Indraprastha Power Generation Company Limited (Rajghat Power House and Gas Turbine Power Plants running on coal and gas, respectively), Pragati Power Corporation Limited (gas) and Pragati III Power Project at Bawana (gas). Apart from these, various new sub-stations are planned at Mundaka, Harish Chandra Mathur Lane, Peeragarhi, Wazirpur, Rohini II, Lodhi Road, Bawana Extension and Harsh Vihar (Planning Department of Delhi 2013).

3.8 Demography Delhi Delhi accounts for 1.38% of the total country’s population and its annual growth rate is 1.92% (Census of India 2011b). The population trend of Delhi indicates steep rise from 0.4 million in 1901 to 13.85 million in 2001 and to 16.75 million in 2011 (Fig. 3.13). The maximum decadal growth period (106.58%) and annual exponential growth rate (7.3%) was recorded in the 1951 census owing to the largescale in-migration from Pakistan to India. Delhi has recorded very high growth

3.8 Demography

83

Fig. 3.13 Growth of total and urban population in Delhi (in millions). Source Based on data from Planning Department of Delhi (2006), Census of India (2011b)

rate of population after 1951. Being a capital city, it has attracted large-scale inmigration from all over the country. Further, the United Nations Report projects the total population of Delhi urban agglomeration is likely to be 22.4 million by 2025 (United Nations 2007). The rise of population in Delhi is mainly observed in the urban areas. Rural–Urban Composition There is constant shrinking of rural area and population in Delhi. The total rural area in 1991 was 797.66 km2 that reduced to 369.35 km2 by 2011. On the contrary, the urban areas rapidly increased from 685.34 km2 in 1991 to 1113.65 km2 in 2011 (Planning Department of Delhi 2013; Census of India 2011b). One of the main causes of increased population concentration in Delhi is in-migration from neighbouring states to the city of Delhi. Since 1981, Uttar Pradesh is the largest contributor of population to Delhi (above 40%) followed by Bihar and Haryana (Planning Department of Delhi 2009). The average annual rate of migration was recorded as 2.18% in 1991–2001 visà-vis average annual rate of natural growth as 1.70%. The urban–rural population composition in 2011 was 97.49 and 2.5%, respectively (Directorate of Economics and Statistics 2012a). Density Population density is an indicator of population concentration and resultant pressure on land resources. The spatial pattern of population density is uneven with maximum densities found in the North-East district followed by Central and East Delhi district. While the average density was 274 persons per km2 in 1901, it increased to 4,194 (1981), 6,352 (1991), 9,340 persons per km2 in 2001 and 11,297 in 2011 (Directorate of Economics and Statistics 2014).

84

3 Geographical Background: Delhi and Mumbai

Literacy and Sex Ratio The literacy rate in Delhi has improved from 61.95% (1961) to 86.34% (2011). The sex ratio in the city remains skewed with slow and steady improvement. The sex ratio has slightly improved from 1901 (862) to 2011 (868) in Delhi, but it is still much lower than national average (943) (Table 3.3). Very low sex ratio of Delhi can be attributed to gender-biased in-migration from other states to Delhi. The migration is male dominant, mainly for employment opportunities. Age-Sex Composition The agewise composition of population suggests that 32.44% population was below 14 years in 2001 and nearly 10.68% were senior citizens (Census of India 2001). There is large proportion (about 43%) of population that is dependent and vulnerable to changes taking place in the city. Nearly, 57% population is adult population. Poverty The varied definitions and assessment methods of poverty have led to changes in the poverty levels over time. However, 49.61% of Delhi’s population was accounted to be Below Poverty Line (BPL) in 1973–74. This reduced to 26.33% in 1983, 12.41% in 1987–88, which in later years increased to 14.69% in 1993–94 and dipped to low of 8.23% in 1999–2000. Nearly 14.7% of the total population was BPL in 2004–05 (Planning Department of Delhi 2013). Overall, the poverty levels have declined in Delhi and India. It is interesting to note that the level of poverty remained lower than national average. Rural–urban difference provides further insight into poverty levels in Delhi. The poverty levels always remained low in urban Delhi as compared to rural Delhi. Although the income levels increased in rural and urban areas, it is to be noted that gap between rural and urban income too have increased over the period. Mumbai Population size of Maharashtra, according to 2011 census, was 112 million. Mumbai city district had total population size of 3 million in 2011, which is 2.80% of total population of Maharashtra. The Mumbai city and suburban district together had total population of 9.2 million in 1901 that rose to 12.4 million in 2011 (Fig. 3.14), whereas the MMR has a total population of 18.4 million. Mumbai megacity or MMR is the largest megacity of India (2011). As per 1971 census, almost after ten years of creation of the states of Maharashtra and Gujarat, Mumbai (earlier known as Bombay) was 3.4 times larger than Ahmedabad Urban Agglomeration (UA) and 5.3 times larger than the Poona UA. Thus, Mumbai proved to be a strong and powerful urban magnet for other states of the country. In 1981, 35% of Maharashtra’s urban population was concentrated in Greater Mumbai, whereas it was only 17% in 1901. The main reasons for this increase were economically generated. In 1960s, 30% of the tertiary employment, 75% of the industrial output and 66% of factory employment of the Maharashtra state were concentrated in Bombay (Kosambi 1986).

793a

862a

Sex ratio

733a

NA

1921–31

722a

NA

1931–41

715a

NA

1941–51

Source Department of Delhi (2000), Census of India (2011b) Note: NA states data Not Available

a Planning

NA

NA

Literacy rate

1911–21

1901–11

Census decades 768a

NA

1951–61

785a

61.95

1961–71

801a

65.08

1971–81

Table 3.3 Trend of literacy rate (per cent) and sex ratio (females per thousand males) in Delhi (1901–2011)

808a

71.94

1981–91

827a

75.29

1991–01

821

81.67

2001–10

868

86.34

2010–11

3.8 Demography 85

86

3 Geographical Background: Delhi and Mumbai

Fig. 3.14 Population growth of Mumbai (1901–2011) (in millions). Source Based on data from Ramachandra et al. (2014), Census of India (2011a)

The study area has experienced very high growth rate of population in the last century. Maharashtra recorded highest growth rate (27.45) during the 1961–1971. Thereafter, it has continuously declined. During the recent decade (2001–2011), it recorded about 15.99% growth rate. The Mumbai city and Mumbai suburban districts recorded highest growth rate during 1941–1951 and 1961–1971, respectively. The Mumbai city even recorded negative growth rate during 1981–1991 and 2001–2011 (Table 3.4). The negative growth rate of Mumbai may be attributed to the out-migration from city to nearby districts of Navi Mumbai and Mumbai suburban. On the other hand, Mumbai suburban district had 8.01% growth rate, which is lower than earlier decades. Greater Mumbai has no rural population and hence this growth was in urban areas only. In comparison, the decadal growth of Maharashtra from 2001 to 2011 was 15.99 with 23.67% in urban areas and 10.34% in rural areas. Density Population density of Mumbai city was 21,261 persons per km2 in 2001 making it the district with highest density in Maharashtra. This was followed by Mumbai suburban district (19,373 persons per km2 ). A decade later in 2011, the Mumbai suburban (20,925 persons per km2 ) occupied the top rank in terms of proportion of density in Maharashtra, whereas the Mumbai city slipped to the second rank with 20,038 persons per km2 . The state average is much lower than Mumbai city and Mumbai suburban districts, i.e. 315 in 2001 and 365 in 2011 (Census of India 2011a). Rural–Urban Composition In 2011, the country had 68.84% population living in rural areas and 31.16 urban areas. Maharashtra state portrayed inverse pattern with 57.57% urban and 42.43%

10.74

23.79

23.79

Maharashtra

Mumbaia (suburban)

Mumbaia

20.17

1.26

1.26

14.91

−2.91

20.17

1921–31

1911–21

Source Census of India (2011a) a Mumbai suburban district was created in 1951 Census

(city)

1901–11

Census Decades

28.87

28.87

11.99

1931–41

Table 3.4 Decadal variation of population (1901–2011) (in percent)

66.23

66.23

19.27

1941–51

19.02

107.41

23.6

1951–61

10.77

110.14

27.45

1961–71

6.99

70.97

24.54

1971–81

27.99 5.14

−3.35

22.73

1991–01 36.15

25.73

1981–91

−5.75

8.01

15.99

2001–11

3.8 Demography 87

88

3 Geographical Background: Delhi and Mumbai

rural population. Of the total 35 districts, two districts constituting the Greater Mumbai had 100% population residing in urban areas (Census of India 2011a). Sex Ratio The Mumbai city and Mumbai suburban district have poor sex ratio as compared to state average. (Census of India 2011a) (Table 3.5). Overall, since 1901, the sex ratio has gradually declined in Maharashtra. However, the Mumbai city and Mumbai suburban districts have experienced an improvement in the status of sex ratio. The overall sex ratio of both the district is very low, which is even lower than Delhi (868). The 2001 census data on wardwise sex ratio reveals large gap among the 24 wards with ward C (Marine Lines) with minimum sex ratio (587 females per thousand males) and T and H/E (Mulund and Khar-Santacruz) having maximum sex ratio (894 females per thousand males) (MCGM 2010b). Literacy Rate Over 80% of total population in Maharashtra is literate with 89.82% male literacy and 75.48% female literacy. Mumbai suburban and Mumbai city have higher literacy rate than state average with 90.90 and 88.48%, respectively. With respect to literacy rate among males and females, above 90% males and nearly 86% females were literate in both the districts as per 2011 census. The literacy has increased from 2001 to 2011 for Greater Mumbai as a whole. In 2001, the corresponding figures for suburb and city were 86.89 and 86.4%, respectively. Age-Sex Composition The population below the age group of 6 years was 8,76,917 and 2,62,229, respectively for Mumbai suburban and the city (Census of India 2011a). In 2001, 11.86% children were below age of 6 in the suburban district and 10.18% in the Mumbai city district. This reduced to 9.4 and 8.34 having decadal change of −14.42% for the suburban district and −22.81 for Mumbai city district. The state constituted nearly 11% child population in the category with −6.02% growth rate for 2001–11.

3.9 Transport Network and Vehicular Traffic Delhi The growth of vehicles, both personal and commercial vehicles, has been remarkable in Delhi that increased from 5,21,457 (0.52 million) in 1980–81 to 74,38,155 (7.43 million) in 2011–12. All through the years, the share of motorcycles and scooters has dominated with over 60% concentration. Cars and jeeps have increased by about 10% during the period of 1980–2012, from 22.48 to 31.5% share in the total vehicular population. Buses and taxis have the lowest share, i.e. 0.86 and 0.94%, respectively.

978

652

652

Maharashtra

Mumbai (suburban)

Mumbai (city)

Source Census of India (2011a)

1901

Census years

570

570

966

1911

Table 3.5 Trend of sex ratio (1901–2011)

561

561

950

1921

592

592

947

1931

616

616

949

1941

574

712

941

1951

626

744

936

1961

670

769

930

1971

729

801

937

1981

791

831

934

1991

777

822

922

2001

838

857

925

2011

3.9 Transport Network and Vehicular Traffic 89

90

3 Geographical Background: Delhi and Mumbai

The road length was 14,316 km in 1980–81 as against 31,183 km in 2010–11 (Directorate of Economics and Statistics 2012a). As the maximum population and area are under MCD, the total road length is also highest all through the period (Fig. 3.15). The number of vehicles per km of road steeply swelled from 26 (1971) to 42 (1981), 125 (1991), 231 (2001–02) and further to 1549 (2004–05) (Firdaus 2012; Directorate of Economics and Statistics 1994; Planning Department of Delhi 2006). The vehicles per thousand population increased steeply from 256 (2001) to 436 (2011) (Government of NCT of Delhi 2013). The ring road and outer ring roads add distinct character to road network in the city. Apart from this, there are five National Highways (NH 1, 2, 8, 10 and 24) along with newly constructed Delhi-NOIDA Direct Flyway and Delhi-Gurgaon Expressway. The mass public transport facilities and infrastructure are acute in Delhi. The Delhi Transport Corporation (DTC) is responsible for providing efficient public transport to city dwellers. DTC had a fleet of 3524 buses in 2001. It has shifted to CNG mode for eco-friendly transport system. The buses, however, constitute a very small proportion of the total vehicular load, i.e. 1.2% in 2003 as against 93.73% personalized vehicles (Firdaus 2012). The total rail network in 2003 was 62.20 km with five main railway stations, namely New Delhi, Old Delhi, Hazrat Nizamuddin, Anand Vihar’s Inter-state Bus Terminus (ISBT) and Sarai Rohilla. The daily total passengers are estimated to be 6,27,000 (0.62 million) and commuters 3,54,000 (0.35 million) in 2001 (Sarkar et al. 2007). The rail network for daily commuting in the city is not much pronounced, except the Delhi Metro in the recent decade. The Delhi Metro offers mass rapid transport system (MRTS) based on efficient rail system that is non-polluting and

Fig. 3.15 Percentage share of length of road under different agencies in Delhi. Source Based on data from Directorate of Economics and Statistics (2012a)

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efficient. The DMRC was started in 2002 and has constructed a massive network of 213 km with 160 stations in record time. Further, 159 km metro lines are under construction. DMRC has been certified as the first metro rail to get carbon credits for reducing Green House Gas (GHG) emissions by the United Nations. It is estimated the Delhi Metro helped reduce pollution levels in the city by 0.63 million tons every year (http://www.dmrc.com/). The city has two airports, namely Palam for domestic passengers and Indira Gandhi International Airport mainly for international flights. Mumbai The total motor vehicle population in Mumbai increased from 10,29,265 (1.02 million) in 2000 (NEERI 2010) to 18,70,311 (1.87 million) in 2011 that is almost one-fifth of Delhi (Motor Vehicle Department 2011). Of the total vehicles plying in Greater Mumbai, only 10.28% used CNG as fuel, whereas the petrol vehicles dominated (76.75%) (Motor Vehicle Department 2011). The maximum vehicular share is of two-wheelers followed by cars and jeeps akin to Delhi. As per the Government of Maharashtra (2007), the total length of roads in Mumbai city is 1350 km while in Mumbai suburban district is 1660 km managed by BMC. Of the total roads in study area, 23.33 km is under Western expressway and 25.5 km in Eastern express highway. The vehicles per km of road length are estimated to be 647 for Mumbai city (Directorate of Economics and Statistics 2012b). As per the record of Air Quality Assessment Report of Mumbai (2010) MCGM maintains 11 flyovers, 47 road over bridge (ROB) and 104 bridges. Further, there are 55 flyovers proposed to support easy flow of traffic, of which 45 are completed. The Mumbai city and the suburban district are well connected by Mumbai local trains (Fig. 3.16a, b). It is considered as the ‘lifeline of the city’. Dense network of north-south rail lines has been constructed. The Mumbai suburban railway system is operated by Indian Railway’s Western (WR) and Central (CR) zone having 36 and 62 stations, respectively (Directorate of Economics and Statistics 2012b). The harbour line is a part of Central Railway with 38 stations. The system carries more than 7.24 million commuters daily (Gardas et al. 2013). If annual ridership (2.64 billion) was taken into account, the suburban rail would be the second-busiest rapid transit system in the world (Gardas et al. 2013). It has the highest passenger density of any urban railway system in the world. As per Government of Maharashtra (2007), total number of passengers in CR main line, CR harbour line and WR are 1.31, 0.828 and 1.4 million daily, respectively. The daily trips by CR main line, CR harbour line and WR are 658, 414 and 923 trains, respectively (Government of Maharashtra 2007). BrihanMumbai Electric Supply and Transport (BEST) (Fig. 3.17a) provide the bus service in Mumbai. It had a total fleet of 4404 in 2010. Double-decker buses are still operational on some routes in the Mumbai city (Fig. 3.17b). The total daily passengers using the services of BEST are 4.12 million (Government of Maharashtra 2007). The average number of BEST buses on road each day in Mumbai city alone was 4652 that carried 4.20 million passengers in 2010–11 (Directorate of Economics and Statistics 2012b). Other than this, Mumbai metro project has been initiated and the first phase from Versova to Ghatkopar is now operational. Also, the monorail

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Fig. 3.16 a Railway tracks at Bandra Station. b Network map of local train

Fig. 3.17 a Bus services and b double-decker bus provided by BrihanMumbai Electric Supply and Transport

project is gaining pace. The project aims to link Wadala to Chembur in first phase and Wadala to Jacob Circle in the second phase. The International Airport at Sahar and domestic airport at Santacruz handle average passenger traffic of 4 million and 4.2 million every day, respectively (Government of Maharashtra 2007). The air traffic in Mumbai is magnanimous that handled 15.3 million international, 8.1 domestic passengers; and 379 thousand tonnes of international and 151 thousand tonnes of domestic cargo in 2008 (MCGM 2010a). The Mumbai port accounted for 30.6 million tons of cargo, i.e. 11% of total seaborne traffic of major ports of India (MCGM 2010a). There are two major ports in Maharashtra, i.e. Mumbai Port Trust (MPT) and Jawaharlal Nehru Port Trust. The heavy road, rail, sea and air transports are matters of concern for the environmentalists

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as living conditions of the city are rapidly deteriorating. The growth and expansion of road, rail and air transport have exceeded the carrying capacity of the city posing threat to health and environmental conditions in Mumbai and its vicinity. Many new projects are planned like Mumbai trans-harbour link, multi-modal corridor from Virar to Alibaug and extension of metro and monorail.

3.10 Health Delhi Directorate of Health Services is the key government organization associated with healthcare delivery in Delhi. As per 2011 data, there were 61 healthcare institutions under MCD, 38 under Delhi Government, 4 under NDMC and 3 with DCB (Directorate of Economics and Statistics 2012a). This includes various kinds of health institutions like hospitals, TB clinics and maternity home. The availability of number of beds is highest in private-registered nursing homes (18,324), Delhi government clinics and hospitals (9834) and Government of India (9078). There are 755 private nursing homes spread across the city (2011) that has sprawled rapidly in last decade. Total of eight primary health centres were established since 2007. The birth rate in the last 4 decades has observed decline by nearly 6 per thousand from 27.18 (1980) to 20.98 births per thousand (2010). The reduction in birth rate can be attributed to the massive family welfare programme of the government that focused on creating awareness regarding ways of contraception, oral pills and sterilization. The death rate, on the other hand, is nearly 6–7% per thousand. The Infant Mortality Rate (IMR), however, has sharply declined from 50 deaths per thousand in 1980–81 to 22 deaths per thousand in 2011, representing improvements in the health care during and post-pregnancy (Directorate of Economics and Statistics 2012a). As a result, there is a constant increase in population. The demographic transition due to increasing life expectancies has led to rise in elderly population totalling to 8,29,917 (0.82 million) in 2004 (National Sample Survey 2012). This shift in age pyramid has spiral impact on policies of the government, insurance and social security. The persons with special abilities require additional resources and attention for survival. It is necessary for the growth of economy that the population is healthy and fit. As per 2001 data, 2,35,886 persons (13,432 rural, 2,22,454 urban; 1,44,872 males, 91,014 females) suffered from disability in seeing/speech, hearing/movement or mind-related. There were estimated more than 56,000 homeless populations in Delhi that add to vulnerable population (Government of NCT of Delhi 2013) (Fig. 3.18). Mumbai The health infrastructure consists of 51 MCGM, 21 government-owned and 1500 private hospitals/maternity homes/nursing homes. Total beds available are 11,700, 9000 and 20,000 by MCGM, government and private hospitals, respectively. Additionally, there are 176 health posts under MCGM. Total dispensaries in Mumbai are 30,235

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Fig. 3.18 Homeless population in Delhi

(185 by MCGM, 50 under government and 30,000 by private hospitals) (MCGM 2010b). For municipal hospitals, population per bed was 579 in Mumbai city, 2763 in Western suburb and 2285 in Eastern suburb (average for 2007 was 1309). While for other hospitals, the population per bed in 2007 was 273 in the city, 634 in Western suburb and 823 in Eastern suburb (MCGM 2010b). The average life expectancy in 2007 was 56.8 years with 52.6 and 58.1 years for females and males, respectively (MCGM 2010b). The major causes for death, as identified by MCGM in 2007, are heart diseases, tuberculosis, cancer, pneumonia and respiratory tract infections in descending order (MCGM 2010b).

3.11 Industrial Growth Delhi The growth of industries plays an important role in the economic growth and development. The city emerged as a major industrial and commercial centre with growth in manufacturing, trading, transport and communication activities. The manufacturing units in Delhi comprise mainly of food, metal, textile, engineering, paper and repair services. These are located in 16 small- and medium-scale industrial centres mostly found in west, south and south-eastern part of the city. In 1951, there were 8160 industrial units that increased to 1,26,000 in 2001 providing employment to 11,36,000 (Massey 2003) to 14,40,000 (Firdaus 2012) persons. The industrial growth in the city steadily increased from 1980–81 till 1999 with 3619 factories. Post-2000 slow yet sturdy decline can be observed in the number of factories and in 2009–10, 2878 factories operated in Delhi (Directorate of Economics and Statistics 2012a).

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Mumbai There are 12,429 industries/factories in the city and 19,351 located in the suburbs (Anonymous 2003) and all these pay the air pollution fees under section 390, MMC Act of 1888. The total area under industrial land use in city is 13.5 km2 (19.9%) and in suburb 41 km2 (11.69%) (Government of Maharashtra 2007). As per Economic Survey of Maharashtra, 2011–12, there were 1248 operational co-operative industrial units and 311 large enterprises in Mumbai (Directorate of Economics and Statistics 2012b). The research on economy in Dharavi reveal some interesting facts like nearly 5000 small-scale industries and 1500 single-room factories are expected to generate the profit of over a half-billion US dollars (Bhagat 2014). Hence, the actual number of small and medium industries and enterprises is not certain. The industries are categorized into three categories on the basis of their pollution levels. These are red, orange and green. Greater Mumbai has 1009 industries in red category, 2628 orange and 4213 green industries that are least polluting (MPCB 2016). The industries in Mumbai are located in north and north-eastern part of the city (Anonymous 2003). Due to the prevalent north-east winds during the cool season, the polluted air from the industries is transferred to other parts of the city raising the pollution levels. Apart from industries, Mumbai is also an attractive centre for IT parks. Nearly 98% of the total IT parks in Maharashtra are located in Greater Mumbai (171), followed by Pune (161) and Thane (109) (Directorate of Economics and Statistics 2012b).

3.12 Air Quality Delhi The CPCB monitors and records the pollution levels in Delhi city. The SO2 levels in Delhi are recorded much below the annual permissible limit of 50 µg/m3 . In post2000, the annual average of SO2 has declined that is attributed to the shifting of industries and reduction in sulphur content in the fuel used in automobiles. Researches on NO2 in Delhi point out that the use of CNG has proved to be a paradigm shift in improving the quality of environment in the city. Nagdeve (2004) presents research on changing air pollution levels from 1995 to 1998 and concludes that the NO2 had declined due to the implementation of strict pollution norms for industries and vehicles. The SPM and RSPM levels are strikingly soaring in the city. These are most prominent during winters, especially in the form of smog formation. In post-2005, sharp rise is observed in PM content. Increased SPM may be due to various factors like the natural sources like road dust and meteorological conditions contribute to PM in the air. Various anthropogenic sources are incomplete combustion from industries and vehicles, agriculture, construction, fireplaces, refuse burning, etc. The vulnerability and susceptibility to ill health from PM is highest and it particularly has profound effect on the lungs, respiratory tract and circulatory system.

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Mumbai As per the executive summary report of Environmental Report of Mumbai (2003), the concentration of NO2 , RSPM and CO has exceeded the permissible air quality standards of CPCB. MCGM monitors air quality at six monitoring stations throughout Mumbai. As per the annual standards, the SO2 and lead in the air is within the permissible limits. A 24-hour analysis shows that NO2 , SO2 , SPM and Pb exceeded at many places. The air pollution levels are noted to be low during the monsoon season and high during the winters. As a result, the pollutants are washed out quickly during the rainy seasons, whereas remain suspended in winters.

3.13 Hazards and Disasters Delhi Like most urban cities of the world, Delhi too is vulnerable to multiple disasters like earthquake, flood, fire, terror strikes, transport accidents, chemical and industrial accidents, heat and cold waves and epidemics. The changes in land use patterns and environmental degradation have caused ecological imbalance in the city. There has been an expansion of industries and reduction in area under agricultural land and water bodies. The haphazard growth of settlements and increasing population density pose major impediment in case of earthquake. Delhi falls in zone IV with respect to earthquake proneness, thus it lies in high risk zone. Flooding in Delhi is now recorded as an annual phenomenon. Heavy rains coupled with low carrying capacity cause flooding along the banks of River Yamuna. Prashar et al. (2012) assessed the resilience of Delhi to climate-related disasters using the Climate Disaster Resilience Index (CDRI) and found that East Delhi is least resilient, whereas New Delhi district is most resilient. Mumbai Mumbai city is prone to a range of natural and human disasters. The growing population and demand for housing and infrastructure has altered the land use of the city leading to aggravated impact of disasters. The city is prone to moderate earthquake with Richter scale up to 6.5 magnitudes (Seismic Zone III), urban flooding, landslides and cyclones. The low-lying areas are under the threats of floods even if there are minor rains. There are 111 places in the city, 26 in Mumbai city district, 73 in the Eastern suburbs and 12 in the Western suburbs that were identified in 1993 as flood-prone areas (Municipal Corporation of Greater Mumbai 2010a). Some areas are also prone to landslides. Road and railway accidents, industrial accidents, incidences of fire, bomb blasts and terror strikes have taken place in the city in recent history. The major concentration of the hazardous industries is found in the Chembur–Trombay belt, spread over an area of about 10 km2 , having major chemical complexes, refineries, fertilizer

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plants, atomic energy establishment and thermal power station. Clustering of various operating units makes them highly vulnerable to disasters. This area is also in close proximity to the port activities of MPT, which handles hazardous cargo. MPT has identified 32 hazardous chemicals, require frequent handling and storage during loading and unloading operations. There are approximately 900 industries involved in use or production of hazardous material in the city. The most vulnerable region is Chembur–Trombay belt followed by the MPT (Municipal Corporation of Greater Mumbai 2010a).

3.14 Squatter Settlements and Slums Delhi According to the NSS Round Survey conducted in 2012, there are about 6343 slums with nearly 1.02 million households in Delhi (Directorate of Economics and Statistics 2015). Of these, 90% of slums are built on public land that is owned by local bodies, railways and state government. As per 2001 Census records, the slum population of Delhi was recorded as second highest (1.85 million) after Mumbai (6.5 million). Mumbai Asia’s largest slum, Dharavi, is located in the outskirts of the Mumbai city. A study by Kamla Raheja Vidhyanidhi Institute of Architecture estimates that the Chamra Bazaar in the central area of Dharavi has density as high as 18,000 persons per acre and density of Dharavi is estimated to be six times of Manhattan (Windle 2009). The MGCM counted the total pavement dwellings as 20,000 in 1952 that grew to 62,000 in 1961 and 22,600 households by the Census of 1981 (Government of Maharashtra 2015). The 2001 data mentioned the total slum population of 58,23,510 (5.8 million) (Indira Gandhi Institute of Development Research 2014). By 2010, slums accounted for 60% of Mumbai’s population and merely 8% of land area (MCGM 2010a). With the exception of Ward C, slums are omnipresent everywhere in Mumbai. The largest cluster is found in Western suburb from Bandra to Dahisar and from Mankhurd to Mulund in the Eastern suburb. Extensive survey by Youth for Unity and Voluntary Action (YUVA), a non-governmental organization, in 2001 counted total of 1959 slums with 5.72 million people in 2001; while the MCGM calculated 2245 slum pockets in 2002–03 and Slum Rehabilitation Authority stated the figure as 2500 (MCGM 2010a). The living condition of the slums and pavement dwellers is much below the standards. Most of these dwellings are informal and illegal. Zopadpattis and squatter settlements are considered synonymous to each other. The squatter settlements have continued to grow in response to increasing migration to the city. The informal settlements occupy fragile and unsafe zones in the city and suburban Mumbai like low-lying marshy areas, hill slopes and along the railway tracks. The living conditions and household amenities are poor making them more vulnerable (Fig. 3.19a, b).

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Fig. 3.19 a and b Living conditions in slum of Mumbai (Parel)

3.15 Concluding Remarks The Delhi and Mumbai are the two largest cities of India and share similar constraints and challenges that may provide an insight into the changing nature of cities of India. The two contrasting yet similar case studies will be helpful in understanding the components of planning for urban environment. The contrasting location setting, climate and historical evolution have imprints on the spatial setting, land use and nature of crisis faced by both the cities. In terms of urban population, Mumbai is 100% urban while Delhi still has nearly 3% rural population occupying 24% rural area. In terms of Human Development Index (HDI) 2012, Mumbai scores better with 0.84 than Delhi (0.75). However, slums and squatter settlements characterized by poor living conditions are much ubiquitous and pronounced in Mumbai. The dilapidated infrastructure, unplanned growth, large in-migration and daily commuting have led to major changes in LULC of both megacities. The number of industries is higher in Mumbai, whereas, the share of private vehicles is higher in Delhi. Industrial and vehicular pollution are main contributors to degrading air quality. The dynamics of population change, LULC along with their underlying causes, is dealt in detail in the next chapter.

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Web References Bombay Community Public Trust (2015) www.bcpt.org.in. Accessed 2 Jan 2015 Delhi Metro rail Corporation (2015) www.dmrc.com. Accessed 2 Jan 2015 The Land Processes Distributed Active Archive Center (2015) lpdaac.usgs.gov/. Accessed 2 Jan 2015

Chapter 4

Changing Urban Environment in Megacities

Abstract This chapter discusses in detail the land use/land cover and population change as factors of urban environment of Delhi and Mumbai. Land use/land cover change and air quality change are considered as indicators of urban environmental change in Mumbai and Delhi. Land use/land cover change has been studied using Landsat satellite images from 1991 to 2011. The air quality change has been studied based on the data collected from Central Pollution Control Board and Maharashtra Pollution Control Board for the same period. Records of air quality were collected from CPCB for Delhi (nine stations) and from MPCB for Mumbai (three stations) for SPM, RSPM, SO2 and NO2 . Detailed analysis of air pollution is supplemented by extensive fieldwork. Keywords Air quality change · Urban population change · LULC change

4.1 Introduction The world has experienced unprecedented urban growth in recent past. As per United Nations Report (2011), the million plus cities have increased from 75 in 1950 to 447 in 2011, and further, they are projected to increase to 527 by 2020. The million plus population cities in India increased from 4 to 53 during the same period (Census of India 2011a, b, c). The total urban population of India was 62.4 million in 1951 that increased to 377 million in 2011 and is projected to increase to 463 million by 2020 and 590 million by 2030 (United Nations 2011; Census of India 2011a, b, c). From merely 17.3% of the total population in 1951, the urban population almost doubled in 2011 with 31.2%. The urban population of NCT of Delhi is 97.5% (Census of India 2011b). Mumbai city recorded 100% urban population in 2011 (Census of India 2011a). The total population is rising steeply in both the cities of Delhi and Mumbai (Fig. 4.1). However, the growth rate in both the cities is on a decline. Mumbai city is experiencing de-urbanization in the recent past. While the growth rate of Mumbai was nearly 4% from 2001 to 2011, it was 20% for Delhi. Steeper decline can be observed for Mumbai city owing to resettlement of people in Mumbai suburban district and Navi Mumbai (Fig. 4.2). © Springer Nature Singapore Pte Ltd. 2020 A. Grover and R. B. Singh, Urban Health and Wellbeing, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-13-6671-0_4

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Population (in millions)

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4 Changing Urban Environment in Megacities 18.00

16.75

16.00

13.85 11.91 12.48

14.00 12.00

9.93

10.00

8.24

8.00

5.97

6.00

0.00

6.22

4.15

4.00 2.00

9.42

0.93

1.03

0.41

0.41 1901

1911

1921

0.92

0.64

0.49

1931

4.07

2.10

1.80

1.58

1.38

1941

1.74

1951

2.66

1961

1971

Year

1981

1991

2001

Delhi

2011

Mumbai

Fig. 4.1 Trend of population growth in Delhi and Mumbai (city and suburban), 1901–2011 (in millions). Source Compiled from Planning Department of Delhi (2006), Census of India (2011a, b)

Growth rate (in per cent)

120 100

97.58 89.99

80 60 52.44 40 20 0

44.27 34.28 18.02 10.77 1.98 1911 1921

52.93 43.79

53

51.45

47.02

38.06

30.25 14.48

13.97

20.41

16.65

20.03 20.96 4.73

1931

1941

1951

1961

Year

1971

1981

1991 Delhi

2001

2011 Mumbai

Fig. 4.2 Growth rate of population in Delhi and Mumbai (city and suburban), 1901–2011 (in per cent). Source Compiled from Planning Department of Delhi (2000, 2006), Census of India (2011a, b)

As per the Census of India (2011b), Delhi has very high density of population (11,297 persons per km2 ) among the administrative units of India (Fig. 4.3). In 1901, Delhi was a small city with total population of only 0.4 million, which increased to 16.75 million by 2011. In the same time period, the urban population of Delhi increased from 52.76 to 97.50%. The Mumbai, as compared to Delhi, has nearly double population density in 2011, i.e. 20,693 persons per km2 . During this process of urban growth and development, substantial significant transformations in LULC of Delhi have taken place. In response to the rising urban

4.1 Introduction

105

25000

Density (persons per km 2)

19758

20693

20000 16460 13670

15000 9901

11297

10000

9340

6885

5000 1538

1704

273

0

1901

2289

279

1911

2620

329

1921

2987

429

1931

618

1941

6352

3484 1176

4194 1792

1951 1961 Year

2738

1971

1981

1991

2001

Delhi

2011 Mumbai

Fig. 4.3 Trend of density of population in Delhi and Mumbai (city and suburban), 1901–2011 (in persons per km2 ). Source Compiled from Planning Department of Delhi (2000, 2006), Census of India (2011a, b)

population, the rural population and rural areas have declined. The number of villages reduced from 300 (1961) to 165 (2001) (Planning Department 2001) (Fig. 4.4). In the period of 40 years, the number of villages reduced by almost 50%, which indicates the LULC change in the city. In the decade 1991–2001, more than 70% in-migration in Delhi was from the neighbouring states of Uttar Pradesh, Bihar and Haryana (Fig. 4.5) (Planning Department of Delhi 2001). The average annual rate of net migration was 2.23% in 1971–81 that decreased marginally to 2.11% in the next decade and further increased to reach 2.18% in 1991–2001 (Kumar 2013). As a result of in-migrations, the urban population in the city grew rapidly from 82.4 (1951) to 97.5% (2011). 350 300

Number of villages

300 258

250

231 209

200

165

150 100 50 0 1961

1971

1981 Year

1991

2001

Fig. 4.4 Growth in number of villages in Delhi (1961–2001). Source Based on data from Planning Department of Delhi (2001)

106

4 Changing Urban Environment in Megacities 49.61

60

40

WB

Punjab

13.87

10.26

MP

5.16

4.72

10

3.18

20

17.39

30

1.85

In-migrants (in per cent)

50

0 Rajasthan Haryana

Bihar

UP

States

Other states

Fig. 4.5 Source states for in-migration to Delhi during 1991–2001 (in per cent). Source Planning Department of Delhi (2009), Department of Environment and Forests (2010)

4.2 Driving Forces of Urban Environmental Change 4.2.1 LULC Change and Population Change There are multiple factors of urban environmental change, but the two most prominent factors that have shaped and reconstructed the Indian urban environment are: (1) population and (2) vehicular growth. Higher population growth rates and rising population density in urban centres propel transformations in permeable LULC. Population growth accompanied with unplanned urbanization is the prime cause of indiscriminate deforestation and reduction in natural habitats at the cost of settlements. The LULC modifications are primarily due to urbanization that is dependent on population and density increase. By 2025, the population of Delhi and Mumbai UA will further increase at steep rate as compared to other cities of the world (Fig. 4.6). It is noticeable that while the UAs are experiencing increase in population, the cities have lowered their population growth rates (Fig. 4.7). Mumbai city is experiencing decline in population growth, whereas the Navi Mumbai is experiencing spurt in population. However, the daily migrants continue to increase, adding load to the city resources. Urban population growth has caused innumerable irreversible changes on the surface of the earth. Apart from inculcating development, the urban areas also have modified their core and surroundings. The process of urban growth has multiple consequences on socio-economic development and environmental conditions. The outward expansion of city consumes large areas of agricultural land, wasteland, water bodies and forest land leading to LULC change. LULC alterations have long-lasting impacts on the environment of the urban areas. This urban sprawl is unavoidably

4.2 Driving Forces of Urban Environmental Change

107

22.4

26.4

25 20

12.4

16.1

Population (in millions)

30

15 10 5 Tokyo

New York Mexico City Sao Polo

Kolkata

Major urban agglomerations of the world

Mumbai 1975

Delhi 2000

2025*

Fig. 4.6 Actual and projected population (*) growth in largest urban agglomerations of the world, 1975–2025 (in millions). Source Based on data from United Nations 2011 120

106.58

107.41

66.23

64.17

110.14

100 80 55.48

60 40 20

70.97 54.57

58.16

46.98 23.79

46.87 36.15 28.87

27.94 20.17

52.37 27.99

11.13 1.26

0

8.01 19.01 10.77

6.99

5.14 -3.35

-20

Delhi

26.56

Mumbai city

-5.75

Mumbai suburban

Fig. 4.7 Decadal variation of population in Mumbai (in per cent). Source Compiled from Census of India (2011a, b), Planning Department of Delhi (2013). Note Since Mumbai suburban district was constituted after 1951, the data prior to it is same as of Mumbai city district

accompanied with concretization, industrial growth, traffic congestion, air pollution, emission of volatile organic compounds, etc., thereby modifying the urban heat budget and raising the temperature of the city core (Roy et al. 2011; Yang and Lo 2002; Lo and Quattrochi 2003). Since land is one of the prime resources, analysis of LULC is important component for resource management and monitoring the environmental changes.

108

4 Changing Urban Environment in Megacities

Population growth induces vehicular growth to meet the demands of movement of people, goods and services. Vehicles discharge gases that harm the environment and human health. As per CPCB (1995), the vehicular PM load in Delhi was estimated to be 10.3 tonnes per day, while for Mumbai, it was 5.59 tonnes per day. Likewise SO2 was also estimated to be more than double for Delhi (8.96 tonnes per day) as compared to Mumbai (4.03 tonnes per day). The NOx load was calculated as 126.46 tonnes per day for Delhi and 70.82 tonnes per day for Mumbai (Central Pollution Control Board 1995). Among the 12 metropolitan cities of India, Delhi followed by Mumbai (other cities: Bangalore, Calcutta, Ahmedabad, Pune, Madras, Hyderabad, Jaipur, Kanpur, Lucknow, Nagpur) had highest concentration of vehicular pollution load (estimated for PM, SO2 , NOx , HC and CO). The paucity of public transport system has encouraged private car ownership that is now a menace causing traffic congestion and emanating harmful gases. This section deals with the analysis of population trends and transport statistics of Delhi and Mumbai and investigates their contribution in LULC and air quality change.

4.2.2 Vehicular Growth Growth of transportation system has direct relationship with air pollution. The vehicular air pollution levels depend on vehicular density, fuel quality, vehicle speed, age of vehicles, condition of roads, and rate of emission and pollution norms. Congestion and traffic jams due to increased vehicles on road and limited road space add to the additional pollution loads in cities. Both Delhi and Mumbai are experiencing increasing private vehicles, insufficient road space accompanied with bleak provisions for use of road by cyclists and pedestrians (Singh 2012). While Mumbai has well-developed mass transit system, Delhi still lags behind. The vehicular growth from 1981 to 2001 in Delhi and Mumbai was 6.78 and 3.36%, respectively (Agarwal 2006).

4.2.2.1

Vehicular Growth in Delhi

Singh (2012) states that Delhi, Bengaluru, Chennai and Hyderabad accounted to nearly 15 million vehicles, which constituted about 16.6% of the total vehicles of India (2007). While Delhi contains 1.4% of India’s population, it accounts for 7% of total vehicular load of the country. Das and Parikh (2004) mentioned that Delhi is the fourth most polluted city in the world and the dominant contributor sector to air pollution was transport sector. They correlate population and GDP growth with vehicular and pollution increase. Recently, Delhi has been regarded as most polluted city in the world. The case studies demonstrate that Mumbai transport has 60% lesser emissions than Delhi. Mumbai has higher GDP but has more reliance on public transport as compared to Delhi.

4.2 Driving Forces of Urban Environmental Change

109

30

Growth of vehicles

25 20 15 10 3.03 3.16 1.92 2.06 2.19 2.37 2.57 2.79

5

3.3

8.05 6.23 7.14 5.32 3.51 4.41

0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2005 2010 2015 2020 2025 Total vehicles (in million) Private cars (in million) Percentage of private car to total vehicles

Growth rate of total vehicles (%) Growth rate of private cars (%)

Fig. 4.8 Actual and projected growth of vehicles in Delhi (1991–2025). Source Compiled from Firdaus and Ahmed (2011), Government of NCT of Delhi (2005)

While the number of total vehicles in Delhi increased from 1.92 million (1991) to 5.32 million (2010), the growth rate declined in the same time period. The private cars have rapidly proliferated in the city making a total of 1.46 million (2010) from merely 0.42 million (1991) (Fig. 4.8). There is high dependence on private vehicles for transportation, which is clearly reflected by the increasing proportion of private cars to total vehicle composition. While in 1991, 22.2% of the total vehicular load was of the private cars, it increased to 27.4 in 2010. The projected estimates by the Transportation Department of Delhi (2005) reflect that increasing trend is expected to continue till 2025. Considering the damaging effects of rise in private cars, especially the diesel vehicles and two stroke vehicles, there is an urgent need to focus on the betterment of mass public transportation system and related policy reforms.

4.2.2.2

Vehicular Growth in Mumbai

The Motor Transport Statistics of Maharashtra (2011) reveals that the total vehicular load in Greater Mumbai increased from 0.28 million in 1980 to 0.609 million in 1990, to 0.96 million in 2000 and a decade later in 2010 nearly doubled to 1.76 million (Fig. 4.9). The rate of growth of vehicles is higher in suburbs than the city. Also, it may be noted that the rise in Western suburbs is much more than in the Eastern suburbs. In Mumbai suburban district, two-wheelers followed by cars and jeeps constitute major portion of the vehicular traffic (Fig. 4.10). It may be noted that three-wheelers are not allowed to ply in Mumbai city. The growth of vehicles in Mumbai reveals that from 1982 to 1993, there was decline in the vehicular growth, while rise in 1994–1997 but again during 1998–2003, vehicular growth slowed down. In 2004 again, 12% growth was noted that declined

110

4 Changing Urban Environment in Megacities

Number of vehicles

1400000 1200000 1000000 800000 600000 400000 200000 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

0

Year Mumbai Central Mumbai Eastern

Mumbai Western Greater Mumbai

Fig. 4.9 Growth of vehicles in Mumbai (1980–2005). Source Based on data from Motor Vehicles Department (2011)

Fig. 4.10 Vehicular traffic at Vikhroli, Mumbai

to 7.9% in the subsequent years (Fig. 4.11). Fuel used is an important component of air quality. Of all, diesel is considered as the least safe fuel and CNG as the most secured fuel. CNG is composed of methane, ethane, carbon dioxide, propane, i-butane, i-pentane, n-pentane, nitrogen and n-butane. Goyal and Sidhartha (2003) mention that there are 56, 55, 86 and 56% reductions in CO, HCs, PM and NOx , respectively, due to use of CNG buses in lieu of diesel buses. In 2011, majority of vehicles were petrol fuelled (1.43 million) followed by 0.23 million diesel fuelled and 0.19 million CNG vehicles (Motor Vehicles Department 2011). In response to the economic prosperity, the number of vehicles possessed by people has exponentially increased (Frumkin 2002). Combustion of fossil fuels used particularly in industries and transportation produce complex mixture of pollutants.

3

7.96 5.05

3.88

6.47

6.18

5.93

7.88

9.71

8.43

2.89

5.57

8.99

9.22

3.7

5

6.26

9.14

8.84

9.91

9.87

8.37

10

7.95

0 -5 -10

-13.69

Growth rate of vehicles (in per cent)

15

12.15

111 10.13

4.2 Driving Forces of Urban Environmental Change

-15

Year

Fig. 4.11 Rate of change in number of vehicles in Mumbai (1981–2005) (in per cent). Source Based on data from Motor Vehicles Department (2011)

Industrial and vehicular growth modifies the atmospheric composition, through addition of GHGs. Analysis of trend of pollutant levels is most often carried out for oxides of nitrogen, sulphur, SPM and RSPM. The urban environment in the context of the present study refers to the changes in LULC and air pollution within the administrative boundaries of the city. The detailed analysis of LULC changes in Delhi and Mumbai and spatio-temporal analysis of pollution levels are done. The LULC changes are associated with increasing population, and the pollution levels have increased largely due to rise in number of vehicles and several other reasons. Therefore, both of these underlying factors responsible for changes in the urban environment, i.e. population and vehicular increase, are studied in detail.

4.3 Data Sources 4.3.1 Datasets Used in LULC Classification The present LULC study has been carried out based on Landsat satellite images and supported by Google Earth images and Survey of India toposheets (only Delhi) on 1:50,000 scale. The Landsat satellite images have been acquired from www. earthexplorer.usgs.gov for three time periods, i.e. 1993, 2000 and 2010, for Delhi and 1991, 2003 and 2010 for Mumbai (Table 4.1). Landsat 5 provides TM images with seven bands; three in visible (Band 1–3) and one in near-infrared (Band 4) regions and two in middle infrared (Band 5 and 7) having spatial resolution of 30 m and one thermal infrared band (Band 6) in 120 m

TM

LANDSAT 5

30 m

30 m

30 m

30 m

30 m

30 m

Resolution

Source Based on the supplementary .txt file downloaded with the images

ETM+

LANDSAT 7

TM

TM

LANDSAT 5

LANDSAT 5

TM

LANDSAT 5

Mumbai

TM

LANDSAT 5

Delhi

Sensor

Satellite

City

Table 4.1 Details of satellite images of Delhi and Mumbai

148/47

148/47

148/47

146/40

146/40

146/40

Path/Row

17-04-2010

22-04-2003

15-05-1991

05-05-2010

09-05-2000

22-05-1993

Date

LT51480472010107KHC00

LE71480472003112ASN00

LT51480471991135ISP00

LT51460402010125KHC00

LT51460402000130XXX01

LT51460401993142ISP00

Scene Id

112 4 Changing Urban Environment in Megacities

4.3 Data Sources

113

Table 4.2 Characteristics of Landsat satellite image used in the present study Band

Spectral range (μm)

Spatial resolution (m2 )

Bands

Gain

Offset/Bias

1

0.450–0.515

30

Blue

0.762823529

−1.520000000

2

0.525–0.605

30

Green

1.442509804

−2.840000000

3

0.630–0.690

30

Red

1.039882353

−1.170000000

4

0.760–0.900

30

Near IR

0.872588235

−1.510000000

5

1.550–1.750

30

Mid IR

0.116980392

0.370000000

6

10.40–12.5

120/60a

Thermal

0.055156863

1.238000000

7

2.080–2.35

30

Mid IR

0.064117647

0.150000000

8b

0.50–0.90

15

Panchromatic

0.786274521

26.1999998

Source Chander et al. (2009); http://landsat.usgs.gov/; http://glcf.umiacs.umd.edu/library/guide/ techguide_landsat.pdf a In Landsat 5 and 7, thermal band is provided at 120 and 60 m spatial resolution, respectively b This band is only available in Landsat 7

spatial resolution (Table 4.2). The Landsat 7 sensor (ETM+) provides an improvised thermal band (Band 7) at 60 m spatial resolution and an additional panchromatic band (Band 8) at 15 m spatial resolution (Table 4.2). The bias value is also known as offset or L min, which is provided with the metadata file of the Landsat satellite image while acquiring from http://landsat.usgs.gov/. The gain is, however, estimated by subtracting L max from L min , and further, it is divided by 255.

4.3.2 Data Sources of Air Pollution With respect to the data on air pollution/quality, the present research relies on various secondary sources. In Delhi, the air quality data is collected and managed by CPCB, Delhi Pollution Control Board (DPCB) and National Environmental Engineering Research Institute (NEERI), while in Mumbai by MPCB and NEERI. The CPCB was constituted in 1974 as Central Board for the Prevention and Control of Water Pollution. The prime aim of this board was to assess the water pollution levels in the country. In 1981, it was entrusted with added responsibilities of air pollution control under Air Prevention and Control of Pollution Act, 1981. The pollution-recording stations are representative of three categories on the basis of their function, i.e. residential, industrial and traffic junctions. All the sites regularly monitor the SPM, RSPM, SO2 and NO2 levels. SPM and RSPM are monitored 8 hourly for 24 h, whereas SO2 and NO2 are monitored every 4 h per day. The permissible limit for each pollutant is given by National Ambient Air Quality (NAAQ) Standards, which is classified into three categories: residential, industrial and rural areas and ecologically sensitive areas (Table 4.3).

114

4 Changing Urban Environment in Megacities

Table 4.3 Permissible limits and sources for selected pollutants 24-h Mean concentration range (μg/m3 )

Sourcesa

Pollutants

Annual mean concentration range (μg/m3 )

SO2

50

80

Power stations, petroleum refineries, industrial boilers

NO2

40

80

Power plants, electric utility boilers, vehicle emission

RSPM/PM10

60

100

Industries, combustion of fossil fuels, vehicle exhaust, anthropogenic sources like agriculture, construction work, refuse burning

SPM/PM2.5

40

60

Anthropogenic sources like agriculture, construction work, refuse burning, natural sources, windblown dust, forest fire, volcanic eruption, combustion

Source CPCB (2012); a Adopted from Patankar (2009)

Records of air quality were collected from CPCB for Delhi (nine stations) and from MPCB for Mumbai (three stations). Of the nine stations in Delhi, six stations, viz. Pitampura, Sarojini Nagar, Town Hall, Nizamuddin, Janakpuri and Siri Fort, are located in residential areas and three stations, viz. Shahdara, Shahzada Bagh and Mayapuri, are located in industrial areas (Fig. 4.12). Besides, the data for ITO was not available, which is a traffic junction-type observatory (Table 4.4). Of the three stations found in Mumbai, two stations (Bandra-Worli and Kalbadevi) are residential and one station (Parel) is located in industrial area (Table 4.5), whereas the data for traffic junctions (Sion and Mulund) was not available. As for the Bandra-Worli station, during 1990–1999, the station was located at Bandra, and post 2000 was shifted to Worli.

4.3 Data Sources Fig. 4.12 Location and nature of air quality monitoring stations in a Delhi and b Mumbai (Background images are Landsat TM). Source CPCB (2012); www.mpcb.gov.in. Note R—residential, I—industrial, TJ—traffic junction. Bandra–Worli station 1990–1999 the station was at Bandra and post 2000 was shifted to Worli; air quality station data used in Delhi are monitored by NAMP/CPCB and in Mumbai by NAMP/NEERI

115

Janak Puri

Siri Fort

6

2006–11

6

2006–11

5

2006–11

5

Number of years

NO2

Number of years

SPM

Number of years

RSPM

Number of years

2

2010–11

13

1990–2000, 2010–11

13

1990–2000, 2010–11

13

1990–2000, 2010–11

Source Data collected from CPCB 1990–2011

2006–2011

7

2005–11

18

1990–2000, 2005–11

18

1990–2000, 2005–11

18

1990–2000, 2005–11

8

2004–11

19

1990–2000, 2004–2011

19

1990–2000, 2004–2011

19

1990–2000, 2004–2011

8

2004–11

18

1990–94, 1996–2000, 2004–11

18

1990–94, 1996–2000, 2004–11

18

1990–94, 1996–2000, 2004–11

8

2004–11

18

1990–94, 1996–2000, 2004–11

18

1990–94, 1996–2000, 2004–11

18

1990–94, 1996–2000, 2004–11

7

2005–11

7

2005–11

7

2005–11

7

2005–11

8

2004–11

18

1990–94, 1996–2000, 2004–11

18

1990–94, 1996–2000, 2004–11

18

1990–94, 1996–2000, 2004–11

Shahdara

Industrial areas Nizamuddin

Mayapuri

Town Hall

Pitampura

Sarojini Nagar

Residential areas

SO2

Station/Pollutant/years

Table 4.4 Details of available data of air pollution for Delhi

8

2004–11

18

1990–94, 1996–2000, 2004–11

18

1990–94, 1996–2000, 2004–11

19

1990–2000, 2004–2011

Shahzada Bagh

116 4 Changing Urban Environment in Megacities

4.4 Methodology

117

Table 4.5 Details of available data of air pollution for Mumbai Station/pollutant/years

Residential areas Bandra-Worli

Industrial area Kalbadevi

Parel 1990–2011

SO2

1991–2011

1990–2011

Number of years

20

21

21

NO2

1990–2011

1990–2011

1990–2011

Number of years

21

21

21

SPM

1990–2011

1990–2011

1990–2011

Number of years

21

21

21

RSPM

1992–2009

1993–2011

1992–2011

Number of years

17

18

19

Source Data collected from CPCB 1990–2011

4.4 Methodology 4.4.1 Pre-processing of Images for Land Use/Cover Classification Standard False Colour Composites (FCCs) of band 4, 3 and 2 were created using layer stack operation in Erdas Imagine. It displays urbanization in cyan with centric pattern usually; water feature in blue colour and vegetation in dark red (Campell 1996). All the satellite images under study were already geo-referenced to UTM projection (WGS 84), and no spatial mismatch was found in the images. Therefore, no further attempt for geo-referencing was made. Relative radiometric normalization (histogram matching) was used for matching data of one image to other images taken on different dates (Bruce and Hilbert 2006). It converts the histogram of one band of an image to resemble other histogram of other image. The satellite images have been radiometrically corrected in order to make them usable. Initially, the subset of study area was taken out of the satellite image. Thereafter, following steps were adopted for pre-processing second (green), third (red) and fourth (near-infrared) bands. The steps are well described in Landsat 7 Science Data Users Handbook, Chander and Markham (2003), Bruce and Hilbert (2006), Chander et al. (2009) and http://giswin.geo.tsukuba.ac.jp (Fig. 4.13). Step 1 Conversion of the DN to spectral radiance (L)

L λ = L MIN + (L MAX − L MIN ) ∗ D N /255

(4.1)

where L λ = Spectral radiance, L MIN = 1.238, L MAX = 15.600 and DN = Digital number

118

4 Changing Urban Environment in Megacities Changing urban environment in megacities: Delhi and Mumbai, 19902010

Land Use / Cover Change Analysis

Air Quality Analysis

Images downloaded and all bands were combined to form multi-spectral image using Erdas Imagine

Secondary data on daily/monthly levels of SO2, NO2, SPM and RSPM collected and compiled

Pre-processing: Histogram matching and radiometric corrections Conversion of the Digital Number (DN) to spectral radiance (L) Conversion of spectral radiance to reflectance

Stations in Delhi: 6 Residential and 3 Industrial; Stations in Mumbai: 2 Residential and 1 Industrial

Annual and Seasonal pattern and trend from 1990 to 2010 analyzed

Image Classification: Supervised classification using Maximum Likelihood for LULC mapping

Factors responsible for changes in urban environment:

Cross classification for LULC change mapping

a. Population increase as a factor of LULC change

Post Classification Processing using Recode methods

b. Vehicular Growth as a factor of air quality

Area of Interest selected

Fig. 4.13 Methodological framework

Step 2 Conversion of spectral radiance to reflectance

ρλ = π d 2 L λ /E 0λ cos θs

(4.2)

where ρ λ = Reflectance, d = Earth–Sun distance (astronomical units), L λ = Radiance, E 0λ = Mean solar exoatmospheric irradiance, π = 3.14159, θ s = Angle of solar zenith (degrees).

4.4 Methodology

119

Step 3 Supervised classification Step 4 Cross-classification and LULC change mapping.

4.4.2 Land Use/Cover Classification, Mapping and Change Detection For the study, supervised classification method has been used in ArcGIS 10.1. The LULC classification was done on the basis of reflectance characteristics of the different LULC types. Decisions were made on the multispectral satellite image to be classified such as water, urban areas and agricultural croplands. Training sites were created by drawing polygons or rectangles, by choosing similar pixels with known land cover to form training sets (here Google Earth has been used for better understating of features of past dates) (Campell 1996). Overall, 117 trainings samples for agriculture, 66 training samples for built up, 34 training samples for water, 60 training samples for vegetation and 29 training samples for wasteland/rock outcrop were created, merged and managed in training sample manger in ArcGIS. Using maximum likelihood as a parametric rule carried out the supervised classification. For LULC classification, mapping and change detection, broadly six categories were identified in Delhi (agriculture, vegetation, built up, water body, wasteland or rock outcrop) and seven in Mumbai in addition to wetlands (Table 4.6). Table 4.6 Land use/cover classification LULC class

Description

Agriculturea,b

Area under cropping including the fallow land

Vegetationa,b

It includes forest areas and tree cover

Urban built

upa,b

Area that is covered with human settlements that may be moderately or densely populated. It also includes all asphalt and concrete structures like roads, railway lines, airport and others

Water bodya,b

Water resources like river, streams, canals and lakes or reservoirs

Wasteland/rock outcropa,b

These are mainly the fluvial sediments that include mixed gravel, sand or other river deposits

Wetlandb

Marshy areas along the creeks generally characterized by mangroves and mudflats

a LULC

for Delhi, b LULC for Mumbai

120

4 Changing Urban Environment in Megacities

4.4.3 Post-classification Processing The supervised classification has many errors of misclassification, particularly in the shadow regions giving a poor pattern on image. Making an AOI in the misclassified region and assigning a correct class value by Recode method have rectified such anomalies in the classification. In this process of sub-setting, the imagery was clipped to find out the image of only study area. Converting the digitized administrative boundaries into AOI format and then clipping the imagery did this. After the sub-setting of image, the imagery of 1993, 2000 and 2010 for Delhi and 1991, 2003 and 2010 for Mumbai time period was reclassified by arranging the class name and value in ArcGIS so that the classified images have same ordered class names.

4.4.4 Analysis and Quantification of Differences in LULC The change in LULC of Delhi was obtained by using the Union Matrix module in Erdas Imagine. As the image of 1993 was classified into five classes, so while comparing two imageries that are of 1993 and 2000, it produced 25 classes in the matrix. The same process was repeated for the 2000 and 2010 imagery. The assessment of land use change as a field of enquiry is many decades old, but the methodologies have changed over time. The remote sensing satellite data has been extensively used in recent years (Torres-Vera et al. 2009; Muttttanon and Tripathi 2005). The LULC studies using the satellite images are abundantly available. Since Landsat datasets are available since 1978, it is possible to undertake long-term analysis.

4.4.5 Estimation of Trends of Air Pollution Air pollution is one of the very important indicators of urban environmental changes. For Delhi, six residential and three industrial stations have been used to represent the status of air pollution in the city. For Mumbai, two residential and one industrial stations are used to understand the pattern of air quality. The spatio-temporal analysis of pollution data was carried out after averaging the daily pollution records to monthly and further to annual averages. The trend of levels of SPM, RSPM, SO2 and NO2 was analysed (1990–2011) with respect to the annual trend, monthly fluctuations and according to the nature of station. The seasonal pattern is conducted for a maximum period of 19 years (1990–2011) as the available data is discontinuous (Table 4.5). Nevertheless, the long-term nature of dataset is an advantage for the present analysis. Simple linear regression has been applied to understand long-term trend of different pollutants in study area of Delhi and Mumbai.

4.5 Results and Discussion

121

4.5 Results and Discussion 4.5.1 Land Use/Cover Change in Delhi and Mumbai The LULC classification maps were cross-classified and analysed to understand the changes in LULC for both Delhi (1993, 2000 and 2010) and Mumbai (1991, 2000 and 2010). The results reveal that there has been an apparent change in the LULC that is associated with expansion and intensification of concrete areas on the surrounding areas including the forest cover, water bodies and agricultural lands. The concrete areas of Delhi and Mumbai have experienced unprecedented horizontal and vertical growth, leading to the major modifications of previously occurring LULC.

4.5.1.1

LULC Changes in Delhi: Patterns and Trends

The LULC classification for Delhi is categorized into five major classes, namely water bodies, wasteland/rock outcrop, vegetation, built up land and agricultural area. The most striking change is observed in built up and agricultural area (Table 4.7). The concrete areas in Delhi increased from 351 to 476 km2 (1993–2000) and further to 591 km2 in 2010. The area under agriculture shows a declining trend. In 1993, it covered an area of 938 km2 that reduced to 821 km2 in 2000 and later in 2010 to 711 km2 . The natural vegetation cover has increased since 1993, while the area under water bodies has shrunk. The forest and tree cover reduced from 100 to 95 km2 (1993–2000) but later increased to 102 km2 (2010) owing to the massive plantation drive of the Delhi government under the Bhagidari Project and other afforestation programmes. The tree cover along the linear features like roads, highways and other open areas has been effective in increasing the vegetation cover in the city. There has been striking fall in the total area under the water bodies. Nearly 10 km2 area was lost or reclaimed since 1993 as the total area under water bodies reduced from 36 to 25 km2 . The pattern of LULC change clearly reflects growth of concrete urban areas at the cost of agricultural land and water bodies. Hence, the rural pervious land is converted to urban impervious land use. Other research groups have undertaken the LULC analysis for Delhi as well. As per Mallick et al. (2012), nearly 38% of the total area of Delhi was covered with high Table 4.7 Land use/cover change in Delhi in 1993, 2000 and 2010 (in km2 )

LULC Class

1993

2000

2010

Water bodies

36.2

30.3

25.3

Wasteland/rock outcrop

46.7

49.3

42.8

Vegetation

100.0

95.6

102.3

Built up land

351.2

476.5

590.8

Agriculture

938.8

821.3

711.7

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4 Changing Urban Environment in Megacities

and low built up areas in 2009. Followed by the built up areas, the dominant land cover of Delhi is agricultural land along the banks of the River Yamuna occupying approximately 36% area, followed by dense and sparse vegetation (15%) and water bodies with merely 1.1% area. Similar results are reflected by Mohan et al. (2011). The research concludes that there has been positive change in urban area while negative in crop land, fallow and scrub land and water bodies (Fig. 4.14). Kumar (2013) states that in 1901, the urban area in Delhi was merely 43.25 km2 that increased by 15 times in 9 decades, i.e. by 1991 covering 624 km2 that further rose to 792 km2 (2001). However, this LULC change has spatial variations (Figs. 4.15, 4.16 and 4.17). In 1993, the dense city was concentrated in south, central and north-east parts of the city. The transportation lines radiated towards North, West and South-West Delhi from the dense built up and accordingly the population moved to these areas. Many small villages are scattered in the extreme South and West Delhi that are surrounded by agricultural land use. A peculiar feature that can be identified is water body located in each village. By 2000, the geographical city expanded towards north, south-east and west. Similar results are also cited by Roy et al. (2011) stating that there is general horizontal spread along the transport corridor that is accompanied by selective vertical expansion in the city. As a result, planned and unplanned residential and industrial growth has taken place especially in North and North-East Delhi. The area under airport expanded, while the vegetation cover declined in central Delhi. The villages in the west and extreme north increased in size and density at the cost of agricultural land. Mixed land uses are found in this part of the city (Roy et al. 2011). In 2010, these expanding villages turned into towns and assimilated with the built up area of the city. The less or sparse dense built up became highly dense areas especially in the North, North-East and South-East Delhi (Fig. 4.18). Massive urban growth took place along the roads and highways. The vegetation cover increased in the central region and around the airport. The airport area occupied almost double

Fig. 4.14 Transformation of agricultural land to built up land use in East Delhi along the River Yamuna

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123

Fig. 4.15 Land use/cover of Delhi in 1993

the area than in 1993. In response to growing urbanization, the small water bodies disappeared and the width of the River Yamuna also reduced to an extent. The loss of pervious LULC has serious implications on environmental health and wellbeing that is dealt in later chapters. The LULC change analysis was carried out in two phases: (a) 1993–2000 and (b) 2000–2010 to understand the decadal changes (Figs. 4.19 and 4.20). The pattern on LULC change detection detects definite loss of natural land covers like water bodies and vegetation in lieu of residential and agricultural land uses. Water bodies in the city declined mainly to agriculture followed by built up areas. Notably, many small lakes and floodplain areas along the River Yamuna were reclaimed to meet the needs of growing population. Similarly, vegetation cover too shrank. The vegetation-covered area was mainly replaced by the concrete residential, transport and commercial uses. Some areas were also encroached for agricultural uses. Agricultural area in the city also expanded due to conversion of rock outcrop/wasteland to agriculture. Built up areas have also substituted the wasteland and rock outcrop.

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4 Changing Urban Environment in Megacities

Fig. 4.16 Land use/cover of Delhi in 2000

4.5.1.2

LULC Changes in Mumbai: Patterns and Trends

To understand the LULC change in Mumbai, six major LULC types were identified. These are agriculture, rock outcrop, urban, vegetation, water bodies and wetlands. Since the British era, the city expanded in lieu of the natural land cover like swamps through the process of reclamation. The reclamation process continued in the postindependence phase to create space for large streams of in-migration to the city from other districts of Maharashtra as well as nearby states. Mukhopadhyay (2005) reveals that built up area constituted 28.93%, while mud, marshes, hills, forests and open areas constituted nearly 60% of the total area in 1970s. But after 1980s till 2001, there was massive growth leading to shrinkage of natural land covers. Mumbai city has been 100% urban since 1991, and since then the area under built up land use and its density has increased tremendously. Surrounded by the Arabian Sea from three sides, the city outgrowth was forced to extend towards the northern part of Mumbai suburban district. The suburban district is not suburban in nature but merely named so and not related to the features of suburban areas.

4.5 Results and Discussion

Fig. 4.17 Land use/cover of Delhi in 2010

Fig. 4.18 East Delhi housing colonies

125

126

4 Changing Urban Environment in Megacities

Fig. 4.19 Land use/cover change in Delhi (1993–2000)

4.5 Results and Discussion

Fig. 4.20 Land use/cover change in Delhi (2000–2010)

127

128

4 Changing Urban Environment in Megacities

The Mumbai city acts as the main Central Business District (CBD) as there is marked expansion of commercial activities (Pathan et al. 1993). The city exhibits spatial segregation of functions, and there is clear presence of the core or CBD near the Fort area in south Mumbai. This CBD was developed under the British rule. The CBD was dominated by big industries, commercial and retail activities, docks and rail yards and textile mills inter-mingled with low-income residential areas (Mukhopadhyay 2005). One of the prominent low-income residential areas near CBD is the present Dharavi slum. The closure of mills and decline in port/dock function led to decline in industrial area in 1980s. Later these mills underwent transformation whereby they were converted to malls, entertainment centres and office complexes (Mukhopadhyay 2005). Post 1990s, activities of CBD like financing, insurance, banking, transportation, hotels, theatres, art galleries and head quarters of multi-national companies attracted population. Due to overgrowth of population and congestions, secondary CBD at Nariman Point was developed in 1990s (see Fig. 3.2, Chap. 3) (Fig. 4.21). Till 1990s, there was dense wholesale market north to the CBD. Later this was shifted to Navi Mumbai. Exclusive streets specializing in jewellery, stationary, cloth and others still exist. The naming commercial areas in Mumbai were the textile industry that dominated the landscape till 1980s. These occupied the regions of Lalbaug, Parel, Nigaum, Worli and Prabhadevi. With the closure of textile mills, the region was developed with high-class infrastructure and facilities like high-rise apartments, shopping malls, hotels and sports complexes. While the Western suburbs attracted upper-class residential groups, the Eastern suburbs exerted a pull on industries. The Malabar hill, Cumballa hills, Bandra, Juhu and Khar are occupied by the high-class income groups. On the other hand, Eastern suburbs developed with heavy engineering, petro-chemical and other industries. This included the regions of Chembur and Ghatkopar. In the recent past, the Mumbai city is experiencing de-urbanization and people are moving towards the

Back Bay

Nariman Point

Koli Fishermen village

Hotel Taj & Gateway of India

Antilia: Mukesh Ambani’s house

Fig. 4.21 Bird’s eye view of CBD, Nariman Point and other important locations in Mumbai

4.5 Results and Discussion

129

Navi Mumbai due to better natural environment and up-marker residential developments. New planned business district is also developed called Bandra Kurla Complex (BKC) located in Mumbai suburban district (Sita 2013). New high-rise buildings were constructed in Greater Mumbai of which Hiranandani Complex (Fig. 4.22) is most prominent. As per the Landsat image analysis, in 1991, agricultural land can be found in the extreme north-west of Mumbai suburban district (Gorai and Malad regions) and along the eastern coast (Mulund east to Ghatkopar east). The natural vegetation is quite healthy in the city as well as suburban district. Mangroves occupy the eastern coast, while the western coast is largely rocky or concretized (Fig. 4.23). The creeks and rivers entering from east and west coasts are clearly visible representing moderate to high water depth. Scattered small lakes in the city and suburb are visible. Twelve years later, in 2003, there is clear intensification of built up area in the city with reduction in vegetative cover (Fig. 4.24). Substantial rise in dense urban built up area is prominent in the Western and Eastern suburbs. There is apparent replacement of natural land covers like vegetation cover and water bodies, and cultivated land (Fig. 4.25). The Powai Lake shrank in 2003 owing to siltation, encroachment and reclamation (Salaskar et al. 2008) (Fig. 4.27). Some portions of Manori and Malad Creek in the west coast were reclaimed for cultivation, while the agricultural areas in the east coast were encroached by urban land uses. There is encroachment of the SGNP from south and south-west that was occupied by the slums and squatter settlements. Further in 2010, there is escalation of built up land following the Eastern and Western suburban railway track (Fig. 4.26). The agricultural, vegetation and water body area in extreme north (east and west) was replaced by settlements. The SGNP is shrinking due to encroachments by people.

Fig. 4.22 Hiranandani Complex in Mumbai suburban district

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Fig. 4.23 Land use/cover of Mumbai in 1991

The Vihar Lake shrank, and patchy settlements are found between the two lakes, i.e. within SGNP area. Small lakes found in the eastern wetlands also have contracted in size. The vegetation cover has drastically reduced especially in the airport area and suburb that is lost to urban built up land use. The research on Mumbai by Ramachandra et al. (2014) reveals parallel findings. According to the study, the share of built up area increased from 7.32 to 14.26%, while vegetation declined from 21.9 to 16.2 and water bodies from 45.8 to 44.7% (1992–2009). The LULC conversion (Figs. 4.28 and 4.29) from various land uses to urban land use in the former decade (1991–2003) saw intensification in the city and expansion in the suburban district. Later in 2000–2010, spreading out of the urban growth took place. There was loss of all forms of LULC (water bodies, wasteland, wetland, vegetation and agricultural land use) for urban growth. However, most affected LULC was vegetation, agricultural area and wetlands (Table 4.8).

4.5 Results and Discussion

131

Fig. 4.24 Land use/cover of Mumbai in 2003

Fig. 4.25 Clearance of vegetated hill areas for urban development in Mumbai suburban district

132

4 Changing Urban Environment in Megacities

Fig. 4.26 Land use/cover of Mumbai in 2010

Fig. 4.27 Shrinking of Powai Lake located in Mumbai suburban district

4.5 Results and Discussion

Fig. 4.28 Land use/cover change in Mumbai (1991–2003)

133

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4 Changing Urban Environment in Megacities

Fig. 4.29 Land use/cover change in Mumbai (2003–2010)

4.5 Results and Discussion Table 4.8 Land use/cover change in Mumbai in 1991, 2000 and 2010 (in km2 )

135 LULC class

1991

2003

2010

Water bodies

17.5

15.1

11.8

Built up area

210.1

244.6

253.8

14.7

17.3

14.9

6.5

5.9

5.4

Vegetation

128.6

111.5

114.8

Agriculture

97.8

80.8

74.5

Wetland Rock outcrop

Note Area under each land use/cover is in km2

Water bodies in Greater Mumbai have shrunk on account of reclamation for built up areas, cultivation and sometimes vegetation. The coastal areas of Mumbai have been rich in mangroves and wetlands. They form a vital component of city land use. These wetlands act as a barrier to high tidal surges, purify coastal water and are crucial for maintaining the ecosystem balance. However, there has been substantive drop in the wetlands owing to ambitious infrastructural projects and urban development. Along with these changes, reclamation mainly of swampy coastal areas is a major cause of LULC change along the coast of Mumbai. Even the dominant vegetation cover of Mumbai, i.e. SNGP, is shrinking. Encroachment along the western and southern boundaries of SGNP by the slum dwellers for constructing hutments and practicing agriculture are becoming common (Zerah 2007). The trend that emerges from the LULC change for both the cities suggests replacement and swapping of all possible land uses for accommodating the residential, industrial and transport needs of city growth. The urban development focuses on provision of infrastructure as against sustainable urban environment. Multiple irreversible amendments have been made on the land as well as water resources that pose threat to urban environmental sustainability (Samant and Subramanyan 1998). These include imbalance in the heat exchange and microclimatic changes that further poses risk to human health.

4.5.2 Status of Air Quality Change in Delhi and Mumbai In response to the industrialization, vehicularization, motorization and increased energy consumption, urban areas are experiencing rise in respiratory organ related diseases. The World Health Statistics (2014) records 18 and 14% of mortality of children below 5 years of age due to acute respiratory infections in India for 2000 and 2012, respectively. It is alarming to note that diseases of the respiratory system are third largest cause of all deaths in the country (Government of India 2010). The proportion of mortality rates from respiratory illness increased from 7% (2000) to 9.5% (2011) in India. The study reflects that there are multiple factors affecting the quality of air over the period of time. These are meteorological and atmospheric conditions, vehicular fleet, fuel quality, fossil fuel burning, industries and power

136

4 Changing Urban Environment in Megacities

plants, road condition and government policies. The pollution inevitably has been recently rising for both the megacities of Mumbai and Delhi, but they follow different annual and seasonal trends for each pollutant under study.

4.5.2.1

Annual Trend of Air Pollution in Delhi

Analysis of annual mean levels of SO2 indicates that it has mostly been lower than the prescribed limit (50 μg/m3 ), since 1990 for both industrial and residential areas. The SO2 concentration has been recorded highest in 1994 at Town Hall (46.7 μg/m3 ) and Shahzada Bagh (30.5 μg/m3 ) for residential and industrial areas, respectively (Fig. 4.30a, b). On the whole, the residential observatories (except Town Hall) record

Sulphur dioxide (in µg/m3)

(a) 50 40 30 20 10 0

Sulphur dioxide (in µg/m3)

(b)

Pitampura

Sarojini Nagar

Town Hall

Nizamuddin

Janakpuri

Siri Fort

35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0

Shahdara

Shahzada Bagh

Mayapuri

Fig. 4.30 a Annual trend of SO2 in residential areas in Delhi (in μg/m3 ). Source Based on data from CPCB 1990–2011. b Annual trend of SO2 in industrial areas in Delhi (in μg/m3 ). Source Based on data from CPCB

4.5 Results and Discussion

137

lower SO2 levels than those of Industrial observatories. All stations (residential and industrial) record declining trend in SO2 concentrations. The main sources of SO2 in Delhi are fossil fuel burning in power sector and thermal power plants followed by the transport sector (Chelani and Devotta 2007; Datta et al. 2010; Sindhwani et al. 2015). Overall lower concentrations of SO2 and further declining trend over the period in Delhi may be due to the improvement in quality of diesel fuel used or stricter policy control measures like the use of CNG in vehicles. Besides, shifting of industries from residential areas to the outskirts of Delhi in the year 1999 is also considered as an effective policy measure to reduce SO2 levels. Post 2004, all residential as well as industrial areas have lower than 10 μg/m3 SO2 levels, which is considerably lower than prescribed limits of 50 μg/m3 suggested by CPCB. The levels of NO2 indicate an increasing trend and are much above the prescribed limit of 40 μg/m3 in recent past (Fig. 4.31a, b). Steep rise in the level of NO2 has been observed in industrial observatories. There is clear difference in the residential and industrial observatories. The residential observatories (except Town hall) have recorded lower levels of NO2 than industrial observatories. During 1990–2000, the levels of NO2 remained below 40 μg/m3 for residential and below 50 μg/m3 for industrial areas. However, during 2000–2011, sharp increase can be noticed industrial areas (up to 75 μg/m3 ), whereas the residential areas observed minor increase wherein the level of NO2 was recorded below 60 μg/m3 . The NO2 is produced due to road traffic and resulted increased burning of fossil fuels at higher temperature. The massive rise in vehicular fleet is considered a major cause for this increase. The rise of NO2 is a threat to environmental and human health as the reactions of NOx in presence of sunlight produce ground-level ozone that is highly dangerous to human health (Lo and Quattrochi 2003). The SPM and RSPM levels are strikingly soaring in both residential and industrial areas (Figs. 4.32a, b, 4.33a, b). The annual permissible limit for SPM and RSPM are 40 and 60 μg/m3 , respectively. Incomplete fuel combustion processes from industries and vehicles are major sources of PM of varied sizes. The natural sources, viz. road dust and meteorological conditions also add to the RSPM and SPM. Other anthropogenic sources are agriculture, construction work, fire places, refuse burning, etc. Sindhwani et al. (2015) mention that re-suspension of road dust by reduced night traffic is an important source of RSPM. These are menace to human health and wellbeing including physical, social and mental wellbeing. In the process of rapid urbanization and need for intra- and inter-city movement, the human activities have tend to produce large quantities of complex structures of particulates. The annual temporal analysis of SPM and RSPM suggests upward trend. On an average, the level of SPM is observed lower in residential areas than that of industrial areas. The level of SPM was always below 400 μg/m3 in residential areas (except Town hall) during 1990–2000, which increased to nearly 500 μg/m3 during 2000–2011. The industrial areas observed SPM between 350 and 480 μg/m3 during 1990–2000, which was very high during 2000–2011. The level of SPM was recorded highest at Nizamuddin station (526) in 2009 and Mayapuri (575) in 2010. The SPM

138

4 Changing Urban Environment in Megacities

Nitrogen dioxide (in µg/m3)

(a) 120 100 80 60 40 20 0

Nitrogen dioxide (in µg/m3)

(b)

Pitampura

Sarojini Nagar

Town Hall

Nizamuddin

Janakpuri

Siri Fort

80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

Shahdara

Shahzada Bagh

Mayapuri

Fig. 4.31 a Annual trend of NO2 in residential areas in Delhi (in μg/m3 ). Source Based on data from CPCB. b Annual trend of NO2 in industrial areas in Delhi (in μg/m3 ). Source Based on data from CPCB

level was always much higher than the prescribed limit but have more than doubled since 1990. The RSPM has been recorded remarkably higher in industrial areas than residential areas. The particulates claim a remarkable share in the pollutant load of the city and hence, have profound effect on the lungs, respiratory tract and circulatory system.

4.5 Results and Discussion

139

(a) 700 600 SPM (in µg/m3)

500 400 300 200 100 0

Pitampura

Sarojini Nagar

Town Hall

Nizamuddin

Janakpuri

Siri Fort

(b) 800.0 700.0

SPM (in µg/m3)

600.0 500.0 400.0 300.0 200.0 100.0 0.0

Shahdara

Shahzada Bagh

Mayapuri

Fig. 4.32 a Annual trend of SPM in residential areas in Delhi (in μg/m3 ). Source Based on data from CPCB. b Annual trend of SPM in industrial areas in Delhi (in μg/m3 ). Source Based on data from CPCB

4.5.2.2

Monthly Average of Air Pollution in Delhi

Due to variations in temperature, rainfall and humidity levels, wind direction and other climatic factors, the levels of pollutant vary across the seasons. The SO2 concentration is observed highest in the winter season, particularly the months of January and December owing to the stagnant stable air masses (Fig. 4.34a). Datta et al. (2010) suggest that high SO2 levels in winters may be due to thermal power plants located in the north-west of the city that may transport harmful gases under the influence of north-westerly winds. Further, the higher concentration may be related to lower

140

4 Changing Urban Environment in Megacities

(b)

300

RSPM (in µg/m3)

RSPM (in µg/m3)

(a) 400

200 100

300 250 200 150 100 50

0

0

Pitampura Town Hall Janakpuri

Sarojini Nagar Nizamuddin Siri Fort

Shahdara Mayapuri

Shahzada Bagh

Pollutant concentration (in µg/m3)

Fig. 4.33 a Annual trend of RSPM in industrial areas in Delhi (in μg/m3 ). Source Based on data from CPCB. b Annual trend of RSPM in residential areas Delhi (in μg/m3 ). Source Based on data from CPCB

(a) 60.00

49.23

50.00

46.62

45.74

46.78

44.56

41.22

38.25

37.41

37.49

40.00

53.44

54.96

56.24

12.52

12.91

14.86

O

N

D

30.00 20.00

14.21

12.83

13.33

F

M

12.52

11.80

10.99

10.25

9.45

9.27

A

M

J

J

A

S

10.00 0.00 J

Months

SO2

NO2

Pollutant concentration (in µg/m3)

(b) 600.00 500.00

448.85 409.77

400.00

378.97

474.59

387.42

294.73

300.00

248.84

240.95 199.86

197.67

214.09

248.36

262.84

497.96

300.44

308.62

97.15

104.92

A

S

N

D

219.03

192.28

200.00

491.08 432.11

417.52

123.34

100.00 0.00 J

F

M

A

M

J

J

Months

O SPM

RSPM

Fig. 4.34 a Monthly average of SO2 and NO2 ; b SPM and RSPM in Delhi (1990–2011) (in μg/m3 ). Source Based on data from CPCBRSPM is one of the most dangerous pollutant components

4.5 Results and Discussion

141

limit of tropopause and further to lower and weak convection currents. The lower air temperature tends to sink and settle down all pollutants in lower atmosphere during winters. Jayaraman and Nidhi (2008) and Mohan and Kandya (2007) also state that SO2 is found to be maximum in winters. The SO2 concentration is lowest in the rainy months of July–September. This is largely due to high humidity content that makes pollutant heavy and settle down at ground (Mohan and Kandya 2007; Aneja et al. 2001). In the dry period of October to January, the SO2 gases are found to be high. There are variations in the level of increase of NO2 across the seasons. The winter months observe maximum concentration of NO2 that gradually decreases in spring and summer to reach its lowest limits in rainy season (Fig. 4.34a). The prime factors for this seasonal variation are same as for SO2 . The mobile sources like vehicles are paramount contributors to oxides of nitrogen, and rising vehicularization has increased NO2 content for human health. The RSPM levels are spectacularly low in the rainy season (Fig. 4.34b). This is due to the settling of minute particles under the influence of rainfall and high humidity. The highest RSPM levels are found in winter season that are much above the prescribed limits (60 μg/m3 ). Similar findings are cited in detailed research by CPCB (2006). Similar to RSPM, the SPM levels are much higher than the prescribed limit in all seasons for residential as well as industrial areas. The peak winter and summer seasons have recorded maximum SPM. In winters, the air is stable and there is less mixing in the atmosphere that contributes to high SPM levels in lower atmosphere. On the other hand, in the summer season the pollenization and dryness of air are responsible for high SPM content. Dry air disintegrates the upper soil layers and brings to suspended state in lower atmosphere. Shandilya et al. (2007) indicate that the finer particles stick together or deposit on surfaces easily and rapidly in the rainy season leading to cleaner air during monsoons (Figs. 4.35, 4.36). The seasonal and annual averages suggest that SPM, RSPM and NO2 have

(a)

(b)

(c)

(d)

20.00

70

600.00

60

500.00

400.00 350.00 300.00 250.00 200.00 150.00 100.00 50.00 0.00

15.00

50

400.00

40

10.00 5.00 0.00

30

300.00

20

200.00

10

100.00

0 J A J O Residential Areas Industrial Areas

J MM J S N Residential Areas Industrial Areas

0.00 J MM J S N Residential Areas Industrial Areas

J MM J S N Residential Areas Industrial Areas

Fig. 4.35 Monthly average of a SO2 , b NO2 , c RSPM and d SPM for residential and industrial areas in Delhi (1990–2011) (in μg/m3 ). Source Based on data from CPCB. Note The x-axis shows months, and y-axis represents pollutant concentration

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4 Changing Urban Environment in Megacities

Fig. 4.36 Monthwise probable causes of high and low pollutant levels in Delhi. Source Compiled by the authors based on CPCB data. Note Red colour represents high pollutant level, and green represents low pollutant level

exceeded the prescribed limits of pollution load. The peaks and troughs of pollutant concentration in different seasons suggest its probable variations in health in different time periods. Winters are most harsh, and due to high pollution load, persistent smog is formed (Guttikunda and Gurjar 2012). All the pollutants have been observed to be concentrated more in industrial areas than residential areas.

4.5.3 Status of Air Quality Change in Mumbai 4.5.3.1

Annual Trend of Air Pollution in Mumbai

The annual averages of pollutants indicate that except SO2 , all other pollutants have increased over the past decade in Mumbai (Fig. 4.37a). The trend analysis shows that the air quality of Mumbai has improved in terms of SO2 largely on account of change of fuel used in the industries and also the quality of fuel due to changes in pollution norms. The level of SO2 was, however, always lower than prescribed limits (50 μg/m3 ). Generally, natural gas or oil has been substituted for coal. Also the closure of many medium- and large-scale industries in Mumbai has led to SO2 decline in the city. The level of SO2 has been observed to be higher in industrial areas (Parel) than residential areas. The difference of the two has, however, declined during 1990–2011. High differences of SO2 were noted during 1990s in residential and industrial areas, which have narrowed down recently. On the contrary, the NO2 levels are consistently rising, although are below the permissible limit (Fig. 4.37b). A clear decline can be seen for all stations in the year 1999–2000 but by 2005–2006 sharp increase is evident. Since the rate of increase is higher for the residential centres, this may be on account of rise in motor vehicles. Bandra-Worli and Kalbadevi recorded NO2 levels at 41.5 and 39.7 μg/m3 (2011) from 25.7 to 33.3 μg/m3 (1990), respectively. Parel, however, observed decline of 4 μg/m3 in the same time period (31.9 in 1990 to 28.7 in 2011 μg/m3 ). The PM values generally exceed the permissible limit for both residential and industrial areas and have increased strikingly post 2004–2005 but have recorded

4.5 Results and Discussion

143

Sulphur dioxide (in µg/m3)

(a) 80.00 60.00 40.00 20.00 0.00

Year

Nitrogen dioxide (in µg/m3)

(b)

Bandra-Worli

Parel

Kalbadevi

Year

Bandra-Worli

Parel

Kalbadevi

Year

Bandra-Worli

Parel

Kalbadevi

Parel

Kalbadevi

60.00 50.00 40.00 30.00 20.00 10.00 0.00

(c) SPM (in µg/m3)

500.00 400.00 300.00 200.00 100.00 0.00

(d) RSPM (in µg/m3)

250.00 200.00 150.00 100.00 50.00 0.00

Year

Bandra-Worli

Fig. 4.37 Annual trend of a SO2 , b NO2 , c SPM and d RSPM in Mumbai (1992–2011) (in μg/m3 ). Source Based on data from www.mpcb.gov.in

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4 Changing Urban Environment in Megacities

decline in 2011 (Fig. 4.37 c, d). Nevertheless, the PM levels are still much above the permissible limit and hence are major contributors of ill health, especially in the children and elderly population. The main contributors to PM are smoke from wood, open air garbage burning, variety of construction activities and incomplete combustion from buses, trucks and two-wheelers. The combined factors of smoke, dust, heat and humidity are responsible for high pollution in the city. The level of RSPM has slightly declined recently.

4.5.3.2

Monthly Average of Air Pollution in Mumbai

Similar to the monthly contrast of pollutants in Delhi, Mumbai too experiences worst air quality in winter months from November to February as compared to the other months. The air quality is observed to be best in the rainy season. The monthly averages of all pollutants form a deep U-shaped trend reflecting the impact of dominant atmospheric factors on air quality (Fig. 4.38 a, b). The poor air quality in winters is mainly on account to calm weather conditions, dusty winds and low humidity. On the other hand, heavy rainfall season leads to sharp dip in pollutant levels. The air pollution level is lowest in the monsoon season and highest during winters. The seasonal fluctuations in air pollution levels are attributed to the meteorological conditions like wind direction, turbulence, rainfall, etc. Most of the industries in Mumbai are located in the north/north-east part and north-easterly winds blow in winters leading to high pollution levels in the city. On the other hand, in the monsoon season the wind direction is south/south-west that causes lower pollutant levels in the city. Most polluted sites identified are Khar and Maravli. As per the research on air pollution in Mumbai by NEERI (2010), the major contributors for PM are bakeries, landfill open burning, construction activity, stone

(b) Pollutant concentration (in µg/m3)

Pollutant concentration (in µg/m3)

(a) 50.00 40.00 30.00 20.00 10.00 0.00 J F MAM J J A S O N D

350 300 250 200 150 100 50 0 J FMAM J J A S OND Months

Months

SO2

NO2

SPM

RSPM

Fig. 4.38 Monthly average of a SO2 and NO2 , b SPM and RSPM in Mumbai (1990–2011) (in μg/m3 ). Source Based on data from www.mpcb.gov.in

4.5 Results and Discussion

145

crushing and unpaved road dust. The major sources of SO2 are identified as power plants, industries, railway and domestic sector. The NOx sources were identified as railways, industries, domestic sources, diesel and heavy vehicles, aircraft and landfill open burning. As per the report, all the sources are classified under three types, namely area source (bakeries, cremation, open eatouts, domestic sector, landfill open burning, railway, aircraft, marine vessels and construction activities), industrial sources (power plants, stone crushers) and mobile sources (vehicles and road dust) (NEERI 2010). The seasonal air quality levels’ study for two main traffic junctions, namely Sion and Mulund, was carried out by NEERI (2010). The records at Sion reveal that the SO2 , NO2 and RSPM concentration has increased, while only SO2 is below permissible limit (2005 to 2007) and the latter two have crossed the limit. RSPM reaches maximum in November 2007 (300 μg/m3 ) and NO2 a high of 235 μg/m3 in January 2006. At Mulund traffic junction, the scenario remains much similar to that of Sion with the winter season, experiencing maximum pollutant concentrations. The vehiclewise emission load from different categories of vehicles at Colaba, Dadar, Dharavi, Khar, Andheri, Mahul and Mulund was also calculated. The heavy-duty diesel vehicles and diesel cars contributed to 25, 11.5 and 859.7 kg/day PM, respectively followed by three-wheelers (618). The major contributors of NOx were heavyduty diesel vehicles (18,835.6 kg/day), diesel cars (2,913 kg/day) and two-wheelers (1,480 kg/day). The SO2 emissions are under control as all three-wheelers and taxis run on CNG. Besides, Colaba and Dadar have banned plying of three-wheelers. As a result, diesel heavy vehicles and cars cause SO2 emissions. Srivastava (2004) states that evaporative emissions of VOCs are dominant pollution contributor in Mumbai. The effective solution includes reduction of benzene content in petrol. The benzene content was 3% in 2001 in Mumbai (Srivastava 2004). Detailed analysis of causes per station was also carried out (NEERI 2010). The results reveal that there are variations in the causes of poor air quality in the seven stations. Some common causes are fuel oil combustion, wood combustion and marine sources. However, variations are caused by locational, site-specific and situational factors. For instance, in Colaba station located in Mumbai city, fuel oil combustion, wood combustion and marine sources dominate. On the contrary, in Dharavi, unpaved soil adds to the pollutant load, while in Mahul biogas burning fertilizer industry, petroleum refinery and power plants are identified as specific sources. In Mulund, secondary smelting industry and use of coal for combustion are main sources of air pollution.

4.6 Concluding Remarks The urban environment of Delhi and Mumbai is deteriorating due to changing composition and share of different LULC types. The dominant forces of environmental changes were identified as population and vehicular increase. The urban atmospheric environment shows rise in PM for both the cities. The NO2 concentrations also are hazardous and are noted to be increasing in Mumbai. There is apparent change in

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favour of residential, industrial, commercial and transportation land uses that is in line with the goals of urbanization. The PM level is steeply increasing and therefore has highest impact on human health. These fine particles tend to easily enter the respiratory and circulatory system and cause congestion and other related illnesses. The inter-linkages between air pollution and human health suggest that PM exerts maximum negative influence. The link between health and environment is composed of complex interactive elements that need systematic investigation in trans-disciplinary perspective. The urban population is rapidly increasing at fast pace especially in developing countries. These cities are expected to foresee higher levels of risk and exposure to health hazards. Therefore, it is necessary to unravel the trends and patterns between air pollution and human health. The challenges of this urbanization are embedded in the policies relating to the urban planning and development. This calls for policy level changes that balance urban growth and environmental protection. For the sustainable growth of cities, it is necessary that the pollutant levels and LULC changes are monitored and controlled. For this, the pollutant standards and LULC regulation may be revised and made stricter. There should be stringent implementation of the rules. For maintaining clean environment in the city, green belt around the city needs to be created and maintained. Cleaner fuels and much efficient diesel and petrol vehicles maybe introduced. The next chapter deals with another aspect on urban environment, i.e. identification of UHI in Delhi and Mumbai. The NDVI and NDBI are computed to understand the intensity of UHI. The spatial and temporal changes in the surface temperature, vegetation and built up index are correlated with population rise and other related factors. The understanding of these components will reflect gaps in policy-making.

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Central Pollution Control Board (CPCB) (2006) Air quality trends and action plan for control of air pollution from seventeen cities. Ministry Environ For 1–218 Central Pollution Control Board (CPCB) (2012) National ambient air quality status and trends in India—2010. Ministry Environ For 1–172 Chander G, Markham B (2003) Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Trans Geosci Remote Sens 41(11):2674–2677 Chander G, Markham B, Helder D (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors. Remote Sens Environ 113:893–903 Chelani AB, Devotta S (2007) Air quality assessment in Delhi: before and after CNG as fuel. Environ Monit Assess 125:257–263 Das A, Parikh J (2004) Transport scenarios in two metropolitan cities in India: Delhi and Mumbai. Energy Convers Manag 45:2603–2625 Datta A, Saud T, Goel A, Tiwari S, Sharma SK, Saxena M, Mandal TK (2010) Variation of SO2 over Delhi. J Atmos Chem 65:127–143 Department of Environment and Forests (2010) State of environment report for Delhi, 2010. Government of NCT of Delhi, retrieved from www.environment.delhigovt.nic.in, pp 1–137 Firdaus G, Ahmad A (2011) Changing air quality in Delhi, India: determinants, trends and policy implications. Reg Environ Change 11:743–752 Frumkin H (2002) Urban sprawl and public health. Public Health Rep 117:201–217 Government of India (2010) Report on medical certification of cause of death—2009. Office of Registrar General, Ministry of Home Affairs, India Guttikunda SK, Gurjar BR (2012) Role of meteorology in seasonality of air pollution in megacity Delhi, India. Environ Monit Assess 184:3199–3211 Goyal P, Sidhartha (2003) Present scenario of air quality in Delhi: a case study of CNG implementation. Atmos Environ 37:5423–5431 Jayaraman G, Nidhi (2008) Air pollution and associated respiratory morbidity in Delhi. Health Care Manage Sci 11:132–138 Kumar A (2013) Delhi: Growing problems of a growing megalopolis. In: Misra RP (ed) Urbanization in South Asia: focus on mega cities. Cambridge University Press India Pvt., Ltd., pp 1–532 Landsat 7 Science Data Users Handbook; National Aeronautics and Space Administration (NASA): Washington, DC, USA (2003), retrieved from http://landsathandbook.gsfc.nasa.gov/ pdfs/Landsat7_Handbook.pdf. Accessed 22 Aug 2013 Lo CP, Quattrochi DA (2003) Land-use and land-cover change, urban heat island phenomenon and health implications: a remote sensing approach. Photogram Eng Remote Sens 69(9):1053–1063 Mallick J, Singh CK, Shashtri S, Rahman A, Mukherjee S (2012) Land surface emissivity retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surface of Delhi. Int J Appl Earth Obs Geoinf 19:348–358 Mohan M, Kandya A (2007) An analysis of the annual and seasonal trends of air quality index in Delhi. Environ Monit Assess 131:267–277 Mohan M, Pathan SK, Narendrareddy K, Kandya A, Pandey S (2011) Dynamics of urbanization and its impact on land use/land cover: a case study of megacity Delhi. J Environ Prot 2:1273–1283 Motor Vehicles Department (2011) Motor transport statistics of Maharashtra, 2010–2011, retrieved from moef.nic.inmahatranscom.in/pdf/STATISTICAL.BOOK.10-11.pdf. Accessed 9 July 2016 Mukhopadhyay T (2005) Decadal changes in the spatial order of Mumbai. In: Raiser S, Volkmann K (eds) Emerging patterns of the global city region: spatial changes in Johannesburg, Mumbai/Bombay, Shanghai and São Paulo, ISSN 1434-419X, pp 31–47 Muttttanon W, Tripathi NK (2005) Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. Int J Remote Sens 26(1):2311–2323 National Environmental Engineering Research Institute (NEERI) (2010) Air quality assessment, emissions inventory and source apportionment studies: Mumbai. National Environmental Engineering Research Institute, Mumbai

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Patankar AM (2009) Health effects of urban air pollution—a study of Mumbai. Unpublished Ph.D thesis, Humanities and Social Science Department, Indian Institute of Technology, Bombay Pathan SK, Sastry SVC, Dhinwa PS, Rao M, Majumdar KLL, Sampat Kumar D, Patkar VN, Pathak VN (1993) Urban growth trend analysis using GIS techniques—a case study of the Bombay metropolitan region. Int J Remote Sens 14(17):3169–3179 Planning Department of Delhi (2000) Economic survey of Delhi—1999–2000, retrieved from http:// delhiplanning.nic.in. Accessed 11 July 2014 Planning Department of Delhi (2001) Economic survey of Delhi—2000–01, retrieved from http:// delhiplanning.nic.in. Accessed 9 June 2014 Planning Department of Delhi (2006) Economic survey of Delhi—2005–06, retrieved from http:// delhiplanning.nic.in. Accessed 9 June 2014 Planning Department of Delhi (2009) Economic survey of Delhi—2008–09, retrieved from http:// delhiplanning.nic.in. Accessed 9 June 2014 Planning Department of Delhi (2013) Economic survey of Delhi—2012–13, retrieved from http:// delhiplanning.nic.in. Accessed 9 June 2014 Ramachandra TV, Bharath HA, Sowmyashree MV (2014) Urban footprint of Mumbai—the commercial capital of India. J Urban Reg Stud 6(1):71–94 Roy SS, Singh RB, Kumar M (2011) An analysis of local spatial temperature patterns in the Delhi metropolitan area. Phys Geogr 32(2):114–138 Salaskar PB, Yeragi SG, Gordon R (2008) Environmental status of Powai Lake, Mumbai (India). In: Sengupta M, Dalwani R (eds) Proceedings of Taal 2008: the 12th world lake conference, pp 1650–1654 Samant HP, Subramanyan V (1998) Land use/land cover change in Mumbai—Navi Mumbai cities and its effects on the drainage basins and channels—a study using GIS. J Indian Soc Remote Sens 26(l&2):1–7 Shandilya KK, Khare M, Bhusham Gupta A (2007) Suspended particulate matter in rural—industrial Satna and in urban—industrial South Delhi. Environ Monit Assess 128:431–44 Sindhwani R, Goyal P, Kumar S, Kumar A (2015) Anthropogenic emission inventory of criteria air pollutants of an urban agglomeration—National Capital Region (NCR), Delhi. Aerosol and Air Qual Res 15:1681–1697 Singh SK (2012) Urban transport in India: issues, challenges, and the way forward. Eur Trans 52(5):1–26 Sita K (2013) Mumbai: the financial capital of India. In: Misra RP (ed) Urbanization in South Asia: focus on mega cities. Cambridge University Press India Pvt, Ltd., pp 78–108 Srivastava A (2004) Source apportionment of ambient VOCs in Mumbai city. Atmos Environ 38:6829–6843 Torres-Vera MA, Prol-Ledesm RM, Garcia-Lopez D (2009) Three decades of land use variations in Mexico city. Int J Remote Sens 30(1):117–138 Transportation Department (2005) Government of NCT, Delhi United Nations Human Settlements Programme (2011) Cities and climate change: global report on human settlements. Earthscan, London and Washington DC, USA World Health Organization (WHO) (2014) World health statistics 2014. ISBN 978 92 4 156471 7, ISBN 978 92 4 069267 1 (PDF), Italy Yang X, Lo CP (2002) Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. Int J Remote Sens 23(9):1775–1798 Zerah M-H (2007) Conflict between green space preservation and housing needs: the case of the Sanjay Gandhi National Park in Mumbai. Cities 24(2):122–132

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Web Reference Earthexplorer (2015) www.earthexplorer.usgs.gov. Accessed on 4 Jan 2015 Maharashtra Pollution Control Board (2015) www.mpcb.gov.in. Accessed on 4 Jan 2015 Spatial Information Science Lab, University of Tsukuba, Japan (2015) http://giswin.geo.tsukub. Accessed on 2 Jan 2015

Chapter 5

Urban Microclimates

Abstract This chapter presents the detailed account of causes of formation of UHI followed by establishing inter-relationships between LST, NDVI and NDBI. It deals with assessment of land surface temperature, UHI and UMC using Landsat satellite data. The two most dominant factors of urban microclimatic changes are considered as state of vegetation and built up land. While the green cover has cooling effect, the built up concrete land has warming effect in the region. NDVI indicates vegetation health and NDBI indicates built up density (concrete surface). NDVI and NDBI have also been assessed as the predictor and factor of urban microclimate. Both NDVI and NDBI are correlated with LST to identify the changes in microclimates of Delhi and Mumbai. Keywords SUHI · NDVI · NDBI

5.1 Introduction The process of urbanization has gained pace during the last few decades, especially in the developing countries of the world. The urban growth mainly owes the causes to large scale in-migrations from rural hinterland to towns and cities. The process of settling added population has transformed LULC. These modifications in existing LULC types have further induced changes of one LULC type to the other, urban heat balance, process of heating and cooling of urban surface and environmental status of cities across the world. The natural land covers like the vegetation areas and the water bodies have been encroached upon on a large scale due to intense human activities. The urban growth and associated concretization is major factor contributing to relatively elevated temperature conditions in concerned areas as compared to the lower temperature in rural hinterland. The differential temperature between rural and urban areas leads to the formation of UHI effect in urban areas. In light of increasing urban activities, the microclimatic changes have intensified in recent decades. These have far reaching and long-lasting consequences on the global climate, environment, human health and wellbeing. UHI phenomenon has led to increased heat event experiences causing illnesses and mortality related to the heat waves. The higher temperatures also have adverse © Springer Nature Singapore Pte Ltd. 2020 A. Grover and R. B. Singh, Urban Health and Wellbeing, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-13-6671-0_5

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effects on the air quality and subsequently human health. Many researches have concluded that degraded air quality coupled with rising temperature causes respiratory and cardiovascular illnesses and diseases. According to Lo and Quattrochi (2003), VOCs and NOx , emitted from industries, power plants, vehicles and combustion of fossil fuels, in presence of sunlight react to form ground-level ozone (Cardelino and Chameides 1990) that is a public health hazard. Increased pollution levels strongly associated with amendments in surroundings and urban lifestyles are the prime cause of degrading health in urban areas. The LST is an effective indicator of microclimatic changes and a function of LULC. LST is also an important marker of urban heat balance and urban health. The relationships of LST with related factors need in-depth analysis to understand UHI effect. Change in LST with reference to LULC has been performed with the help of thermal infrared satellite images of various satellites’ sensors at different spatial resolutions. The thermal infrared sensors primarily used for mapping and analysis of LST are Geostationary Operational Environmental Satellite (GOES), NOAA–AVHRR, MODIS, ASTER, Synthetic Aperture Radar (SAR) imager, Satellite Pour I’Observation de la Terre (SPOT) and Landsat (4 and 5) Thematic Mapper (TM) and Landsat (7) Enhanced Thematic Mapper Plus (ETM+) (Kant et al. 2009; Muttttanon and Tripathi Muttttanon and Tripathi 2005). The Operational Land Imager (OLI) has been recently introduced as part of Landsat 8. While the GEOS data has 4 km spatial resolution, AVHHR and MODIS—1 km and ASTER—90 m, the Landsat TM have 120 m, ETM+ have 60 m and OLI have 100 m spatial resolution that makes it efficient in providing finer details necessary to understand the spatial variations. Among the available thermal satellite data, the Landsat TM provides longest time series data (1987–2011) making it an efficient source for long-term changes. Research on changes and impact of urbanization in altering thermal balance has been conducted all over the world, e.g. Lo and Quattrochi (2003) on Atlanta Metropolitan Area, Li et al. (2011) on Shanghai and Bagan and Yamagata (2012) for Tokyo, Zhang and Wang (2008) in different parts of China and Chen et al. (2006) on Pearl River delta. The magnitude and extent of UHIs have been found to be positively correlated with the size of cities, level of urbanization and population size, indicating the significant impact of urban growth on microclimates (Hung et al. 2006). The present chapter explores the relationships between LST, NDBI and NDVI for Delhi and Mumbai with the help of Landsat thermal satellite data. The two most dominant factors of urban microclimatic changes are considered as state of vegetation and built up land. While the green cover has cooling effect, the built up concrete land has warming effect in the region. The vegetation is studied using NDVI and NDBI is used to study the built up area. Both NDVI and NDBI are correlated with LST to identify the changes in microclimates of Delhi and Mumbai.

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153

5.1.1 Urban Environment Urban areas are defined as areas or places, where the predominant economic activities are non-agricultural. The population densities and total numbers in urban areas are very high as compared to rural areas. These areas are characterized by large scale in-migrations from the surrounding hinterland. They are also marked by wellorganized social and economic infrastructure. The urban areas evolve through the process of transformation of natural environment into built environment. It is difficult to differentiate the built urban environment as it includes mixed range of structures, infrastructure, slums and squatter settlements patches of open land, etc. However, the built urban environment can be classified by LULC composition. In the present research, ‘urban’ is in context of spatial unit that is demarcated by the Government of India. The definition of ‘urban’ adopted from Census of India, Government of India is: (a) demographic criteria: total population of more than 5,000 persons; population density of 400 persons per km2 ; (b) administrative criteria: these are the urban areas, as identified by Census of India and (c) economic criteria: more than 75% of the total male population to be engaged in non-primary activities (Census of India 2011). This definition is contested with respect to population and economic criterions. The population variation in the country is immense and the same yardstick cannot be used for all states/union territories. The definition also ignores the contribution of women in secondary and tertiary sectors. Ignoring these nuances and considering the administrative status, the city boundaries (as identified by the government) are selected for the study. These areas may not be strictly contiguously urban but likely to be urban in near future. The determinants of physical environment of the city include sanitation, drainage, drinking water supply, sewage management, air, noise water and soil pollution and the built environment (Vlahov et al. 2007). For the present research, urban environment is taken in context of the physical environment of the city, i.e. represented and analyzed by air pollution, LULC change and UHI.

5.1.2 Urban Heat Island The phenomenon wherein the temperature of urban centres is recorded about 2.5–6 °C higher than the surrounding rural hinterland is called UHI. Various factors including concentration of human activities in urban areas, high density of concrete buildings and surfaces and high traffic density create an ‘island’ of heat surrounded by a ‘sea’ of cooler rural areas called UHI. The first documentation of UHI was done in 1833 by Luke Howrad’s ground-breaking study of London’s climate using metrological data (Stewart 2011). He found that the city had excess heat as compared with surrounding areas. Later, satellite data was used to examine spatial thermal patterns and its causes

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(Voogt and Oke 2003; Li et al. 2012). It has been noted the UHIs are much severe in summers especially in the cities of the tropical climatic zones (Dhakal 2002). Based on the source of dataset, there are two broad types of UHIs—Surface Urban Heat Island (SUHI) and Atmospheric Urban Heat Island (AUHI). The SUHI analysis depends on surface temperature while AUHI depends on atmospheric temperature data analysis. For SUHI, LST that can be acquired from satellite images, whereas atmospheric temperature (from fixed or mobile weather stations) is used to analyze AUHI. Due to different nature of surface and atmospheric temperature, AUHI and SUHI exhibit differences in temporal occurrence. Researches conclude that SUHI is greatest in the day and summers (Oke 1982) whereas AUHI in the night or pre-dawn and in the winters (Lo and Quattrochi 2003). However, sun’s intensity, local weather conditions and location determine seasonal and diurnal UHI variations are different for different areas. SUHI is present at all times of the day and night; only the intensity varies. On the other hand, AUHI may be small or non-existent during the day and is less intense due to continuous mixing of air through convection currents and updraft and downdraft, etc. The variations in SUHI are more prominent than AUHI, owing to the heat-retaining capacity. Since the variations in SUHI are larger and intense, it is more reliable to understand UHI than through AUHI. Also, the SUHI datasets, i.e. satellite images are continuous and readily available for most cities in the world. The AUHI, on the basis of layers in urban atmosphere, is further divided into two types—Canopy Layer Urban Heat Island (CLUHI) and Boundary Layer Urban Heat Island (BLUHI). CLUHI exists from ground level till top layer of trees and roofs. BLUHI starts from tree and rooftop and extends till the point, where urban landscapes do not affect the atmosphere (Voogt and Oke 2003).

5.1.3 Factors Affecting UHI and LST There are various factors affecting UHI such as geographic location, climate, topography, time (season, day), city size, wind direction, status of cloud cover, city geometry, green spaces, city morphology and material used, energy use, pollution and rural surroundings (Voogt and Oke 2003) (Fig. 5.1; Table 5.1). The UHI phenomenon is closely associated with the spatial patterns of LST, which primarily depends on the type and properties of LULC (Lo and Quattrochi 2003; Oke 1995; Singh et al. 2014). Due to accelerated urbanization, natural environment has been drastically replaced by the concrete surface and high rise buildings that have degraded natural landscapes like forests and depleted water bodies. The pervious soil and vegetation surfaces (grass, thatch roofs, dry soil, sand) are substituted by impervious urban built up materials like concrete, metal, steel, stone, tiles and asphalt (Lo and Quattrochi 2003; Zhang et al. 2012; Dhakal 2002). The construction material used in urban areas has lower albedo due to which the energy absorption is high and thus the heat-retaining capacity is comparatively higher (Table 5.2). Additionally, they have less or no water absorption capacity. Apart from this, urban geometry,

5.1 Introduction

Factors affecting UHI Intensity

155

Geographic location

Coastal / Interior

Climate

Season

Local weather

Cloud cover / Winds

Time

Day / Night / Season

City geometry

Building spacing / Height

Air pollution

Green house gases concentration

Energy used

Demand / Fuel / Vehicles / Industries / Power plants

City morphology

Land use/cover/ Construction material used / Albedo / pervious and impervious material

City size

Fig. 5.1 Factors affect creation and intensity of Urban Heat Island (continuous line boxes indicate natural factors and dash line boxes indicate human factors). Source Compiled by the authors from Voogt and Oke (2003); Lo and Quattrochi (2003); Giridharan et al. (2004); Xiao and Weng (2007); Zhang et al. (2012)

particularly, role of height of building is critical in formation and intensification of UHI. Balanced heat budget is critical for maintaining the temperatures of any area. However, both the incoming solar radiations and the outgoing long-wave radiations are trapped between the buildings leading to increase in temperature (Fig. 5.2). Added to this is the anthropogenic heat released from air conditioners, vehicles and industries further intensify UHI. Also, the movement of air is restricted by the high rise buildings. Consequently, the mixing of air is also restricted that is responsible for moderating the temperature. The change in surface material has number of implications on microland—atmosphere energy exchange process, albedo, ratio of reflection and absorption by surface and LST. Generally, higher surface temperature is observed in the cities due to built up—impervious surface and high rise buildings constructed though heavy use of metal, concrete and glass as compared to surrounding rural landscapes characterized by green cover and pervious surfaces. The UHI phenomenon is manifestation of these LULC modifications (Xiao and Weng 2007). There exist intricate relationships between UHI, LST and LULC that are extensively studied for various cities of the

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Table 5.1 Major causes of UHI formation with explanation Cause

Explanation

Human-made changes in the urban environment

Human-made changes in the urban environment have worsened the heat balance in which the radiation balance is disrupted, and on the one hand, there is high absorption of long-wave radiation, and on the other, the absorbed radiation is unable to radiate back to the environment

Higher use of energy sources

In urban areas, there is higher use of energy sources, increased traffic and industries, decrease in surface evaporation and higher anthropogenic heat discharge. Higher population densities and per capita consumption releases large amount of GHGs in the atmosphere, changes surface morphology and geometry that intensify UHIs

LULC changes

Changes in the nature of composition of surface due to LULC changes have altered the albedo, solar reflectivity, evaporative efficiency and radiation. The surfaces covered with asphalt, steel, stone, concrete and tiles have lower albedo than grass, thatch roofs, dry soil, sand and other permeable land uses

Impervious surface

Conversion of pervious to impervious surfaces and LULCs like from vegetative area to urban areas. Built up areas have lesser moisture and evaporation, lower albedo (reflectivity) and as a result higher temperature

Urban geometry

Urban geometry refers to dimensions (height) and spacing of buildings within a city and it influences wind flow, energy absorption and ability of the surface to reradiate absorbed long-wave radiation back to the atmosphere. In the regions of high building density and high rise areas, the heat is trapped due to building geometry and UHI is intensified

Local weather and location

Local weather and location are also important causes of UHI. The calm winds and clear sky intensify UHI while strong winds and cloud cover reduce UHI. This is why the rainy season has lower UHI than the summers. Locational factors like topography, site and situation also influence UHI creation. Presence of large water body or nearby mountain range reduces high temperatures

Source Compiled by the authors from Voogt and Oke (2003); Lo and Quattrochi (2003); Giridharan et al. (2004); Xiao and Weng (2007); Zhang et al. (2012)

5.1 Introduction

157

Table 5.2 Albedo of selected surfaces and cover types used in urban areas Material of surface

Albedo range

Tar and gravel

0.03–0.18

Asphalt

0.05–0.20

Corrugated roof

0.10–0.15

Red and brown tile

0.10–0.35

Concrete

0.10–0.35

Trees

0.15–0.18

Coloured paint

0.15–0.35

Brick and stone

0.20–0.40

Grass

0.25–0.30

White paint

0.50–0.90

Highly reflective roof

0.60–0.70

Source Adopted from United States Environmental Protection Agency (2003); http://weather.msfc. nasa.gov/urban/urban_heat_island.html

Fig. 5.2 Role of urban geometry in trapping heat and intensifying Urban Heat Island. Source Adopted from United States Environmental Protection Agency (2008)

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Table 5.3 Prominent researches on different factors affecting UHI Factors affecting UHI and LST

Prominent research papers

Geographic location

Kolokotroni and Giridharan (2008)

Climate

Pandey et al. (2009), Singh et al. (2014), Arnfield (2003), Taha (1997)

Time

Nichol (2005), Jongtanom et al. (2011)

City geometry

Barring et al. (1985), Eliasson (1995), Voogt and Oke (1998)

Air pollution

Elminir (2005), Feizizadeh and Blaschke (2013)

Energy

Roy et al. (2011)

City morphology/LULC

Li et al. (2011), Mallick et al. (2008), Zhang et al. (2010), Xiao and Weng (2007)

City size

Hung et al. (2006), Zhang and Wang (2008), Mallick et al. (2012)

Source Compiled by the authors

world. Some recent studies include Li et al. (2011) on Shanghai, Mallick et al. (2008) on Delhi and Beijing by Zhang et al. (2010) (Table 5.3). The spacing of buildings, street geometry (Eliasson 1995) and urban geometry influence wind velocity, movement, direction and energy heat balance also influence UHI intensity. High building density and high rise buildings trap the solar radiation, thereby increasing temperature. Other human-made changes like growth of industries, power plants and vehicular traffic increase the concentration of GHG that further intensify UHIs. The influence of population density, city size and LULC was deeply studied by Zhang and Wang (2008). Coupled with natural factors like on calm, cloud-free day or night, the UHI may be higher, especially in the absence of water body or mountain in vicinity. There has been ample research on diurnal and seasonal variations in UHI (Nichol 2005; Jongtanom et al. 2011; Singh et al. 2014).

5.1.4 Inter-Relationship Between LST, NDVI and NDBI As green cover acts as a heat moderator, they are regarded as heat sinks, and hence, vegetative cover and density have been regarded as one of the prime determinants of LST (Weng et al. 2004). It may be noted that the vegetation density is inversely proportional to LST. Consequently, the areas of healthy vegetation have lower LST and vice versa (Kawashima 1994). NDVI has been used to study vegetation degradation, stress, moisture, etc. making it an important indicator of vegetation health. The higher values of NDVI indicate healthy vegetation and lower values of poor vegetation (Yuan and Bauer 2007). The NDVI is usually estimated using red and near-infrared bands of satellite images. Most of the moderate resolution satellite images have these electromagnetic spectrums. The study of LST and NDVI relationship is very important (Mallick et al. 2008) in present context in view of increased

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159

incidences of heatwaves in summers that cause serious health problems, especially in summer season. In contrast to NDVI, NDBI captures the status and intensity of built up land. The NDBI is positively correlated with UHI and LST phenomenon. Higher temperatures have been found in the densely built up land surface and vice versa. The higher values of NDBI indicate higher density of built up land, which are very well related to higher LST and lower NDBI values are found in poorly built up land, e.g. water bodies, vegetation cover, etc., which correspond to lower surface temperature. Abundant researches on UHI in relation to NDVI and NDBI have been conducted. Yuan and Bauer (2007) correlated the impervious surfaces with UHI phenomena and NDVI. Other like Kawashima (1994), Yue et al. (2007), Weng et al. (2004), Zhang et al. (2012) interlinked LST with UHI and NDVI. Scholars such as Xu (2007), Liu and Zhang (2011) have examined the intensity of UHI with the help of NDBI Weng and Yang (2004), Lo and Quattrochi (2003) have intensively examined the impact of UHI on urban environment and human health.

5.1.5 UHI Studies in India Of the four metropolitan cities in India, Delhi is most extensively studied for UHI and LST phenomenon (Singh and Grover 2014). The Landsat images are analysed by Mallick et al. (2008, 2012), Singh et al. (2014), while Pandey et al. (2009) used MODIS data for UHI research analysis for Delhi. Faris and Reddy (2010) discussed the relationship between LST, LULC and UHI with respect to Chennai using Landsat image. The LST and UHI researches on Mumbai are few. Grover and Singh (2015), Dwivedi et al. (2015) have visually analyzed UHI and LST patterns with NDVI and other underlying factors in Mumbai. These studies confirm the presence of UHI but for different seasons, e.g. Grover and Singh (2015) for April 2009 and Dwivedi et al. (2015) for December 2009. However, none of the studies have analyzed and quantified the degree of influence of various factors of UHI and LST such as vegetation cover, built up land, water bodies, etc.

5.2 Data Sources The analysis of LST, NDVI and NDBI has been done using Landsat TM 5 satellite images that were acquired from www.earthexplorer.usgs.gov. Since, the spatial resolution of thermal band is high and they are available for relatively longer period as compared to other sensors, e.g. AVHHR, MODIS and Landsat images are well-suited for investigation in dynamics of LST and their relationships with NDVI and NDBI and further with LULC. The third (red) and fourth (NIR) bands of Landsat data are used to estimate NDVI, fourth and fifth bands for estimating NDBI and the sixth band (TIR) is primarily used for mapping of surface temperature.

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Table 5.4 Details of satellite images of Delhi and Mumbai City

Satellite

Sensor

Resolution (m)

Path/row

Date

Scene Id

Delhi

LANDSAT 5

TM

30

146/40

09-05-2000

LT51460402000130XXX01

LANDSAT 5

TM

30

146/40

05-05-2010

LT51460402010125KHC00

LANDSAT 5

TM

30

148/47

15-05-1991

LT51480471991135ISP00

LANDSAT 5

TM

30

148/47

17-04-2010

LT51480472010107KHC00

Mumbai

Source Based on the supplementary.txt file downloaded with the images

Keeping in mind the objectives of the study and based on availability of quality images, the two satellite images for Delhi (2000 and 2010) and two for Mumbai (1993 and 2010) were selected (Table 5.4). The main consideration was to opt for images representing summer season, as the SUHI is much intense in summers. The images used are the same used for LULC change analysis. However, for Mumbai, it is not possible to compare the two images as both represent different seasons. The Landsat images for Mumbai, from April to October 1991–2010, were unavailable or covered with high cloud/haze making them unsuitable for analysis. Only the 15 May 1991 was closest to the image of 2010 (17 April 2010) and hence that is used for the present study.

5.3 Methodology 5.3.1 Image Pre-processing The satellite images were already geometrically rectified to UTM projection (WGS 84). The images were thoroughly checked to identify the spatial miss-match, if any. Since no spatial miss-match was noticed, no attempt for further geometric correction was made. The satellite images have been radiometrically corrected in order to make them usable for LST, NDVI and NDBI estimation. The images were also checked carefully for line striping and gaps and required corrections (de-striping, etc.) were made in case of need. Thereafter, following steps were adopted for pre-processing of image for further analysis, which are well described in Landsat 7 Science Data Users Handbook, Bruce and Hilber (2006), Chander et al. (2009) and http://giswin.geo.tsukuba.ac.jp. Conversion of the DN to Spectral Radiance (L) L λ = LMIN + (LMAX − LMIN) ∗ DN/255

(1)

where: L λ = Spectral radiance, LMIN = 1.238, LMAX = 15.303 and DN = Digital number Conversion of Spectral Radiance (L) to Reflectance

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161

ρλ = π d 2 L λ /E 0λ cos θs

(2)

where: ρ λ = Reflectance, d = Earth–Sun distance (astronomical units), L λ = Radiance as a function of bandwidth, E 0λ = Mean solar exoatmospheric irradiance, π = 3.14159, θ s = Angle of solar zenith (°). The stated processes of conversion from digital number (pixel values) to radiance and further to reflectance have been followed and suggested in many studies (Landsat 7 Science Data Users Handbook, Bruce and Hilber (2006), Chander et al. (2009), considering them as very important for production of accurate results of ratio analysis (NDVI, NDBI and others).

5.3.2 Relative Radiometric Correction (RRC) RRC is a very important method when more than one image of same area or of two areas is to be compared, e.g. change detection analysis (Bruce and Hilbert 2006). Therefore, histogram matching was applied between the images of Delhi and Mumbai to make them comparable. In this case, based on one reference image, the histogram of another image is calibrated. The method can be applied across the time series and over the space.

5.3.3 Estimation of NDVI NDVI is an important indicator of vegetation health, stress and greenness or biomass. The values of NDVI range between −1 and +1, where higher values indicate healthy vegetation and lower and negative values represent poor vegetation health/cover (built up, water, barren, snow regions, etc.) (Lo and Quattrochi 2003; Yuan and Bauer 2007). The NDVI using Landsat 5 image is estimated using NIR (fourth band) and IR (third band) (Amiri et al. 2009; Chander et al. 2009). However, specific bands are initially corrected to produce reflectance images based on Eqs. 1 and 2 for estimating NDVI (Bruce and Hilbert 2006). These steps are essential before the NDVI is computed. The NDVIs were calculated using Eq. 3. NDVI = (band 4−band 3)/(band 4 + band 3) or (NIR − R)/(NIR + R) where NIR = band 4, R = band 3

(3)

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5.3.4 Estimation of NDBI NDBI is an indication of built up land and also ranges between +1 to −1. Positive value corresponds to highly built up land and negative value to other LULC types. Equations 1 and 2 are applied to correct the specific bands needed for estimation of NDBI. The NDBI is estimated (Chen et al. 2006; Xu 2007) using following Eq. 4 NDBI = (band 5−band 4)/(band 5 + band 4) or (MIR−NIR)/(MIR + NIR) where MIR = band 5, NIR = band 4

(4)

5.3.5 Estimation of LST The sixth band (TIR) of TM5 has been extensively used for mapping and estimating LST and UHI in many studies (Lo and Quattrochi 2003; Singh et al. 2014). It involves three steps for estimating the LST from Landsat thermal data viz. (1) Conversion of raw DN of sixth band to spectral radiance (Eq. 1) (2) Conversion of spectral radiance to temperature (Kelvin scale) (Eq. 5) (3) Conversion of temperature in Kelvin to degree Celsius scale (Eq. 6). These steps are mentioned in details in Murayama and Lwin (2010) (http://giswin. geo.tsukuba.ac.jp), which particularly deals with LST estimation using Landsat 5 TM thermal data. Chander and Markham (2003) also describe these methods with special reference to Landsat 5TM. Chander et al. (2009) mentioned the process for all the Landsat thermal datasets (4, 5 and 7), whereas Landsat 7 Science Data Users Handbook for Landsat ETM+ also explains these processes. Conversion of Spectral Radiance (L) to Temperature in Kelvin TB = K 2 /I n {(K 1 /L λ ) + 1}

(5)

where: K 1 = Calibration constant 1 (607.76) and K 2 = Calibration constant 2 (1260.56) for thermal band of TM data, T B = Surface temperature Conversion of Kelvin to Celsius TB = TB − 273

(6)

5.3 Methodology

163

Thus, the temperature values are obtained, which are referred as black body temperature. They therefore need correction for spectral emissivity based on the nature of land cover (Yue et al. 2007; Lo and Quattrochi 2003). The Lo and Quattrochi (2003) further suggest that the temperature remains nearly unchanged with very minor difference (±0.3°) after emissivity correction, thus there is no need for correcting the black body temperature for emissivity. We have therefore limited the calculation of LST to black body temperature based on Lo and Quattrochi (2003). Further, NDVI and NDBI change were computed and relationships between surface temperature, NDVI and NDBI were established (Chen et al. 2006; Xu 2007). The profiling of NDVI, NDBI and LST for the same sample pixels was done using line graphs (Figs. 5.3 and 5.4). The profiles of NDVI, NDBI and LST representing LULC classes for both the years were then compared using line graphs. The comparison has also been done using the maps of LULC, surface temperature, NDBI and NDVI. The profiles were created in such a way that they cross and cover most LULC types. The profiles were further compared using simple line graph. All the steps taken for analysis are explained in Fig. 5.5.

Fig. 5.3 Location of profile lines (north-south and west-east) for comparison of LST, NDVI and NDBI in Delhi

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5 Urban Microclimates

Fig. 5.4 Location of profile lines (west-east) for comparison of LST, NDVI and NDBI in Mumbai

5.4 Results and Discussion 5.4.1 Spatial Patterns and Trends of LST and UHI in Delhi The images of 2000 and 2010 when visually correlated reveal that there exists strong positive relationship between LULC and LST in Delhi, meaning that the surface characteristics play a dominant role in changing LST. As vegetation area acts as heat sinks, forested areas and vegetation cover have minimum surface temperatures. Similarly, water bodies like presence of River Yamuna (23–25 °C in 2000; 24–27 °C in 2010) and lakes like Bhalswa, Sanjay Gandhi Lake (25 °C in 2010) help moderate the temperatures around them. This is due to differential heating of water bodies, difference in albedo and high absorption capacity of water. In contrast, higher temperatures are associated with non-porous materials like surfaces made of concrete, glass, tiles, asphalt and metal (29–31 °C in 2010) (Yue et al. 2007) (Fig. 5.7). The spatial pattern of LST shows that the surface temperature for south-west part of Delhi is maximum (29 °C in 2000; 36 °C in 2010) owing to the presence

5.4 Results and Discussion

165

Urban microclimate of Delhi and Mumbai

Surface urban heat island mapping (1991-2010)

Landsat TM and ETM+ satellite image were acquired for Delhi (2000 and 2010) and Mumbai (1991 and 2010)

Image pre-processing:Radiometric Corrections include: a. Conversion of the Digital Number to Spectral Radiance, b. Conversion of Spectral Radiance to Reflectance, c. Histogram matching

Estimation of LST Conversion of raw Digital Number (DN) of 6th band to spectral radiance Conversion of spectral radiance to temperature (Kelvin scale) Conversion of temperature in Kelvin to degree Celsius scale Mapping of LST, NDVI and NDBI as factors of SUHI for Delhi (2000 and 2010) and Mumbai (1991 and 2010)

Inter-relationship between LULC, LST, NDVI, NDVI as a function of UHI through profile lines for Delhi (2 profile lines) and Mumbai (3 profile lines) Fig. 5.5 Methodological framework

of fallow land and sandy soil. However, in central part of Delhi, the temperatures are not highest (27–29 °C in 2000 and 28–30 °C in 2010). Unlike many other large cities of the world, like Tokyo, Shanghai, Georgia, Beijing and many Asian cities, this pattern is an atypical phenomenon. Delhi is exceptionally unique in this sense as urbanization and expansion have parallelly taken place leading to creation of mixed land uses. Additionally, most of the highly compact built up areas in central Delhi are accompanied with dense tree cover, thereby fostering balanced surface temperature. Delhi has perennial river, Yamuna River that crosses through seven of total nine

166

5 Urban Microclimates

Fig. 5.6 Low building height in Delhi

districts of Delhi that plays a significant role in balancing the temperatures in the city. Another possible factor that plays crucial role in minimising the UHI intensity in the landlocked city of Delhi may be the low building height (Fig. 5.6). The growth of Delhi city has been more horizontally and not vertically and therefore the occurrence of heat trap is minimal. Due to the combination of these multiple factors, very strong heat island does not exist in the centre of Delhi, but it may be noted that there are clear distinction and variation in thermal properties according to the LULC type. It is clear that the LST has increased for all LULC types in Delhi.

5.4.2 Spatial Patterns and Trends of LST, NDVI and NDBI in Delhi To further investigate the association between LULC and LST, NDVI was calculated. There exists an inverse relationship between NDVI and LST (Fig. 5.7). Since the

5.4 Results and Discussion

167

Fig. 5.7 Spatial distribution of LST, NDVI and NDBI in Delhi (2000 and 2010)

green cover increase increased from 2000 to 2010 in South and New Delhi, the NDVI values too elevated. In comparison to May 2000, in 2010, sections of South Delhi, New Delhi and North Delhi have experienced an increase in NDVI values. On the other hand, the values decreased in North-East, East and North-West Delhi. It is clearly visible that the city of Delhi has expanded in the last census decade. As per the relationship between NDVI and LST, North, North-East and South-West Delhi has low NDVI and hence should exhibit higher LST, but this is not the case owing to patchy green cover and low building height. The presence of River Yamuna in these

168

5 Urban Microclimates

districts has also contributed to lowering the temperature. The surface temperature pattern depicts a varied scenario. The city temperature was much lower for the hottest month of the year in 2000 and ten years later the surface temperature for the same month increased significantly for all districts. As per the survey records of Survey of Forest in Delhi (2001), total forest cover in Delhi was 111 km2 , i.e. 7.6% of the total geographical area of Delhi. This comprised of 38 km2 dense forest cover and open forest of 73 km2 . New Delhi (27.88%) had maximum forest cover followed by South Delhi (21.02%) and Central Delhi with 9.20% in 2001. Of the total nine, rest six districts had less than 5% forest cover. However, as per the Forest Survey of India Report (2011), the total forest land increased to 176.2 km2 which is a rise of 4.32% in a decade. South Delhi recorded maximum forest cover with 78.32% (rise of 57.3 km2 ) followed by South-West Delhi with 41.8% and minimum of 4.1 and 2.99% for North-East and East Delhi. The decadal change in surface temperature (°C) and NDVI are represented in Fig. 5.7a3, b3. The change in surface temperature ranges from −9 to 13 °C. Apart from the border areas of west, south-west and south central areas, the change ranges between 0 and 2 °C except a few pockets. The maximum change has taken place in South-West district.

5.4.3 Relationship Between LULC, LST, NDVI and NDBI in Delhi The north-south and west-east profile lines were drawn cutting across Delhi and LST, NDVI and NDBI values were correlated along the profile lines (Fig. 5.3, Fig. 5.8a–f). The west-east profile shows that overall the temperature has increased in Delhi. In 2010, the temperature gradually decreases from west to east and the Central Delhi experiences 27–32 °C temperature. It reaches maxima (35 °C) in West Delhi and minimum of 24 °C in east near River Yamuna. Most of the East Delhi areas have maximum temperature of 30 °C except the border areas. Overall in 2000, temperature was much lower, especially in the western portion (Fig. 5.8a). The temperature of River Yamuna has also increased (22 °C in 2000 and 24 °C in 2010). NDVI results of west-east profile are highest in East Delhi, moderate in West Delhi and least in Central Delhi. The low NDVI in Central Delhi may be attributed to mixed LULC and better NDVI in East is due to high tree cover (Fig. 5.8c). There exists inverse relationship between NDVI and NDBI (Fig. 5.8e). The NDBI is highest for the west and lowest for the eastern parts of Delhi. The north-south profile lines assert similar relationships between LST, NDVI and NDBI. The southern Delhi shows maximum temperatures in both 2000 (31 °C) and 2010 (34 °C) (Fig. 5.8b). It has high NDBI and low NDVI. On the contrary to the general belief, core of the city has less LST (26 °C), high NDVI (0.7 and above) and low NDBI (Fig. 5.8d, f) asserting weak UHI in Delhi. The north-south profile lines pass through the districts of North Delhi, Central Delhi, New Delhi and South

5.4 Results and Discussion

169

Fig. 5.8 Spatial relationship of a LST, c NDVI and e NDBI in west-east profile and b LST, d NDVI and f NDBI in north-south profile in Delhi (2000–2010)

Delhi. The temperature increases towards the extreme South while in Central and New Delhi, it is low. Similar to the west-east profile lines, the thermal conditions have observed an increase from 2000 to 2010, and the NDVI also experienced gain at many places in north-south profile of the city. The NDVI is recorded highest in New Delhi. In extreme north and south, however, the NDVI values are much lower. There is clear correlation between surface temperature graph and NDVI values as areas having higher vegetation index have comparatively low temperature conditions and vice versa. The high level of greenness in the capital city is accountable for maintaining relatively low temperature and the absence of clear UHI even during the hottest month of the year in May.

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5 Urban Microclimates

5.4.4 Spatial Patterns and Trends of LST and UHI in Mumbai The spatial distribution of LST reveals that there is higher temperature in the city district than its periphery/suburban Mumbai (Fig. 5.9). In Mumbai, the LST ranges from high of 38 °C to lowest of 27 °C (1991), as compared to 40 and 28 °C (2010), respectively. The city and suburb are surrounded by sea on the west coast and is covered with multiple LULC types ranging from the water bodies (lakes and rivers) to forest cover (SGNP) and high density high rise buildings in the city centre, interspersed with large congested areas of low height covered with slums. In 2010, built up concrete surfaces exhibit high temperatures (35 °C on western coast and 30 °C on eastern coast) in the city. The Bandra-Kurla urban complex near Mahim bay records a high of 31–32 °C. The LST soars to maximum (37 °C) at International Airport of Mumbai (2010). The Eastern and Western suburbs along with the SGNP record moderate temperatures (29 °C). In the Western suburbs, the high density urbanized region tends to have LST ranging between 32 and 33 °C. In the midst of these hotspots, the Indian Institute of Technology, Bombay campus (29 °C), bordered by Powai Lake (26 °C) and SGNP is greener and cooler. Gorai, a fairly adjacent to the Manori Creek, has a maximum temperature of 28 °C, owing to the presence of mangroves and green areas. The pervious zones along the coastal areas covered with mangroves show moderate temperature ranges (26–27 °C), whereas the lakes have lower temperatures of 24–25 °C (Fig. 5.9a2).

5.4.5 Spatial Patterns and Trends of LST, NDVI and NDBI in Mumbai NDVI, the greenness index, in Mumbai varies from maximum of 0.55 to minimum of −0.33 (2010) (Fig. 5.9 b1, b2). Apart from the SGNP in Mumbai suburb, the greenest areas in Mumbai are the coastal areas that covered with mangroves. The NDVI is estimated highest in dense vegetation areas of SGNP. Most parts in the national park have an NDVI of more than 0.5, though a few patches in the periphery have lower NDVI indicating sparse vegetation owing to encroachment. The mangrove-covered Thane Creek, Malad Creek, Gorai and Mahim Bay have moderate NDVI values (0.3–0.55). These green areas absorb the heat and therefore act as heat moderators in the dense urban jungle of Mumbai. The NDVI however is mostly negative in the built up area, especially in Mumbai city. The temperature hotspots coincide with the low and negative NDVI values. These are the dense urban built up areas and heavy steel, iron and concretized transportation lines. The southernmost point of Mumbai, Colaba Point, exhibits higher NDVI values (0.55) along the coast but reduces to 0.06 towards the urban areas. Similarly, the Malabar Point NDVI ranges from 0.52 to 0.11 depending on the LULC type. The International Airport has very low NDVI values (0.01) with patches of

5.4 Results and Discussion

Fig. 5.9 Spatial distribution of LST, NDVI and NDBI in Mumbai (1991–2010)

171

172

5 Urban Microclimates

tree cover possessing higher NDVI values (0.4). The NDVI dips along the roads and highways confirming the absence of tree cover in the city (Fig. 5.9c1–c3).

5.4.6 Relationship Between LULC, LST, NDVI and NDBI in Mumbai The existence of UHI can be interpreted with the help of LST and validated using the NDVI and NDBI values. The three indicators are closely related. The areas characterized by higher temperature are generally with higher NDBI values and consequently lower NDVI values. Thus, there is positive relationship with LST and NDBI (in case when high LST is due to built up areas) and negative relationship between LST and NDVI. By drawing three transect lines (Fig. 5.4), these relationships and the degree of correlation among LST, NDVI and NDBI are clearly represented through various profile lines (Fig. 5.10). The northernmost profile line (Figs. 5.4A–a) passes through the northern part of Mumbai suburban district representing the mangroves and marshy lands in the

Fig. 5.10 Spatial relationship of LST (a1, b1, c1), NDVI (a2, b2, c2) and NDBI (c1, c2, c3) for A–a west-east profile (a1, a2, a3), B–b west-east profile (b1, b2, b3), C–c west-east profile (c1, c2, c3) (1991–2010). Note See Fig. 5.4 for reference of A–a, B–b and C–c

5.4 Results and Discussion

173

west near coast, urban areas in the centre and dense vegetation cover under SGNP in the east. In the west, i.e. mangrove-covered areas, the temperature observed is very low (26–27 °C) as compared to very high temperature (34–35 °C) in central urban built up areas (Gorai, Borivilli and Khandiwali) and very low temperature in eastern vegetation areas (SGNP) (2010). The patterns of LST, NDVI and NDBI are clearly interlinked. The peaks of LST and NDBI correspond to the built up impervious urban areas, whereas, vegetation cover has NDVI as high as 0.5 in the eastern portion (Fig. 5.10a1–a3). The central profile line (Fig. 5.4B–b) running from west to east passes through southern part of Mumbai suburban district. In the west, it coincides with urban areas of Santa Cruz, Kurla and Ghatkopar, followed by International Airport and Mithi River in the centre of the profile and mangrove-covered areas are found on the eastern side of profile representing the eastern coast. The LST profile lines present apparent peaks and troughs. The very high LST and NDBI values and negative NDVI values correspond well to urban built up settlements. The LST reaches a high of 35 °C at the International Airport, 33 °C at Kurla and nearly 32 °C at Ghatkopar, which are also characterized with lowest NDVI and highest NDBI. Some prominent lows of LST and NDBI are patchy tree cover zones, gardens, Mithi River. The LST dips to 25–27 °C in the extreme east, along the Thane Creek, that is covered with mangrove and other tree varieties (Fig. 5.10b1–b3). The southernmost transect (Fig. 5.4C–c) represents the main Mumbai district that is densely covered with concrete, stone and metal having negligible natural vegetation cover. This region has high density of high rise buildings that act as heat trap zones. The southern profile line primarily crosses the urban built up areas of Cumballa Hill, Mahalaxmi, Mumbai Central, Byculla and Mazagaon from west to east. Since the NDBI is high here, NDVI values in the range between 0 and −0.1. Most of the built up areas record high LST (up to 33 °C) and NDBI (0.2–0.3) values. Two highs of LST can be seen near Cumballa Hill in West Mumbai and another two at Mumbai Central and Mazagaon. Overall, high urbanization has led to high NDBI values and high LST. There is sudden increase of temperature at Mumbai Central that is also representative of high density road and railway network (Fig. 5.10c1–c3) (Fig. 5.11). In comparison to Delhi, the UHI phenomenon is clearly visible for Mumbai (Singh et al. 2014) but unlike many cities of the world, like Shanghai, Hong Kong, Beijing, the intensity of UHI is not very strong. This is mainly on account of the impact of the coast, large coastal areas under mangroves and relatively lower heights of buildings of slums that are apparent all through the city. The city of Delhi has lower UHI intensity due to persistent tree cover along the roads, railway lines and in other open spaces. In Mumbai, despite fairly good public transport network, private vehicles are on the rise (Pacione 2006). The in-migrations, population increase, and vehicular and industrial pollution are main contributors to UHI creation. The preservation of existing water bodies and mangroves is vital to maintain the balance between population needs and resources available in the city (Kamini et al. 2006). People’s participation with the public and private sector on programmes related to afforestation, green roofs and terrace gardens needs to be promoted to uphold the livability and sustainability of Mumbai.

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Fig. 5.11 Dense high rise buildings in Mumbai city

5.5 Concluding Remarks On the basis of various geographic factors, such as location, climate and evolution of urban growth, Delhi and Mumbai contrast each other. The two largest cities of the country have undergone differential pace and spatial spread of urbanization. In Mumbai, the daily movement of people from nearby districts is much more prominent than Delhi. Mumbai also has a larger number of high rises than Delhi. The concrete area in Mumbai is much greater than in Delhi. The agricultural land has diminished over the years and also has been shifted to the periphery, but it is to be noted that in Delhi, agricultural activities still do persist along River Yamuna. In contrast, the agricultural land in Mumbai is almost non-existent. All of these factors signify that the nature of urban development in the two cities is dissimilar. This is also reflected in the UHI formation, whereby in Mumbai, there exists strong UHI, but this is weak for Delhi. The regression analysis between LST and NDVI proves that Delhi has a larger area under green cover, and hence, the UHI effect is diminished. In Mumbai, the absence of tree cover along with other factors related to city geometry has led to increased LST. In this scenario, it becomes imperative to focus on impacts of increased LST on human health and implementation of urban planning norms.

References

175

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Kant Y, Bharath BD, Mallick J, Atzberger C, Kerle N (2009) Satellite—based analysis of the role of land use/land cover and vegetation density on surface temperature regime of Delhi, India. J Indian Soc Remote Sens 37:201–214 Kawashima S (1994) Relation between vegetation, surface temperature and surface composition in the Tokyo region during winter. Remote Sens Environ 50:52–60 Kolokotroni M, Giridharan R (2008) Urban heat island intensity in London: An investigation of the impact of physical characteristics on changes in outdoor air temperature during summer. Sol Energy 82(11):986–998 Landsat 7 Science Data Users Handbook; National Aeronautics and Space Administration (NASA): Washington, DC, USA, 2003. Retrieved from http://landsathandbook.gsfc.nasa.gov/ pdfs/Landsat7_Handbook.pdf. Accessed 22 Aug 2013 Li J, Song C, Cao L, Zhu F, Meng X, Wu J (2011) Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sens Environ 115:3249–3263 Li YY, Zhang H, Kainz W (2012) Monitoring patterns of urban heat islands of the fast -growing Shanghai metropolis, China: using time-series of Landsat TM/ETM+ data. Int J Appl Earth Obs Geoinf 19:127–138 Liu L, Zhang Y (2011) Urban heat island analysis using the Landsat TM Data and ASTER data: a case study in Hong Kong. Remote Sens 3:1535–1552 Lo CP, Quattrochi DA (2003) Land-use and land-cover change, urban heat island phenomenon, and health implications: a remote sensing approach. Photogramm Eng Remote Sens 69(9):1053–1063 Mallick J, Kant Y, Bharath BD (2008) Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J Indian Geophys Union 12(3):131–140 Mallick J, Singh CK, Shashtri S, Rahman A, Mukherjee S (2012) Land surface emissivity retrieval based on moisture index from Landsat TM satellite data over heterogeneous surfaces of Delhi city. Int J Appl Earth Obs Geoinf 19:348–358 Murayama Y, Lwin KK (2010) Estimation of Landsat TM surface temperature using ERDAS imagine spatial modeler, University of Tsukuba: Ibaraki, Japan. Retrieved from http://giswin. geo.tsukuba.ac.jp/sis/tutorial/koko/SurfaceTemp/SurfaceTemperature.pdf Muttttanon W, Tripathi NK (2005) Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. Int J Remote Sens 26:2311–2323 Nichol J (2005) Remote sensing of urban heat islands by day and night. Photogramm Eng Remote Sens 71(5):613–621 Oke TR (1982) The energetic basis of the urban heat Island. Q J Royal Meteorol Soc 108:1–24 Oke TR (1995) The heat island of urban boundary layer: characteristics, causes and effects in wind climate in cities. In: Cermak JE, Davenport AG, Plate EJ, Viegas DX (eds) Wind climate in cities, Springer, ISBN: 0792332024, pp 81–107 Pacione M (2006) City profile—Mumbai. Cities 23(3):229–238 Pandey P, Kumar D, Prakash D, Kumar K, Jain VK (2009) A study of the summertime urban heat island over Delhi. Int J Sustain Sci Stud 1(1):27–34 Roy SS, Singh RB, Kumar M (2011) An analysis of local spatial temperature patterns in the Delhi Metropolitan Area. Phys Geogr 32(2):114–138 Singh RB, Grover A, Zhan J (2014) Inter—seasonal variations of surface temperature in urbanized environment of Delhi using Landsat thermal data. Energies 7:1811–1828 Singh RB, Grover A (2014) Remote sensing of urban micro climate with special reference to urban heat island using Landsat thermal data. Geographia Polonica 87:555–568 Stewart ID (2011) A systematic review and scientific critique of methodology in modern urban heat island literature. Int J Climatol 31:200–217 Taha H (1997) Urban climates and heat islands: Albedo, evapotranspiration, and anthropogenic heat. Energy Build 25(2):99–103 United States Environmental Protection Agency (2003) EPA—Global warming. Retrieved from http://yosemite.epa.gov/oar/globalwarming.nsf/content. Accessed 6 Oct 2016

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United States Environmental Protection Agency (2008) Reducing urban heat islands: Compendium of strategies—Urban heat island basics. Retrieved from http://www.epa.gov/heatisland/index. html. Accessed 16 July 2016 Vlahov D, Freudenberg N, Proietti F, Ompad D, Quinn A, Nandi V, Galea S (2007) Urban as a determinant of health. J Urban Health: Bull New York Acad Med 84(1):i16–i26 Voogt JA, Oke TR (1998) Effects of urban surface geometry on remotely-sensed surface temperature. Int J Remote Sens 19:895–920 Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86:370–384 Weng Q, Yang S (2004) Managing the adverse thermal effects of urban development in a densely populated Chinese city. J Environ Manage 70:145–156 Weng Q, Zengshang D, Jacquelyn S (2004) Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89:467–483 Xiao H, Weng Q (2007) The impact of land use and land cover changes on land surface temperature in a karst area of China. J Environ Manage 85:245–257 Xu H (2007) Extraction of urban built-up land features from Landsat imagery using a thematic oriented index combination technique. Photogramm Eng Remote Sens 73(12):1381–1391 Yuan F, Bauer ME (2007) Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ 106:375–386 Yue W, Xu J, Tan T, Xu L (2007) The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data. Int J Remote Sens 28(15):3205–3226 Zhang XX, Wu PF, Chen B (2010) Relationship between vegetation greenness and urban heat island effect in Beijing city of China. Proced Environ Sci 2:1438–1450 Zhang Y, Yiyun C, Qing D, Jiang P (2012) Study on urban heat island effect based on normalized difference vegetated index: a case study of Wuhan city. Proced Environ Sci 8:574–581 Zhang J, Wang Y (2008) Study of the relationships between the spatial extent of surface urban heat islands and urban characteristic factors based on Landsat ETM+ data. Sensors 8:7453–7468

Web Reference Marshall Space Flight Center Earth Science Office (2016) http://weather.msfc.nasa.gov/urban/ urban_heat_island.html. Accessed 3 July 2016

Chapter 6

Urban Health Risk Analysis

Abstract This chapter studies the urban health risk in Delhi and Mumbai. Impact of air pollution on human health has been particularly dealt with. Data related to deaths caused by different diseases including circulatory and respiratory system has been collected from various governmental sources. The temporal and spatial pattern of air pollution and increased temperature-related diseases are analyzed. The extensive fieldwork was also done for understanding the disease pattern across pollution strata (occupation, gender, age, income, etc.). Further, the incomewise analysis of mortality caused by different diseases related to air pollution was studied. Also, agewise analysis of deaths has been presented for Delhi. Keywords Diseases of respiratory system · Circulatory system · Tuberculosis · Neoplasms · Infections and parasitic diseases

6.1 Introduction The purview of health is no longer limited to the absence of disease; rather, it encompasses multifold facets like physical, mental and social wellbeing. Health shares its interface with milieu of factors, e.g., environmental surroundings, lifestyle and hereditary and dietary habits. Health and state of health are ever changing in response to the changing environment. A disease may be acquired or inherited, but it is triggered, activated and aggravated due to environmental factors. Human health is influenced by extreme climatic events, urbanization and associated intertwined ingredients of concretization, industrial growth, transportation, pollution, etc. (Chen and Kan 2008). Unplanned, unregulated and haphazard urban growth and associated microclimatic changes impose challenges for human health. As a result, urban areas that are the prime centres of development are experiencing rise of burden of diseases. Often, urban areas are considered as centres of paramount health facilities, but the population growth and their needs modify the physical environment to the extent that it exerts negative impacts. The atmospheric composition of gases has been altered by industrial and vehicular growth and changes in consumption patterns and lifestyles of human beings. There is rising concentration of oxides of nitrogen, sulphur and carbon. Added to this, © Springer Nature Singapore Pte Ltd. 2020 A. Grover and R. B. Singh, Urban Health and Wellbeing, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-13-6671-0_6

179

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the SPM and trace elements, e.g., benzene and O3 (Knowlton et al. 2004) are also increasing. Due to trapping of heat energy by GHGs and other related causes, the microclimatic conditions change in urban areas. The combined effect of these two environmental changes is the increase in temperature that has far-reaching and longlasting consequences on human and environmental health (Fig. 6.1). Though it is very difficult to understand the potential magnitude of interaction between air pollution and temperature, it is clear that there exists an intricate relationship between increased temperature and air pollution that translates to impact human health in a complex manner (Williams et al. 2012). From the previous two chapters, it can be concluded that seasons of extreme temperature are also the seasons of increased pollution. Higher temperature when corresponds with higher emissions, the temperature may further peak up. UHI too amplifies the heat waves, and the foremost impact is dramatic rise in heat-related deaths (McMichael 2000; Bentham 1992; Reid et al. 2009; Tan et al. 2010). Correspondingly, many studies confirm that season is an important proxy for variations in pollutant and mortality (Roberts 2004). For instance, summers have maximum PM and likewise high asthma-related illness and deaths (Roberts 2004; Qian et al. 2008; Stafoggia et al. 2008). There is synergetic effect of high temperature

Demographic and behavioral determinants

Social and economic determinants

Fig. 6.1 Inter-relationship between temperature, air quality and human health

6.1 Introduction

181

with PM of median aerodynamic diameter of 10 µm (PM10 ) on cardio-respiratory morbidity and mortality, especially in hot season (Ren and Tong 2006). On hot days, people generally are likely to spend more time outdoor that increases their exposure to PM (Roberts 2004). Thermal discomfort and oppressive heat too increase the susceptibility to illness. Beggs and Bambrick (2005) reported that pollen quantities depend on temperature conditions. As the temperature and CO2 levels increase during the summer season, the pollen quantity and duration becomes longer causing rise in asthma cases. On the other hand, higher NO2 and O3 levels were co-related with temperature where it was found that combination of high pollutants and temperature elevated the risk factor for heat stroke in Tokyo (Piver et al. 1999). Study by Katsouyanni et al. (1997) found significant effect of interaction between SO2, and high temperature on mortality. Elminir (2005) suggests that the pollution levels are maximum when wind speed is low, and the foremost impact is creation of smog in winters. Williams et al. (2012) acknowledge that air pollutants contribute to the existing temperature–mortality relationship. McMichael et al. (2008) present the results of heat- and cold-related mortality in 12 urban centres of the world. The research concludes that there is strong positive association between high temperature and mortality in Delhi, but little evidence is there for cold temperature and mortality. The incidence of heat stroke is not much evident for Delhi as it experiences hot summers, and populations adapt to their local climate. Urban air pollution causes spectrum of health effects ranging from eye irritation, skin infections, spread and rise of infectious diseases, asthma and allergic disorders, chronic respiratory disorders, chronic heart and lung disease (Frumkin 2002), asthma (Magas et al. 2007) cancer (Chen and Kan 2008; Barakat-Haddad et al. 2015; Bentham 1992), minor eye, nose or skin irritations, allergy (Kim et al. 2013) and even death in some cases (Lvovsky 1998). The gravity of problem is reflected by Mohan and Kandya (2007) where they stated that apart from cases of illness, air pollution is estimated to cause over 20,50,000 deaths every year in South Asia. Barakat-Haddad et al. (2015) state that the degrading air quality is significant contributor of increasing respiratory health problems in adolescents in United Arab Emirates. Magas et al. (2007) noted that the number of asthma hospitalization cases have increased in North America and Europe. Beggs and Bambricks (2005) also pointed out to the global rise in asthma cases. They correlate it with the anthropogenic causes like pollution increase and warming. Climate change also tends to increase the incidences of vector and parasite borne diseases. In addition, lower atmospheric ozone formation is enhanced by heat and pollution. Knowlton et al. (2004) predict that worldwide O3 -related acute mortality would steeply rise by 2050. Heat stroke is aggravated in the urban centres particularly by trapping of heat by air pollutants. The frequency and severity of extreme events like the heat and cold waves are rising. Heat waves are considered as health hazard as it leads to fainting, swelling, cramps, vomiting, nausea, weakness and even death. The ‘Global Burden of Disease’ ranked air pollution as one of the top ten causes of mortality in the world causing nearly 3.2 million deaths annually (Centre for Science and Environment 2013). Of these, two-thirds of the deaths due to air pollution are

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estimated to take place in the developing countries. The ‘Global Burden of Disease’ further states that of all the causes of death, air pollution is ranked sixth most dangerous cause in South Asia and fifth in India. It was responsible for six times increase in premature deaths from 1,00,000 (2000) to 6,20,000 (2013). This figure may be under-estimation as a study by TERI (1998) estimated 2.5 million premature deaths and total morbidity and mortality cost of 885–4,259 billion annually due to exposure to PM10 . The World Health Organization (2014) states that 14% mortality of children below five years of age was due to acute respiratory infections in India in 2012. It is alarming to note that diseases of the respiratory system are third largest cause of all deaths in the country (Government of India 2010). The proportion of mortality rates from respiratory illness increased from 7% (2000) to 9.5% (2011). Among the various respiratory infectious diseases, pneumonia and asthma are notable causes of mortality. However, depending upon the level of contact with impure environment, duration of exposure and immunity levels, children and elderly constitute the most vulnerable groups. The age composition reveals that 11.7% children and above 50% elderly died of respiratory problems. It was also responsible of 2.8% of the total infant deaths, 5.5% of deaths of children between 1–4 years and 9.1% of children between 5–14 years (Government of India 2010). In the backdrop of the complex relationships between environmental change, pollution and rising populations in urban areas, the present chapter has an important place. The large cities are growing at a faster pace in developing countries unlike the developed world. As a consequence, large section of urban population is at risk with respect to exposure to health hazards due to air pollution and environmental degradation. Hence, the present chapter tries to examine the impact of rising pollution and temperature on human health.

6.2 Data Sources and Methodology To examine the impact of pollutants on human health, the data on cause of mortality has been used. This is because unlike the developed countries, the morbidity data in India is poorly recorded. The patient records are generally not shared with community. Hence, the causes of death records maintained by the government are utilized for analysis. The MCD records the data for births and deaths. Similarly, the BMC maintains the records for Greater Mumbai. The Registration of Births and Deaths Act, 1969 provides certification for the cause of death by a medical practitioner. This medical certification has been made compulsory since July 2003 for all hospitals including nursing homes, private and government hospitals of Delhi. The various causes of mortality due to air pollution are from illness analyzed at 3 levels: (1) impact on system, (2) impact on major classification of system and (3) diseases caused. The systems considered are respiratory, circulatory, TB, infections and parasitic diseases (Table 6.1).

Bronchitis, chronic and unspecified emphysema

Asthma

Other lower respiratory disorders

Acute laryngitis and tracheitis

Acute upper respiratory infections

Other diseases of URT

All other diseases of the respiratory system

Pleurisy

Pneumonia

Influenza

Other respiratory system diseases (ORD) Heart attack

Respiratory TB

Tuberculosis (TB)

Other malignant neoplasm of respiratory and intra-thoracic organs

Malignant neoplasm of trachea, bronchus and lung

Malignant neoplasm of larynx

Malignant neoplasm of respiratory and intra-thoracic organs

Neoplasms

Source Compiled from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b)

Acute bronchitis and acute broncholitis

Acute pharyngitis and acute tonsillitis

Level III: disease

Diseases of the lower respiratory tract (LRT)

Diseases of circulatory system

Diseases of respiratory system

Diseases of the upper respiratory tract (URT)

Indirect diseases

Direct diseases

Level II: major classification

Level I: system

Table 6.1 Classification of causes of death due to air pollution

Whooping cough

Other bacterial diseases

Infections and parasitic diseases

6.2 Data Sources and Methodology 183

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Further, at the level of major classification, the diseases of respiratory system are most critical and are divided into three parts: Diseases of the URT, Diseases of the LRT and ORD. URT have Upper Respiratory Symptoms (URS) that involves symptoms like runny and stuffy nose, sinusitis, sore throat, wet cough, dry cough, cold head, fever, burning or red eyes. The LRT have Lower Respiratory Symptoms (LRS) that include wheezing, phlegm, shortness of breath, chest discomfort or pain. Most of the respiratory diseases underlying these symptoms are caused by bacterial, fungal or viral infections or structural or functional damage to the respiratory system. These broad categories are divided into minor classifications, i.e., diseases caused. The Upper Respiratory Diseases (URD) are acute pharyngitis and acute tonsillitis, acute laryngistis and trachetis, acute upper respiratory infections and other diseases of upper respiratory tracts. The types of Lower Respiratory Diseases (LRD) are acute bronchitis, broncholitis, chronic and unspecified emphysema, asthma and other lower respiratory disorders. The rest, i.e., influenza, pneumonia and pleurisy are classified under other forms of respiratory illnesses. Other than respiratory system diseases, heart attacks, respiratory TB, different neoplasms, whooping cough and diphtheria are considered for the present study. The mortality data for Delhi is available for NDMC, MCD and DCB; agewise and genderwise from 2001 to 2011. Comparatively, the data for Mumbai is minimal, i.e., totals from 2007 to 2011 for Greater Mumbai as a whole. Other than these government documents, data on health is gathered from the National Sample Survey Reports, Government of India reports and data by the Census of India (Fig. 6.2). Morbidity and mortality can be an accumulation of many factors and not only air pollution. But considering that human being’s closest interaction is with the surrounding atmosphere, pollution certainly has a vital role to play. The disease or illness tends to aggravate as the pollution levels increase. Therefore, the analysis helps in providing an insight to the nature and level of impact. Since the nature of data for pollutants and health is different, the spatial correlation and mapping could not be possible. This is because, the pollution data is available for nine stations, but the health data is for the city as a whole. Therefore, temporal analysis with respect to the impact of pollution levels on children and elderly population is determined using basic descriptive statistics. The complexity of influence and role of air pollution in determining the health of people in Delhi have been investigated under the principles of system analysis. Correlation and regression between the two variables were undertaken. The coefficient of correlation (r 2 ) was computed to understand the influence of pollutant on various types of diseases. Apart from the analysis of mortality data, primary survey was also carried out in both the cities. Household survey about diseases related to air pollution and high temperature was conducted. Selection of households was based on random sampling. In total, 145 households were surveyed (75 in Delhi and 70 in Mumbai). Cross tabulation is done to understand the pattern of disease with respect to socio-economic, age and other criterion. The results are presented through statistical diagrams and maps. Further, correlation technique is applied to understand the relationship between air

6.2 Data Sources and Methodology

185

Urban health risk analysis: Delhi and Mumbai Secondary Data: Cause of death (2001 to 2012 except 2002-2003 for Delhi and 2007 to 2011 for Mumbai)

Causes Identified: Diseases of Respiratory System, Circulatory System, Tuberculosis, Neoplasms, Infections and Parasitic diseases

Region wise*; Age wise*; disease wise temporal analysis^ conducted

Correlation and regression with air pollutants conducted

Primary Survey: Questionnaires at household level

Random Sampling, Total samples: 145

Cross tabulation

People’s perception on urban health and environment

Regression analysis between health of respondents and income

Fig. 6.2 Methodological framework. Note The analysis is *only for Delhi, ˆDelhi and Mumbai

quality, Urban Heat Islands and associated disease. Added to this, doctors specialized in internal medicine, respiratory and cardiovascular illness were consulted to understand the impacts.

6.3 Results and Discussion The imbalances in the atmospheric compositions can have far-reaching impacts on human health. The nature of illness depends on the pollutant (Table 6.2). The pollutants and toxins both have negative impacts on human health, but in the present study, four pollutants (SO2 , NO2 , SPM and RSPM) are considered as the long-term data is available. The irritation in skin, nose and eyes, headache, breathlessness and minor difficulties in breathing are generally common forms of illness. However, these are not reported at initial stages of treatment. The most affected organs are lungs and heart. Most widespread diseases associated with impacts from air pollution are pneumonia, asthma, bronchitis, heart disease, cancer and influenza.

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6 Urban Health Risk Analysis

Table 6.2 Sources and human health impacts of major air pollutants Pollutant

Sources

Health impacts

Sulphur dioxide

Power stations, petroleum refineries, industrial boilers

Heart problem, respiratory problems including pulmonary emphysema, cancer, eye burninga , headache, damage to lungs and skin, aggravate asthmab , chest tightness, nose and throat irritationa , premature mortalitya

Oxides of nitrogen

Power plants, electric utility boilers, vehicle emission

Lung irritation, viral infection, airway resistance, chest tightness, eye irritation, diabetesc

SPM/PM2.5

Windblown dust, forest fire, volcanic eruption, combustion, construction

Pneumoconiosis, restrictive lung disease, asthma, cancerd

RSPM/PM10

Industries, combustion of fossil fuels, vehicle exhaust

Chronic Obstructive Airway/Pulmonary Disease (COPD), influenzaf , dry cough, wheeze, breathlessness and chest discomfort, hypertension, lower respiratory tract illness, dry cough, wet cough, wheezing, whistling sound while breathing, pain in lungs, sinusitis, rhinitis (running or stuffy nose), sneezing, sore throat, common cold with fever, respiratory hospital admissionse , diabetesc

Carbon Monoxide

Incomplete combustion of carbon fuels, motor vehicles

Cherry lips, unconsciousness, death by asphyxiation, cancer

Ozone

Chemical reaction of NO2 with VOC

Impaired lung function, chest pain, coughing, eye and nose irritation

Lead

Vehicle exhaust, lead smelting, processing plants

Decrease haemoglobin synthesis, anaemia, damage to nervous system, renal system

Sources Compiled from Patankar (2009), Nagdeve (2004), Curtis et al. (2006), Cropper et al. (1997), Firdaus (2010), Agarwal et al. (2006), Rizwan et al. (2013), Siddique et al. (2010), Nidhi and Jayaraman (2008) a Department of Environment and Conservation NSW (2005) b Kim et al. (2013) c Eze et al. (2014) d Brook et al. (2015) e Namdeo et al. (2011) f Xu et al. (2013)

6.3 Results and Discussion

187

6.3.1 Impact of Air Pollution on Mortality in India Air pollutants have maximum effect on diseases of the circulatory system and respiratory system. In India, 30.6% of the total deaths were due to diseases of the circulatory system and 8.6% from diseases of respiratory system (2009). The circulatory system diseases reveal that it increased by 10% in 19 years (1990–2009) (Fig. 6.3). The mortality due to respiratory illness increased in 1991 (8.1%) and thereafter declined consistently till 2000 (7%) (Fig. 6.4). But the mortality from respiratory system illness swelled to 8.9% in the subsequent years that further increased to 9.6% (2003). There was slowing down of mortality from respiratory system diseases after 2007, but 2008 and 2009 again show minor increase. Under the diseases of respiratory system group, 3,997 cases of URD and 3,20,405 of LRD as a cause of death were recorded for 2009. Most prominent diseases were pneumonia and asthma (23.5 and 10.7% deaths, respectively) for 2009 alone. Pneumonia, an inflammatory illness of the lung, is the leading cause of death among the elderly and children less than five years of age. The above 65 years of age is most vulnerable age group for respiratory system diseases. Overall, males are more susceptible to respiratory illness than females. This may be attributed to the level and time of exposure to pollutants (Table 6.3).

Fig. 6.3 Percentage of deaths from diseases of circulatory system in India (1990–2009). Source Based on data from Office of the Registrar General, India 2009

Fig. 6.4 Percentage of deaths from diseases of respiratory system in India (1990–2009). Source Based on data from Government of India (2010)

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6 Urban Health Risk Analysis

Table 6.3 Age distribution of deaths due to diseases of respiratory system in India (2009) Age group

Number of males

Males (%)

Number of females

Females (%)

Total numbers

Total (%)

Below 1

3,454

6.8

2,896

9.5

6,350

7.8

1–4

1,697

3.3

1,436

4.7

3,133

3.8

5–14

1,246

2.4

948

3.1

2,194

2.7

15–24

1,613

3.2

1,424

4.7

3,037

3.7

25–34

2,459

4.8

1,721

5.7

4,180

5.1

34–44

4,061

8.0

2,145

7.1

6,206

7.6

45–54

6,280

12.3

2,902

9.6

9,182

11.3

55–64

8,783

17.2

3,991

13.1

12,774

15.7

65–69

5,548

10.9

2,833

9.3

8,381

10.3

70 and above

14,577

28.6

9,453

31.1

24,030

29.5

Not stated Total

1,331

2.6

633

2.1

1,964

2.4

51,049

100.0

30,382

100.0

81,431

100.0

Source Government of India (2010)

TB alone accounted for 4.8% of the total medically certified deaths. The most vulnerable age groups are 35–44 years with 6,803 death cases, 45–54 years (6,103 deaths) and 55–64 (5,000 deaths). The least vulnerable groups are of children below five years of age accounting for 830 deaths in 2009 from respiratory TB.

6.3.2 Temporal Analysis of Mortality from Circulatory and Respiratory System in Delhi The mortality from circulatory system diseases was 15.45% in 2001 that increased in 2004 (20.54%) but declined to 11.43% in 2006. In 2008, it doubled to 22.74% and later reduced to 10.76% in 2010 but again started showing an increasing trend in 2011 and 2012 (Fig. 6.5). The trend analysis (2001–2012) reflects steady rise in deaths from chronic respiratory illness. The deaths from respiratory system diseases were 7.17% in 2001 that increased by 1% in 2003 but started to decline after 2004 to reach 3.84% in 2006. Since 2007 there has been slow increase in deaths from respiratory system diseases to reach 6.22% in 2012.

6.3 Results and Discussion

189

Fig. 6.5 Proportion of deaths due to respiratory and circulatory illness in Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b)

6.3.2.1

Mortality from Diseases of Respiratory System (URT, LRT and ORD) in Delhi and Mumbai

The data on causes of death reveals interesting facts about state and severity of respiratory diseases in the two cities (2001–12). The data records that are comparable and available for both the cities are from 2007 to 2011. However, the lacuna with Mumbai data is the exceptionally low values for ORD because of which the data excludes an important cause, i.e., pneumonia. The data reveals that deaths due to respiratory diseases are constantly increasing by multiple folds. The URT diseases in Delhi were merely nine in 2006 that recorded 499 after just four years span in 2010 that further increased to 653 (2011). On the contrary, URT mortality in Mumbai was comparatively much lower ranging between 20 and 35 deaths. The LRT diseases, reduced to nearly half from 1,108 to 598 (2001–2010) but rose to 1,218 (2012) (Table 6.4). It is clear that Mumbai has larger mortality cases due to lower respiratory illness (Fig. 6.6a–c) including asthma, bronchitis, broncholitis and unspecified emphysema. The LRT deaths in Mumbai are much higher than Delhi. There can be various reasons like poor air quality, land use, weather and living conditions of people. ORD have always been the largest contributor to mortality.

6.3.2.2

Major Diseases of the Respiratory System and Others Caused from Poor Air Quality in Delhi and Mumbai

The major respiratory diseases causing deaths like pneumonia, influenza, asthma and bronchitis reflect overall increase (Fig. 6.7a–d). An insight on the major respiratory diseases causing deaths like pneumonia reflects fluctuations with maximum

NA

Mumbaia

NA

2,074

Mumbai

Delhi NA

1,350

NA

695

NA

243

2004

NA

1,678

NA

1,043

NA

48

2005

NA

1,218

NA

1,089

NA

9

2006

20

1,500

6,248

922

20

43

2007

25

1,730

5,037

949

35

17

2008

28

2,006

5,920

1,088

23

21

2009

36

2,649

6,309

598

22

499

2010

16

2,403

5,024

886

25

653

2011

NA

2,485

NA

1,218

NA

512

2012

Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b); Municipal Corporation of Greater Mumbai (2007–2011) a Excludes pneumonia, NA states data not available

ORD

1,108

Delhi

NA

Mumbai

Diseases of LRT

49

Delhi

Diseases of URT

2001

Year/city

Cause of death

Table 6.4 Total deaths from disease of URT, LRT and ORD of respiratory system in Delhi and Mumbai

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6.3 Results and Discussion

191

Fig. 6.6 Deaths from a diseases of URT, b diseases of LRT and c ORD in Delhi (2001–2011) and Mumbai (2007–2011). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b); Municipal Corporation of Greater Mumbai (2007–2011). Note The dotted line is for Delhi and continuous line for Mumbai, excludes mortality from pneumonia in Mumbai, data not available for 2002 and 2003

deaths observed in 2010 (4,386) and minimum in 2006 (1,678) in Delhi (2001–2011) (Fig. 6.7a). The deaths from pneumonia in Mumbai were 5,875 (1989–90) (Directorate of Economics and Statistics 1991) and 4,634 in 2000 (Directorate of Economics and Statistics 2001), but the unavailability of data for 2001–11 restricts comparison between the two cities. Mortality from influenza and whooping cough is higher for Delhi than Mumbai (Fig. 6.7b, d). While influenza rose from 6 to 635 in Delhi (2001–10) but later declined to 222 (2011), Mumbai observed decline from 18 to 15 (2007–11). The whooping cough-related mortality though is low as compared to other diseases but still have experienced fast increase from 1 in 2001 to 60 (2008) that reduced in 2009 (12) but again increased in 2010 (50). Mumbai has minimal cases of death from whooping cough (Fig. 6.7d). Bronchitis, broncholitis, asthma and unspecified emphysema together were responsible for 1,405 and 282 deaths in Mumbai and Delhi, respectively, in 2011 (Fig. 6.7c). The mortality from these diseases has always been much higher for Mumbai (2007–2011). Respiratory TB is one kind of TB that is caused due to exposure to air pollutants and toxins. The trend for Mumbai reveals that more than 9,000 deaths (2007) reduced to 8,162 (2010) but again increased in 2011 with 8,712 deaths. The overall trend shows slight decline in respiratory TB in Mumbai. In contrast, 235 deaths (2007) took place in Delhi that reduced to 25 (2009) but sharply increased in 2010. The lowest mortality from respiratory TB in Delhi was recorded in 2005 (24) and highest in 2011 (3,039) (Fig. 6.8). An important emerging cause of death is cancer. The cancer of respiratory and intra-thoracic organs too has seen a steep rise. The cancer cases have steeply increased

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6 Urban Health Risk Analysis

Fig. 6.7 Deaths due to major respiratory diseases from a pneumonia (Institutional and hospital deaths in Delhi only), b influenza, c bronchitis, broncholitis, asthma and unspecified emphysema and d whooping cough in Delhi and Mumbai. Source Based on data from Government of NCT of Delhi (2001a, 2004a, 2005a, 2006a, 2007a, 2008a, 2009a, 2010a, 2011a); Municipal Corporation of Greater Mumbai (2007–2011)

Fig. 6.8 Deaths from respiratory TB, Delhi and Mumbai. Source Based on data from Government of NCT of Delhi (2001a, 2004a, 2005a, 2006a, 2007a, 2008a, 2009a, 2010a, 2011a); Municipal Corporation of Greater Mumbai (2007–2011)

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Fig. 6.9 Deaths due to cancer of respiratory and intra-thoracic organs in Delhi and Mumbai. Source Based on data from Government of NCT of Delhi (2001a, 2004a, 2005a, 2006a, 2007a, 2008a, 2009a, 2010a, 2011a); Municipal Corporation of Greater Mumbai (2007–2011)

in Delhi from 13 (2001) to 425 (2011) (Fig. 6.9). In comparison, Mumbai experienced much higher death from cancer of respiratory and intra-thoracic organs (941 in 2011). The trend (2007–11) reveals that it increased from 721 (2007) to 941 (2011), i.e., 7.6 times increase. The respiratory TB is one of the major causes of death from diseases of respiratory system in Mumbai. Total number of deaths in Mumbai was 3,238 as against 917 in Delhi (2007–2010), i.e., eight times than Delhi. Among the cancer of respiratory and intra-thoracic organs, most prominent in Mumbai is malignant neoplasm of trachea, bronchus and lung (2007–2011) (Fig. 6.10).

6.3.2.3

Spatial Analysis of Impact of Air Pollution on Human Health in Delhi

The area of NCT of Delhi has remained the same; however, its rural–urban composition has undergone change due to urbanization of villages (Census of India 2011). In 2011, urban area was 1,113.65 km2 and rural area 369.35 km2 (Directorate of Statistics and Economics 2014). The urban area is administered by three agencies. These three distinct statutory towns are NDMC, MCD and DCB (Table 6.5). Other than these, data for segregated rural pockets is also collected. MCD occupies maximum area (94%) and population (97% in 2001) and proportionally accounted for maximum deaths from 2001 to 2012. Among the direct health effects, i.e., respiratory-related mortality diseases, maximum deaths are due to pneumonia and followed by bronchitis and asthma, influenza and whooping cough in decreasing order (Table 6.6). The indirect effects of changing air quality are TB and heart diseases. But air pollutants cause not all kinds of TB and heart diseases. For instance, only respiratory TB

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Fig. 6.10 Deaths from different kinds of neoplasm in Mumbai (2007–2011). Source Based on data from Government of NCT of Delhi (2001a, 2004a, 2005a, 2006a, 2007a, 2008a, 2009a, 2010a, 2011a); Municipal Corporation of Greater Mumbai (2007–2011)

Table 6.5 Area, population and mortality details of statutory towns and rural areas in Delhi Statutory towns and rural areas

Area (2001) (in km2 )

Area (2001) (in %)

Population (2001)

Density (2001) (in persons per km2 )

Total institutional deaths (2001–2012)a

MCD

1,397

94

13,423,227 (97%)

9,607

460,038

NDMC

43

3

302,363 (2%)

7,074

224,902

DCB

43

3

124,917 (1%)

2,907

16,035

Urban

925

62

NA

13,957

NA

Rural

558

38

NA

1,692

NA

Source Directorate of Statistics and Economics (2014) a Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b)

is believed to be aggravated or caused by air pollution. In case of heart-related diseases, lifestyle, diet and age are dominant control factors. The mortality from both TB and heart diseases occupies majority of deaths in all regions of Delhi (Fig. 6.11a–d). While the maximum heart-related deaths were reported in NDMC area, maximum TB deaths were reported from MCD. Since the mortality from indirect causes dominates the analysis, detailed analysis for only respiratory-related deaths was done (Fig. 6.12a–d). Among all the regions,

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Table 6.6 Total deaths from 2001 to 2012 in Delhi Statutory towns and rural areas

Direct health effects

Indirect health effects

Respiratory diseases Pneumonia

Influenza

Bronchitis and asthma

Whooping cough

TB

Heart disease

MCD

6,441

2,588

5,330

300

24,022

72,542

NDMC

32,813

6,592

74

3,687

2

3,636

DCB

550

3

403

0

364

2,570

Rural

623

115

566

41

1,918

8,422

Total

3,0352

2,780

9,986

343

30,352

116,347

Source Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b) Note Total figures do not include data for 2002 and 2003

Fig. 6.11 Direct and indirect causes of death in statutory towns and rural areas in Delhi (in percent) in a MCD, b NDMC, c DCB and d rural areas (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003

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Fig. 6.12 Deaths from respiratory diseases in statutory towns and rural areas in Delhi (in percent) for a MCD, b NDMC, c DCB and d rural areas (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003

maximum deaths from influenza were recorded in MCD (Fig. 6.12a). In NDMC region, there is no case of deaths from whooping cough and only 1% deaths from influenza but highest pneumonia-related mortality was observed here (Fig. 6.12b). Among the four regions, the DCB is only region with nil mortality from influenza and whooping cough, but bronchitis and asthma cases were the highest (Fig. 6.12c). Rural areas are found in discrete segregated pockets in Delhi. Though rural areas account for only 4% of the total deaths from whooping cough, these are highest as compared to other regions. (Fig. 6.12d). Further, the regionwise trend analysis and regression of the four major diseases of respiratory system from 2001 to 2012 were conducted. It is interesting to note that in all the urban areas, bronchitis and asthma have strongest relationship and have observed maximum rise. The r 2 values for MCD, NDMC and DCB for bronchitis and asthma are 0.621, 0.75 and 0.48, respectively (Fig. 6.13a–c). On the other

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Fig. 6.13 Trend analysis of deaths from respiratory diseases in statutory towns and rural areas in Delhi for a MCD, b NDMC, c DCB and d rural areas (2001–2012); x-axis represents number of deaths and y-axis represent year. Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003

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6 Urban Health Risk Analysis

Fig. 6.13 (continued)

hand, highest gain is in deaths from pneumonia cases in rural Delhi with r 2 as 0.72 (Fig. 6.13d). The results reflect on the fact that living conditions and environment too influence human health.

6.3.2.4

Spatial Variation in Genderwise Mortality from Respiratory and Circulatory System Diseases in Delhi

Differentials in spatial and temporal pattern of mortality based on disease caused are presented using statistical diagrams and maps. Of all the diseases that are caused by air pollutants and increasing temperature, most prominent are TB, heart attacks, pneumonia, influenza, bronchitis and asthma. Mortality due to TB shows an increasing trend for all regions except in DCB (2001–12). Maximum rise was noted in the rural areas followed by NDMC. The MCD, NDMC and rural areas experienced sudden increases in deaths from TB after 2009 (Fig. 6.14). On the other hand, the deaths from heart diseases notably increased in DCB (Fig. 6.15). Pneumonia is one of the major causes of death due to respiratory illness. Impure environmental conditions aggravate the problem, and this is reflected in the spatiotemporal analysis too. Rural areas having poor amenities, infrastructure and living conditions have highest proportion of deaths from pneumonia (2001–2012) (Fig. 6.16). MCD that represents mixed population with relatively low quality of environment in comparison to NDMC and DCB also experienced rise in mortality from pneumonia (2001–2012). The trend of deaths from influenza shows erratic and fluctuating pattern for all regions of Delhi (Fig. 6.17). It is to be noted that post-2009 the influenza cases suddenly raised for all regions and later started to decline. However, maximum deaths from influenza were caused in MCD and minimum in DCB. The growth of bronchitis- and asthma-related mortality is highest among all the diseases. All the urban and rural regions of Delhi observed steep increase in cases of bronchitis- and asthma-related deaths since 2002 (Fig. 6.18).

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y = 88.63x + 1914.7 R² = 0.1825

4000 3000 2000 1000

y = 28.812x + 205.13 R² = 0.1969

0 2009

200

0 2001 2004 2005 2006 2007 2008 2009 2010 2011 2012

400

20

2011

600

40

2007

2009

2007

2011

800

2005

60

y = -4.1939x + 59.467 R² = 0.5576

2001

80

2005

2001

0

RURAL 400 300

y = 23.152x + 64.467 R² = 0.6941

200

Total Male Female

100

2001 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

Fig. 6.14 Trend of mortality due to tuberculosis in statutory towns and rural areas of Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003

Bronchitis and asthma are majorly caused due to SPM (Department of Environment and Conservation NSW 2005), and the health condition becomes severe due to the exposure to SO2 (Chen and Kan 2008). While SPM is mainly released due to incomplete combustion from industries and vehicles, road dust and other natural atmospheric conditions, SO2 is released from thermal power plants and vehicles.

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6 Urban Health Risk Analysis

15000 10000

y = 39.6x + 7036.4 R² = 0.0015

5000 2001 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

RURAL Total Male Female

Fig. 6.15 Trend of mortality due to heart disease and heart attack in statutory towns and rural areas of Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003

6.3.2.5

AgeWise Analysis of Impact of Air Pollution on Mortality from Respiratory and Circulatory System Diseases in Delhi (2006–2010)

The age composition of total deaths from all causes comprised of 50% working age group, 12% below 14 years, and 38% were above 60 years of age (2006–2010). The IMR in Delhi reduced by 30 per thousand populations in 2011 (Government of NCT of Delhi 2013), but still is highest among the metropolitan cities of India (IMR for India is 14.5 per thousand). Though the mortality rates from respiratory system disease have declined for children (812 in 2006 to 507 in 2010), it has increased for the elderly. Of the total

6.3 Results and Discussion

201

RURAL Total Male Female

Fig. 6.16 Trend of mortality due to pneumonia in statutory towns and rural areas of Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003

deaths from respiratory disease, 1,126 elderly people died in 2006 that increased to 1,546 (2010). Notably, the most vulnerable age group is above 70 years of age, followed by 55–64 years group (Fig. 6.19). The circulatory diseases are another major cause of mortality among children and elderly. More than 55,000 deaths took place due to diseases from circulatory system (2006–2010). In all years, the old population is most susceptible, with over 3,000 deaths in 2006 that increased to over 4,000 in 2010 (Fig. 6.20). Among children, 524 deaths from circulatory problems took place in 2006 that increased to 1,188 (2007) and 1,323 (2009) but further reduced to 518 in 2010.

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6 Urban Health Risk Analysis

RURAL Total Male Female

Fig. 6.17 Trend of mortality due to influenza in statutory towns and rural areas of Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003

Agewise trend of mortality due to diseases of the respiratory system (URD, LRD and ORD), Delhi The mortality from ORD (pneumonia, influenza and pleurisy) was highest followed by LRD. Overall trend of deaths from various kinds of respiratory diseases reflects that ORD have increased steeply after 2008 while LRD are experiencing a declining trend, for both children and elderly. The URD are also increasing slowly for both children and elderly (Fig. 6.21a, b). The elderly population remains most vulnerable to URD. While in 2006, five elderly died from URT illness, in 2010, it rose to 208 (Fig. 6.22b). The number of children from 1 to 4 years that died of URT increased from nil (2006) to 30 (2010) (Fig. 6.22a). The children mortality from LRT illness dipped to 42 (2010) from 106 (2006). Likewise, the old population too experienced reduction in mortality from

6.3 Results and Discussion

203

RURAL Total Male Female

Fig. 6.18 Trend of mortality due to bronchitis and asthma in statutory towns and rural areas of Delhi (2001–2012). Source Based on data from Government of NCT of Delhi (2001b, 2004b, 2005b, 2006b, 2007b, 2008b, 2009b, 2010b, 2011b, 2012b). Note Does not include data for 2002 and 2003

LRT diseases. Of the total elderly population, 64–69 years age groups are most vulnerable (Fig. 6.22a, b). The mortality rates due to ORD account for a major share in all age groups. The children aged from 1 to 14 years experienced twice as much as deaths from 2006 to 2010. There is sharp rise in infant deaths from 151 (2006) to 414 (2010). The elderly population is also susceptible to ORD. In 2006, the number of elderly that died of ORD illness was 383 that increased to 869 (2010) (Fig. 6.22c). Agewise analysis of major diseases of the respiratory system and others Acute bronchitis and broncholitis, acute asthma, influenza and pneumonia are the four main diseases of the respiratory system that are major causes of mortality. The statistics reflect that pneumonia cases have increased by three times, acute bronchitis

204

6 Urban Health Risk Analysis

Fig. 6.19 Agewise composition of deaths from diseases of respiratory system. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b)

Fig. 6.20 Agewise composition of deaths from diseases of circulatory system. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b)

Fig. 6.21 Trend of mortality due to URD, LRD and ORD in (a) Children from 1 to 14 years and (b) elderly. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b)

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Fig. 6.22 Agewise composition of deaths due to major divisions of the respiratory systems, a URT, b LRT and c ORD. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b)

and broncholitis by four times and influenza by nearly six times (2006–2010). Similar analysis is also presented by CPCB (2008). Acute bronchitis and broncholitis was highest for children in 2007, but in 2010, it was highest for the elderly (Fig. 6.23a–d). In case of population above 55 years, all respiratory diseases seem to exert stronger influence on the existing weak vulnerable respiratory system of the elderly. The deaths from malignant neoplasm of respiratory and intra-thoracic organs doubled from 161 (2006) to 313 (2010) (Fig. 6.24). Maximum cases of malignant neoplasm of respiratory and intra-thoracic organs were reported with persons above

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6 Urban Health Risk Analysis

Fig. 6.23 Agewise composition of deaths due to major respiratory diseases a acute bronchitis and broncholitis, b asthma, c influenza and d pneumonia where x-axis shows age groups and y-axis represents number of deaths. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b)

55 years of age. Among elderly, total of 114 deaths occurred in 2006 that rose to 204 (2010). The children were less susceptible to malignant neoplasm of respiratory and intra-thoracic organ disease.

6.3.2.6

Relationship Between Air Pollutants and Diseases in Delhi

The inter-relationship of these diseases with SO2 suggests 73% of the cases of abnormalities in breathing, 64% of the pneumonia and 62% of heart attack cases are dependent on SO2 levels. Contrary to this, NO2 shares positive correlation with all diseases except breathing abnormalities. Of this, whooping cough has over 93% dependence of NO2 levels followed by influenza (59%). The PM of various sizes has maximum impact on human health. PM with 2.5 µm or less in diameter is one of the biggest concerns. It is one hundredth the thickness of human hair and, therefore, penetrates easily into the lungs and blood stream (UNEP 2014). RSPM and SPM are positively correlated with all the diseases. The 89% of breathing abnormalities, 88% pneumonia and 68% influenza cases were dependent on rising RSPM levels. The increasing

6.3 Results and Discussion

207

Fig. 6.24 Agewise composition of deaths due to malignant neoplasm of respiratory and intrathoracic organs. Source Based on data from Government of NCT of Delhi (2006b, 2007b, 2008b, 2009b, 2010b)

concentrations of SPM have high degree of correlation with pneumonia, influenza, heart attacks and breathing difficulties. Various authors have concluded strong positive relationship between increasing pollution levels in Delhi and rise in related diseases. Nidhi and Jayaraman (2008), using the daily count of patient with respiratory illness data with temperature and pollution levels, investigate the relationship between the two variables. The Pearson’s correlation results show that most pollutants were positively correlated with high temperature. Further, it was found that ozone, NO2 and RSPM have significant impacts on human health and have resulted in increasing the daily count of patients having respiratory problems. Nidhi and Jayaraman (2007) correlated the monthly count of patients with respiratory diseases from seven hospitals with meteorological and pollution levels for Delhi. The study shows that with the changing seasons, the dominant pollutant that is causing illness changes. However, SPM and SO2 are most harmful. Agarwal et al. (2006) investigated status of respiratory morbidity in Delhi and found that winters have higher pollutant levels. It is well established by Mohan and Kandya (2007) that pollutant levels are higher in Delhi in winters. The SPM and RSPM are found to have direct influence on increase in daily count of patients. Cropper et al. (1997) also correlated pollutants and health and found that there is positive significant relation between PM and respiratory and cardiovascular problems. In accordance with our results, they state that 70% of deaths (from air pollution) in India occur before 65 years of age and 20% before the age of 5. The review of literature points out to the rise in cases of illness and death from respiratory and cardiovascular diseases. Nonetheless, there exist variations in vulnerability. In India, particularly in Delhi, children and elderly are most vulnerable. Siddique et al. (2010) state that the numbers of lung dis-functioning cases are reported most for children.

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6 Urban Health Risk Analysis

Another study by Siddique et al. (2011) estimated that over 30% of the children in Delhi suffered from respiratory problems, and this is strongly correlated with rise in RSPM levels. In addition to the research articles, fairly respectable amount of research is conducted by the CPCB. The investigation of negative effect of poor air quality on the elderly population in Delhi is carried out by CPCB (2008). The results indicate rise in cancer, asthma, bronchitis, respiratory infections and other disease in Delhi. There are changes in lung size in children and young people. Upward trend is observed for respiratory ill health in elderly and children. The sixtieth round of NSS was devoted to status of morbidity and conditions of elderly persons in India. Of the total hospitalized cases, 2.4% were due to respiratory illness, 2.1% of bronchial asthma, 1.1% TB, and 0.2% pertaining to whooping cough (Directorate of Statistics and Economics 2004).

6.3.3 People’s Perception on Urban Environment and Health of Delhi and Mumbai Urban populations are more risk prone to heat and pollution-related morbidity and mortality (McGeehin and Mirabelli 2001). As the evidences suggest, there is presence of numerous heat spots in Delhi, and the intensity of pollution, especially PM, is at critical levels. Overall, there has been increase in heat and pollution-related deaths in the city. However, all the socio-economic groups do not necessarily bear the burden of disease and death equally. There are variations according to one’s vulnerability, exposure, living conditions, environment, behavioural adaptations, income level, place of work, daily routine and other factors. To investigate the perception of general population on state of environment, health problems and suggestions, primary survey was conducted. A total of 75 households in Delhi were randomly selected for the primary survey. Of the total households surveyed, 41 were natives of the city, while 34 were migrants. In Mumbai, 70 households were randomly selected. Majority of respondents were migrants and were staying in the city for over three years. Only 25 respondents were born in Mumbai, 33 were from other parts of Maharashtra and rests were from other states of India. Majority of households interviewed in Delhi had family size of four and above members, but Mumbai had majority family size of two persons. Agewise composition of the respondents was dominated by below 30 years of age in Delhi (45 respondents) and 30–40 years (29 respondents) in Mumbai. The occupational composition was dominated by students in Delhi and traders and business persons in Mumbai. The people’s perception was represented by government employees, hawker, drivers (auto and taxi), road side hawker and other persons like guard, construction worker, farmer and domestic helpers (Fig. 6.25). Urban health risks are often mitigated by household conditions and facilities like number of air conditioners, exhaust fans, windows, rooms and kind of fuel used.

6.3 Results and Discussion

209

Fig. 6.25 Occupational composition of respondents in Delhi and Mumbai. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016

Most of the households in Delhi that were interviewed had more than three rooms, having toilet and tap water within the house. Majority of the household relied on LPG (60 respondents) as the source of energy followed by PNG (eight respondents). Few also used kerosene (three respondents) for cooking food, thus making them more susceptible to respiratory illness by indoor air pollution. Other than these, induction cooking is also picking up in the city. Nearly 50% had air conditioning in the house. Similarly, exhaust fan was also installed in almost all houses (36 respondents), and small and large windows were available (57 respondents). In Mumbai too, the LPG fuel dominates as cooking fuel (66 respondents), but PNG users are still low (three respondents). The use of air conditioning in Mumbai was mush lower with only 20 households having air conditioners. The houses are smaller having at least one window (31 households) or more (25 households). Nearly, 55% households had one or more exhaust fans. The response about foul smell in air was clearly negative, and the fact that air quality has declined was reported in both the cities (Fig. 6.26). Forty-eight respondents in Delhi asserted the presence of hazy clouds in winter months, whereas in Mumbai the air was considered much cleaner than Delhi. On further inquiry regarding the reason for foul smell, the presence of nala nearby house followed by dumping ground was considered as a major cause in Delhi. In Mumbai, traffic congestion and presence of butcher house were leading causes of foul smell. Some also mentioned the pollution along the coast and presence of nala (Fig. 6.27). The surrounding living environment influences the urban environment. On inquiring if there is a park nearby, 50% of respondents in Delhi agreed, whereas only 26% of respondents in Mumbai had park around their houses (Fig. 6.28). Majority of respondents in Delhi agreed that the green cover in the city has improved, but only 1% in Mumbai responded that there has been increase in green cover in the past decade. The use of means of transportation also reflects on the

210

6 Urban Health Risk Analysis

Fig. 6.26 People’s perception on state of urban environment in Delhi and Mumbai. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016

Fig. 6.27 People’s perception on reason of foul smell in Delhi and Mumbai. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016

exposure levels, and 56% of Mumbai respondents and 54% of Delhi respondents used public transport 3–4 times a week or every day. The lifestyle and daily routine followed by urban dwellers also has significant impact on the state of physical health. To understand the influence of human habits on physical health, questions on smoking, food habits, consumption of alcohol and tobacco were asked for all the family members (Fig. 6.29). The total family members in Delhi were 331 and in Mumbai were 208. Majority people did not go for regular walk in Delhi, while in Mumbai, none of them responded positively. The current smokers were 24 and 13 in Delhi and Mumbai, respectively. Similarly, very few respondents’ family members were current consumers of alcohol and tobacco in

6.3 Results and Discussion

211

Fig. 6.28 People’s perception on quality of environment around their place of living. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016

Fig. 6.29 People’s response on lifestyle and habits of their family members. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016

both the cities. It is to be noted that despite majority respondents agreed that the air quality has degraded in the past decade, only seven in Delhi and three in Mumbai used the mask for protection. While most researches point out that the mobile sources are the main cause of urban pollution, the transportation related issues were also discussed. As per the people of Delhi, the most important means of transportation are metro, bus and auto-rickshaw in order of importance. However, it is interesting to understand that maximum people use buses followed by private vehicle (car/two-wheeler) and metro to commute. This reflects the dichotomy between people’s thinking and behaviour.

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The share of bus and private vehicle users is almost equal. While the reasons for avoiding public transportation in comparison to private car were over-crowding, traffic jams, eve-teasing and civic sense. Traffic jams and creation of bottlenecks on the road are critical determinants of air quality at traffic junctions and roads. Delhi, except in peak hours, does not experience heavy traffic jams though less than 1 h jam per 10 km was reported by 48 households. Some (22 respondents) also faced 1–2 h of jam per 10 km. In Mumbai, the traffic jams were mostly during the rainy season. On daily basis, there is less than 1-h traffic jam (36 respondents). The transportation pattern in Mumbai is different from Delhi. More than 81% responded that local train is the most important means of transportation in the city and majority also travelled by the same. The second most important means of commuting was bus transport. Of 70 respondents, 15 used private vehicle daily and 25 used it for 3–4 times in a week. The severity of impact of deteriorated environment is reflected by illnesses reported by people. In Delhi, prominent health problems were breathlessness, eye irritation, skin redness, while travelling, cough (dry and wet), common cold and fever and sore throat (Fig. 6.30). Few people reported of heat stroke, bronchitis and none reported of asthma, headache, malaria, dengue and redness in eyes. This may be due to under reporting or lack of awareness. It is surprising to note that there are few cases of heat stroke and that it may be because the temperatures are already high in Delhi and, therefore, people are accustomed to it (McMichael et al. 2008). In contrary, maximum respondents reported that they face breathlessness, eye irritation, skin redness while travelling (24% in Delhi and 25% in Mumbai) (Figs. 6.30 and 6.31). Respiratory illnesses, common cold, cough and sore throat were most common forms of illnesses reported by respondents. Fig. 6.30 People’s perception on health in Delhi. Source Primary survey conducted by the authors in Delhi 2013–2016

6.3 Results and Discussion

213

Fig. 6.31 People’s perception on health in Mumbai. Source Primary survey conducted by the authors in Mumbai 2013–2016

To avoid misinterpretation of illness between hereditary and acquired, the respondents were inquired about the duration of illness. It was observed that most illnesses persisted overtime and once the person is disease struck, it tends to continue. This may also be because of nature of treatment and seriousness of response to these illnesses. It was believed that due to household conditions, inaccessibility to health care and lifestyle poor people tends to experience higher morbidity and mortality due to heat and air pollution. Therefore, care was taken in selection of households that would represent all income groups. The correlation of health problems with income groups reflects that unlike the perception that the poor are more vulnerable, though the rich too are at risk (Fig. 6.32). The analysis reveals that lower income group faced more health risk than the higher income groups for both Delhi and Mumbai. The regression analysis also reflects that there is more dependence of income on human health in Mumbai (r 2 = 0.361) than Delhi (r 2 = 0.041). However, the results suggest that health is not only a function of physical environment but also social, economic environment and lifestyle.

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Fig. 6.32 Scatter plot representing the relationship between income groups and human health in Delhi and Mumbai. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016

6.4 Concluding Remarks The urban environment of Delhi and Mumbai is deteriorating due to changing composition of atmosphere. The urban atmospheric environment shows rise in PM for both the cities. The NO2 concentrations also are hazardous and are noted to be increasing in Mumbai. The PM is steeply increasing and, therefore, has high impact on human health. These fine particles tend to easily enter the respiratory and circulatory system and cause congestion and other related illnesses. The inter-linkages between air pollution and human health suggest that PM exerts maximum negative influence. The link between health and environment is composed of complex interactive elements that need systematic investigation in trans-disciplinary perspective. The challenges of this urbanization are embedded in the policies relating to the urban planning and development. This calls for policy-level changes that balance urban growth and environmental protection. For the sustainable growth of cities, steps are taken in three areas namely, infrastructure, institution and knowledge. Systems approach can be used to improve the health and wellbeing in cities.

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Chapter 7

Strategic Plan for Urban Health and Wellbeing for the Indian Megacities

Abstract This chapter deals with reviews of governmental plans and policies regarding the human health, environment including air pollution, transport and land use, etc. The chapter tries to encapsulate the efforts at international, national level and in context of Delhi and Mumbai. Despite some drastic steps, coherent strategy and mitigation actions, the desirable results have not been much evident for Delhi and Mumbai. Therefore, the weaknesses of the strategies, plans and other actions are presented as lacunas. Further, it presents strategic plan for urban health and wellbeing for Indian megacities, i.e. Delhi and Mumbai, which can be further applied to other cities for sustain urban development of Indian cities. The perception of respondents on suggestions and recommendations to improve health and wellbeing is also presented in the chapter. Keywords Systems approach · Urban planning · People’s perception

7.1 Introduction Health and wellbeing are the key ingredients of improved urban quality of life. The physical environment is responsible for creating healthy physical and psychological wellbeing (Tzoulas and James 2004). The complex haphazard urban growth is, however, causing unhealthy environment that poses threat to human health and wellbeing. The sporadic rise of unplanned urban areas has been incapable of creating conducive and healthy environment. The fact that while 54% (2014) of the global population is urban and is expected to increase to 66% by 2050, of which, India, China and Nigeria are expected to account for 37% of projected world urban growth (United Nations 2014) is of concern. Interpolating from the past trends, it can be concluded that Indian megacities will observe massive growth in the coming decade, and in this scenario, it becomes imperative to prepare well for forthcoming challenges. The major goals that are designed at the international level are manifested in national- and lower-level policies. An insight on the historical events at international level indicates that while in 1972 the focus shifted to environment, the sustainable development concept was introduced after two decades in 1992 at Rio Conference. © Springer Nature Singapore Pte Ltd. 2020 A. Grover and R. B. Singh, Urban Health and Wellbeing, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-13-6671-0_7

219

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Further, role and importance of cities became the subject of debate and UN Sustainable Cities Programme (1990) and other initiatives gave impetus to sustainable and green cities. Post-2010, the focus has shifted to the cities of developing nations, and in response, the national governments are gaining momentum to build ‘sustainable and green’ cities. The components of sustainable city are strong healthy and just society that respects environmental limits and involves people in the governance process. The sustainable urban development must be resilient, safe and healthy, planned, productive, inclusive and green (UN Habitat 2012). Hence, Indian cities need consistent, sustainable and more aggressive strategies. All the plans and policies must be trans-disciplinary and inter-departmental with foundation built on integration. Considering large-scale diversity of the country, both, top-down and bottom-up approaches may be applied. Given the nature of cities characterized with mixed land uses, large low-income group population, rising traffic and pollution and inadequate health infrastructure, it is certain that isolated initiatives will not give adequate results. The strategic plans must promote sustainable cities encompassing social, economic and environmental sustainability. The plans and policies must direct towards reducing negative impacts of the city on human health, wellbeing and local environment. The chapter tries to encapsulate the efforts at international, national level and in context of Delhi and Mumbai. Despite some drastic steps, coherent strategy and mitigation actions, the desirable results have not been much evident for Delhi and Mumbai. Therefore, the weaknesses of the strategies, plans and other actions are presented as lacunas. To build on a strategic plan for urban cities of India, the systems approach is integrated within the framework of urban systems (Silva et al. 2012). As McPherson (2012) mentions ‘mitigation of UHI can lead to sustainability of cities’ and four key areas, i.e. air quality, LULC, UHI and health and wellbeing are identified. The blueprints of desired actions to be taken under these four key areas are classified under infrastructure, institution and knowledge.

7.2 Existing Plans and Policy for Health and Wellbeing in Changing Urban Environment 7.2.1 International Level There have been many significant international efforts on themes related to sustainable development. These are UN conference on human environment, Stockholm (1972), UN Rio de Janeiro conference (1992), the first European conference on sustainable town and cities, Denmark (1994), UN Convention on Climate Change (COP I), Berlin (1995), UN International conference on human settlements—Habitat II, Istanbul (1996), COP 2, Geneva (1997), COP 3 and Kyoto Protocol, Japan (1997), COP 4, Buenos Aires (1998), and COP 5, Bonn (1999) in the twentieth century. The

7.2 Existing Plans and Policy for Health and Wellbeing …

221

year 1992 is a landmark in the history of development and growth in the world. The first formal framework on sustainable development was proposed in Rio Conference. In the twenty-first century, the concept of sustainable development was integrated with various facets of environment and human beings. The century saw international exchange of ideas and 37 countries ratified the Convention on Climate Change in COP 6, The Hague and Bonn (2000, 2001). COP 7 marked the year 2001, Marrakesh followed by UN World Summit on Sustainable Development, Johannesburg (2002). This was the second landmark and had significant impact on shaping of environmental policies worldwide. COP 8 took place in New Delhi, 2002, followed by COP 9, Milan, in subsequent year. The first meetings between Meeting of Kyoto Protocol (MOP I) and COP 11, Montreal (2005), COP 13, Bali (2007), COP 15, Copenhagen (2009), COP 21, Paris (2015), are some important international meets (Brandon and Lombardi 2011). Other important steps undertaken are in form of international agreements, formation of international organizations and compilation of reports and publications. The 1970s observed First Earth Day (22nd April), creation of Environmental Protection Agency (EPA) under National Environmental Policy Act and formation of United National Environment Programme (UNEP) and United Nations Centre for Human Settlement (UNCHS). The 1980s saw the signing of Montreal Protocol international treaty succeeded by beginning of Intergovernmental Panel on Climate Change (IPCC). The decade 1990–2000 was marked by the Agenda 21, UN Earth Summit at Brazil, UN Sustainable Cities Programme (1990), negotiation of Kyoto Protocol and formation of World Green Building Council (Whitehead 2003). Post-2000, International Initiative for a Sustainable Built Environment (IISBE) was formed, and IPCC 4th Global Warming Report released. The IAP-Global Network of Science Academies on Disaster Risk Reduction (DRR), UNISDR Science and Technology Conference for implementing Sendai Framework of DRR and Future Earth Initiatives are some important steps in achieving sustainable development. Other than this, the ICSU supported core projects ‘Health and Wellbeing in Changing Urban Environment’ (2011). The Millennium Development Goals (MDGs) were adopted in 2000 having 2015 as the deadline, followed by Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development that came into operation from 1 January 2016. Total 17 goals were underlined of which one is on health and wellbeing. International Year of Global Understanding (IYGU), 2016, aims to bridge the gap between global thinking and local actions to tackle climate change and other international challenges. In accordance with the developments at the international level, India too advanced by signing the Montreal protocol in 1993 and committed to reduce the harmful chemical pollutants that cause ozone hole. The Ozone Depleting Substances (Regulation and Control) Rules were also implemented in 2000. India also signed the Kyoto Protocol and submitted its initial National Communication to the UNFCCC in June 2004. India is also a partner of the Asia-Pacific Partnership on Clean Development and Climate (APPCDC; commonly referred to as AP6). The Partnership consists of Australia, China, India, the Republic of Korea and the USA (ASCI and UNDP 2009).

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7.2.2 National Level Revi (2008) states that India is the 4th largest pollution emitter in the world and with 53 cities having more than one million populations makes the situation grim. The Ministry of Environment and Forests (now Ministry of Environment, Forest and Climate Change) established in 1985 is the government nodal centre for the various programmes and policies regarding environmental issues in India. It is entrusted with governance of all environmental indicators like air, water, solid waste and vegetation. The important acts introduced are, e.g. The Air (Prevention and Control of Pollution) Act (1981), Industrial Disputes Act (1982), Environment Protection Act (1986), Building Bye-Laws (1998), National Environmental Policy (2006) and Forest Conservation Act and Fly Ash Management Rule, 2008 (ASCI and UNDP 2009; Massey 2003). Key programmes relating to improvement of air quality are National Policy Statement on Abatement of Pollution (1992) and The Environment Action Programme (1993) (Schwela et al. 2006). It is essential that prevention-based environmental policy is strengthened. In 2008, India released its National Action Plan on Climate Change (NAPCC) to outline its strategy to meet the challenge of climate change. Other important programmes launched to combat climate change challenges are the National Solar Mission and the National Mission on Sustainable Habitat (Planning Commission 2011). The National Mission on Sustainable Habitat attempts to promote energy efficiency in buildings, biodiesel and hydrogen in transport and efficient management of solid waste. Control of CO2 and PM emissions is one of the indicators of air quality under SDGs (Ministry of Statistics and Programme Implementation 2013). To monitor and regulate the air quality, the CPCB monitors ambient air quality at national level under National Ambient Air Quality Monitoring Programme (NAAQM). The major pollutants identified under NAAQM are SO2 , NO2 and PM10 /RSPM2.5 . There have been various steps taken by the Government and Supreme Court of India for environmental protection. Some of the recent and effective steps have been introduction of Euro I (nationwide 1 April, 2000), Euro II/Bharat Stage II (nationwide 1 April 2005) followed by Euro III/Bharat Stage III in 2010 (nationwide). The pollution from point sources like industries is increasing. CPCB under the Environmental (Protection) Act, 1986, states separate permissible limits of pollutants. These standards are periodically reviewed and new ones are notified. On the basis of pollution potential, the industrial units are classified into three categories: red, orange and green, in decreasing order of their polluting capacity. The red industries are further classified into special red and ordinary red categories. This categorization is applicable to all states and union territories of India, though the same classification may not be followed. The classification of industries helps in easy monitoring, control and planning. Other than this, there are various schemes introduced by CPCB like environmental audit, adoption of clean technologies in small-scale industries and environmental statistics and mapping to maintain air quality (CPCB 2013; ASCI and UNDP 2009).

7.2 Existing Plans and Policy for Health and Wellbeing …

223

For conservation of energy, the Standards and Labelling programme has been initiated with the objective to provide energy-efficient equipment and appliances to general public. These include household equipments like air conditioners, refrigerators, water heater, electric motors, agriculture pump sets, etc. The energy labelling is also done on many of the electrical appliances. The government is encouraging the construction of green buildings. The concept of green buildings was incorporated in planning various buildings in Delhi, e.g. Thyagraj Stadium, Delhi International Airport and Chhatrasal Stadium (Mehta 2009). To generate awareness about health and environment, several days are celebrated all through the year like March 21: World Forestry Day, April 7: World Health Day, April 22: Earth Day, June 5: World Environment Day, June 21: International Yoga Day and September 16: World Ozone Day. To mitigate degradation of land and increase green cover, the Eleventh Five Year Plan emphasized on social forestry and engaging Panchayati Raj Institutions for sustainable management of the common property resources (ASCI and UNDP 2009). Other than these, some vital steps have been taken in the education sector. Eco-clubs were started in educational institutes to create awareness about environment. Disaster management course has been made compulsory at school level for Xth standard. University Grants Commission has initiated a compulsory course on Environmental Studies at graduation level in all Central Universities. To reduce the negative impact of air pollution and to improve the health and wellbeing of people, ICMR has initiated an initiative on Global Environment and Health, where four areas of impact of climate change are listed. These are (a) climate change and vector borne diseases, (b) aerosols and respiratory diseases, (c) UV-a and UV-b and corneal damage and cataract and (d) environment and heart diseases. As per these four critical health challenges, ICMR has formed task groups on (a) vector borne diseases and climate change (b) respiratory diseases and air pollutants and (c) eye health and environment for promotion of research and innovation in these areas (Planning Commission 2011).

7.2.3 Delhi Delhi being the capital city received much attention due to its degraded air quality and changing urban environment. Subsequently, many steps have been taken by the government to improve the urban environment of the city. In 1999, a year earlier than the nationwide implementation, Euro I was introduced in Delhi. Further, Euro II and Euro III were implemented by 2005. In 2010, Euro IV was implemented in Delhi. Other important steps to reduce vehicular pollution are conversion of all public vehicles to CNG in 2002. To minimize the diesel pollution, truck traffic was diverted. Also age cap of about 15 years was introduced for commercial vehicles plying in the city (Shrivastava et al. 2013). Other important steps are a ban on the registration of new auto-rickshaws with front engine, replacement of all pre-1990 autos and taxis with ones using clean fuels and ban on over 8-year-old diesel buses (CPCB

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1999). The pollution check for all vehicles was made compulsory and stringent monitoring is also done. The Supreme Court also directed for shifting of all largescale industries from the city to the peripheral areas in National Capital Region (Department of Environment and Forests 2010). With this, open burning was also banned. The success of these programmes led to lower levels of pollution, but this success could not sustained for a long time. This is because of rampant rise in private vehicles and resultant rise in PM pollution. The EPCA report (2014) states that 1,400 vehicles are added every day in Delhi. Presently, REVA cars and e-bikes are encouraged, and Air Ambience Fund is created for reimbursement of concession and DVAT on purchase of battery operated vehicles (Planning Department of Delhi 2013). With respect to green cover, the social forestry programme started in 1980s and joint forestry programme in 1990. Bhagidari initiative and other afforestation programmes were successful in increasing the green cover area of Delhi from 26 km2 (1996–97) to more than 300 km2 (2009). Along with these measures, the expansion of metro: 190 km (2013), creation of cycle tracks and footpaths. Increase in number of buses and removal of bottlenecks by construction of over 50 flyovers/ROBs/RUBs/grade separators and more than 60 foot over bridges and subways after Eleventh Five Year Plan were initiated (Planning Department of Delhi 2011). Healthcare infrastructure and services are being provided in the NCT of Delhi by a number of agencies, i.e. Government of NCT of Delhi, local bodies, i.e. MCD, NDMC and DCB, Ministry of Health and Family Welfare through its network of hospitals and other specialized institutions, Ministry of Railways, Ministry of Defence, Ministry of Labour and Central Government undertakings. In addition to these government and public sector agencies, private sector is also contributing in provision of healthcare services in Delhi. As per Directorate of Economics and Statistics (2014), the three statutory towns of Delhi, namely MCD, NDMC and DCB had 245, 10 and 3 health institutions, respectively, having the number of beds as 3,797, 220 and 2,128, respectively (Table 7.1). The trend of growth of beds in health institutions reveals that there has been negligible increase since 2002. There were 34 allopathic, two ayurvedic and unani and two homeopathic hospitals that provided secondary and tertiary healthcare services managed by Government of Delhi (2009). There were 427 dispensaries of allopathic, ayurvedic and unani and homeopathic system managed by Delhi Government for providing primary healthcare services in the city (Planning Department of Delhi 2011). Total number of beds in 807 medical institutions was 40,342 (23,120 beds in 131 government/public and 17,222 beds in 676 private institutions). Three new super specialty hospitals for liver, cancer and paediatric have started (Planning Department of Delhi 2011). To provide health insurance cover particularly to the Economically Weaker Section (EWS) category, the schemes for Rashtriya Swasthya Bima Yojana, Aapka Swasthya Bima Yojana are being implemented.

15

15

25

25

25

52

63

56

56

61*

245

245

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

3,797

3,797

4,138

4,091

4,091

4,046

3,988

4,064

4,064

3,625

3,436

3,565

Beds

10

10

4

4

4

4

2

4

4

2

4

2

Number

NDMC

220

200

220

220

220

220

200

220

220

200

220

200

Beds

3

3

3

3

3

3

3

3

3

3

3

3

Number

DCB

2,128

2,128

1,850

1,850

1,832

1,855

1,855

1,855

1,855

1,855

1,850

1,850

Beds

*13 hospitals, 9 TB clinics, 6 IPPvII, 26 Maternity homes and 2 Ayurvedic/unani Source Directorate of Economics and Statistics (2014)

Number

Year

MCD

Table 7.1 Distribution of health institutions and beds in Delhi

9,680 9,102

39

9,894

9,466

7,510

7,518

6,813

6,655

6,655

6,551

5,507

5,391

Beds

38

38

38

38

37

32

32

32

32

25

25

Number

Government of Delhi

15

15

25

22

22

20

23

25

25

25

22

22

Number

4,823

4,847

9,078

8,778

8,550

8,336

8,299

9,970

9,970

9,970

8,281

8,281

Beds

Government of India

930

857

755

676

590

581

611

629

611

564

509

496

Number

21,425

19,636

18,324

17,222

15,722

15,144

15,079

14,984

12,381

12,429

11,230

10,980

Beds

Private regd. nursing homes

7.2 Existing Plans and Policy for Health and Wellbeing … 225

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7.2.4 Mumbai The transport system of Mumbai is unique. Unlike other metropolitan Indian cities, the public transport is quite efficient in Mumbai. The lifeline of the city, i.e. suburban rail system and buses are well-developed. Share of use of public transport in Mumbai is nearly 80%, i.e. 11.2 million journeys per day (MCGM 2010a, b). The eastern, western and central railway systems along with BEST bus service are major service providers. The Mumbai suburban railway network caters to 6.3 million commuters daily making it highest passenger density in the world (MCGM 2010a, b). The government has expanded the road network, developed mono rail and metro service in the city to provide better transport. Three North–South arterial roads (Western Express Highway, Eastern Express Highway and Sion-Panvel Highway) have also been constructed and many projects are underway. There are attempts to interlink Greater Mumbai with Mumbai Metropolitan Area in order to decongest the former. The roads of Mumbai are narrower than Delhi, and to control traffic congestion and maintain air quality, the auto-rickshaws and rickshaw are banned in the city. Vehicular norms and fuel quality are same as Delhi, and all public vehicles have been converted to CNG. Also many industries have been closed or shifted according to the amount of their pollution generating capacity. In 1979, use of coal rule of Urban Development Department prohibited issuing of coal by new units (Schwela et al. 2006). The Industrial Location Policy (1984) of MMR prohibited the expansion of industrial units (large, medium and small) in Mumbai (Schwela et al. 2006). The environmental concepts are incorporated in the school syllabi to generate awareness among children. Eco-clubs and National Green Corps are working attentiveness towards nature. There are many publications like ‘Paryawaran Sevak’ in Marathi and Environmental Information Centre (IGIDR 2014). The health infrastructure of Mumbai is better to most cities of the country with 18 general hospitals, a tuberculosis hospital, an infectious diseases hospital, an ENT hospital, an eye hospital and a leprosy hospital under MCGM (2010a). There are 163 dispensaries, 26 maternity homes, 14 maternity wards, five tuberculosis clinics, five clinics for sexually transmitted diseases and 168 health posts (MCGM 2010a). Under private ownership, there are 1,258 dispensaries, 175 private hospitals and five super specialty hospitals (MCGM 2010b). While in private hospitals number of people per bed are 487, it is 10,147 people per bed for municipal hospitals (MCGM 2010b). Despite large number of health services, there is imbalance in terms of their spatial distribution and are inadequate with respect to high population.

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7.3 Lacuna in Existing Policies and Plans 7.3.1 Land Use/Cover The LULC change of Delhi has been mapped and studied extensively by many authors. The Delhi Metropolitan Plan has been prepared and updated from time to time. The planning of new residential areas, commercial zones and transportation has not been sufficient to cater to the rising needs and demands. Also the pace of demographic growth is higher than of construction of infrastructure. The planning of residential areas have not kept in mind the urban geometry to facilitate free movement of wind, extended parking lots, green spaces and building material. These lacunas have led to creation of UHIs in dense concrete residential areas. In Mumbai city district, the situation is severe as the city has negligible vegetation accompanied with heavy traffic and scores of high rise buildings that trap the heat creating stronger UHI. The suburban Mumbai is little better off as the lakes, rivers and SGNP that act as heat absorbers. Since the population rapidly advancing towards the suburban district, it is imperative that proper planning is initiated so as to avoid the shortcomings experienced in Mumbai city. For efficient LULC planning, a base map of existing LULC, population distribution and expected direction of growth are needed. The government authorities still do not have any such map to scale or with geographic co-ordinates. Planning according to mere sketches may result in false results. Another complication that one encounters in Mumbai is that the boundary of the coastal areas is not stable, and with the change in height of tides and mangrove areas, the boundary changes. This is particularly critical in case of the eastern boundary along the Thane creek. In absence of codified map, the temporal changes cannot be analysed with precision. The afforestation programmes have failed to translate to ecosystem needs as the native varieties and pollution combating species have not been encouraged. The demarcation and scientific calculation of water bodies and water levels in ponds and lakes are still missing. Forests and water bodies are most essential LULCs to minimize UHI and pollution impact on human health.

7.3.2 Air Quality The air quality for Delhi and Mumbai is regularly monitored for majorly four pollutants (SO2 , NO2 , SPM and PM10 ). The comparative analysis of NAAQ pollutant limits with European Union (EU) and World Health Organization (WHO) reveals that Indian pollution standards need revision and rethinking. The desired limits decided by NAAQ, except for 24 hourly limits for SO2 , are much lenient than EU and WHO (Table 7.2). Apart from this, a major drawback is that many harmful pollutants and toxins are yet not monitored for all cities, i.e. O3 , Pb, CO, VOCs and HCs such as benzene

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Table 7.2 Comparative permissible limits of key pollutants in residential areas NAAQ (µg/m3 )a,b

EU (µg/m3 )

WHO (µg/m3 )

Pollutant

Time frame

Comments

SO2

Annual

60

20

50

SO2

24 h

80

125

125

Stricter than EU but lenient than WHO

NO2

Annual

60

40

40

Lenient than EU and WHO

PM10

Annual

60

40

20

Lenient than EU and WHO

PM10

24 h

100

50

50

Lenient than EU and WHO

Lenient than EU

Source Based on Schwela et al. (2006), CPCB (2013) Note a Annual average of minimum of 104 measurements in a year, taken twice a week, 24-hourly at uniform intervals, b 24-hourly/eight-hourly values should be met 98% of the time in a year. It could exceed in two per cent of the time, but even then not on two consecutive days; EU—European Union, WHO—World Health Organization, µg/m3 —microgramme per cubic metre

and PAHs (ASCI and UNDP 2009). The share of these pollutants is negligible, but worldwide research shows that these have far-reaching consequences on human health. The smaller cities should also comply with same rules and regulations much before the problem become acute. There are selected locations for which the real-time data is also available. But to understand the trend, long-term data is needed. It is essential that the data sharing between the government authorities for research purpose is eased and both complement each other. The unavailability and reluctance to share the data is an impediment to find solutions to environmental problems. Also, the number of locations for air quality data is insufficient and do not represent critical areas like near an educational institute, petrol pump and health centres. Vehicles constitute major share of mobile source of pollutants. The weak mass transportation system further aggravates the problem. The primary survey results on problems associated with public transportation indicate that while together for Delhi and Mumbai, traffic jams and congestion are major issues; over-crowing was dominant problem for Delhi (Fig. 7.1). The pollution checking norms, quality of fuel and use of old vehicles need to have stricter implementation. Similarly, aggressive monitoring of pollutants released from coal based power plants is needed. Garg et al. (2006) state that India is the single largest source of CO emissions, wherein 50% contribution is from the power sector. On the basis of intensity of hazardousness, there is clear-cut classification of industries notified by colours, red (high polluting industries that require immediate closure and relocation), orange (polluting but could operate till 1993) and green (could operate till 1995) (Massey 2003). But Directorate of Industries has not accounted for the exact number of industries. The only data on number of industries is through Supreme

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Fig. 7.1 Frequency of response by respondents on problems associated with public transport. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016

Court orders of 1996 that lists 1,328 hazardous industries in Delhi (Massey 2003). The large-scale industries are not the only way to reduce point source pollution. Rather, detailed account of small and medium industries is needed and accordingly regulations and standards shall be set for them too. In addition, the unorganized enterprises (not registered as per SIDO—Small Industries Development Organisation) are estimated to about 15 million in total. These units use 40% of the total energy, most of which is from biomass fuels like groundnut and paddy husk, firewood, waste oil, cotton waste, etc. These primitive methods of energy generation cause enormous pollution and should come under the purview of CPCB regulations (ASCI and UNDP 2009). Other than these, role of aviation industry, biogas and crematoriums may also be assessed (Schwela et al. 2006).

7.3.3 Urban Heat Island Despite the growing awareness on air quality, LULC change, UHI and increasing related health problems are still under-researched area. For Delhi, scores of studies have been conducted on spatial and temporal changes in UHI but many areas are still unexplored. These being inter-relationships between UHI, health and wellbeing, role of building density, design and material on UHI and innovative ways of mitigation, to name a few. On the other hand, UHI research on Mumbai is limited with less than five research papers. The facets of microclimate in Mumbai limited to factors of creation and spatial and temporal occurrence. Of 53 cities with over 1 million population in India, only handful of them have been studied for UHI like Surat, Guwahati (Borbora and Das 2014), Bangalore (Ramachandra and Kumar 2009, 2010), Ahmedabad (Raykar 2005), Pune and Chennai.

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7.3.4 Health The availability, accessibility and affordability of health services remain most critical issues of health sector. The slow growth of health infrastructure coupled with unawareness about health problem, loss of faith in health system has resulted in reluctance in engaging with treatment. The response on problems associated with accessibility and availability of health services as reflected from primary survey reveals that financial constraint for treatment is the foremost health lacuna followed by availability of doctors and reliable treatment. However, the responses vary with respect to city (Fig. 7.2). While distance or accessibility to hospital as a problem is reported higher for Mumbai (23 responses) than Delhi (16 responses), finance and availability of doctors and reliable treatment were more critical for Delhi. There are a couple of health institutes promoting research on impact of pollution (outdoor and indoor) on human health, but these are working in compartmentalized structure. Trans- and inter-disciplinary research is the need of hour. The sciences are still not merging with social science leading to ineffective and partial success in health sector. The data sharing is another lacuna. It is almost impossible to access morbidity and mortality data. The correlations between air pollution and respiratory morbidity are abundant, and however, its linkages with diabetes, heart attacks, hypertension, premature deaths and other diseases are missing. The agewise and income groupwise research is plentiful, but probable occupational hazard groups comprising of traffic police, street vendor and petrol pump employees need attention. While people are aware of the government’s initiatives and policies for improving urban health and environment in respective cities, majority feels that they are

Fig. 7.2 Frequency of response by respondents on problems associated with health facilities. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016

7.3 Lacuna in Existing Policies and Plans

231

not well-implemented. For Delhi, 83% (62 of 75 respondents) of the sample population reported that some problems exist with policy implantation, while 43% (32 of 75 respondents) agreed on the same in Mumbai (Fig. 7.3). Only 11 and 37% of the respondents in Delhi and Mumbai, respectively, were satisfied with policy implementation in respective cities. Further, on enquiring about the nature of problems that have led to poor policy implementation, corruption followed by lack of public participation and gaps in policy making were reported in decreasing order of importance (Fig. 7.4). Nearly 41% (40 of 97 responses) respondents in Delhi and 49% (37 of 76 responses) in Mumbai stated that corruption is the main reason for poor execution of policies.

Fig. 7.3 Response on implementation of policies. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016

Fig. 7.4 Frequency of response by respondents on problems associated implementation of policies. Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016

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7.4 Systems Approach and Sustainable Urban Environment The ICSU identified ‘systems analysis approach’ for improving the health and wellbeing of people in the rapidly changing urban environment. The urban areas have capacity to provide ample opportunities for better health and wellbeing but at the same time present risks of ill health (ICSU 2011). Cities have multifarious populations and governance structures along with wide spatial interactions. It has been recognized that urbanization has systematic response on natural, built and social environment (ICSU 2011) that are in constant dynamicity. Therefore, the systems analysis approach is put forth by the ICSU (2011) to comprehensively understand the problems, feedbacks across the boundary of scale and system through interdisciplinary approach. The present chapter uses the basic premise of system analysis approach and translates it to find solutions to create sustainable urban areas. Parallel to system analysis, Silva et al. (2012) present a simplified conceptual model to analyse urban systems. The model identifies three types of networks, i.e. infrastructure, institution and knowledge necessary for wellbeing and ecosystem (Fig. 7.5). Infrastructure network involves physical and technological aspects like building design, energy, healthcare services, etc. The knowledge network focuses on access to information, learning resources and research opportunities. Lastly, the institutional network refers to the issues of governance, decision making, role of civil society and other agencies. Silva et al. (2012) also elaborate on measuring resilience and urban system boundary. However, only the idea of three pillars is taken and merged to form a basis for creation of strategic plan for urban health and wellbeing for the Indian megacities. Wellbeing

Ecosystem

Health

Urban environment

NETWORKS

Fig. 7.5 Networks in city system analysis. Source Adopted and modified after Silva et al. (2012)

7.4 Systems Approach and Sustainable …

233

The infrastructure, institution and knowledge are three different units but still inter-linked with each other. With knowledge, i.e. research, results and findings in the form of innovations are disseminated to the institutions concerned. These institutions have political and financial power to implement new programmes through creation of infrastructure both, physical and social. In case of present research objectives, the desired wellbeing outcome is health for the urban ecosystem. Four broad areas, i.e. air quality, LULC, UHI and health, are identified in which desirable steps can be taken that can be replicated in all Indian megacities for improvement of health and wellbeing (Table 7.3).

7.4.1 Air Quality India has set the air quality standards, but many air pollutants and toxins are not monitored in all cities. Also, the data availability for research is an impediment. More transparency needs to be maintained to facilitate research and innovation. It is a challenge to link the air quality standards with the epidemiological needs. Air quality monitoring stations shall be placed in sensitive locations like petrol pump and near hospitals and educational institutes. Diesel emissions are categorized as Class 1 carcinogen, and therefore, health concerns due to dieselization have increased. Delhi, Mumbai and 11 other cities (Kolkata, Chennai, Bengaluru, Pune, Kanpur, Agra, Surat, Hyderabad, Ahmedabad, Sholapur and Lucknow) have 50 ppm sulphur level in diesel (w.e.f. 2010), but rest of the country still has 350 ppm sulphur. Comparatively, many cities worldwide are using diesel with 10 ppm sulphur content. Availability of clean fuel and immediate implementation of Euro IV norms at national level is an urgent call. There must be stringent implementation of norms and rules with regular inspection. Other than inducing road and environmental tax on private car owners, incentives can also be given to those choosing the cleaner fuel options. Fuel policy and its pricing requires modification and research shows that the lesser is the gap between CNG and other fuels, i.e. diesel and petrol, more is the diminishing interest in usage of CNG vehicles. It is hence necessary to maintain adequate gap between fuel prices and lower CNG rates would encourage its use. It is also noted that the GHG emission from agriculture is over 28% that needs to be monitored (CSE 2013). Along with these, promotion and availability of affordable electric vehicles must be encouraged. Electric station for charging must be planned to meet the demands. Vehicles using LPG, bio-ethanol and other safer energy options must be promoted and made available at affordable prices. The grants for energy saving projects should be promoted. The US Pennsylvania Department of Environmental Protection’s Energy Harvest Programme provides such incentives since 2003. Traffic management and removal of bottlenecks is essential to reduce the pollution levels along the roads. Infrastructure development in Delhi and Mumbai has taken fast pace in the recent past, and many flyovers and roads have been constructed. But, the rate of vehicular increase is much higher than the capacity of new roads and

Air quality

• Bus transport needs to be connected with metro through feeder services • Park and ride facilities should be developed • Introduce technology to reduce pollution from generators and waste burning • Measures such as pollution bulletins and air pollution forecasts should be started on a regular basis • Create multi-level or basement parking areas • Create parks under flyover • Create car-free zones • Promote terrace gardening • Increase school buses to reduce private vehicular traffic in school hours

Infrastructure • Planting indigenous and pollutant absorbing tree species • Reduce tax on buses • Implement Euro IV nationwide • Set an early timeline for introduction of Euro V and Euro VI • Check small-scale industrial pollution • Strong enforcement of legal parking areas • Introduce cleaner fuel and check fuel adulteration • Frame transport policy • Record and monitor CO, CO2 , O3 , benzene, etc. for all cities • Industrial waste minimization and utilization policy to be encouraged • Increase the gap between pricing of CNG with diesel and petrol • Increase parking charges • Monitor pollution levels near school/hospital/petrol pump and open areas • Make NAAQ standards legally binding • Industrial social responsibility

Institution

Table 7.3 Measures suggested for strategic plan for Indian megacities

(continued)

• Role of agriculture in pollution generation • Private and Community participation • Information dissemination and awareness: transparency and access to the data to be improved • LPG, bio-ethanol and other options to be explored • Promotion of clean technology • Utilization of fly ash • Monitor pollution levels near school/hospital/petrol pump and open areas • Focusing on effects on foetus, brain, hyper tension • Affordable electric vehicles

Knowledge

234 7 Strategic Plan for Urban Health and Wellbeing …

• Plant grass/shrubs in open areas along pavements • Green façade8 • Living walls/bio-walls/vertical gardens9 • Green roof on vehiclesa • Cool roofs like white and green roofs (which absorb little “insolation”)1,5 Plant shade trees along building • Use of reflective surfaces (rooftops and pavements)2 • Greening of parking lots6 • Use of high albedo surfaces • Plant vines • Increase tree cover in playgrounds, school yards and sports fields

UHI

Infrastructure

• Apply remote sensing and GIS technology to prepare planning maps • Enhance vegetative cover in city parks • Material of building (high albedo) • Permeable pavements • Plan ventilation corridors

Land use/cover

Table 7.3 (continued)

• Promotion of affordable solar equipments especially for all government institutions • Ensure access to timely meteorological forecasts • Initiate eco-roof development bonus • Switch-off air conditioners in malls and offices in lean hours • Discourage use of auto-sensor taps

• ‘Plant a sapling’ day on equinox and solstice • ‘Plant a tree’ on graduation day • Appropriate design of green belts/barriers • Acknowledgement of gardeners

Institution

(continued)

Colour the buildings and roofs Proper siting of the industry UHI creating in small cities Permeable pavement system Living walls and roofs Low water using plumbing fixtures Linking basin water with washing machine or other uses • Building design, geometry and orientation10

• • • • • • •

• Identify areas suitable for non-motorized transport and pedestrianisation • Map building geometry for all cities • Uniform creation of small-scale LULC maps • Sea level change analysis

Knowledge

7.4 Systems Approach and Sustainable … 235

Infrastructure

• Increase health infrastructure • Create wind paths/ventilation corridors while planning new city areas3,4 • Development of urban agriculture—community gardens, roof gardens, backyard gardens, aquaculture7 • Establish eco-clubs and green gyms

• Implementation of daily air quality alert with health advisory • Make health facilities accessible, affordable and available to all • Compulsory free quarterly check up of all students (public private partnership) • Introduce compulsory ‘health hour’ in institutes and offices

Institution

• Use of mask by vulnerable groups (traffic police, workers at petrol pump, auto drivers, street vendors) • Create awareness on impact of concretization on health • Create awareness on safe places to live • Encourage trans-disciplinary research • Disease statistics to be made available for research (without personal information of patient)

Knowledge

Source 1 Akbari and Konopacki (2005), 2 Akbari (2005), 3 Akashi (2008), 4 Wong et al. (2010), 5 Susca et al. (2011), 6 Onishi et al. (2010), 7 Qiu et al. (2013), 8 Yeh (2016), 9 Timur and Karaca (2013), 10 Gago et al. (2013) a The Telegraph (2015)

Health and wellbeing

Table 7.3 (continued)

236 7 Strategic Plan for Urban Health and Wellbeing …

7.4 Systems Approach and Sustainable …

237

flyovers. It is, therefore, necessary to focus on speedy construction and at the same time, work in increasing the capacity of mass transportation system. It is noted that in line with the demographic need, the number of schools have rapidly increased in the last decade. Since the buses allotted for school transportation are not sufficient or plying on all routes, use of private vehicles has increased. In this context, it is necessary to allocate more the buses for schools and encourage students and school authorities to travel through bus. Locations or markets suitable for non-motorization shall be identified to create vehicle-free zones. The bus and feeder services must be well-connected with metro and local train. Research on probable and most suitable locations of bus/taxi and auto stands must be conducted to avoid policy failure. Park and ride facilities should be developed at selected locations near to a park or recreational ground and sports club. The people’s perception on suggestions and recommendations to improve air quality as reflected by the primary survey, asserts that pollution control measures must be made stricter and innovative technological advancements be made to achieve desired results. In Delhi, the respondent’s main focus was on strict pollution control measures (38%) followed by innovations in technology, efficient policy implementation and lastly, environmental impact assessment. For Mumbai, majority responses were on pollution control measures (30%) followed by corruption-free policy implementation, new innovations and environmental impact assessment (Fig. 7.6). Other than these, respondents were also asked to provide their suggestions for improving urban environment. In Delhi, public transport needs to be revamped and to encourage ridership, interchange points; safe walking and cycling zones need to be planned. The respondents suggested that awareness regarding civic sense need to be generated for safer use of public transport. Other than this, the government shall work on means to reduce eve teasing and create safe and well-lit transport infrastructure. The respondents from Mumbai stated that ‘odd-even scheme’ must be replicated in

Fig. 7.6 Response of people on suggestions and recommendations to improve air quality (in per cent). Source Primary survey conducted by the authors in Delhi and Mumbai 2013–2016

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all cities of India. They also focused on car-pooling, stricter traffic rules, shifting of all industries in outskirts, afforestation programmes, safe storage for chemicals and increasing the carrying capacity of public transportation. The guidelines for industries require creation of compulsory green belt, ensuring safety of workers at the occupational site, utilization of fly ash and encouraging industrial partners to work with the government through ‘industrial social responsibility’. According to OECD (2006), small and medium enterprises (SMEs) account for 40% of the industrial production but generate 70% of the total industrial pollution, and therefore, strict guidelines for SMEs must be prepared by CPCB. Other than vehicles, power plants and industries, agriculture too contributes immensely to the pollution load. These emissions are seasonal due to burning of agricultural waste. Methods must be invented to reduce these emissions.

7.4.2 Land Use/Cover and Urban Heat Island The LULC changes for minimizing UHI effect should focus on four key areas, i.e. green cover and vegetation, albedo, pavement surfaces and material of building (Gago et al. 2013). The LULC maps for all cities and towns must be prepared using remote sensing and GIS technology. The planning programmes and policies must consider the development of ventilation corridors to keep the city cool by making wind movement free. The building material shall have higher albedo so as to increase reflection that helps in cooling the surface. Gago et al. (2013) mentions that nearly 16% of the total city area is under pavements. These pavements are usually rough and are made of concrete that contribute in increasing the surface temperature. Light coloured smooth surfaces should be preferred in hot climatic urban areas. There are experiments on creation of permeable pavements and likewise Indian cities may also explore the new technology. It is to be noted that Mumbai’s streets cover about 11% of its surface, while comparably Delhi has 21% (MCGM2010). Pavements and sidewalks can also be made by combining concrete and asphalt with soil, gravel and grass (EPA 2003a, b). Studies confirm that white roofs, permeable and cool pavements and high albedo building material help stabilize temperature fluctuation and also reduce energy demand (Akbari 2005). Building design and orientation are also important components of urban morphology. Krüger et al. (2005) conducted field studies and found that size and design have substantial influence on microclimate. They suggest wide streets with east–west axis and north–south orientation of buildings to allow cooling. Further, EPA (2003a, b) mentions that to maximize the impact of trees, they shall be located towards east and west of the house. New parameters like green plot ratio, sky view factor, building density and wall surface area can be studied to evaluate the impact of urban morphology on energy consumption (Gago et al. 2013; Rizwan et al. 2008). Since green cover and water bodies are two most essential elixirs of sustainable urban growth, LULC planning should be focused on them. This is proved in Chap. 5

7.4 Systems Approach and Sustainable …

239

on UHI and LULC, whereby the green spaces and water bodies show lower surface temperature than other LULC. Urban green spaces contribute in improving the human physical and psychological wellbeing that thereby improves health (Tzoulas and James 2004). The temperature of the city centre can be moderated by planting vines and climbers and greening of parking lots, playgrounds, road sides and parks. Urban greening moderates temperature through evapo-transpiration and shading process (Fig. 7.7). However, the selection of specie must be native and pollutant absorbing. Care has to be taken on the specie selection and blindly planting trees may be harmful. Khera et al. (2009) mentions that Prosopis juliflora is one of the most abundant tree in Delhi, while Singh and Kumar (2003) reported that it causes human allergies. The plants that produce allergens and biogenic emissions may contribute to ground-level ozone formation and hence must be avoided. Gago et al. (2013) conclude that trees and vegetation can also have negative effect on microclimates especially in cool climates. Experiments in USA showed that conifers increased heating and therefore suggest the in-depth knowledge of local climate and tree species shall be selected for afforestation programmes. They also state that grass has negative effect on cool island formation. The general building material are impermeable that have low albedo and high absorption capacity. Trees contribute to the beauty, provide shade and reduce energy demand. Green spaces minimize the urban heating and air and noise pollution (Oliveira et al. 2011). Some innovative solutions to reduce city temperature are through green roofs, white roofs, green facade and living walls. Green roofs are also called eco-roofs, living roofs and cool roofs. Green roofs have been installed in Chicago, Oregon, Philadelphia (EPA 2003a, b). These are also common in Germany, France, Australia, Taiwan and Japan. The green roofs remain cool as it is made of planted rooftops using traditional heat absorbing materials. Green wall or vertical gardens are walls covered completely or partially with vegetation. These can be placed indoor as well as outdoor. In India, they have been generally adapted in places

Fig. 7.7 Outdoor green façade in Delhi

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like shopping malls, schools and hotels. The two broad types of vertical gardens are green façade and living wall or biowall. While the former consists of climbing plants that climb the walls directly, the latter are made of three layers, namely, metal frame, a PVC layer and an air layer (EPA 2003a, b). To increase green cover, LULC planning must be conducted seriously. Social forestry, agro-forestry and community forestry are some of the successful steps taken by Government of India to increase the green cover. Here, public-private-community partnership can play a crucial role. Religious centres must be encouraged to plant trees in their vicinity or courtyard. Minor but very important initiative can be planting of grass at all barren patches along pavements, market areas, open spaces and parking lots. Incentives must be given to societies/co-operatives/individuals for their dedication in voluntary planting of trees. It has been noted that there is growing interest in celebration of designated days, and therefore, ‘plant a sapling’ day on equinoxes and solstices can be introduced and promoted through social media and radio. The University of Delhi conducts annual flower show in which the gardeners are awarded (www.du.ac.in). Such good practices should be implemented at larger scale at all levels for their valuable contribution to maintain the ecosystem. In proportion to the total area covered by the educational and health institutes, multinational company offices and malls must cover 20% area with trees. The concept of living walls and green roofs may not be possible to implement on large scale due to financial and maintenance issues but innovative small steps can be taken to reconnect the nature–human bond. Courses on gardening, bonsai making and indoor ornamental plant training should be started. To generate connectivity between nature and human beings, ‘climb a tree or selfie with tree’ and related innovate activities must be promoted. It should be made compulsory to plant a tree on graduation day/degree award function for students. A very interesting article from Kolkata, India, was cited in ‘The Telegraph’ newspaper (2015) wherein a taxi driver covered the roof of his car with grass creating an example for others (Fig. 7.8). Water bodies are another ecosystem that is decisive in maintaining the city temperature. As discussed, the number of lakes has drastically disappeared in Delhi and the amount of water is diminishing in rivers and other water bodies (Fig. 7.9a, b). This is a warning sign, and thus, it is important to first map the existing water bodies and their volume through geo-spatial techniques and plan their restoration accordingly. In case of Mumbai, seasonal sea level changes must be mapped and studied extensively. At household level, awareness generation to reuse basin water or minimal use of water is necessary. Innovative plumbing fixtures are required that consume least water.

7.4.3 Health Health is the final outcome of physical environmental changes. The health facilities suffer from many gaps in our country. The government is well-committed to improve the healthcare services and the budget allocation is a fair reflection of this fact. But

7.4 Systems Approach and Sustainable …

241

Fig. 7.8 Cab with green roof in Kolkata. Source The Telegraph, 17 May 2015

these funds need to be channelized in proper manner keeping in mind the dynamicity of health impacts, especially in urban areas. There are many programmes and financial aid for AIDS, child birth, Polio and family planning programmes. At the same time, many diseases and health concerns are lagging behind. Health and air quality need to be built together and intensive research is required on impact of air pollution on human health. But these researches must be trans-disciplinary in nature and not only from medical point of view. There is need to look beyond the impact on respiratory system and research must be promoted to interlink air quality with effects on heart, effects on foetus, brain, hyper tension and diabetes. Educational institutes and all work places must encourage regular health checkups for all employees and students. These check-ups can be basic but would be helpful in generating awareness about the health status and encourage preparedness. Similar to lunch break, the government may declare 30 min compulsory ‘health break’ in all offices, institutes and organizations for personal health care. Personal health care can include many activities like yoga, breathing exercises, pranayam, gym and other outdoor sports activity or simply going out for a walk. There is a need to scale up the research on ways to mitigate air pollution through behavioural changes and innovations. Health impact assessment should be done for all sections of the society in terms of gender, age, socio-economic class, occupation and pollutants. Health sector in India requires robust data and guidelines. Computerization of health database excluding the personal details of patient must be made accessible to research groups. However, it is necessary to monitor the dispersal and use of data.

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Fig. 7.9 a River Yamuna in Delhi, b diminishing water along Mumbai coast

7.5 Strategic Planning for Delhi and Mumbai The review of literature, case studies, secondary and primary research results are used to present suggestions for better health and environment in Delhi and Mumbai. The LST is classified into three zones: high, medium and low. For the same image, the four pollutant levels are interpolated to understand the spatial inter-linkages between the two indicators of urban environment (Figs. 7.9 and 7.10). With the help of secondary and primary research results, suggestions are presented for major problem areas in Delhi and Mumbai. The districtwise understanding of LST in Delhi for 2010 suggests that Central, North-Eastern and Northern areas of South Delhi experience low temperatures. Though these are densely populated, the high density of tree cover helps reduce the LST. On the contrary, the south-western periphery of Delhi records highest LST

7.5 Strategic Planning for Delhi and Mumbai

243

Fig. 7.10 Spatial inter-linkages between LST and pollution levels in Delhi. Note The LST in °C and pollutant average in micro g/m3

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due to the presence of fallow land. The western part of the city falls under moderate LST and eastern part in low LST. This may be due to the cooling effect by River Yamuna. Interpolation of pollutants for all the monitoring stations reveals that all pollutants are almost double in eastern Delhi than western Delhi (Fig. 7.10). The interpolation of pollution data reveals that since the pollution recording stations reconcentrated in the central areas of the city, for both Delhi and Mumbai, the peripheral areas are not well-represented (Figs. 7.10 and 7.11). For better spatial air quality planning, new air quality monitoring stations need to be opened in the peripheral areas of the city. The pollution as well as the LST is higher for the Mumbai city than the Mumbai suburban district, and hence, the future planning must focus Mumbai suburb and LULC improvements be carried out in the city. The critical regions and land uses require special attention. The suggestions are proposed accordingly (Tables 7.4 and 7.5). The extreme south-western parts of Delhi are agricultural in nature. The low green cover and large fallow land increased the susceptibility to heat related health problems, especially due to PM. Here, agroforestry may be promoted through which the farmers will also benefit and the green cover will help in reducing LST and PM levels. Even due to high LULC changes in the eastern and north-eastern Delhi, substantially low temperatures are found. Hence, the focus should be on maintain and improve the quality and quantity of water in River Yamuna. The city is expected to expand towards the peripheral areas. Here, the policies must focus on living walls and terraces (South-West Delhi; North and North-West Mumbai suburb). Care should be taken in selection of building material and colour of built up areas. The use of high albedo surfaces especially for pavements and roads may be beneficial in reducing city temperatures. In moderate LST regions, i.e. residential areas, urban agriculture shall be promoted (Dense residential areas of North, East and West Delhi; Mumbai city). The parking areas in residential and commercial areas should be covered with green roof or grass floor. Policies for managing the vehicular pollution in Delhi and industrial pollution in Mumbai need to be envisaged. The plans must incorporate improvement in fuel quality and implementation of Euro V.

7.5.1 Concluding Remarks Since cities are functional, it is necessary to plan them holistically rather than sectorwise. The use of system analysis integrated with remote sensing and GIS technology can prove to be beneficial in this regard. The research on issues and problems of cities cannot be isolated and therefore, inter-disciplinary and trans-disciplinary knowledge generation is vital. The methodology should be harmonized as per the international level but modified according to the needs of the city. The national goals and local actions should be glued and gelled in the manner that they go hand-in-hand. Also, the infrastructure, institution, knowledge and research must be inter-woven to achieve

7.5 Strategic Planning for Delhi and Mumbai

245

Fig. 7.11 Spatial linkages between LST and pollution levels in Mumbai. Note The LST in °C and pollutant average in micro g/m3

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7 Strategic Plan for Urban Health and Wellbeing …

Table 7.4 Regionwise suggestions to improve urban environment of Delhi and Mumbai Issues/reason (region)

Solution

Dense residential areas (NE, N, W Delhi; Mumbai city)

• To promote plantation of vines, climbers and other trees along buildings • Increase tree cover in existing playgrounds and parks • Colour: green/white roofs • Building surfaces to have smooth textures • Development of urban agriculture/roof gardens

Still rural; may have urban LULC in future PM pollution (SW Delhi; N, NW Mumbai suburb)

• • • •

Presence of River Yamuna (Heat moderator) (E, N, NE Delhi)

• Revival of water bodies • Cleaning of River Yamuna

Planning city morphology Creating ventilation corridors Living walls Establish hospitals

Source Compiled by the authors

Table 7.5 Feature/land use-wise recommendations to improve urban environment of Delhi and Mumbai Feature

Reason

Solution

Pavements/sidewalks

21% area Delhi and 11% area in Mumbai

• Made by combining concrete and asphalt with soil, gravel and grass

Streets

Allow cooling

• Wide with EW and NS axis

Trees

Heat absorbers

• Located in E and W side of houses • Indigenous pollutant tolerant • Tree species like neem, peepal, babool

Vehicles

GHGs add to UHI effect

• Traffic management and interlinking at changing points • Fuel quality improvement; Lesser sulphur content • Implement Euro IV nationwide and Euro V in all Class I cities

desired results. It is imperative to remain close to traditional methods that are exclusive for each region. There is growing realization that there cannot be one yardstick for all the cities as each is unique in its historical, physical and other characters. Therefore, it is essential to maintain the uniqueness but at the same time apply system analysis to combat the environment-health and wellbeing nexus.

References

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Mehta R (2009) Climate change agenda for Delhi 2009–2012. Directorate of Information and Publicity, Government of NCT of Delhi Ministry of Statistics and Programme Implementation (2013) India country report—2013 statistical appraisal, SAARC development goals, Central Statistics Office, pp 1–98. Retrieved from http://mospi.nic.in/mospi_new/upload/saarc_development_goals_%20india_ country_report_29aug13.pdf. Accessed 18 Sept 2016 Municipal Corporation of Greater Mumbai (2010a) Disaster risk management plan, Mumbai: City profile of Greater Mumbai-2010 (DRMMP Mumbai, 2010–11). Disaster management and CCRC Department, Government of Maharashtra, pp 1–121 Municipal Corporation of Greater Mumbai (MCGM) (2010b) Mumbai human development report 2009, Oxford University Press, pp 1–285. Retrieved from mhupa.gov.in/writereaddata/Mumbai% 20HDR%20Complete.pdf. Accessed 11 July 2016 OECD (2006) Environmental compliance and enforcement in India: rapid assessment. Retrieved from http://www.oecd.org/dataoecd/39/27/37838061.pdf. Accessed 18 Feb 2008 Oliveira S, Andrade H, Vaz T (2011) The cooling effect of green spaces as a contribution to the mitigation of urban heat: a case study in Lisbon. Build Environ 46:2186–2194 Onishi A, Cao X, Ito T, Shi F, Imura H (2010) Evaluating the potential for urban heat-island mitigation by greening parking lots. Urban Forestry and Urban Greening 9:323–332 Planning Commission (2011) Climate change and 12th five year plan: report of sub-group on climate change, Government of India, pp 1–97 Planning Department of Delhi (2011) Issues and challenges for the 12th Five Year Plan (2012–17), Government of NCT of Delhi. Accessed 4 July 2016 Planning Department of Delhi (2013) Economic survey of Delhi—2012–13. Retrieved from www. delhi.gov.in/wps/wcm/…./Economic+Survey+of+Delhi+2012-13. Accessed 18 Sept 2016 Qiu G-y, Li H-y, Zhang Q-t, Chen W, Liang X-j, Li X-z (2013) Effects of evapo-transpiration on mitigation of urban temperature by vegetation and urban agriculture. J Integr Agric 12(8):1307–1315 Ramachandra TV, Kumar U (2009) Land surface temperature with land cover dynamics: multiresolution, spatio-temporal data analysis of Greater Bangalore. Int J Geoinf 5(3):43–53 Ramachandra TV, Kumar U (2010) Greater Bangalore: emerging urban heat island, GIS Development. Retrieved from http://www.ces.iisc.ernet.in/energy/paper/Bangalore_heatisland/ introduction.htm. Accessed 19 Sept 2016 Raykar PS (2005) Defining relationship between urban heat islands and urban morphology. CEPT University, Ahmedabad, School of Planning, pp 1–96 Revi A (2008) Climate change risk: an adaptation and mitigation agenda for Indian cities. Environment and Urbanization, Sage Publications. Retrieved from http://eau.sagepub.com/cgi/reprint/ 20/1/207. Accessed 16 June 2016 Rizwan A, Dennis M, Leung YC, Liu C (2008) A review on the generation, determination and mitigation of urban heat island. J Environ Sci 20:120–128 Schwela D, Haq G, Huizenga C, Han W-J, Fabian H, Ajero M (2006) Urban air pollution in Asian cities: status, challenges and management. Routledge Publication, London Shrivastava RK, Saxena N, Gautam G (2013) Air pollution due to road transportation in India: a review on assessment and reduction strategies. J Environ Res Dev 8(1):69–77 Silva J, Kernaghan S, Luque A (2012) A systems approach to meeting the challenges of urban climate change. Int J Urban Sustain Dev 1–21. Retrieved from http://dx.doi.org/10.1080/19463138.2012. 718279. Accessed 17 Sept 2012 Singh AB, Kumar P (2003) Aeroallergens in clinical practice of allergy in India: an overview. Ann Agric Environ Med 10:131–136 Susca T, Gaffin SR, Dell’Osso GR (2011) Positive effects of vegetation: urban heat island and green roofs. Environ Pollut 159:2119–2126 The Telegraph, 2015, Cool cab with a rooftop garden, 17 May 2015, Calcutta, India. Retrieved from http://www.telegraphindia.com/1150517/jsp/calcutta/story_20517.jsp#. V3gEUfl97Dc. Accessed 2 July 2016

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Timur OB, Karaca E (2013) Vertical gardens, advances in landscape architecture. Intech Chap. 22, 587–622. Retrieved from http://dx.doi.org/10.5772/55763. Accessed 2 July 2016 Tzoulas K, James P (2004) Finding links between urban biodiversity and human health and wellbeing. Urbanisation and sustainable human settlements, pp 208–217 United Nations (2014) World urbanization prospects: the 2014 revision. Department of Economic and Social Affairs, New York UN Habitat (2012) Join the world campaign: better city, better life, World Urban Campaign. Retrieved from mirror.unhabitat.org. Accessed 28 Feb 2012 Whitehead M (2003) (Re) Analysing the sustainable city: nature, urbanisation and the regulation of socio-environmental relations in the UK. Urban Studies 40(7):1183–1206 Wong MN, Nichol JE, To PH, Wang J (2010) A simple method for designation of urban ventilation corridors and its application to urban heat island analysis. Build Environ 45:1880–1889 Yeh Y-P (2016) Green wall—the creative solution in response to the urban heat island effect. Retrieved from http://www.nodai.ac.jp/cip/iss/english/9th_iss/fullpaper/3-1-4nchu-yupengyeh. pdf. Accessed 2 July 2016

Web Reference University of Delhi (2016) www.du.ac.in. Accessed on 1 Mar 2016

Chapter 8

Health Policy, Programmes and Initiatives

Abstract Environment as an input for good health and wellbeing should be protected and preserved. Reflecting on the growing urban spaces associated with increasing number of city inhabitants, it becomes imperative to plan for particularly urban health. Keeping in mind the future needs and demands, the government plans to build 100 new smart cities in India. This is in cognizance with the Sustainable Cities and Human Settlements and Sustainable Development Goals to build planned resilient sustainable cities that are prepared for disasters and promote health and wellbeing. The present chapter encapsulates landmark policies, acts and programmes undertaken by the government of India to promote good health and wellbeing and pave way for bringing sustainable urban development. Keywords Five year plans · Health programmes · Policies · Missions · Constitutional provisions · Judiciary and health · International treaties and conventions

8.1 Introduction Good health and wellbeing are foundation of human resource quality and determine the development strategy of any country. It is well established that health and environment are closely and intimately interlinked (Haque and Singh 2017). Environment as an input for good health and wellbeing should be protected and preserved. Reflecting on the growing urban spaces associated with increasing number of city inhabitants, it becomes imperative to plan for particularly urban health. Keeping in mind the future needs and demands, the government plans to build 100 new smart cities in India. This is in cognizance with the Sustainable Cities and Human Settlements goal to build planned resilient sustainable cities that are prepared for disasters and promote health and wellbeing. It is essential to focus on low carbon-emitting energy resources for transportation, industry and agriculture for the development of sustainable cities. The present chapter encapsulates landmark policies, acts and programmes undertaken by the government of India to promote good health and wellbeing and pave way for bringing sustainable urban development.

© Springer Nature Singapore Pte Ltd. 2020 A. Grover and R. B. Singh, Urban Health and Wellbeing, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-13-6671-0_8

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India presently is the second-largest populated nation comprising of 18% of the world’s population (United Nations 2017). This is approximately combined population of the six countries, namely the USA, Indonesia, Brazil, Pakistan, Bangladesh and Japan (Patel 2015). However, with large human resource base comes many inherent challenges. India is bearing the dual burden of diseases where on the lower end, malnutrition, hygiene, immunization, sanitation and infectious diseases are major concerns; and on the higher end, environmental health and lifestyle diseases and other non-communicable diseases have raised alarm. Cardiovascular diseases, tuberculosis, cancer, diabetics, malaria, dengue fever, chikungunya, respiratory infections, vector and water-borne diseases continue to be major challenges among the latter group (Central Bureau of Health Intelligence 2016). Added to this is the threat of emerging infectious disease like Ebola, SARS and H1N1 influenza virus-related diseases. As per the Central Bureau of Health Intelligence (2016), India is facing the ‘Triple burden of diseases’, i.e., unfinished agenda of communicable diseases, noncommunicable diseases and emerging infectious diseases. Among the communicable diseases, in 2015, morbidity reported from acute respiratory infections was the highest (67%) followed by acute diarrhoeal diseases (23%), whereas mortality reported was highest from influenza A H1N1 (23%), acute respiratory infections (20%) and pneumonia (18%) (Central Bureau of Health Intelligence 2016). Hence, the concern of the future decade is not only population increase and composition but also quality of human resource. India is growing at a rapid pace and is already the fourth-largest economy in the world. With achievements in economic sector, India achieved many strides in other sectors too like, life expectancy increased to 65 year and Infant Mortality Rate, Maternal Mortality Rate and Death Rate have been reduced significantly (Planning Commission 2012). Diseases like polio, smallpox, guinea worm and leprosy have been nearly eliminated. The birth rate too is showing a declining trend. The numbers of doctors, health clinics and nursing have increased to provide healthcare services to many remote parts of the country. The success of these is attributed to increase penetration of healthcare services, improved immunization, growing literacy and innumerable initiatives by the government and private sector (Central Bureau of Health Intelligence 2016, 2018). The insurance sector has played an important role in contributing for the betterment of health. With the liberalization of health insurance, there has been an increase in the private player, but, still 74% of the insurance falls under various Governmentsponsored schemes. As percentage of GDP, the public expenditure on health was 1.12% in 2009–10 that reduced to 1.07 in subsequent year. In 2013–14, only 1% of GDP was the expenditure on public health. Later in 2015–16, it again increased to 1.12% but in comparison with problems the multiplicity of problems of the rising population, it seems quite less (Central Bureau of Health Intelligence 2016). The backdrop document of the NHP-2017 mentions that the private healthcare industry encompasses insurance and equipment which accounts for about 15%, pharmaceuticals for more than 25%, diagnostics about 10% and hospitals and clinical care about 50% having total value of $ 40 billion that is expected to grow to $ 220 billion by 2020. On the other hand, the government has heavily invested in the form of levying

8.1 Introduction

253

lower direct taxes in healthcare industry, higher depreciation in medical equipment, income tax exemptions for five years for rural hospitals and custom duty exemptions for lifesaving equipment (Government of India 2017a, b). India’s healthcare spend is significantly low in comparison with the other highly populated developing countries as well as the developed countries. As percentage of GDP, India spent 4.10% of the total GDP on health care in 2010 (World Health Statistics 2010; ASSOCHAM 2011). In comparison, the global average was 9.7% with the USA having maximum share (15.70%) followed by UK and Brazil (8.40% each). Break-up of public and private spending reveals that 26.20% and 73.80%, respectively, were the contribution. This is highly skewed and the public sector spending is lowest in comparison with USA (45.5%), UK (81.70%), Brazil (41.60) and China (44.70%). On the other hand, the private sector contribution is highest in India while the global average is 40.40% (2010). The per capita spending on health care is also among lowest in India (40 USD). However, it is notable to see that the healthcare industry is growing in India owing to population increase, expected increase in geriatric population, lifestyle-related diseases, rising literacy and disposable income that makes health care more affordable. Despite massive amount of investments, there exist wide gaps between targets and reality. It is imperative for India to provide quality healthcare services at affordable rates to its population. There have been strides of growth in health sector since its inception from the recommendations of Bhore Committee in 1946. The Bhore Committee was set up in 1943 for a comprehensive health survey for development of the country. It laid down the basic structure for health planning that later shaped the nature of programmes and policies in India. The most important recommendations were setting up of well-structured public health system with high priority to child and maternal health care. The new agenda for public health in India includes: • Epidemiological transition (rising burden of non-communicable diseases). • Demographic transition (increasing elderly population). • Environmental changes.

8.2 Health Sector in India—Structure, Roles and Functions The health sector in India is public, government, private or individual owned. Private sector healthcare providers, registered under the Clinical Establishment Act, are owned and run by individuals or a group of individuals. These consist of dispensaries, clinics, nursing homes and hospitals that may practice Allopathic, Ayurvedic, Homeopathic or Unani systems of medicine. Public sector, on the other hand, comes under the Ministry of Health and Family Welfare (MoHFW), Government of India. They too consist of dispensaries, clinics, nursing homes and hospitals that follow various kinds of medicine systems. Additionally, it includes all India networks of government health facilities in the form of sub-centres, primary health centres, community

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health centres and rural hospitalizing, urban health centres, municipal and other government hospitals. Charitable institutions, religious organisations like churches and NGOs and public sector bodies like atomic energy, railways, port trust, reserve bank and armed forces also own many of these also. Additionally, pharmaceutical companies, chemist shops, research organisations, medical colleges and other health-related training and research institutes that may be public or privately owned also are a part of the health sector. The roles and responsibilities of public sector vary from the private sector. While the private sector institutions are more inclined towards curative aspects, the public sector takes more holistic approach including research, disease prevention and control, sanitation and cleanliness missions. At the level of operation, federal nature of the Constitution allows for two levels: Union and State governments. The Seventh Schedule of the Constitution describes the three lists: Union, State and Concurrent entailing the details of roles and responsibilities at each level.

8.2.1 Role of Government of India in Preservation and Promotion of Public Health: Health Missions, Five Year Plans and National Health Policies The central government provides a broader framework and direction to all programmes to be undertaken like smallpox, malaria, tuberculosis, HIV/AIDS, leprosy and others. These programmes are implemented all over the country uniformly. It is responsible to provide funds to the state government for implementation and execution of all the initiatives. The states also implement all centrally funded programmes like family planning, Swachh Bharat Abhiyan (Clean India Mission) and universal immunization. The Union Ministry of Health and Family Welfare is responsible for the implementation of various programmes related to health and family welfare, prevention and control of major communicable diseases and promotion of traditional and indigenous systems of medicines at the national level. It also undertakes research, provides technical assistance and funds for control of seasonal disease outbreaks and epidemics. The Ministry is also responsible for the implementation of World Bank-assisted programmes like control of malaria, tuberculosis, AIDS and others. Programmes having implications at the national level come under the Concurrent list like family welfare and population control, medical education and prevention of food adulteration. Public health, hospitals, dispensaries and sanitation fall under the State list (Government of India 2015). With respect to missions on health, NRHM and NUHM have had significant achievements. Recently, the Swachh Bharat Mission (2014–19) aims to achieve sanitation facilities, cleaner environment and surroundings for all. One of the main objectives of this nationwide campaign is to eliminate open defecation by the construction of toilets and awareness generation. AMRIT launched in 2015 aims to reduce the expenditure incurred by patients on treatment of non-communicable diseases like

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Table 8.1 National health missions in India Year

Name of mission

1996

Intellectual Disability-related Schemes (Vikaas, Samarth, Gharaunda, Niramaya, Sahyogi, Gyan Prabha, Prerna, Sambhav, Bhadte Kadam and Disha)

2002

Sarwa Shiksha Abhiyan

2005

National Rural Health Mission (NRHM)

2008

National Mission on Medicinal Plants

2012

National AYUSH Mission

2013

National Urban Health Mission (NUHM)

2014

Swachh Bharat Mission (Clean India Mission)

2015

Affordable Medicines and Reliable Implants for Treatment (AMRIT)

2018

National Health Protection Mission (Ayushman Bharat Yojana/Pradhan Mantri Jan Arogya Yojana—PMJAY)

cancer and heart diseases (Table 8.1). With 11 centres established till 2018, it is reaching out fast to the public. The world’s largest health insurance scheme, Ayushman Bharat Yojana (National Health Protection Mission), was launched in 2018. It promises health cover worth Rs. 500,000 to every poor family for treatment of serious ailments.

8.2.2 Historical Evolution of Health Policies, Plans and Programmes in India The first comprehensive health policy and plan document, Health Survey and Development Committee Report, i.e., Bhore Committee Report, was prepared in 1946. Herein, detailed plan for National Health Service with universal coverage was envisaged. The Bhore Committee presented a detailed analysis of the present situation with suggestions. Further, the Sokhey Committee (established in 1938) report was released in 1948. This was a sketchy report as compared the Bhore Committee report. Nevertheless, the recommendations of both concurred. Unfortunately, the health disparity and coverage of health services still remain grave. In post-independence, it was not until 1983 when the first health policy was formulated and adopted. But before 1983, schemes made under the Five Year Plans were fulfilled. These had specific targets like, in the 1950s and the 1960s, the focus was on managing the epidemics. Widespread national-level campaigns were started to overcome the loss by malaria, smallpox, tuberculosis, leprosy, filaria, cholera and others. The approach was techno-centric wherein the health workers were trained to prevent and control disease spread. International experts and ideologies influenced the mission. The necessary chemicals, medicines and vaccines were dependent on international agencies. The role of social and economic conditions, environment, diet, nutrition, housing

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and clothing was ignored. Moreover, the structure of public healthcare delivery system remained unchanged in first two Five Year Plans and urban areas continued to receive major share of resources. By the end of second plan, there was one Primary Health Unit per 140,000 rural populations (14 times less as per Bhore Committee recommendation) and one hospital for 320,000 rural populations. On the contrary, in urban areas, the ratio for hospital and dwellers was 1:36,000 and 1:440 for hospital bed per population. Clearly, the health disparities were quite high and needed urgent attention. Murlidhar Committee was set up in 1959 to evaluate the progress made in the first two plans and provide recommendations. Though there were success stories with regard to the control of disease-specific deaths, improvements in life expectancy and reduction in death rate; the committee bought forward the issues of availability and accessibility to healthcare services. The primary health centres (PHC) were understaffed and ill-equipped, the health practitioners were less in number and urgent need to improve healthcare facilities was asserted (Sharma 2017). Subsequently, the Third Five Year Plan proposed the establishment of medical colleges, research institutes and training centres for doctors, nurses and auxiliary staff. Though the family planning programme started in 1951, it was actively pursued in this period. Additionally, family planning was made an independent department in the Ministry of health. Thereafter, in 1969, the fourth plan was released that continued the previous approach and goals. Other than this, water supply and sanitation were given separate allocations under the sector of Housing and Regional development. The Fifth Five Year Plan was landmark as it acknowledged the widening gap between rural and urban areas with respect to all health indicators. It thus focused on accessibility of health services in the rural areas through the Minimum Needs Programme. The emphasis on eradication of communicable diseases and provision of health infrastructure continued. In the middle of this plan, emergency was declared and family planning received undue attention. The provision of safe drinking water and sanitation remained inadequate or absent in majority of the areas. Many waterborne diseases such as diarrhoea, cholera, typhoid, jaundice and others affected the Indian population. Later in 1979–80, India faced with acute drought. Hence, subsequent plants prioritized the issue of safe drinking water and sanitation. The Sixth Five Year Plan was influenced by the international declaration ‘Health for all by 2000 AD’. Many radical measures were suggested by sixth and seventh plan but the action taken was minimal. Privatization became an overarching characteristic in the 1980–90s. Finally, in 1983, the first National Health Policy (NHP) was announced. It aimed to achieve the goal of universal health care that is affordable and as per the needs of the people. Emphasis was laid on preventive, promotive and rehabilitative primary health care; decentralization and community participation and increased role of private investors. Due to attention on selective health care, increased privatization and delink with the ground realities, the policy could not make much success stories. The Seventh Five Year Plan too emphasized on AIDS, cancer and coronary heart diseases with the development of super-specialized centres. This led to a boom in corporate hospitals and diagnostic centres.

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The Eighth Five Year Plan laid focus on the health for underprivileged but with selective healthcare approach. However, the ninth plan refers back to Bhore Committee and other significant recommendations and came up with innovative strategies such as evolving state-specific strategies, integration of medical education and health, provision of PHC in slums, horizontal and vertical integration of programmes and improvement of disease surveillance. It also asserted the need for new Health Policy. Despite novel solutions and ideas, the plan failed at ground level. On the eve of the tenth plan, the draft of NHP was announced and called for feedbacks from the public. Finally in 2002, NHP document was released with the objective of achieving acceptable standards of good health of Indian population, decentralization, equity, accessibility of health services and provision of affordable private health care (Duggal 2014). The role of traditional medicines was also acknowledged by this policy. Further, in the Eleventh Five Year Plan, the central theme with respect to the health sector is ‘inclusive growth’. It envisaged the provision of healthcare facilities in rural areas through National Rural Health Mission (NRHM). The Twelfth Five Year Plan was prepared after the consultation of public. It called for Universal Health Coverage through Essential Health Package and to assess the social determinants of health. Major thrust areas were reducing out of pocket expenditure (OOP), ensuring accessibility of vaccines, medicines and technology, increasing staff, AYUSH (Ayurveda, Yoga and Naturopathy, Unani, Siddha and Homeopathy) doctors, disaster management areas, nutrition promotion, improve sanitation and provide safe drinking water facilities (Planning Commission 2013a, b). The National Health Policy 2017 came after 14 years gap and therefore the context of health changed in many ways. The growing number of non-communicable diseases and infectious diseases; rise of private sector; increased expenditure on health and rising economic growth enabling enhanced fiscal capacity have shaped the 2017 policy (Gupta and Kumari 2017). The policy aims at providing health care in an ‘assured manner’ to all. There is shift from sick-care to wellness and wellbeing of individuals. The Make in India model governs the manufacturing of drugs and devices. AYUSH is given special emphasis, especially yoga. While the policy is a comprehensive document, it is yet to be seen whether the targets are achieved or not (Planning Commission 2013a, b; Government of India 2017a, b). Other than the NHPs, many other policies were announced from time to time that are closely linked with improving the health status of people. These are National Population Policy, National Nutrition Policy, National Water Policy and National Environmental Policy to name a few (Table 8.2).

8.3 Constitutional Provisions: Acts and Statues in India The Government of India envisages the goal of ‘Health for all’ as health care is important component of social security and development. As per the Constitution, public health, sanitation, dispensaries and hospitals come under the purview of state list (Entry 6, State List II) while population control and family planning are in

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Table 8.2 National health policies/other related policies for promotion of health Year

Name of policy

1983

National Health Policy

1992

National AIDS Control and Prevention Policy

1993

National Nutrition Policy

1999

National Policy on Older Persons

2000

National Population Policy

2001

National Policy for Empowerment of Women

2002

National Blood Policy

2002

National Policy on Indian System of Medicine and Homeopathy

2002

National Health Policy

2003

National Policy for Access to Plasma-derived Medicinal Products from Human Plasma for Clinical/Therapeutic use

2003

National charter for children

2005

National Rural Health Mission

2006

National Environment Policy

2009

Right of children to Free and Compulsory Education Bill—2009 (education to children aged between 6 and 14 years)

2012

National Pharmaceutical Pricing Policy

2012

National Water Policy

2013

National Policy for Children

2015

National Youth Policy

2017

National Health Policy

Under the purview of policies, many programmes for communicable and non-communicable diseases were launched listed in Tables 8.3 and 8.4. Other than these, Ministry of Health and Family Welfare launched Pradhan Mantri Swasthya Suraksha Yojana (PMSSY) in 2006, Janani Shishu Suraksha Karyakram (JSSK) and Janani Suraksha Yojana for insuring the health care. Various programmes undertaken by the Ministry of Social Justice and Empowerment/Ministry of Child Development and Women are Integrated Child Development Services (ICDS) scheme, Mid-day Meal (MDM) Programme, Special Nutrition Programme, National Nutritional Anaemia Prophylaxis Programme (NNAPP), Reproductive and Child Health Programme and School Health Programme. With respect to supply of clan drinking water, Ministry of drinking water and sanitation introduced the Rajiv Gandhi National Drinking Water Mission (RGNDWM) (Lakshminarayanan 2016; http:// shodhganga.inflibnet.ac.in/bitstream/10603/145095/15/15_chapter%205.pdf; Patel 2015)

Concurrent list (Entry 20 A, List III). The relevant constitutional provisions are stated as following (Gupta 2002; Government of India 2015): 1. 2. 3.

Article 21 guarantees the fundamental right to life that casts an obligation upon the State to preserve the life of every person by offering immediate medical aid. Article 23 prohibits traffic in human beings—important in the context of prostitution, STD and HIV AIDS. Article 24 prohibits child labour (below age 14).

8.3 Constitutional Provisions: Acts and Statues in India

259

Table 8.3 National health programmes: communicable diseases Year

Name of programme

1955

National Leprosy Eradication Programme (NLEP)

1955

National Filaria Control Programme (NFCP)

1962

National TB Control Programme (NTC)

1978

Universal Immunization Programme (UIP launched in 2005)/Mission Indradhanush)

1983

National Guinea Worm Eradication Programme (NGEP)

1990

National Vector Borne Disease Control Programme (NVBDCP)

1992

National AIDS Control Programme (NACP)

1993

Revised National TB Control Programme (RNTCP)

1996

Yaws Control Programme

2000

Integrated Disease Surveillance Projects (IDSP)

NA

Voluntary Blood Donation Programme (VBDP)

Table 8.4 National health programmes: non-communicable diseases, injury and trauma Year

Name of programme

1950s

National STD Control Programme

1962

National Goitre Control Programme (NGCP)

1975

National Cancer Control Programme (NCCP)/National Programme for Prevention and Control of Cancer (NPPCC)

1976

National Programme for Control of Blindness (NPCB)

1982

National Cancer Registry Programme (NCRP)

1982

National Mental Health Programme (NMHP)

1988

Drug De addiction Programme (DDAP), Revised in 1993

1992

National Goitre Control Programme (NGCP) was renamed National Iodine Deficiency Disorder Control Programme (NIDDCP)

1992

National AIDS Control Programme (NACP)

1995

Pulse Polio Immunisation programme

1996

District Mental Health Programme

1998

National Programme for Control and Treatment of Occupational Diseases (NPCTOD)

2006

National Programme for Prevention and Control of Deafness (NPPCD)

2007

National Tobacco Control Programme (NTCP)

2008

National Programme for Prevention and Control of Fluorosis (NPPCF)

2010

National Programme on Prevention and Control of Diabetes, CVD and Stroke

2010

National Programme for Health Care in Elderly (NPHCE)

2014

National Oral Health Programme

260

4. 5. 6. 7. 8. 9.

10.

11.

12.

13.

14.

8 Health Policy, Programmes and Initiatives

Article 32 empowers every citizen of India to move the courts for violation of fundamental rights. Article 38 enjoins upon the state to minimize the inequalities in income, facilities (including health facilities) and opportunities. Article 39 reads ‘the state shall direct its policy towards securing health and strength of men, women and children and to see to it that they are not abused’. Article 41 is about the provision of public assistance in case of old age, sickness and disability. Article 42 is about provision of just and humane conditions of work and maternity benefits. Article 47 reads ‘The State shall regard raising the level of nutrition and the standard of living of its people and improvement of public health as among its primary duties. The State shall endeavour to bring about prohibition of the consumption, except for medical purposes, of intoxicating drinks and of drugs injurious to health’. As per the 7th schedule of the constitution, provision of health care is the responsibility of the State governments but the central government also plays a vital role in supporting the access to quality health. Article 246 pertains to scheme of distribution of legislative powers between centre and states as given in the 7th schedule of the constitution among Union, State and Concurrent list. Article 243G is inserted as the 73rd amendment of the constitution 1992 to endow the Panchayats with various powers including matters related to drinking water, health, sanitation, PHCs, family welfare, women and child development and welfare of the handicapped and mentally retarded. Article 243 W, added by the 74th amendment in 1992, pertains to the powers given to Municipalities to perform the functions entrusted with them regarding water supply, public health, sanitation and solid waste management, vital statistics registration, regulation of slaughterhouses and tanneries. Article 263 provides for the formation of inter-state council for investigating subjects in which states and centre have common interest and recommending the action for better co-ordination.

The government passes various acts and laws to promote healthy lives for all. These acts pertain to medical profession and education, nursing profession and education, pharmacists and pharmacy education, dental profession and education, mental health, drugs standards, advertisements relating to drugs and medicines, prevention of the extension from one State to another of infectious or contagious diseases affecting human beings and prevention of adulteration of foodstuffs and drugs.

8.4 Role of Judiciary

261

8.4 Role of Judiciary The Supreme Court is the original, appellate and advisory body for jurisdiction in India. In addition, Article 32 of the Constitution gives an extensive original jurisdiction to the Supreme Court in regard to enforcement of Fundamental Rights. The Supreme Court also deals with ‘Public Interest Litigations’, i.e. matters in which interest of the public at large is involved and the Court can be moved by any individual or group of persons either by filing a Writ Petition (Gupta 2002). The High Court is the highest body at state level. It has the power to issue jurisdiction directions, orders, or writs to any person within its state. Lok Adalats are voluntary agencies monitored by the State Legal Aid and Advice Boards. They help resolve the dispute through conciliatory method.

8.4.1 Some Important Legislation Related to Health The Indian Medical Council Act, 1956 and Regulations 2002; the Indian Nursing Council Act, 1947; the Dentists Act, 1948; the Pharmacy Act, 1948; the Rehabilitation Council of India Act, 1992; the Indian Medicine Central Council Act, 1970, and the Homeopathy Central Council Act, 1973 and the Clinical Establishment Act 2010 are related to quality of education and training of health personnel (Kishore 2012; Government of India 2011). • Registration of Births and Deaths Act, 1969 • Spread of Epidemics Disease Act, 1994 • The Cigarettes and other Tobacco Products (Prohibition of trade, commerce, production, supply and distribution) Act, 2003 • The Mental Health Act, 1987 • The Narcotic Drugs and Psychotropic Substances Act, 1985 • The Drugs and Cosmetics Act, 1940 • The Prevention of Food Adulteration Act, 1954 • Persons with Disabilities (Equal Opportunities, Protection of Rights and Full Participation) Act, 1995 • Various women health-related acts are The Maternity Benefit Act, 1961, Family Court Act 1984, The Dowry Prohibition Act, 1961 and The Immoral Traffic (Prevention) Act, 1956 • To protect children and their rights are The Prenatal Diagnostic Techniques (Regulation and Prevention of misuse) Act, 1994, The Infant Milk Substitutes, Feeding Bottlers and Infant Foods (Regulation of Production, Supply and Distribution) Act, 1992, The Juvenile Justice Act, 1986, The Child Labor (Prohibition and Regulation) Act, 1986 and The Child Marriage Restraint Act, 1929. • For the protection of workers and their families a number of acts have been passed. These include the Minimum Wages Act, 1948; The Dangerous Machine (Regulation) Act, 1983; The Plantation Labor Act, 1951; The Factories Act, 1948; The

262

•

•

•

•

8 Health Policy, Programmes and Initiatives

Mines Act, 1952; The Employees State Insurance (ESI) Act, 1948; The Workmen’s Compensation Act, 1923; The Bonded Labor System (Abolition) Act; The Trade Union Act, 1926; The Dock Workers (Safety, Health and Welfare) Act, 1986; The Mines Labor Welfare Fund Act, 1972; The Bidi Workers Welfare Fund Act, 1972; The Cigar Workers (Conditions of Employment) Act, 1966; and The Contract Labor (Regulation and Abolition) Act, 1970. Environmental legislations are very important to ensure good health and wellbeing. In this regard, the government has enacted number of such as the Destructive Insect and Pest Act, 1914; Wild Life (Protection) Act, 1942; The Atomic Energy Act, 1962; The Water (Prevention and Control of Pollution) Act, 1974; The Air (Prevention and Control of Pollution) Act, 1981; The Environment (Protection) Act, 1986; The Motor Vehicles Act, 1988. Government of India has made provisions for voluntary groups to work in social, educational, environmental, and health domains through acts such as The Societies Registration Act, 1860 and The Red Cross Society (Allocation of Property) Act, 1936. Recently in 2010, The Clinical Establishments (Registration and Regulation) Act was introduced 2010 which aims at providing registration and regulation of clinical establishments in the country with a view to prescribing the minimum standards of facilities and services for them. In 2011, pictorial health warnings on cigarettes and other tobacco products have come to effect (Government of India 2011). Other than these many acts/statutes come under the jurisdiction of MoHFW including The Prevention of Food Adulteration Act, 1954 (37 of 1954), Medical Termination of Pregnancy Act, 1971 (34 of 1971), Pre-conception and Pre-natal Diagnostic Techniques (Prohibition of Sex Selection) Act, 1994 (57 of 1994), The Food Safety and Standards Act, 2006 (34 of 2006) and The Clinical Establishments (Registration and Regulation) Act 2010.

8.5 Ministries Related to Improving Health Since good health and wellbeing have overlapped with various other dimensions, many ministries together have to work for the promotion of healthcare facilities. Various ministries that directly or indirectly contribute towards good health of the Indian population. Of the total 58 ministries, the following 26 are related to provision of healthcare services and promotion of good health. 1. 2. 3. 4. 5. 6. 7.

Ministry of Health and family welfare Ministry of Social, Justice and Empowerment Ministry of Women and Child Development Ministry of Human Resource Development Ministry of Rural Development Ministry of Urban Development Ministry of Housing and Urban Poverty Alleviation

8.5 Ministries Related to Improving Health

8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25.

263

Ministry of Water Resources Ministry of Drinking Water and Sanitation Ministry of Environment, Forests and Climate Change Ministry of Earth Sciences Ministry of New and Renewable Energy Ministry of Petroleum and Natural Gas Ministry of Power Ministry of Panchayati Raj Ministry of Tribal Affairs Ministry of Minority Affairs Ministry of Labour Ministry of Youth Affairs and Sports Ministry of Consumer Affairs, Food and Public Distributions Ministry of Agriculture Ministry of Food Processing Industries Ministry of Science and Technology Ministry of Electronics and Information Technology Ministry of Home Affairs

8.6 International Treaties and Conventions Ratified by India There has been a significant role of international treaties and declarations on the course of direction of the health policies of India. Notably, when the international scenario was focused on curing the communicable diseases, India too planned accordingly. Later, when the holistic definition of health was released by WHO, the perspectives on health widened. It can be undoubtedly said that India has aligned itself with the global needs and concerns. Some important landmarks in health sector at the international level are as follows: • Alma Ata Declaration, 1978: The Declaration of Alma Ata was adopted at the International Conference on Primary Health Care at Kazakhstan in 1978. It is the first international declaration and hence an important landmark. The goal of this declaration was to achieve ‘Health for all’ particularly through primary health care. • International Conference on Population and Development (ICPD) Cairo, 1994: The International Conference on Population and Development was organized by the United Nations to discuss various issues related to population such as immigration, infant mortality, birth control, family planning, education of women and protection for women from unsafe abortion services. The conference called for universal education, reduction in IMR, MMR and child mortality and access to safe methods for family planning.

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8 Health Policy, Programmes and Initiatives

• Millennium Development Goals, 2000: The United Nations declared Millennium Development Goals, 2000 to be achieved by 2015. The eight international goals under it were adopted by all 191 member states. These goals are: 1. 2. 3. 4. 5. 6. 7. 8.

To eradicate extreme poverty and hunger To achieve universal primary education To promote gender equality and empower women To reduce child mortality To improve maternal health To combat HIV/AIDS, malaria, and other diseases To ensure environmental sustainability To develop a global partnership for development

• WHO Framework for Tobacco Control—WHO Geneva, Convention 2003: The World Health Organization Framework Convention on Tobacco Control (WHO FCTC) treaty was adopted in the 56th World Health Assembly held at Geneva, Switzerland. It was signed by 168 countries. The treaty called for the protection of present and future generations from the devastating health, social, environmental and economic consequences of tobacco consumption and exposure to tobacco smoke. • Sustainable Development Goals (SDG), 2016: The SDGs are a set of 17 goals to be accomplished by 2030. They cover issues including poverty, hunger, health, education, global warming, gender equality, water, sanitation, energy, urbanization, environment and social justice. Good health and wellbeing for people is the third goal states ‘Ensure healthy lives and promote wellbeing for all at all ages’ (Fig. 8.1) while clean water and sanitation, affordable and clean energy, sustainable cities and communities, climate action and zero hunger are other important goals related to health.

8.7 Concluding Remarks Within the wide framework of nested health missions, policies, programmes, acts and statues lie the overarching objective of providing good health and wellbeing to all. The diverse challenges pose threat to achieving targets. Concerted efforts and dedicated research are still falling short of targets. The health sector is wide and demanding and requires comprehensive detailed review of all previous committee reports and recommendations and in-depth ground reality checks. The role of government needs to be enhanced along with more revenue allocation for successful results in health sector.

References

265

Fig. 8.1 Highlights of Sustainable Development Goal 3: good health and wellbeing. Source Adopted from United Nations Organisation

References ASSOCHAM (2011) Emerging trends in healthcare—a journey from bench to bedside, pp 1–50 Central Bureau of health Intelligence (2016) National health profile 2016. Directorate General of Health Services, Ministry of Health and Family Welfare. Retrieved from http://www. indiaenvironmentportal.org.in/files/file/National%20Health%20Profile%202016212.pdf Central Bureau of health Intelligence (2018) National health profile 2018. Directorate General of Health Services, Ministry of Health and Family Welfare Retrieved from http://www.cbhidghs. nic.in/WriteReadData/l892s/Before%20Chapter1.pdf Duggal R (2014) Health planning in India. Retrieved from http://www.cehat.org/cehat/uploads/ files/a168.pdf Government of India (2011) Annual report to the people on health. Ministry of Health and Family Welfare, pp 1–76 Government of India (2015) Manual on health statistics in India. Ministry of Statistics and Programme Implementation. Retrieved from http://www.mospi.gov.in/sites/default/files/ publication_reports/Manual-Health-Statistics_5june15.pdf. Accessed 1 Dec 2018 Government of India (2017) National health policy 2017. Ministry of health and family welfare, pp 1–31 Government of India (2017) Situation analysis: backdrop to the national health policy 2017, Ministry of Health and Family welfare. Retrieved from https://mohfw.gov.in/sites/default/files/ 71275472221489753307.pdf. Accessed 1 Dec 2018 Gupta MC (2002) Health and law—a guide for professionals and activists. Kanishka Publishers, New Delhi Gupta RK, Kumari R (2017) National health policy 2017: an overview. JK Sci 19(3):135–136

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Haque S, Singh RB (2017) Air pollution and human health in Kolkata, India: a case study. Climate 77(5):1–16. https://doi.org/10.3390/cli5040077 Kishore J (2012) Legislation and health promotion in India. DRUNPP Rev Global Med Healthcare Res 3(2):75–87 Lakshminarayanan S (2016) Role of government in public health: current scenario in India and future scope. J Family Commun Med 18(1):26–30 Patel RK (2015) Health status and programmes in India. New Century Publications, New Delhi Planning Commission (2012) Report of the steering committee on health for the 12th five year plan. Health division, Government of India, pp 1–77. Accessed 1st Dec 2018 Planning Commission (2013a) Twelfth five year plan (2012–2017) faster, more inclusive and sustainable growth, 1:1–370. Retrieved from http://planningcommission.gov.in/plans/planrel/ 12thplan/pdf/12fyp_vol1.pdf Planning Commission (2013b) Twelfth five year plan (2012–2017) faster, more inclusive and sustainable growth, 2:1–438. Retrieved from http://planningcommission.gov.in/plans/planrel/ 12thplan/pdf/12fyp_vol2.pdf Sharma KK (2017) Government programmes to improve health and environment. Ministry of Health and Family Welfare, Government of India. Retrieved from www.nams-india.in/downloads/CMENAMSCON2017/9M2017.pdf United Nations (2017) World population prospects. Department of Economic and Social Affairs, Population division New York World Bank and Institute for Health Metrics and Evaluation (2016) The cost of air pollution: strengthening the economic case for action—2016. Retrieved from http:// documents.worldbank.org/curated/en/781521473177013155/pdf/108141-REVISED-Costof-PollutionWebCORRECTEDfile.pdf World Health Organization (2010) World health statistics, pp 1–177. Retrieved from https://www. who.int/gho/publications/world_health_statistics/EN_WHS10_Full.pdf

Web Reference Health related Policies and Programmes of Government of India, Karnataka and Kerala (2018). http://shodhganga.inflibnet.ac.in/bitstream/10603/145095/15/15_chapter%205.pdf. Accessed 2 Nov 2018

Appendix

9.1 Questionnaire CHANGING URBAN ENVIRONMENT and URBAN HEALTH – DELHI/MUMBAI S.No. Date 1. SECTION I: Basic Information i. Name ii. Gender iii. Age iv. Address/location v. Pin Code vi. District vii. Number of family members viii. Annual Income 2. Place/State of birth: 3. Years of stay in Delhi/Mumbai i. 1-5 years ii. 5-10 years iii. More than 10 yrs SECTION II: Urban Environment 4. Household characteristics

© Springer Nature Singapore Pte Ltd. 2020 A. Grover and R. B. Singh, Urban Health and Wellbeing, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-13-6671-0

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268 4.1

Appendix 4.2

4.3

Owned/Rented Number Sourc e of of water rooms

4.4

4.5

4.6

4.7

Toilet inside/outsid e the house

No. of Air Fuel conditioner exhaust used fans in house, for cooking if yes how many

4.8 Number of rooms having window

Choose from the options given below: Owned/Rented : 4.1.1 Owned 4.1.2 Rented Number of rooms: 4.2.1 One room 4.2.2 Two rooms 4.2.3 Three rooms 4.2.3 Four rooms Source of water: 4.3.1 Tap water inside the house 4.3.2 Tap water outside the house 4.3.3 Packaged water 4.3.4 Any other, specify Toilet: 4.4.1 Toilet inside the house 4.4.2 Toilet outside the house Cooking Fuel: 4.5.1 LPG 4.5.2 Kerosene 4.5.3 PNG Pipe 4.3.4 Any other, specify Availability of AC: 4.6.1 Yes 4.6.2 No Number of ACs: None, one, two, three Number of Exhaust Fans: 4.7.1 None 4.7.2 One 4.7.3 Two 4.7.4 Three/more Number of rooms with windows: 4.8.1 None 4.8.2 One 4.8.3 Two 4.8.4 Three 5. Do you feel any change in air pollution level over last 10 years? i. Increased in 10 years/20 years ii. Reduced in 10 years/20 years iii. Constant 6. Is there any foul smell in air? i. Yes, where ii. No iii. Sometimes yes, where?

Appendix

269

7. What is the reason of foul smell? i. Nala nearby ii. Presence of dumping ground iii. Presence of industry or gas station iv. Congestion v. Butcher house near the house vi. Any other, specify 8. Do the hazy clouds develop early morning/smog during Nov-Dec-Jan-Feb? i. Yes ii. No iii. Sometimes yes 9. Do you have a park nearby? Yes/No 10. Has the green area increased or decreased in the city over past 10 years? i. Yes increased in 10 years/20 years ii. No decreased in 10 years/20 years 11. What are the main means of transportation used in the city (RANK From 1-5) Rank (please tick) 1: Most Importan t i

Scooty

ii

train

iii

Taxi

iv

Bus

v

Auto

vi

Metro/local/Mo no Rail

vii

Private vehicle

viii Any other, specify(phat phat/ company vehicle/ car pool)

2: Importan t

3: Sometime s

4: Less used

5: Rarely used/not important

270

Appendix 12. Means of transportation used by you and your family members Number of cars/bike/scooty you own Means of Transportation used by your family members for going to office/school/recreation Family Member

Taxi

Bus

Auto

Metro/mono Private vehicle rail

Any other, Specify

1 2 3 13. How often you use public transport in a week? i. Sometimes / less than twice a week ii. 3-4 times week iii. Everyday 14. How long are you stuck in traffic jams during peak hours per 10 kms? i. Less than 1 hour per 10 kms ii. 1-2 hours per 10 kms iii. 2-3 hours per 10 kms iv. 3- 4 hours per 10 kms v. More than 4 hours per 10 kms 15. How often you use your vehicle in a week? i. Sometimes / less than twice a week ii. 3-4 times week iii. Everyday SECTION III: URBAN HEALTH 16. Basic characteristics and lifestyle of family members S.No. Age Sex occupat ion of family mem ber

Morning/ev ening walk/any other recreation

Smoki Alcohol ng consump tion

Tobacco Food Blood consump (Veg Pressure( /Mix) High or tion Low)

16.1

16.5

16.6

16.8

1 2

16. 16. 16.4 2 3

16.7

16. 9

16.10

Appendix

271

Choose from the options given below: 16.2 Age: 16.2.1: 0-18 years 16.2.2: 19-35 years 16.2.3: 36-65 years 16.2.4: above 65 years 16.3 Gender: 16.3.1: Male 16.3.2: Female 16.4 Occupation: 16.4.1: Student / Unemployed 16.4.2: Trader /Business 16.4.3: Govt Employee 16.4.4: Teacher 16.4.5: house maker 16.4.6: Auto rickshaw driver 16.4.7: Car/taxi/Bus driver 16.4.8: Factory worker 16.4.9: Labourer 16.4.10: Retired 16.4.11: Road side Hawker 16.4.12: any other, specify 16.5 Walk/Recreation: 16.5.1 Yes 16.5.2 No 16.6 Smoking: 16.6.1 Current Smoker 16.6.2 Ex-smoker 16.6.3 Never smoked 16.7 Alcohol consumption 16.7.1 Currently takes alcohol 16.7.2 Ex-consumer / used to take earlier 16.7.3 Never takes alcohol 16.8 Tobacco Consumption 16.8.1 Currently consume 16.8.2 Ex-consumer / used to take earlier 16.8.3 Never takes tobacco 16.9: Food Habit 16.9.1 Vegetarian 16.9.2 Both veg and non veg 17. Health Profile of the household 17.1 17.2 17.3 17.4 S. No. of Height Weight Health Profile in family last 6 months member

17.5 Health Profile in last 12 months

17.6 Health Profile in last 24 months

17.7 Health Profile in last 36 months

1 2 3 4 17.4: Health Profile 17.4.1: Sinusitis 17.4.3: Sore throat 17.4.5: Heaviness in chest or pain 17.4.6: Headache 17.4.8: TB 17.4.10: Eye irritation 17.4.12: Dizziness 17.4.14: Heat stroke 17.4.16: Dengue 17.4.18: Bronchitis

17.4.2: Common cold, running nose and fever 17.4.4: Dry cough or wet cough 17.4.6: Respiratory illness (breathlessness etc.) 17.4.7: Asthma 17.4.9: Heart illness 17.4.11: Redness of eyes 17.4.13: Skin allergies 17.4.15: Malaria 17.4.17: Fainted due to heat stroke 17.4.19: Any other, specify

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Appendix

18. Nature of treatment for any of the above illness 18.1 18.2 18.3 18.4 18.5 S. No. of family member

Illness S.No.

Household Local treatment clinic

Treatment from doctor + duration of treatment (public/private)

18.6.1 Public; 18.6.2 Private

18.7

Hospitalized ever (public or private)

Others, specify

19. If anyone in the family ever fainted due to heat stroke give details (who, when and where)? 20. Do you /any member experience breathlessness/ eye irritation / skin redness? (Yes/No) If yes, where? i. Bus stop ii. Traffic intersection iii. Place of job iv. Any other, pls specify 21. Any history of death due to these diseases. Yes/No If yes, give age of death, cause, years of stay in Delhi/Mumbai, occupation, duration of treatment, (the problem should not be hereditary or due to lifestyle like smoking habit) 22. Do you wear mask while moving out within the city? (Yes/No) If yes, when (any particular season and why sun stroke/dust/breathing problem/any other reason)?

SECTION IV: PROBLEMS and SUGGESTIONS 23. What are the problems associated with public transportation? i. Overcrowding ii. Traffic jams and Congestion iii. Any other, pls specify 24. Problems in access and availability of health facilities i. Distance: Availability of dispensary/hospital near your house ii. Money: The cost of health care is too high iii. Availability of doctor and reliable treatment iv. Any other, pls specify

Appendix 25. Are you aware of any govt plan / policy on air pollution increase in the city? i. Green action plan ii. Afforestation efforts iii. CNG use in vehicles iv. Any report on air quality and health v. Any other, pls specify 26. What are the problems with these policies? i. Public participation lacking ii. Corruption iii. Gaps in policy making iv. Any other, pls specify 27. These Government policies are well-implemented or have flaws in it? i. Well implemented ii. Some problems do exist 28. What are your suggestions and recommendations to improve air quality of the city? i. Policy Implementation ii. Strict pollution control measures iii. Innovations in technology iv. Environmental Impact Assessment v. Any other, pls specify 29. What are your suggestions and recommendations to improve health care facilities in

the city?

273