case study of pollution in india

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Ganga Pollution Case: A Case Study

This article is written by Abhinav Anand , a student pursuing B.A.LL.B(Hons.) from DSNLU, Visakhapatnam. The article deals with the Ganga pollution case and the peruses into reasons behind the pollution. It also discusses some of the schemes of the government to purify the river and critically analyses its impact. It further suggests changes that should be done to make the effective implementation.

Table of Contents

Introduction

Water Pollution has become a global crisis. The perennial threat of the water crisis is exacerbating because of uncontrolled and unbalanced development of the allied sectors such as industries and agriculture. According to the reports of NITI Aayog, 21 major Indian cities, including Delhi will completely run out of groundwater. This article deals with reasons behind the pollution of the river Ganga and it examines the effective measures taken by the government. It also suggests changes to expedite the cleaning process of the river.

Reasons behind the Pollution of Ganga

There are 4600 industries in Uttarakhand out of which 298 are seriously polluting industries. There are many industries which have not taken permission from the Uttarakhand pollution control board for their operations and they started their operation based on the advisory of the government in which the government exempted certain classes of industries from taking permission. The sewage treatment and advanced technology for the treatment of the wastes are not used despite government strict regulations.

Sewage is an important source of pollution and contributes 75% to the pollution caused by all sources of pollution. Urban development of different sizes contributes to sewage pollution in the river. The considerable efforts by the Ganga Action Plan are not able to improve the situation.

The report says that despite the failure of the Ganga Action Plan there is no disapproval on the part of the citizens as well as their representative living in urban areas on the banks of the river. The failure is on the part of the government agencies responsible for the effective implementation of the plan. 

The urban citizens residing near the river show a lack of interest in the cleanliness of the river. The representatives of the urban areas are not receiving enough complaints from the citizens and as a result, they refrain from raising this issue to the higher authorities. Based on the analysis done by the independent authorities, the political parties show reluctance to increase the taxes because they may lose the support of their voters. The taxes will help the authorities to have financial validity. The Kanpur Nagar Nigam has to pay operation and management taxes to the Uttar Pradesh Jal Nigam for the operation and maintenance of the services in the Ganga Action Plan. 

However, the Kanpur Nagar Nigam is unable to collect taxes from the users of the services of Ganga Action Plan to pay to the Uttar Pradesh Jal Nigam. So, the government directly transfers the money to the Uttar Pradesh Jal Nigam by cutting the share of the Kanpur Nagar Nigam. 

It has been contended that the decentralisation of funds and functionaries will help in improving the condition of the governance at Urban Level. But, it is evident that the urban local bodies are neither motivated nor passionate to do the assigned duty. 

Municipal Corporation

These are the following factors contributing to the waste in the river:

The use of plastic by people at large and its improper disposal ultimately reach in the river. Plastic pollution has been considered as one of the significant reasons for the pollution in the river. The government has failed in the implementation of Management and Sewage Waste Rules to curb the menace of plastic pollution.

The state should declare a complete ban on the use of plastic. The authorities pay no attention to the rampant use of plastics and the improper treatment of wastes before releasing them in the river. The pollution level of water has exponentially risen because of plastic wastes. The Tribunal while dealing with the matter of pollution on the ghats has banned the use of plastic in the vicinity of ghats.

However, the ban imposed by the tribunal has no effect on the ground level and the plastics are used rampantly. The plastic bags can be replaced by the jute bags which are nature friendly.

The Ghats are also one of the major sources of pollution in the river. Ganga is one of the important parts of our Indian culture due to which different kinds of pujas and other religious tasks are performed on the ghats, and the materials used are disposed of in the river. The materials are non-decomposable, highly toxic and hence pollute the river. 

Agriculture Waste

Agricultural water pollution includes the sediments, fertilizers and animal wastes. The unbalanced use of inorganic fertilizers and other fertilizers have immensely contributed to water pollution. The fertilizers rich in nitrates create toxic composition after reaching several other entities. Large quantities of fertilizers, when washed through the irrigation, rain or drainage to the river, and pollutes the river. The fertilizers rich in nitrate content are used to get more productivity from the land. This led to pollution in the entire food chain wherever the by-product of the produce is consumed. When these fertilisers wash away due to rain or other factors and pollute the river.

Effective Measures by Government to stop the Pollution

Ganga Action Plan

The Ganga Action Plan was started in 1986 for control of water pollution in the river Ganga. The main function of this plan was to make Ganga River free from the pollution from the disposal of waste from the cities settled on the banks of the river. The plan was to make Ganga pollution free from Rishikesh to Kolkata. The central pollution control board had prepared a plan of 5 years in 1984 to make Ganga pollution-free. The central Ganga authority was formed in 1985 and a Ganga action plan was launched in 1986 to make the Ganga pollution free. 

The first phase of the Ganga action plan was inaugurated by late Rajiv Gandhi at Rajendra prasad ghat of Banaras. The National Protection Agency was constituted for its implementation. During the first phase of Ganga Action Plan 256 schemes of 462 crores were undertaken in Uttar Pradesh, Bihar and West Bengal. Special stations have been created to check the quality of water.

The experts from Bharat Heavy Electricals Limited and National Environment Engineering Research Institute were appointed to check the quality of the water. Despite so much effort, the Ganga action plan failed miserably and crores of money were spent on the Ganga action plans. The failure of such a big plan has led to economic pollution.

The government launched the second phase of the Ganga Action Plan in 2001 wherein the central pollution board, central public works department and public works department are the bodies to carry out the plan. 

Namami Ganga Programme

A flagship Namami Ganga Programme was launched under separate union Water Ministry created under river rejuvenation programme. The project aims to integrate Ganga conservation mission and it is in effect to clean and protect the river and gain socio-economic benefits by job creation, improved livelihoods and health benefits to the population that is dependent on the river.

The key achievement of the Namami Ganga projects are:

• Creating sewage treatment capacity- 63 sewerage management project under implementation in the states of Uttarakhand, Uttar Pradesh, Bihar and West Bengal. 12 sewerage management projects launched in these projects.
• Creating riverfront development: 28 riverfront development projects and 33 entry-level projects for construction, management and renovation of 182 ghats and 118 crematoria has been initiated.
• River surface cleaning: The river surface cleaning is the collection of solid floating waste on the ghats and rivers.after collection, these wastes are pumped into the treatment stations.
• Public Awareness: Various activities such as seminars, workshops and conferences and numerous activities are organised to aware the public and increase the community transmission.
• Industrial Effluent Monitoring: The Grossly Polluting Industries monitored on a regular basis. Industries are following the set standard of the environmental compliances are checked. The reports are sent directly to the central pollution control board without any involvement of intermediaries.

Suggestions

These are the following suggestion for making the existing machinery robust to expedite cleanliness process of the Ganga:

Development of a comprehensive and basic plan

We need to develop a plan by which we can reach the problem in a holistic way. The already devised plans involve many intermediaries wherein the transparency factor is cornered and only paper works are shown to the people at large. 

The strategy should be formulated for different areas according to their demand. The people having apt knowledge of that area should be involved to know the actual problem of pollution in the river. A thorough check should be done and a customer-friendly platform should be formed wherein the views of every individual should be considered.

Measurement of the quality

The apt instruments are required to measure the quality of the water. We have many schemes for the cleanliness of the Ganga but the officials assigned the duty of measuring the quality of water either have authoritarian pressure or lack of knowledge to assess the quality of water. The quality of water should be measured by a recognised testing agency. Further, the research should be made to evolve better machinery for precision in quality measurement.

Getting the institutions right

The main task is to get the involved institution on the right path. The river cleaning task demands leadership, autonomy and proper management. The cities need to be amended. Ultimately they will be the custodians of the networks developed for the cleanliness process. Many cities have weak financial powers and their revenue generation is also weak so they should be given extra incentives. An awareness campaign should be launched in small cities where people have no idea about the pollution of the river and how it affects the environment. 

Engaging and mobilising all the stakeholders

The inhabitants of the river Ganga are people, elected representatives, and the religious leaders who consider the river as a pious and clean river. The mass awareness campaign can launch only when these people will be under sound financial conditions. So, if a portion is invested in these people, then it will help to develop their thinking on a large scale. 

A similar situation has arisen in Australia where the government has invested 20% of the funds in creating mass awareness among the people for the cleanliness of the Murray river basin. It has shown a great impact on the productivity of the programmes implemented in Australia. So, when we promote all the stakeholders in one or the other way we can see a holistic development in the situation.

Rejuvenation requires equal attention to quality and quantity

The rejuvenation of rivers requires quality and quantity at the same time. The old adage of “ solution to pollution is dilution” should be kept in mind while making any kind of plan. 

The improvement of water quality in Ganga during the Kumbh Mela is the result of the release of water barrage of the water upstream. The water in the upper stream is used in the agriculture process by the respective states. So, if the water is released on a regular basis it will also help to improve the quality of the water and reduce the pollution level in the water. 

Ganga is considered a pious river in the religious scriptures. The current situation demands holistic accountability from the authorities and people to make it clean. The global image is projected by the cleanliness of our rivers. The river Ganga is a part of our culture and it is our duty to maintain its sanctity. The government should formulate a more stringent policy to develop the quality of the water in the river. The environmental laws should be strictly followed and the violators should be punished. 

•   https://www.theigc.org/blog/ganga-pollution-cases-impact-on-infant-mortality/

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Air pollution and human health in kolkata, india: a case study.

1. Introduction

2. study area, 2.1 sources of air pollution in kolkata, 3. database and methodology, 3.1. monitoring stations and criteria pollutants, 3.2. air quality assessment.

• Critical pollution (C): when EF is more than 1.5;
• High pollution (H): when the EF is between 1.0–1.5;
• Moderate pollution (M): when the EF between 0.5–1.0; and
• Low pollution (L): when the EF is less than 0.5.

3.3. Health Assessment

3.4. data analysis, 4. results and discussion, 4.1. concentration and trends of ambient air quality, 4.2. interpreting health outcomes of surveyed dispensaries in kolkata, 4.3. outdoor pollution-averting activities, 4.4. diseases analysis, 5. conclusions, acknowledgments, author contributions, conflicts of interest.

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Haque, M.S.; Singh, R.B. Air Pollution and Human Health in Kolkata, India: A Case Study. Climate 2017 , 5 , 77. https://doi.org/10.3390/cli5040077

Haque MS, Singh RB. Air Pollution and Human Health in Kolkata, India: A Case Study. Climate . 2017; 5(4):77. https://doi.org/10.3390/cli5040077

Haque, Md. Senaul, and R. B. Singh. 2017. "Air Pollution and Human Health in Kolkata, India: A Case Study" Climate 5, no. 4: 77. https://doi.org/10.3390/cli5040077

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• 10 November 2023

Why is Delhi’s air pollution so bad right now?

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Air pollution is spiking in Delhi, a megacity of more than 30 million people. Credit: Arun Thakur/AFP via Getty

As the Hindu festival of Diwali kicks off on 10 November, the Indian capital of Delhi, already blanketed in choking smog, is bracing for pollution to worsen. Over the past week, children struggling to breath the acrid air have flooded hospital emergency departments , and schools have been forced to close . Why is Delhi’s air pollution so bad right now?

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Catalyzing Clean Air in India

Beyond Boundaries - Understanding Airsheds and PM2.5

Story highlights.

• All of India’s 1.4 billion people (100% of the country’s population) are exposed to unhealthy levels of ambient PM2.5 – the most harmful pollutant - emanating from multiple sources.
• The health impacts of pollution also represent a heavy cost to the economy. Lost output from premature deaths and morbidity attributable to air pollution accounted for economic losses of US$28.8 billion (21.4–37.4) and $8 billion (5.9–10.3), respectively, in India in 2019. This total loss of $36.8 billion (27.4–47.7) was 1.36% of India's gross domestic product (GDP).
• The World Bank program is introducing tools for airshed management and planning to support state and regional air quality management approaches. These efforts aim to facilitate the creation of India's inaugural State-wide Air Quality Action Plans and the first extensive Regional Airshed Action Plan for the Indo Gangetic Plains (IGP), covering seven union territories and states.

Globally, air pollution is a silent killer. The air pollution levels in India are among the highest in the world, posing a heavy threat to the country's health and economy. All of India’s 1.4 billion people are exposed to unhealthy levels of  ambient PM 2.5  – the most harmful pollutant - emanating from multiple sources. These small particulates with a diameter of less than 2.5 microns, is about one-thirtieth the width of a human hair. Exposure to PM 2.5 can cause such deadly illnesses as lung cancer, stroke, and heart disease. 1.67 million deaths were attributable to air pollution in India in 2019, accounting for 17.8% of the total deaths in the country. The health impacts of pollution also represent a heavy cost to the economy. Lost output from premature deaths and morbidity attributable to air pollution accounted for economic losses of US$28.8 billion and $8 billion, respectively, in India in 2019. This total loss of $36.8 billion was 1.36% of India's gross domestic product (GDP).

PM 2.5 comes from a variety of sources. Some of the most common sources include emissions from burning fossil fuels such as coal or oil and biomass such as wood, charcoal, or crop residues. PM 2.5 can also come from windblown dust, including natural dust as well as dust from construction sites, roads, and industrial plants.

Over half of PM 2.5 emissions in India are formed in “secondary” way in the upper atmosphere when different types of gaseous pollutants from one area such as ammonia (NH3), mix with other gaseous pollutants like sulfur dioxide (SO2), and nitrogen oxides (NOx) from another place. Agriculture, industry, power plants, households, and transport all contribute significantly to the formation of secondary PM 2.5. This secondary form spreads farther and wider than primary PM2.5 and travels across states, cities, and crosses jurisdictional borders.

The air pollution challenge in India is therefore inherently multi-sectoral and multi-jurisdictional, requiring an “airshed” approach. An airshed can be defined as a region that shares a common flow of air, which may become uniformly polluted and stagnant. Air quality within an airshed will largely depend on pollution sources within it. Because the formation of secondary particles and the transporting of primary and secondary particles take place over large geographic areas, airsheds can extend over several hundred kilometers, well beyond the boundaries of cities. India, therefore, needs to look beyond its cities and take action at the sub-national level for effective air pollution control strategies and apply new set of tools for airshed-based management. Standardizing tools across India is important so control strategies and relevant data sets can be linked . 

Air Pollution - A Major Health Risk

India is taking many significant steps in responding to this problem. The Government of India is envisaging a revision of its the ambient air quality standards and has strengthened vehicular and industrial emission standards in recent years. A strong emphasis on expanding renewable energy, promoting electric vehicles, and supplying LPG cooking fuel to millions of households are some examples of the actions India is taking to combat air pollution.

The Government of India’s  National Clean Air Programme (NCAP)  is a powerful step in acknowledging and resolving the problem of deteriorating ambient air quality. The NCAP has set a time-bound goal for improving air quality across the country, with a focus on around 132 “non-attainment” cities where air pollution standards are not being met. The NCAP provides cities an overall framework for developing air quality management plans, with guidance on policies across a range of sectors.

In 2020, based on the recommendations of the 15 th  Finance Commission, the Government of India has set aside about $1.7 billion to fight air pollution over the next five years for the 42 Indian cities that have million-plus populations – provided they reduce their air pollution levels by 15 percent every year. This is the world’s first performance-based fiscal transfer funding program for air quality management in cities.

Recognizing the need for concerted cross-jurisdiction and airshed level action and coordination, India’s Parliament approved a law in August 2021 to establish the Commission of Air Quality Management in the National Capital Region and adjoining areas.

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Subsidize farmers who use organic fertilizers and manage sustainably

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World Bank Support

The World Bank has been aiding India in Air Quality Management through a phased strategy (under its  Country Partnership Framework ) aimed at enhancing knowledge, building capacity, involving stakeholders, transferring expertise in airshed management tools, facilitating analysis for policy adjustments, and mobilizing finance for more effective government programs. This AQM initiative has leveraged insights from decades of Bank projects in other countries such as Mexico and China, which encountered comparable peaks in air pollution levels. The greatest effort is being placed on the Indo-Gangetic Plain where the population density and pollution intensity are the highest and most concerning, and capacity and systems to tackle the challenge are in most need of support.

Building on the work that is already underway through India’s national strategic framework of the NCAP,  the World Bank program  is introducing tools to support state and regional air quality management approaches. These initiatives will help formulate India’s  first State Air Quality Action Plans  and  India’s first large Regional Airshed Action Plan for the Indo Gangetic Plains (IGP), spanning seven union territories and states.  Measures such as these will be prioritized to reduce the greatest amount of air pollution at the lowest cost based on scientific evidence.

In the IGP states, the World Bank program supports the Network in connecting with academic institutions and city and state practitioners to carry out air quality management work such as modeling.

As part of the India Lighthouse initiative, experts from India and around the world have been exchanging their experiences to develop India-specific practices using state-of-the-art tools to make the full extent of the air pollution problem in India more effectively understood, managed, and controlled.     

• Air Quality Management Program

Way Forward

Air quality management is an ongoing process. It needs to be integrated into the capabilities of the government, as well as incorporated into the behavior of businesses and individuals. This requires sufficient funding and a sustained focus on building capacity. Airshed wide coordination is vital in Indian states where a significant portion of PM2.5 pollution originates from sources outside the cities as well. Individual cities cannot achieve substantial pollution reductions by eliminating local emissions alone. Collaborative efforts between states are crucial to meet WHO Air Quality Interim Targets cost-effectively, considering the persistent high pollution levels in many urban clusters across the country.

The good news is that many other countries have demonstrated that air pollution control is possible when there is strong commitment and a well targeted and cost-effective plan in place.

Due to its convergence with climate change, India has already put in motion many of the essential “sector transitions” needed in air quality management. For example, India is spearheading a solar-energy revolution. Today, 60 percent of Delhi Metro’s daytime energy requirement is being met through solar power from the  750 MW Rewa Solar Project  in Madhya Pradesh, reducing its dependence on coal, as well as saving over $170 million on its energy bill over the next 25 years.

What’s more, a study by the World Bank and the  International Institute for Applied Systems Analysis (IIASA)  show that focusing on air pollution through a clean air pathway out to 2030 could bring about significant climate change co-benefits for India. Such a pathway, for example, will reduce India’s CO 2  emissions by 23 percent by 2030, and 42 percent by 2040-50. In fact, most of the policy measures and management practices are well-known. If pursued, they have the potential to reduce India’s air pollution within a single generation.

During the medium- to long-term, the World Bank will support Indian cities and states, as well as the Indo-Gangetic Plain in implementing state and regional airshed plans for cleaner air for all. The focus will be on developing institutional capabilities and implementing systems that are vital for change. Working with the government and various stakeholders will help to bring the best local and international experts to bear on the air quality issue.

Tackling Air Pollution through Information, Incentives and Institutions.

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Air pollution is a world-wide challenge. Multiple sectors contribute to it and air knows no boundaries but sustained political commitment can make a difference.

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Poor air quality is a concern across India

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The human toll of air pollution in India

Pollution-related deaths numbered 1.67 million in 2019, according to a new report led by Boston College researchers. Economic losses were in the billions.

Pollution takes many forms in India, including the use of poorly-ventilated stoves and open fires for cooking inside dwellings. Photo by Mark Katzman.

Air pollution in India resulted in 1.67 million deaths in 2019—the largest pollution-related death toll in any country in the world—and also accounted for $36.8 billion in economic losses, according to a new study led by researchers from the Global Observatory on Pollution and Health at Boston College, the Indian Council of Medical Research, and the Public Health Foundation of India.

The 2019 death toll attributed to air pollution in India accounted for 17.8 percent of all deaths in the country in 2019, according to the study’s findings, published in the journal Lancet Planetary Health .

The $36.8 billion in economic loss was 1.36 percent of the country’s gross domestic product, according to the report, titled “The health and economic impact of air pollution in the states of India.”

Pollution-related losses "could impede India’s aspiration to be a $5-trillion economy by 2024," the researchers concluded. “Successful reduction of air pollution in India would lead to substantial benefits for both the health of the population and the economy.”

“Pollution takes an enormous human toll in India,” said lead researcher Boston College Professor of Biology Philip J. Landrigan, M.D., director of the Global Observatory on Pollution and Health . “It is causing 1.67 million premature deaths per year—many more than from COVID-19.”

The consequences will be long-lasting without efforts to reduce air pollution in the nation of 1.35 billion people, according to Landrigan, whose research was funded in part by the United Nations Environment Programme.

“It is also having a profound effect on the next generation of Indians,” said Landrigan. “It increases future risk for heart disease, diabetes, and respiratory disease for today’s children when they become adults. It is reducing children’s IQ. It will be very difficult for India to move forward socially or economically if they don’t do something about the problem.”

Additional Boston College contributors to the report were Global Observatory on Pollution and Health Senior Data Analyst Samantha Fisher, Observatory intern Gabrielle Taghian ’20, Assistant Professor of Social Work Praveen Kumar, BC School of Social Work Dean Gautam Yadama, and Vice Provost for Research and Academic Planning and DeLuca Professor of Biology Thomas Chiles.

The toll of both indoor and outdoor air pollution amounted included the deaths of 1.67 million people in India in 2019, according to a new study in Lancet Planetary Health.

Researchers also found rapidly changing patterns of air pollution and pollution-related disease in India, according to the report. The death rate from indoor air pollution, which is caused mainly by poorly ventilated home cook stoves, has decreased by 64.2 percent since 1990.

In the same time period, the death rate due to ambient (outdoor) particulate matter pollution increased by 115.3 percent and the death rate due to ambient ozone pollution increased by 139.2 percent. These increases in deaths from ambient air pollution reflect increasing emissions from cars, trucks, and buses, as well as the widespread use of coal to generate electricity in India.

Among the many costs associated with increased mortality and illness caused by air pollutants, the researchers estimate the air pollution-related costs to India’s health care system at nearly $12 billion in 2019.

Climate change exacerbates pollution, the researchers noted, through atmospheric stagnation, temperature-driven increases in particulate matter, and ground-level ozone formation, which are likely to be particularly severe in India.

State-by-state analysis showed a more than three-fold variation in air pollution death rates across the states of India. Southern Indian states have put policies in place to reduce air pollution when compared to states in the north, where pollution and its consequences showed a greater impact in mortality and economic costs, said Landrigan.

Landrigan said there are ample solutions and examples of successful pollution reduction policies that can be developed to meet the specific needs of the country and its states. China, a country with a similar size population and equally ambitious economic goals, adopted pollution control targets in its most recent five-year plan and is making progress on pollution control, he said.

“We point to countries like the United States where we reduced air pollution by 70 percent since passage of the Clean Air Act in the 1970s,” said Landrigan. “At the same time, U.S. GDP grew by 250 percent. There are similar statistics from Europe, Australia, and Japan. Pollution control does not stifle economic growth.”

While researchers report a decline in indoor air pollution produced primarily by cook stoves used in millions of homes throughout the country, further reductions will require additional strategies that address poverty as well as energy needs, said School of Social Work Dean Yadama.

“One of our challenges is to provide the poor with greater access to devices and clean fuels that can be sustainably used in a variety of real-world conditions,” said Yadama. “The more these are developed and tested in collaboration with communities — particularly the women, the devices’ end users—the more likely their uptake.”

The health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019 : DOI 10.1016/S2542-5196(20)30298-9

Ed Hayward | University Communications | January 2021

Related links

"The health and economic impact of air pollution in the states of India: The Global Burden of Disease Study 2019"

Boston College Global Observatory on Pollution and Health

Boston College School of Social Work

Air Pollution in India: Major Issues and Challenges

This article foregrounds the challenges India is currently facing in reducing air pollution and bringing the level of air quality to a certain standard. It also discusses solutions that could be adopted to combat the national crisis.

Rising urbanisation, booming industrialisation, and associated anthropogenic activities are the prime reasons that lead to air pollutant emissions and poor air quality. It is expected that by 2030, around 50% of the global population will be residing in urban areas (Gurjar, Butler, Lawrence, et al. 2008). More than 80% of population in urban areas is exposed to emissions that exceed the standards set by World Health Organization (WHO 2016). Air pollution is one of the key global health and environmental concerns (Nagpure, Gurjar, Kumar, et al. 2016) and has been ranked among the top five global risk factors of mortality by the Health Effects Institute (HEI 2019). According to HEI's report, particulate matter (PM) pollution was considered the third important cause of death in 2017 and this rate was found to be highest in India. Air pollution was considered to cause over 1.1 million premature deaths in 2017 in India (HEI 2019), of which 56% was due to exposure to outdoor PM 2.5 concentration and 44% was attributed to household air pollution. As per WHO (2016), one death out of nine in 2012 was attributed to air pollution, of which around three million deaths were solely due to outdoor air pollution.

The rising trends in population growth and the consequent effects on air quality are evident in the Indian scenario. For example, the megacities of Delhi, Mumbai, and Kolkata combined holds a population exceeding 46 million (Gurjar, Ravindra, and Nagpure 2016). Over the years, there has been a massive-scale expansion in industries, population density, anthropogenic activities, and the increased use of automobiles has degraded the air quality in India (Gurjar and Lelieveld 2005). In the last few decades, the greenhouse gas (GHG) emissions and other emissions resulting from anthropogenic activities have increased drastically (Gurjar and Nagpure 2016).

As per WHO (2016) estimates, 10 out of the 20 most populated cities in the world are in India. Based on the concentrations of PM 2.5 emissions, India was ranked the fifth most polluted country by WHO (2019), in which 21 among the top 30 polluted cities were in India. The Indian cities, on average, exceeded the WHO threshold by an alarming 500%.

The consistent population growth has led to an excessive strain on the energy consumption, thereby affecting the environment and the air quality of the megacities (Gurjar and Nagpure 2016). Kumar, Khare, Harrison, et al. (2015) calculated the increase in the total energy demand for both mobile and point sources and inferred that in Delhi, the energy demand had grown by 57.16% from 2001 to 230,222 TJ in 2011. A subsequent rise in energy consumption can be expected in the coming years, with no reliable sources available for monitoring the rate of energy consumption.

The continuous degradation of ambient air quality in the urban centres of India demands effective measures to curb air pollution. Though various policy measures are being introduced by the Government of India (GoI) to reduce the vehicular and industrial emissions, the extent to which these measures are implemented is questionable. The lack of infrastructural facilities, inadequacy of financial resources to implement advanced infrastructural innovations, difficulty in relocation of the industries from the urban centres even after mandatory court decisions, and above all, the behavioural patterns among people in accepting the green solutions are some of the crucial impediments on the road to environmental protection that our country seems to be struggling to overcome today.

There have been various efforts to study the air quality in Indian cities. The potential of the atmospheric carcinogenic emissions to put human health at risk has been studied by Gurjar, Mohan, and Sidhu (1996). Gurjar, Aardenne, Lelieveld, et al. (2004) framed a comprehensive emission inventory model to understand the emission trends in Delhi, India's capital, for a period from 1990 to 2000. A multi-pollutant index (MPI) rating scale was used by Gurjar, Butler, Lawrence, et al. (2008) to rank the megacities with respect to their ambient air quality. According to this study, out of 18 megacities considered worldwide, the Indian cities, namely, Delhi, Kolkata, and Mumbai were ranked 7, 9, and 11, respectively. Gurjar, Nagpure, Kumar, et al. (2010) evaluated the vehicular emissions in Kolkata between 2000 and 2010 and inferred that the older vehicles in the city contributed more to the pollution load and should be phased out. A Vehicular Air Pollution Inventory (VAPI) model was developed by Nagpure and Gurjar (2012) that could estimate the vehicular emissions from road traffic in Indian cities. Later, Gurjar, Nagpure, and Kumar (2015) evaluated the potential gaseous emissions from the agricultural wetlands of Delhi and inferred that man-made wetlands were responsible for 48–49% of the total GHG emissions in the capital city. The study intended to develop an emission inventory for agricultural activities to evaluate their contribution to pollution in Delhi.

Several policy measures have been taken by the Ministry of Environment, Forest and Climate Change (MoEFCC), GoI to tackle the adverse effects of air emissions in short and long terms. The government's decision to adopt compressed natural gas (CNG) as an alternative fuel to petrol and diesel, the odd-even measures introduced in Delhi, and the improvements in fuel and vehicle quality to lower emissions are some of the recent commendable steps towards curtailment of air pollution. Moreover, the increasing number of studies related to this field shows the importance of research on this subject. Several studies have assessed the trends of air pollutant emissions from different sources across several cities in India. However, there is an urgent need for a comprehensive review of the existing issues in the Indian scenario. More focus is needed on studying the impacts of these pollutant emissions on various forms, such as the ecosystem, biodiversity, buildings and materials, and primarily the health risks that people are vulnerable to due to breathing foul air.

A comprehensive review is done to understand the current scenario in the Indian context. The following section comprises a detailed review focusing on air pollution studies in India, the various sources, and the effects of the pollutants on the ecosystem, biodiversity, materials and buildings, and on human health, which are discussed in the later sections of this article. The various air quality standards followed by countries worldwide are included as well. The Discussion section of the article consists of the mitigation strategies adopted for emission control in India, the challenges posed by various sectors in the Indian scenario, and the research gaps that have been identified from the available literature. The key conclusions and a few recommendations form part of the last section.

Reviewing Literature

The present review is divided into three sub-sections: The first sub-section discusses the literature that focuses on air pollution in India on a national scale; the next segment highlights the various sources of air pollution and the effects of the pollutants. The major sources are categorised into seven sectors. Thereafter, the various effects of pollutant emissions are pointed out. The air quality standards adopted by various countries for controlling air pollution have been discussed in the later sections of this article.

Studies on air pollution in India

Though various studies have addressed the issue of air pollution and its impacts on urban Indian cities, most of these studies are limited to specific cities and do not necessarily give a complete picture of the situation. Some of the highlights of these studies are discussed in the following paragraphs.

Pandey and Venkataraman (2014) evaluated the effects of emissions from various modes of transport in India. Their study inferred that on-road transportation contributed over 97% of the estimated emissions in India, when compared to other modes of transport, such as railways, waterways, and airways. Gurjar, Ravindra, and Nagpure (2016) did a comprehensive study on various anthropogenic emission sources in Indian megacities, such as Delhi, Mumbai, and Kolkata. The global impact of urban pollution is also discussed in their study. Upadhyay, Dey, Chowdhury, et al. (2018) evaluated the major anthropogenic sources of PM 2.5 and the potential benefits to human health, if sufficient control measures are applied to curb emissions. A recent study by Jat, Gurjar, and Lowe (2021) examined the extent of pollution during the winter months in India. The study used a WRF-Chem model, that is, Weather Research and Forecasting (WRF) coupled with chemistry, to evaluate the concentrations of pollutants, such as PM 2.5 , oxides of sulphur (SOX), oxides of nitrogen (NOX), black and organic carbons, and non-methane volatile organic carbons (NMVOCs) that were identified for the winter months. The various sources of air pollution can be classified into seven major sources and the consequent effects are discussed in this article.

Sources of air pollution

The various sources of air pollution are classified into seven major sectors, which include transportation, industries, agriculture, power, waste treatment, biomass burning, residential, construction, and demolition waste.

Vehicular/Transport Emissions

The transportation sector is the main contributor of air pollutants in almost every city, but this phenomenon is worse in urban cities (Gurjar, Aardenne, Lelieveld, et al. 2004). This could be due to the increased number of vehicles when compared to the existing infrastructural facilities, e.g., roads, fuel stations, and the number of passenger terminals provided for public transport. In India, the amount of motorised transport increased from 0.3 million in 1951 to 159.5 million in 2012 (Gurjar, Ravindra, and Nagpure 2016). A significant share of vehicular emissions comes from urban cities, such as Delhi, Mumbai, Bengaluru, and Kolkata. Carbon monoxide (CO), NOX, and NMVOCs are the major pollutants (>80%) from vehicular emissions (Gurjar, Aardenne, Lelieveld, et al. 2004). Other trace emissions include methane (CH4), carbon dioxide (CO2), oxides of sulphur (SOx), and total suspended particles (TSPs).

In an urban environment, road traffic emissions are one of the prime contributors of air pollution. Road dust is a major contributor to PM emissions in Delhi (37%), Mumbai (30%), and Kolkata (61%). Road transport is the largest source of PM 2.5 in Bengaluru (41%), Chennai (34%), Surat (42%), and Indore (47%) (Nagpure, Gurjar, Kumar, et al. 2016). In the Indian context, some of the essential factors of high traffic emissions include extreme lack of exhaust measures, the highly heterogeneous nature of vehicles, and poor quality of fuel.

Industrial Processes

Over the last few decades, India has witnessed large-scale industrialisation. This has degraded the air quality in most urban cities. The Central Pollution Control Board (CPCB) has categorised the polluting industries into 17 types, which fall under the small and medium scale (Gurjar, Ravindra, and Nagpure 2016). Out of these categories, seven have been marked as 'critical' industries that include iron and steel, sugar, paper, cement, fertiliser, copper, and aluminium. The major pollutants comprise SPM, SOX, NOX, and CO2 emissions.

The small-scale industries, which are not regulated like the major industries, use several energy sources apart from the primary source of state-provided electricity. Some of these fuels include the use of biomass, plastic, and crude oil. These energy sources are neglected in the current emission inventory studies. In Delhi, after the intervention of the judiciary in 2000, many industries were relocated from urban areas to adjacent rural areas (Nagpure, Gurjar, Kumar, et al. 2016). In Delhi, a major fraction of the pollution load comes from the brick manufacturing industries, which are situated at the outskirts of the city. Rajkot (42%) and Pune (30%) are the two cities where industries play a prominent role in contributing to the highest amount of PM 2.5 (Nagpure, Gurjar, Kumar, et al. 2016).

Agriculture

Agricultural activities produce emissions, which have the potential to pollute the environment. Ammonia (NH3) and nitrous oxide (N2O) are the key pollutants released from agricultural activities. The other agricultural emissions include methane emissions from enteric fermentation processes, nitrogen excretions from animal manure, such as CH4, N2O, and NH3, methane emissions from wetlands, and nitrogen emissions from agricultural soils (N2O, NOX, and NH3) due to the addition of fertilisers and other residues to the soil (Gurjar, Aardenne, Lelieveld, et al. 2004). Agricultural processes, such as 'slash and burn' are prime reasons for photochemical smog resulting from the smoke generated during the process. Crop residue burning is another process that results in toxic pollutant emissions. This is how neighbouring cities of Delhi contribute to the agricultural pollution load. This is an example of how external sources contribute to the menace of air pollution in the city (Nagpure, Gurjar, Kumar, et al. 2016).

Power Plants

The contribution of power plants to air emissions in India is both immense and worrisome. The thermal power plants manufacture around 74% of the total power generated in India (Gurjar, Ravindra, and Nagpure 2016). According to The Energy and Resources Institute (TERI), the emissions of SO2, NOX, and PM increased over 50 times from 1947 to 1997. Thermal power plants are the main sources of SO2 and TSP emissions (Gurjar, Aardenne, Lelieveld, et al. 2004), thereby contributing significantly to the emission inventories. In Delhi, power plants contributed 68% of SO2 emissions and 80% of PM10 concentrations over a period from 1990 to 2000 (Gurjar, Aardenne, Lelieveld, et al. 2004). Thus, there is an urgent need to adopt alternative power sources including green and renewable resources for meeting the energy requirements.

Waste Treatment and Biomass Burning

In India, about 80% of municipal solid waste (MSW) is still discarded into open dumping yards and landfills, which leads to various GHG emissions apart from the issues of foul odour and poor water quality at nearby localities. The lack of proper treatment of MSW and biomass burning has been responsible in causing air pollution in urban cities. In Delhi alone, around 5300 tonne of PM10 and 7550 tonne of PM 2.5 are generated every year from the burning of garbage and other MSW (Nagpure, Gurjar, Kumar, et al. 2016).

Methane (CH4) is the major pollutant released from landfills and wastewater treatment plants. Ammonia (NH3) is another by-product, which is released from the composting process. The open burning of wastes, including plastic, produces toxic and carcinogenic emissions, which are a grave concern (Gurjar, Aardenne, Lelieveld, et al. 2004).

Domestic Sector

Households are identified as a major contributor of air pollution in India. The emissions from fossil fuel burning, stoves or generators come under this sector, thereby affecting the overall air quality. Domestic energy is powered by fuels, such as cooking gas, kerosene, wood, crop wastes or cow dung cakes (Gurjar, Aardenne, Lelieveld, et al. 2004).

Though liquefied petroleum gas (LPG) is used as an alternative source of cooking fuel in most urban cities, the major share of India's rural population continues to rely on cow dung cakes, biomass, charcoal or wood as the primary fuel for cooking and other energy purposes and demands. These lead to severe implications on air quality, especially the indoor air quality, which could directly affect human health. According to HEI (2019), about 60% of India's population was exposed to household pollution in 2017.

Construction and Demolition Waste

Another major source of air pollution in India is waste, which is an outcome of construction and demolition activities. Guttikunda and Goel (2013) inferred from their study that around 10,750 tonne of construction waste is generated in Delhi every year. Even after the construction phase, these buildings have the potential to be the major contributors of GHG emissions. Nowadays, the increasing interest in green building technologies and the application of green infrastructure and materials during construction could tackle this issue to a large extent, thereby preserving our biodiversity and maintaining cleaner air quality.

On the Ecosystem

The terrestrial ecosystem is widely affected by ground air pollution. The ill-effects include respiratory and pulmonary disorders in animals and humans (Stevens, Bell, Brimblecombe, et al. 2020). The effects on the marine ecosystem include acidification of lakes, eutrophication, and mercury accumulation in aquatic food (Lovett, Tear, Evers, et al. 2009). These processes may indirectly affect the health of the living beings. Similarly, soil acidification is another phenomenon that is common in forest ecosystems as a result of long-term pollutant accumulation. The deposition of sulphate, nitrate, and ammonium is the main reason for soil acidification. Bignal, Ashmore, Headley, et al. (2007) inferred that traces of heavy metals were found in soil samples in areas adjacent to roadways due to cumulative deposition of pollutants. Soil pollution indirectly affects the ecosystems of plants and animals that are reliant on soil for nutritional intake. Nitrogen deposition in wet and dry forms on vegetation and soil surfaces can occur from vehicular and agricultural activities (Driscoll, Whital, Aber, et al. 2003). The results of these activities on the ecosystem have long-term environmental implications, such as global warming and climate change (Lovett, Tear, Evers, et al. 2009). A recent study by Stevens, Bell, Brimblecombe, et al. (2020) discussed four threats to the global ecosystem from pollution, namely, the effects of primary pollutants, such as SO2 and NO2 in a gaseous state, the consequences of wet and dry depositions from SOX and NOX emissions, effects of eutrophication by nitrogen deposition, and the impact of ground-level ozone concentrations.

On Biodiversity

The ill-effects of air pollutant emissions could impact the biological diversity. Though it is evident that air pollution contributes to ground-level emissions, limited studies have been conducted to address the effects on our biodiversity. Acid rain, which is a result of air pollution, is caused by the oxidation and wet deposition of SO2 and NOX emissions in the atmosphere (Rao, Rajasekhar, and Rao 2016). Therefore, acid rain can have harmful effects on our biodiversity.

Nitrogen deposition on plants is a serious outcome of air pollution (Lovett, Tear, Evers, et al. 2009). Bignal, Ashmore, Headley, et al. (2007) investigated three sites adjacent to roadways in the UK to study the impact of pollution on the health of oak and beech trees. Several damages, such as increased defoliation, discolouration, poorer crown condition, and increased pest attacks were observed during the study. It was inferred that significant effects on plants could be found within 100 m from the roadways due to NO2 emissions.

Ozone is another pollutant which is toxic to both plants and animals. Ozone results in reduced photosynthesis and slower growth in plants. In animals and humans, ozone can affect the lung tissues causing respiratory conditions, such as asthma (Stevens, Bell, Brimblecombe, et al. 2020). The effect of ground-level ozone on the crop yield was studied by Sharma, Ojha, Pozzer, et al. (2019), where the researchers evaluated the pan India losses in crop yield and financial problems incurred during 2014–15 due to the ozone. Poor air quality and exposure to anthropogenic pollution had a negative effect on the health of animals as well (Isaksson 2010).

Moreover, the reproductive performance of animals also gets affected due to increased oxidative stress (Isaksson 2010), thereby impacting the population of any species. This may not prove healthy especially for the endangered species. Considering the rapid urbanisation, more focus should be given to this study area in the future.

On Materials and Buildings

SOX and NOX emissions can harm the flora, fauna, material surfaces, and even damage buildings and structures. The negative effects may be in the form of discolouration, loss of material, structural failing, and soiling. This can reduce the service life of buildings and can severely damage historical monuments and structures. One such example is India's white-marble Taj Mahal, which is turning yellow as a result of being exposed to SOX emissions from industries and acid rain. Another historical monument in India is Hyderabad's Charminar, which is turning black due to it being situated in a highly polluted area (Rao, Rajasekhar, and Rao 2016). The erosion of such heritage zones poses a grave concern.

On Human Health

People residing in areas exposed to poor air quality and high pollution levels are prone to hazardous health risks. Such deleterious implications can lead to both minor respiratory disorders and fatal diseases (Gurjar, Jain, Sharma, et al. 2010). Molina, Molina, Slott, et al. (2004) inferred that the studies conducted worldwide had similar conclusions regarding the impact of pollutants on humans. Emissions such as PM, O3, SOX, and NOX have the potential to damage the cardiovascular and respiratory systems of humans. In recent years, the study of human health risks as an outcome of poor air quality has been of prime focus. Gurjar, Jain, Sharma, et al. (2010) evaluated the health risks people in urban areas were prone to due to air pollution in terms of mortality and morbidity. However, there are several limitations associated with the application of this health risk assessment methodology, which must be addressed in the future studies. The HEI (2019) assessed the impact of PM 2.5 concentrations in India and concluded that around 1.1 million deaths in 2015 were a result of being exposed to air pollution. Upadhyay, Dey, Chowdhury, et al. (2018) inferred that a total of 92,380 lives would have been saved if control measures were applied in the transport, residential, industries, and energy sectors, which are some of the prominent contributors of air pollution.

Gurjar, Ravindra, and Nagpure (2016) concluded in their study that around 30% of Delhi's population complained of respiratory issues due to air pollution in the selected year. Another study by Nagpure, Gurjar, and Martel (2014) evaluated that the mortality rate due to air pollution had doubled between 1990 and 2010 in the capital city. According to Gurjar, Mohan, and Sidhu (1996), the number of premature deaths in Mumbai due to air pollution was recorded at 2800 in 1995, which later increased exponentially to 10,800 in 2010 (Gurjar, Ravindra, and Nagpure 2016). In Kolkata, the premature deaths were estimated to be around 13,500 in 2010. Similarly, Delhi reported about 18,600 premature deaths per year (Lelieveld, Evans, Fnais, et al. 2015).

Air quality standards

The acceptable threshold level of air pollution in terms of its potential impacts on health and environment is defined as the ambient air quality standards. These standards are adopted and enforced by a regulatory body or authority. Every standard should have a standalone definition and its threshold values should be justified appropriately (Molina, Molina, Slott, et al. 2004). The air quality standards may vary for different countries due to various factors, such as economic conditions, technological know-how, and indigenous air pollution-related epidemiological studies. These are known as the National Ambient Air Quality Standards (NAAQS) in countries, such as India, China, and the US. However, in Canada and the European countries, the limit values are predefined (WHO 2005). Table 1 gives a representation of the different standards adopted by different countries (WHO 2005).

For India, the NAAQS developed by the Central Pollution Control Board (CPCB 2009) are given in Table 2.

Mitigation strategies for emission control in India

In India, the central and state governments have taken several steps to control air pollution and improve the ambient air quality. Various initiatives, such as the use of compressed natural gas (CNG) as an alternative fuel, the odd-even measures implemented in Delhi, the introduction of Bharat Stage VI vehicle and fuel standards, the Pradhan Mantri Ujjwala Yojana (PMUY), and the National Clean Air Programme (NCAP) are some examples in this endeavour. The CPCB ensures the monitoring and regulation of the NAAQS in the cities, towns, and industrial areas with the cooperation of the respective state pollution control boards (SPCBs). Under these plans, various sector-wise measures have been implemented in the urban cities of India. For the transport sector, for instance, some of these measures include the use of electric vehicles (EVs) as a mode of public transportation, development of cycling infrastructure, use of bioethanol as fuel, and the construction of multi-level car parking facilities and peripherals to tackle congestion. Within the industrial sector, some of the measures undertaken comprise the implementation of zig-zag technology for the stack emissions from brick kilns, online monitoring of discharges through the Online Continuous Emission Monitoring Systems (OCEMS), and the installation of web cameras in highly polluting industries. To tackle the problem of open burning of garbage and household wastes, door-to-door collection of segregated wastes has been introduced and several compost pits have been established in urban cities. In the residential sector, the government has set a target of achieving 100% usage of LPG for cooking purposes. Further, to control the concentrations of particulate matter (PM) and dust particles, various steps, such as the green buffer around cities, maintenance of 33% green cover around urban areas, installation of water fountains across the cities have been taken over the years (Ganguly, Kurinji, and Guttikunda 2020; Sharma, Mallik, Wilson, et al. 2018; Sharma, Rehman, Ramanathan, et al. 2016).

Other potential mitigation strategies

Air quality management in megacities is a four-stage process that involves problem identification, formulation of policies, their implementation, and control strategies (Molina, Molina, Slott, et al. 2004). The various management tools to ensure emission control and attainment of air quality standards include, air quality modelling, emission inventories, monitoring the concentration of pollutants, and source apportionment studies. These methodologies involve a complex analysis of extensive data sets for the effective management of air quality standards. Due to the lack of transparency and unavailability of data, uncertainties are introduced in the estimation of atmospheric concentrations. Minimising these uncertainties with our scientific understanding is one of the major challenges towards addressing the issues related to air quality (Gurjar and Ojha 2016).

The increase in private vehicles is the prime contributor of air pollution in Indian cities (Molina, Molina, Slott, et al. 2004). Therefore, there should be some policy norms that would set a certain limit to private vehicle ownership. Second, the age of vehicles degrades the air quality and such ageing vehicles should be phased out over a period of 10 years or so. Threshold limits should be imposed on emissions from all sources, primarily vehicles and industries, and the violators should be penalised.

Infrastructural modifications to limit traffic in polluted areas, development of efficient public transport facilities, such as the Bus Rapid Transit (BRT) system or other public transit systems, improved facilities for walking, biking, and public transport, and relocation of point sources out of urban centres could help curb emissions significantly.

Technology modifications, such as the introduction of hybrid vehicles or fuel cell vehicles or fuel modifications, such as ultra-low sulphur fuels, or alternative fuels like CNG, methanol in Brazil or hydrogen fuel in Japan (Molina, Molina, Slott, et al. 2004) could reduce air pollution levels. In recent years, owing to the reduced sulphur fractions in the fuels, decreasing trends in SOX have been observed (Gurjar, Ravindra, and Nagpure 2016) and such a development could further control air pollutant emissions.

Control points should be identified and prioritised in urban areas that would help reduce pollutant emissions significantly. The development of sustainability matrices could help monitor and regulate the emissions. Emission trading, also known as cap and trade, is another control strategy that could be applied in urban cities, a practice already prevalent in the US, where economic incentives are offered to reduce the pollutant concentrations (Molina, Molina, Slott, et al. 2004). Congestion pricing, as followed in London, where a driver is charged each time they enter the peak zones of a city could be another avenue to explore within the Indian context as well. However, such a strategy would require strong public awareness and support to become successful.

A combination of effective policies, technologies, and land-use planning could help develop a control strategy for emission control. Stricter emission standards, cleaner fuels, advancements in engines, manufacture of cleaner and green vehicles, and post-emission treatment technologies could curtail pollution levels in urban areas to a great extent. Concrete policy measures could be imposed that would further limit the exposure of people to pollutant emissions. Relocation of industries to the outskirts of the city is a fine example (Molina, Molina, Slott, et al. 2004) to consider in this regard.

Limiting the emissions from combustion sources could curb pollution. One such example was the use of CNG-fuelled vehicles in Delhi from 2001 to 2006, which had reduced the emissions of PM, CO, NOX, and SO2 levels considerably.

Challenges in the Indian scenario

Air pollution poses serious risks to human health, economic assets, and the overall environment (Gurjar, Butler, Lawrence, et al. 2008). In the current Indian scenario, urban cities are mostly polluted by vehicular emissions, industries, and thermal power plants (Gurjar, Ravindra, and Nagpure 2016). Nagpure, Gurjar, Kumar, et al. (2016) studied and inferred that vehicular emission is the major contributor of increasing pollution in Delhi. Gurjar, Aardenne, Lelieveld, et al. (2004) had earlier indicated that there is a lack of India-specific emission factors for several air pollutants, which could be a major concern towards developing realistic emission inventories for Indian cities. Further, Nagpure, Sharma, and Gurjar (2013) observed that neither ratio nor realistic numbers are available for two-stroke and four-stroke two-wheelers or for light and heavy commercial vehicles. Similarly, for evaluating the utilisation factors for vehicles, which indicate how frequently a vehicle is being used in a given period of time, the escalating travel demand in the country is not considered. This results in uncertainties in the estimated emissions of air pollutants.

Over the last decades, industrialisation has boomed and India ranks among the top 10 industrialised countries, globally (Gurjar, Ravindra, and Nagpure 2016). Guttikunda and Calori (2013) studied and listed the improvements that could be made in the emission estimates from Indian cities by monitoring capacity, regular documentation of pollutant sources, fuel usage patterns, and receptor modelling studies.

In India, the methodologies associated with emission estimation from biomass burning have certain limitations. For instance, Gurjar, Aardenne, Lelieveld, et al. (2004) exempted several sources from estimation due to non-reliable data sets pertaining to biogenic emissions. Guttikunda and Calori (2013) estimated that the burning of roadside garbage and the landfill fires have an uncertainty of ±50%. The study also estimated that the data on fuel used for cooking and heating in the domestic sector have an uncertainty of ±25%. This uncertainty of fuel usage data for the in-situ generators used in large institutions, hospitals, and hotels was ±30% for the year 2010.

As discussed in the previous section, the implementation of strict policy measures and the use of advanced technologies and infrastructure could tackle the problem of air pollution to a great extent. Though stringent measures and policies are being adopted to curb vehicular and stack emissions, most Indian cities lack the technological and infrastructural wherewithal. In a developing country like India, financial constraints faced during the timely planning and implementation of advanced urban infrastructural changes could pose a serious hurdle to air pollution mitigation strategies (Gurjar and Nagpure 2016).

Irresponsible human behaviour is another major issue that makes the existing challenges difficult to overcome. The lack of public interest in the emission control measures and inefficient traffic management system are major hurdles to realising the goal of clean air. The lack of public interest in certain measures taken by the government could result in significant losses of investments in infrastructural facilities (Gurjar and Nagpure 2016).

Research Gaps

Several studies focus on air pollutant emissions in Indian urban cities and industrial clusters. However, India-specific emission factors are either unavailable or difficult to interpret for various sources in most cases (Gurjar, Aardenne, Lelieveld, et al. 2004). Also, there is a lack of adequate research on the extent of pollution concentrations in medium-scale cities, which are likely to expand in the near future. For a country like India, nearly 68% of population (Chandramouli 2014) resides in rural areas and is dependent on domestic cooking fuels, such as wood and cow dung cakes. Moreover, practices such as biomass and crop burning create additional point sources of air pollution. This further gives an opportunity to evaluate the strategies to reduce emissions from such sources.

A recent national-level emission inventory for India at fine resolution is not available in the public domain and research on policy measures using regional air quality modelling mostly depends on global emissions inventories, which are at coarser resolutions. For Indian cities with limited or no air quality monitoring infrastructure, researchers and authorities are dependent on the data available through secondary sources. However, these data sets are non-reliable and the accuracy of such data is also uncertain (Gurjar, Jain, Sharma, et al. 2010)Risk of Mortality/Morbidity due to Air Pollution (Ri-MAP).

With the increasing rate of industrialisation, Gurjar, Aardenne, Lelieveld, et al. (2004) discussed the lack of factual data on industrial production and fuel statistics for Indian cities.

The urban population in India is anticipated to increase exponentially and the number of cities will grow as well. This suggests that the MSW generation will also increase, which must be managed efficiently. However, in India, proper MSW management and treatment techniques need to be implemented other than the current practices of landfilling and composting. Moreover, data sets on detailed MSW statistics regarding the amount of wastes collected, segregated, stored, and treated were absent (Gurjar, Aardenne, Lelieveld, et al. 2004).

Over the years, indoor air quality (IAP) has become an area of scientific interest and researchers worldwide are studying the threats IAP poses to human life. However, in the Indian context, there are limited studies which have stressed on the impact of indoor air pollution concentrations.

Conclusion and Recommendations

An effective and successful emission control strategy should be holistic (Molina, Molina, Slott, et al. 2004). It must be a combination of successfully applied scientific ideas and technological advancements; should support the economy and be supported by the public. Various steps taken by the Government of India to control air pollution in Indian cities have been highlighted in the previous sections. These measures have the potential to tackle pollution only if implemented successfully in the coming years.

India is facing serious issues of poor air quality in many urban areas. Apart from the much discussed megacities, like Delhi, various reports suggest that several medium-scale cities are equally at the brunt of filthy air. The ill-effects could impact human health in a negative way, also affecting the biodiversity, other life forms, heritage, cultural buildings and even climate in the longer term. It is about time that the government comes forward to support cities for the development of infrastructure and treatment facilities.

The control strategies adopted to tackle air pollution must be sustainable in nature. For example, the urban air pollution control strategy should depend mainly on sustainable means of public transportation modes, such as BRTs, metros, trams, cycle lanes and well-connected pedestrian facilities, which can further ensure minimum use of private vehicles, thereby reducing air pollution levels. People must be motivated to opt for an efficient public transport system instead of relying on private vehicles. Similarly, some strict laws must be enforced, such as emission trading and congestion pricing, which have the potential to reduce emissions drastically. Apart from these, the use of alternate fuels and e-cars, e-bikes and hybrid vehicle types must be promoted by the government. All these measures could reduce city emissions significantly.

The residents of rural areas are seldom aware of the harmful effects of air-borne pollutants and their consequence to human health. Public awareness programmes should be initiated by the government in every city, both rural and urban, highlighting the importance of managing air pollution at source and the various control measures that could be adopted to reduce pollutant emissions. Such initiatives could significantly reduce the activities, such as open burning of wastes, crop burning, use of biomass as a fuel for cooking and burning of plastic and rubber materials during winters. A holistic approach incorporating all of the mentioned measures could be beneficial to attain cleaner air quality in Indian cities and guarantee a healthier place to inhabit.

In this context, the NCAP launched by the Government of India appears to be a timely intervention. It is based on a long-term, time-bound, national-level strategy to tackle air pollution in a comprehensive manner with targets to achieve 20–30% reduction in particulate matter (PM) concentrations by 2024, keeping 2017 as the base year for the comparison of concentration levels. A total of 122 non-attainment cities have been identified across the country based on the 'Air Quality' data obtained for the period 2014–2018 under NCAP. The city-specific action plans are being prepared which, inter-alia, include measures for strengthening the monitoring network, developing emission inventories, carrying out source apportionment studies, reducing vehicular/industrial emissions, and generating public awareness, among others. It is expected that such initiatives by the central and state governments along with the participation of local bodies and other stakeholders comprising academia, research institutions, and public interest groups would result in ensuring better air quality in India.

Dr Bhola Ram Gurjar is Professor of Civil (Environmental) Engg., and Dean of Resources & Alumni Affairs (DORA), Indian Institute of Technology, Roorkee. He can be reached at [email protected]. This article was originally published in the January to March 2021 issue of Energy Futures magazine .

Acknowledgements

I thank my students, who have helped me in conducting the literature survey and compiling the necessary information from various bibliographical resources.

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Air pollution in Delhi, India: It’s status and association with respiratory diseases

Abhishek dutta.

Department of Environmental Science, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok, Thailand

Wanida Jinsart

Associated data.

Data Availability: Air quality data of Delhi that support the findings of this study are owned by the Central Pollution Control Board (CPCB). For further information about the air quality data please visit https://cpcb.nic.in/real-time-air-qulity-data/ or https://app.cpcbccr.com/ccr/#/dashboard-emergency-stats . Meteorological data of Delhi can be obtained from the Regional Meteorological Centre, India Meteorological Department ( https://rmcnewdelhi.imd.gov.in/ ). Both for data and permission to use the data, please contact the Deputy Director General of Meteorology (DDGM), Regional Meteorological Centre, Lodi Road, New Delhi – 110003 via E-mail: moc.liamg@ihledwencfwr . Daily hospital visit data between the years 2016 and 2018 for respiratory diseases (ICD-10) J00-J99, used in this study, were collected from Vardhman Mahavir Medical College Safdarjung hospital, Ansari Nagar East, New Delhi. For data and permission to use data please contact the Medical Superintendent M.S. Office, New OPD Building, Safdarjung Hospital, New Delhi-110 029.Tel (011-26190763), e mail: ni.cin.hjs-cmmv@eciffosm .

The policymakers need research studies indicating the role of different pollutants with morbidity for polluted cities to install a strategic air quality management system. This study critically assessed the air pollution of Delhi for 2016–18 to found out the role of air pollutants in respiratory morbidity under the ICD-10, J00-J99. The critical assessment of Delhi air pollution was done using various approaches. The mean PM 2.5 and PM 10 concentrations during the measurement period exceeded both national and international standards by a wide margin. Time series charts indicated the interdependence of PM 2.5 and PM 10 and connection with hospital visits due to respiratory diseases. Violin plots showed that daily respiratory disease hospital visits increased during the winter and autumn seasons. The winter season was the worst from the city’s air pollution point of view, as revealed by frequency analyses. The single and multi-pollutant GAM models indicated that short-term exposure to PM 10 and SO 2 led to increased hospital visits due to respiratory diseases. Per 10 units increase in concentrations of PM 10 brought the highest increase in hospital visits of 0.21% (RR: 1.00, 95% CI: 1.001, 1.002) at lag0-6 days. This study found the robust effect of SO 2 persisted in Delhi from lag0 to lag4 days and lag01 to lag06 days for single and cumulative lag day effects, respectively. While every 10 μg m -3 increase of SO 2 concentrations on the same day (lag0) led to 32.59% (RR: 1.33, 95% CI: 1.09, 1.61) rise of hospital visits, the cumulative concentration of lag0-1 led to 37.21% (RR: 1.37, 95% CI:1.11, 1.70) rise in hospital visits which further increased to even 83.33% (RR: 1.83, 95% CI:1.35, 2.49) rise at a lag0-6 cumulative concentration in Delhi. The role of SO 2 in inducing respiratory diseases is worrying as India is now the largest anthropogenic SO 2 emitter in the world.

1. Introduction

Time and again, the policymakers felt the requirements of understanding the status of air pollution in growing cities and association of short-term air pollution exposures spanning one or a few days on morbidity. This is particularly more relevant for the world’s fast-growing cities to accrue benefits of sustainable development. Epidemiological studies conducted in the past in cities held air pollution responsible for inducing different health hazards. The quasi-poison regression model within over-dispersed Generalized Additive Model (GAM) has been very handy for many researchers for exploring the association of air pollution with different morbidity and mortality [ 1 – 6 ]. In a time series where the respondent variable depends on the nonlinear relationship of independent variables, GAM model finds its best applicability. In GAM, the nonlinear confounders can be controlled using smooth functions to correctly estimate the best connection between dependent and independent variables [ 7 – 12 ]. Accordingly, researchers used the GAM model extensively to indicate the role of air pollution in causing health effects for US and European cities [ 13 , 14 ].

Chinese and Indian cities frequently grabbed the world’s attention because of increasing air pollution and reported health effects on city dwellers. Indian cities were in the limelight because of the uncontrollable nature of air pollution in already declared polluted cities. Different Chinese cities have been put under strict scanners by the researchers who continuously reported or updated the policymakers on air pollution and health hazards so that policy-level initiatives may defuse the situation. Recently Lu et al. [ 15 ] reported that research ably supported the polluted Chinese cities to progress in air pollution control and place the much-needed strategic air quality management system. Another recent article indicated that out of 31 research papers published during 2010–2020 investigating the role of different air pollutants on the health of city dwellers using the GAM model, the majority, i.e., 17 were in the backdrop of Chinese cities and 3 for Indian cities [ 16 ]. GAM successfully explored the role of different pollutants in establishing their relationships with different types of respiratory morbidity/mortality for 21 cities of China, India, Iran, Brazil. Denmark and Kuwait ( S1 Table ). Zhao et al. [ 17 ], using GAM, reported that Dongguan city dwellers in China faced the threat of enhanced respiratory diseases due to short term exposure to CO. Song et al. [ 18 ] found respiratory diseases amongst the children of Shijiazhuang city of China due to PM 10 , SO 2 , NO 2 presence in the air. Cai et al. [ 19 ], studied the total respiratory diseases mortality of Shenzhen, China, and linked them with PM 2.5 presence in ambient air through GAM modelling. Liang et al. [ 20 ] used GAM model to indicate a direct relationship between pulmonary disease in Beijing with air pollution. Very recently Wang et al. [ 21 ] confirmed the role of particulate matter (PM) with pneumonia hospitalizations of children in Hefei, China.

Delhi, the capital city of India, is the second most populated and one of the most polluted cities in the world and should be the obvious choice for pollution and health hazard research. The recent air quality report of IQ Air has ranked Delhi first out of the air-polluted capital cities of 106 countries based on PM 2.5 concentration [ 22 ]. According to WHO, Delhi is the sixth-worst polluted city amongst 13 notable other Indian cities. Indeed, the city-dwellers had terrible times when PM 2.5 of Delhi stood at 440 μg m -3 during October 2019, i.e., 12 times the US recommended level. Past studied blamed the huge transport sector with the largest vehicle stock of the country as the critical emission source [ 23 – 27 ]. Chen et al. [ 28 ] demonstrated that local transport emissions and neighboring states contributed dominantly to PM 2.5 and O 3 concentration strengthening in Delhi. Sreekanth et al. [ 29 ] found high PM 2.5 pollution persists across all the seasons in Delhi despite pollution control efforts in vogue. In the pan-Indian context, air pollution significantly contributed to morbidity and premature mortality in India for a long time [ 30 ]. Sharma et al. [ 31 ] reviewed 234 journal papers and noted the knowledge gaps in connecting hospital admissions of patients with air pollution of Delhi. Balyan et al. [ 32 ] also noted that a deeper understanding of ambient pollutants at the city level and their effect on morbidity was lacking.

Against the background above, the primary objective of this paper to explore the environmental data of Delhi for confirming the poor air quality status of the city and, after that, assess the role of air pollutants with morbidity (respiratory diseases) through the application of the GAM model. A more profound grasp of the city air quality and influences of ambient air pollution on respiratory diseases is much needed. Such studies may provide all critical information for initiating actions to curb air pollution, health risk, developing public health policies, and above all, a strategic environmental management system for Delhi.

2. Study location

As a highly populated and polluted city, Delhi provides an opportunity to apply the GAM model for ascertaining how much the prevailing air pollution is responsible for respiratory diseases of the city dwellers. Delhi has spread over 1,483 km 2 and a population size of about 11 million per the 2011 census study. With time Delhi emerged as a significant city of the country so far as commerce, industry, medical service, and education are concerned. As per Köppen’s climate classification, Delhi’s climate is extreme with five seasons. The summer is scorching (April–June), while winter is freezing (December-January). The average temperature range during the summer is between 25°C to 45°C, while the winter temperature range is between 22°C to 5°C [ 33 ]. The comfortable season spring prevails from February to March, and autumn runs from mid-September to late November. The rainy monsoon season spans almost three months, starting from July. Air pollution varies across seasons due to the influence of climatic conditions [ 34 ].

3. Materials and methods

3.1. air pollution data.

Daily average data for three years, January 2016 to December 2018, (1096 data points) of key air pollutants were collected from the State Pollution Control Board (SPCB), Delhi. The pollutants were sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO), particulate meter 10 micrometers or less (PM 10 ), and particulate meter 2.5 micrometers or less (PM 2.5 ) as recorded by 11 NAMP (National Air Quality Monitoring Programme) stations of the city as shown in Fig 1 and S2 Table .

3.2. Meteorological data

Time series meteorological data for 1 January 2016–31 December 2018 were collected from Regional Meteorological Department located in Delhi. The data were of a total of 1096 days and included daily average temperature (T), daily average relative humidity (RH), daily average wind speed (WS), and daily rainfall (RF). The collected meteorological and air monitoring data will be adequate to estimate the confounding effect of meteorological conditions on morbidity related to respiratory diseases using GAM model.

3.3. Hospital visit data

We considered respiratory diseases covered by J00-J99 under the ICD-10 classification system. Data related to daily hospital outpatient visits of patients for respiratory diseases under International Classification of Diseases-10 (ICD-10), J00-J99 for 2016–2018 (1096 days) were collected from Safdarjung Medical College and Hospital (SMCH) of Delhi. The SMCH had its existence from pre-independence days of India and now functioning under the Ministry of Health and Family Welfare, Government of India. SMCH has many different specialties and super specialty departments, and Respiratory Medicine (RM) is one. Fig 1 shows that all the 11 air pollution monitoring stations considered in this study are located within a road distance of 12 km from SMCH. The hospital records contained information related visit date of patients, age, gender, and final medical diagnosis for each patient. The patient data were grouped age-wise under three categories (i) elderly people (more than or equal to 65 years), (ii) middle-aged (45–64 years), and (iii) young (less than or equal to 44 years). For hospital data collection formal request letter was submitted to the hospital authority. As the data were old data without identifiers and not having any possibility of ascertaining the identities of the individuals to whom the data belong, the hospital waived IRB approval.

3.4. Methods of analysis

3.4.1 summary statistics and analysis of time series.

Summary statistics of climatic variables, air pollutants, and hospital visits of the patients such as mean, standard deviation, maximum, minimum, and different quartiles were computed using the SPSS 25 version of the software. Daily hospital visit counts for three years (2016–2018) in SMCH were structured based on the patient’s age, sex, and visit dates. Violin plots were developed for three air pollutants (PM 10 , PM 2.5 , and CO), two climatic variables (T, RH), and hospital visits of patients regarding five seasons of Delhi, indicating the distribution of data prevailing in the city during different seasons. Violin plots have been drawn with XLSTAT statistical software. Time series plots were developed using the SPSS 25 version of the software with time dimensions on the horizontal axis and hospital visits, pollutants and, meteorological variables on the vertical coordinate axes to shed light on the data distribution for three years.

3.4.2 Frequency analysis

The seasonal distribution of PM 2.5 and PM 10 concentrations in Delhi during 2016–18 has been done by frequency analysis [ 35 ]. Under frequency analysis, first, the city level average concentrations of PM per day were calculated by averaging the concentration of 11 monitoring stations. Then, PM concentrations (both for PM 10 and PM 2.5 ), i.e., number of per day observations for the period 2016–18 falling under six categories like 0–25, 25–50, 50–100, 100–200, 200–300, and more than 300 μg m -3 worked out. So, the three-year period (2016–18) data or 1096 observations were segregated session-wise for each of the six categories, and the frequency of their appearances was then expressed in percentage terms. The calculations were done with the help of data analysis ’ToolPak’ of excel. As per the air quality index (AQI) Of India, the range 0–100 is considered a good category, 100–200 as moderate, 200–300 as poor, and above 300 as very poor or severe.

3.4.3 Correlation analysis

To understand the interrelationship between climatic variables and air pollutants data for Delhi (2016–2018), we executed Pearson correlation analysis using SPSS version 25.0 (SPSS Inc., Chicago, IL, USA) software. The coefficients of correlations were established between daily meteorological variables and air pollutants for Delhi. The correlation coefficients at p < .01 were accepted as statistically significant [ 36 ]. For better visualization, correlation matrix plots have been drawn with R software’s ’corrplot’ package.

3.4.4 Generalized Additive Models (GAM)

The nonlinear associations of various independent variables (climatic variables and criteria pollutants) and the outcome variable (hospital visits due to respiratory diseases) of Delhi can be better explained by (GAM) model. GAM explicitly allows the relationship between outcome variables independent variables to be developed based on the smooth functions fitted to some independent variables, thereby bringing the flavor of parametric relationships of the covariates in a regression model [ 37 , 38 ]. Accordingly, in this study, the potential confounding effects of few independent variables that entered the regression model were controlled with non-parametric smoothening splines. Smoothening splines of 7 degrees of freedom (df) per year were fitted to calendar time (time since 1 January 1970) to control long-term trends and possible calendar effects [ 39 , 40 ]. In line with Wei [ 41 ] smoothening splines with 7 df were also applied to mean RH and mean temperature (T) to control their respective confounding effects on the regression model. A linear term of mean wind speed (WS) was allowed to prevail. A dummy variable as the day of the week (DOW) was additionally introduced in the categorical form to control for week effects. As per Peng et al. and Zheng et al. [ 39 , 42 ], the dfs for smoothing splines were allowed to be determined by the generalized cross-validation (GCV) scores. Finally, based on the description of the regression model formation above, we formed the following GAM model ( Eq 1 ) in our present study with usual notations and applied.

where i denotes the day of observation; E ( Y i ) denotes the daily hospital visits expected due to respiratory diseases; β denotes regression coefficient; X i denotes the daily mean concentration of pollutants; s stands for the smoothing spline applied, and α is the intercept. Once the basic GAM model is set with the smoothing splines for RH, T, and time variables, the independent variables like PM 2.5 , PM 10 , NO 2 , SO 2 , and CO (per day concentrations) were added to the basic model to make it the multi-pollutant GAM model. We also constructed two single pollutant models for PM 2.5 and PM 10 , respectively, to understand their respective sole effects on respiratory diseases related to hospital visits in the city under study. In the single-pollutant model, PM 2.5 and PM 10 concentrations, in turn, were entered as independent variables in the base model. Generally, single pollutant models do not reflect the synergistic effect of pollutants on morbidity, but in consideration with the multi-pollutant models, they provide crucial complementary understanding.

The respective coefficients of pollutants of the multi-pollutant and single-pollutant GAM models, found out as regression model output, were the inputs in deriving the relative risk (RR) of hospital visits due to one unit rise of each modelled air pollutants in the ambient air.

Past studies have shown that the air pollutants remain in the ambient air and create lingering effects on morbidity. Accordingly, we have considered pollutant concentrations for a single day and multiple days in the study. We tested the lingering effects of air pollution for single-day lags and cumulative lag days. Single-day lag (lag0) means air pollutant concentrations on the same day of the hospital visit, while lag6 indicates air pollutant concentrations of 6 days before the hospital visit. Similarly, for cumulative concentrations of pollutants lag0-1indicate the mean of pollutant concentration of the current day and previous day of the hospital visit (i.e., 2 days mean). Similarly, lag 0–2 indicates the mean of current day pollutant concentration, 1 day before and 2 days before the visit (i.e. 3 days mean). In the same way, lag0-3, lag0-4, lag0-5, and lag0-6 means 4 days, 5 days, 6 days, and 7 days mean pollutant concentrations, respectively. We used single lags of 0, 1, 2, 3, 5, and 5 days (lag0–lag 5) and cumulative lags of 0–1, 0–2, 0–3, 0–4, 0–5, and 0–6 days (lag 0–1 to lag0-6) to explore the lag pattern of health effects in the multi pollutants and single pollutant models. The R software with "mgcv" package (version 4.0.2) was applied to construct the GAM models. For visualizations of GAM models developed in this study, we have used visual tools of the mgcViz R package.

3.5. Relative Risk (RR)

Relative risk (RR), often used in epidemiological studies, helps understand the risk of the outcome of an intervened event with non-intervened events. Thus, RR compares one group with another group. In this study, the exposure-response coefficient β of pollutants obtained from the GAM models under different lag conditions have been used to estimate RR and their 95% confidence intervals (95% CIs). RR for the i th predictor variable and its confidence intervals were calculated using the following Eqs 2 , 3 and 4 .

where Δ C i is the rise of the i th pollutant concentration in air and S.E i is the standard error of i th pollutants. Here, Δ C will be 1 unit increase in CO and 10 units increase in all other pollutants. RR provides information on the rise of hospital visits due to each unit increase of a pollutant’s concentration level. To make the RR estimates of daily hospital visits due to air pollution more expressive, we also calculated the percentage change (PC, %,) at 95% CI in the following way ( Eq 5 ).

PC = Percentage change of daily hospital visits due to air pollution

In all analyses p-value < 0.05 considered significant.

4. Results and discussion

4.1 data distribution and time-series analyses.

The distribution of criteria pollutants, climatic variables (T and RH), and daily counts of hospital visits in Delhi are placed in Table 1 for 2016–18. Table 1 indicates that the mean value of PM 2.5 and PM 10 concentrations exceeded the guidelines of NAAQS and WHO both by a wide margin. They shoot to as high as 693.08 μg m - ³ for PM 10 and 478.25 μg m - ³ for PM 2.5 during 2016–2018. The mean RH value of 58.5% (range, 98.3% to 12.5%) in Delhi indicates the city’s humid condition higher than the ideal level relative humidity for health and comfort of 30–50%. The three years mean temperature of 25.63 ± 7.65 °C with a maximum as high as 45°C and a minimum of 0.5°C along with a higher level of RH indicates the extreme climate of Delhi. Daily mean hospital visits of patients for respiratory diseases during 2016–18 was 20±23.52.

Table 2 reveals that a total of 22,253 patients visited SMCH, Delhi, either for outpatient consultation or admission for respiratory diseases during 2016–2018, as retrieved from hospital records. The maximum number of people who visited the hospital for respiratory ailments for a day was 176, and the minimum 0 patients. Out of the total patients, 63.5% were female, and 30% had ≥65 years of age. Similarly, out of male patients, 52% were aged ≥65 years, as shown in Table 2 .

Time series charts in ( Fig 2A–2F ) depict behaviors of meteorological variables (RH, temperature), air pollutants (PM 2.5 , PM 10 , and CO), hospital visits, and their interrelationship during 2016–2018 for Delhi. PM 2.5 and PM 10 were positively correlated in Delhi during 2016–18, indicating the interdependency ( Fig 2A ) while maintaining a positive correlation with hospital visits due to respiratory diseases ( Fig 2B and 2C ). Fig 2D–2E shows that hospital visits tended to negatively correlate with RH and temperature. Fig 2(F) shows a positive correlation of hospital visits with CO concentration too in the city’s environment.

The time series of Delhi from 2016–2018 (A) PM 2.5 Vs Hospital visit, (B) PM 10 Vs Hospital visit, (C) RH Vs Hospital visit, (D) T Vs Hospital visit, (E) CO Vs Hospital visit, (F) PM 2.5 Vs PM 10 .

Violin plots of three air pollutants (PM 10 , PM 2.5 , and CO), two meteorological variables (T, RH), and hospital visits of patients were drawn for the five distinct seasons of Delhi have been provided in ( Fig 3A–3F ) below. Fig 3A indicates that PM 2.5 dominates the city environment during winter and autumn. Fig 3B indicates that PM 10 dominates the city air during the winter and summer seasons, but the median value of PM 10 concentrations was higher during winter. The concentration of CO in the air remains high during winter and low during the monsoon season ( Fig 3C ). Fig 3D clearly shows that the city experiences comparatively higher RH during summer and monsoon, with the highest median value during monsoon. Fig 3E indicates that the city experiences the hottest season during summer and autumn. From Fig 3F , it can be observed that during the winter and autumn season’s daily hospital visits due to respiratory diseases increased. The rectangles within the violin plots indicate finishing points of the first and third quartile of data distribution with central dots as medians. The upper and lower whiskers show data spread.

(A) PM 2.5 , (B) PM 10 , (C) CO, (D) RH, (E) Temperature, (F) Hospital visit.

4.2 Seasonal distribution of PM 2.5 and PM 10 in Delhi

The frequency distribution of PM 2.5 and PM 10 concentrations for five Delhi seasons are shown in Fig 4 . Fig 4 indicates that the winter season was terrible from the air pollution point of view as almost 95.2% of the time, the ambient PM 2.5 concentrations recorded to be more than 100 μg m -3 . Alarmingly, 100% of the time, the ambient PM 10 concentrations crossed the 100 μg m -3 benchmark during winter, indicating very harsh wintertime for the city dwellers. The spring season brought some relief for the city dwellers when 42.2% of the time PM 2.5 concentrations crossed 100 μg m -3 benchmark, but PM 10 remained very strong with 99.4% of the time crossing the 100 μg m -3 benchmark. During summer, about 76.9% of the time PM 2.5 concentrations were under the ’good’ category, and 15.8% of the time PM 2.5 concentrations were more than the 100 μg m -3 benchmark. During summer PM 2.5 concentrations improved considerably with only 15.8% of the time, its concentrations were more than the 100 μg m -3 benchmark, but PM 10 remained razing with 97.8% time crossing 100 μg m -3 benchmark. However, two and half months of monsoon (July, August, and mid-September) brought relief from PM 2.5 pollution. Almost 100% of the time, PM 2.5 concentrations remained under the ’good’ category, but PM 10 remained 51.1% crossing the 100 μg m -3 benchmark during monsoon. From autumn (mid-September to late November), PM pollution built up with 97.8% of the time PM 2.5 concentrations crossing 100 μg m -3 benchmark, as shown in Fig 4 . In summary, the frequency distribution of PM 2.5 and PM 10 concentrations indicates that except winter, the PM concentrations remained very high, which could be a possible cause of health hazards for the city dwellers.

4.3 Correlation between pollutants and meteorological variables

Positive correlation existed between two important gaseous pollutants SO 2 and NO 2 (r = 0.341), while PM 10 maintained a mild positive correlation with SO 2 (r = 0.281). PM 10 almost had linear positive correlation both with NO 2 (r = 0.783) and CO (r = 0.733) as shown in Table 3 and Fig 5 . PM 2.5 also had positive correlation with SO 2 (r = 0.137), and positive linear correlation with NO 2 (r = 0.673) and CO (r = 0.757). Also, PM 10 and PM 2.5 maintained positive linear correlation.

Blue, red, and while indicate positive, negative, and no correlation respectively.

**. Correlation is significant at the 0.01 level (2-tailed).

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

4.4 Association of criteria pollutants with respiratory diseases, Delhi

Multi-pollutant and single pollutant GAM models were formed for Delhi to understand the impact of air pollutants on hospital visits due to respiratory diseases. Multi pollutant models indicate combined effects of the involved pollutants on the hospital visits, whereas single pollutant GAM models cast light on the sole effect of pollutants. The models were tested with different lag concentrations to comprehensively understand the impact of short-term exposure of pollutants on hospital visit counts due to respiratory diseases.

4.4.1. Association of criteria pollutants with respiratory diseases in Delhi (multi-pollutant models)

In the multi-pollutant model, criteria pollutants for 2016–18 were included in the base GAM model. Table 4 and Fig 6 indicate the relative risks (RR) of hospital visits due to a rise of 1 unit increase in CO and 10 units for all other pollutant concentrations for different single lag days. The RR patterns in Table 4 indicate synergistic effects of criteria pollutants on respiratory diseases related hospital visits in the city. Table 4 reveals that both PM 2.5 and PM 10 concentrations of all the 6 single lag days had no significant effect on respiratory disease-related hospital visits. The effect of NO 2 on hospital visits was there during lag1 day concentrations only but without any positive acceleration. The effect of SO 2 on respiratory diseases-related hospital visits was found to be robust instantaneously, i.e., the increase of every 10 ppb SO 2 on the same day (lag 0) resulted in a 32.6% (RR: 1.326, 95% CI: 1.089, 1.614) rise in hospital visits. The effect of SO 2 on hospital visits persisted throughout the lag days from lag0 up lag4. The increase in CO on hospital visits throughout the different lag days (lag0 to lag6) was found to be non-significant for respiratory diseases.

* Figs. in the brackets indicates PC (% change in hospital visits)

Note: p < 0.05, p < 0.01, and p < 0.001 considered significant

Table 5 and Fig 6 below indicate the relative risks (RR) pattern of change in hospital visits due to a rise of 1 unit increase in CO and 10 units for all other pollutant concentrations for different cumulative concentrations of pollutants. Both for PM 2.5 and PM 10 , in terms of cumulative days effect of air pollution, no significant effect could be found. NO 2 and CO were also not significantly responsible for enhancing respiratory diseases in the city. However, per 10 ppb rise in cumulative lag days, concentrations of SO 2 led to a comparatively more robust effect on respiratory diseases than single-day lag effects. At lag0-1 per 10 ppb, rise in concentrations of SO 2 was associated with the percentage change in hospital visits of 37.21% (RR: 1.372, 95% CI: 1.107, 1.701), which increased to 83.34% (RR: 1.833, 95% CI: 1.351, 2.489) during the lag0-6 day. The result indicates the robust effect of pollutants SO 2 on respiratory disease-related hospital visits in Delhi.

Note: p < 0.05, p < 0.01, and p < 0.001 considered significant.

Figs ​ Figs7 7 and ​ and8 8 below, drawn with the "mgcViz" R software package (Fasiolo et al., [ 43 ], provide the visual representation of the smoothing applied to the non-parametric terms and performance of the GAM model at lag0 respectively.

4.4.2. Association of criteria pollutants with respiratory diseases in Delhi (Single-pollutant models)

Two single-pollutant models were developed with pollutants PM 2.5 and PM 10, respectively, to understand the sole effect of PM pollution on respiratory diseases. We fitted different single lag days and cumulative lag days to express the association of daily hospital visits for respiratory diseases with a 10μg m -3 increase in PM 10 or PM 2.5 in Delhi. Both PM 2.5 and PM 10 did not show any significant association with the number of respiratory disease-related hospital visits in Delhi for all the single lag days considered here, as revealed by the p values ( Table 6 and Fig 9 ). In other words, the association of PM 2.5 and PM 10 with the respiratory disease was negligible as RR was found to be less than the baseline (RR<1).

*Note: p < 0.05, p < 0.01, and p < 0.001 considered significant

However, in cumulative exposure single-pollutant models, PM 10 was found to have persistently enhanced hospital visits of patients with the respiratory disease excepting lag 0–2 days, as shown in Table 6 . Table 6 shows that per 10 units increase in concentrations of PM 10 brought the highest increase in hospital visits of 0.21% (RR: 1.002, 95% CI: 1.001, 1.002) at lag0-6 days. PM 2.5 association with respiratory disease-related hospital visits found to be non-significant during all the cumulative lag days considered.

5. Conclusion and discussion

The study investigated first the level of air pollution in Delhi and then assessed the impact of air pollution on respiratory diseases. The result suggests that Delhi has been struggling to cope up with the increasing nature of criteria pollutants in the first place. A total of 22,253 patients visited the Delhi hospital either for outpatient consultation or admission for respiratory diseases for 2016–2018. The study found that the mean value of PM 2.5 and PM 10 concentrations for the period 2016–2018 were 107.32±71.06 μg m -3 and 210.61±95.90 μg m -3 for Delhi, respectively, which were substantially higher than the NAAQS and WHO standards. Out of the five seasons in Delhi, the winter season is hugely dominated by PM 2.5 and PM 10 pollution, as revealed by frequency analyses. Initial time series analysis revealed that PM 2.5 maintained a positive correlation with PM 10 have while PM 2.5 , PM 10 , and CO maintained a positive correlation with hospital visits during 2016–18 in Delhi. Pearson correlation analysis confirmed that PM 10 in Delhi had almost positive linear correlations with NO 2 and CO while PM 10 maintained a strong positive correlation with PM 2.5 . Interestingly, SO 2 too maintained a significant positive correlation with PM 2.5 , PM 10 , NO 2 , and CO. Previous studies in the Indian city of Mumbai highlighted the strong positive correlation of PM 2.5 with NO 2 and referred to them as a dummy indicator of air pollution due to transport-related emissions in the city [ 44 ]. In the same line, significant positive correlations between PM concentrations and gaseous pollutants, shown by air pollution data, point towards transport-related pollution, solvent evaporation, and waste disposal as sources [ 45 , 46 ].

This study shows PM 10 to have persistent enhancing effects on the number of hospital visits with the respiratory disease during all the cumulative lag days excepting lag 0–2 days. Luong et al. [ 47 ] reported PM 10 and respiratory disease-related hospital admission in polluted Hanoi city of Vietnam. Past studies confirmed the role of PM in inducing oxidative stress in the human respiratory system [ 48 ]. PM 10 impact on respiratory diseases in Delhi may be aggravated due to the road dust fraction of PM 10 that has significant oxidative potential [ 49 ]. It was interesting to note that in multi-pollutant models, the role of PM 10 causing respiratory diseases got subdued due to the combined presence of other pollutants in Delhi city.

This study found that short-term exposure to SO 2 and PM 10 led to increased hospital visits of the city dwellers due to respiratory diseases under (ICD-10) J00-J99. The present study reports the mean SO 2 in ambient air for three years (2016–18) as 14.65 ppb or 38.25 μg m -3 . SO 2 is a very critical gaseous pollutant connected with public health [ 50 ]. Past studies reported that an ordinary person could withstand only 2.62 μg m -3 of SO 2 in the ambient air without any respiratory problem [ 51 ]. However, short but higher concentration exposure to SO 2 gas can cause persistent pulmonary problems [ 52 ]. Orellano et al. [ 53 ], in a more recent and extensive review and metadata analysis, confirmed that short-term exposure to SO 2 , varying from few hours to days, can lead to an increased risk of respiratory morbidity/mortality. Our findings agree with that and found a robust effect of SO 2 on respiratory diseases hospital visits in Delhi. This study shows the robust effect of SO 2 persisted in Delhi throughout the single lag days (from lag0 up lag4) and had an instantaneous (same day, lag 0) increase of 32.6% (RR: 1.326, 95% CI: 1.089, 1.614) of hospital visits. The cumulative concentrations of SO 2 were more robust than the single lag day concentration in Delhi. While every 10 μg m -3 SO 2 concentrations on the same day (lag0) showing 32.59% (RR: 1.326, 95% CI: 1.089, 1.614) rise of hospital visits, the cumulative concentration on the day and its previous day (lag0-1) showing 37.21% (RR: 1.372, 95% CI: 1.107, 1.701) rise in hospital visits which further increased to even 83.33% (RR: 1.833, 95% CI: 1.351, 2.489) rise at a lag0-6 cumulative concentration of the pollutant in Delhi. Ren et al. [ 54 ], using the GAM model, confirmed the SO 2 effect on respiratory diseases in the fast-industrializing Chinese city of Wuhan and found that a 10 μg m -3 rise in SO 2 concentrations led to a rise of RR for respiratory disease mortality by 1.9% at lag0 day or same day. More recently, another two highly industrializing cities of Zhoushan and Hangzhou of China with the comparatively lesser presence of average SO 2 of 6.12 μg m -3 and 17.25 μg m -3 in ambient air, respectively, confirmed the active role of SO 2 in enhancing hospital visits of the patient for respiratory diseases [ 55 ]. Phosri et al. [ 56 ] also reported the effect of SO 2 for hospital admissions for respiratory diseases in industrializing Bangkok city of Thailand.

Recent COVID-19 and air pollution studies in Delhi indicated that even during the rigorous ’lockdown’ period, there was only a marginal decrease of mean SO 2 in the ambient air than in the regular times [ 33 , 57 ]. Therefore, it proves that a significant portion of ambient SO 2 in Delhi is likely to be from non-local origins like distant transfer, fossil fuel-fired thermal power plants in the bordering areas of Delhi, and biomass burning in the neighboring states. India’s recognition as the largest anthropogenic SO 2 emitter replacing China in recent times will be much more worrisome in the context of this study’s findings [ 58 , 59 ].

Suneja et al. [ 60 ], through an experimental study in Delhi, reported the seven-year (2011–2018) mean value of SO 2 level was 2.26 ppb, while this study found a much higher three-year average (2016–18) of 14.65 ppb, indicating the rise of SO concentrations in Delhi in the more recent years. The association of respiratory diseases with PM 10 and SO 2 was found stable in different lag days analyses, indicating the problem’s depth for the city dwellers. The robust and instantaneous nature of the relationship between SO 2 and respiratory morbidity indicated in this study and evidence of similar relationships found in the previous studies highlight the necessity of taking policy-level measures to reduce SO 2 in the ambient air. Limited GAM model application in Indian cities to link air pollution and health effects is not a limitation of the present study findings but rather a call for more sponsored research in the area.

Supporting information

Acknowledgments.

The authors thank the Central Pollution Control Board and the Indian Meteorological Department of Delhi city for providing air pollution and meteorological information, respectively.

Funding Statement

This study was supported by the Graduate School Thesis Grant GCUGR1225632064D, Chulalongkorn University, Bangkok, Thailand. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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5 Biggest Environmental Issues in India

In its latest climate assessment, the Intergovernmental Panel on Climate Change (IPCC) warned that it is “now or never” to limit global warming to 1.5C. The consequences of global warming are felt everywhere in the world. However, some nations suffer more than others. In this article, part of our ‘ Environmental Issues ‘ series, we look art some of the biggest environmental issues in India right now and how the country is dealing with them.

1. Air Pollution

Undoubtedly, one of the most pressing environmental issues in India is air pollution. According to the 2021 World Air Quality Report, India is home to 63 of the 100 most polluted cities, with New Delhi named the capital with the worst air quality in the world. The study also found that PM2.5 concentrations – tiny particles in the air that are 2.5 micrometres or smaller in length – in 48% of the country’s cities are more than 10 times higher than the 2021 WHO air quality guideline level. 

Vehicular emissions, industrial waste, smoke from cooking, the construction sector, crop burning, and power generation are among the biggest sources of air pollution in India. The country’s dependence on coal, oil, and gas due to rampant electrification makes it the world’s third-largest polluter , contributing over 2.65 billion metric tonnes of carbon to the atmosphere every year.  

The months-long lockdown imposed by the government in March 2020 to curb the spread of Covid-19 led to a halt in human activities. This unsurprisingly, significantly improved air quality across the country. When comparing the Air Quality Index (AQI) data for 2019 and 2020, the daily average AQI in March-April 2019 was 656, the number drastically dropped by more than half to 306 in the same months of 2020.  

More on the topic: India’s Coal Dilemma Amid Record-Breaking Heatwave

Unfortunately, things did not last long. In 2021, India was among the world’s most polluted countries, second only to Bangladesh. The annual average PM2.5 levels in India was about 58.1 µg/m³ in 2021, “ending a three-year trend of improving air quality” and a clear sign that the country has returned to pre-pandemic levels. Scientists have linked persistent exposure to PM2.5 to many long-term health issues including heart and lung disease, as well as 7 million premature deaths each year. In November 2021, air pollution reached such severe levels that they were forced to shut down several large power plants around Delhi. 

In recent years, the State Government of the Indian capital has taken some stringent measures to keep a check on air pollution. An example is the Odd-Even Regulation – a traffic rationing measure under which only private vehicles with registration numbers ending with an odd digit will be allowed on roads on odd dates and those with an even digit on even dates. Starting from January 2023, there will also be a ban on the use of coal as fuel in industrial and domestic units in the National Capital Region (NRC). However, the ban will not apply to thermal power plants, incidentally the largest consumers of coal. Regardless of the measures taken to curb air pollution, as the World Air Quality Report clearly shows – the AQI in India continues to be on a dangerous trajectory.

More on the topic: 15 Most Polluted Cities in the World

2. Water Pollution

Among the most pressing environmental issues in India is also water pollution. The Asian country has experienced unprecedented urban expansion and economic growth in recent years. This, however, comes with huge environmental costs. Besides its air, the country’s waterways have become extremely polluted, with around 70% of surface water estimated to be unfit for consumption. Illegal dumping of raw sewage, silt, and garbage into rivers and lakes severely contaminated India’s waters. The near-total absence of pipe planning and an inadequate waste management system are only exacerbating the situation. Every day, a staggering 40 million litres of wastewater enter rivers and other water bodies. Of these, only a tiny fraction is adequately treated due to a lack of adequate infrastructure.

In middle-income countries like India, water pollution can account for the loss of up to half of GDP growth, a World Bank report suggests. Water pollution costs the Indian government between US$6.7 and $7.7 billion a year and is associated with a 9% drop in agricultural revenues as well as a 16% decrease in downstream agricultural yields.

Besides affecting humans, with nearly 40 million Indians suffering from waterborne diseases like typhoid, cholera, and hepatitis and nearly 400,000 fatalities each year, water pollution also damages crops, as infectious bacteria and diseases in the water used for irrigation prevent them from growing. Inevitably, freshwater biodiversity is also severely damaged. The country’s rivers and lakes often become open sewers for residential and industrial waste. Especially the latter – which comprises a wide range of toxic substances like pesticides and herbicides, oil products, and heavy metals – can kill aquatic organisms by altering their environment and making it extremely difficult for them to survive.

Fortunately, the country has started addressing the issue by taking steps to improve its water source quality, often with local startups’ help. One strategy involves the construction of water treatment plants that rely on techniques such as flocculation, skimming, and filtration to remove the most toxic chemicals from the water. The upgrade process at one of the country’s largest plants located in Panjrapur, Maharashtra, will enable it to produce more than 19 million cubic metres of water a day , enough to provide access to clean water to approximately 96 million people. 

The government is also looking at ways to promote water conservation and industrial water reuse by opening several treatment plants across the country. In Chennai, a city in Eastern India, water reclamation rose from 36,000 to 80,000 cubic metres between 2016 and 2019. 

Finally, in 2019, Gujarat – a state of more than 70 million citizens – launched its Reuse of Treated Waste Water Policy , which aims to drastically decrease consumption from the Narmada River. The project foresees the installation of 161 sewage treatment plants all across the state that will supply the industrial and construction sectors with treated water.

3. Food and Water Shortages

According to the Intergovernmental Panel on Climate Change (IPCC), India is the country expected to pay the highest price for the impacts of the climate crisis. Aside from extreme weather events such as flash floods and widespread wildfires, the country often experiences long heatwaves and droughts that dry up its water sources and compromise crops. 

Since March 2022 – which was the hottest and driest month recorded in 120 years – the North West regions have been dealing with a prolonged wave of scorching and record-breaking heat . For several consecutive days, residents were hit by temperatures surpassing 40 degrees Celsius, while in some areas, surface land temperatures reached up to 60C. There is no doubt among experts that this unprecedented heatwave is a direct manifestation of climate change .

The heatwave has also contributed to an economic slowdown due to a loss of productivity, as thousands of Indians are unable to work in the extreme heat. The agriculture sector – which employs over 60% of the population – is often hit hard by these erratic droughts, impacting food stability and sustenance. Currently, farmers are struggling to rescue what remains of the country’s wheat crops, piling on existing fears of a global shortage sparked by the war in Ukraine.

More on the topic: Water Scarcity in India

Already among the world’s most water-stressed countries , the heatwave is causing further water shortages across the nations. Even though water tankers are keeping communities hydrated, the supply is not enough to cover the needs of all residents. But heat is not the only factor contributing to water scarcity. In an interview with the Times of India , lead researcher at Pune-based Watershed Organisation Trust Eshwer Kale described the national water policy as very ‘irrigation-centric’. Indeed, over 85% of India’s freshwater is used in agriculture. This has led to a crisis in several states, including Punjab, Haryana, and western Uttar Pradesh. The indiscriminate use of water for irrigation, coupled with the absence of conservation efforts and the huge policy gap in managing water resources has left over 10% of the country’s water bodies in rural areas redundant. A 2019 report predicts that 21 major cities – including New Delhi and India’s IT hub of Bengaluru – will run out of groundwater by 2030, affecting nearly 40% of the population. 

4. Waste Management

Among the most pressing environmental issues in India is also waste. As the second-largest population in the world of nearly 1.4 billion people, it comes as no surprise that 277 million tonnes of municipal solid waste (MSW) are produced there every year. Experts estimate that by 2030, MSW is likely to reach 387.8 million tonnes and will more than double the current value by 2050. India’s rapid urbanisation makes waste management extremely challenging. Currently, about 5% of the total collected waste is recycled, 18% is composted, and the remaining is dumped at landfill sites .

The plastic crisis in India is one of the worst on the planet. According to the Central Pollution Control Board (CPCB), India currently produces more than 25,000 tonnes of plastic waste every day on average, which accounts for almost 6% of the total solid waste generated in the country. India stands second among the top 20 countries having a high proportion of riverine plastic emissions nationally as well as globally. Indus, Brahmaputra, and Ganges rivers are known as the ‘highways of plastic flows’ as they carry and drain most of the plastic debris in the country. Together with the 10 other topmost polluted rivers, they leak nearly 90% of plastics into the sea globally. 

To tackle this issue, in 2020 the government announced that they would ban the manufacture, sale, distribution, and use of single-use plastics from July 1 2022 onwards . Furthermore, around 100 Indian cities are set to be developed as smart cities . Despite being still in its early phase, the project sees civic bodies completely redrawing the long-term vision in solid waste management, with smart technologies but also awareness campaigns to encourage community participation in building the foundation of new collection and disposal systems. 

You might also like: 14 Biggest Environmental Problems of 2024

5. Biodiversity Loss

Last but not least on the list of environmental issues in India is biodiversity loss. The country has four major biodiversity hotspots , regions with significant levels of animal and plant species that are threatened by human habitation: the Himalayas, the Western Ghats, the Sundaland (including the Nicobar Islands), and the Indo-Burma region. India has already lost almost 90% of the area under the four hotspots, according to a 2021 report issued by the Centre for Science and Environment (CSE), with the latter region being by far the worst affected.

Moreover, 1,212 animal species in India are currently monitored by the International Union for Conservation of Nature (IUCN) Red List, with over 12% being classified as ‘endangered’ . Within these hotspots, 25 species have become extinct in recent years.

Due to water contamination, 16% of India’s freshwater fish, molluscs, dragonflies, damselflies, and aquatic plants are threatened with extinction and, according to the WWF and the Zoological Society of London (ZSL) , freshwater biodiversity in the country has experienced an 84% decline. 

Yet, there is more to it. Forest loss is another major driver of biodiversity decline in the country. Since the start of this century, India has lost 19% of its total tree cover . While 2.8% of forests were cut down from deforestation, much of the loss have been a consequence of wildfires, which affected more than 18,000 square kilometres of forest per year – more than twice the annual average of deforestation. 

Forest restoration may be key to India’s ambitious climate goals, but some argue that the country is not doing enough to stop the destruction of this incredibly crucial resource. Indeed, despite committing to create an additional carbon sink of 2.5-3 billion tonnes of CO2 equivalent through additional forest and tree cover by 2030, Narendra Modi’s government faced backlash after refusing to sign the COP26 pledge to stop deforestation and agreeing to cut methane gas emissions. The decision was justified by citing concerns over the potential impact that the deal would have on local trade, the country’s extensive farm sector, and the role of livestock in the rural economy. However, given these activities’ dramatic consequences on biodiversity, committing to end and reverse deforestation should be a priority for India.

This article was originally published on June 17, 2022

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Environmental Pollution and Control: A Case Study of Delhi Mega City

• Population and Environment 25(5):461-473
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Plastic Waste Management: A Case Study From Dehradun, India

To enhance the Plastic Waste Management at Dehradun, India, Earth5R , an Environmental Organization based in India initiated a project called ‘ Know Your Plastics ’. The project aims at raising awareness about plastic waste and also aspires to increase recycling rates of products.

Clean-Up And Classification Of Plastic Waste 

As part of the project, volunteers visited 10 locations in their neighborhood to collect the maximum amount of plastic waste possible. A time limit was dedicated to segregating waste into six different categories:   MLP(multi-layer packaging), PET( Polyethylene terephthalate) plastics, LDPE(Low Density Polyethylene), HDPE(High Density Polyethylene), Tetra packs and Synthetic fibers. Any other kind of waste that was found was included in the ‘other’ category.

After the waste was segregated, the data put together is analyzed to figure out what category contributes to most of the pollution. It also assists in finding out which companies are generating most of the plastic waste.

Waste Data Utilised For Research Work And Creating Awareness 

As an effort to bring into perspective the ongoing issue of plastic waste and how it hinders the implementation of sustainable development goals and environmental growth, some data has been represented below:

• The Changing Markets  Foundation stated that as of 2020, Coca-Cola was the largest plastic footprint on earth with 2.9 million metric tonnes of plastic packaging produced annually. While Pepsico, came second with 2.3 millon metric tonnes of plastic waste.
• A Central Pollution Control Board ( CPCB ) report from 2018-19 puts the total annual plastic waste generation in India at a humongous 3.3 million metric tonnes per year.
• More than half of the plastic waste (approx.60%) goes in for recycling whereas the rest of it goes unpicked in the natural environment.
• The current situation of the COVID-19 pandemic has aggravated the issue with large amounts of plastic and medical waste being disposed of carelessly.
• As per data from 2019, metropolitan cities like Chennai, Bengaluru and Delhi contribute to more than 50% of the plastic waste deposition.

Recklessly increasing dependency on plastics simply because of their durability is choking our waterways and is becoming an immeasurable threat to the terrestrial as well as the aquatic ecosystem!

Predictions  say that the amount of plastic waste in the environment will only keep increasing if no strict action is taken against it.

Plastic Waste Management Initiative At Dehradun, India

Arya Mitra ,  an   Earth5R volunteer   from Dehradun took the initiative to go about the Global Plastic Waste Crisis from his hometown. He conducted a sequence of 10 cleanup sessions, analysed the waste collected and provided the material.

His views on why he wanted to join the project were, “I wanted to join the ‘Know Your Plastics’ project because I wanted to understand the types of waste and how I could help in achieving  a long term goal, not only by picking up waste right now but actually encouraging the society around me to assist in accomplishing the objective of sustainable development. With the help of Earth5R, I would like to raise awareness about plastic waste not only in my city but outside the boundaries too and also do the required steps that need to be implemented in order to bring the crisis under control and gradually solve it.”

With the help of Earth5R, I would like to raise awareness about plastic waste not only in my city but outside the boundaries too and also do the required steps that need to be implemented in order to bring the crisis under control and gradually solve it-ARYA MITRA, EARTH5R VOLUNTEER @DEHRADUN, INDIA

Plastic Waste Data Collected In Dehradun

Cleanup and segregation of data was carried out in 10 different locations by Arya Mitra in his locality.

He collected and analyzed the data, the results are as follows:

• A total of 246 plastic waste items were collected.
• 150 Multi-Layer Packaging(MLP)Products constituted the highest amount of the plastic waste i.e. 60.9%.
• This was followed by 48 Low Density Polyethylene Products  (LDPE) waste which made up 19.5% of the total.
• 19 Tetra Packs were found which formed 7.7% of the total.
• 9 High Density Plastic (HDPE) Products were found forming 3.6% of the total plastic waste.
• 5 Polyethylene terephthalate (PET)Products were found which made up 2.03% of the total plastic waste.

Lack of proper waste management leads to waste being found at places which are harmful for the environment. Arya also stated, “According to my findings, most of the waste was found near school boundaries and comparatively lesser around the residential areas. I also wanted to mention that most of the plastic waste material consisted of things which are usually tabooed in the society for example: pregnancy test kits other contraceptives and packets of tobacco. Maybe people are not comfortable with disposing these off at home and so unfortunately, they happen to litter the streets outside!”

I also wanted to mention that most of the plastic waste material consisted of things which are usually tabooed in the society for example: pregnancy test kits other contraceptives and packets of tobacco. Maybe people are not comfortable with disposing these off at home and so unfortunately, they happen to litter the streets outside!– ARYA MITRA, EARTH5R VOLUNTEER

Burning Of Plastic Waste

Due to lack of management in the city, all the waste is littered on the roads and is highly hazardous for the environment. As an outcome of lack of segregation and recycling, plastic is left in the soil to decompose or to be burnt which again poses detrimental effects on the environment.

Another important point that Arya brings up is “I am positive that the rate of plastic consumption in my city is very high. People are not even responsible enough to throw their plastic waste in segregated dustbins that have been set up. Due to their careless behaviour, the entire ecosystem has to bear the consequences.” 

This behavior highlights the lack of education and awareness of the people belonging to the city. 

How To Solve The Global Plastic Waste Issue?

The responsibility of solving the Plastic Waste Crisis falls directly on the shoulders of the people. They must switch to recyclable and reusable plastics or things that do not pose a threat to the environment. 

The government must make policies or laws encouraging plastic ban, use economic incentives to stimulate manufacturers to adopt alternatives to plastic or create revenue that can fund plastic waste cleanup efforts.

As Sylvia Earle, a marine biologist says,  “It is the worst of times but it is the best of times because we still have a chance,”  we must not let go of that chance to protect our ecosystem and the environment around us. Instead, we must work together towards a brighter plastic-free future leading us on the road to sustainable development. It is all in the hands of those in power after all and as citizens of the world we must be responsible enough to give back to Mother Earth for she has granted to us the gift of life.

Reported by Arya Mitra; Edited by Krishangi Jasani

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Noise pollution assessment and management in rare earth mining areas: a case study of Kollam, Kerala, India

• Published: 05 August 2024
• Volume 196 , article number  787 , ( 2024 )

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• Sravanth Tangellamudi 1 ,
• Akhil Vikraman 1 &
• Saurabh Sakhre 1  

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Noise pollution is an unintentional consequence of mining activities, needing rigorous assessment, monitoring, and mitigation techniques to reduce its impact on local residents and ecosystems. The study specifically examines the noise pollution from rare earth mining activities in the Neendakara-Kayamkulam (NK) coastal belt, Kollam, Kerala, India, a region rich in ilmenite, rutile, sillimanite, zircon, and monazite. Despite the known environmental and health impacts of noise pollution, there is limited specific data on its magnitude and sources in this region, as well as a lack of effective mitigation strategies tailored to rare earth mining operations. Studies have indicated that mining operations, such as the movement of heavy mineral sands, considerably elevate noise levels, which have an effect on the environment’s quality and public health. This study seeks to fill the gap by geospatial mapping and assessing the noise levels and recommend measures to effectively mitigate noise pollution. Systematic noise measurements were conducted at 48 suitable locations within the NK coastal belt, including residential, commercial, industrial, coastal, and silence zones. The noise levels vary from 49.1 dB(A) near a religious place to 82.4 dB(A) near the local industry. The study employs geospatial noise mapping and land cover superimposition to implement class-specific mitigation measures for noise pollution in a coastal vicinity mixed land use area, including natural and vegetative barriers, operational scheduling, zoning, and land use planning.

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The data and materials used in this study are not publicly available due to privacy concerns. Restrictions apply to the availability of these data, and they are thus not included in the supplementary information. However, reasonable requests for collaboration or clarification can be directed to the corresponding author.

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Acknowledgements

The authors are thankful to the Director, CSIR-NIIST Thiruvananthapuram, for providing infrastructural support.

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All authors contributed to this paper in various capacities. Sravanth Tangellamudi played a key role in conceptualizing and designing the study, including draft preparation as well as analyzing the data collected. Akhil Vikraman has been involved in conducting fieldwork, including noise level measurements at different locations within the Neendakara-Kayamkulam coastal belt and interpolation part for noise mapping and analysis. Saurabh Sakhre contributed expertise in environmental impact assessment and mitigation strategies, aiding in the interpretation of results and proposing measures to reduce noise pollution in the mining region.

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Tangellamudi, S., Vikraman, A. & Sakhre, S. Noise pollution assessment and management in rare earth mining areas: a case study of Kollam, Kerala, India. Environ Monit Assess 196 , 787 (2024). https://doi.org/10.1007/s10661-024-12931-5

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DOI : https://doi.org/10.1007/s10661-024-12931-5

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Noise pollution assessment and management in rare earth mining areas: a case study of Kollam, Kerala, India

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• 1 Environmental Technology Division, CSIR - National Institute for Interdisciplinary Science and Technology, Kerala, 695019, Thiruvananthapuram, India.
• 2 Environmental Technology Division, CSIR - National Institute for Interdisciplinary Science and Technology, Kerala, 695019, Thiruvananthapuram, India. [email protected].
• PMID: 39103555
• DOI: 10.1007/s10661-024-12931-5

Noise pollution is an unintentional consequence of mining activities, needing rigorous assessment, monitoring, and mitigation techniques to reduce its impact on local residents and ecosystems. The study specifically examines the noise pollution from rare earth mining activities in the Neendakara-Kayamkulam (NK) coastal belt, Kollam, Kerala, India, a region rich in ilmenite, rutile, sillimanite, zircon, and monazite. Despite the known environmental and health impacts of noise pollution, there is limited specific data on its magnitude and sources in this region, as well as a lack of effective mitigation strategies tailored to rare earth mining operations. Studies have indicated that mining operations, such as the movement of heavy mineral sands, considerably elevate noise levels, which have an effect on the environment's quality and public health. This study seeks to fill the gap by geospatial mapping and assessing the noise levels and recommend measures to effectively mitigate noise pollution. Systematic noise measurements were conducted at 48 suitable locations within the NK coastal belt, including residential, commercial, industrial, coastal, and silence zones. The noise levels vary from 49.1 dB(A) near a religious place to 82.4 dB(A) near the local industry. The study employs geospatial noise mapping and land cover superimposition to implement class-specific mitigation measures for noise pollution in a coastal vicinity mixed land use area, including natural and vegetative barriers, operational scheduling, zoning, and land use planning.

Keywords: Coastal environment; Land use; Noise pollution; Open-cast mining; Rare earth.

© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

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Memo to the supreme court: clean air act targeted co2 as climate pollutant, study says, the new paper digs into congressional archives to settle a legal debate, arguing that climate science had determined by 1970 that greenhouse gases would warm the planet—and that lawmakers knew..

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Baltimore Judge Tosses Climate Case, Hands Win to Big Oil

Montana’s High Court Considers a Constitutional Right to a Stable Climate

Supreme Court Overturns Chevron Doctrine: What it Means for Climate Change Policy

Among the many obstacles to enacting federal limits on climate pollution, none has been more daunting than the Supreme Court. That is where the Obama administration’s efforts to regulate power plant emissions met their demise and where the Biden administration’s attempts will no doubt land.

A forthcoming study seeks to inform how courts consider challenges to these regulations by establishing once and for all that the lawmakers who shaped the Clean Air Act in 1970 knew scientists considered carbon dioxide an air pollutant, and that these elected officials were intent on limiting its emissions.

The research, expected to be published next week in the journal Ecology Law Quarterly, delves deep into congressional archives to uncover what it calls a “wide-ranging and largely forgotten conversation between leading scientists, high-level administrators at federal agencies, members of Congress” and senior staff under Presidents Lyndon Johnson and Richard Nixon. That conversation detailed what had become the widely accepted science showing that carbon dioxide pollution from fossil fuels was accumulating in the atmosphere and would eventually warm the global climate.

Explore the latest news about what’s at stake for the climate during this election season.

The findings could have important implications in light of a legal doctrine the Supreme Court established when it struck down the Obama administration’s power plant rules, said Naomi Oreskes, a history of science professor at Harvard University and the study’s lead author. That so-called “major questions” doctrine asserted that when courts hear challenges to regulations with broad economic and political implications, they ought to consider lawmakers’ original intent and the broader context in which legislation was passed.

“The Supreme Court has implied that there’s no way that the Clean Air Act could really have been intended to apply to carbon dioxide because Congress just didn’t really know about this issue at that time,” Oreskes said. “We think that our evidence shows that that is false.”

The work began in 2013 after Oreskes arrived at Harvard, she said, when a call from a colleague prompted the question of what Congress knew about climate science in the 1960s as it was developing Clean Air Act legislation. She had already co-authored the book Merchants of Doubt, about the efforts of industry-funded scientists to cast doubt about the risks of tobacco and global warming, and was familiar with the work of scientists studying climate change in the 1950s. “What I didn’t know,” she said, “was how much they had communicated that, particularly to Congress.”

Oreskes hired a researcher to start looking and what they both found surprised her. The evidence they uncovered includes articles cataloged by the staff of the act’s chief architect, proceedings of scientific conferences attended by members of Congress and correspondence with constituents and scientific advisers to Johnson and Nixon. The material included documents pertaining not only to environmental champions but also other prominent members of Congress.

“These were people really at the center of power,” Oreskes said.

When Sen. Edmund Muskie, a Maine Democrat, introduced the Clean Air Act of 1970, he warned his colleagues that unchecked air pollution would continue to “threaten irreversible atmospheric and climatic changes.” The new research shows that his staff had collected reports establishing the science behind his statement. He and other senators had attended a 1966 conference featuring discussion of carbon dioxide as a pollutant. At that conference, Wisconsin Sen. Gaylord Nelson warned about carbon dioxide pollution from fossil fuel combustion, which he said “is believed to have drastic effects on climate.”

The paper also cites a 1969 letter to Sen. Henry “Scoop” Jackson of Washington from a constituent who had watched the poet Allen Ginsberg warning of melting polar ice caps and widespread global flooding on the Merv Griffin Show. The constituent was skeptical of the message, called Ginsberg “one of America’s premier kooks” and sought a correction of the record from the senator: “After all, quite a few million people watch this show, people of widely varying degrees of intelligence, and the possibility of this sort of charge—even from an Allen Ginsberg—being accepted even in part, is dangerous.”

Jackson then sent the letter to presidential science advisor Lee DuBridge, who responded by detailing the latest science, which showed that while there was uncertainty about the effects of increased levels of carbon dioxide, the greenhouse gas effect was real and a product of fossil fuel combustion.

“We just felt that strengthens the argument that this is not some little siloed scientific thing,” Oreskes said of the episode. “It’s not just a few geeky experts.”

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The new paper is not the first to assert that climate science was well established by the mid-1960s and that congressional leaders were aware of it. Other work, including some cited in the study, has shown that hearings on Clean Air Act amendments explicitly addressed global climate change.  

The new work adds breadth and depth to this body of evidence, said Michael Burger, executive director of the Sabin Center for Climate Change Law at Columbia Law School.

In a landmark 2007 decision, the Supreme Court held that the Clean Air Act authorizes the Environmental Protection Agency to regulate carbon dioxide as a pollutant. However, even that ruling claimed that climate science was in its “infancy” when Congress enacted the key provisions in 1970.

The ruling, known as Massachusetts v. EPA, quickly became a target for some political conservatives. Project 2025, a conservative playbook for a next Republican administration, calls for undoing the EPA “endangerment” findings the ruling enabled, though Donald Trump has sought to distance himself from Project 2025 as he campaigns for reelection. The Trump administration reportedly considered such a move in its final days but opted against it.

Michael Oppenheimer, a professor of geosciences and international affairs at Princeton University, said the new study could be “of critical importance” if the Supreme Court were to hear a challenge to Massachusetts v. EPA.

The authors published their paper in a law journal, rather than one focused on history or science, because they hope it will shape future legal challenges and court decisions, Oreskes said.

While several legal and policy experts praised the work for its additions to the history, some questioned how much influence it will have on the Supreme Court’s rightwing majority.

“Unfortunately there’s a big question mark as to how much weight the court will, in any individual instance, place on actual history as opposed to select history that supports the ultimate conclusion that they favor,” Burger said. “One of the big criticisms of the major questions doctrine and the current majority’s use of history is that it does tend to be quite selective.”

He added that the current challenge to the Biden administration’s power plant regulations does not focus on whether the EPA has authority to regulate carbon dioxide but on unrelated, technical questions about which technologies can be used to limit pollution.

What the new research should do, Burger said, is conclusively refute the notion that the EPA cannot regulate carbon dioxide.

“The argument that the Clean Air Act for some reason should not include the regulation of greenhouse gases is simply wrong,” Burger said.

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Much of what consumers buy is marked “sustainable,” “humane” or “green.” In the sugar cane fields of India, that papered over the worst abuses.

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Megha Rajagopalan reported from villages across Maharashtra, India, and from London, the home of the sugar industry’s standards body.

Bags of sugar that leave the Dalmia Bharat Sugar mill in the western Indian city of Kolhapur come with an industry guarantee: It was harvested humanely, in fields free of child labor, debt bondage and abuse.

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Even the auditor who said she inspected the Dalmia mill said turning up problems was extremely rare.

“I’ve been auditing for the last two years, and I have not found any violations,” said Swapnali Hirve, who said she also inspected a mill owned by NSL Sugars. Both mills are in the state of Maharashtra.

But women who cut sugar cane that ends up in these mills work in brutal conditions. In interviews, they told us that they were pushed into underage marriages so that they could cut sugar with their husbands. They were locked into years of debt by sugar mill contractors. Some, like thousands of other working-age women in this region, said they felt pressured to get unneeded hysterectomies to resolve common ailments like painful periods and keep working in the fields.

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