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What is polluting delhi’s air a review from 1990 to 2022.

1. Introduction

2. geography and meteorology, 3. ambient air quality, 3.1. ground measurements, 3.1.1. pre-2006 period, 3.1.2. 2006–2018 period, 3.1.3. post-2018 period, 3.2. satellite observations and reanalysis, 4. receptor, source, and other modelling studies, 4.1. source apportionment, 4.2. air quality forecasting and alert systems.

• The Copernicus Atmosphere Monitoring Service (CAMS) forecasting system is a service provided by the European Union ( https://atmosphere.copernicus.eu/data , accessed 22 February 2023) that uses a combination of mathematical models and satellite data to provide air quality forecasts at 40 km spatial resolution globally and at 10–12 km spatial resolution for select regions. It is designed to provide reliable forecasts of air quality across Europe with the use of the ESA’s geostationary satellite data. The global data can be visualised at https://www.windy.com (accessed 22 February 2023). The CAMS reanalysis archives from 1990 are also available for studying long-term trends;
• The Early Warning System (EWS) for Delhi by IITM is hosted at https://ews.tropmet.res.in (accessed 22 February 2023) and includes results from the WRF-Chem regional model and GEOS and WACCM global modelling systems as a combination of national, region, and cit- level hourly maps, time series, and comparison with data from the CPCB’s monitoring network. The system also includes the forecast of fog onset and visibility for Delhi and a summary of air quality forecasts for other cities;
• The NASA-GEOS system is operated by the Global Modelling and Assimilation Office (GMAO) to support a wide range of applications, including air, weather, and climate modelling ( https://gmao.gsfc.nasa.gov , accessed 22 February 2023). A 10-day air quality forecast for Delhi from the GEOS-5 model is included on the EWS portal. Like CAMS, the GEOS system also includes a data assimilation system (GEOS-DAS) with reanalysis archives from 1990 for studying long-term trends (like MERRA-2— https://giovanni.gsfc.nasa.gov/giovanni , accessed 22 February 2023);
• SAFAR ( https://safar.tropmet.res.in , accessed 22 February 2023) uses a combination of on-ground measurements, emission inventories, and mathematical models to predict air quality for the next three days. Since its inception for Delhi, the model has been replicated for the cities of Mumbai, Pune, and Ahmedabad;
• SILAM (System for Integrated modeLing of Atmospheric composition) is a global chemical transport model developed and maintained by the Finnish Meteorological Institute (FMI). As part of a memorandum of understanding, FMI shares air quality forecasts customised for the NCR Delhi region with the Indian Meteorological Department (IMD). These results are also included on the EWS portal;
• The Urban Emissions program (by the authors) uses the WRF-CAMx modelling system covering the Indian Subcontinent and Delhi’s airshed as a nest. The city results are shared at https://www.delhiairquality.info (accessed 22 February 2023) in the form of hourly and daily average maps, city-level hourly and daily average PM 2.5 source apportionment, district-level concentration and source apportionment time series, and real-time (updated every 6 h) comparison of results with data from CPCB’s monitoring network.

5. Sectoral History

5.1. transport sector, 5.1.1. vehicle and fuel standards, 5.1.2. pollution-under-check (puc) programme, 5.1.3. public transportation and cng introduction, 5.1.4. bus rapid transit (brt) system, 5.1.5. metrorail system, 5.1.6. para-transit system, 5.1.7. odd–even experiment, 5.1.8. electric vehicle (ev) promotion, 5.1.9. new expressways, 5.2. agricultural waste burning, 5.3. residential emissions, 5.4. waste management, 5.5. construction sector, 5.6. road dust, 5.7. electricity consumption and load sharing, 5.8. diwali firecrackers, 6. judicial and institutional engagement, 6.1. role of the judicial system, 6.1.1. environment pollution (prevention and control) authority (epca), 6.1.2. diesel to cng conversion, 6.1.3. diwali firecracker ban, 6.1.4. leapfrogging from bs4 to bs6 vehicle emission and fuel standards, 6.1.5. petcoke ban, 6.1.6. installation of smog towers, 6.1.7. national green tribunal (ngt).

• Vardhaman Kaushik vs. Union of India case [ 140 ]—the NGT ordered de-registration of all diesel vehicles older than 10 years and all petrol vehicles older than 15 years.
• Smt. Ganga Lalwani vs. Union of India and Ors. case [ 141 ]—the NGT took cognisance of crop burning as a significant cause of Delhi’s air pollution and ordered various steps to reduce crop burning in adjoining states. These include converting crop waste into organic manure, use of ISRO’s services to alter lice on crop burning incidents, etc.
• Almitra H. Patel and Ors. vs. Union of India [ 142 ]—the NGT prohibited open burning of waste and directed all states to implement the solid waste management rules. Using its authority, the NGT under the polluter pays principle, in October 2022, imposed an environment compensation fee of INR 9,000,000,000 on the Delhi government for undisposed waste in its landfills.
• Mayank Manohar and Paras Singh vs. Government of Delhi and Ors. [ 143 ]—the NGT directed the government to immediately shut down 4770 industrial units running illegally in the residential areas of Delhi and directed it to adopt coercive measures to recover compensation for illegal operation of such units in accordance with law apart from prosecution.

6.2. Role of Union Government

6.2.1. graded response action plan (grap).

• When AQI conditions land in the poor category, actions include ensuring strict enforcement of controls on garbage burning, brick kilns, power plants, ash ponds, construction sites, fireworks, and periodic wet sweeping of roads; vigilance on polluting vehicles, vehicles touting PUC norms and out of state trucks; deploying more traffic police; and posting information on social media.
• When AQI conditions land in the very poor category, actions include banning diesel generator sets, increasing parking fees, increasing bus services, stopping coal and wood burning at hotels, opening eateries and stationing guards at markets in residential areas, and increasing public awareness.
• When AQI conditions land in the severe category, actions include shutting down brick kilns, hot-mix plants, stone crushers, and power plants, intensifying public transport services, and wet-sweeping roads more frequently.
• Under emergency conditions, actions include closing entry of non-commodity trucks, closing all construction activities, introducing the odd–even formula, and additional measures as the authority sees fit (for example, in January 2023, all coal use was banned in the NCR region).

6.2.2. National Clean Air Programme (NCAP)

6.2.3. commission for air quality management (caqm), 6.2.4. fifteenth finance commission grant (xvfc), 7. final remarks, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Guttikunda, S.K.; Dammalapati, S.K.; Pradhan, G.; Krishna, B.; Jethva, H.T.; Jawahar, P. What Is Polluting Delhi’s Air? A Review from 1990 to 2022. Sustainability 2023 , 15 , 4209. https://doi.org/10.3390/su15054209

Guttikunda SK, Dammalapati SK, Pradhan G, Krishna B, Jethva HT, Jawahar P. What Is Polluting Delhi’s Air? A Review from 1990 to 2022. Sustainability . 2023; 15(5):4209. https://doi.org/10.3390/su15054209

Guttikunda, Sarath K., Sai Krishna Dammalapati, Gautam Pradhan, Bhargav Krishna, Hiren T. Jethva, and Puja Jawahar. 2023. "What Is Polluting Delhi’s Air? A Review from 1990 to 2022" Sustainability 15, no. 5: 4209. https://doi.org/10.3390/su15054209

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Delhi, the world’s most air polluted capital fights back

Subscribe to global connection, vinod thomas and vinod thomas distinguished fellow - asian institute of management, manila, former senior vice president - world bank chitranjali tiwari ct chitranjali tiwari associate fellow - jk lakshmipat university, jaipur.

November 25, 2020

After an unexpected respite as coronavirus lockdowns stalled economic activity, air  pollution  has returned to  pre-COVID-19 levels in Delhi, the world’s most air polluted capital city  (Figure 1).

Last month, ahead of the usual spike in winter, the Delhi administration launched an antipollution campaign. But to win, nothing short of sustained action on  multiple fronts  will suffice. Other Asian capitals too have faced pollution crises. But Delhi’s is extreme because of a combination of smoke from thermal plants and brick kilns in the capital region, effluents from a congested transportation network, stubble or biomass burning by farmers in neighboring states, and the lack of cleansing winds that causes air pollution to hang over the city. Even as technical solutions are within reach, the campaign must overcome the poor policy coordination among central, city, and local governments.

Delhi’s toxic haze is a deadly health risk to its residents, particularly children, the elderly, and the ill. Particulate matter—PM2.5 and PM10—far exceeds national and World Health Organization limits and is the  main culprit  for Delhi’s high incidence of cardiovascular damage. The city’s toxic air also contains high quantities of sulfur dioxide, nitrogen oxide, and carbon monoxide, putting people at  higher risk  of strokes, heart attacks, and high blood pressure, and worsening the respiratory complications from  COVID-19.

The main sources of Delhi’s particulate emissions are, in equal measure, particles from large power plants and refineries, vehicles, and stubble burning. The experiences of Bangkok, Beijing, and Singapore suggest that an ambitious but feasible goal is to cut air pollution by one-third by 2025, which, if sustained, could extend people’s  lives  by two to three years. The current effort is designed to confront all three sources, but strong implementation is needed.

Delhi is moving simultaneously on three fronts: energy, transport, and agriculture. In each case, East Asia offers valuable lessons.

• Coal-fired plants. Delhi’s environment minister has called for the closure of  11 coal-fired power plants operating within 300 kilometers of Delhi. But policy implementation must improve: All the plants have missed two deadlines to install flue-gas desulfurization units to reduce sulfur dioxide emissions. Last year,  10 coal-fired  power plants missed a December deadline to install pollution control devices.  Beijing provides valuable lessons in cutting concentrations of PM2.5 more than  40 percent  since 2013. Beijing substituted its four major coal-fired stations with natural gas plants. The city government ordered  1,200 factories  to shut with stricter controls and inspections of emitters.  Bangkok  had success with its inspection and maintenance program.
• Cleaner transport . Delhi has tried  pollution checking of vehicles by mobile enforcement teams, public awareness  campaigns , investment in mass rapid transport systems, and phasing out old commercial vehicles. The Delhi government’s recent  push  for electric vehicles shows promise, while the response of industry and the buy-in from customers will be key. Overall results in cutting pollution have been weak because of poor governance at every level. Better outcomes will be predicated on investment in public transportation, including integration of transport modes and last-mile connectivity. Unfortunately, Delhi Transport Corporation’s fleet  shrank  from 6,204 buses in 2013 to 3,796 buses in 2019, with most of the bus fleet aging. Delhi should look at  Singapore’s  regulation on car ownership and use; its improved transit systems; and promotion of pedestrian traffic and nonmotorized transport.
• Better farming practices . Burning of crop stubble in Delhi’s neighboring states has become a serious source of  pollution in the past decade. In 2019, India’s Supreme Court ordered a complete halt to the practice of stubble burning and reprimanded authorities in two of these states, Punjab and Haryana, for allowing this illegal practice to continue. Needed is the  political will to act , as poor farmers complain that they receive no financial support to dispose of post-harvest stubble properly. Delhi’s  “Green War Room”  signaling the fight against the smog, is analyzing satellite data on farm fires from Punjab and Haryana to identify and deal with the culprits. The  Indian Agricultural Research Institute  has proposed a low-cost way to deal with the problem of stubble burning by spraying a chemical solution to decompose the crop residue and turn it into manure. Better coordination is needed. In 2013, when  Singapore  faced a record-breaking haze due to agricultural waste burning in neighboring countries, the Environment Agency and ministries of education and manpower together issued guidelines based on a Pollution Standards Index to minimize the health impacts of haze.  Stubble burning  has been banned or discouraged in China, the United Kingdom, and Australia.

Delhi, projected to be the world’s most  populous  city by 2030, is motivated by a sense of urgency. Facing a growing environmental and health calamity, antipollution efforts are being strengthened. But to succeed, the different levels of government must harness the political will to invest more, coordinate across boundaries, and motivate businesses and residents to do their bit.

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Suggested citation: Kurinji, L. S,  Adeel Khan , and  Tanushree Ganguly . 2021.  Bending Delhi’s Pollution Curve: Learnings from 2020 to Improve 2021 . New Delhi: Council on Energy, Environment and Water.

The study intends to support the Delhi government, the Central Pollution Control Board (CPCB), and the Delhi Pollution Control Committee (DPCC) in identifying priority areas of interventions needed in controlling air pollution in Delhi during the winter season of 2021. It establishes the primary drivers of Delhi's winter air pollution during the different phases of the season. It considers meteorological parameters, source activity levels, and its contributions to investigate how winter 2020 was, if at all, different in the pandemic circumstances compared to the previous years.

Key Highlights

• Delhi residents were exposed to air that does not meet the National Ambient Air Quality Standards (60 µg/m3) for more than half of 2020 despite low economic activity levels for close to eight months (March to November) due to the pandemic-induced lockdown.
• Delhi’s air quality in winter 2020 was worse than winter 2019 with 92 severe and very poor air quality days in the winter of 2020 compared to 80 such days in 2019.
• Relative contribution from farm fires was the highest (~30 per cent) between 15 October and 15 November 2020. In the following months, the contribution from household emissions (including domestic cooking, space heating, water heating, and lighting) primarily drove poor air quality in Delhi.  

Source: Author’s analysis

• Calmer winds in October and November amplified the impact of farm fires on Delhi’s air quality. The stubble burning phase (15 October to 15 November) in 2020 experienced 172 hours (70 per cent higher) of calm and light winds (<5 km/h) compared to 101 hours in 2019.
• Activity levels were low at the start of the winter. But most activities, including vehicular traffic and power generation, bounced back to the previous year’s levels (proxied by indicators such as congestion and electricity generation levels) as the season progressed.
• Adverse meteorological conditions in Delhi intensified the impact of local and regional emissions on Delhi’s air quality. While meteorological conditions cannot be controlled, sustained air quality gains can be realised only by steeper emission cuts across sectors.

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Executive Summary

Delhi is among the most polluted cities in the world (IQ Air 2021). In 2019 alone, air pollution caused over 17,000 premature deaths and an economic loss of 1,207 million USD in the national capital (Pandey et al. 2021). With regards to air quality, 2020 was an aberration. The pandemic-induced lockdown measures provided temporary respite from the year-round poor air quality. 

Despite the lockdown, Delhi’s annual average PM 2.5 in 2020 was more than 2-times its permissible limit

Barring a few days in April and May, Delhi experienced National Ambient Air Quality Standard (NAAQS)1 compliant air quality on most days during the lockdown. Yet, the annual average PM 2.5 concentration in 2020 was 93 µg/m3, which is more than twice the permissible limit for PM 2.5 in India. Despite low activity levels for close to eight months (March to November) in 2020, Delhi residents were exposed to NAAQS non-compliant air for more than half of the year (Figure ES1).

Winters saw poor quality despite proactive measures by the government

While the NAAQS non-compliant air quality in Delhi is not a new phenomenon, the winter of 2020 witnessed proactive measures from the State Government in the wake of COVID and evidence pointing at the association between high air pollution and COVID mortality (Petroni et al. 2020; Cole, Ozgen, and Strobl 2020; Wu et al. 2020). This includes the Yuddh Pradushan Ke Virudh (war against pollution) campaign and a seven-point action plan to combat air pollution in Delhi which listed measures ranging from combating dust and mitigating hotspots to a mobile application called Green Delhi for complaints and a ‘war room’ for monitoring air pollution control activities (PTI 2020a). Similar to 2019, the Graded Response Action Plan (GRAP), also came into force on 15th October 2020 and the Environmental Pollution Control Authority (EPCA)2  oversaw its implementation until the announcement of its dissolution on 28th October 2020 (Koshy 2020; EPCA 2020). However, despite these measures, the PM 2.5 levels remained almost three times higher than the NAAQS on an average between October 2020 and January 2021. We also observe that PM 2.5 levels in winter 2020 were higher than those in 2019. To explain this end, we analyse meteorological parameters, source activity levels, and contributions to establish primary drivers of pollution during different phases of the winter season. Through this brief, we intend to help the Delhi government, the Central Pollution Control Board (CPCB) and the Delhi Pollution Control Committee (DPCC) to identify priority areas of intervention for the year 2021. We summarise key highlights as follows

Air quality in winter 2020 was worse than winter 2019

Delhi observed 92 severe and very poor air quality days in the winter of 2020 compared to 80 such days in 2019. Compared to an average PM 2.5 concentration of 161 μg/m3  in 2019, between October and November 2020, this value was 172 μg/m3. It further shot up to an average level of 192 μg/m3  in the period between December 2020 to January 2021 compared to 178 μg/m3 during the same period previous year.

Contributions from stubble burning and household emissions from cooking and space heating were significant fractions of the pollution pie

Modelled source contribution estimates of particulate matter (PM 2.5 ) by UrbanEmissions suggest that relative contribution from farm fires was the highest (~30 per cent) in the period between 15 October and 15 November 2020 (Figure ES2). We find that compared to the stubble burning period in 2019, a longer harvesting season in 2020 led to a significant increase in the number of fires. In the following months, contribution from household emissions (including domestic cooking, space heating, water heating, and lighting) primarily drove poor air quality in Delhi. It is worth highlighting that these values are modelled estimates and are subject to the sector-specific assumptions used in the model.

Calmer winds in October and November amplified the impact of farm fires on Delhi’s air quality

The stubble burning phase (15 October to 15 November) in 2020 experienced 172 hours (70 per cent higher) of calm and light winds (<5 km/h) compared to 101 hours in 2019. Winds predominantly from the north-west direction facilitated the transport of smoke emanating from farm fires and calm winds in Delhi further intensified its adverse impact on air quality. Interestingly, for brief periods in the season, even when high fire counts were reported in Punjab and Haryana, Delhi’s air quality was not affected due to favourable meteorological conditions (Easterly and southerly winds) (Figure ES3). Unfavourable meteorological conditions include low wind speeds and shallow mixing height height3 .

Lowered activity levels at the start of winter due to lockdown bounced back to the previous year’s levels as the winter progressed

While Delhi’s average PM 2.5 concentration during the stubble burning period (October’20 and November’20) was 172 µg/m3, it increased to 192 µg/m3 during peak winter (December’20 and January’21). The higher PM 2.5 levels in December 2020 and January 2021 were primarily caused by locally emitted pollutants and added burden of household emissions from space heating. Activity levels were low at the start of the season, but most activities, including vehicular traffic and power generation, bounced back to the previous year’s levels (proxied by indicators such as congestion and electricity generation levels in Figures ES4 and ES5) as the season progressed. 

Delhi needs a dedicated air quality forecasting cell to facilitate roll out of preventive measures

Delhi needs a dedicated air quality forecasting cell to facilitate the rollout of preventive measures.

“I see skies so blue and clouds so white … What a wonderful world,” wrote Bob Thiele and George David Weiss in 1967. I t took a pandemic and complete shutdown of activities for blue skies and white clouds to appear in Delhi. This respite from year-round air pollution was short-lived and the gains from cessation of activities were lost with the unlock of economic activities and the arrival of winter in Delhi. Despite the reduced activity levels for close to eight months (March to November) in 2020, Delhi residents were exposed to NAAQS non-compliant air for more than half of the year. Our analysis compared the anthropogenic activity levels and meteorological conditions in 2020 with those in 2019. We also explain how these factors influenced air quality in the winter of 2020. We find that air quality in the winter of 2020 was worse than in the winter of 2019. Lower vehicular congestion and power generation levels in October and November 2020 are indicative of reduced emissions from these two activities. A relatively longer stubble burning period, colder and drier winter conditions, and calmer winds in October and November 2020 were primarily responsible for the worsening Delhi’s air quality that year. As the winter season progressed, most anthropogenic activities such as power generation and vehicular levels bounced back to previous year’s levels. Household heating and cooking contributed to a significant share (40 per cent) to the pollution burden in December 2020 and January 2021. We stress that the interplay of meteorological conditions on Delhi’s air quality cannot be discounted, but there is need for steeper cuts in emissions across sectors. The GRAP presents the state government with an opportunity to constitute an air quality forecasting cell that can advise the government to take necessary measures to prevent severe air quality episodes in the capital city. We recommend that in addition to supporting source identification studies, the government should also encourage air quality modelling and forecasting efforts. Augmenting the existing monitoring infrastructure would help air quality modellers validate their forecasts. The state government and the city administration could also work collaboratively with the modellers in developing necessary databases to track emissions from local anthropogenic sources. 

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A vision to tackle Delhi’s dirty air

When thirty-eight-year-old Jasmine Shah moved to Delhi in 2013 and found himself engulfed in the city’s toxic winter smog, everything changed for him. 

“This scale of air pollution in Delhi, and the fact that it is among the top ten most polluted cities in the world, resulted in my urgent call to tackle the issue,” said Shah.

Targeting public transport seemed like the most logical way to start, with potential for bigger impact than tackling individuals, and Shah began by working with the government, creating momentum for policy change and more electric buses.

From UN Environment, Valentin Foltescu, Senior Programme and Science Officer of the Climate & Clean Air Coalition, added: 

“If current policies aimed at reducing air pollution are effectively enforced, air quality will be no worse in 2030 than now, despite population growth, rapid urbanization and an ever-increasing demand for goods and services, but neither will air quality be better.

“What do we need then to make the air breathable amid rapid economic development? We must move quickly to low and zero emissions solutions and comprehensive transformations. We should aim for full electrification of sectors in combination with clean energy.”

Today, as Vice Chairperson of the Dialogue and Development Commission of Delhi, an internal think tank of the Delhi government, we asked Shah what advice he has for young people interested in working alongside government towards effective policy change.

What inspired you to tackle air pollution?

A couple of winters in Delhi brought me face to face with the toxic smog that engulfs Delhi during this period. I realized that we needed more long-term measures to tackle air pollution since what we have is a health emergency in India. Particulate matter—hazardous pollution particles suspended in the air—is highly concentrated in most Indian cities and nearly 10–15 times the World Health Organization’s safety limits. And unfortunately, the problem disproportionately affects children and the elderly. Something had to be done.

What inspired you to get involved in the transport industry specifically?

The city of Delhi is well devoted to tackling air pollution and the government had already prioritized this issue on their agenda. In 2016, Delhi became the first city in India to launch a very successful Odd-Even scheme as an emergency measure to restrict the movement of private vehicles on polluted days. But since the transport sector is a top contributor to particulate emissions, I was motivated to look for long-term solutions. In February 2018, I advised the Delhi government in preparation of a comprehensive Green Budget—a 26-point long-term action plan to tackle air pollution in Delhi, with a major focus on the transport sector. Two of the most important initiatives in this were the introduction of 1,000 electric buses in Delhi and a comprehensive electric vehicle policy.

What challenges have you faced in introducing electric buses to Delhi?

With the political will already in place, the challenges we faced in Delhi were largely infrastructural and operational. We had to undertake a detailed study of the city and its transport networks before planning the electric bus routes. We undertook site visits to China to learn from best practices, since no other city outside of China has committed to bringing such a large number of electric buses. I am happy to say that we have already initiated procurement of 385 e-buses for Delhi, which should arrive by November. By 2020, we aim to bring in 1,000 e-buses.

What is your advice to youth interested to inspire change through policy action?

I encourage all young people to be persistent, creative and innovative. We have the know-how to beat air pollution. Build networks and connect with relevant people whose vision aligns with yours, and together we can combat air pollution. I believe this is achievable but requires patience and hard work.

What is your hope for the future in terms of tackling air pollution?

Air pollution is a complex problem. There are various sources of pollutants—dust, transport, industrial emissions. A comprehensive set of strategies is required to tackle the problem, and our vision for the future in Delhi is to work on all of these. In the transport sector, we are taking many steps to work towards a zero-carbon city and to ensure 25 per cent of all new vehicles in Delhi are electric by 2024. We are developing more pedestrian walkways and cycle lanes in the city to encourage walking and cycling. My vision for the future of India is to see the political leadership at national level fully acknowledge air pollution as a health emergency and act upon it. There are many solutions to tackle air pollution but we need political commitment and specific budgets and action plans to address this rising health issue.

Air pollution is the theme for  World Environment Day  on 5 June 2019 hosted by China. The quality of the air we breathe depends on the lifestyle choices we make every day. Learn more about how air pollution affects you, and what is being done to clean the air. What are you doing to reduce your emissions footprint and #BeatAirPollution? 

The 2019 World Environment Day is hosted by China.

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India’s Air-pocalypse: Understanding the air pollution crisis in Delhi and beyond

A toxic spectre is haunting the nation. how shall we tackle it.

Published : Nov 06, 2023 11:55 IST - 2 MINS READ

Students wear masks amid dense smog near the India Gate in New Dellhi on November 4, 2023. | Photo Credit: PTI

As of November 6, the air quality in Delhi remains critically poor, according to the System of Air Quality and Weather Forecasting and Research (SAFAR)-India. Reports suggest that this situation is expected to deteriorate further. Pollution levels in and around Delhi have consistently been in the “critical” category, with air quality index (AQI) readings in the range of 400 at various locations for the past few days. On the morning of November 6, SAFAR recorded an overall average AQI of 471. In response, the Delhi government has implemented a “work from home” policy for 50 per cent of government employees as part of their pollution control plan.

Data from the Central Pollution Control Board reveals that Delhi’s AQI has worsened by more than 200 points since October 27. The most severe air quality was reported on November 3, surpassing the previous high of 471 recorded on November 12, 2021. There has been some improvement due to better wind speed, but a dense and toxic haze still blankets the national capital for the sixth consecutive day.

Unfortunately, the issue of air pollution is not a new one and is not limited to Delhi or the National Capital Region (NCR). Several cities in India have been grappling with air pollution for decades, with little substantial action taken beyond political and policy discussions.

In the case of Delhi, there have been numerous studies, reports, papers, seminars, monographs, speeches, and explanations on the subject. A recent study has identified Delhi as the most polluted city in the world, with residents potentially losing a significant portion of their lifespan due to pollution. The study, called the Air Quality Life Index (AQLI), was published in August 2023 by the Energy Policy Institute at the University of Chicago. It also indicated that the entire Indian population of 1.3 billion people lives in areas with an annual average particulate pollution level exceeding the WHO’s limit of 5 μg/m3.

Still, nothing much has been done to check air pollution and the issue continues to persist like a haunting spectre in India. In this context, we have selected a collection of insightful stories from our archives to help you better understand the air pollution issue and make informed decisions regarding your use of fossil fuels. Please feel free to share your comments with us at [email protected].

Delhi's air pollution: Taming a killer

An odd (-even) formula

Season of smog: Not just Delhi, many north Indian cities are suffering

Breathing space for the capital

A scripted performance in Chandigarh puts stubble burning in focus

Mumbai in a haze: Pollution hits alarming levels

Maharashtra has the second-highest number of air pollution–related deaths in the country, air pollution can affect bone density.

Greenpeace study claims that Visakhapatnam and Hyderabad air most toxic in south India

India ranks at the bottom in a list of 180 countries in the 2022 Environmental Performance Index

India in search of clean air, global call to tackle air pollution.

India is haunted by an unprecedented economic deprivation

Editor’s note: will india vote for what really matters.

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• Published: 02 September 2024

Transforming air pollution management in India with AI and machine learning technologies

• Kuldeep Singh Rautela 1 &
• Manish Kumar Goyal 1  

Scientific Reports volume  14 , Article number:  20412 ( 2024 ) Cite this article

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A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM 2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM 2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM 2.5 concentrations across India. The results reveal its exceptional precision in PM 2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m 3 . However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM 2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.

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Introduction.

Air pollution has emerged as a critical global environmental health issue, with 92% of the world's population exposed to pollutant levels exceeding air quality guidelines 1 , 2 . This widespread exposure poses significant health risks, including increased incidence of respiratory diseases, cardiovascular problems, and premature mortality 3 , 4 . In India specifically, ambient particulate matter (PM) exposure has been linked to an estimated 1.1 million premature deaths annually, with air pollution becoming the fourth leading cause of mortality nationwide 5 , 6 . The economic impact is also substantial, with the World Bank estimating that air pollution costs India 3–8% of its GDP due to healthcare expenses, reduced productivity, and premature deaths 7 .

Atmospheric aerosols, particularly black carbon, organic carbon, dust, sea salt, and sulfates, have been extensively researched in South and Southeast Asia over the past two decades 8 . However, the magnitude of these impacts is largely influenced by spatio-temporal variability and the composition of these aerosols 9 . Aerosols, including, are significant constituents of atmospheric PM and account for approximately 30–70% of the fine aerosol mass over urban areas in India 5 , 10 . However, in recent decades, this concern has increased notably, primarily attributed to the rapid surge in population, unplanned urban development, and the expansion of industries 11 , 12 . India, home to the world's largest population share at 17.76%, faces a significant environmental challenge, with many of its cities (eg; Delhi, Mumbai, Kolkata) ranking among the most polluted on the global scale 13 , 14 . An investigation based on World Health Organization (WHO) data from 2008–2013 brought attention to India's status among the most polluted nations 15 . India has faced alarming and extensive air pollution incidents in the last twenty years, prompting substantial concern among regulatory authorities. The Indo-Gangetic Plain (IGP) is highly susceptible to severe pollution incidents, notably prevalent in the post-monsoon and winter period 16 . Similarly, in many metropolitan cities across India, such as Delhi, air quality has deteriorated to hazardous levels. Concentrations of particulate matter (PM 2.5 and PM 10 ) have surged beyond 500 µg/m 3 , while nitrogen oxides (NO 2 ) have exceeded 10 µg/m 3 . Additionally, ozone (O 3 ) and sulfur dioxide (SO 2 ) levels have surpassed 5 µg/m 3 , alongside other pollutants 17 . The concentration of these pollutants, often surpassing 500 µg/m 3 , far exceeds WHO's safe annual limit of 10 µg/m 3 and India’s national ambient air quality standards (NAAQS) of 40 µg/m 3 during winters 18 . According to the Economic Times, 12.25 million vehicles are registered in Delhi, growing at a rate of 7% per annum, and they account for 67% of the total pollution 19 , 20 . Additionally, Coal-based thermal power plants and small-scale industries each contribute 12% to the pollution, including emissions from various industrial units followed by the agricultural and biomass burning in Delhi and surrounding areas 20 . This increased pollution level has raised considerable concern among authorities and stakeholders, prompting focused efforts towards addressing this critical issue 9 . The urgency of addressing air pollution in India is evident through compelling data illustrating its significant impact across various sectors.

AI & ML have become pivotal in addressing air pollution by harnessing big data analytics, utilising advanced computing systems, scalable storage, and parallel processing technologies 21 , 22 , 23 . These innovations enable comprehensive management and mitigation strategies for various air pollutants, bridging the gap between atmospheric and climate sciences through sophisticated data-driven approaches. Previous studies have proposed various AI&ML-based models as pivotal components for air pollution and aerosol transport 5 , 8 , 24 , 25 , 26 . Initially, researchers have introduced succinct and efficient statistical models for practical applications. These statistical models primarily encompass multiple linear regression (MLR) 27 and autoregression moving average (ARMA) 28 methods. The predominant use of linear hypotheses in developing statistical models contrasts with the inherent nonlinear properties exhibited by pollutant concentrations. Consequently, researchers have advocated for integrating data mining methods 29 and machine learning models 30 , 31 , 32 designed to accommodate nonlinear predictions in studying air pollutants. However, the notably nonlinear and non-stationary nature of pollutants poses challenges for achieving high prediction accuracy with these models. As a result, several studies have turned to various deep learning techniques 8 , 33 , 34 , 35 , 36 to enhance the prediction of air pollutant levels.

Despite numerous efforts to forecast concentration of major pollutants, comprehending the complex relationship among diverse influencing factors remains a persistently challenging task. Studies exploring the relevance of these factors in predicting pollutants have been scarce and constrained in scope 37 , 38 . Typically, researchers tend to utilize all accessible features and input them into prediction models. While it holds true that AI&ML models exhibit superior performance in scenarios with abundant data availability, the effectiveness of these models in pollutant prediction hinges on understanding and incorporating the most influential factors. Figure  1 illustrates the comprehensive AI/ML model development workflow for environmental or traffic-related predictions. The process includes data collection across various domains, preprocessing, algorithm selection, model development, training, testing, and validation. The process completes with prediction, incorporating a feedback loop for model refinement if needed, ensuring adaptability and continuous improvement in predictive accuracy.

Charting the sequential steps of AI and ML involvement in predicting air pollution concentrations.

Previous studies have conducted comparative analyses between AI&ML-based methodologies for forecasting concentrations of various pollutants. Initially, Mc Kendry 39 evaluated Artificial Neural Networks (ANN) with MLR for simulating the concentrations of PM 2.5 and PM 10 . Similarly, Dutta and Jinsart 40 compared the performances of decision tree and ANN algorithms in estimating PM 10 concentrations. Other comparisons include Turias et al. 41 pitting back-propagation based ANN against ARIMA for predicting the Sulfur Dioxide (SO 2 ), concentrations of Carbon Monoxide (CO) and Suspended Particulate Matter (SPM), over an industrialized region. Shang and He 42 formulated an innovative prediction method by coupling of ANN and Random forest (RF) to forecast hourly PM 2.5 concentrations. Bozdağ et al. 43 presented a comprehensive analysis for the simulation of PM 10 concentrations by comparing various modelling approaches—ANN, KNN (K-Nearest Neighbour Algorithm), SVM (Support Vector Machine) , LASSO (Least Absolute Shrinkage and Selection Operator), RF, and xGBoost.

This study systematically explores the consequences of severe air pollution in India, focusing on contributors like PM, Organic Aerosols (OAs), BC, Water-Soluble Brown Carbon (WS-BrC), and Volatile Organic Compounds (VOCs). Remediation techniques, including legislation, NAAQS, and an Air Quality Index (AQI), are inspected alongside the evolution of emission load studies and management strategies. Additionally, the study investigates the integration of AI&ML in mitigating and predicting air pollution. It details the application of AI&ML models and underscores the potential of deep learning algorithms, exemplified through a case study predicting PM 2.5 concentrations over India. Identifying challenges like technological barriers, regulatory hurdles, public awareness gaps, agricultural practices, urbanization impacts, cross-border pollution, climate change interlinkages, and socio-economic disparities, the study emphasizes the urgency of comprehensive solutions. Looking forward, the study discusses prospects involving emerging technologies and global collaborations. The study emphasizes the imperative to address air pollution in India holistically, leveraging AI&ML advancements, global cooperation, and technological innovations to formulate effective strategies for combatting the multifaceted challenges posed by air pollution in the region.

Results and discussion

Consequences of air pollution in india.

Air pollution in India specially in metropolitan cities has dire consequences for public health, stemming from increased levels of particulate matter, nitrogen oxides, and various pollutants. This increase pollution level is consistently linked to increased respiratory diseases, particularly asthma, chronic obstructive pulmonary disease (COPD), and bronchitis 7 , 44 . Children, with developing respiratory systems, are particularly vulnerable to irreversible health issues upon prolonged exposure, while the elderly, with compromised immune systems, face pre-eminent risks, including deep lung penetration, inflammation, and enduring damage caused by PM 2.5 . Beyond respiratory implications, air pollution has severe cardiovascular consequences, with nitrogen oxides significantly contributing to an increased risk of heart attacks and strokes, leading to heightened cardiovascular mortality with prolonged exposur 7 . The significant study conducted by the CPCB in Delhi highlighted robust correlations between air quality levels and negative health effects. Comparative analysis against a rural control population in West Bengal indicated a 1.7-fold higher occurrence of respiratory symptoms in Delhi, emphasizing the direct impact of air quality on public health 20 , 45 , 46 , 47 . Odds ratios for upper and lower respiratory symptoms were 1.59 and 1.67, respectively, emphasizing the profound impact of air pollution. The study also highlighted a significantly higher prevalence of current and physician-diagnosed asthma in Delhi, with lung function notably reduced in 40.3% of Delhi's participants compared to 20.1% in the control group 20 .

In addition to respiratory effects, non-respiratory impacts were observed in the cities as compared to rural controls. The prevalence of hypertension was notably higher in cities (36% vs. 9.5% in controls), correlating positively with respirable suspended particulate matter (PM 10 ) levels in ambient air 48 . Chronic headaches, eye irritation, and skin irritation were significantly more pronounced in most of the cities. Community-based studies consistently affirm the association between air pollution and respiratory morbidity. Studies focusing on indoor air pollution reveal similar correlations with respiratory morbidity, extending to conditions such as attention-deficit hyperactivity disorder in children, increased blood levels of lead, and decreased serum concentration of vitamin D metabolites 49 . Beyond health impacts, the environmental consequences of air pollution are profound. Pollutants harm plants and animals, disrupt ecosystems, and lead to biodiversity loss 50 . The issue extends beyond health and the environment, impacting economics and society, straining healthcare, productivity, and social equity, demanding holistic strategies spanning economic, social, and environmental facets making it imperative, in this crisis, to understand the existing and potential remediation techniques 51 .

The economic and social ramifications are substantial, with healthcare costs soaring as the incidence of pollution-related illnesses rises 7 . Treating respiratory and cardiovascular diseases places a significant burden on the healthcare system, affecting both public and private healthcare expenditures 44 . Air pollution in India incurred an estimated economic toll of $95 billion in 2019, amounting to 3% of the country's GDP, attributable to decreased productivity, increased work absences, and premature fatalities 52 . The economic implications of air pollution extend beyond direct healthcare costs, affecting labor markets and overall productivity 53 . Social disparities are accentuated by air pollution, with vulnerable communities facing disproportionate exposure to pollutants. Factors such as socio-economic status, access to healthcare, and geographic location contribute to disparities in exposure and health outcomes 54 . Addressing these social dimensions is crucial for devising equitable solutions that prioritize environmental justice. As India grapples with the immediate consequences of air pollution, emerging challenges require attention. Also, climate change exacerbates existing issues, influencing weather patterns and contributing to the persistence of stagnant air masses that trap pollutants and their transportation mechanism 8 . The increasing frequency of extreme weather events further complicates pollution dynamics 55 . Moreover, the complex interplay of indoor and outdoor air pollution adds another layer of complexity, with indoor air pollution often stemming from household activities such as cooking with solid fuels, compounding the overall burden on public health 49 . However, government policies and initiatives take center stage in this exploration, with regulatory measures, such as emission standards and vehicle restrictions, scrutinized for their effectiveness and implementation challenges 12 . Sustainable urban planning, including the creation of green spaces and transportation planning for pollution reduction, is examined as a proactive approach to mitigate pollution at its source 56 . Technological solutions, ranging from air purifiers to pollution monitoring devices, are also evaluated 57 . The challenges of scalability, accessibility, and integration into existing infrastructure are dissected to discern the practicality and potential impact of these technologies. Emerging technologies and global collaborations are explored as potential catalysts for change 57 , 58 .

Contributors to air pollution in India

Air pollution in India is a complex issue with multiple sources and contributors, as highlighted by various studies conducted by Lalchandani et al. 59 , Tobler et al. 60 , Rai et al. 61 , Talukdar et al. 62 and Wang et al. 63 . The sources and contributors to air pollution can be broadly categorized into particulate matter (PM 2.5 and PM 10 ), organic aerosols (OAs) including black carbon (BC), water-soluble brown carbon (WS-BrC), and volatile organic compounds (VOCs). Each of these components plays a signifsicant role in the overall air quality of the region.

Particulate matter (PM)

Particulate matter is a key component of air pollution, and Lalchandani et al. 59 conducted studies using the Positive Matrix Factorization (PMF) model to identify and apportion different sources of PM. The sources identified included traffic-related emissions, dust transportation, solid-fuel burning emissions, and secondary factors 62 , 64 . Traffic-related emissions in metropolitan cities were found to be the significant contributor to the total concentration of PM, for example, at the IIT Delhi site, emphasizing the impact of vehicular activities on air quality. Additionally, solid fuel burning emissions, often associated with residential cooking and heating, were identified as a major contributor to PM, particularly at night 62 . Rai et al. 61 conducted source apportionment of elements in PM 10 and PM 2.5 , identifying nine source profiles/factors, including dust, non-exhaust sources, solid fuel combustion, and industrial/combustion aerosol plume events. The contribution of anthropogenic sources to elements associated with health risks, such as carcinogenic elements. The geographical origins of these sources were also determined, emphasizing the regional and local influences on element concentrations in atmosphere 65 .

Organic aerosols (OAs)

Organic aerosols are another crucial component of air pollution, and studies by Tobler et al. 60 and Lalchandani et al. 62 revealed three main components of OAs: solid fuel combustion OAs (SFC OAs), hydrocarbon-like OA (HOAs) from vehicular emissions, and oxygenated OAs (OOAs). Lalchandani et al. 65 further categorized these components into sub-factors, providing a detailed understanding of the OA composition. Emissions stemming from traffic emerged as the primary contributor to the overall OA mass, underscoring the profound influence of vehicular pollution 59 .

Black carbon (BC)

BC, a product of incomplete combustion, was studied by Using the Absorption Ångström Exponent (AAE) method, contributions from biomass burning and vehicular emissions were apportioned 66 . Vehicular emissions were found to be a dominant source of BC, contributing around 67.5% 62 , 67 . The distinction between BC and brown carbon (BrC), which absorbs light in the near-UV to visible region, was also discussed, highlighting the need to consider multiple light-absorbing aerosols in air quality assessments.

Water-soluble brown carbon (WS-BrC)

Rastogi et al. 68 performed a PMF analysis of WS-BrC spectra, identifying six factors representing specific sources of BrC. The study revealed diurnal variability in BrC absorption, with factors associated with different emission sources. The presence of secondary BrC was indicated, suggesting the importance of atmospheric processes in the formation of brown carbon. This finding adds another layer of complexity to the sources of light-absorbing aerosols in the atmosphere 69 .

Volatile organic compounds (VOCs)

Wang et al. 63 investigated the characteristics and sources of VOCs, identifying six factors related to traffic, solid fuel combustion, and secondary sources. Traffic-related emissions were found to be the dominant source of VOCs at the urban site, while at the suburban site (MRIIRS), contributions from secondary formation and solid fuel combustion were more significant. The study highlighted the major role of anthropogenic sources in VOC pollution 70 .

Current remediation techniques

India has faced escalating challenges in managing air pollution over the years, necessitating the implementation of diverse remediation techniques. Figure  2 illustrates the legislative evolution of air quality management in India across three eras: Pre-Internet (1905–89), Transition (1990–99), and Internet Era (2000 onwards). This timeline showcases key acts and regulations implemented over time to address air pollution. The bottom timeline highlights the progression of NAAQS in India, from monitoring just 3 pollutants in 1982 to 7 in 1994, and 12 in 2009. The latest phase (2019–24) involves a comprehensive review of air quality standards under the National Clean Air Programme (NCAP) in 2019, demonstrating India's ongoing commitment to improving air quality management.

Legalisation and Evaluation of NAAQS in India 12 .

Legislation and regulatory measures

India's legislative landscape has evolved significantly to address air pollution. The introduction of key acts such as the Air (Prevention and Control of Air Pollution) Act in 1981 and subsequent amendments empowered central and state pollution control boards to handle severe air pollution emergencies 71 . The Environment (Protection) Act of 1986 served as an umbrella act for environmental protection, while the Motor Vehicles Act has been periodically amended to regulate vehicular pollution 72 . Recent developments include the Motor Vehicles (Amendment) Bill of 2019, allowing the government to recall vehicles causing environmental harm 73 . The establishment of institutions like the National Green Tribunal (NGT) and the National Environment Tribunal reflects a commitment to environmental accountability 74 .

National ambient air quality standards (NAAQS) and air quality index (AQI)

The formulation and periodic revision of National Ambient Air Quality Standards (NAAQS) have been pivotal in regulating air quality 18 . Beginning in 1982, the Central Pollution Control Board (CPCB) introduced NAAQS, initially covering SO 2 , NO 2 , and SPM 47 . Subsequent amendments expanded the list to include RSPM, Pb, NH 3 , and CO 75 . The National Air Quality Index (NAQI) was introduced to enhance public awareness, categorizing air quality into six levels from 'Good' to 'Severe' 76 . This index, based on the concentration of eight pollutants, guides interventions for improved air quality.

Air pollution monitoring network

India's air quality monitoring network has witnessed substantial growth. The initiation of the National Ambient Air Quality Monitoring (NAAQM) Network in 1984, expanded to the National Air Quality Monitoring Programme (NAMP), marked a critical step 77 . The network, comprising both manual and Continuous Ambient Air Quality Monitoring System (CAAQMS) stations, now stands at 1082 locations 78 , 79 . Real-time monitoring, as exemplified by CAAQMS, provides valuable data for prompt decision-making. The introduction of the System of Air Quality and Weather Forecasting and Research (SAFAR) further enhances forecasting capabilities 80 .

Evolution of studies on emission load

Emission inventories, critical for formulating air pollution control policies, have evolved over time. Initiatives by CSIR-NEERI and CPCB in the late twentieth century laid the foundation 12 . Emission inventory data, collected through GIS, has become integral in mapping pollution sources and understanding spatial distribution 81 . The Air Pollution Knowledge Assessments (APnA) city program and organizations like TERI contribute to city-specific inventories 82 . The emphasis on utilizing secondary data streamlines the process, enabling the creation of comprehensive databases for national and urban pollution inventories. The secondary data refers to datasets that include emission loads from various sources such as vehicular emissions, industrial outputs, construction activities, residential heating, and biomass burning 83 .

Management strategies and control policies

India's air pollution management strategies encompass a multifaceted approach, with a blend of judicial interventions and executive actions.

Judicial interventions

The judiciary, particularly through petitions filed by M.C. Mehta, has been instrumental in setting guidelines and policies 84 . For instance, interventions in the Taj Trapezium Zone and the oversight of air quality management plans for non-attainment cities by the National Green Tribunal (NGT) are notable 74 . The judiciary has played a significant role in shaping policies for better governance and legislation.

Executive actions

Several executive measures contribute to air pollution control. The Auto Fuel Policy, initiated in 2003 and updated in 2014, addresses vehicular emissions 85 . Emphasis on alternative fuels, as seen in the National Auto Fuel Policy and the Pradhan Mantri Ujwala Yojana (PMUY) for subsidized LPG connections, aligns with cleaner fuel initiatives 86 . Stricter emission standards for thermal power plants and the push for Hybrid and Electric Vehicles (EVs) under schemes like Faster Adoption and Manufacturing of Hybrid & Electric Vehicles (FAMHE) contribute to pollution reductions 87 .

AI&ML Techniques for addressing and forecasting air pollution

Overview of ai&ml models.

Various AI&ML techniques, such as ANN, Fuzzy logic (FL), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Recurrence Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Autoencoder (CA) etc., are commonly used in previous studies to predict and forecast earth and atmospheric variables 8 , 25 , 88 , 89 , 90 , 91 (Table 1 ). AI&ML models have become pivotal in processing and simulating non-linear information, with a notable focus on ANNs 92 . ANNs emulate the human nervous system, comprising interconnected neurons that collectively address a spectrum of challenges, from function approximation to clustering and optimization 93 . The three-stage process involved in ANN modelling, encompassing design, training, and validation, underscores its versatility 92 . During the design phase, crucial parameters such as architecture, layers, neurons, and learning algorithms are thoroughly chosen 94 . Training involves iterative adjustments of synaptic weights to minimize errors, while validation gauges the network's generalization performance for unknown data.

Multilayer Perceptron (MLPs), a prominent type of ANN, have proven effective in predicting atmospheric pollution events. Typically featuring input, hidden, and output layers, MLPs can adapt to complex patterns by incorporating multiple hidden layers 92 . Configuring neurons in the hidden layers is of utmost importance, as an incorrect count can lead to over-fitting or under-fitting. Techniques like thumb rule and trial and error, network reduction offer solutions to optimize neuron numbers. FL, another AI technique, operates on a different paradigm by assigning truth values in a range. Developed from fuzzy set theory, it accommodates linguistic variables, making it adept at handling uncertainty in natural language statements. Fuzzy logic's three main phases—fuzzification, inference, and defuzzification—form a robust modelling system capable of addressing nuanced problems. SVM are popular for supervised learning, excelling in classification, prediction, density estimation, and pattern recognition. SVM seeks an optimal hyperplane to segregate data into predefined classes, with kernel functions playing a pivotal role in introducing non-linearity.

Deep Neural Networks (DNNs) represent an advanced version of ANNs, characterized by structural depth and scalability 8 . DNNs, with more than three layers, can automatically extract features from raw inputs, known as feature learning. Notable architectures within DNNs, such as CA, LSTM, CNNs and RNNs have demonstrated superior performance, especially in air pollution forecasting. The training of DNNs demands significant computational power, leading to advancements in processing capabilities and the development of sophisticated algorithms. Overcoming challenges like vanishing gradient and overfitting has prompted the application of advanced algorithms like SVM, RF, Greedy layer-wise, and Dropout. The application of these models extends across various domains due to their versatility and robust performance. The modelling of complex atmospheric variables such as air pollution forecasting, LSTM, CA, and CNNs emerge as particularly effective and popular architectures.

Application of AI&ML in addressing and forecasting air pollution

The application of AI&ML models, particularly ANNs, FL, SVM and DL models, have emerged as a crucial tool in addressing and forecasting air pollution. ANNs have helped in a transformative era in air pollution forecasting, with a diverse range of applications capturing the attention of researchers. Numerous studies attest to the success of ANNs in predicting both particulate and gaseous pollutants with desired accuracy over various spatio-temporal resolution. The early forays into air pollution forecasting by Mlakar et al. 95 marked a significant milestone, employing a trained nonlinear three-layered back propagation feed forward network. This model successfully predicted the concentration of SO 2 over a thermal power plant, showcasing the potential of ANNs. Subsequent research expanded the scope and sophistication of ANN applications. Similarly, Arena et al. 96 demonstrated the efficacy of multi-layer perceptron in predicting concentration of SO 2 over an industrial area, emphasizing the model's accuracy across diverse weather conditions. Sohn et al. 97 extended the ANN approach to model multiple pollutants, including NO, SO 2 , NO 2 , CO, O 3 , CH 4 and total hydrocarbons. The results indicated reasonable accuracy within a limited prediction range, highlighting the need for further optimization by incorporating additional weather-related input parameters. The application of ANNs in gaseous pollutants forecasting continued with studies by Slini et al. 98 and Kandya 99 both emphasizing the importance of optimizing input parameters for improved accuracy. Comparative assessments with other forecasting techniques consistently positioned ANNs as superior for gaseous pollutants. Chaloulakou et al. 100 found that ANN outperformed Multiple Linear Regression (MLR) in predicting ozone concentrations, showcasing the model's superior accuracy. Similar findings were reported by Mishra and Goyal 101 , compared Principal Component Analysis (PCA)-based ANN model with MLR for estimating the concentrations of NO 2 . In the realm of particulate matter forecasting, ANNs have proven equally effective. Fernando et al. 102 successfully used multi-layered MLP to predict PM 10 concentrations, considering parameters such as hourly meteorological data, particulate, matter with statistical indicators. Grivas and Chaloulakou 103 employed an ANN model for hourly PM 10 predictions, showcasing consistent accuracy even in the presence of noisy datasets. The versatility of ANNs extends to predicting roadside contributions to PM 10 concentrations, as demonstrated by Suleiman et al. 104 . Comparative studies with other models have affirmed the efficacy of ANNs in particulate matter forecasting. Zhang et al. 105 utilized BPANN to forecast the concentrations of PM 10 and found BPANN outperforming other models in predictive accuracy. Paschalidou et al. 106 evaluated the multi-layer perceptron-based ANN those models provided superior results compared to Radial Basis Function models, establishing the former's dominance in terms of forecasting capability. Contrasting trends were observed in certain studies, such as those by Mishra et al. 107 and Moisan et al. 108 , where alternative models outperformed ANN during extreme events. This highlights the nuanced nature of model performance, with specific conditions favouring different approaches. However, recent progress has witnessed researchers utilizing ensemble methods to improve both the stability and accuracy of ANN models. Liu et al. 109 combined Wavelet Packet Decomposition (WPD), Particle Swarm Optimization (PSO), and BPNN to create an ensemble model for PM 2.5 forecasting, demonstrating superior precision compared to individual models.

FL, renowned for its capacity to manage uncertainty, enhanced fault tolerance, and adeptness in handling highly complex nonlinear functions, has garnered extensive adoption in the realm of air pollution prediction. The advantages of FL are exemplified in various studies. For example, Chen et al. 110 innovatively introduced a novel fuzzy time series model specifically for O 3 prediction, showcasing its superior performance when compared to traditional fuzzy time series models. Jain and Khare 111 applied a neuro-fuzzy model to predicts the concentration of CO in Delhi, achieving accurate estimates at complex urban levels. Carbajal-Hernández et al. 112 predicts air quality in Mexico City by utilising FL model alongside autoregression model and signal processing. The introduction of a novel algorithm, the "Sigma operator," allowed for precise evaluation of air quality variables, showcasing the effectiveness of fuzzy-based models. Moreover, Al-Shammari et al. 113 , evaluates stochastic and FL-driven models to estimate the daily maximum concentrations of O 3 . The findings indicated that the FL-based model exhibited a marginal superiority over the statistical model particularly in instances of severe pollution events. Innovative approaches like the Fuzzy Inference Ensemble (FIE), as proposed by Bougoudis et al. 114 , demonstrated high accuracy in air pollution forecasting for Athens. Another significant application was presented by Song et al. 115 , where different probability density functions were employed to enhance particulate matter (PM) forecasting. They developed an adaptive neuro-fuzzy model, emphasizing the importance of density functions in addressing uncertainty associated with future PM trends. Furthermore, Wang et al. 116 presented a hybrid model for forecasting air pollution. This model merges uncertainty analysis with fuzzy time series, demonstrating precision in predicting PM and NO 2 concentrations. Behal and Singh 117 leveraged FL within an intelligent IoT sensor framework to monitor and simulate benzene, demonstrating satisfactory statistical efficacy in recent advancements. The versatility of fuzzy logic extends to unconventional pollutants as demonstrated by Arbabsiar et al. 118 , who modelled the leakage of CH 4 and H 2 S using a fuzzy inference technique. The suggested model demonstrated satisfactory performance when evaluating these contaminants.

Support Vector Machines (SVM), when combined with other machine learning algorithms, have been helpful in forecasting diverse types of pollutants. Feng et al. 119 compared SVM with other models for forecasting daily maximum concentrations of O 3 in Beijing, highlighting its stable and accurate performance. Yeganeh et al. 120 assessed the efficacy of a forecasting model utilizing SVM integrated with Partial Least Squares (PLS) for the prediction of CO concentrations, demonstrating positive outcomes. García Nieto et al. 121 conducted a comparative analysis of various prediction models for PM 10 concentrations, determining that the SVM method exhibited superior accuracy and robustness. Luna et al. 122 utilized Principal PCA in combination with SVM and ANN for the prediction of O 3 levels in Rio de Janeiro. Their study specifically investigated the influence of meteorological parameters on the concentrations of O 3 . Wang et al. 123 proposed hybrid adaptive forecasting models combining SVM and ANN for predicting PM 10 and SO 2 , demonstrating superior performance compared to individual models. FL and SVM in the forecasting air pollution levels have proven to be highly effective in addressing the complexities and uncertainties associated with predicting pollutant concentrations.

While still in its early stages, the potential of DNNs in this domain is evident from a review of various applications such as forecasting of variables in earth and atmospheric sciences. Early on, Freeman et al. (2018) employed a combination of Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to predict ozone concentrations in an urban area. While showing strong predictability in 8 h average ozone concentrations, various model runs revealed overfitting concerns, underscoring the necessity for further refinement. Wang and Song 125 introduced an ensemble method using a deep LSTM network with fuzzy c-means clustering for air quality forecasting. This ensemble approach outperformed individual models, showcasing its efficacy in both short-term and long-term predictions. Zhou et al. 126 explored the application of LSTM and deep learning algorithms for multi-step ahead forecasting of PM 2.5 , PM 10 , and NO x . Their deep learning architecture, integrating dropout neurons and L 2 regularization, demonstrated exceptional capabilities in capturing variations in the processes of air pollutant generation. Recent research highlights the growing preference for employing deep neural networks to capture dynamic spatiotemporal features from historical air quality and climatological datasets. Fan et al. 91 introduced stacked LSTM (LSTME), spatiotemporal deep learning (STDL), time delay neural network (TDNN), autoregressive moving average (ARMA), and support vector regression (SVR) for modelling of air pollutants over different spatiotemporal resolutions. The inclusion of auxiliary inputs resulted in a model with exceptional performance, outshining other machine learning techniques. Soh et al. 127 proposed a STDL integrating ANN, CNN, and LSTM for PM 2.5 prediction. The model exhibited stability over extended time periods, with noise reduction achieved through Airbox sensor source models, further enhancing prediction accuracy. Qi et al. 128 presented a novel forecasting approach employing a fusion of Graph Convolutional and LSTM (GC-LSTM) neural networks, aiming to investigate spatial interdependence within air quality data. The spatial correlation modelling highlighted the consistency of the GC-LSTM model for short-term forecasting, suggesting potential improvements for long-term predictions with enhanced spatiotemporal considerations. Fan et al. 91 developed a LSTM-based deep–RNN for predicting PM 2.5 for different spatiotemporal frames showcasing superior specificity measures compared to baseline models. In a novel approach, Li et al. 129 and Zhang et al. 130 incorporated large-scale datasets of graphical images for air pollution estimation, utilizing CNN. The models, trained on images capturing various atmospheric conditions, demonstrated improved prediction accuracy, emphasizing the adaptability of deep learning to diverse data types. These models offer robust solutions, demonstrating superior performance in various studies and showcasing their potential to contribute significantly to the field of environmental monitoring and public health.

Performance analysis

The evaluation is based on the comparison of their performances using statistical measures such as RMSE and R 2 , widely accepted metrics in air pollution forecasting studies. Previous research, utilizing a range of datasets, has yielded disparate results 134 . While certain studies advocate for ensemble methods, others find negligible disparities in the overall accuracy of the outcomes. The efficacy of AI and ML-driven methodologies relies heavily on the precise curation of influential parameters, especially when addressing various pollutants such as PM, O 3 , NO 2 , SO 2 , and CO 29 . For example, for PM forecasting, critical elements such as precipitation, pressure, humidity, land utilization, wind speed and direction, traffic flow on roads, and population density exert significant influence. Similarly, different influential parameters are identified for SO 2 , NO 2 , O 3 , and CO, emphasizing the importance of tailoring models to specific pollutants. The precision of the methods is notably impacted by the direct correlation between these factors and forecasted levels of pollutants. Additionally, the efficacy of AI&ML models hinges upon variables including network structure, intricacy, learning algorithms, correspondence between input and output information, and the presence of data interference. A comprehensive analysis shows the varying performances of DNN, SVM, ANN, and Fuzzy techniques across different pollutants. DNNs emerge as particularly effective in forecasting PM concentrations, outperforming other techniques with R 2 and mean RMSE values of 0.96 and 7.27 μg/m 3 , respectively 91 , 126 , 133 . In O 3 prediction, SVM, FL and DNN exhibit superior accuracy, with DNNs once again leading with R 2 and mean RMSE values of 0.92 and 3.51 μg/m 3 , respectively 119 , 120 . SVM excels in forecasting NO 2 concentrations, although Fuzzy and DNN techniques also demonstrate reasonable accuracy 116 , 118 , 131 . Notably, the DNN approach consistently stands out, showcasing the best statistical performance for O 3 and CO categories. For CO, DNN achieves an exceptional RMSE of 0.69 × 10 –5  ppm and an R 2 of 0.95 119 , 120 , 124 , 125 . The overall analysis represents the superiority of DNN across all pollutants, with the lowest overall RMSE score of 5.68. However, despite DNN's dominance, it is crucial to note the underdeveloped application of ensemble methodologies based on DL models for the forecasting of air pollution 131 , 135 , 136 . These approaches, involving multiscale spatiotemporal predictions, have untapped potential to further advance the field, incorporating more explanatory variables to represent air pollution episodes with robust dynamical forcing. The DNN emerges as the leading AI&ML system for the forecasting and prediction of air pollution based on statistical evidence, the exploration of ensemble approaches presents an avenue for future developments in enhancing predictive accuracy.

Prediction of PM 2.5 concentrations

The study used a convolutional autoencoder (CA) for analysing PM 2.5 concentrations. The dataset was divided into training (70%), testing (20%), and validation (10%) sets, trained over 30 epochs (Fig.  3 ). This PM 2.5 -focused CA processes sequences of ten consecutive images, using acquired features to reconstruct subsequent images. The visual representation of the model's capabilities includes sequences of 10 input images, their corresponding 11 th ground truth, and the model's predictions (Fig.  4 ). The model demonstrates promising performance in predicting PM 2.5 concentration patterns across India. Comparing the actual 11 th image with the predicted one reveals that the model successfully captures the broad spatial distribution of PM 2.5 concentrations. Key findings show that the model accurately predicts high concentration areas in the northern regions, particularly in the IGP (Fig.  4 ). It also effectively represents lower concentrations in southern and eastern coastal areas. The model captures the general gradient from northwest to southeast quite effectively. The prediction tends to slightly overestimate PM 2.5 levels in the northwestern region. Additionally, some localized high-concentration areas in central India are not fully captured in the prediction. Furthermore, the model's prediction shows a smoother distribution compared to the more granular actual data. (Fig.  4 ). Performance evaluation employed established image quality metrics: Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) (Fig.  5 ). SSIM, which assesses image similarity, predominantly ranged from 0.50 to 0.70 during training, slightly lowering to 0.45 to 0.55 during testing, and stabilizing at 0.50 to 0.60 in validation. PSNR peaked at 25 to 30 dB during training, followed by 24 to 28 dB in testing, and 28 to 30 dB in validation. Lower MSE values (10 to 15 µg/m 3 in training, 10 to 20 µg/m 3 in testing, and 8 to 11 µg/m 3 in validation) signify improved accuracy at the pixel level.

RMSE loss during the training, testing and validation phase.

Example set for predicting the 11th image of PM 2.5 by providing a batch of 10 images of concentration and comparing with the 11th actual image. The maps were generated using Python in a Jupyter Notebook with Matplotlib (v3.3.4) and Basemap (v1.2.2) libraries ( https://matplotlib.org/ and https://matplotlib.org/basemap/ ).

Model evaluation parameters used for prediction the PM 2.5 concentrations.

These metrics offer insights into image quality, indicating some variation between training, testing, and validation, yet within acceptable ranges. Consistently higher SSIM and PSNR values and lower MSE values highlight the model's exceptional precision compared to benchmarks. The model's excellence traces back to its ability to capture complex spatio-temporal features through Autoencoder-based models and strategic integration of Conv2d, Batch Normalization, and Upsampling layers. The model outperforms prior methodologies in predicting PM 2.5 concentrations, achieving precise and high-quality predictions across phases. Attempting to forecast PM 2.5 levels for the next 4 days led to efficiency parameter decreases (SSIM, PSNR, MSE) with increased time frames, suggesting the need for more parameters for model efficiency improvement (Fig.  6 ). Predicting PM 2.5 concentrations remains challenging due to intricate spatiotemporal features, where DL models offer promise. Leveraging deep learning architectures and transfer learning, this study fine-tuned models, achieving promising PM 2.5 prediction results. Despite ongoing challenges in precise location predictions due to PM 2.5 's dynamic nature, the model demonstrated spatial distribution prediction abilities, evident in visual comparisons between predicted and actual PM 2.5 concentration maps.

Example set of predictions of PM 2.5 for next 4 days compared with their actual images. The maps were generated using Python in a Jupyter Notebook with Matplotlib (v3.3.4) and Basemap (v1.2.2) libraries ( https://matplotlib.org/ and https://matplotlib.org/basemap/ ).

Challenges and limitations

Technological barriers.

One of the primary challenges lies in overcoming technological barriers. While advanced pollution control technologies exist, their widespread adoption is hindered by factors such as high costs and limited access to cutting-edge solutions. Many regions, particularly in rural areas, lack the infrastructure necessary to deploy and maintain sophisticated air quality monitoring and purification systems. Bridging this technological divide is essential for comprehensive pollution control.

Regulatory and enforcement challenges

India grapples with the challenge of implementing and enforcing air quality regulations consistently. While the country has established regulatory frameworks to curb emissions from industries, vehicles, and other pollution sources, enforcement remains uneven. This inconsistency is often compounded by resource constraints, bureaucratic hurdles, and the need for stronger mechanisms to penalize non-compliance. Strengthening regulatory frameworks and enhancing enforcement mechanisms are critical steps in addressing this challenge.

Public awareness and participation

Creating widespread awareness and fostering public participation are essential components of any successful pollution control strategy. However, there is a considerable gap in public awareness regarding the causes and consequences of air pollution. Engaging citizens in proactive measures, such as adopting sustainable practices and reducing individual carbon footprints, requires comprehensive educational campaigns and community involvement. Overcoming societal inertia and instigating behavioral change are significant challenges in this regard.

Agricultural practices and crop burning

Agricultural practices, particularly the prevalent practice of crop burning, contribute significantly to air pollution. The burning of crop residues releases substantial amounts of particulate matter and pollutants into the air. Farmers resort to this practice due to a lack of viable alternatives and time constraints between harvest seasons. Developing and promoting sustainable agricultural practices, coupled with providing farmers with effective alternatives to crop burning, is a complex challenge that requires a holistic approach.

Urbanization and infrastructure development

Rapid urbanization and infrastructure development, while essential for economic growth, often contribute to increased pollution levels. The construction industry, in particular, releases pollutants into the air. Balancing the need for development with sustainable and environmentally conscious practices poses a significant challenge. Implementing green building technologies, stringent emission norms for construction activities, and incorporating urban planning strategies that prioritize air quality are vital steps in addressing this challenge.

Cross-border pollution

Air pollution knows no boundaries, and India contends with the impact of cross-border pollution. Transboundary movement of pollutants, especially during crop burning seasons, contributes to elevated pollution levels in various regions. Collaborative efforts with neighbouring countries are necessary to address this challenge effectively. Developing joint strategies, sharing data, and fostering regional cooperation are imperative for tackling the transboundary dimension of air pollution.

Climate change interlinkages

The interlinkages between air pollution and climate change present a complex challenge. Mitigating air pollution often aligns with climate action goals, but there are trade-offs and synergies that need careful consideration. Striking a balance between addressing immediate air quality concerns and contributing to long-term climate resilience requires integrated policies and strategic planning.

Socio-economic disparities

Air pollution disproportionately affects vulnerable communities, exacerbating existing socio-economic disparities. The challenge lies in designing interventions that address environmental concerns and promote social equity. Ensuring that pollution control measures do not inadvertently burden marginalized communities and providing equitable access to clean technologies are critical to overcoming this challenge.

Future prospects

India stands at the cusp of a pivotal moment in its battle against air pollution, with promising avenues emerging on both technological and collaborative fronts.

Emerging technoloagies

The integration of cutting-edge technologies offers hope for India's future in pollution control. Advancements in AI&ML, when coupled with sophisticated numerical weather prediction models, present a potent toolset for predicting and managing air pollution. These technologies can enhance real-time monitoring, improve predictive capabilities, and facilitate data-driven decision-making, allowing for more precise and targeted interventions. Additionally, the fusion of AI&ML with numerical weather prediction (NWP) models can refine pollution control strategies by providing a deeper understanding of atmospheric dynamics and pollutant dispersion patterns. Furthermore, exploring potential breakthroughs in sustainable energy sources offers a transformative pathway. Shifting from traditional, pollutant-intensive energy sources to sustainable alternatives is crucial for reducing the overall carbon footprint. Investments in research and development, coupled with policy incentives, can accelerate the adoption of clean and renewable energy solutions, fostering a paradigm shift in India's energy landscape.

Global collaborations

Recognizing that air pollution transcends national boundaries, India looks toward global collaborations as a key driver for progress. International efforts in combating air pollution gain significance as countries join forces to address shared challenges. Collaborative platforms provide opportunities for knowledge sharing, exchange of best practices, and collective research initiatives. India's participation in these global endeavours not only enriches its own understanding of air pollution dynamics but also contributes to the global pool of knowledge. By fostering partnerships with other nations, India can access expertise, technologies, and resources that augment its capacity to implement effective pollution control measures. Knowledge sharing and collaborative research initiatives form the cornerstone of global efforts. Platforms that facilitate the exchange of data, research findings, and innovative solutions enable nations to collectively tackle the intricate and interconnected challenges of air pollution. As India engages in these collaborative endeavours, it not only benefits from the collective wisdom of the global community but also contributes its unique insights and experiences, enriching the collective understanding of air pollution dynamics.

India's strategic focus on emerging technologies and global collaborations holds immense promise in navigating the future. By harnessing the power of advanced technologies and participating in international initiatives, India can chart a course toward a cleaner, more sustainable future where the skies are clear, and the air is a testament to the collective commitment to environmental well-being.

Materials and methods

Maintaining fresh air quality is a complex undertaking influenced by various factors over time. These elements encompass air pollutant emissions, deposition, weather patterns, traffic dynamics, and human activities, among others 8 , 64 . The complexity of these interrelated factors makes it challenging for traditional shallow models to offer precise portrayals of air quality attributes. Based on the above review, deep learning algorithms were found most suitable for predicting air quality variables without needing prior knowledge. This capability enhances the potential for more accurate predictions regarding air quality, signifying a valuable contribution to addressing the intricacies associated with sustaining optimal air quality levels.

The case study utilized MERRA-2 reanalysis data from the NASA GESDISC DATA ARCHIVE application 137 , 138 . This dataset, spanning from January 1, 2015, to December 31, 2022, features a spatial resolution of 0.5° × 0.625° and a temporal resolution of 1 h (Fig.  7 ). It includes five key variables: black carbon surface mass concentration (BCSMASS), dust surface mass concentration—PM 2.5 (DUSMASS25), organic carbon surface mass concentration (OCSMASS), sea salt surface mass concentration—PM 2.5 (SSSMASS25), and SO 4 surface mass concentration (SO 4 SMASS). These variables are analysed across three dimensions: latitude, longitude, and time. The concentration of the PM 2.5 (µg/m 3 ) for each grid cell was computed as 139 , 140 :

Surface PM 2.5 concentration over India during ( a ) Winters and ( b ) Summers; Maps were generated using R Studio (v4.3.3, https://www.rstudio.com/ ).

Convolutional Autoencoder model

Air quality monitoring and predicting PM 2.5 concentrations accurately stands crucial for public health and environmental management 8 . The case study explores an innovative approach employing an Autoencoder-based DL model for forecasting PM 2.5 concentrations from spatiotemporal data over India. The study begins by complexly handling the datasets, leveraging PyTorch's Dataset and data loader classes. The ATMriver Dataset class is crafted to capture the dataset, enabling sequential data handling 9 . The data, formatted into tensors and split into training, testing, and validation subsets in a ratio of 70, 20 and 10, respectively, undergoes a custom transformation via the tensor class, ensuring compatibility with the neural network model 8 , 141 . The core of this methodology lies in the architecture of the Autoencoder, a neural network comprising convolutional and transposed convolutional layers. Specifically, the model comprises convolutional layers (conv1, conv2, conv3) responsible for feature extraction and transposed convolutional layers (conv1_d, conv2_d, conv3_d) for data reconstruction (Fig.  8 ). Each convolutional layer is paired with batch normalization and dropout (set at 25%) to regularize the network and prevent overfitting. The use of five layers in this Autoencoder architecture allows for hierarchical feature extraction and reconstruction, enhancing the model's ability to learn complex representations. The learning rate, a critical hyperparameter governing the magnitude of parameter updates during optimization, is set to 0.0025 for the Adam optimizer. This value influences the convergence speed and stability of the training process. A higher learning rate might lead to faster convergence but risks overshooting the optimal parameters, while a lower rate might result in slower convergence. The chosen learning rate balances the trade-off between convergence speed and stability, aiming to facilitate efficient model training while preventing divergence or oscillation in the optimization process.

Convolution autoencoder architecture for PM 2.5 data processing with model features an encoding phase with three autoencoder stages, followed by a decoding phase with two transpose convolution stages; structure enables dimensionality reduction and subsequent reconstruction of PM 2.5 concentration maps.

To train the Autoencoder, a custom root mean squared error (RMSE) loss function is defined. This loss function quantifies the disparity between predicted and actual PM 2.5 concentrations, guiding the model toward more accurate predictions. The training process iterates through the dataset multiple times (epochs), optimizing the model parameters using the Adam optimizer. The evaluation phase of the model involves assessing its predictive capabilities on separate testing and validation sets. The model's outputs are compared against the original images PM 2.5 concentrations, and the RMSE loss is computed. The best-performing model, based on its performance on the testing set, is identified and saved for the prediction. Further the records and reports the losses incurred during training, testing, and validation across epochs, providing insights into the model's loss curve and performance stability. Additionally, the best model's loss metric is highlighted, signifying its capability to accurately predict PM 2.5 concentrations. The evaluation of the trained model's predictive capability in this study primarily relied on two widely accepted image quality metrics: Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). The Structural Similarity Index (SSIM) serves as a measure to assess the similarity between the predicted and actual images 142 . SSIM evaluates the perceived change in structural information, including luminance, contrast, and structure, between the predicted and actual images. A higher SSIM score, closer to 1, indicates a greater similarity between the two images, implying better predictive performance of the model. Peak Signal-to-Noise Ratio (PSNR) is another commonly used metric for quantifying the quality of reconstructed or predicted images. PSNR measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Higher PSNR values signify lower image distortion or higher image fidelity, implying better prediction accuracy in capturing the details of the actual images.

Addressing the complex challenges of air pollution in India necessitates a multifaceted and technically informed approach. The existing impediments, including technological barriers and limited access to advanced pollution control technologies, underline the urgency of bridging the technological divide, particularly in rural areas. While regulatory frameworks are in place, inconsistent enforcement due to resource constraints and bureaucratic hurdles requires strategic strengthening. Public awareness and participation, integral components of effective pollution control, demand targeted educational campaigns to instigate behavioural change. Agricultural practices, notably crop burning, pose a significant challenge, and resolving this requires not only viable alternatives but a holistic approach that integrates sustainable agricultural practices. Rapid urbanization and infrastructure development, while essential for economic growth, necessitate the incorporation of green building technologies, stringent emission norms, and urban planning strategies prioritizing air quality. Cross-border pollution adds a transboundary dimension, demanding collaborative efforts with neighbouring countries. The intricate interlinkages between air pollution and climate change underscore the need for carefully balanced policies that address immediate air quality concerns while contributing to long-term climate resilience. Moreover, the disproportionate impact of air pollution on vulnerable communities emphasizes the importance of interventions that promote social equity alongside environmental considerations. Looking towards the future, the convergence of emerging technologies offers a beacon of hope. The integration of AI&ML with numerical weather prediction models presents a potent toolset for real-time monitoring, precise predictive capabilities, and data-driven decision-making. This amalgamation not only enhances our understanding of atmospheric dynamics and pollutant dispersion patterns but also refines pollution control strategies. Exploring breakthroughs in sustainable energy sources becomes imperative for reducing the overall carbon footprint. Shifting from traditional, pollutant-intensive energy sources to clean and renewable alternatives require concerted efforts through research, development, and policy incentives.

Furthermore, global collaborations stand out as a key driver for progress, given the transboundary nature of air pollution. Participating in international efforts fosters knowledge sharing, exchange of best practices, and collective research initiatives. By engaging in these collaborative activities, India not only enriches its understanding of air pollution dynamics but contributes to the global pool of knowledge. Platforms facilitating data exchange, research findings, and innovative solutions enable nations to collectively tackle the complex challenges of air pollution. In navigating the future, India's strategic focus on emerging technologies and global collaborations holds immense promise. The careful harnessing of advanced technologies and participation in international initiatives can chart a course toward a cleaner, more sustainable future. The fusion of AI&ML with numerical weather prediction (NWP) models positions India to proactively manage air quality, with the skies serving as a testament to the collective commitment to environmental well-being. As India progresses, the synergy of technological advancements and global cooperation emerges as the cornerstone for effective, informed, and sustainable solutions to combat air pollution.

Data availability

Data will be made online on a reasonable request to the corresponding author.

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We would like to express our sincere gratitude to the Department of Civil Engineering, Indian Institute of Technology, Indore for their support and resources, which have been instrumental in the successful completion of the present study.

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Rautela, K.S., Goyal, M.K. Transforming air pollution management in India with AI and machine learning technologies. Sci Rep 14 , 20412 (2024). https://doi.org/10.1038/s41598-024-71269-7

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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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Book contents

• Global Change and Future Earth
• Special Publications of the International Union of Geodesy and Geophysics Series
• Global Change and Future Earth: The Geoscience Perspective
• Copyright page
• Contributors
• Acknowledgments
• Abbreviations
• Part I Future Earth and Planetary Issues
• Part II Future Earth and Geodetic Issues
• Part III Future Earth and the Earth’s Fluid Environment
• Part IV Future Earth and Regions
• Part V Future Earth and Urban Environments
• 15 Nutrition, Urban Environments, and Future Earth
• 16 Nutrition Science and Future Earth: Current Nutritional Policy Dilemmas
• 17 Air Pollution and Human Health Risk Reduction: The Case Study of Delhi Megacity, India
• Part VI Future Earth and Food Security
• Part VII Future Earth and Risk, Safety and Security
• Part VIII Climate Change and Global Change
• Subject Index

17 - Air Pollution and Human Health Risk Reduction: The Case Study of Delhi Megacity, India

from Part V - Future Earth and Urban Environments

Published online by Cambridge University Press:  22 October 2018

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• Air Pollution and Human Health Risk Reduction: The Case Study of Delhi Megacity, India
• By R. B. Singh , Aakriti Grover
• Edited by Tom Beer , Jianping Li , Beijing Normal University , Keith Alverson
• Book: Global Change and Future Earth
• Online publication: 22 October 2018

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

• Population and Environment 25(5):461-473
• 25(5):461-473

• International Institute for Population Sciences

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Battling the Winter Smog: Delhi's Pollution Predicament

• 28 Oct 2023
• 11 min read
• GS Paper - 2
• Government Policies & Interventions
• Environmental Pollution & Degradation

This editorial is based on “Delhi's battle against pollution” which was published in The Hindustan Times on 24/10/2023. It talks about the deteriorating air quality and the factors that contribute to the pollution crisis.

For Prelims: Air quality index , SAFAR , stubble burning, Smog , Temperature inversion,

For Mains: Delhi Air pollution and its causes, Government initiatives and Way Forward

Recently, Delhi got a trailer of the inevitable environmental misery that awaits it in the coming months: Air pollution. On Some day, the air quality index surpassed 300 on a scale topping out at 500, as a blanket of haze shrouded the skies and a distinct smell of dust and smoke pervaded the outdoors. Luckily, winds picked up the following day and the skies cleared up.

For the 20 million residents of the city (and millions more in neighboring states), it was a relief for the air to improve — from “very poor” to just “poor”.

Delhi’s pollution is a serious health hazard that affects millions of people every year. According to a study by the Indian Council of Medical Research , air pollution was responsible for 1.67 million deaths in India in 2019 , and Delhi had the highest per capita mortality rate due to air pollution among all states.

What are the Reasons behind Rising Pollution levels in Delhi during Winters?

• According to SAFAR , in 2021, stubble burning's contribution to Delhi pollution was 25%.
• Stubble burning emits toxic pollutants in the atmosphere containing harmful gasses like Carbon Monoxide (CO), methane (CH4), carcinogenic polycyclic aromatic hydrocarbons, volatile organic compounds (VOC).
• According to a study conducted by National Physical Laboratory, 72% of Delhi’s wind in winters comes from the northwest.
• For example, on October 25, 2023, the air quality improved marginally when the wind changed direction from north to northeast.
• Temperature inversion affects Delhi’s pollution in winter, when the weather is cold and calm. The pollutants from stubble burning, vehicle emissions, industrial emissions, and other sources accumulate in the lower atmosphere and form a thick layer of smog .
• Dry and Still Air: In winters, there is less rainfall and wind speed, which means that the pollutants do not get washed away or diluted by fresh air. The pollutants remain suspended in the air for longer periods of time.
• A study by IIT Delhi noted that vehicular emissions contribute around 25% to Delhi’s PM2.5 levels.
• A 2015 study conducted by IIT-Kanpur states that 17-26% of all particulate matter in Delhi in winters is because of biomass burning.

Government Initiatives to Control Delhi's Pollution

• Green War Room: A nine-member team that monitors the actions taken by 20 government agencies against pollution on a real-time and daily basis.
• Anti-Pollution Campaign: Delhi Government has recently launched a major anti-pollution campaign, Yuddh Pradushan Ke Viruddh, which includes a tree transplantation and other such initiatives.
• Green Delhi App: A mobile app that allows citizens to report any instances of pollution such as garbage burning, industrial emissions, or traffic congestion.
• Bio-Decomposer : A solution developed by PUSA institute that helps farmers decompose the crop residue in their fields without burning it. The government provides free spraying of bio-decomposer in Delhi’s farmlands.
• Water Sprinklers: The use of water sprinklers, mechanized road sweeping machines, anti-smog guns, and sprinkling facilities on high-rise buildings to reduce dust and particulate matter in the air.
• Industry Pollution: The monitoring of industrial sites and ensuring that they use clean and authorized fuel. The government has also extended piped natural gas (PNG) to industries and set up the country’s first e-waste eco-park in Delhi.
• PUC Certificates: The enforcement of pollution under control (PUC) certificates for vehicles and banning trucks that carry non-essential goods from entering the city. The government has also hired 1,000 private CNG vehicles to augment the public transport system.
• Smog Towers: The installation of smog towers that use large fans and filters to purify the air. The first smog tower has been set up at Connaught Place and has shown positive effects.
• Pollution Hotspots: The identification of 21 pollution hotspots in Delhi and deploying special teams to monitor and mitigate the sources of pollution in these areas.

What Measures should be taken to Control Delhi’s Pollution?

• A congestion charge is a fee that drivers have to pay to enter or use certain areas or roads that are prone to traffic congestion.
• Cap-and-Trade for Industrial Emissions: A cap-and-trade system sets a limit on industrial emissions and promotes a market-driven approach to reducing pollution. This system creates financial incentives for industries to reduce their emissions and invest in cleaner technologies, ultimately leading to a decrease in overall pollution.
• For instance, the New Engineering Education Transformation (NEET) cohort’s drone system is designed to provide real-time air quality data with a 15-meter resolution that is publicly accessible through a user-friendly interface.
• Vertical Gardens: Vertical gardens are an aesthetically pleasing and environmentally beneficial addition to urban areas. They not only enhance the visual appeal of the city but also help purify the air by absorbing carbon dioxide and releasing oxygen. Additionally, they can provide habitats for birds and insects, contributing to urban biodiversity.
• Rewards for Low-Carbon Lifestyles: Encouraging citizens to adopt low-carbon lifestyles through a rewards system is an innovative approach. By providing incentives like points or vouchers or tax benefits for eco-friendly behaviors such as using public transport or carpooling, people are more likely to make environmentally conscious choices, reducing their carbon footprint.

UPSC Civil Services Examination Previous Year Question (PYQ)

Q. In the cities of our country, which among the following atmospheric gases are normally considered in calculating the value of Air Quality Index? (2016)

• Carbon dioxide
• Carbon monoxide
• Nitrogen dioxide
• Sulfur dioxide

Select the correct answer using the code given below:

(a) 1, 2 and 3 only (b) 2, 3 and 4 only (c) 1, 4 and 5 only (d) 1, 2, 3, 4 and 5

Q. Describe the key points of the revised Global Air Quality Guidelines (AQGs) recently released by the World Health Organisation (WHO). How are these different from its last update in 2005? What changes in India’s National Clean Air Programme are required to achieve revised standards? (2021)

Crop residue burning in Punjab not main culprit of air pollution in Delhi: Study

About the Author

Vinod Kumar is with The Times of India’s Punjab Bureau at Chandigarh. He covers news concerning Punjab politics, Health, Education, Employment and Environment.

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