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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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• v.38(1); Jan-Mar 2013

“Air pollution in Delhi: Its Magnitude and Effects on Health”

Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India

Baridalyne Nongkynrih

Sanjeev kumar gupta.

Air pollution is responsible for many health problems in the urban areas. Of late, the air pollution status in Delhi has undergone many changes in terms of the levels of pollutants and the control measures taken to reduce them. This paper provides an evidence-based insight into the status of air pollution in Delhi and its effects on health and control measures instituted. The urban air database released by the World Health Organization in September 2011 reported that Delhi has exceeded the maximum PM10 limit by almost 10-times at 198 μ g/m3. Vehicular emissions and industrial activities were found to be associated with indoor as well as outdoor air pollution in Delhi. Studies on air pollution and mortality from Delhi found that all-natural-cause mortality and morbidity increased with increased air pollution. Delhi has taken several steps to reduce the level of air pollution in the city during the last 10 years. However, more still needs to be done to further reduce the levels of air pollution.

Pollution refers to the contamination of the earth's environment with materials that interfere with human health, quality of life or the natural functioning of the ecosystems. The major forms of pollution include water pollution, air pollution, noise pollution and soil contamination. Other less-recognised forms include thermal pollution and radioactive hazards. It is difficult to hold any one particular form responsible for maximum risk to health; however, air and water pollution appear to be responsible for a large proportion of pollution related health problems.

Of late, the air pollution status in Delhi has undergone many changes in terms of the levels of pollutants and the control measures taken to reduce them. This paper provides an evidence-based insight into the status of air pollution in Delhi and its effects on health and control measures instituted.

Status of Air Pollution in Delhi

Delhi (or the National Capital Territory of Delhi), is jointly administered by the central and state governments. It accommodates nearly 167.5 lakh people (2011 Census of India).( 1 )

Metros across the world bear the major brunt of environmental pollution; likewise, Delhi is at the receiving end in India.

A study funded by the World Bank Development Research Group was carried out in 1991-1994 to study the effects of air pollution.( 2 ) During the study period, the average total suspended particulate (TSP) level in Delhi was approximately five-times the World Health Organization's annual average standard. Furthermore, the total suspended particulate levels in Delhi during this time period exceeded the World Health Organization's 24-h standard on 97% of all days on which readings were taken. The study concluded that the impact of particulate matter on total non-trauma deaths in Delhi was smaller than the effects found in the United States of America, but found that a death associated with air pollution in Delhi caused more life-years to be lost because these deaths were occurring at a younger age.

A report by the Ministry of Environment and Forests, India, in 1997 reviewed the environmental situation in Delhi over concerns of deteriorating conditions.( 3 ) Air pollution was one of the areas of concern identified in this study. It was estimated that about 3000 metric tons of air pollutants were emitted every day in Delhi, with a major contribution from vehicular pollution (67%), followed by coal-based thermal power plants (12%). There was a rising trend from 1989 to 1997 as monitored by the Central Pollution Control Board (CPCB). The concentrations of carbon monoxide from vehicular emissions in 1996 showed an increase of 92% over the values observed in 1989, consequent upon the increase in vehicular population. The particulate lead concentrations appeared to be in control; this was attributable to the de-leading of petrol and restrictions on lead-handling industrial units. Delhi has the highest cluster of small-scale industries in India that contribute to 12% of air pollutants along with other industrial units.

Vehicular pollution is an important contributor to air pollution in Delhi. According to the Department of Transport, Government of National Capital Territory of Delhi, vehicular population is estimated at more than 3.4 million, reaching here at a growth rate of 7% per annum. Although this segment contributes to two-thirds of the air pollution, there has been a palpable decline compared to the 1995-1996 levels.

The PM 10 standard is generally used to measure air quality. The PM 10 standard includes particles with a diameter of 10 μm or less (0.0004 inches or one-seventh the width of a human hair). These small particles are likely to be responsible for adverse health effects because of their ability to reach the lower regions of the respiratory tract. According to the Air Quality Guideline by the World Health Organization, the annual mean concentration recommended for PM 10 was 20 μg/m 3 , beyond which the risk for cardiopulmonary health effects are seen to increase.( 4 ) Major concerns for human health from exposure to PM 10 include effects on breathing and respiratory systems, damage to lung tissue, cancer and premature death. Elderly persons, children and people with chronic lung disease, influenza or asthma are especially sensitive to the effects of particulate matter. The urban air database released by the World Health Organization in September 2011 reported that Delhi has exceeded the maximum PM 10 limit by almost 10-times at 198 μg/m 3 , trailing in the third position after Ludhiana and Kanpur.( 5 ) Vehicular emissions and industrial activities were found to be associated with indoor as well as outdoor air pollution in Delhi [ Table 1 ].( 6 – 9 )

Air pollutants in Delhi

Effects of Air Pollution on Health

A large number of studies in Delhi have examined the effect of air pollution on respiratory functions and the associated morbidity. The most comprehensive study among them was the one conducted by the Central Pollution Control Board in 2008, which identified significant associations with all relevant adverse health outcomes.( 10 ) The findings were compared with a rural control population in West Bengal. It was found that Delhi had 1.7-times higher prevalence of respiratory symptoms (in the past 3 months) compared with rural controls ( P < 0.001); the odds ratio of upper respiratory symptoms in the past 3 months in Delhi was 1.59 (95% CI 1.32-1.91) and for lower respiratory symptoms (dry cough,wheeze, breathlessness, chest discomfort) was 1.67 (95% CI 1.32-1.93). Prevalence of current asthma (in the last 12 months) and physician-diagnosed asthma among the participants of Delhi was significantly higher than in controls. Lung function was reduced in 40.3% individuals of Delhi compared with 20.1% in the control group. Delhi showed a statistically significant ( P < 0.05) increased prevalence of restrictive (22.5% vs. 11.4% in control), obstructive (10.7% vs. 6.6%) as well as combined (both obstructive and restrictive) type of lung functions deficits (7.1% vs. 2.0%). Metaplasia and dysplasia of airway epithelial cells were more frequent in Delhi, and Delhi had the greater prevalence of several cytological changes in sputum. Besides these, non-respiratory effects were also seen to be more in Delhi than in rural controls. The prevalence of hypertension was 36% in Delhi against 9.5% in the controls, which was found to be positively correlated with respirable suspended particulate matter (PM 10 ) level in ambient air. Delhi had significantly higher levels of chronic headache, eye irritation and skin irritation.

Several other community-based studies have found that air pollution is associated with respiratory morbidity.( 11 – 13 ) Numerous studies have reported an association between indoor air pollution and respiratory morbidity.( 14 – 19 )Some of these studies have concentrated on children's respiratory morbidity.( 15 , 17 , 19 ) Other studies in children have found similar correlations between particulate matter in ambient air and attention-deficit hyperactivity disorder( 20 ) between vehicular air pollution and increased blood levels of lead (a potential risk factor for abnormal mental development in children)( 21 ) and between decreased serum concentration of vitamin D metabolites and lower mean haze score (a proxy measure for ultraviolet-B radiation reaching the ground).( 22 )

Studies that have examined the compounding effect of meteorological conditions on air pollution found that winter worsened the air quality of both indoor air and outdoor air. They also found a positive correlation between the winter weather and rise in the number of patients with chronic obstructive airway disease in hospitals.( 12 , 16 )

There was a relative paucity of studies that measured outdoor air pollutant levels first hand and then tried to objectively correlate them to adverse health effects. However, some studies measured air pollutant levels and found a correlation with health-related events.( 17 , 19 )

A time-series study on air pollution and mortality from Delhi found that all-natural-cause mortality increased with increased air pollution.( 23 ) In another study, gaseous pollutants, in spite of being at a level lower than the permissible level, showed more consistent association with respiratory admissions.( 24 ) In a hospital-based study, an increase in emergency room visits for asthma, chronic obstructive airway disease and acute coronary events was reported with an increase in air pollutant levels.( 25 ) These studies are summarized in Table 2 .

Effects of air pollution in Delhi on health

Control Measures Instituted by the Government of Delhi

The nodal ministry for protecting the environment is the Ministry of Environment and Forests at the Centre and the Department of Environment of the Government of National Capital Territory of Delhi. The Central Pollution Control Board set up in 1974 under the Water Act is the principal watchdog for carrying out the functions stated in the environmental acts, implementation of National Air Quality Monitoring Programme and other activities. The Delhi Pollution Control Board is the body responsible at the state level.

From time to time, the judiciary has taken strong note of the deteriorating environmental conditions in Delhi in response to public litigations. One of the earliest such instances was the judgement passed by the Supreme Court of India to deal with the acute problem of vehicular pollution in Delhi in response to a writ petition filed in 1985. Subsequently, it ordered the shutdown of hazardous, noxious industries and hot-mix plants and brick kilns operating in Delhi.

Vehicular Policy

Control measures so far instituted include introduction of unleaded petrol (1998), catalytic converter in passenger cars (1995), reduction of sulfur content in diesel (2000) and reduction of benzene content in fuels (2000). Others include construction of flyovers and subways for smooth traffic flow, introduction of Metro rail and CNG for commercial transport vehicles (buses, taxis, auto rickshaws), phasing out of very old commercial vehicles, introduction of mandatory “Pollution Under Control” certificate with 3-month validity and stringent enforcement of emission norms complying with Bharat Stage II/Euro-II or higher emission norms. Introduction of The Air Ambience Fund levied from diesel sales and setting up of stringent emission norms for industries and thermal power stations are the other measures. Environmental awareness campaigns are also carried out at regular intervals. The Delhi Pollution Control Board conducts monthly Ambient Air Quality Monitoring at 40 locations in Delhi, and takes corrective action wherever necessary.

Industrial Policy

The first Industrial Policy for Delhi was introduced in 1982. Subsequently, a second Industrial policy (2010–2021) was issued by the Department of Industries, Government of Delhi. It is a comprehensive document envisioning higher industrial development in Delhi, with one of its mandates being to develop clean and non-polluting industries and details of steps to be undertaken in this direction have been described.

There are many other organizations that work synergistically with the government efforts to reduce air pollution. These include the Centre for Science and Environment and The Energy and Resources Institute, and the Indian Association for Air Pollution Control. Representatives of the industries include Confederation of Indian Industry and Society of Indian Automobile Manufacturers. Government agencies like Factories Inspectorate are also involved in the control of pollution. Research and academic institutions include National Environmental Engineering Research Institute, Indian Institute of Technology, Council of Scientific and Industrial Research institutions, Indian Agricultural Research Institute and various other academic institutions in and around Delhi. Professional organizations like the Indian National Science Academy, the Indian Institute of Chemical Engineers and the Indian Institute of Engineers are also involved in pollution control.

Benefits Accrued as a Result of Control Measures

Since the first act on pollution was instituted, huge progress has been made in terms of human resource, infrastructure development and research capability. Some studies tried to gather evidence for the effectiveness of control measures by comparing pre- and post-intervention health status. The study conducted by the Central Pollution Control Board demonstrated that spending 8-10 h in clean indoor environment can reduce health effects of exposure to chronic air pollution.( 10 ) A recent study found significant improvement in the respiratory health following large-scale government initiatives to control air pollution.( 26 ) It was reported that use of lower-emission motor vehicles resulted in a significant gain in disability-adjusted life-years in Delhi.( 27 ) Another study found significant evidence for reduction in respiratory illness following introduction of control measures.( 24 )

Most of the studies were ecological correlation studies, which are severely limited in their ability to draw causal inferences. But, considering the context that demanded the research, these were probably the best available designs to produce preliminary and,sometimes, policy-influencing evidences, as any other methodology would be unethical or operationally impossible.

The Government of National Capital Territory of Delhi has taken several steps to reduce the level of air pollution in the city during the last 10 years. The benefits of air pollution control measures are showing in the readings. However, more still needs to be done to further reduce the levels of air pollution. The already existing measures need to be strengthened and magnified to a larger scale. The governmental efforts alone are not enough. Participation of the community is crucial in order to make a palpable effect in the reduction of pollution. The use of public transport needs to be promoted. The use of Metro rail can be encouraged by provision of an adequate number of feeder buses at Metro stations that ply with the desired frequency. More frequent checking of Pollution Under Control Certificates needs to be undertaken by the civic authorities to ensure that vehicles are emitting gases within permissible norms. People need to be educated to switch-off their vehicles when waiting at traffic intersections. Moreover, the “upstream” factors responsible for pollution also need to be addressed. The ever-increasing influx of migrants can be reduced by developing and creating job opportunities in the peripheral and suburban areas, and thus prevent further congestion of the already-choked capital city of Delhi.

Health, as we all know, is an all-pervasive subject, lying not only within the domains of the health department but with all those involved in human development. Many great scholars from Charaka to Hippocrates have stressed the importance of environment in the health of the individual. Therefore, all those who play a role in modifying the environment in any way, for whatever reason, need to contribute to safeguard people's health by controlling all those factors which affect it.

Source of Support: Nil

Conflict of Interest: None declared.

Suggested citation: Khan, Adeel, Uday Suryanarayanan, Tanushree Ganguly, and Karthik Ganesan. Improving Air Quality Management through Forecasts: A Case Study of Delhi’s Air Pollution of Winter 2021. New Delhi: Council on Energy, Environment and Water.

This study assesses Delhi’s air pollution scenario in the winter of 2021 and the actions to tackle it. Winter 2021 was unlike previous winters as the control measures mandated by the Commission of Air Quality Management (CAQM) in Delhi National Capital Region and adjoining areas were rolled out. These measures included the Graded Response Action Plan (GRAP) and additional emergency responses instituted on the basis of air quality and meteorological forecasts. Given that the forecasts play a major role in emergency response measures, the study assesses the reliability of different forecasts. Further, it gauges the impact of the emergency measures on Delhi’s air quality levels. It also discusses the primary driver of air pollution in winter 2021.

Key Findings

• While air quality forecasts picked up the pollution trends, they are not yet very accurate in predicting high pollution episodes ('very poor' and 'severe' air quality days)
• When the restrictions were in place like ban on entry of trucks, construction & demolition activities and others, air quality did not descend into the ‘severe +’ category. Moreover, air quality improved from ‘severe’ to ‘poor’ when all the restrictions were in place simultaneously, aided by better meteorology.
• However, when the restrictions were finally lifted, the air quality spiralled back into the ‘severe’ category resulting in the longest six days ‘severe’ air quality spell of the season.
• There has been no significant improvement in Delhi's winter air quality since 2019. In winter 2021, air quality was in the ‘very poor’ to ‘severe’ category on about 75 per cent of days.
• In the winter of 2021, transport(∼ 12 per cent), dust (∼ 7 per cent) and domestic biomass burning (∼ 6 per cent) were the largest local contributors.
• About 64 per cent of Delhi’s winter pollution load comes from outside of Delhi’s boundaries.

HAVE A QUERY?

Executive Summary

With every passing winter, the need to address Delhi’s air pollution grows more urgent. During the winter of 2021, the Supreme Court, the Delhi Government, and the Commission for Air Quality Management in the NCR and Adjoining Areas (CAQM), all sprang into action to arrest rising pollution levels in Delhi. The interventions ranged from shutting down power plants and restricting the entry of trucks into Delhi to school closures and using forecasts to pre-emptively roll out emergency measures. However, the impact of these interventions on Delhi’s air quality begs further investigation.

Through this study, we intend to examine what worked and what did not this season. As is the case every year, meteorological conditions played an important role in both aggravating and alleviating pollution levels. To assess the impact of meteorological conditions on pollution levels, we analysed pollution levels during the months of October to January vis-a-vis meteorological parameters. To understand the driving causes of pollution in the winter of 2021, we tracked the changes in relative contribution of various polluting sources as the season progressed.

While pre-emptive actions based on forecasts was a step in the right direction, an assessment of forecast performance is a prerequisite to integrating them in decision-making. We also assessed the performance of forecasts by comparing them with the measured onground concentrations. We also studied the timing and effectiveness of emergency directions issued in response to forecasts.

We sourced data on pollution levels from Central Pollution Control Board’s (CPCB) real-time air quality data portal and meteorological information from ECMWF Reanalysis v5 (ERA5). For information on modelled concentration and source contributions, we used data from publicly available air quality forecasts, including Delhi’s Air Quality Early Warning System (AQ-EWS) (3-day and 10-day), the Decision Support System for Air Quality Management in Delhi (DSS), and UrbanEmissions.Info (UE).

Figure ES1 In Delhi, 75% of Winter 2021 saw 'very poor' to 'severe' air quality

Source: Authors’ analysis, data from Central Pollution Control Board (CPCB). Note: Air quality index (AQI) for the day is calculated using the PM2.5 concentration at the same stations with a minimum of 75 per cent of the data being available.

A. 75 per cent of days were in ‘Very poor’ to ‘Severe’ air quality during winter 2021

The number of ‘Severe’ plus ‘Very poor’ air quality days during the winter has not decreased in the last three years (Figure ES1). During the winter of 2021 (15 October 2021 - 15 January 2022), about 75 per cent of the days, air quality were in the ‘Very poor’ to ‘Severe’ category. Interestingly, despite more farm fire incidents in Punjab, Haryana, and Uttar Pradesh in 2021 compared to 2020, Delhi’s PM2.5 concentration during the stubble burning phase (i.e., 15 October to 15 November) was lesser in 2021. This was primarily due to better meteorological conditions like higher wind speed and more number of rainy days during this period.

B. Regional influence predominant; Transport, dust, and domestic biomass burning are the largest local contributors to air pollution

We find that about 64 per cent of Delhi’s winter pollution load comes from outside Delhi’s boundaries (Figure ES2(a). Biomass burning of agricultural waste during the stubble burning phase and burning for heating and cooking needs during peak winter are estimated to be the major sources of air pollution from outside the city according to UE (Figure ES2(b). Locally, transport (12 per cent), dust (7 per cent), and domestic biomass burning (6 per cent) contribute the most to the PM2.5 pollution load of the city. While transport and dust are perennial sources of pollution in the city, the residential space heating component is a seasonal source. However, this seasonal contribution is so significant that as the use of biomass as a heat source in and around Delhi starts going up as winter progresses, the residential sector becomes the single-largest contributor by 15 December (Figure ES2(b)). This indicates the need to ramp up programmes to encourage households to shift to cleaner fuels for cooking and space heating.

Figure ES 2(a) Transport, dust, and domestic biomass burning are the largest local contributors to the PM2.5 pollution load in Delhi

Source: Authors’ analysis, source contribution data from DSS and UE. Note: Modelled estimates of relative source contributions retrieved from UE and DSS.

Figure ES 2(b) Both local and regional sources need to be targeted for reducing Delhi’s pollution

Source: Authors’ analysis, source contribution data from UE. Note: Source contribution data retrieved from UE district products which have larger geographical cover and lower resolution.

C. Forecasts picked up the pollution trend but could not predict high pollution episodes

The availability of multiple forecasts provides decisionmakers with a range of options to choose from. At the same time, this is an obstacle to effective onground action. To streamline the flow of relevant information from forecasters to decision-makers, it is important to analyse the forecasts and assess their reliability. We found that all the forecasts identified pollution trends accurately (Figure ES3) but their accuracy in predicting pollution episodes (‘Severe’ and ‘Very Poor’ air quality days) decreases with future time horizon.

D. Though forecasts were used to impose restrictions, the lifting of the curbs was ill-timed

In November–December 2021, apart from the Graded Response Action Plan (GRAP) coming into effect in DelhiNCR, the CAQM introduced several emergency response measures through a series of directions and orders. The Supreme Court also stepped in from time to time to direct the authorities to act on air pollution.

As a first, the CAQM used air quality and meteorological forecasts to time and tailor emergency response actions. The first set of restrictions was put in place on 16 November 2021, and all were lifted by 20 December 2021, save the one on industrial operations.

Figure ES3 All the forecasts can predict the trend accurately

Source: Authors’ analysis, data from Central Pollution Control Board (CPCB), AQ-EWS, and UE. Note: r represents correlation.

During this period, all the forecasts except AQ-EWS (3-day) underpredicted PM2.5 levels. Therefore, by looking at the difference between forecasted and measured concentrations, it is not possible to gauge the effectiveness of the restrictions conclusively. Hence, multiple models or different modelling experiments are needed to estimate the impact of the intervention.

It should be noted that during the restriction period, air quality did not descend into the ‘Severe +’ category. Further, when all the restrictions were in place along with better meteorology, air quality did improve from ‘Severe’ to ‘Poor’. The first prolonged ‘Severe’ air quality period in December was witnessed between 21 December and 26 December. While the forecasts sounded an alarm for high pollution levels during this period, all restrictions barring those on industrial activities were lifted. Subsequently, PM2.5 levels remained above 250 µgm -3 for six straight days resulting in the longest ‘Severe’ air quality spell of the season. (Figure ES4).

Figure ES4 The lifting of the restrictions was ill-timed with high pollution levels forecasted in the following days

Source: Authors’ analysis, data from Central Pollution Control Board (CPCB). Note: C&D stands for construction and demolition activities. Work from home (WFH) stands for the 50% cap on employee attendance in the office. Industrial restrictions stand for compulsory switching over to Piped Natural Gas (PNG) or other cleaner fuels within industries and non-compliant industries being allowed to operate restrictively.

The discussion above highlights that despite the emergency measures taken in winter 2021, the air quality conditions were far from satisfactory. Calibrating emergency responses to forecasted source contributions may result in a greater impact on air quality. Our study recommends the following to help the Government of National Capital Territory of Delhi ( GNCTD) and CAQM plan and execute emergency responses better:

• GRAP implementation must be based strictly on modelled source contributions obtained from forecasts and timed accordingly. This will eliminate the need for ad-hoc emergency directions to restrict various activities. For instance, restrictions on private vehicles can be brought in when the air quality is forecasted to be ‘Very poor’ as transport is a significant contributor.
• Surveys or assessments are required in the residential areas across NCR to explore the prevalence of biomass usage for heating and cooking purposes. Based on this, a targeted support mechanism is required to allow households and others to use clean fuels for cooking and heating. There is also a need to assess and promote alternatives for space heating.
• Air quality forecasts should be relayed to the public via social media platforms to encourage them to take preventive measures such as avoiding unnecessary travel and wearing masks when stepping out. This will help reduce individual exposure and activity levels in the city.
• Ground level data and insights need to be incorporated in forecasting models. Data from sources like social media posts (text and photos), camera feeds from public places, and pollution related grievance portals like SAMEER, Green Delhi, and SDMC 311 can provide near-real time information on pollution sources. Then aggregated representation of polluting activities based on recent days or weeks can be used as an input in models. Ultimately, a crowd-sourced emissions inventory for NCT/NCR will benefit modellers and policymakers alike while also making pollution curtailment efforts transparent.
• Combining the available air quality forecasts through an ensemble approach can help improve the accuracy of the forecasts and prompt better coordination within the modelling community.

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• CORRESPONDENCE
• 08 February 2022

New Delhi: air-quality warning system cuts peak pollution

• Sachin D. Ghude 0 ,
• Rajesh Kumar 1 ,
• Gaurav Govardhan 2 ,
• Chinmay Jena 3 ,
• Ravi S. Nanjundiah 4 &
• M. Rajeevan 5

Indian Institute of Tropical Meteorology, Pune, India. Ministry of Earth Sciences, New Delhi, India.

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National Center for Atmospheric Research, Boulder, Colorado, USA.

India Meteorology Department, Delhi, India. Ministry of Earth Science, New Delhi, India.

Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru, India.

Ministry of Earth Sciences, Delhi, India.

A sophisticated early-warning and decision-support system is minimizing air-pollution events in and around the Indian capital of New Delhi. This system helped to cut the city’s pollution peak last November by 18–22%.

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Identification of urban street trees for green belt development for optimizing pollution mitigation in Delhi, India

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• Shilky   ORCID: orcid.org/0000-0001-8292-0385 1 ,
• Ratul Baishya   ORCID: orcid.org/0000-0002-4149-6466 2 &
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The current study evaluated the effects of air pollution on selected street trees in the National Capital Territory during the pre- and post-monsoon seasons to identify the optimally suitable tree for green belt development in Delhi. The identification was performed by measuring the air pollution tolerance index (APTI), anticipated performance index (API), dust-capturing capacity (DCC) and proline content on the trees. The APTI of street trees of Delhi varied significantly among different tree species ( F 11,88.91  = 47.18, p  < 0.05), experimental sites ( F 3,12.52  = 6.65, p  < 0.001) and between seasons ( F 1,31.12  = 16.51, p  < 0.001), emphasizing the relationships between trees and other types of variables such as the climate and level of pollution, among other factors. This variability emphasizes the need to choose trees to use for urban greening in the improvement of air quality in different environments within cities. Ascorbic acid (AA) concentration and relative water content (RWC) had a strong influence on APTI with an extremely significant moderate positive correlation between AA concentration and APTI ( r  = 0.65, p  < 0.001) along with RWC and APTI ( r  = 0.52, p  < 0.001), indicating that higher levels of AA concentration and RWC are linked to increased air pollution tolerance. The PCA bi-plot indicates AA has poor positive loading coefficients with PC1 explaining 29.49% of the total variance in the dataset. The highest APTI was recorded in Azadirachta indica (22.01), Leucaena leucocephala (20.65), Morus alba (20.62), Ficus religiosa (20.61) and Ficus benghalensis (19.61), irrespective of sites and seasons. Similarly, based on API grading, F. religiosa and F. benghalensis were identified as excellent API grade 6 (81–90%), A. indica and Alstonia scholaris as very good API grade 5 (71–80%), M. alba , Pongamia pinnata and Monoon longifolium as good API grade 4 (61–70%) and Plumeria alba as moderate API grade 3 (51–60%) in different streets of Delhi. As these plants are indigenous to the region and hold significant socio-economic and aesthetic significance in Indian societies, they are advisable for avenue plantations as part of various government initiatives to support environmental sustainability.

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Acknowledgements

The authors express their sincere gratitude to the Department of Botany, University of Delhi for providing support for conducting experiments. The authors are also thankful to the DST-FIST Program of the Department of Environmental Sciences, Central University of Jharkhand (CUJ), Ranchi sponsored by DST, Govt. of India (Ref. No. SR/FST/ES-I/2019/55 (C) dated 25th March 2021).

Financial support in the form of an institutional fellowship was provided by the CUJ to Shilky to carry out her Ph.D. research.

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Shilky, Baishya, R. & Saikia, P. Identification of urban street trees for green belt development for optimizing pollution mitigation in Delhi, India. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-34802-9

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

Roles Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

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

Roles Conceptualization, Methodology, Supervision, Visualization

* E-mail: [email protected]

• Abhishek Dutta, 
• Wanida Jinsart

• Published: September 20, 2022
• https://doi.org/10.1371/journal.pone.0274444
• Reader Comments

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.

Citation: Dutta A, Jinsart W (2022) Air pollution in Delhi, India: It’s status and association with respiratory diseases. PLoS ONE 17(9): e0274444. https://doi.org/10.1371/journal.pone.0274444

Editor: Yangyang Xu, Texas A&M University, UNITED STATES

Received: October 1, 2021; Accepted: August 29, 2022; Published: September 20, 2022

Copyright: © 2022 Dutta, Jinsart. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: 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: [email protected] . 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: [email protected] .

Funding: 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.

Competing interests: The authors have declared that no competing interests exist.

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 .

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https://doi.org/10.1371/journal.pone.0274444.g001

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 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)

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.

https://doi.org/10.1371/journal.pone.0274444.t001

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 .

https://doi.org/10.1371/journal.pone.0274444.t002

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 .

https://doi.org/10.1371/journal.pone.0274444.g002

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.

https://doi.org/10.1371/journal.pone.0274444.g003

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.

https://doi.org/10.1371/journal.pone.0274444.g004

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.

https://doi.org/10.1371/journal.pone.0274444.g005

https://doi.org/10.1371/journal.pone.0274444.t003

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.

https://doi.org/10.1371/journal.pone.0274444.g006

https://doi.org/10.1371/journal.pone.0274444.t004

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.

https://doi.org/10.1371/journal.pone.0274444.t005

Figs 7 and 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.

https://doi.org/10.1371/journal.pone.0274444.g007

https://doi.org/10.1371/journal.pone.0274444.g008

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).

https://doi.org/10.1371/journal.pone.0274444.g009

https://doi.org/10.1371/journal.pone.0274444.t006

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

S1 table. air pollutants and their association with respiratory mortality/morbidity for 18 cities using gam model during 2000–2020..

https://doi.org/10.1371/journal.pone.0274444.s001

S2 Table. Monitoring stations and their geographic coordinates, Delhi.

https://doi.org/10.1371/journal.pone.0274444.s002

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.

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

Affiliation.

• 1 Department of Environmental Science, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok, Thailand.
• PMID: 36126064
• PMCID: PMC9488831
• DOI: 10.1371/journal.pone.0274444

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 PM2.5 and PM10 concentrations during the measurement period exceeded both national and international standards by a wide margin. Time series charts indicated the interdependence of PM2.5 and PM10 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 PM10 and SO2 led to increased hospital visits due to respiratory diseases. Per 10 units increase in concentrations of PM10 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 SO2 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 SO2 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 SO2 in inducing respiratory diseases is worrying as India is now the largest anthropogenic SO2 emitter in the world.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Fig 1. Delhi city, air quality monitoring…

Fig 1. Delhi city, air quality monitoring stations, and hospital location.

The time series of Delhi…

The time series of Delhi from 2016–2018 (A) PM 2.5 Vs Hospital visit,…

Fig 3. Violin plots of three air…

Fig 3. Violin plots of three air pollutants, two metrological variables, and hospital visits in…

Fig 4. Frequency distribution of PM concentrations…

Fig 4. Frequency distribution of PM concentrations across five seasons, Delhi.

Fig 5. Pearson correlation matrix, 2016–2018, Delhi…

Fig 5. Pearson correlation matrix, 2016–2018, Delhi generated using R program.

Blue, red, and while…

Fig 6. Relative risk pattern (95% CIs)…

Fig 6. Relative risk pattern (95% CIs) of respiratory diseases related hospital visits in multi-pollutant…

Fig 7. Exploratory variables with confidence bands…

Fig 7. Exploratory variables with confidence bands and smoothers for Delhi city.

Fig 8. GAM model performance for Delhi…

Fig 8. GAM model performance for Delhi city.

Fig 9. Relative risk pattern (95% CIs)…

Fig 9. Relative risk pattern (95% CIs) of respiratory diseases related hospital visits in single…

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Delhi govt releases winter plan to curb pollution; drone surveys, WFH key points

Delhi launches a winter action plan to combat air pollution, featuring real-time drone surveys and a special task force..

New Delhi: A real-time drone survey will be carried out as part of Delhi’s winter action plan to curb air pollution in the Capital, Delhi environment minister Gopal Rai said on Thursday, while releasing the 21-point plan.

Rai said that nodal agencies for each of the 21 action points were also decided, with the plan to be implemented as soon as each department submits its internal action plan on September 12.

ALSO READ- Delhi breathed clean air for 128 days in first half of 2024, says report

“For the first time, drone monitoring will be done to reduce pollution at hot spots. Also, a special task force will be formed. The departments concerned have been instructed to submit their action plan on the winter action plan by September 12,” Rai said.

“The 2024 Winter Action Plan focuses on pollution hot spots, vehicular and dust pollution, work from home, stubble and garbage burning, industrial pollution, upgrading the war room and green app, maintaining dialogue with the central government and neighbouring states, and preparation for odd-even (vehicle rationing) and artificial rain as emergency measures,” Rai said.

ALSO READ- Delhi HC pulls up Wikipedia for non-compliance of order

The plan was released following a meeting with 35 government departments and agencies, focussing on 14 key areas. Rai said suggestions made by experts and agencies in the past few weeks led to the government adding more measures to the plan, which includes a work-from-home (WFH) policy for private organisations, voluntary vehicular restriction and odd-even vehicle rationing. A green award for organisations taking part in environment-friendly activities is also planned.

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Last year, the government adopted a 15-point winter action plan, with Rai stating additional measures are aimed at helping bring down pollution peaks further.

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For assessing pollution through drones, the minister said the environment department, DPCC, MCD, Delhi Traffic Police, DDA and DSIIDC were appointed nodal agencies, and the environment department was tasked with forming a special task force (STF) on air pollution.

Other measures include a green award, the “Harit Ratna”, in recognition of agencies or companies working in the field of environment, for which the environment department is the nodal agency.

The environment department, jointly with the transport department, will also be preparing modalities if the odd-even scheme is required to be imposed during pollution peaks in winter, besides framing the work-from-home policy framework for private organisations, which may also include staggered timings.

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Review on air pollution of Delhi zone using machine learning algorithm

• Journal of Air Pollution and Health 5(4)

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