case study of delhi air pollution

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• 10 November 2023

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

• Dyani Lewis

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

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

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doi: https://doi.org/10.1038/d41586-023-03517-1

Bikkina, S. et al. Nature Sustain. 2 , 200–205 (2019).

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Kulkarni, S. H. et al. Environ. Sci. Technol. 54 , 4790–4799 (2020).

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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 @vthomas14 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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Air pollution in Delhi, India: It’s status and association with respiratory diseases

Abhishek dutta.

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

Wanida Jinsart

Associated data.

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

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

1. Introduction

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

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

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

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

2. Study location

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

3. Materials and methods

3.1. air pollution data.

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

3.2. Meteorological data

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

3.3. Hospital visit data

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

3.4. Methods of analysis

3.4.1 summary statistics and analysis of time series.

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

3.4.2 Frequency analysis

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

3.4.3 Correlation analysis

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

3.4.4 Generalized Additive Models (GAM)

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

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

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

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

3.5. Relative Risk (RR)

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

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

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

In all analyses p-value < 0.05 considered significant.

4. Results and discussion

4.1 data distribution and time-series analyses.

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

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

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

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

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

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

4.2 Seasonal distribution of PM 2.5 and PM 10 in Delhi

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

4.3 Correlation between pollutants and meteorological variables

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

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

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

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

4.4 Association of criteria pollutants with respiratory diseases, Delhi

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

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

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

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

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

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

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

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

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

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

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

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

5. Conclusion and discussion

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

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

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

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

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

Supporting information

Acknowledgments.

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

Funding Statement

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

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New Delhi’s Air Turns Toxic, and the Finger-Pointing Begins

Schools and factories close. India’s Supreme Court blasts the government’s do-nothing response. But Delhi residents continue to suffer from the bad air.

By Hari Kumar and Emily Schmall

NEW DELHI — A thick blanket of noxious haze has settled over the Indian capital of New Delhi, burning eyes and lungs, forcing schools to close and prompting ardent calls from residents for action.

India’s leaders have responded with what has become an annual tradition: by pointing fingers at one another.

The central government, run by Prime Minister Narendra Modi, is accusing city officials of inaction, and vice versa. The country’s Supreme Court has stepped in to shut down factories and order farmers to stop burning fields. But the court’s other efforts, which last year included ordering the installation of a pair of air-scrubbing filter towers, have been derided as ineffectual .

The airborne murk and the towers stand as symbols of India’s deep political dysfunction. The choking pollution has become an annual phenomenon , and the country’s scientists can accurately predict the worst days. But deep partisanship and official intransigence have hindered steps that could help clear the air.

New Delhi’s residents don’t agree who is at fault, but they agree that more must be done.

“These last three weeks I became a refugee. I was so sick that I couldn’t take it anymore,” said Jai Dhar Gupta, the owner of a business that sells air pollution mitigation tools, such as home air purifier machines and face masks.

Mr. Gupta, who now lives between Delhi and Mussoorie, a city in the foothills of the Himalayas that boasts better air, became an antipollution activist and entrepreneur in 2013, after developing asthma.

“It’s really sad for a nation where every time there is a health emergency the Supreme Court needs to intervene. That tells you everything about the apathy toward the health and life in our country,” said Mr. Gupta. “Nobody cares.”

The court stepped in over the weekend in response to a petition filed by an 18-year-old environmental activist and after the city had endured pollution levels comparable to levels generated by major wildfires . It criticized officials for what it called their “don’t take any step” position.

Earlier this week, Delhi’s emergency measures went into effect. Construction activity, diesel generators and trucks were banned. Schools were closed and employers were asked to keep half of their staffs at home. Six power plants outside New Delhi were ordered shut down.

Under pressure from the court, city officials also deployed other steps that have inspired more jokes than optimism. They include anti-smog guns that create artificial mist, fire trucks to douse the streets with water and chemical dust suppressants.

“These are hardly measures,” said Bhavreen Kandhari, a member of Warrior Moms, a group of mothers lobbying for cleaner air. “These are reactions, knee-jerk reactions. Till you have the political intent, you know nothing is going to happen.”

Broadly, India’s air quality suffers from its appetite for fossil fuels, which has only grown after two decades of rapid economic growth. Last year, India was home to 15 of the 20 cities with the most hazardous air globally , and health experts have detailed how such conditions can lead to brain damage , respiratory problems and early death.

Weaning the country off coal and other dirty fuels will be difficult, a reality underscored by climate negotiations that took place in Glasgow, Scotland, this month. India already struggles to meet its basic power needs. During the Scotland talks, India and China teamed up to insist upon a last-minute amendment to the language of the accord, to “phase down” coal rather than ease it out.

Mr. Modi argues that India’s increasing use of coal and other fossil fuels is helping build an economy that is lifting millions out of poverty. But emissions from burning coal make the pollution problem worse for city dwellers, particularly the poor, who cannot afford air purifier machines or the electricity to run them.

Cities also suffer from growing car emissions and the fires that the poorest residents burn to cook food and keep warm, especially when colder weather hits in November. New Delhi’s air quality takes a particular hit from the burning of crop stubble by farmers in the neighboring states of Punjab and Haryana. The bad air settles over the Indo-Gangetic plain of northern India, trapped on either side by the Thar Desert and the Himalayas, forming a toxic stew.

Mr. Modi’s government says that Delhi, which is run by an opposition political party, has failed to enforce its own pollution-reduction policies, such as a limit on vehicular traffic on days when pollution surges. Earlier this week, Adesh Gupta , the Delhi president of Mr. Modi’s Bharatiya Janata Party, said that Delhi’s top elected official, Arvind Kejriwal, should resign.

“Instead of making Delhi a world-class city as he claimed, Kejriwal has made it a smog city,” Mr. Gupta said.

Delhi officials in turn say Mr. Modi’s government has failed to persuade farmers in nearby states to stop clearing their fields with fires.

“Farmers in neighboring states are compelled to burn stubble as their governments are doing nothing for them,” Mr. Kejriwal said.

The Supreme Court stepped in last year, too, ordering the two sides to take steps like enforcing a ban on farm fires and capturing power plant emissions. It also ordered Delhi early last year to build the two experimental smog towers, despite experts’ doubts about their impact. A study last year in the peer-reviewed journal Atmosphere called the approach unscientific.

“Can we vacuum our air pollution problem using smog towers? The short answer is no,” the researchers said.

Still, they are a tempting refuge for people desperate to escape the city’s bad air.

As a coppery sun set behind smoky skies, Jasmer Singh rested under a smog tower in central Delhi as it sucked in polluted air. A monitor measuring the levels of dangerous particulate matter showed that the air it spit out was slightly cleaner, but far from what the World Health Organization considers safe.

Still, Mr. Singh, a volunteer at a nearby Sikh temple, said, “around here, the air is good, lighter and better.”

Some members of both Mr. Modi’s party and the opposition say they want to take a serious, nonpartisan look at the problem.

“The blame game will be always there,” said Vikas Mahatme, a lawmaker with the B.J.P. Summing up the attitudes of many politicians, he said, “Why one should bother about other states? They are not voters to consider.”

Still, getting all sides to work together will be difficult, he acknowledged. “We are not very active,” he said. “I tell you freely.”

Hari Kumar is a reporter in the New Delhi bureau. He joined The Times in 1997. More about Hari Kumar

Emily Schmall is a South Asia correspondent based in New Delhi. More about Emily Schmall

Doctors Advising Kids, Vulnerable Populations To Leave Delhi-NCR Due to Toxic Air

Delhi engulfed by smog on November 2, 2023. Photo: Shekhar Tiwari/The Wire

Humans of Air Pollution: India’s Growing Tribe of Environmental Refugees

This is the third and final part of a three-part series on India’s small but growing tribe of environmental migrants. Currently, these “pollution refugees” come from the privileged class of the educated aware. But as awareness grows and people connect the dots around the health harm pollutants trigger – in this case, the human cost of air pollution – the country will see a greater migration from extremely high to relatively low pollution areas, says Jyoti Pande Lavakare, who has been tracking this space since 2014. Read part 1 here and part 2 here .

It isn’t as if leaving for a few days during episodic peaks can save us from the long-term impact of breathing toxic air. Many suffering folk leave Delhi during Diwali and around Christmas, the two highest peak pollution times, some under the express advice of their doctors and when fortunately, school holidays allow them to flee. Pulmonologist Dr Randeep Guleria is just one among the several doctors who have told me they have begun advising patients, especially the elderly to get out of the city that its own chief minister calls a “gas chamber.” (It’s a separate matter that his rival party, the right-wing Bharatiya Janata Party, instead of trying to improve pollution, politicised the matter by calling him Hitler.)

It’s also a separate matter that even though people can escape episodic peaks, which cause the maximum and most intensive damage, there are enough reports that show that lower levels of air pollution still trigger disease and disability. Experts speak in one voice when they say, “There are no safe levels of air pollution,” whether it is the World Health Organisation , the National Institutes of Health , the American Journal of Respiratory and Critical Care Medicine or others. Very simply put, it is the difference between smoking 2 cigarettes or 20. Both harm our health, but smoking two will harm relatively less than smoking 20 cigarettes. Dr Naresh Trehan was probably among the earliest Indian doctors to call attention to how exactly harmful Delhi air was through pictures of healthy pink and sooty black lungs of two similar aged non-smoking men, one living in Himachal Pradesh and the other in Delhi. Many more doctors have joined these ranks and there is now a network of “passionate and informed” doctors called Doctors for Clean Air who are fighting against air pollution.

As far back as 25 years ago, a study by doctors had shown that high episodic air pollution increased cardiovascular and respiratory events, leading to higher emergency room visits at AIIMS, with increased morbidity and mortality from acute asthma, acute exacerbation of chronic obstructive airway disease and acute coronary events. 

Have you ever noticed, even anecdotally, how many more heart attacks and lung failures happen just after Diwali and in the polluted north Indian winter? Dilshad Master-Kumar’s father-in-law Narendra “Bull” Kumar passed away in polluted December. Another example is Ratan Lal, film-maker and clean air evangelist Nutan Manmohan’s father. “P ost-Diwali pollution created a complex set of medical problems for my father,” she says with conviction. Escaping Delhi’s post-Diwali air in 2018, Manmohan was jolted out of her sleep early one polluted morning with a phone call that informed her that her father, who she says was “a robust man,” had fainted. Since he had past cardiac history, he was rushed to Delhi’s Escorts Heart hospital. But what he was diagnosed to have was severe lung infection. Fortunately, her father pulled through, but doctors advised him to stay strictly indoors to protect himself from the high pollution levels. But when he finally resumed work in February, “tests showed that the winter had certainly left its impact,” says Nutan. Basically, “pollution triggered a lung infection. With his lungs compromised, his heart had to work harder, which affected the already weak heart valves. He was absolutely fine before Diwali. By the end of winter, he had a massive heart attack and we lost him.”

Studies correlating high episodic air pollution with ER visits, morbidity and mortality hide several stories similar to this.

As recently as 2021, a study correlating high pollution levels to emergency room visits from acute respiratory symptoms  in two Delhi hospitals showed that children were even more severely affected . 

Children are the most vulnerable because their airways and lungs are smaller and still developing, They also absorb greater amounts of pollution as they breathe more rapidly. In fact, children born and raised (and breathing) in Delhi have been found to have smaller lung size , said Dr S.K. Chhabra, former director-professor at Vallabhbhai Patel Chest Institute in the Journal of Indian Paediatrics.  They also have lower lung function compared to children who grow up in areas of lower pollution. A study by the Chittaranjan National Cancer Institute showed that 53% of children in polluted cities have impaired lung function, liver and brain ailments with boys five times more prone than girls We are setting up our young for failure from an early age – and this, in a country where we talk proudly of our young demographic. 

Ishita Mohindra, a highly educated first-time mother in her early 30s belongs to this young demographic. Mohindra moved back to Delhi from London where she and her husband were working in order to be close to parents, family and homeland. She sent me a desperate text Thursday morning even as I was wrapping up writing this story. “ I’m just so distraught about the quality of air. The helplessness and lack of effort from multiple agencies is starting to affect me so much that I want to start having conversations about moving again. How did you deal with it? The stress of it all is getting to me and affecting my mental health!” 

I met Mohindra three years ago through our dog playgroup, when she was considering starting a family. I sense the panic of a young, new mother in her text. She knows what I went through with my young children when we moved back to India. She just wants to protect her two-month old, enjoy and bond with her daughter while on maternity leave from the multinational she works for. “What to do though? Just sit inside with the purifiers on? I was looking forward to taking my girl out on a stroller but I can’t even imagine it at this point for the next few weeks (possibly months),” she texts. Today’s AQI in Delhi is 343 according to SAFAR’s monitor and is predicted to rise to 349 tomorrow. Independent weather agencies show much higher levels, with Delhi’s Anand Vihar levels going off the charts at 999 .  If people flout the cracker ban again this year, this number could go up beyond 3200, which is unreal, but was measured by my friend Barun with his trusty DustTrak in Diwali 2013. Young, educated Indians like Mohindra, who are in their prime productive stage of life still have the agency to migrate away from their homeland. And they will, if nothing strong and significant is done to bring levels down urgently, becoming pollution migrants despite wanting to stay in their home country, close to their parents and extended family.

Transparency precedes accountability; need data, science but also year-round action

After the sharp peak in north India’s air pollution in October-November, often the wind and rain end up cleaning up the air by early December. However, the dip in temperature towards the end of December leads to another cluster of fires – that of the urban and rural poor, who burn biomass for heating in addition to cooking. 

All this information on fires is usually publicly and transparently available on government websites, from where air pollution experts and atmospheric scientists scrape it to analyse data and devise mitigation strategies that policy makers can execute. But this year, in an unexpected move mid-October, as soon as AQI started worsening, the government walled this data without any explanation. Real-time data adds to the science behind air pollution. And since the most precise measuring machines – large, expensive, reference grade instruments – are owned by the government and paid for by tax money, there is no reason why this data shouldn’t remain public.

In a similar oblique move aimed at discrediting data by low-cost sensors, the CPCB on March 25 released a circular stating that it would not be using measurements from low-cost sensors for regulatory purposes as ‘its accuracy, linearity, reliability, and long-term performance are not yet fully established.’ Low cost sensors bridge the critical gap between ‘no monitoring’ and ‘very precise monitoring’ by expensive machines and these have been accepted by the government’s own National Clean Air Programme. India needs 4,000 continuous air monitoring stations, but has less than 20% of that number. Additionally, these are skewed towards urban metros, with Delhi-NCR hogging the lion’s share.

Transparency precedes accountability, and without data to make the invisible visible, like my friend’s DustTrak did for me, it is easy to sidestep this “wicked” problem that kills more people in India than Covid ever did. What the eye cannot see, the mind doesn’t know. But more than all of this is the need for serious, urgent, proactive and long-term action in reducing emissions.

In fact, during the COVID-19 pandemic, air pollution continued to play its stealthy role as a hidden killer. Research showed that more people got COVID and more severely in areas that were more polluted. Simple logic suggests that had India’s pollution levels been lower, fewer people could have suffered and died from COVID. When the rest of the world was going through a pandemic, India was going through a twindemic.

If COVID-19 was a visible, viciously virulent, insanely infectious pandemic, killing swiftly and mercilessly, air pollution is its invisible, non-communicable evil twin, killing unhurriedly, under the radar, but equally ruthlessly. It is a non-communicable disease pandemic in slow-motion, matching – if not surpassing – the cataclysmic fury of SARS-CoV-2. Air pollution in India kills nearly 1.7 million each year. COVID-19 killed less than one-third of that number in one year.

In this context, it may sound dramatic when people like Jaidhar Gupta, a long-time Delhi resident, call this “mass genocide,” but air pollution is definitely a violation of human rights , according to David Boyd, the United Nations Special Rapporteur on Human Rights and Environment, when the involuntary act of just breathing can slowly and silently kill you.

“I have bronchial asthma. My child has bronchial asthma.” We have no option but to leave north India during its extreme pollution peaks, Gupta says bluntly. “I’m a pollution refugee.”   

Band-aid measures normalise high levels of pollution

Despite these winter pollution peaks, all that the government does is announce short-term band aid measures, some under a reactive, knee-jerk emergency plan called the “ Graded Response Action Plan “, a misnomer which has neither action, nor any real planning embedded in it.  

This plan gets activated only when pollution levels get to “ very poor ” (above 300) and severe (above 400) levels, when activities such as banning construction and entry of commercial diesel vehicles, or closing schools, staggering work timings or even worse, red herrings like anti-smog guns (imagine a diesel truck going around the city spraying scarce water to wash down particulate matter like fake rain ) are implemented, even as company and government-sponsored marathons like the annual “Run for Unity” continue to exhort people to come and exercise in these hazardous levels of pollution, without sharing any health warnings. Every year, telecom giant Airtel’s well-known half-marathon takes place in the most polluted time of the year. This year’s Vedanta half-marathon on October 15 took place on the first day the AQI slipped to the “poor” category.  These events, more than anything else, normalise high levels of air pollution. 

That may finally be changing. For the first time in history an Indian cricket team captain has expressed concern over poor air quality publicly. The issue of air quality had been raised earlier during the World Cup by Joe Root following England’s defeat to South Africa in Mumbai.

“I’ve not played in anything like that before,” Root had said. “I’ve obviously played in hotter conditions, and probably more humid conditions. But it just felt like you couldn’t get your breath. It was like you were eating the air . It was unique.” These public statements have nudged the Board of Cricket Control of India to issue a statement saying they won’t add to the pollution by bursting firecrackers at the end of the matches.

This is an improvement from December 2017, when a Sri Lankan player vomited on the field, was escorted off the ground as heavily polluted air continued to plague an international cricket test match in Delhi and yet many, including some BCCI officials, dismissed this saying that if the Indian team and viewers on the stands had no problems, why were the Sri Lankans making such a big deal about it. But just not bursting crackers just isn’t enough. 

Fortunately, the Supreme Court, forced to step in in 2016, in response to a petition by three toddlers for their fundamental right to breathe clean air, has already banned crackers. First, it banned the distribution and sale of fireworks, followed the next year by a ban on their manufacture, an excellent ruling because in addition to PM 2.5, firecrackers add toxic metals to the air, making post-Diwali air even more poisonous.  But more needs to be done proactively and urgently through the year instead of just not “adding” to the pollution. 

With Mumbai air becoming as bad or worse than Delhi air, my worst fear is that the Maharashtra government may also succumb to the kind of  pseudo-science and wasteful use of public funds in building giant outdoor air purifiers that atmospheric scientists have declared ineffective and inefficient.  

Three years ago, for instance, despite the science, the courts directed the Delhi government to build two anti-smog towers, which cost the public exchequer around Rs 44 crore. After these were commissioned with great fanfare, the government admitted in parliament that the towers were ineffective.

There is only one way to reduce pollution: bring down emissions at source. Nothing else works. This isn’t rocket science – and don’t forget, India has the capability of getting to the moon. Let us contextualise this debate in the words of Chandra Bhushan , the c hief executive officer at International Forum for Environment, Sustainability & Technology (iFOREST) who put it very simply on NDTV last evening. “Air pollution is nothing but what we burn. India burns 2 billion tonnes of solid material… Of this 1.1 billion tonnes is coal.” Another 600 million tonnes is crop residue burnt by farmers and biomass for cooking. “There is no action on solid fuel. No country in the world has been able to address pollution without addressing the issue of pollution from solid fuels,” he says. It’s that simple, really. We need to strictly ban open waste burning, (even our public landfills are constantly catching fire) crop stubble burning (which is another story of growing the wrong crop in the wrong state at the wrong time) and have a solid waste burning policy in addition to clean energy and renewable energy fuelling public mobility. So far, the focus has been on liquids (petrol, diesel) and gases (CNG, LPG). As for useless burning like crackers, that shouldn’t even cross our minds.

So it isn’t as if the solutions to this problem, this twindemic, this public health emergency aren’t known. They are, and countries like Mexico and China have demonstrated they can clean the air, and quickly. The COVID-19 lockdown also showed us blue skies in Delhi, demonstrating that the problem is solvable and is reversible.  

The real reason why my tribe of pollution migrants is growing is different. It is because we are losing hope, because we know we are running out of time. And that is because some of us have begun to realise that, as founder and former president of the World Resources Institute, co-founder of the Natural Resources Defense Council and adviser to two former US Presidents, Gus Speth articulated very succintly:

“I used to think the top environmental problems were biodiversity loss, ecosystems collapse and climate change. I thought that with 30 years of good science we could address those problems. But I was wrong. The top environmental problems are selfishness, greed and apathy… and to deal with those we need a spiritual and cultural transformation and we, (lawyers) and scientists, don’t know how to do that.” 

And selfishness, greed and apathy is harder to get rid of than pollution. 

Read part 1 here and part 2 here .

Jyoti Pande Lavakare is the author of Breathing Here is Injurious to Your Health: The Human Cost of Air Pollution , published by Hachette in November 2020. 

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Source Governance-Oriented Zoning for Heavy Metal Pollution in Farmland Soil: a Case Study of an Industrial Park in Mid-Western Shaanxi Province, China

• Published: 17 May 2024
• Volume 235 , article number  330 , ( 2024 )

Cite this article

• Tao Chen   ORCID: orcid.org/0000-0001-9162-4078 1 , 2 ,
• Rui Zhang 3 ,
• Honglei Wang 1 ,
• Xinping Dong 1 ,
• Shunan Zheng 1 , 4 &
• Qingrui Chang 1  

Soil heavy metal pollution in farmland has raised widespread concern. Implementing zoning governance is critical for enhancing the efficiency of pollution prevention and control practices. Traditional zoning approaches focus more on the status of soil pollution rather than its sources, potentially resulting in subsequent governance measures being either irrelevant or only having short-term effects. Integrating information about heavy metal sources into zoning strategies is beneficial for effective pollution control. This study analyzed heavy metals in topsoil within a typical industrial park, identifying their primary sources through multivariate statistics and spatial pattern analysis. The contributions of these sources were then quantified using absolute principal component score-multiple linear regression (APCS-MLR), positive matrix factorization (PMF), and ensemble models. Based on the differences in source contributions, the study area was divided into different sub-regions by fuzzy clustering method (FCM), and appropriate management strategies were proposed for each. The results showed that cobalt (Co), chromium (Cr), copper (Cu), and nickel (Ni) were primarily derived from natural sources, with their contributions ranging from 82.82% to 89.38%. Lead–Zinc smelting was identified as a significant contributor, accounting for 65.18% of cadmium (Cd), 73.33% of lead (Pb), and 44.41% of zinc (Zn). Mercury (Hg) emissions were predominantly attributed to power plants, constituting 53.41% of the total. The study area was divided into three sub-regions, each with distinct major pollution sources. A high-risk area covering 1.553 km 2 was identified in the sub-region with the most severe pollution, primarily caused by Pb–Zn smelting activities. For this high-risk area, targeted interventions at the source were recommended, such as optimizing smelting processes, increasing recovery efficiencies, reducing waste emissions, and creating buffer zones. This study confirmed the feasibility of adopting source-based zoning strategies, which yielded valuable insights for the effective management of other pollutants.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This research was supported by the Fundamental Research Funds for the Central Universities (2452018143).

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College of Natural Resources and Environment, Northwest A&F University, Yangling, 712100, Shaanxi, China

Tao Chen, Honglei Wang, Xinping Dong, Shunan Zheng & Qingrui Chang

Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture and Rural Affairs, Yangling, 712100, Shaanxi, China

School of Geography and Ocean Science, Nanjing University, Nanjing, 210093, JiangSu, China

Rural Energy & Environment Agency, Ministry of Agriculture and Rural Affairs of China, Beijing, 100125, China

Shunan Zheng

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Tao Chen sampling, supervision, reviewing, editing. Rui Zhang sampling, investigation, writing. Honglei Wang model validation. Xinping Dong software analysis. Shunan Zheng supervision, resource. Qingrui Chang supervision.

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Chen, T., Zhang, R., Wang, H. et al. Source Governance-Oriented Zoning for Heavy Metal Pollution in Farmland Soil: a Case Study of an Industrial Park in Mid-Western Shaanxi Province, China. Water Air Soil Pollut 235 , 330 (2024). https://doi.org/10.1007/s11270-024-07131-3

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Received : 09 January 2023

Accepted : 03 May 2024

Published : 17 May 2024

DOI : https://doi.org/10.1007/s11270-024-07131-3

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