case study of air pollution in kerala

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Burden of Outdoor Air Pollution in Kerala, India—A First Health Risk Assessment at State Level

Myriam tobollik.

1 Department of Environment and Health, School of Public Health, Bielefeld University, Bielefeld, Universitätsstraße 25, Bielefeld 33615, Germany

2 Federal Environment Agency, Section Exposure Assessment and Environmental Health Indicators, Corrensplatz 1, Berlin 14195, Germany; E-Mails: [email protected] (D.W.); [email protected] (D.P.)

Oliver Razum

3 Department of Epidemiology and International Public Health, School of Public Health, Bielefeld University, Bielefeld, Universitätsstraße 25, Bielefeld 33615, Germany; E-Mail: [email protected]

Dirk Wintermeyer

Dietrich plass.

Ambient air pollution causes a considerable disease burden, particularly in South Asia. The objective of the study is to test the feasibility of applying the environmental burden of disease method at state level in India and to quantify a first set of disease burden estimates due to ambient air pollution in Kerala. Particulate Matter (PM) was used as an indicator for ambient air pollution. The disease burden was quantified in Years of Life Lost (YLL) for the population (30 + years) living in urban areas of Kerala. Scenario analyses were performed to account for uncertainties in the input parameters. 6108 (confidence interval (95% CI ): 4150–7791) of 81,636 total natural deaths can be attributed to PM, resulting in 96,359 (95% CI : 65,479–122,917) YLLs due to premature mortality (base case scenario, average for 2008–2011). Depending on the underlying assumptions the results vary between 69,582 and 377,195 YLLs. Around half of the total burden is related to cardiovascular deaths. Scenario analyses show that a decrease of 10% in PM concentrations would save 15,904 (95% CI : 11,090–19,806) life years. The results can be used to raise awareness about air quality standards at a local level and to support decision-making processes aiming at cleaner and healthier environments.

1. Introduction

Air pollution is a well-known risk factor causing human ill-health. It is responsible for thousands of premature deaths, particularly in South Asia [ 1 ]. Considering global levels of ambient particulate matter (PM), India ranks tenth and thus is among the most polluted countries with an annual average PM 10 level of 134 μg/m 3 . 42 Indian cities are listed among the 100 most polluted cities worldwide [ 2 ]. According to the latest update of the Global Burden of Disease (GBD) 2010 study 627,426 (95% CI : 528,681–726,434) deaths were caused by ambient PM pollution in India. Combining mortality and morbidity effects of air pollution, and using the Disability-Adjusted Life Year (DALY) as a measure for population health, 17,760,000 healthy life years (95% CI : 15,201,700–20,705,000) were lost in India in 2010. Most of the DALYs were lost due to mortality effects (95.6%) with only 4.4% attributable to outcomes of morbidity [ 1 ].

To estimate the health risk of air pollution an indicator needs to be defined that approximates the level of air pollution. One of the best studied indicators are PM 10 (coarse particles smaller than 10 µm in aerodynamic diameter) and PM 2.5 (fine particles smaller than 2.5 µm in aerodynamic diameter) [ 3 , 4 ]. PM is a mixture of small components and thus not a homogeneous stressor and its composition varies by location and sources [ 5 , 6 , 7 ]. The respirable fraction of PM consists mainly of organic and elemental carbonaceous materials; inorganic components such as sulfate, nitrate, and ammonium; and metal components such as iron, aluminum, nickel, copper, zinc, and lead. Coarse particles comprise primarily organic and elemental carbon and metals such as silicon, magnesium, iron, ions like sulphates, nitrates, and ammonium [ 8 ]. In India the main anthropogenic sources of PM are road traffic emissions, industrial combustion plants, commercial, and residential combustion such as cooking with solid fuels, and agricultural activities [ 8 ]. Additional regional sources are road dust, waste burning, and sea salt [ 9 ].

Exposure to PM can cause several adverse health effects, including mortality and morbidity outcomes [ 5 ]. The exposure to PM is associated with an increased health risk when inhaling fine particles. Once inhaled, these particles can harm the cardiovascular system by inflammation or coagulation. Additionally, the respiratory system can be harmed, because PM can trigger pulmonary oxidative stress [ 6 ].

Based on scientific evidence regarding adverse health consequences of air pollution the World Health Organization (WHO) recommends an annual mean of not more than 20 µg/m 3 and 10 µg/m 3 for PM 10 and PM 2.5 , respectively [ 3 ]. To prevent and control air pollution, India issued the Air Prevention and Control of Pollution Act in 1981 and developed National Ambient Air Quality Standards (NAAQS) to regulate pollutant emissions. In 2009, the standards were updated and 12 air pollutants are currently regulated. The annual mean standards for PM 10 and PM 2.5 are 60 and 40 µg/m 3 [ 10 ].

To tackle the air pollution problem, regional assessments are necessary especially in countries such as India where large health and environmental disparities exist. The differences between states in terms of population, climate, and air pollution are large and need to be considered in state-specific assessments. Such adapted risk assessments can help to raise awareness for ambient air pollution and the resulting health risks. Furthermore, they can support policy and programs and help to introduce measures to reduce ambient air pollution [ 11 , 12 , 13 ].

For our purpose, we focus Kerala, a state on the southern tip of the Indian subcontinent with a coastline of about 580 km. In 2011, Kerala had 33.4 million inhabitants (16.0 million males and 17.4 million females). Nearly half of these people reported living in urban areas (47.7%). With a sex ratio of 1084 women per 1,000 men, Kerala has the highest share of females in the population among all Indian states [ 14 ].

In this study we aim to test the feasibility of the environmental burden of disease at state level in India. In addition, we quantify a first set of disease burden estimates due to ambient air pollution in urban areas of Kerala.

2. Data and Methods

2.1. quantification method.

In our study, the disease burden of ambient air pollution was quantified by the mortality component (Years of life Lost) of the DALY. The DALY measure generally combines morbidity (Years Lived with Disability-YLDs) and mortality (Years of Life Lost-YLLs) in one measure and thus allows comparisons of different diseases, interventions, populations, and periods [ 15 , 16 ].

The YLLs were calculated using the Environmental Burden of Disease (EBD) approach, an extension of the burden of disease approach, which was developed by WHO, the World Bank and the Harvard School of Public Health [ 17 , 18 ]. The number of deaths in a certain age-group attributable to ambient PM exposure was multiplied with the remaining life expectancy at the age of death. The deaths attributable to PM exposure were calculated as a population attributable fraction (PAF) and suitable concentration-response functions [ 17 ]. The concentration-response functions are available with a confidence interval (CI) and the upper and lower bounds were used to calculate the CI of the YLLs. The PAF was calculated with this formula [ 19 , 20 ]:

The calculations were performed in an Excel environment using predefined and adapted spreadsheets as published by the WHO [ 21 ]. Uniform age-weights and no time-discount were applied for the estimates. To account for the state-specific setting, the life expectancies from the urban population of Kerala was used [ 22 ].

2.2. Data Input

Several datasets are needed to perform the calculation of EBD [ 17 ]. Table 1 summarizes the input data used in this study.

Input data used for the calculation of EBD due to ambient air pollution in urban Kerala.

ICD: International Classification of Diseases, a standard diagnostic tool to classify diseases.

2.2.1. Particulate Matter Data

PM 10 is measured all over India. It is monitored in an eight-hour sampling twice a week, which results in 104 observations per year [ 8 ]. For Kerala, these data were available on the internet (open access) in form of regularly published reports by the Central Pollution Control Board (CPCB) [ 23 ]. Table 2 shows the data available for Kerala, which were based on 17 measurement sites in six cities. Therefore only the burden of diesease of the urban population can be assessed.

Annual mean PM 10 concentration (in μg/m 3 ) measured by CPCB at six locations in Kerala from 2008 to 2011, Source [ 23 ].

For the quantification of the disease burden attributable to air pollution an annual mean value of 44.9 µg/m 3 PM 10 was calculated by taking into account all measured values from 2008 to 2011 to smooth out annual outliers. The most current evidence on concentration-response functions relate to PM 2.5 instead of PM 10 but for Kerala comprehensive PM 2.5 data were not available, therefore, PM 10 measurements were converted into PM 2.5 by using recommendations of the WHO [ 3 ] and two recent studies from India [ 23 , 28 ] suggesting PM 2.5 to PM 10 ratios of 0.4, 0.5, and 0.7.

Furthermore, a counterfactual value was needed to assess the negative health effects above the comparative exposure concentration. For PM 2.5 so far no threshold was identified below which no negative health effects of PM 2.5 occur [ 3 , 29 ]. Nevertheless zero pollution is not a realistic assumption due to natural sources of PM 2.5 . Therefore we used two different counterfactual levels: (a) a theoretical minimum exposure of 7.3 µg/m 3 derived from the largest cohort study on air pollution in the United States of America [ 29 , 30 , 31 ]; and (b) the guideline value of 10 µg/m 3 as recommended by the WHO air quality guidelines [ 3 ].

2.2.2. Concentration-Response Functions

Two concentration-response functions were used in this assessment. One is from an international meta-analysis because it was assumed that pooled results of different studies increases the consistency and validity of the concentration-response function. The other is from a Chinese cohort study because the exposure situation in China is rather comparable with the one in India ( Table 3 ). For clear assignment of concentration-response functions and health data, International Classification of Disease 10 (ICD-10) codes were used.

Selected concentration-response functions for all-cause mortality and cardiovascular mortality and PM 2.5 /PM 10 exposure.

2.2.3. Population Data

Population data were obtained from the Indian census 2011 [ 32 ]. The data are stratified by states, sex, five-year age groups, and the rural-urban status. Life tables for Kerala with reference years from 2006 to 2011 are also available from the census [ 22 ].

2.2.4. Mortality Data

For mortality data, two data sources were used. For the EBD calculation only natural deaths (ICD 10: A00-R99) and for the scenario analysis only cardiovascular deaths (ICD 10: I00-I99) are needed. Therefore data from the Report on Medical Certification of Cause of Death 2010 [ 26 ] and the Vital statistics of India based on the Civil Registration System 2011 [ 27 ] were combined by applying the cause of death rates to the total number of deaths. These data are stratified by states, sex, five-year age groups) and urban-rural status.

Table 4 shows the demographic data for the urban population of Kerala in the year 2010. From the about 33.4 million inhabitants 7,610,740 men and 8,307,037 women lived in urban areas. The largest numbers of individuals are in the age group 0 to 14 years (676,030 boys and 647,412 girls). A sex difference is visible with 696,297 more women in the total population. The opposite distribution can be found in the mortality data. More men than women died in 2010 (48,292 men and 33,346 women). Most of the deaths occurred in older age groups (age group 70+:5727 per 100,000 men and 3,872 per 100,000 women, respectively).

Demographic data of the population living in urban areas of Kerala in 2010, stratified by age groups and sex, Sources: [ 14 , 22 , 26 , 27 ].

2.2.5. Scenario Analyses

To reflect existing uncertainties in the input data, several input parameters were altered: two concentration-response functions, three PM 2.5 to PM 10 ratios, and two counterfactual values were used to estimate the impact of parameter changes on the EBD estimates. Combining the different options resulted in ten scenarios ( Table 5 ). The baseline scenario ( Natural Deaths (ND)_Baseline (1)) summarizes the assumption of a conservative concentration-response function, the mid value of the PM 2.5 to PM 10 ratio, and the theoretical minimum risk exposure as counterfactual value.

Parameter scenario descriptions by considered concentration-response functions, PM 2.5 to PM 10 ratios, and counterfactual values. ND: Natural Deaths, CD: Cardiovascular Deaths.

a Hoek et al. [ 24 ], b Zhang et al. [ 25 ], c WHO [ 3 ], d Satsangi et al. [ 28 ], e Lim et al. [ 31 ].

Air pollution is not a constant environmental factor and the counterfactual values used for the quantification are currently not achievable in India, therefore two additional and more realistic assumptions were considered: a possible decrease and increase in PM 2.5 by 10% each ( Table 6 ).

Air pollution scenario descriptions by considered concentration-response functions, PM 2.5 to PM 10 ratios, counterfactual values, and assumptions on the development of PM.

a Hoek et al. [ 24 ], c WHO [ 3 ], e Lim et al. [ 31 ].

3. Results and Discussion

3.1. results.

In the recent years (2008–2011), the annual mean PM 10 concentrations in ambient air in urban areas of Kerala did not exceed the national guideline value of 60 μg/m 3 —except for two values in Trivandrum in 2008 and 2009 which were slightly above the guideline value ( Table 2 ). However, the measured values were considerably higher than the guidelines recommended by WHO (20 µg/m 3 PM 10 ).

In the baseline scenario ( ND_Baseline (1) ), 6,108 ( CI : 4150–7791) of the 81,636 total natural deaths in the urban population of Kerala can be attributed to ambient air pollution by PM 2.5 ( Figure 1 ). Hence 7.5% of deaths can be attributed to PM 2.5 . Stratified by sex and in absolute numbers, more attributable deaths were modeled for men with 3,613 ( CI : 2455–4609) deaths, as compared to 2495 ( CI : 1695–3183) deaths for women. According to the assumptions in the different scenarios the results vary markedly. The lowest burden in terms of premature deaths was estimated for scenario ND_High PM 2.5 to PM 10 ratio (3) , using the more conservative value for the conversion factor from PM 2.5 to PM 10 (0.4). The highest burden can be found in scenario ND_Alternative CRF (5) , with 22,785 ( CI : 21,912–23,909) attributable deaths.

Deaths attributable to air pollution (PM) by different scenarios for the male and female urban population of Kerala. ND: Natural Deaths, CD: Cardiovascular Deaths.

The scenario analysis shows that many deaths which can be attributed to ambient air pollution by PM 2.5 are due to cardiovascular causes. In the baseline scenario ( Cardiovascular Deaths (CD)_Baseline (6) ) 51% of the male and 49% of the female cardiovascular deaths can be attributed to air pollution. The sex difference was smaller than for the total natural deaths. The lowest number of premature death cases occur in scenario CD_Low PM 2.5 to PM 10 ratio (7) and the highest in scenario CD_Alternative CRF (10).

Comparable patterns of disease burden were estimated for YLLs attributable to ambient PM 2.5 ( Table 7 ). In the baseline scenario ( ND_Baseline (1) ) for total natural deaths, 96,359 ( CI : 65,479–122,917) life years were lost due to PM 2.5 . The absolute burden was higher in the male population with 58,868 ( CI : 40,003–75,094) YLLs compared to 37,490 ( CI : 25,476–47,823) YLLs in the female population. Per 100,000 people 605 ( CI : 411–772) years of life are lost. Scenario ND_ Low PM 2.5 to PM 10 ratio (2) and ND_ High PM 2.5 to PM 10 ratio (3) show the impact of the change in the conversion factor from PM 2.5 to PM 10 . If a conversion factor of 0.4 was applied, the burden was calculated to 69,582 ( CI : 46,873–89,463) YLLs. If the ratio was 0.7, the burden more than doubles to 146,020 ( CI : 100,860–183,589) YLLs. Scenario ND_ Alternative counterfactual value (4) represents the results of using a counterfactual value of 10 µg/m 3 PM 2.5 , with 80,434 ( CI : 54,375–103,085) YLLs. These results are lower compared to the baseline scenario because adverse health effects below 10 µg/m 3 PM 2.5 were not included. The highest burden was estimated for scenario ND_ Alternative CRF (5) with 359,465 ( CI : 345,695–377,195) YLLs, which is more than 3.5 times that of the baseline scenario.

YLLs and YLLs per 100,000 inhabitants due to PM 2.5 in urban areas of Kerala, stratified by sex, CI in parentheses.

In scenario analyses, which specifically estimate the disease burden for cardiovascular diseases in the baseline scenario ( CD_Baseline (6) ) 47,966 ( CI : 24,998–62,688) years of life are lost due to ambient PM 2.5 pollution. As in the natural deaths calculation, a sex difference is visible with more male YLLs.

The age pattern of the disease burden in the baseline scenario ( ND_Baseline (1) ) is shown in Figure 2 . For both sexes the disease burden is increasing throughout the age-groups, with some minor decreases. The highest burden in natural deaths is in the oldest age group, with over 27% of YLLs in men and even 42% of YLLs in women. The cardiovascular death burden is also highest in the oldest age group.

Age patterns of YLLs per 100,000 people due to PM in the baseline scenarios ( ND_Baseline (1) and CD_Baseline (6) ), in urban Kerala.

Assuming that air pollution and thus the PM 2.5 concentration levels will change in the future, Figure 3 shows the impact of a 10% decrease of PM 2.5 ( ND_ 10% decrease in PM 2.5 (12) and C D_ 10% decrease in PM 2.5 (14) ) and a 10% increase ND_ 10% increase in PM 2.5 (11) and C D_ 10% increase in PM 2.5 (13) ). Improved air quality regarding PM 2.5 would reduce the burden by 15,904 ( CI : 11,090–19,806) to 80,455 ( CI : 54,389–103,111) YLLs as compared to the baseline scenario ( ND_Baseline (1) ). In scenario C D_ 10% increase in PM 2.5 (13) , 41,745 ( CI : 21,519–54,992) YLLs still can be attributed to air pollution by PM 2.5 , which would be 5954 ( CI : 3479–7696) YLLs less compared to the cardiovascular baseline scenario ( CD_Baseline (6) ).

Impact on the burden of disease in urban Kerala of 10% less and 10% more PM 2.5 compared to the baseline scenario, scenarios 11 to 14.

A worsening of air quality by 10% more PM 2.5 would increase the burden to 109,242 ( CI : 74,547–13,826) YLLs. In total 12,884 ( CI : 9068–15,909) YLLs more would occur due to higher PM 2.5 concentrations. The cardiovascular burden would increase to 53,930 ( CI : 28,405–69,951) YLLs.

3.2. Discussion

The aim of the study was to test the feasibility of the environmental burden of disease approach at state level in Kerala, India, and to quantify a first set of disease burden estimates due to ambient air pollution by PM 2.5 . In general, despite some limitations in data availability, the method was applicable at state level. The disease burden due to ambient air pollution for the urban population was estimated to the best of our knowledge for the first time, using state specific data such as PM 10 concentrations, population and mortality data. Data on air pollution were freely available, but the locations of the single measurement sites were missing as well as exposure data for the rural population. Therefore, population-weighted exposure modeling was not possible. Nevertheless, the results show the importance of air pollution as a threat to population health in Kerala.

The ambient PM 10 values in Kerala did not exceed the Indian national guidelines. However, these standards are lagging far behind other national and international standards [ 9 ]. Our results support this criticism by showing the burden of PM 2.5 . Further, realistic future scenarios of PM 2.5 were assessed, demonstrating that a worsening of air pollution (a 10% increase in PM 2.5 ) would increase the mortality-associated disease burden by around 13%. By improving air quality (a 10% decrease in PM 2.5 ), around 17% of the disease burden attributed to PM 2.5 could be prevented.

The scenario analysis shows that around half of the natural deaths which can be attributed to PM 2.5 are due to cardiovascular causes ( ND_Baseline (1) and CD_Baseline (6) ): natural deaths 6108 ( CI : 4150–7791) and cardiovascular deaths 3,311 ( CI : 1725–4327) attributable to air pollution). The other 50% of the natural deaths may have other causes like lung cancer, chronic obstructive pulmonary disease, or other respiratory diseases.

In our assessment, we used air pollution data published by CPCB of India. Comparing these data to other sources provides some differences in the EBD results. The national annual average of PM 10 concentration in ambient air in India from 2009 to 2012 was 132 µg/m 3 [ 33 ]. This value is much higher than the value we used for our calculations (44 µg/m 3 PM 10 ). In the last GBD study a weighted annual mean PM 2.5 of 27.2 µg/m3 was used to quantify the burden of air pollution in India [ 34 , 35 ], which is in the range of PM 2.5 we applied (18.0 to 31.5 µg/m 3 PM 2.5 ). Data from the same source extracted for Kerala provide an even lower value of 14.5 µg/m 3 PM 2.5 . This population weighted mean refers to the state of Kerala in total and thus includes rural areas [ 35 ].

In the scenario analyses different PM 2.5 to PM 10 ratios were assessed because so far PM 2.5 is not comprehensively measured in India and no agreed and exact ratio is available. The ratios applied in our study vary from 0.4 to 0.7 [ 23 , 28 ]. The results differ accordingly: when applying a ratio of 0.4, the burden is 69,582 ( CI : 46,873–89,463) YLLs for natural causes, which is around half of the burden when applying a ratio of 0.7 (146,020 ( CI : 100,860–183,589).

The disease burden estimates are a first set of results and should be interpreted with caution. No estimates for Kerala were available so far, therefore estimates from India and South Asia were used to compare the results. In the recent burden of disease estimates published by WHO, the premature deaths attributable to ambient air pollution in South East Asia were 52 per 100,000 persons for 2012 [ 36 ]. In our study, 38 natural deaths per 100,000 people ( CI : 26 – 49) can be attributed to ambient air pollution in Kerala. However, considering the much broader reference area in the WHO estimates, the differences are reasonable—particularly so as the WHO applied much higher PM level values than we did in our assessment.

In the GBD 2010 study the mortality part of disease burden of ambient PM in India was estimated to be 1,358 (95% CI : 1192 – 1617) YLLs per 100,000 people, which is much higher than our estimate (605 ( CI : 411–772) YLLs per 100,000 people). Reasons for the large differences could be the different input data. In the GBD 2010 study, many more deaths were considered in the calculation (896 male and 771 female deaths per 100,000 for India and our numbers are 635 males and 401 females deaths per 100,000 for Kerala) and different concentration-response functions and life expectancies were applied because in the GBD study an international comparison was targeted [ 31 ]. Another reason for the comparably low disease burden calculated in the present assessment could be that nearly half of the population in Kerala was living in urban areas, but only around 34.4% of deaths were reported there.

Because of the complex and data-demanding calculations, our study also faces some limitations, mostly related to data quantity and quality. The best available data were used, but still limitations and uncertainties occur which are discussed in the following.

No comprehensive data on the cause of death were available. Therefore, data from the Report on Medical Certification of Cause of Death 2010 and the Vital statistics of India based on the Civil Registration System 2011 were combined, while keeping in mind that the combination of two data sources can lead to several uncertainties. Data on causes of death were classified in 10-year age groups. In order to enable reliable quantifications it was necessary to distribute these data to five-year age groups using population data. This does not give an accurate distribution of the death causes. Nevertheless, for the assignment of the remaining life expectancy to the age groups and the quantification of the YLLs it is sufficiently detailed.

The highest available age group in the mortality data is 70 years and older. In this age group the disease burden caused by PM is the highest for natural deaths as well as for cardiovascular deaths with respect to absolute numbers of premature deaths. More detailed data for the age groups older than 70 years would allow more accurate results because there is evidence that elderly people are more susceptible to the effects of air pollution [ 37 ]. Additionally, young children, undernourished people, and those with pre-existing health problems should be considered in more detail because they would benefit most from better air quality [ 38 , 39 ].

Beside the uncertainties related to the PM exposure, the conversion factor from PM 10 to PM 2.5 and mortality data, the applicability of concentration-response functions is questionable. Over the last decades a growing number of experimental and epidemiological studies increased the knowledge of the association between PM exposure (especially PM 2.5 ) and adverse health effects [ 3 , 4 , 24 , 40 ], but evidence concerning the statistical relationship, which is needed for an health risk assessment, is still limited, especially for India [ 13 ]. During the last few years, two time series studies for Delhi and Chennai were conducted to assess the link between PM exposure in ambient air and the number of natural deaths. However, these results cannot be directly used in this assessment, because the study focused on short-term exposure solely [ 41 ]. So far no cohort studies on long term exposure to air pollution and mortality have been reported for India [ 42 ]. Therefore, and because of broad consistency of Asian time-series studies with European and North American studies, the Health Effects Institute (HEI) supports the use of results from Western cohort studies, if data for estimating the burden of disease attributable to air pollution in Asia is missing [ 42 ].

Nevertheless, this approach has limitations because the concentration-response functions were derived at lower levels of air pollution than observed in Asia and thus the results must be interpreted with caution. Therefore, two concentration-response functions were used in this assessment to show the impact of this input variable. Comparing scenario ND_Alternative counterfactual value with an excess risk of 6% (95% CI : 4%–8%) per 10 µg/m 3 increase in PM 2.5 exposure with scenario ND_Alternative CRF and a 24% (95% CI : 22%–27%) excess risk per 10 µg/m 3 PM 10 increase, shows a difference of 279,031 ( CI : 291,321–274,110) YLLs. Thus, applying the concentration-response function estimated for China resulted in a burden four times as high as when using the pooled concentration-response function. These large differences prove the strong influence of the concentration-response function on the EBD calculation and the need to research on concentration-response functions for India and other South Asian countries. Compared to the other input variables the choice of the concentration-response function has the largest effect on the results.

Several other adverse health effects of exposure to PM are discussed, but convincing evidence is still lacking [ 4 , 40 ]. As soon as sufficient evidence is available, further disease endpoints need to be included in the estimation processes to better illustrate and underline the importance of air pollution as a major health threat. Additionally the related health data (mortality and morbidity) is needed to assess the effects of PM comprehensively. If, for example, prevalence data on cardiovascular and respiratory health outcomes would be available, the morbidity part (years lived with disability) could be quantified as well.

The approach presented here can be adopted by other Indian states by applying respective population and ambient air pollution data. However, the availability of concentration-response functions should be examined because, compared to other Indian states, the air pollution levels in Kerala are relatively low. Thus if necessary, an adapted concentration-response function for higher air pollution levels and another slope (e.g. supralinear) should be applied to avoid an overestimation [ 43 ]. Finally, the assessment needs to be further developed in the direction of an integrated approach by including rural settings as well as indoor exposure as suggested by Balakrishnan, Dhaliwal and Shah [ 12 ].

3.2.1. Implication for Further Research

• Conduct a cohort study to assess the effects of long-term air pollution exposure on health outcomes (mortality and morbidity) and to derive representative concentration-response functions for Indian settings.
• Expand the number of measurement parameters of air pollution, like PM 2.5 , to provide more specific and reliable data for health risk assessments. Likewise, the number of measurement sites should be increased to also cover rural areas. This would allow a much more comprehensive risk assessment.
• Assess indoor air pollution as well and include measurements in the YLL estimations at state level.

3.2.2. Practical Implications

The identification of the sources of air pollution is another step to develop effective mitigation policies. India’s development over the last decades is characterized by a social and economic progress which is directly linked to industrialization, urbanization, and motorization, all leading to an increase in ambient air pollution. This development will most probably continue in the future [ 44 ]. In particular, the demand for personal transport and the amount of goods transportation are increasing steadily [ 45 ]. This in turn leads to an increase of pollutant emissions from vehicle exhausts. Therefore, actions to reduce hazardous emissions are needed, such as fuel emission standards or a shift to a safer and cleaner public transport alternative [ 9 ]. Because public transport is not an option for everybody, actions addressing the road conditions and the traffic itself are needed. Poor road conditions, the high number of vehicles, waterlogging during monsoons, and people on the street interrupt the traffic and lead to traffic congestions, which in turn can increase the fuel consumption [ 44 ]. Better road maintenance, paving of unpaved roads, and silt removal would be possible actions. The low quality of fuel and lubricating oil currently used also contributes to poor air quality [ 44 ]. CPCB is already discussing a road map for fuel quality improvement in India.

Further pollution sources are solid fuels used for cooking and industrial emissions. The latter need to be regulated by appropriate policies, for example by a shift from coal based industry to the use of cleaner and renewable fuels such as wind or solar energy. A shift to cleaner fuel is also needed for cooking, because indoor air pollution causes a considered health burden [ 12 ].

4. Conclusions

Our findings show that the EBD method is applicable at state level and can be applied to other Indian states as well. The results indicate, that even if local air quality standards are met, a considerable health burden for the population living in urban Kerala can be assumed, which can be partly prevented by taking actions to reduce air pollution. Compared to other Indian states Kerala shows relative low annual PM levels, thus the burden of disease due to PM in other Indian states is expected to be even higher. Further estimates for other Indian states can help to complete the overall picture and allow for state-wise comparisons.

Acknowledgments

Myriam Tobollik was supported with a traveling grant from the project “A new passage to India” kindly provided by DAAD (German Academic Exchange Service).

We gratefully acknowledge support for the Article Processing Charge by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.

Author Contributions

Myriam Tobollik conceived the study, built the models, gathered and compiled the data and performed the analyses. Myriam Tobollik drafted the first version of the manuscript with editorial input from Dietrich Plass. Oliver Razum, Dirk Wintermeyer, Dietrich Plass interpreted the results and provided critical feedback on the manuscript. Oliver Razum helped setting up the study. All authors read and approved the final version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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Phytoremediation for urban landscaping and air pollution control—a case study in Trivandrum city, Kerala, India

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• Volume 28 , pages 9979–9990, ( 2021 )

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• Ancy S Watson 1 &
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Air pollutant concentration of Trivandrum, the capital of Kerala, exceeded the limits of National Ambient Air Quality (NAAQ) standards, according to a study conducted in 2015 by NATPAC. These polluted corridors harbour vegetation on roadsides and traffic islands, planted solely for aesthetic appeal. Analysis of air pollution tolerance levels of existing plants can act as a scientific basis for efficient planning of the urban landscape. Sixty-seven species, including flowering, fruit-bearing, ornamental, shade-providing and timber-yielding species, were screened for their relative resistance to air pollution. Based on leaf pH, relative water content, chlorophyll and ascorbic acid levels, the Air Pollution Tolerance Indices (APTI) of each species were formulated and they were grouped into the following: tolerant, moderately tolerant, intermediate and sensitive groups. Agave americana (18.40), Cassia roxburghii (17.63), Anacardium occidentale (11.97), Cassia fistula (11.60), Mangifera indica (11.59) and Saraca asoca (10.88) may be considered for planting near green spaces like roundabouts and near pollution prone industrial areas, as they belong to tolerant category. Comparison of APTI during summer and monsoon also revealed the stability of Agave americana , Saraca asoca , Ficus benghalensis , Peltophorum pterocarpum , Ficus elastic a, Ixora finlaysoniana , Mangifera indica , Canna indica and Delonix regia in maintaining pollution tolerance even during water disparity. Agave americana , Anacardium occidentale , Ficus elastica , Mangifera indica , Syzygium cumini , Ficus benghalensis , Nerium oleander and Ficus benjamina were found to be suited for mass planting, as was evident from their Anticipated Performance Indices (API).

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Impact and pollution indices of urban dust on selected plant species for green belt development: mitigation of the air pollution in ncr delhi, india.

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Acknowledgements

Facilities: The Principal, University College, University of Kerala, Trivandrum.

Secondary data: Kerala State Pollution Control Board (KSPCB), Kerala Forest Department (KFD) and National Transportation Planning and Research Centre (NATPAC).

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Watson, A.S., Bai R, S. Phytoremediation for urban landscaping and air pollution control—a case study in Trivandrum city, Kerala, India. Environ Sci Pollut Res 28 , 9979–9990 (2021). https://doi.org/10.1007/s11356-020-11131-1

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Analysis of Air Pollution in Three Cities of Kerala by Using Air Quality Index

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Air pollution has an appalling effect on human health and our planet has a whole. This study quantifies air pollution using a parameter – Air Quality Index and compare the pollution of air in six major sites across Kerala, India (South over bridge and Eloor in Ernakulam district, Pettah and Veli in Thiruvananthapuram district, Chavara and Kadappakada in Kollam district). The significant differences in seasonal variation of pollutants in the three districts are also studied. The conclusion of the study shows: a) Air Quality Index in all these areas is predominantly determined by Particulate Matter concentration; b) Ernakulam district has the highest Air Quality Index among both industrial and residential areas and Kadappakada, Kollam has the lowest Air Quality Index among all the six areas studied; c) The air quality deteriorates during winter and summer because of limited pollutant dispersion.

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• DOI: 10.1007/s11356-020-11131-1
• Corpus ID: 226274406

Phytoremediation for urban landscaping and air pollution control—a case study in Trivandrum city, Kerala, India

• Ancy S. Watson , Sudha Bai R
• Published in Environmental science and… 7 November 2020
• Environmental Science

15 Citations

Assessment of air pollution tolerance potential of selected dicot tree species for urban forestry.

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Environmental impacts of air pollution and its abatement by plant species: A comprehensive review

The tolerance index for different growing tree plant species in jubail industrial city, a polluted area, ksa, evaluation of air pollution tolerance index and anticipated performance index of six plant species, in an urban tropical valley: medellin, colombia, phytoremediation: the sustainable strategy for improving indoor and outdoor air quality, a comparative study of air pollution tolerance capabilities of four tree species in xi’an city, china, assessment of air pollution tolerance index (apti) and anticipated performance index (api) of selected roadside plant species for the green belt development at ratnagiri city in the konkan region of maharashtra, india, selection of tropical trees and shrubs for urban greening in coal mine complex: a case study of singrauli, madhya pradesh., phytoremediation as a potential technique for vehicle hazardous pollutants around highways., investigating the biochemical responses in wheat cultivars exposed to thermal power plant emission, 57 references, environmental assessment of air pollution on roadside plants species at dehradun, uttrakhand, india, comparative evaluation of air pollution tolerance of plants from polluted and non-polluted regions of bengaluru, evaluation of air pollution tolerance index of selected plant species along roadsides in thiruvananthapuram, kerala., air pollution tolerance index of plants, evaluation of selected plant species as a bio-indicators of particulate automobile pollution using air pollution tolerance index (apti) approach, impact and pollution indices of urban dust on selected plant species for green belt development: mitigation of the air pollution in ncr delhi, india, variation in air pollution tolerance index of plants near a steel factory: implications for landscape-plant species selection for industrial areas, physiological responses of some tree species under roadside automobile pollution stress around city of haridwar, india, phytoremediation of air pollutants: a review, air pollution tolerance index of various plant species growing in industrial areas, related papers.

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The public interest litigation was instituted with a view to ensure healthy living and eradication of air pollution even though initially the provocation was the formation of fog in the city of Cochin. The scope of the writ petition was expanded by impleading industries and departments which were suspected to be the cause for the air pollution within the city and the adjoining areas of the city. An expert body like NEERI was entrusted with the work of study of the air pollution. The Government of Kerala had already notified under Sec.19(1) of the air (Prevention and Control of Pollution) Act, 1981 dated 31-7-1984 declaring the area coming under the Corporation of Cochin as an Air Pollution Control Area KEA No.1. The various sources of air pollution caused in Cochin are from industrial plants, motor vehicles, sewers and domestic drains emanating foul gases. Some of the major pollution are ammonia, sulphur dioxide, carbone monoxide, nitrogen dioxide etc..The Executive engineer, Air Pollution Control Cell of the Kerala State Pollution Control Board submitted a detailed report as per the directions of this Court. The report has given a background of the unusual phenomena called Smog in the city of Cochin, concluding that the presence of ammonia in humid winter conditions is one the principle cause of the often seen fog in Cochin city. The respondents replied that their factories have conformed to the standards prescribed and the emissions from their factories were within the norms prescribed by the Government. The Court, based on the reports of both these expert bodies, found that some of the major industries are yet to comply with the conditions of the consent orders. Similarly the Corporation of Cochin and the State Government are yet to make effective steps for the control of pollution, even with regard to the the emissions of gases from the open sewers,being one of the major air pollutant, and to pollution deriving from traffic congestion in the city. The Court held that the conclusion and recommendations of the NEERI are to be followed for maintaining the Cochin area clear of pollution,and are to be considered by the Pollution Control Board as to form part of the conditions for granting consent orders. Some of the recommendations of the NEERI are to be carried out by the State Government and the other departments. It direct The Executive Engineer, AIR Pollution Control Cell of the Kerala State Pollution Control Board to monitor and evaluate the performance of the industries and also the Corporation of Cochin and other bodies who are responsible in containing the air pollution in and around the city of Cochin and submit yearly reports on or before 31st December of the year, of the action taken in this regard to the Court.

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Burden of Outdoor Air Pollution in Kerala, India—A First Health Risk Assessment at State Level

Affiliations.

• 1 Department of Environment and Health, School of Public Health, Bielefeld University, Bielefeld, Universitätsstraße 25, Bielefeld 33615, Germany. [email protected].
• 2 Federal Environment Agency, Section Exposure Assessment and Environmental Health Indicators, Corrensplatz 1, Berlin 14195, Germany. [email protected].
• 3 Department of Epidemiology and International Public Health, School of Public Health, Bielefeld University, Bielefeld, Universitätsstraße 25, Bielefeld 33615, Germany. [email protected].
• 4 Federal Environment Agency, Section Exposure Assessment and Environmental Health Indicators, Corrensplatz 1, Berlin 14195, Germany. [email protected].
• 5 Federal Environment Agency, Section Exposure Assessment and Environmental Health Indicators, Corrensplatz 1, Berlin 14195, Germany. [email protected].
• PMID: 26343701
• PMCID: PMC4586631
• DOI: 10.3390/ijerph120910602

Ambient air pollution causes a considerable disease burden, particularly in South Asia. The objective of the study is to test the feasibility of applying the environmental burden of disease method at state level in India and to quantify a first set of disease burden estimates due to ambient air pollution in Kerala. Particulate Matter (PM) was used as an indicator for ambient air pollution. The disease burden was quantified in Years of Life Lost (YLL) for the population (30 + years) living in urban areas of Kerala. Scenario analyses were performed to account for uncertainties in the input parameters. 6108 (confidence interval (95% CI): 4150-7791) of 81,636 total natural deaths can be attributed to PM, resulting in 96,359 (95% CI: 65,479-122,917) YLLs due to premature mortality (base case scenario, average for 2008-2011). Depending on the underlying assumptions the results vary between 69,582 and 377,195 YLLs. Around half of the total burden is related to cardiovascular deaths. Scenario analyses show that a decrease of 10% in PM concentrations would save 15,904 (95% CI: 11,090-19,806) life years. The results can be used to raise awareness about air quality standards at a local level and to support decision-making processes aiming at cleaner and healthier environments.

Keywords: Air pollution; India; Kerala; Years of Life Lost (YLL); environmental burden of disease; particulate matter.

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• Does utilizing WHO's interim targets further reduce the risk - meta-analysis on ambient particulate matter pollution and mortality of cardiovascular diseases? Liu Z, Wang F, Li W, Yin L, Wang Y, Yan R, Lao XQ, Kan H, Tse LA. Liu Z, et al. Environ Pollut. 2018 Nov;242(Pt B):1299-1307. doi: 10.1016/j.envpol.2018.07.041. Epub 2018 Jul 28. Environ Pollut. 2018. PMID: 30121484 Review.
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Journal of Industrial Pollution Control

ISSN (0970-2083)

AN ANALYSIS OF AIR POLLUTION IN KERALA

Amogh P. Kumar 1 , Mohandas K 2 , kshama AV 1 * , Paul lazarus T 3 , Salma muslim 1 and Santha AM 4

1 M.Sc. (Agricultural Economics) students, Department of Agricultural Economics, College of Agriculture, Vellayani, Kerala Agricultural University, Kerala, India.

2 PhD (Agricultural Economics) Student, Department of Agricultural Economics, College of Agriculture, Vellayani, Kerala Agricultural University, Kerala, India.

3 Assistant Professor (SS), Department of Agricultural Economics, College of Agriculture, Vellayani, Kerala Agricultural University, Kerala, India.

4 Associate Professor and Head, Department of Agricultural Economics, College of Agriculture, Vellayani, Kerala Agricultural University, Kerala, India.

Received date: 02 March, 2017; Accepted date: 23 May, 2018

Visit for more related articles at Journal of Industrial Pollution Control

Air pollution index is an overall scheme that transforms the weighted values of individual air pollution related parameters into a single number. In the Indian context, most commonly used air pollution index (API) is a four parameter model. The longer and more intense the exposure of people to air pollutants such as particulate matter (PM), nitrogen oxides, carbon monoxide, sulphur dioxide etc., the greater the negative impact on their health. The effects range from minor eye irritation, respiratory symptoms, to decreased lung and heart function, hospitalization and even premature death. Hence this study was conducted to study the quality of air in all the districts of Kerala for a period of nine years during the period 2008-2016.

Air Pollution Index, Kerala.

Introduction

Air is an important and vital component of earth’s environment and slight change in its composition can have varied effects on growth and development of organisms on this planet. Air pollutants released from various sources exert detrimental effects on vegetation. The major reason for air pollution is industrial emissions including automobile emissions. Air pollutants have a lot of adverse effects on various platforms. So it is very much important to monitor the air quality status of an area to know whether it is polluted or not. According to source apportionment studies conducted by the Central Pollution Control Board (CPCB, 2010), in cities such as Delhi, Kanpur, Bangalore, Pune, Chennai and Mumbai show that transport sector contributes to more than 70% of the ambient air pollution.

The scientific evidence about the health effects of air pollution is compelling. The longer and more intense the exposure of people to air pollutants such as particulate matter (PM), nitrogen oxides, carbon monoxide, sulphur dioxide etc., the greater the negative impact on their health. The effects range from minor eye irritation, respiratory symptoms, to decreased lung and heart function, hospitalization and even premature death. Hence this study was conducted to study the quality of air in all the districts of Kerala for a period of nine years during the period 2008-2016 (Biju and Vijayan, 2014; Bindu, 2008).

Materials and Methods

All the 14 districts of Kerala were selected for the study. With in each district, few locations were chosen for a period of nine years from 2008 to 2016 and they are as follows Thiruvananthapuram (4 locations), Ernakulam (7 locations), Kollam, Alappuzha, Kottayam, Kozhikode, Kannur and Kasargode (2 locations each), Pathanamthitta, Idukki, Thrissur, Palakkad, Malappuram and Wayanad (1 location each). The monthly and yearly averages of different pollutants such as SO 2 , NO 2 , SPM and RSPM were studied.

Air Pollution Index (API)

Air pollution index is an overall scheme that transforms the weighted values of individual air pollution related parameters into a single number. In the Indian context, most commonly used air pollution index (API) is a four parameter model as, shown below.

where SO 2 , NO 2 , SPM (suspended particulate matter) and RSPM (respirable suspended particulate matter) are measured values, SSO 2 , SNO 2 , SSPM and SRSPM are standard values as per the National Air Quality Standard, 2009. The range of air quality index and its interpretations are given in the Table 1 below.

Table 1: Range of air quality index and its interpretation

Fine particles (PM 2.5 ) pose greatest health risk. These fine particles can get deep into lungs and some may even get into the bloodstream, Exposure to these particles can affect a person’s lungs and heart. Coarse particles (PM 10-2.5 ) are of less concern, although they can irritate a person’s eyes, nose and throat (United States Environmental Protection Agency, 2018). Hence Central Pollution Control Board (CPCB) eliminated SPM in ambient air from the standard in November 2009 (National Ambient Air Quality Status and Trends in India-2010). (Kerala State Pollution Control Board, 2010) (KSPCB) is a subsidiary of CPCB and this instruction has been followed. Subsequently in all the Water and Air Quality Directories published by the KSPCB after 2010 have not included data on SPM (Cropper, et al ., 1997; Dcruz, et al ., 2017; Khan and Ghouri, 2011; Waseem, et al ., 2013).

However, the formula for calculating API remains unchanged and the resultant API values tend to be smaller by ignoring SPM values from the calculation of API. The entire data for this study were obtained from various issues of Water and Air Quality Directory published by KSPCB.

Results and Discussion

The annual average of SO 2 , NO 2 , RSPM and SPM from 14 districts of Kerala have been shown in ( Fig.1 ). The value of highest SO 2 was observed during the year 2008 (4.24 μg/m 3 ) and the lowest during the year 2015 (2.89 μg/m 3 ). The value of NO 2 was highest during the year 2016 (15.86 μg/m 3 ) and the lowest during 2013 (9.71 μg/m 3 ), average RSPM level was highest during the year 2013 with a value of 45.58 μg/m 3 and the lowest during 2011 (38.4 μg/m 3 ).

Fig 1: Air pollutants in Kerala during 2008-2016.

The values of SPM were available only for three years during 2008-10. We can see that SPM values were highest during the year 2008 with a value of 79.21 μg/m 3 followed by 2009 (76.17 μg/m 3 ) and least in the year 2010 with a value of 64.03 μg/m 3 . There is a gradual decrease in SPM in the ambient air during this period.

( Fig. 2 ) shows the Air Pollution Index for three years with SPM. The figure shows that during the year 2008 API value was highest ie.,70.89 followed by the year 2009 with a value of 68.18 and least in the year 2010 with a value of 59.34. The values of API fall within the range of 50-75 during three years. Hence we can say that in Kerala as a whole, moderate air pollution existed during the years 2008-10. Moreover, the quality of air has improved during the period under consideration. API values calculated by including SPM are higher than those calculated by excluding SPM.

Fig 2: Air pollution index including SPM during 2008-2010.

Air pollution index excluding SPM during 2011- 2016 has been shown in ( Fig. 3 ). The API was highest during the year 2016 (38.41) followed by 2012 (36.28), 2013 (36.16), 2014 (36.07), 2015 (35.64) and least in the year 2011 (32.67). The values of API fall within the range of 25-50 during the six years. Hence we can say that all the districts of Kerala had light air pollution during 2011-16.

Fig 3: Air pollution index excluding SPM during 2011-2016.

From Tables 2 and 3 , we can classify the districts in Kerala on the API both by including and by excluding SPM into four ranges of air pollution index. Idukki has least API both with and without SPM among all districts of Kerala.

Table 2: Average API values including SPM in different districts of Kerala during 2008-2010

Table 3: Average API values excluding SPM in different districts of Kerala during 2011-2016

Table 4 shows that four districts viz., Thiruvanathapuram, Alappuzha, Palakkad and Kasargode had experienced heavy air pollution which needs to be addressed as it is affecting the quality of ambient air. Seven districts fall under moderate air pollution category which is indicating the need for measures to control which otherwise would fall under heavy air pollution category.

Table 4: API including SPM during 2008-2010

Table 5 shows that eleven districts fall under the range of light air pollution and two districts under moderate air pollution. Idukki and Patanamthitta districts had clean air.

Table 5: API excluding SPM during 2011-2016

( Fig. 4 ). shows the SO 2 , NO 2 , RSPM and SPM levels over the different months in an year in 14 districts of Kerala. The SO 2 is at its peak in the month of October (5.09 μg/m 3 ) and falls to 3.11 μg/m 3 in July. NO 2 levels reach a peak during the month of July (18.89 μg/m 3 ) and drastically reach the lowest in the next month of August (10.8 μg/m 3 ). The RSPM values are high during the initial two months of the year (January and February with values of 52.58 and 51.16 μg/m 3 respectively) and it falls to 35.72 μg/m 3 in the month of August. Level of SPM starts with a value of 81.98 and 80.94 μg/m 3 during January and February respectively and falls to the lowest value of 62.26 μg/ m 3 in July. Similar trend is observed in both RSPM and SPM values during the year.

Fig 4: Average values of pollutants from all districts during different months.

( Fig. 5 ). shows the average Air Pollution Index over different months in an year. The maximum API is observed in the month of January (76.41) followed by February (74.83) and the least during the month of August (56.58). Majority values of API fall within the range 50-75 but only in the month January it falls within the range of 75-100. Hence we can conclude that during February to December there was moderate air pollution whereas the month of January witnessed heavy air pollution.

Fig 5: API from all districts during different months.

Air pollution index is an important measure to check the quality of the air that surrounds the environment within which we live. The analysis carried out suggests that when the air is mixed with suspended particulate matter the quality of air decreases. The district of Thiruvananthapuram, the capital city of Kerala, experienced heavy air pollution which attracts attention of the government, NGOs, private organizations and the public to reduce the pollution of air by planting more trees, more usage of public transport than the private vehicles, pollution test for all the vehicles periodically, setting up of new standards for the control of air pollution, creating awareness among the people by organizing street plays or through programmes or campaigns etc. so that the people will understand the ill effects of low quality air and come forward to reduce the pollution and thereby improve the quality of air.

• Biju, B. and Vijayan, N. (2014). Estimation of health impacts due to air pollution in Thiruvananthapuram City. Int. J. of Innovative Res. in Sci. Eng. Technol . 3(7) : 14900-14907.
• Bindu, G. (2008). Interpretation of air quality data using air quality index for the city of Cochin. India. J. of Ind. Pollut. Control . 24(2) : 115-159.
• Cropper, L.M., Simon, B.N., Alberini, A., Arora, S. and Sharma, P.K. (1997). The health benefits of air pollution control in Delhi. Am. J. of Agrl. Econ . 79(5) : 1625-1629.
• Dcruz, J.J., Kalaiarasan, P. and Nath, G.A. (2017). Air quality study of selected areas in Kerala State. Int. Res. J. of Eng. Technol . 4(4) : 3517-3521.
• Kerala State Pollution Control Board. (2010). Thiruvananthapuram, Water and Air Quality Directory (Various issues).
• Khan, A.M. and Ghouri, M.A. (2011). Environmental pollution: Its effects on life and remedies. Int. Refereed Res. J . 2(2) : 276-286.
• National Ambient Air Quality Status and Trends in India. (2010). Central Pollution Control Board, Ministry of Environment and Forests, GOI.
• United States Environmental Protection Agency. (2018). http://www.3epa.gov/region/airquality/pm-human-health.html [25 May 2018]
• Waseem, S., Ashraf, A., Khanam, S. and Ahmad, A. (2013). Effects of indoor air pollution on human health: A micro-level study of Aligarh City-India. Merit Res. J. of Educ. And Rev . 1(6) : 139-146.

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• Published: 02 September 2024

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

• Kuldeep Singh Rautela 1 &
• Manish Kumar Goyal 1  

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

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

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Introduction.

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

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

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

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

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

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

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

Results and discussion

Consequences of air pollution in india.

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

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

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

Contributors to air pollution in India

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

Particulate matter (PM)

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

Organic aerosols (OAs)

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

Black carbon (BC)

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

Water-soluble brown carbon (WS-BrC)

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

Volatile organic compounds (VOCs)

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

Current remediation techniques

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

Legalisation and Evaluation of NAAQS in India 12 .

Legislation and regulatory measures

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

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

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

Air pollution monitoring network

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

Evolution of studies on emission load

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

Management strategies and control policies

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

Judicial interventions

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

Executive actions

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

AI&ML Techniques for addressing and forecasting air pollution

Overview of ai&ml models.

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

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

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

Application of AI&ML in addressing and forecasting air pollution

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

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

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

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

Performance analysis

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

Prediction of PM 2.5 concentrations

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

RMSE loss during the training, testing and validation phase.

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

Model evaluation parameters used for prediction the PM 2.5 concentrations.

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

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

Challenges and limitations

Technological barriers.

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

Regulatory and enforcement challenges

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

Public awareness and participation

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

Agricultural practices and crop burning

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

Urbanization and infrastructure development

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

Cross-border pollution

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

Climate change interlinkages

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

Socio-economic disparities

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

Future prospects

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

Emerging technoloagies

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

Global collaborations

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

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

Materials and methods

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

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

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

Convolutional Autoencoder model

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

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

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

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

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

Data availability

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

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Acknowledgements

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

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

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A case study of air quality data of Ernakulam district

R. Saraswathy 1 and C. Sathidevi 2

1 P.G Student, Department of Mathematics, Amrita Vishwa Vidyapeetham, Kerala, India. 2 Assistant Professor, Department of Mathematics, Amrita Vishwa Vidyapeetham, Kerala, India.

* Corresponding author: [email protected]

The main objective of an economist is to predict the future of any problem concerning the common man. Considering the human health in India, air pollution becomes a matter of major concern. The present study analyses the air quality data of Ernakulam district in Kerala by assessing various atmospheric pollutants and meteorological factors. The air pollution analysis of southern state, says that Ernakulam district has not been able to sustain the air quality gains of 2020 in the year 2021. As per 2021 air quality categorisation of analysis of days, it is seen that the number of days with worse air quality index has increased two times compared to previous year. The study comes to the conclusion that the meteorological factors have a great impact on the atmospheric pollutants and increasing humidity help to improve pollution. Also, the particulate matters are the major contributer for the increase in air quality index level. The most recent XLSTAT version and python programming were used to infer the results in this study. The researcher contends that, steps are to be taken to reduce the impact of solar radiations which helps in reducing the air quality level.

Key words: Air Quality Index / Meteorological factors / Air pollution

© The Authors, published by EDP Sciences, 2023

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PCB suggests study to spot air pollution sources in Kochi, Thiruvananthapuram

Project may cost nearly ₹2 crore depending on size of city and its rate of development.

Published - July 18, 2021 01:09 am IST - Kochi

The State Pollution Control Board (PCB) has proposed a year-long study to identify the sources and extent of air pollution in Kochi and Thiruvananthapuram.

The recommendation was placed before the Southern Bench of the National Green Tribunal (NGT) on July 14 in the case related to air pollution and related issues in eight cities in the State. A detailed investigation into the sources and extent of air pollution in Kochi and Thiruvananthapuram has been proposed as a pilot initiative before extending it to other regions.

The tribunal had asked the authorities to ascertain the probable peak period and conduct the ambient air quality study to curb air pollution. They were also directed to carry out emission inventory of air pollutants and source apportionment studies to ascertain the nature of pollution. An expert committee constituted by the NGT had pointed out that the study to identify the sources and extent of pollution would take at least 15 months, as it included monitoring and air quality modelling.

The official representing the Central Pollution Control Board (CPCB) had said that the study would cost nearly ₹2 crore depending on factors like the size of the city and its rate of development. The board had informed the tribunal of the time required for carrying out an elaborate research. It proposed that a pilot study be done in Kochi and Thiruvananthapuram.

Funding is also a major hurdle, as the board lacks the wherewithal to meet it. A funding pattern where the CPCB will meet 50% of the expenses is on the anvil. The remaining 50% will be met by the State government and the board, it is learnt.

The CPCB has already framed a conceptual methodology for carrying out emission inventory and source apportionment studies based on inputs from organisations like IITs, Automotive Research Association of India, National Environmental Engineering Research Institute, and The Energy and Resources Institute. The methodology to be followed for studies in Kochi and Thiruvanananthapuram will be decided by the expert committee.

Published - July 18, 2021 01:09 am IST

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Stanisci, I.; Sarno, G.; Curzio, O.; Maio, S.; Angino, A.A.; Silvi, P.; Cori, L.; Viegi, G.; Baldacci, S. Air Pollution and Climate Change: A Pilot Study to Investigate Citizens’ Perception. Environments 2024 , 11 , 190. https://doi.org/10.3390/environments11090190

Stanisci I, Sarno G, Curzio O, Maio S, Angino AA, Silvi P, Cori L, Viegi G, Baldacci S. Air Pollution and Climate Change: A Pilot Study to Investigate Citizens’ Perception. Environments . 2024; 11(9):190. https://doi.org/10.3390/environments11090190

Stanisci, Ilaria, Giuseppe Sarno, Olivia Curzio, Sara Maio, Anna Antonietta Angino, Patrizia Silvi, Liliana Cori, Giovanni Viegi, and Sandra Baldacci. 2024. "Air Pollution and Climate Change: A Pilot Study to Investigate Citizens’ Perception" Environments 11, no. 9: 190. https://doi.org/10.3390/environments11090190

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Sudha Nambudiri reports from the southern state of Kerala. She writes on climate change, science and technology, social issues, and culture.

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