Special Issue on Air Pollution and its Impact in South and Southeast Asia (VI)

Sreyashi Debnath This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Rama Krishna Karumuri3, Gaurav Govardhan1,4, Rajmal Jat1, Himadri Saini5, Akash Vispute1, Santosh H. Kulkarni6, Chinmay Jena7, Rajesh Kumar8, D.M. Chate6, Sachin D. Ghude This email address is being protected from spambots. You need JavaScript enabled to view it.1 

1 Indian Institute of Tropical Meteorology (IITM), Ministry of Earth Sciences, Pune, India
2 Development of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, India
3 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
4 National Center for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida, UP, India
5 University of New South Wales (UNSW), Sydney, Australia
6 Centre for Development of Advanced Computing (C-DAC), Pune, India
7 India Meteorological Department, New Delhi, India
8 National Center for Atmospheric Research, Boulder, USA

Received: March 21, 2022
Revised: July 7, 2022
Accepted: July 28, 2022

 Copyright The Author's institutions. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. 

Download Citation: ||https://doi.org/10.4209/aaqr.220112  

Cite this article:

Debnath, S., Karumuri, R.K., Govardhan, G., Jat, R., Saini, H., Vispute, A., Kulkarni, S.H., Jena, C., Kumar, R., Chate, D.M., Ghude, S.D. (2022). Implications of Implementing Promulgated and Prospective Emission Regulations on Air Quality and Health in India during 2030. Aerosol Air Qual. Res. 22, 220112. https://doi.org/10.4209/aaqr.220112


  • Adoption of promulgated (S2) and ambitious prospective regulations (S3) in 2030.
  • Evaluation of air quality and health benefits under S2 and S3 scenarios in India.
  • Decrease by 15% and 49% in ambient PM2.5 exposure under S2 and S3 respectively.
  • 38% and 62% states would meet NAAQS of 40 µg m3 under S2 and S3 respectively.
  • S2 and S3 are projected to prevent 8.3% and 23.6% premature mortalities in 2030.


To improve ambient air quality, India has laid out strict action plans to reduce the increment in emissions over regional to urban scale by the year 2030. This study evaluates policy-induced improvement in air quality and associated health benefits achievable due to reduction in PM2.5 exposure under the adoption of promulgated (S2) and ambitious prospective regulations (S3) with respect to the scenario for Business As Usual (BAU) in 2030. The Weather Research and Forecasting model coupled with online chemistry (WRF-Chem) has been used to simulate ambient PM2.5 exposure to the population under BAU, S2 and S3 emission scenarios. Results show 15% (9 µg m–3) and 49% (32 µg m–3) decreases in all India ambient PM2.5 exposure under S2 and S3 scenarios, respectively, with respect to the BAU scenario. Throughout India, under the S2 and S3 scenarios, 38% and 62% of states would meet the annual National Ambient Air Quality Standard (NAAQS) of 40 µg m–3, respectively. We projected that the S2 emission regulation scenario would prevent 274,000 (8.3%) premature mortalities and improve mean life expectancy by about 0.6 ± 0.2 years in 2030 relative to the BAU scenario. On the other hand, pursuing an ambitious emission scenario, S3 would prevent 775,000 (~23.6%) premature mortality burden and improve mean life expectancy by about 1.9 ± 0.7 years in 2030. Results indicate that ambitious actions beyond the ambitious prospective regulations are vital to gain significant health benefits.

Keywords: Air quality, Premature mortality, Emission scenarios, Particulate matter, WRF-Chem


As part of the initial commitments to the Paris agreement in December 2015, India has committed to the conservation and protection of the environment with sustainable development within the ambit of targeted goals to reduce the increment in emissions with respect to the scenario for Business As Usual (BAU) by the year 2030 (MoEFCC, 2015). Though India’s propositions are observed to be ambitious to meet all the targets in a span of 15 years, the government is committed to enforcing policy-induced measures to cut down emissions. As an emerging economy, India faces enormous challenges due to various issues, including increasing energy demand due to its higher population growth rate. These challenges can be related to ensuring intended economic growth, overall infrastructure development and an increase in industrial and service sectors' activities. In the past three decades, the industrial developments in India have escalated to new heights, which consequently have led to multiple urban environmental issues (Hakkim et al., 2019; Ghude et al., 2020a), especially deteriorating air quality and giving rise to health hazards (Ghude et al., 2016; Chatterjee, 2016). Therefore, air pollution has become a matter of serious concern for public health in India. As per United Nations Development Programme, India is the world’s second-largest populated country with the most densely populated Indo-Gangetic Plain region (UNDP, 2017), consuming 6% of the world’s total primary energy resources (IEA, 2015). Purohit et al. (2019) identified that the largest PM2.5 concentrations occur in the Indo-Gangetic Plain and a clear north-south gradient exists in the PM2.5 concentrations over India. In a recent study by Pandey et al. (2021), the authors showed that the ambient particulate matter exposure was 91.7 µg m–3 in India in 2019 with higher exposure to particulate matter concentrations of 123.5–217.6 µg m–3 found in the northern states of the Indo-Gangetic Plain region. Several past and recent studies using observational and model simulations (Di Girolamo et al., 2004; Jethva et al., 2005; Nair et al., 2007; Chate et al., 2013; Ghude et al., 2013, 2020b; Jena et al., 2015a, 2021; Kumar et al., 2018; Wu et al., 2019; Kaginalkar et al., 2022; Sengupta et al., 2022) have shown higher levels of air pollutants in densely populated urban areas of India making the population of these urban centres prone to higher health risk due to poor air quality (Chate et al., 2013; Greenstone et al., 2015; Surendran et al., 2015; Ghude et al., 2016; Cohen et al., 2017; Conibear et al., 2018a, 2018b; Guo et al., 2018; Chowdhury et al., 2019; David et al., 2019). Lelieveld (2017) examined that the steepest increase in mortality attributable to air pollution occurs in South Asia, especially in India. Using the regional Community Multi-scale Air Quality (CMAQ) model over northern India, Karambelas et al. (2018) estimated 463200 premature adult deaths from PM2.5 exposure using emission estimate for the year 2010. A recent study by Balakrishnan et al. (2019) revealed that India has a disproportionately high mortality and disease burden due to ambient air pollution, where the burden is highest in the states of north India with low Socio-demographic Index (SDI). According to World Health Organization (WHO) statistics, about 1 million non-accidental deaths/premature mortality estimated in 2017 were due to population exposure to high levels of air pollution in India (IHME, 2018). The Global Burden of Disease Studies (Murray et al., 2020) reported that exposures to ambient and household particulate matter pollution are among the major causes of premature mortality in India.

Given the high pollution level and associated health impacts, targets and actions for reducing emissions are integral components of the sustainable growth agenda of the Indian government. Therefore, in an affirmative and ambitious approach, the Indian government has laid out several mission modes, and strict action plans to reduce the regional to urban scale emissions. In principle, these action plans limit the emissions by 2030 and 2050 to the extent that would achieve the intended economic growth. In April 2015, the Ministry of Environment and Forest and Climate Change (MoEF & CC, Govt. of India) launched a National Clean Air Program (NCAP) as a major assertive program to improve urban air quality in urban regions mitigating public health risk. In this context, the Indian government has taken many strategic plans under the sustainable development goals (SDGs) with relevant administrative and regulatory measures considering the advancement in technology, infrastructure availability, development of path transformation, and enhanced public awareness. The Government of India launched Deen Dayal Upadhyay Grameen Jyoti Yojana in 2014 (https://www.india.gov.in/spotlight/deen-dayal-upadhyaya-gram-jyoti-yojana), and Pradhan Mantri Ujjwala Yojana 2016 (https://www.pmujjwalayojana.com/about.html) which was intended to replace kerosene and solid-fuel with cleaner fuel for household lighting and cooking. The recent introduction of urban development plans such as the Atal Mission for Rejuvenation and Urban Transformation (AMRUT), Smart Cities Mission, and Housing for All (Urban) by the government of India are modes to increase the opportunities for megacities which are considered as “growth engines”. India is poised to have 100 smart cities and 20 million houses to be constructed in the next few years. Major policy decisions that the government has undertaken within the ambit of targeted SDGs to augment abatement of air pollution are: (1) promulgated nationally determined contribution of 40% to renewable energy in 2030, (2) share of growth in public vehicles (25–30%), (3) leapfrogging from BS-IV standard to BS-VI standard and awareness about large scale usage of electric vehicles, (4) shift to energy-efficient technologies and fuels (55% in 2030), (5) 35% phase-out in agricultural residue burning by 2030, and (6) expansion of cleaner fuels like CNG, LPG, natural gas and ethanol blending. With these recent policy interventions, emissions are projected to reduce in 2030 and 2050, particularly with respect to fine particulate matter (PM2.5) levels as compared to no policy-induced baseline interventions for Business As Usual (BAU) emission scenarios (Venkataraman et al., 2018).

It is, therefore, important to address the broader question of whether current policies are already putting India on a path of mitigating public health risks. This work aims to look into the policy projections of emission reduction by 2030 to improve air quality. In this perspective, we assess the implications of ongoing assertively implemented policies aimed at reducing emissions and thus improving public health by reducing population exposure to PM2.5 ambient levels by 2030. Apte et al. (2015) re-iterated that major improvement in air quality is a crucial requirement to significantly reduce mortality from PM2.5 in the polluted regions of India. Their findings showed that in order to keep PM2.5 attributable mortality rates (deaths per 100000 people per year as per 2010 estimate) constant, the average PM2.5 levels would need to decline by ~20−30% by 2025 to balance the increase in PM2.5 attributable mortality from ageing populations. Using the WRF-Chem model, Conibear et al. (2018b) estimated the impacts of three different air pollution control pathways viz. (i) New Policy Scenario (NPS), (ii) Clean Air Scenario (CAS), both developed by the International Energy Agency (IEA), and (iii) Emission source-based scenarios (such as residential, energy, industry, and transport), on the ambient PM2.5 concentrations and human health in India in 2050. The NPS includes all existing and planned policies (as of 2016) that contribute to the reduction in air pollution and the CAS represents a set of aggressive policy actions aimed to achieving significant additional reductions in pollutant emissions, and customized to national circumstances (IEA, 2016). The authors found that the NPS and CAS could reduce population-weighted ambient PM2.5 concentrations below 2015 levels by 9% and 68%, respectively. Purohit et al. (2019) have explored the effectiveness of policy interventions on PM2.5 exposure and health impacts using a multi-disciplinary scientific framework consisting of the Global Change Assessment Model (GCAM) model and the Greenhouse gas - Air pollution INteractions and Synergies (GAINS) model. Their analysis highlighted that in many Indian States, the emission sources outside the immediate state jurisdictions make the dominating contributions to the ambient particulate pollution levels. Therefore, reduction in emissions in the surrounding regions and regionally coordinated cost-effective strategies are necessary to mitigate the exposure to ambient particulate pollution in a region. Considering four different emission scenarios, they have also found that advanced technical emission control measures, combined with national sustainable development strategies, could provide NAAQS-compliant air quality for majority of the Indian population by 2030.

It is imperative to have a proper understanding of pollutant emissions from different emission sectors, considering both the present-day conditions and future time-elapsed evolution under various socioeconomic pathways of development along with shifts in science and technology to undertake strategies to mitigate air pollution. In this work, considering the projected population of India by 2030, we have considered three emission scenarios developed by Venkataraman et al. (2018) which extend from 2015–2050 as: (1) reference scenario Business As Usual (BAU), (2) aspirational scenario (S2) and (3) ambitious scenario (S3). These scenarios capture varying levels of emission control, with BAU representing no change in current (2015) regulations corresponding to slow implementations of new technologies, S2 representing adoption of promulgated regulations corresponding to the effective achievement of targets and S3 representing adoption of ambitious prospective regulations corresponding to those well beyond promulgated regulations. In their study, the methodology adopted for the future projection of emissions included growth in sectoral demands, technology mix, energy consumption, and technology-linked emission factors. The growth levels in sectoral demand were estimated as ratios of 2050 to 2015 demands, where the growth estimates were 5.1, 3.8, 3.2, 1.3, and 1.4 in the building sector, electricity generation, heavy industries, residential sector, and agricultural residue burning, respectively. However, despite expanding sector-wise demands in both S2 and S3 scenarios, there is reduced energy consumption due to the adoption of clean energy technologies. Venkataraman et al. (2018) provide a detailed description of these scenarios.

In our study, we estimate the potential health benefits in terms of improvement in life expectancy and averted premature mortalities due to ischemic heart disease (IHD), cerebrovascular disease (CEV, stroke), chronic obstructive pulmonary disease (COPD), and lung cancer (LC) for the adult population, and acute lower respiratory illness (ALRI) for infants (< 5 years old) linked to PM2.5 exposure in the year 2030 that is achievable with the implementation of S2 and S3 scenarios with respect to the baseline scenario (BAU).


2.1 Model Description

We have used the regional Weather Research and Forecasting model coupled with chemistry (WRF-Chem) v3.9.1 with MOZART-4 gas-phase chemistry and GOCART aerosol scheme. Since emissions are influenced much by regional factors, the WRF-Chem being a regional transport model has the advantage of resolving emissions at finer grid scales by considering the regional impacts of emissions. It also has the ability to simulate full coupling between chemistry and meteorology compared to the global model GEOS-Chem. Although both GEOS-Chem and WRF-Chem employ a bulk aerosol approach to represent aerosol processes, certain aerosol processes such as the ageing of aerosols, deposition parameterization, and parameterizations of online natural emissions (e.g., dust and sea-salt) are treated differently in WRF-Chem. The MOZART-4 gas-phase chemistry incorporates 84 gas-phase species, 39 photolysis and 127 gas-phase reactions, and 12 bulk aerosols compounds. The GOCART aerosol model simulates five major types of aerosols, e.g., sulfate, black carbon, organic carbon, dust, and sea salt. Anthropogenic emissions of CO, NOx, SO2, NMVOC, NH3, PM2.5, BC and OC are taken from the HTAP-v2 inventory (http://edgar.jrc.ec.​europa.eu/htap_v2/) that consists of 0.1° × 0.1° grid maps using nationally reported emissions combined with region-specific inventories in the format of sector-specific grid maps for the year 2010. Details of the HTAP emission inventory can be found in Janssens-Maenhout et al. (2015). The simulations were run at a spatial resolution of 30 km × 30 km covering the Indian Subcontinent (0–40°N to 60–120°E) and 27 vertical levels from surface up to 50 hPa. We used the Thompson scheme (Thompson et al., 2008) to parametrize the cloud microphysical processes, the Grell 3D Cumulus Parameterization scheme (Grell and Dévényi, 2002) for cumulus parameterisation, the Bougeault and Lacarrere Planetary Boundary Layer (PBL) scheme (Bougeault and Lacarrere, 1989) for the boundary layer and Noah Land Surface Model (Chen and Dudhia, 2001) for providing heat and moisture fluxes over land. The short wave radiation was parameterized using the Goddard scheme (Chou and Suarez, 1994) and the Rapid Radiative Transfer Model (RRTM) (Mlawer et al., 1997) was used for the parametrization of long wave radiation. The aerosol-radiation feedback was allowed during the simulations. A summary of the physical and chemical parameterization schemes used in the model setup is given in Table 1. The performance of the model for simulating surface PM2.5 concentration over India is provided in a number of previous studies (Ghude et al., 2013, 2016, 2020b; Jena et al., 2015a, 2015b; Kumar et al., 2015; Krishna et al., 2019; Kulkarni et al., 2020) and these studies are consistent with the ground-based and satellite measurements. Kumar et al. (2012) showed the index of agreement for key meteorological parameters to be greater than 0.6, highlighting the fidelity of WRF-Chem in simulating the variations around the observed mean. Ghude et al. (2016) showed the excellent capability of the model in capturing the seasonality of surface PM2.5 over major sites in India, especially over the IGP region. Recent studies by Jena et al. (2021), Sengupta et al. (2022) and Ghude et al. (2022) demonstrate the efficacy of the WRF-Chem model with detailed performance statistics in capturing the variability of PM2.5 concentration, simulation of meteorology w.r.t in-situ observations and in assisting the decision-makers in air quality management activities, respectively. However, the GOCART aerosol scheme used in the present simulation does not include nitrate and secondary organic aerosols in the simulated PM2.5 concentration. The relative contribution of nitrate and SOA in PM2.5 is about 10% and 15% in India (Behera and Sharma, 2010), adding about 3% and 5% uncertainty to the simulated PM2.5 exposure. The model simulated hourly surface PM2.5 concentrations at 30 km horizontal resolution covering the entire Indian sub-continent. We performed simulations for reference (BAU), aspirational (S2) and ambitious (S3) scenarios for the entire year of 2030. BAU, S2, and S3 emission scenarios for the year 2030 are derived from the HTAP anthropogenic emissions by scaling HTAP emissions with yearly scaling factors based on the projections given in Venkataraman et al. (2018) for India, which are shown in Table 2. Detailed information on emission projections and methods is presented in Venkataraman et al. (2018). For NH3 emissions, we used projections based on RCP8.5 and RCP6.0 scenarios (Moss et al., 2010). All simulations are performed using meteorology and boundary conditions for the year 2018 based on National Centers for Environmental Prediction Final (GFS/FNL) meteorological reanalysis fields. In this study, we considered 2018 as the base year since this was the latest year with available meteorological data when the experiments were conducted. Spatially and temporally (6 hourly) varying climatological mean chemical boundary conditions were provided by the global model simulations from the Model for Ozone and related Chemical Tracers (MOZART-4) from 2007–2017 (Emmons et al., 2010).

Table 1. Model Configuration.

Table 2. Emission incremental factor for different pollutants.

2.2 Health Benefit Estimate

According to the Registrar General and Census Commissioner, Government of India, for the year 2011, around 31% of the residents are 0–14 years old, and 5% of the residents are above 65 years old. We scaled the 2011 population uniformly to project the count for 2030 but kept the same age grouping for the projected population in 2030. These population data are re-mapped to the 30 km model grid using GIS-based methodology. The premature mortality due to PM2.5 exposure in 2030 has been estimated for BAU, S2 and S3 scenarios using the human health impact function described in Murray et al. (2020). Premature mortalities (ΔM) were estimated (Eq. (1)) as a function of Population count (P) for the specific age category, the baseline mortality rate of a particular disease category δc for India and attribution fraction ((RR – 1)/RR) for a specific relative risk (RR). Here we used State-specific baseline mortality for COPD, IHD and Stroke in India, which is based on an estimate reported in Chowdhury and Dey (2016).


The relative risk (RR) due to COPD, CEV, IHD, ALRI and LC related mortality associated with long term exposure to PM2.5 concentrations is calculated using the integrated exposure-response function given in Murray et al. (2020). In the present study, the values of RR attributable to PM2.5 exposure for IHD, CEV (stroke), COPD, and LC disease categories are calculated for the adults (25–95 year) population, and acute lower respiratory illness (ALRI) is calculated for infant population. IHD estimates were split into the 5-year age group of the adult population. We also estimated lower and upper limits of premature mortality associated with the above disease categories.

Using the simulated annual mean PM2.5, the premature mortalities for five different diseases in the year 2030 under the scenarios BAU, S2 and S3 are estimated for the population in each model grid. The state and national level mortalities are estimated for the year 2030 by summing all grids within the state and national boundaries (Fig. S1) for each scenario. Finally, we estimated averted premature mortalities by calculating the decrement in mortalities under S2 and S3 with respect to the baseline scenario BAU. For calculating lost life expectancy due to PM2.5 exposure, we followed the estimate described in Pope et al. (2009). As per their estimate, an increase of 1 µg m–3 in PM2.5 exposure decreases the mean life expectancy by about 0.061 ± 0.02 years. Using this relationship, state and national level lost life expectancy is estimated for the year 2030 under the BAU, S2 and S3 scenarios. Finally, the decrease in health burden in terms of improvement in life expectancy under S2 and S3 with respect to the baseline scenario is estimated.


3.1 Improvement in Ambient PM2.5 Exposure under S2 and S3

Spatial pattern of simulated annual mean ambient PM2.5 concentration in BAU scenario and for emission mitigation scenario S2 and S3 in 2030 are illustrated in Figs. 1(a), 1(b) and 1(c), respectively. Fig. 1(a) illustrates that although the exposure to ambient PM2.5 varies widely across India, the annual mean ambient PM2.5 concentration for the BAU scenario exceeds the National Ambient Air Quality Standard (NAAQS) of 40 µg m–3 (http://www.indiaenvironmentportal.org.in/​files/file/Permissible%20Level%20for%20Pollutants.pdf) in most parts of India in 2030. Among the different regions, the Indo-Gangetic plain of India showed higher exposure to ambient PM2.5 concentration with values as high as 120–130 µg m–3 in the northwestern and northeastern regions of the Indo-Gangetic Plain (IGP). Figs. 2(a) and 2(b) show spatial patterns of the change in exposure to simulated ambient annual mean PM25 concentrations in 2030 for S2 and S3 emission scenarios with respect to the BAU scenario, respectively. In the S2 scenario, all India mean surface PM2.5 concentration exposure decreases from about 63 ± 23 µg m–3 to 54 ± 18 µg m–3, i.e., by about –9 µg m–3 (15%), with respect to the BAU scenario. In most parts of India, exposure to annual mean PM2.5 concentration decreases by about 5–15 µg m–3, whereas regions of northeastern and western India exhibit a decrease of about 20–30 µg m–3 (Fig. 2(a)). Despite these decreases, the annual mean ambient PM2.5 concentration in most regions of India exceeds NAAQS, maintaining similar high concentrations over IGP (Fig. 1(b)). However, simulations show that pockets of exceedingly high ambient annual mean PM2.5 concentration (> 100 µg m–3) reduced significantly to smaller levels in the north-western and northeastern regions of IGP. It indicates that achieving the S2 emission scenario in 2030 would result in only modest improvement in high levels of PM2.5 exposure with respect to the BAU scenario. The estimated values of exposure to ambient PM2.5 concentration in our study under the BAU and S2 scenarios are found to be consistent with Venkataraman et al. (2018). They have shown an increase in all India PM2.5 concentration to 160 µg m–3 i.e., a 62.37% increase in REF scenario and modest improvement in S2 scenario w.r.t 2015 (base year considered in Venkataraman et al. (2018)) using the GEOS-Chem model. Modest expansion of renewable energy (40%) in power generation, a modest increase in energy efficiency in industries, slower shifts to BS-VI standards in the transport sector, and a slow shift to energy-efficient technologies and fuels for residential purposes have resulted in moderate improvement in the air quality in the S2 scenario keeping the overall annual mean ambient PM2.5 concentration in most regions of India above the NAAQS. The north-western and north-eastern states of the IGP are quite densely populated and hence, adoption of the aspirational policies under the S2 scenario has larger implications on these states which consequently results in a significant decrease in the ambient PM2.5 concentration in these states w.r.t the rest of the country.

 Fig. 1. Spatial pattern of the simulated annual mean ambient PM2.5 concentration in 2030 under (a) BAU (b) S2 and (c) S3 scenario.Fig. 1. Spatial pattern of the simulated annual mean ambient PM2.5 concentration in 2030 under (a) BAU (b) S2 and (c) S3 scenario.

If the emissions are mitigated as per the S3 scenario, then all India mean surface PM2.5 concentration is estimated to be 32 ± 11 µg m–3, which results in a significant decrease of about 49% (–31 µg m–3) in PM2.5 exposure with respect to BAU scenario. In most parts of India, simulations with the S3 scenario reveal a significant drop (about 25–45 µg m–3) in ambient annual mean PM2.5 exposure with respect to the BAU scenario (Fig. 2(b)), except in the Indo Gangetic Plain where it indicates even a larger drop of PM2.5 exposure of about 50–70 µg m–3. Venkataraman et al. (2018) also showed that significant reductions in PM2.5 exposure are achievable in 2030 in the S3 scenario. Under the S3 scenario, simulated air quality is also projected to improve relative to the S2 scenario. Fig. 2(c) demonstrates a spatial pattern of the change in annual mean PM2.5 concentrations if the ambitious scenario (S3) is followed instead of the aspirational (S2) emission scenario. Fig. 2(c) follows the similar spatial pattern as that of Fig. 2(a) but with more reduction in PM2.5 exposure in northern, north-eastern, and north-western regions, and the magnitude of a dip in PM2.5 exposure between S2 and S3 is found double than between BAU and S2. This implies that the implementation of the S3 scenario in 2030 is expected to yield a significant improvement in air quality relative to the BAU scenario. Adoption of aggressive policies attributes to the significant improvement in the ambient PM2.5 exposure in the S3 scenario in comparison to the S2 scenario. Expansion of 75–80% of non-fossil-power generation in the power sector, a near-complete shift to high-efficiency industrial technologies, a large shift to public vehicles and increased share of electric and CNG vehicles, aggressive shifts to LPG, electricity, and solar energy for residential activities and a slow shift (35% phase-out by 2030) towards mitigating agricultural residue burning resulted in significant improvement in the ambient air quality in the S3 scenario. Again, since the population density in the states of the IGP region is quite high w.r.t the rest of the country, shifting to cleaner energy and more sustainable modes of development in the S3 scenario would affect a larger population thus resulting in a substantial reduction in the PM2.5 exposure in these states. Fig. 1(c) illustrates that although the PM2.5 exposure is below 40 µg m3 in the southern region of India in the S3 scenario, the central, north-eastern, and north-western regions still do not meet the NAAQS.

Fig. 2. Spatial change in the simulated ambient annual mean PM25 concentrations in 2030 for (a) S2 and (b) S3 emission scenario with respect to BAU scenario. (c) Spatial change in annual mean PM2.5 concentrations in 2030 if ambitious scenario (S3) is followed instead of aspirational (S2) emission scenario.Fig. 2. Spatial change in the simulated ambient annual mean PM25 concentrations in 2030 for (a) S2 and (b) S3 emission scenario with respect to BAU scenario. (c) Spatial change in annual mean PM2.5 concentrations in 2030 if ambitious scenario (S3) is followed instead of aspirational (S2) emission scenario.

To illustrate the state-wise pattern of PM2.5 exposure for BAU, S2 and S3 scenarios, we present bar charts showing annual mean ambient PM2.5 exposure and change in annual mean ambient PM2.5 in S2 and S3 relative to BAU scenario in Figs. 3(a) and 3(b), respectively. Similarly, Fig. 4 shows a state-wise percentage of PM2.5 exposure above or below NAAQS under the BAU, S2 and S3 scenarios. Under the BAU scenario, in 2030, the annual mean PM2.5 exposure in most of the Indian states is above NAAQS, except for north-eastern states (Sikkim and Arunachal Pradesh) and Himalayan states (Jammu and Kashmir, and Uttarakhand). Out of twenty-nine states, five states fall below the NAAQS and the exposure to ambient PM2.5 concentrations in these states would be 5–50% lower relative to NAAQS (Fig. 4(c)). In six states, exposure to ambient PM2.5 concentrations stands 3–25% above NAAQS and for the remaining 18 states, the exposure is 50 –190% above NAAQS. Delhi ranks highest on the list (117 µg m–3) with ~2 times higher exposure followed by West Bengal (100 µg m–3) and Bihar (97 µg m–3) with ~1.5 times higher exposure relative to NAAQS. The population of the five states (having PM2.5 exposure below NAAQS), is quite lower than the maximum of the other 22 states with PM2.5 exposure above the NAAQS. Since the Indian national emissions of primary PM2.5 are dominated by residential sectors, therefore, anthropogenic emissions would be more from states with higher populations and hence cause higher pollution in their immediate vicinity at the state level. Secondly, in terms of geographical location, the states in the IGP region have higher PM2.5 concentrations as compared to other states owing to the blockage of wind circulations by the Himalayas, which results in the trapping of polluted air over these states. The southern states, experiencing a larger share of coastal areas, have lower PM2.5 pollution due to relatively enhanced wind flow in terms of land and sea breezes, which disperses emissions considerably.

 Fig. 3. (a) State-wise pattern of PM2.5 exposure for BAU, S2, and S3 scenario. (b) State-wise pattern of annual mean ambient PM2.5 exposure change in annual mean ambient PM2.5 in S2 and S3 relative to BAU.Fig. 3. (a) State-wise pattern of PM2.5 exposure for BAU, S2, and S3 scenario. (b) State-wise pattern of annual mean ambient PM2.5 exposure change in annual mean ambient PM2.5 in S2 and S3 relative to BAU.

Fig. 4. State-wise percentage of PM2.5 exposure above or below NAAQS under the BAU, S2, and S3 scenario.Fig. 4. State-wise percentage of PM2.5 exposure above or below NAAQS under the BAU, S2, and S3 scenario.

Even if the S2 scenario is achieved in 2030, most states do not meet the NAAQS. However, in S2 scenario, six more states would rank below the NAAQS, and in five states, exposure to high ambient PM2.5 would fall in the range between 3–25% above NAAQS. Slower shifts to the use of cleaner, energy-efficient technologies in industries, transport, and residential sectors, a moderate share of renewable energy in power sectors, and the absence of any strict policy in reducing the agricultural residue burning, can be attributed to the moderate improvement in the ambient air quality in the S2 scenario keeping a majority of states still above the NAAQS. As a result, with the S2 scenario, 21% of additional states in India would meet the NAAQS, and overall, 38% of states would remain below the NAAQS with exposure 5–55% lower relative to NAAQS (Fig. 4(b)). Our estimates are consistent with findings of Venkataraman et al. (2018), wherein they have also projected significant increases in PM2.5 concentration w.r.t present-day values under REF and S2 scenarios in many states of the IGP region such as Bihar, Haryana, Jharkhand, Uttar Pradesh, Orissa, West Bengal.

Further, pursuance to achieving the S3 emission mitigation scenario ambitiously in 2030 would lower the exposure to ambient PM2.5 concentration below NAAQS in 17 more states (~59% states) relative to BAU. In the S3 scenario, the exposure would fall more towards the lower side in 38% of states (Fig. 4(a)), where exposure to ambient PM2.5 concentrations would fall 10–65% lower relative to NAAQS. This implies a significant improvement in ambient air quality in these states compared to other states which fall below NAAQS under the S3 scenario. However, even by pursuing the S3 scenario, eight states in India would continue to violate NAAQS. Of these eight states, PM2.5 exposure would remain 37% above NAAQS in Delhi, and in the remaining seven states, exposure to high ambient PM2.5 would fall in the range of 1–15% above NAAQS. Similar to the estimates of REF and S2 scenarios, these estimates are again consistent with that of Venkataraman et al. (2018) wherein they have projected the majority of states and union territories to fall below the NAAQS except for Delhi and other 9 states in the S3 scenario.

3.2 Health Benefits under S2 and S3 Scenarios

In a recent study by Pandey et al. (2021), it is found that nearly 1.67 million deaths i.e., 17.8% of the total deaths in India were attributable to air pollution in 2019. The majority of the deaths were from ambient particulate matter pollution (~0.98 million) and household air pollution (~0.61 million). Although the death rate due to household air pollution decreased by 64.2% in a span of thirty years (i.e., from 1990 to 2019), the death rate due to ambient particulate matter pollution increased by 115.3%. Therefore, the high burden of death and diseases due to the degrading air quality as highlighted in Pandey et al. (2021) has encouraged us to look into the health benefits achievable by 2030 in India by adopting the policies under S2 and S3 scenarios. We have assessed the health benefits of implementing aspirational (S2) and ambitious (S3) scenarios by estimating averted excess mortalities and improved life expectancies across India with respect to BAU. We have estimated excess numbers of mortalities using the integrated exposure-response function (Eq. (1) for each scenario S2 and S3) and averted mortalities are calculated by subtracting S2 and S3 excess mortalities from those of BAU. We further estimated the health burden in terms of life years lost due to PM2.5 exposure for each scenario following the estimates given in Pope et al. (2009) and used in Ghude et al. (2016). Improved life expectancy across India is calculated by subtracting life years lost from the BAU scenario. The averted numbers of excess mortalities in 2030 due to ambient PM2.5 exposure in the S2 and S3 scenarios for different health outcomes across India are summarized in Table 3. Pandey et al. (2021) have shown that of the total deaths attributable to air pollution in India in 2019, the largest proportions were due to COPD (32.5%) and IHD (29.2%), followed by stroke (16.2%) and lower respiratory infections. Our results show that reducing all India PM2.5 exposure by about 15% under the S2 scenario would prevent ~274,000 (8.3%) nationally aggregated total excess mortality burden with a 95% confidence interval of 301,000–237,000. For adults (age > 25 years), all India avoided excess mortalities linked to IHD, CEV (stroke), COPD, and LC are estimated to be about 78,000 (95%CI: 93,000–60,000), 76,000 (95%CI: 82,000–64,400), 118,000 (95%CI: 125,000–110,000), and 400 (95%CI: 117–600) people, respectively. Avoided mortalities of ALRI for children < 5 years old are estimated about 1,400 (95%CI: 1120–1500). Nationally, under the S2 scenario, reduced ambient PM2.5 exposure is estimated to improve all India mean life expectancy by about 0.6 ± 0.2 years.

Table 3. Averted numbers of excess mortalities in 2030 due to ambient PM2.5 exposure under S2 and S3 scenario for different health outcomes across India.

Following the S3 scenario would drop all India ambient PM2.5 exposure by 49% which would prevent a total of 775,000 (~23.6%) excess mortality burden with a 95% confidence interval, 820,000–688,000. Compared to the S2 scenario, the S3 scenario would potentially reduce nationally integrated annual excess mortalities estimate by 501,000 people. Averted mortalities linked to IHD, CEV (stroke), COPD, LC and (ALRI) are estimated to be about 268,000 (95%CI: 300,000–211,000), 245,000 (95%CI: 256,000–210,000), 258,000 (95%CI: 257,000–260,000), 1200 (95%CI: 815–1600), and 3600 (95%CI: 2900–3900) people, respectively. On the other hand, improvement in all India mean life expectancy in the S3 scenario is estimated to be about 1.9 ± 0.7 years, which is about 1.3 years more than that of the S2 scenario.

The avoided mortalities for different respiratory disease categories and improvement in life expectancy across different states of India estimated for S2 and S3 scenarios relative to BAU are shown in Figs. 5(a), 5(b) and 5(c), wherein a pattern similar to changes in state-wise ambient PM2.5 exposure is seen (Fig. 3). Highlighting the concern related to ambient particulate matter pollution, Pandey et al. (2021) showed that 11.5% of the total disability-adjusted life years (DALY) rate in 2019 was attributable to air pollution with the highest rates in several northern states of India. However, our study ignites the hope indicating a substantial improvement in averted mortalities and life expectancy that is achievable with the implementation of aspirational and ambitious regulations under the S2 and S3 scenarios, respectively. State-wise highest number of averted mortalities under S2 and S3 scenarios is seen in Bihar (BR) (S2:104,000, S3:142,800), followed by West Bengal (WB) (S2:46,400, S3:80,500) and Uttar Pradesh (UP) (S2:27,000, S3:135,000). In terms of life expectancy, the highest benefits are seen in Delhi which shows 1.3 years and 3.8 years increase in life expectancy for S2 and S3 scenarios, respectively. Similarly, increase in life expectancy for West Bengal (WB) under the S2 and S3 scenarios were 1.2 and 3.5 years, respectively, and that for Bihar in S2 and S3 scenarios were 1.3 and 3.4 years, respectively. In 70% of states, life expectancy would increase by 0.5 years and 1.5 years in 2030 under the S2 and S3 scenarios, respectively.

 Fig. 5. Avoided mortalities for different disease category in 2030 for (a) S2 and (b) S3 emission scenarios with respect to BAU. (c) Improvement in life expectancy across different states of India for S2 and S3 scenarios relative to BAU.Fig. 5. Avoided mortalities for different disease category in 2030 for (a) S2 and (b) S3 emission scenarios with respect to BAU. (c) Improvement in life expectancy across different states of India for S2 and S3 scenarios relative to BAU.


The present study evaluates the improvement in the air quality and subsequent health benefits that could be achieved due to a reduction in ambient PM2.5 exposure in 2030 under the adoption of promulgated regulations (S2) and ambitious prospective regulations (S3) relative to business-as-usual (BAU) industrial and economic growth. Improvement in ambient air quality is evaluated by estimating the decrease in annual mean ambient PM2.5 exposure and the number of states that meet the annual mean NAAQS of 40 µg m–3 under S2 and S3 emission pathways. Health benefits are calculated by estimating number of averted excess mortalities and improvement in life expectancy. Achieving the S2 emission scenario in 2030 instead of BAU could result in a modest 15% (9 µg m–3) decrease in all India ambient PM2.5 exposure. Out of 29 states, 38% of states would meet the NAAQS with exposure lowered by about 5–55% relative to NAAQS. Modest improvement in air quality under the S2 scenario would avoid 8.3% (~274,000 persons) of nationally aggregated total excess mortality burden and improve life expectancy by ~0.6 years as compared with BAU emission pathways. On the other hand, pursuing S3 emission pathways can result in a much larger decrease of about 49% (32 µg m–3) in all India ambient PM2.5 exposure. In 59% of states, annual PM2.5 exposure would meet the NAAQS. Despite this, eight states in India under the S3 scenario would continue to have exposure above NAAQS. However, ambient PM2.5 would fall in the range of 1–15% above NAAQS in seven of these states and in Delhi it would be 37% above NAAQS. In terms of health benefits, a significant improvement in ambient air quality would result in avoiding 23.3% (755,000 persons) of all India excess mortalities and an increase in life expectancy by about 1.9 years. This implies that the reduction in ambient PM2.5 exposure is relatively larger than the reduction in associated excess mortalities burden. It is important to note that 15% and 49% reductions in all India ambient PM2.5 exposure for the S2 and S3 scenarios lead to 8.3% and 20.3% decreases in excess mortalities, respectively. Overall, our results suggest that adopting promulgated regulations (S2) would result in a modest improvement in ambient air quality and noticeable health benefits in 2030. In contrast, adopting ambitious prospective regulations (S3) would significantly improve air quality (meeting NAAQS in many states), resulting in moderate health benefits.

It is to be noted that the estimated improvement in PM2.5 exposure, life expectancy and averted premature mortalities are subject to several sources of uncertainty, including uncertainties in the model parameterizations and exposure-response functions. Additionally, the variability in emissions for South Asia adds some uncertainty (~30%) in the PM2.5 emissions (Kurokawa et al., 2013), resulting in ~12% uncertainty in the estimated premature mortalities due to PM2.5 exposure (Ghude et al., 2016). Besides, the GOCART aerosol model used in the present study does not include nitrate and secondary organic aerosols, which gives rise to uncertainties in the simulated PM2.5 exposure. Uncertainty may also be due to the assumptions used in population projection and the mortality estimates. It should be noted that the present study does not focus on estimating premature mortalities from the individual source sectors and associated policy implications on the particulate matter pollution across different states in India in 2030. Therefore, the future prospect of the present study lies in conducting higher-resolution simulations with a more explicit aerosol model that includes nitrates and the formation of SOA (as performed by Debnath et al. (2021)), thus addressing the uncertainty in the simulated PM2.5 concentration to an extent. It will be also worth exploring the implications of individual sectoral policy implementations in 2030 across different states of India. Despite the limitations, this study highlights that reduction in the PM2.5 exposure over regional to urban scale in India, an increase in the number of averted mortalities, and an improvement in life expectancy by the year 2030 are achievable. It is worth noting that ambitious actions beyond the prospective regulations are warranted to achieve the major health benefits.

The findings in this study could be potentially beneficial to the pollution control authorities such as the central and state pollution control boards, Niti Aayog, and the Ministry of Environment and Climate Change (MoEFCC) in planning the implementation of strategic policies to mitigate air pollution and public health risk in different parts of India. The results might be helpful for the central government, state governments, and municipal corporations to allocate sufficient funding towards implementing the policies under the NCAP, thus leading to achieving the SDG targets. Additionally, the environmental ministry should also take initiatives toward developing a detailed emission inventory at national, state, and city levels to support air-quality management. Air pollution control in India should be treated as an essential investment. The existing efforts to mitigate air pollution should be further strengthened to foster the country’s future economic growth.


We thank the Director, IITM, for his encouragement in carrying out the research work. We acknowledge the use of the WRF-Chem preprocessor tools provided by the Atmospheric Chemistry Observations and Modeling Laboratory (ACOM) of NCAR. This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977. The WRF-Chem model simulation was carried out on the HPC System ‘Aditya’ at IITM, India. We would like to thank Prof. Michael Brauer, Institute for Health Metrics and Evaluation, University of Washington for his kind assistance in providing the updated data for the calculation of Relative Risk used in this study (Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Particulate Matter Risk Curves. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME), 2021, https://doi.org/10.6069/KHWH-2703). Supplementary data contains model mean data and all results for the Indian state per scenario. This work was supported by the National Supercomputing Mission (NSM) program grant to the authors at C-DAC (SK and DMC) and we are grateful to the Executive Director and the Director General of C-DAC. The authors would sincerely thank the anonymous reviewers and the editor of the journal for their insightful comments and valuable suggestions.


  1. Apte, J.S., Marshall, J.D., Cohen, A.J., Brauer, M. (2015). Addressing global mortality from ambient PM2.5. Environ. Sci. Technol. 49, 8057–8066. https://doi.org/10.1021/acs.est.5b01236

  2. Balakrishnan, K., Dey, S., Gupta, T., Dhaliwal, R.S., Brauer, M., Cohen, A.J., Stanaway, J.D., Beig, G., Joshi, T.K., Aggarwal, A.N., Sabde, Y., Sadhu, H., Frostad, J., Causey, K., Godwin, W., Shukla, D.K., Kumar, G.A., Varghese, C.M., Muraleedharan, P., Agrawal, A., et al. (2019). The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: The Global Burden of Disease Study 2017. Lancet Planet. Heath 3, e26–e39. https://doi.org/​10.1016/S2542-5196(18)30261-4

  3. Behera, S.N., Sharma, M. (2010). Reconstructing primary and secondary components of PM2.5 composition for an urban atmosphere. Aerosol Sci. Technol. 44, 983–992. https://doi.org/​10.1080/02786826.2010.504245

  4. Bougeault, P., Lacarrere, P. (1989). Parameterization of orography-induced turbulence in a mesobeta--Scale model. Mon. Weather Rev. 117, 1872–1890. https://doi.org/10.1175/1520-0493(1989)117<1872:POOITI>2.0.CO;2

  5. Chate, D., Beig, G., Satpute, T., Sahu, S.K., Ali, K., Parkhi, N., Ghude, S. (2013). Assessments of population exposure to environmental pollutants using air quality measurements during Commonwealth Games-2010. Inhalation Toxicol. 25, 333–340. https://doi.org/10.3109/​08958378.2013.788103

  6. Chatterjee, P. (‎2016)‎. India takes steps to curb air pollution. Bull. World Health Organ. 94‎, 487–488. https://doi.org/10.2471/BLT.16.020716

  7. Chen, F., Dudhia, J. (2001). Coupling and advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Weather Rev. 129, 569–585. https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2

  8. Chou, M.D., Suarez, M.J. (1994). An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo. 104606, 85.

  9. Chowdhury, S., Dey, S. (2016). Cause-specific premature death from ambient PM2.5 exposure in India: Estimate adjusted for baseline mortality. Environ. Int. 91, 283–290. https://doi.org/​10.1016/j.envint.2016.03.004

  10. Chowdhury, S., Dey, S., Guttikunda, S., Pillarisetti, A., Smith, K.R., Di Girolamo, L. (2019). Indian annual ambient air quality standard is achievable by completely mitigating emissions from household sources. PNAS 166, 10711–10716. https://doi.org/10.1073/pnas.1900888116

  11. Cohen, A.J., Brauer, M., Burnett, R., Anderson, H.R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., Pope, C.A., et al. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. Lancet 389, 1907–1918. https://doi.org/​10.1016/S0140-6736(17)30505-6

  12. Conibear, L., Butt, E.W., Knote, C., Arnold, S.R., Spracklen, D. V. (2018a). Residential energy use emissions dominate health impacts from exposure to ambient particulate matter in India. Nat. Commun. 9, 1–9. https://doi.org/10.1038/s41467-018-02986-7

  13. Conibear, L., Butt, E.W., Knote, C., Arnold, S.R., Spracklen, D.V. (2018b). Stringent emission control policies can provide large improvements in air quality and public health in India. GeoHealth 2, 196–211. https://doi.org/10.1029/2018gh000139

  14. David, L.M., Ravishankara, A.R., Kodros, J.K., Pierce, J.R., Venkataraman, C., Sadavarte, P. (2019). Premature mortality due to PM2.5 Over India: Effect of atmospheric transport and anthropogenic emissions. GeoHealth 3, 2–10. https://doi.org/10.1029/2018GH000169

  15. Debnath, S., Jena, C., Ghude, S.D., Kumar, R., Govardhan, G., Gunwani, P., Saha, S.K., Hazra, A., Pokhrel, S. (2021). Simulation of Indian Summer Monsoon Rainfall (ISMR) with fully coupled regional chemistry transport model: A case study for 2017. Atmos. Environ. 268, 118785. https://doi.org/10.1016/j.atmosenv.2021.118785

  16. Di Girolamo, L., Bond, T.C., Bramer, D., Diner, D.J., Fettinger, F., Kahn, R.A., Martonchik, J.V., Ramana, M.V., Ramanathan, V., Rasch, P.J. (2004). Analysis of multi-angle Imaging SpectroRadiometer (MISR) aerosol optical depths over greater India during winter 2001-2004. Geophys. Res. Lett. 31, 1–5. https://doi.org/10.1029/2004GL021273

  17. Emmons, L.K., Walters, S., Hess, P.G., Lamarque, J.F., Pfister, G.G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S.L., Kloster, S. (2010). Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4). Geosci. Model Dev. 3, 43–67. https://doi.org/10.5194/gmd-3-43-2010

  18. Ghude, S.D., Pfister, G.G., Jena, C., Van Der A, R.J., Emmons, L.K., Kumar, R. (2013). Satellite constraints of nitrogen oxide (NOx) emissions from India based on OMI observations and WRF-Chem simulations. Geophys. Res. Lett. 40, 423–428. https://doi.org/10.1029/2012GL053926

  19. Ghude, S.D., Chate, D.M., Jena, C., Beig, G., Kumar, R., Barth, M.C., Pfister, G.G., Fadnavis, S., Pithani, P. (2016). Premature mortality in India due to PM2.5 and ozone exposure. Geophys. Res. Lett. 43, 4650–4658. https://doi.org/10.1002/2016GL068949

  20. Ghude, S.D., Karumuri, R.K., Jena, C., Kulkarni, R., Pfister, G.G., Sajjan, V.S., Pithani, P., Debnath, S., Kumar, R., Upendra, B., Kulkarni, S.H., Lal, D.M., Vander A, R.J., Mahajan, A.S. (2020a). What is driving the diurnal variation in tropospheric NO2 columns over a cluster of high emission thermal power plants in India? Atmos. Environ. 5, 100058. https://doi.org/10.1016/j.aeaoa.​2019.100058

  21. Ghude, S.D., Kumar, R., Jena, C., Debnath, S., Kulkarni, R.G., Alessandrini, S., Biswas, M., Kulkrani, S., Pithani, P., Kelkar, S., Sajjan, V., Chate, D.M., Soni, V.K., Singh, S., Nanjundiah, R.S., Rajeevan, M. (2020b). Evaluation of PM2.5 forecast using chemical data assimilation in the WRF-Chem model: A novel initiative under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India. Curr. Sci. 118, 1803–1815. https://doi.org/10.18520/cs/v118/i11/1803-1815

  22. Ghude, S.D., Kumar, R., Govardhan, G., Jena, C., Nanjundiah, R.S., Rajeevan, M. (2022). New Delhi: air-quality warning system cuts peak pollution. Nature 602, 211–211. https://doi.org/10.1038/​d41586-022-00332-y

  23. Greenstone, M., Nilekani, J., Pande, R., Ryan, N., Sudarshan, A., Sugathan, A. (2015). Lower pollution, longer lives: Life expectancy gains if india reduced particulate matter pollution. Econ. Polit. Wkly 50, 40–46. 

  24. Grell, G.A., Dévényi, D. (2002). A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett. 29, 38-1–38-4. https://doi.org/​10.1029/2002GL015311

  25. Guenther, A., Karl, T., Harley, P., Weidinmyer, C., Palmer, P.I., Geron, C. (2006). Edinburgh Research Explorer Estimates of global terrestrial isoprene emissions using MEGAN ( Model of Emissions of Gases and Aerosols from Nature ) and Physics Estimates of global terrestrial isoprene emissions using MEGAN ( Model of Emissions of Gases an. Atmos. Chem. Phys. 3181–3210. https://doi.org/10.5194/acp-6-3181-2006

  26. Guo, H., Kota, S.H., Chen, K., Sahu, S.K., Hu, J., Ying, Q., Wang, Y., Zhang, H. (2018). Source contributions and potential reductions to health effects of particulate matter in India. Atmos. Chem. Phys. 18, 15219–15229. https://doi.org/10.5194/acp-18-15219-2018

  27. Hakkim, H., Sinha, V., Chandra, B.P., Kumar, A., Mishra, A.K., Sinha, B., Sharma, G., Pawar, H., Sohpaul, B., Ghude, S.D., Pithani, P., Kulkarni, R., Jenamani, R.K., Rajeevan, M. (2019). Volatile organic compound measurements point to fog-induced biomass burning feedback to air quality in the megacity of Delhi. Sci. Total Environ. 689, 295–304. https://doi.org/10.1016/j.​scitotenv.2019.06.438

  28. Institute for Health Metrics and Evaluation (IHME) (2018). Findings from the Global Burden of Disease Study 2017. Institute for Health Metrics and Evaluation, Seattle.

  29. International Energy Agency (IEA) (2015). IEA World Energy Outlook 2015. International Energy Agency, France.

  30. International Energy Agency (IEA) (2016). Energy and Air Pollution. World Energy Outlook, International Energy Agency, France.

  31. Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier Van Der Gon, H., Kuenen, J.J.P., Klimont, Z., Frost, G., Darras, S., Koffi, B., Li, M. (2015). HTAP-v2.2: A mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution. Atmos. Chem. Phys. 15, 11411–11432. https://doi.org/10.5194/acp-15-11411-2015

  32. Jena, C., Ghude, S.D., Beig, G., Chate, D.M., Kumar, R., Pfister, G.G., Lal, D.M., Surendran, D.E., Fadnavis, S., van der A, R.J. (2015a). Inter-comparison of different NOx emission inventories and associated variation in simulated surface ozone in Indian region. Atmos. Environ. 117, 61–73. https://doi.org/10.1016/j.atmosenv.2015.06.057

  33. Jena, C., Ghude, S.D., Pfister, G.G., Chate, D.M., Kumar, R., Beig, G., Surendran, D.E., Fadnavis, S., Lal, D.M. (2015b). Influence of springtime biomass burning in South Asia on regional ozone (O3): A model based case study. Atmos. Environ. 100, 37–47. https://doi.org/10.1016/j.​atmosenv.2014.10.027

  34. Jena, C., Ghude, S.D., Kumar, R., Debnath, S., Govardhan, G., Soni, V.K., Kulkarni, S.H., Beig, G., Nanjundiah, R.S., Rajeevan, M. (2021). Performance of high resolution (400 m) PM2.5 forecast over Delhi. Sci. Rep. 11, 1–9. https://doi.org/10.1038/s41598-021-83467-8

  35. Jethva, H., Satheesh, S.K., Srinivasan, J. (2005). Seasonal variability of aerosols over the Indo-Gangetic basin. J. Geophys. Res. 110, D21204. https://doi.org/10.1029/2005JD005938

  36. Kaginalkar, A., Ghude, S.D., Mohanty, U.C., Mujumdar, P., Bhakare, S., Darbari, H., Dwivedi, A.K., Gavali, P., Gavhale, S., Islam, S., Kadam, G., Kedia, S., Khare, M., Kharkar, N., Kulkarni, S.H., Meher, S.S., Nath, A.K., Niyaz, M., Pokale, S., Valappil, V.K., et al. (2022). Integrated urban environmental system of systems for weather ready cities in India. Bull. Am. Meteorol. Soc. 103, E54–E76. https://doi.org/10.1175/BAMS-D-20-0279.1

  37. Karambelas, A., Holloway, T., Kinney, P.L., Fiore, A.M., Defries, R., Kiesewetter, G., Heyes, C. (2018). Urban versus rural health impacts attributable to PM2.5 and O3 in northern India. Environ. Res. Lett. 13, 064010. https://doi.org/10.1088/1748-9326/aac24d

  38. Krishna, R.K., Panicker, A.S., Yusuf, A.M., Ullah, B.G. (2019). On the contribution of particulate matter (PM2.5) to direct radiative forcing over two urban environments in India. Aerosol Air Qual. Res. 19, 399–410. https://doi.org/10.4209/aaqr.2018.04.0128

  39. Kulkarni, S.H., Ghude, S.D., Jena, C., Karumuri, R.K., Sinha, B., Sinha, V., Kumar, R., Soni, V.K., Khare, M. (2020). How much does large-scale crop residue burning affect the air quality in Delhi? Environ. Sci. Technol. 54, 4790–4799. https://doi.org/10.1021/acs.est.0c00329

  40. Kumar, R., Naja, M., Pfister, G.G., Barth, M.C., Wiedinmyer, C., Brasseur, G.P. (2012). Simulations over South Asia using the Weather Research and Forecasting model with Chemistry (WRF-Chem): Chemistry evaluation and initial results. Geosci. Model Dev. 5, 619–648. https://doi.org/​10.5194/gmd-5-619-2012

  41. Kumar, R., Barth, M.C., Pfister, G.G., Nair, V.S., Ghude, S.D., Ojha, N. (2015). What controls the seasonal cycle of black carbon aerosols in India? J. Geophys. Res. 120, 7788–7812. https://doi.org/10.1002/2015JD023298

  42. Kumar, R., Barth, M.C., Pfister, G.G., Delle Monache, L., Lamarque, J.F., Archer-Nicholls, S., Tilmes, S., Ghude, S.D., Wiedinmyer, C., Naja, M., Walters, S. (2018). How will air quality change in South Asia by 2050? J. Geophys. Res. 123, 1840–1864. https://doi.org/10.1002/2017JD02735

  43. Kurokawa, J., Ohara, T., Morikawa, T., Hanayama, S., Janssens-Maenhout, G., Fukui, T., Kawashima, K., Akimoto, H. (2013). Emissions of air pollutants and greenhouse gases over Asian regions during 2000-2008: Regional Emission inventory in ASia (REAS) version 2. Atmos. Chem. Phys. 13, 11019–11058. https://doi.org/10.5194/acp-13-11019-2013

  44. Lelieveld, J. (2017). Clean air in the Anthropocene. Faraday Discuss. 200, 693–703. https://doi.org/10.1039/c7fd90032e

  45. Madronich, S. (1987). Photodissociation in the atmosphere: 1. Actinic flux and the effects of ground reflections and clouds. J. Geophys. Res. 92, 9740–9752. https://doi.org/10.1029/​JD092iD08p09740

  46. Ministry of Environment, Forest and Climate Change (MoEFCC) (2015). India: First Biennial Update Report to the United Nations Framework Convention on Climate Change. Ministry of Environment, Forest and Climate Change, Government of India, India. https://unfccc.int/​resource/docs/natc/indbur1.pdf

  47. Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J., Clough, S.A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res. 102, 16663–16682. https://doi.org/10.1029/97jd00237

  48. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., Van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., Wilbanks, T.J. (2010). The next generation of scenarios for climate change research and assessment. Nature 463, 747–756. https://doi.org/10.1038/nature08823

  49. Murray, C.J.L., Aravkin, A.Y., Zheng, P., Abbafati, C., Abbas, K.M., Abbasi-Kangevari, M., Abd-Allah, F., Abdelalim, A., Abdollahi, M., Abdollahpour, I., Abegaz, K.H., Abolhassani, H., Aboyans, V., Abreu, L.G., Abrigo, M.R.M., Abualhasan, A., Abu-Raddad, L.J., Abushouk, A.I., Adabi, M., Adekanmbi, V., et al. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1223–1249. https://doi.org/10.1016/S0140-6736(20)30752-2

  50. Nair, V.S., Moorthy, K.K., Alappattu, D.P., Kunhikrishnan, P.K., George, S., Nair, P.R., Babu, S.S., Abish, B., Satheesh, S.K., Tripathi, S.N., Niranjan, K., Madhavan, B.L., Srikant, V., Dutt, C.B.S., Badarinath, K.V.S., Reddy, R.R. (2007). Wintertime aerosol characteristics over the Indo-Gangetic Plain (IGP): Impacts of local boundary layer processes and long-range transport. J. Geophys. Res. 112, D13205. https://doi.org/10.1029/2006JD008099

  51. Neu, J.L., Prather, M.J. (2012). Toward a more physical representation of precipitation scavenging in global chemistry models: Cloud overlap and ice physics and their impact on tropospheric ozone. Atmos. Chem. Phys. 12, 3289–3310. https://doi.org/10.5194/acp-12-3289-2012

  52. Pandey, A., Brauer, M., Cropper, M.L., Balakrishnan, K., Mathur, P., Dey, S., Turkgulu, B., Kumar, G.A., Khare, M., Beig, G., Gupta, T., Krishnankutty, R.P., Causey, K., Cohen, A.J., Bhargava, S., Aggarwal, A.N., Agrawal, A., Awasthi, S., Bennitt, F., Bhagwat, S., et al. (2021). Health and economic impact of air pollution in the states of India: The Global Burden of Disease Study 2019. Lancet Planet. Heath 5, e25–e38. https://doi.org/10.1016/S2542-5196(20)30298-9

  53. Pope, C.A., Ezzati, M., Dockery, D.W. (2009). Fine-particulate air pollution and life expectancy in the United States. N. Engl. J. Med. 360, 376–386. https://doi.org/10.1056/NEJMsa0805646

  54. Purohit, P., Amann, M., Kiesewetter, G., Rafaj, P., Chaturvedi, V., Dholakia, H.H., Koti, P.N., Klimont, Z., Borken-Kleefeld, J., Gomez-Sanabria, A., Schöpp, W., Sander, R. (2019). Mitigation pathways towards national ambient air quality standards in India. Environ. Int. 133, 105147. https://doi.org/10.1016/j.envint.2019.105147

  55. Sengupta, A., Govardhan, G., Debnath, S., Yadav, P., Kulkarni, S.H., Parde, A.N., Lonkar, P., Dhangar, N., Gunwani, P., Wagh, S., Nivdange, S., Jena, C., Kumar, R., Ghude, S.D. (2022). Probing into the wintertime meteorology and particulate matter (PM2.5 and PM10) forecast over Delhi. Atmos. Pollut. Res. 13, 101426. https://doi.org/10.1016/j.apr.2022.101426

  56. Surendran, D.E., Ghude, S.D., Beig, G., Emmons, L.K., Jena, C., Kumar, R., Pfister, G.G., Chate, D.M. (2015). Air quality simulation over South Asia using Hemispheric Transport of Air Pollution version-2 (HTAP-v2) emission inventory and Model for Ozone and Related chemical Tracers (MOZART-4). Atmos. Environ. 122, 357–372. https://doi.org/10.1016/j.atmosenv.2015.08.023

  57. Thompson, G., Field, P.R., Rasmussen, R.M., Hall, W.D. (2008). Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Weather Rev. 136, 5095–5115. https://doi.org/10.1175/2008MWR2387.1

  58. United Nations Development Programme (UNDP) (2017). Annual Report 2017. United Nations Development Programme, New York. 

  59. Venkataraman, C., Brauer, M., Tibrewal, K., Sadavarte, P., Ma, Q., Cohen, A., Chaliyakunnel, S., Frostad, J., Klimont, Z., Martin, R. V., Millet, D.B., Philip, S., Walker, K., Wang, S. (2018). Source influence on emission pathways and ambient PM2.5 pollution over India (2015-2050). Atmos. Chem. Phys. 18, 8017–8039. https://doi.org/10.5194/acp-18-8017-2018

  60. Wesely, M.L. (1989). Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models. Atmos. Environ. 23, 1293–1304. https://doi.org/10.1016/​0004-6981(89)90153-4

  61. Wiedinmyer, C., Akagi, S.K., Yokelson, R.J., Emmons, L.K., Al-Saadi, J.A., Orlando, J.J., Soja, A.J. (2011). The Fire INventory from NCAR (FINN): A high resolution global model to estimate the emissions from open burning. Geosci. Model Dev. 4, 625–641. https://doi.org/10.5194/gmd-4-625-2011  

  62. Wu, X., Xu, Y., Kumar, R., Barth, M. (2019). Separating emission and meteorological drivers of mid-21st-century air quality changes in India based on multiyear global-regional chemistry-climate simulations. J. Geophys. Res. 124, 13420–13438. https://doi.org/10.1029/2019JD030988

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