We use a recent version of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem, version 4.6.1) primarily developed by the National Center for Atmospheric Research (NCAR) (Grell et al., 2005). WRF-Chem is a mesoscale air quality model that online couples atmospheric chemistry (including aerosols) and meteorology (Fast et al., 2006; Chapman et al., 2009), allowing the simulation of the aerosol feedback on regional meteorology that are particularly critical in regions with high aerosol loadings (Zhou et al., 2019; Sharma et al., 2023; Huang et al., 2023). WRF-Chem is thus widely used to simulate surface PM_2.5 pollution over India (Govardhan et al., 2019; Agarwal et al., 2024; Venkataraman et al., 2024; Xie et al., 2024).

We conduct a baseline simulation using the model configurations described in Sect. 2.1 and the updated anthropogenic emission inventory described in Sect. 2.2. We perform a thorough model evaluation against PM_2.5 and AOD observations in Sect. 2.3 that establishes the robustness of model results.

To attribute surface PM_2.5 concentrations to specific sources, we next conduct a series of additional WRF-Chem simulations in which emissions from individual sources inside and outside India are sequentially zeroed out. For the main analysis, we individually remove six domestic anthropogenic sectors within India (i.e., power, industry, residential, transportation, open burning, and agriculture emissions), two natural sources within India (i.e., dust and biogenic emissions), and transboundary emission sources from outside India (i.e., natural and anthropogenic emissions). We also conduct additional simulations to further separate transboundary anthropogenic emissions and transboundary non-anthropogenic emissions, and to evaluate the nonlinearities associated with partial versus complete emission reduction. We provide a summary of emission scenarios for all WRF-Chem simulations in Table 1.

For the main analysis, the contribution of each source is first estimated by subtracting the results of the source-zeroed simulation from those of the baseline simulation, using Eq. (6). However, due to non-linearities in the relationship between partial emission reductions (i.e., less than 100 %) and resulting decreases in PM_2.5 concentrations, which are primarily driven by secondary PM_2.5 formation (Liu et al., 2021) and aerosol-meteorology feedbacks (Zhou et al., 2019), the sum of individual source contributions does not equal the total concentration in the baseline simulation. To address this disparity, we apply a scaling factor to each source's contribution, based on the ratio of baseline concentration to the summed contributions at each WRF-Chem grid cell for PM_2.5 and its components, using Eq. (7). This ensures that the sum of individual source contributions equals the total concentration in the baseline simulation for each WRF-Chem grid. 6Contribchem,i=Cchem,baseline-Cchem,i-off7Contribscaled,var,i=Contvar,i⋅Cvar,baseline∑i=19Contvar,i Here, Contrib_chem,i represents the source attribution (in concentration units) of chemical species chem to the ith emission source; Cchem,baseline and Cchem,i-off are the WRF-Chem simulated concentrations of chemical species chem in the baseline simulation and in the simulation where the ith emission source is turned off, respectively; Contrib_{scaled,chem,i} is the scaled source attribution (in concentration units) of variable chem to the ith emission source. See Sect. S2.1 in the Supplement for the comparison of source-attributed concentrations before and after scaling.

We present annual national total and sectoral emissions for SO₂, NO_x (as NO₂), and PM_2.5 in India from 2015 to 2022 (Fig. 2), showing results from five inventories: our updated 2022 inventory, the Speciated MultipOllutant Generator (SMoG) inventory developed in Indian Institute of Technology (IIT) Bombay (Venkataraman et al., 2018, 2024), and three widely used global inventories (recent releases): CEDS (released on 8 July 2024), EDGAR (version 8.1, released in 2024), and Hemispheric Transport of Air Pollution (HTAP, version 3.1, released in 2025) (Hoesly et al., 2018; Crippa et al., 2018; Guizzardi et al., 2025). For global inventories, while CEDS and EDGAR provide emissions up to 2022, HTAP extends only through 2020. For India, HTAP adopts the Regional Emission inventory in ASia (REAS, version 3.2.1 (Kurokawa and Ohara, 2020)) for 2015 and applies country-sector-pollutant-specific emission trends derived from EDGAR to estimate emissions from 2016 to 2020.

Annual primary PM_2.5 emissions in India, reported only by EDGAR (2015–2022), HTAP (2015–2020), and SMoG (2015 and 2019), exhibit interannual variations, with emissions increasing from 2015 to 2018, declining to 2020, and rising again thereafter. Specifically, EDGAR reports a total PM_2.5 emission of 4.3 Tg in 2022, while HTAP reports 5.0 Tg for 2020, its latest available year. The residential and industrial sectors were two leading contributors to total PM_2.5 emissions in India, accounting for 42±2 % and 41±5 % of total emissions, respectively (Table S2). Residential PM_2.5 emissions from the SMoG inventory are substantially higher than other inventories, likely resulting from fundamental differences in the source data used to estimate fuel consumption (Sect. S3 in the Supplement).

For annual total SO₂ emissions, all three global inventories indicate a similar emission trend from 2015 to 2020, showing an increase from 10.6±0.9 Tg in 2015 to a peak of 11.8±1.2 Tg in 2018, followed by a reduction to 10.3±0.8 Tg in 2020 due to COVID lockdown. After 2020, the CEDS and EDGAR inventories report increases in annual total SO₂ emissions from 9.4 and 10.7 Tg in 2020 to 10.9 and 12.5 Tg in 2022, respectively. The cross-inventory uncertainty for annual SO₂ emissions is 15±3 % from 2015 to 2022. The power sector consistently dominates SO₂ emissions in India from 2015 to 2022, accounting for 61±2 % of total emissions across inventories. This dominance is driven by India's growing electricity consumption (e.g., an increase of 86 % from 2010 to 2022) and continued heavy reliance on coal-fired power generation (e.g., 72 % in 2022), along with limited implementation of end-of-pipe pollution controls (Kumar and Dahiya, 2023). The industry and residential sectors contribute 32±2 % and 6±0 %, respectively, to total SO₂ emissions in India over the same period.

Similarly, annual total NO_x emissions in India increased from 9.5 ± 1.0 Tg in 2015 to 10.2 ± 1.1 Tg in 2018, then declined to 9.1 ± 1.0 Tg by 2020 according to all three global inventories. Post-2020, the CEDS and EDGAR inventories show increases in annual total NO_x emissions from 8.5 and 8.6 Tg in 2020 to 9.3 and 9.8 Tg in 2022, respectively. The power sector remains the largest contributor to NO_x emissions over the period of 2015 to 2022 in all inventories, accounting for 39 ± 0 % of the total, followed by the transportation (29 ± 1 %), industry (19 ± 1 %), and residential (8 ± 0 %) sectors. The cross-inventory uncertainty for annual NO_x emissions is 16 ± 8 % from 2015 to 2022.

Solid fuel had been historically widely used in India's residential sector, such as biomass for residential cooking and kerosene for lighting, which leads to high PM_2.5 emission due to their inefficient and incomplete combustion (Chowdhury et al., 2019). The residential sector has recently benefited from mitigation efforts under the NCAP which has promoted the use of liquified petroleum gas (LPG) as a cleaner fuel replacing solid fuels (Bhaskar, 2019). This clean energy transition in the residential sector is not captured by any of the global inventories, in which residential emissions remain largely unchanged after 2017 (Fig. 2). Therefore, we adopted a recently developed residential emission inventory that accounts for recent consumption pattern changes and cleaner fuel adoption in 2022 (Velamuri et al., 2024). As a result of incorporating the updated residential inventory, annual total residential emissions are reduced by 0.5 Tg for primary PM_2.5 (33 % relative to EDGAR), reduced by 0.4 Tg for SO₂ (82 % relative to CEDS), and reduced by 0.7 Tg for NO_x (80 % relative to CEDS).

In addition to update the residential emissions, we replace power sector emissions with our coal-fired power plant emission inventory described in Sect. 2.2.2. Our coal-fired plant emission inventory covers all operating units regulated by India's CEA, using detailed plant-level generation reports archived by the CEA to improve the accuracy of activity data. We estimate annual total emissions from coal-fired power generation across India in 2022 to be 6.2 ± 1.5 Tg for SO₂, 4.6 ± 0.3 Tg for NO_x, and 0.8 ± 0.1 Tg for primary PM_2.5, with the range reflecting uncertainties in emission factors without mitigation as reflected in the literature (Table S1). These total emissions, along with the estimated emission factors per unit of electricity generated, are comparable to those reported in four earlier studies focused on India's coal-fired power plant emissions (Guttikunda and Jawahar, 2014; Cropper et al., 2021; Singh et al., 2024; Velamuri et al., 2024) (Table S3). In addition, the spatial distributions of gridded power plant emissions among CEDS, EDGAR, and our inventory are similar (Fig. S1). As a result of incorporating our coal plant inventory, annual total emissions for the power sector are increased by 0.4 Tg for PM_2.5 (90 % relative to EDGAR), reduced by 0.6 Tg for SO₂ (9 % relative to CEDS), and increased by 0.4 Tg for NO_x (10 % relative to CEDS). The adoption of India-specific emission factors (Table S1), informed by a literature review of India's high-ash coal, may explain why our estimates of primary PM_2.5 emissions are substantially higher than those from EDGAR. The SMoG 2019 emission inventory developed by multiple Indian institutions reported an even higher annual primary PM_2.5 emission of 1.7 Tg from the power sector (Venkataraman et al., 2024).

In 2022, our updated emission inventory reports India total emissions at 9.5 Tg for SO₂, 10.1 Tg for NO_x, and 4.3 Tg for primary PM_2.5. Compared with existing global inventories in 2022, our total SO₂ emissions are 1.4 Tg (13 % relative to CEDS) lower, while total PM_2.5 and NO_x emissions are 0.04 Tg (1 % relative to EDGAR) and 0.8 Tg (9 % relative to CEDS) higher, respectively. These differences result from the updates in emissions from the residential, power, and transportation (i.e., road dust) sectors in 2022.

The improved WRF-Chem simulations capture the spatial distribution of annual mean surface PM_2.5 concentrations across India and adjacent regions well, achieving a Pearson correlation coefficient (R) of ∼0.7 between modeled and observed concentrations (Fig. 3; daily comparison in Fig. S2). Annual model bias across the entire domain (116 model grids and 288 measurement sites) is 0.2 ± 16.9 µg m⁻³ (0 ± 31 %). Model biases in annual mean surface PM_2.5 are within ± 10 µg m⁻³ in 57 % of the WRF-Chem grids which have measurement sites, whereas biases exceed ± 30 µg m⁻³ in 9 % of these grids. Across the Indo-Gangetic Plain (IGP), the region with the most severe PM_2.5 pollution in India, simulated annual mean surface PM_2.5 concentrations differ from observed concentrations by -1.9 ± 21.2 µg m⁻³ (3 ± 31 %). Specifically, in Delhi, the modeled annual mean surface PM_2.5 concentration (100.2 µg m⁻³) is virtually the same as the observed mean (100.7 µg m⁻³). These results indicate the models' ability to reproduce the spatial pattern of annual mean surface PM_2.5 levels across India and nearby regions in 2022, providing large improvements over previous air quality modeling studies for India (Conibear et al., 2018; Reddington et al., 2019; Singh et al., 2021; Pai et al., 2022). Model simulations using the global emission inventories without improvements for Indian sectoral emissions and without improved near-surface mixing of pollutants show a significant overestimation of annual PM_2.5 by 23.0 ± 29.0 µg m⁻³ (42 ± 53 %) across the domain and by 92.5 ± 40.9 µg m⁻³ (92 ± 41 %) in Delhi (The “Default” Simulation in Fig. 4).

Beyond annual averages, WRF-Chem also effectively captures daily PM_2.5 variations throughout the four-month period (Fig. 3e–h). In the IGP and Delhi, the Pearson correlation coefficients between modeled and observed regional mean daily PM_2.5 concentrations are 0.93 and 0.81, respectively. In addition, the twin-peak pattern in diurnal PM_2.5 concentrations are well reproduced by WRF-Chem, though the morning peak in Delhi is underestimated. In contrast, model simulations without the emission and near-surface mixing updates show much stronger diurnal variability in hourly PM_2.5 concentrations than the observation, resulting from overestimated local emission fluxes and insufficient nighttime near-surface mixing (Fig. 4).

Despite the good model performance of the baseline simulation discussed above, notable biases remain in several regions (Fig. 3b). Specifically, modeled annual mean surface PM_2.5 concentrations exceed observations by more than 30 µg m⁻³ in West Bengal (e.g., Kolkata) and a few stations in Gujarat, Punjab, and Rajasthan, while modeled concentrations underestimate observations by more than 30 µg m⁻³ in a few stations in Bihar and Uttar Pradesh. We summarize the state-level model performance in Table S4. We find the largest negative model bias in annual mean surface PM_2.5 concentrations in Bihar (-19±12 µg m⁻³, -23±17 %). In contrast, the largest positive model bias occurs in West Bengal, where modeled annual mean surface PM_2.5 concentrations exceed observations by 32±19 µg m⁻³ (61±47 %). Model biases in January play a dominant role in these annual biases, contributing, on average, 51 % (10 µg m⁻³) of the annual negative bias in Bihar and 57 % (18 µg m⁻³) of the annual positive bias in West Bengal. PM_2.5 concentrations observed at CPCB stations in Kolkata (West Bengal) are systematically lower compared to those recorded at the nearby US Air Now station, by 60 µg m⁻³ (39 %) in January and 30 µg m⁻³ (44 %) annually. A model evaluation at Kolkata using only the US Air Now station data, instead of averaging across all available measurement stations, significantly reduces the positive model bias from 145 % to 49 % in January, and from 147 % to 45 % annually.

We then utilize satellite-derived AOD data to evaluate the model's performance in simulating the spatial distribution of aerosol column loadings across India. Satellite data provides greater spatial coverage than the surface PM_2.5 measurement network. WRF-Chem reproduces the spatial pattern of AOD, with a Pearson correlation coefficient of 0.84 between annual modeled and observed AOD across India, and monthly correlations ranging from 0.72 to 0.82 (except for July when too much data is missing) (Fig. S3). However, WRF-Chem exhibits a consistent negative bias in AOD across India, with an annual mean bias (normalized mean bias) of -0.12±0.06 (-29±14 %) compared to satellite observations. AOD underestimation persists even in regions where surface PM_2.5 concentrations are significantly overestimated (e.g., West Bengal). Evaluation of daily AOD model values demonstrates that while the model performs well in capturing general AOD trends throughout the year, it fails to reproduce the magnitude of extreme AOD events over Delhi and the IGP. Previous modeling studies have attributed similar AOD underestimation over India primarily to the underrepresentation of large particles (diameter >2.5 µm) (David et al., 2018; Singh et al., 2021). Consistent with this, our baseline simulation underestimates annual mean surface coarse particulate matter (PM_coarse) by 37.3±25.7 µg m⁻³ (59±41 %) compared to CPCB measurements (Fig. S4). Additional discussion regarding missing PM_2.5 emissions and other factors contributing to the AOD bias is provided in Sect. S2.2 in the Supplement.

We analyze the national and regional surface PM_2.5 concentrations (both total and attributed to specific sources) using the PW mean metric (see Sect. 2.2.3 for PW mean calculation). PW mean concentrations reflect population exposure and is indicative of associated health risks. In addition, we investigate the spatial distribution of PM_2.5 concentrations originating from various sources in order to identify local hotspots associated with specific emission sources.

Our WRF-Chem simulation estimates a 2022 national PW mean annual surface PM_2.5 concentration of 47.4 µg m⁻³ (Fig. 5a), similar to the 51.6 µg m⁻³ reported in a recent satellite-based machine learning study (Kawano et al., 2025). In 2022, 37 % of India's population lived in areas where annual PM_2.5 concentrations met the national air quality standard of 40 µg m⁻³ (Fig. 5c), representing an increase from 17 % in 2016 (Apte and Pant, 2019). This change indicates an improvement in India's PM_2.5 air quality from 2016 to 2022 under the NCAP, aided by favorable meteorological conditions that enhanced pollutant dispersion and removal (Xie et al., 2024). However, in 2022, only 29 % of the national population was exposed to PM_2.5 levels below the least stringent annual World Health Organization (WHO) standard (35 µg m⁻³), and less than 0.1 % met the most stringent annual WHO standard (5 µg m⁻³). Regionally, the IGP experienced the highest annual PW mean PM_2.5 concentration in 2022 at 58.9 µg m⁻³, followed by Northwest India (58.3 µg m⁻³) and Central India (46.3 µg m⁻³). In contrast, Northeast India (28.1 µg m⁻³), South India (27.3 µg m⁻³) and the Himalayan states (25.3 µg m⁻³) had lower annual PW mean PM_2.5 concentrations.

At the national level, emissions originating within India accounted for 73 % (34.5 µg m⁻³) of the annual PW mean PM_2.5 concentration in 2022, while transboundary emission sources contributed the remaining 27 % (12.8 µg m⁻³) (Fig. 6). We summarize the national and state-level PW mean PM_2.5 concentrations attributed by source in Table S5.

Among domestic sources, the industrial sector was the leading contributor to the national annual PW mean PM_2.5 concentration in 2022, accounting for 18 % (8.6 µg m⁻³). Spatially, its contribution was particularly dominant in heavily polluted areas where annual PM_2.5 levels exceeding 80 µg m⁻³ (twice the national standard) (Fig. 5c). In some local hotspots within these areas, including major urban centers such as those of Delhi and Mumbai, industrial emissions alone contributed more than 40 µg m⁻³ to simulated annual surface PM_2.5 concentrations (Fig. 6), suggesting that to achieve national air quality standards in these hotspots it will be necessary to regulate industrial emissions.

India's residential sector, which has been undergoing a clean energy transition toward LPG since 2016, was the second-largest domestic contributor (15 %; 7.3 µg m⁻³) to the national PW mean PM_2.5 concentration in 2022. Spatially, however, the residential sector remained the dominant PM_2.5 source across large areas of the IGP, especially during winter (Fig. 7). Consequently, ∼500 million people in India lived in areas where residential emissions were the dominant source of annual PM_2.5 pollution in 2022, the highest among all emission sectors within India. This highlights the continued substantial health burden associated with residential emissions, and the need to augment recent progress in adoption of cleaner cooking fuels.

India's power sector, based on our updated emission inventory, was the third-largest domestic contributor to the national PW mean PM_2.5 concentration in 2022 (13 %; 6.1 µg m⁻³). Spatially, the power sector primarily influenced PM_2.5 air quality in Central India, particularly in Chhattisgarh and Jharkhand (Figs. 6 and 7). This is due to central India generating 52 % of India's annual coal-based electricity in 2022 and power plants typically having emission controls only on primary particulates and not on SO₂ or NO_x which contribute to the formation of secondary inorganic aerosols.

India's transportation sector made a smaller contribution to the national annual PW mean PM_2.5 concentration in 2022 (8 %; 3.8 µg m⁻³), compared with the industry, residential, and power sectors. Spatially, its impact was most notable across much of the IGP and eastern Rajasthan, where it contributed moderately (∼5 to 10 µg m⁻³) to annual PM_2.5 (Fig. 5). In Delhi, the transportation sector recorded its highest state-level contribution to annual PM_2.5, reaching 11.6 µg m⁻³ (11 %, Table S5). However, transportation was not the dominant domestic source of annual PM_2.5 in any of India's populous regions in 2022 (Fig. 7).

India's agricultural sources contributed 8 % (3.7 µg m⁻³) to the national annual PW mean PM_2.5 concentration. This PM_2.5 was primarily derived from secondary inorganic aerosols formed from NH₃ emitted from fertilizer use and livestock as well as a minor source from NO_x emitted from agricultural fields.

India's open burning emissions, derived from the FINN inventory and representing satellite-detectable burning of crop residues, municipal waste and wildfires, contributed 8 % (3.6 µg m⁻³) to the annual PW mean PM_2.5 concentrations across India in 2022. Their impacts were strongly seasonal, with elevated contributions of 15 % (8.3 µg m⁻³) and 10 % (4.3 µg m⁻³) to the national PW mean PM_2.5 in April and October, respectively. Spatially, the regional hotspots switched between months. In April, open burning is most influential in Central India and Northeast India (Fig. 7). In October, open burning became the dominant source of PM_2.5 in northeastern IGP, including Delhi, with significant monthly contributions by ∼27 µg m⁻³ (Fig. S6). Previous studies have shown that groundwater conservation policies in the northeastern IGP (one of India's major crop harvesting and residue burning regions) have shortened the turnover period between crop seasons and delayed agricultural burning. As a result, burning has shifted later in the year, often extending into late fall, when meteorological conditions are less favorable for pollutant dispersion, thereby amplifying the impact of open burning on surface PM_2.5 concentrations (Liu et al., 2022). These earlier findings underscore the complexity of effectively controlling open burning emissions.

India's natural dust emissions, derived from desert dust simulated by WRF-Chem, contributed 4 % (1.9 µg m⁻³) to the national annual PW mean PM_2.5 concentration in 2022. Spatially, it had a substantial impact on PM_2.5 levels in Northwest India, where grid-level annual contributions exceed 40 µg m⁻³ in its western part. However, due to the low population density in Northwest India and the limited impact of natural dust on surface PM_2.5 outside this region (Figs. 5b and 6), natural dust was not a major factor for PM_2.5 exposure at the national level. Despite limited exposure among the population, the natural dust zones overlap substantially with India's solar energy generation centers. This spatial coincidence may lower solar power generation efficiency due to aerosol-induced dimming and soiling, though the soiling impact can be mitigated if panels are cleaned on a regular basis (Li et al., 2020).

India's biogenic emissions had a small but net negative contribution to annual PW mean PM_2.5 concentrations across the country (-1 %; -0.5 µg m⁻³). Biogenic emissions contribute to OA formation, but in our simulations, the OA concentration increase (<0.1 µg m⁻³) is outweighed by secondary inorganic PM_2.5 concentration decrease (-0.5 µg m⁻³) when biogenic emissions are included. We find a reduction in all secondary inorganic PM_2.5 components (i.e., sulfate, nitrate, and ammonium), as well as in the sulfate oxidation ratio and nitrate oxidation ratio, following the inclusion of India's biogenic emissions (Fig. S7). Biogenic VOCs reduced the oxidation capacity of the atmosphere by consuming OH and HO₂ radicals, thereby decreasing the conversion of SO₂ to sulfate and NO₂ to nitrate, which also reduced ammonium formation and led to reductions in secondary inorganic PM_2.5. Consequently, biogenic emissions from within India resulted in a slight net reduction in total PM_2.5 concentrations in 2022. However, they significantly enhanced annual mean O₃ concentrations (by up to 20 µg m⁻³, Fig. S7), deteriorating O₃ air quality across India.

Transboundary sources (emissions from outside of India) accounted for 27 % (12.8 µg m⁻³) of annual national PW mean PM_2.5 in India in 2022, exceeding the contribution of any individual domestic emission source (Fig. 6). These transboundary sources include emissions from six anthropogenic sectors (i.e., power, industry, residential, transportation, agriculture, and open burning) and two natural sources (i.e., dust and biogenic) from outside India, representing emissions beyond the jurisdiction of the Indian government. Spatially, the influence of transboundary sources exhibits a northwest-to-southeast gradient. In 2022, transboundary sources contributed more than 20 % to grid-level annual PM_2.5 concentrations across most of India, with contributions exceeding 40 % in western states such as Punjab, Haryana, Rajasthan, and Gujarat (Fig. S8). In Delhi, transboundary sources contributed 20 % (21 µg m⁻³) to the annual PM_2.5 concentration in 2022. Further decomposition reveals that anthropogenic emissions originating from neighboring countries within our modeling domain accounted for 11 % (5.2 µg m⁻³) of the 2022 annual national PW mean PM_2.5 in India. Meanwhile, other transboundary components, comprising regional natural sources (dust and biogenic) and long-range transport of pollutants entering via model lateral boundary conditions, contributed the remaining 16 % (7.7 µg m⁻³) (Fig. S9).

We analyze the contributions of individual PM_2.5 components simulated by WRF-Chem to total PM_2.5 concentrations across India in 2022, along with their respective source attributions. Figure 8 shows the spatial distribution of annual concentrations of all components. Figure 9 and Table S6 present the source contributions to national PW mean concentrations of PM_2.5 components.

Among components resolved by WRF-Chem, organic aerosols (including both primary and secondary) had the largest contribution to the national PW mean total PM_2.5 concentrations in 2022, at 34 % (16.1 µg m⁻³) (Fig. 8). Residential emissions from within India were the dominant source of population exposure to organic PM_2.5, accounting for 6.6 µg m⁻³ of its annual national PW mean concentration (Fig. 9). Transboundary (3.1 µg m⁻³) and industrial (2.6 µg m⁻³) sources were also significant contributors to organic PM_2.5 levels across the country. Spatially, organic PM_2.5 had a north-to-south gradient, with its dominance closely overlapping with regions where residential emissions were also dominant, particularly the IGP.

The dust component, including both anthropogenic and natural sources, was the second-largest contributor to the national PW mean total PM_2.5 concentration in 2022, at 26 % (12.4 µg m⁻³) (Fig. 8). Transboundary emissions dominated dust PM_2.5 at the national level, contributing 5.0 µg m⁻³ to its annual PW mean concentration (Fig. 9). In addition, industrial (2.0 µg m⁻³) and natural dust (1.8 µg m⁻³) emissions from within India were the other major contributors to dust PM_2.5 across India. Spatially, dust PM_2.5 exhibited a west-to-east gradient, with annual concentrations exceeding 40 µg m⁻³ across most of Rajasthan and Gujarat (Fig. 8).

Sulfate PM_2.5 was the third-largest contributor to the national PW mean total PM_2.5 level in 2022, at 14 % (6.8 µg m⁻³), and was the dominant component of secondary inorganic PM_2.5 (including sulfate, nitrate, and ammonium) (Fig. 8). India's power sector, the largest domestic SO₂ emitter, was the dominant source for sulfate, accounting for 2.8 µg m⁻³ of its annual PW mean concentration, followed by transboundary pollution and the industrial sector (Fig. 9). Spatially, sulfate concentrations exhibited a relatively small gradient compared to organic and dust PM_2.5, though highest concentrations were found in Central India around Chhattisgarh and Jharkhand.

Nitrate PM_2.5 contributed 5.3 µg m⁻³ (11 %) to national PW mean total PM_2.5 concentration in 2022, smaller than that of sulfate PM_2.5 (Fig. 8). However, spatially nitrate PM_2.5 was the dominant component among secondary inorganic PM_2.5 across the IGP, especially in Bihar, Haryana, and Delhi. Unlike organic, dust, and sulfate PM_2.5, nitrate PM_2.5 exhibited highly nonlinear relationships between precursor (i.e., NO_x) emissions and resulting concentrations. Specifically, the agriculture sector, which contributed only 2 % of national NO_x but 80 % of national NH₃ emissions in 2022, were identified as the largest contributor (2.0 µg m⁻³) to nitrate PM_2.5 in 2022 (Fig. 9). In the atmosphere, nitric acid (HNO₃, from NO_x oxidation) reacts with NH₃ remaining after neutralizing sulfuric acid to form ammonium nitrate. Removing agricultural emissions reduced NH₃ availability across India by 77 %, cutting the national average NO3- fraction of total NO3-+ HNO₃ from 51 % (baseline) to 25 % and leading to the largest nitrate reduction among all simulations that removed individual sources (Fig. 10).

Ammonium PM_2.5 contributed 4.0 µg m⁻³ (8 %) to national PW mean total PM_2.5 concentration in 2022, with spatial hotspots overlapping those of sulfate and nitrate (Fig. 8). Like nitrate, ammonium's response to precursor (NH₃) emissions was highly nonlinear. Notably, we identified India's power sector as the largest contributor (1.1 µg m⁻³) to ammonium PM_2.5 using the zero-out emission method (Fig. 9). While the power sector did not emit NH₃ directly, its dominance in SO₂ and NO_x emissions within India increased the availability of sulfuric and nitric acids, which react with NH₃ to form ammonium aerosols. Removing power sector emissions therefore left a larger fraction of total reduced nitrogen (NHx= NH3+ NH4+) as NH₃, leading to greater reductions in ammonium PM_2.5 than any other single-source removal scenarios (Fig. 10). In contrast, when removing agricultural emissions (dominant NH₃ source domestically), transport of NH₃ from outside India partially offset the decrease in NH₃ supply, sustaining some ammonium formation and leading to smaller reductions in ammonium than the simulation in which power-sector emissions were removed. These findings illustrate how non-linear chemistry, when source emissions are entirely removed, can yield results that deviate from those of attributional methods (e.g., tagging precursor emissions) (Koo et al., 2009).

Black Carbon, primarily from the incomplete combustion of solid fuels, contributed 2.3 µg m⁻³ (5 %) to national PW mean total PM_2.5 concentration in 2022, with India's industrial sector the dominant source.

Sodium and chloride together contributed only 0.4 µg m⁻³ (1 %) to national PW mean total PM_2.5 concentration in 2022, primarily from transboundary sources. Previous observational studies reported relatively high chloride concentrations in particulate matter in Delhi (e.g., 15-month average of 8.6 µg m⁻³ during 2017–2018), Kanpur (e.g., monthly average of 19.3 µg m⁻³ in January 2016), and Chennai (episodically), suggesting possible local emissions of hydrochloric acid from plastic-contained waste burning and industry (Gani et al., 2019; Thamban et al., 2019; Gunthe et al., 2021). However, because the emission inventories used in this study (i.e., CEDS, EDGAR, and FINN) do not include anthropogenic emissions of chloride-containing species, such elevated chloride levels observed in these cities were not reproduced in our model simulations. A recent modeling study that incorporated anthropogenic chlorine emissions reported a spatial average increase of 3–4 µg m⁻³ in PM_2.5 concentrations in the IGP during January–March 2018 (Patel et al., 2024). By comparison, our model simulated a spatial average of 88 µg m⁻³ for January 2022 and 56 µg m⁻³ for the annual mean in 2022 across the IGP, which suggests a relatively small impact of incorporating chlorine emissions into our source attribution studies.