# Transboundary emission contribution to PM2.5 concentrations in Indian cities

Raj M Lal, Ajay S Nagpure, Abhinav Anand, Sujit Maji, Kushal Tibrewal, Ganesh Gupta, Chandra Venkataraman, Kangkang Tong

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Raj M Lal, Ajay S Nagpure, Abhinav Anand, Sujit Maji, Kushal Tibrewal, et al. Transboundary emission contribution to PM2.5 concentrations in Indian cities. Environmental Research Letters, 2025, 20 (5), pp.054008. 10.1088/1748-9326/adc147. hal-05045809

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Transboundary emission contribution to PM<sub>2.5</sub> concentrations in Indian cities

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LETTER

# Transboundary emission contribution to PM<sub>2.5</sub> concentrations in Indian cities

Raj M Lal<sup>1,2,*</sup> <sup>1 ID</sup>, Ajay S Nagpure<sup>3,4 ID</sup>, Abhinav Anand<sup>1,5 ID</sup>, Sujit Maji<sup>6 ID</sup>, Kushal Tibrewal<sup>7 ID</sup>, Ganesh Gupta<sup>1</sup>, Chandra Venkataraman<sup>1,2 ID</sup> and Kangkang Tong<sup>8 ID</sup>

¹ Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Mumbai, India

² Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India

³ Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States of America

⁴ World Resources Institute, New Delhi, India

⁵ Department of Mechanical Engineering, Colorado State University, Ft. Collins, CO, United States of America

⁶ Ministry of Earth Sciences, Indian Institute of Tropical Meteorology, Pune, India

⁷ Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Paris, France

⁸ China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai, People's Republic of China

* Author to whom any correspondence should be addressed.

E-mail: rajlal@umich.edu

**Keywords:** urban air quality, National Clean Air Program, transboundary emissions, STILT, WRF-chem

Supplementary material for this article is available [online](#)

## Abstract

Targeting urban air quality improvements in India, the National Clean Air Program (NCAP) was launched in 2019 to reduce PM<sub>2.5</sub> concentrations by 20%–30% in 122 initial cities over a 7 year period (2017–2024). However, considering the regional nature of air pollution nationwide and the significant emission load from rural areas, a potentially large fraction of urban PM<sub>2.5</sub> might originate from emissions outside of a city’s boundary, i.e. transboundary emissions. Here, we couple top-down (STILT-PM<sub>2.5</sub>) and bottom-up (WRF-Chem) modeling approaches with a new, nationwide, monthly-resolved, and fine-scale (5 km × 5 km) anthropogenic emission inventory to assess the impact of transboundary emissions to urban PM<sub>2.5</sub> concentrations across 143 cities (122 NCAP and 67 million plus -cities with population >1 million, among which 46 cities are also NCAP cities). We find that, on average, ~85% (STILT-PM<sub>2.5</sub>: 82% [95% CI: 80–85%; IQR: 77%–94%] & WRF-Chem: 89% [95% CI: 87%–91%; IQR: 88%–96%]) of urban PM<sub>2.5</sub> across the 143 cities originates from transboundary emissions, with domestic biomass burning (32%), energy generation (16%) and industry (15%) being the leading average emission sources to the transboundary contribution. In addition, 107 of the 122 NCAP cities from both modeling approaches have annual transboundary PM<sub>2.5</sub> contributions exceeding 80%, indicating that an entire mitigation of within-boundary emissions alone in these cities will not achieve the most conservative targets outlined as part of NCAP. Our findings underscore the need for multi-scale, regional action planning and implementation to achieve PM<sub>2.5</sub>-air quality targets throughout India.

## 1. Introduction

India is widely recognized for its poor air quality, frequently reporting some of the highest levels of PM<sub>2.5</sub> (particulate matter with aerodynamic diameter 2.5 µms or less) globally. Given that exposure to PM<sub>2.5</sub> is one of the leading causes of premature mortality globally—in India specifically, it contributes

to ~1.6 million premature mortalities each year [1, 2]—understanding PM<sub>2.5</sub> profiles and subsequently implementing interventions to reduce exposures can have far reaching health benefits. Throughout India, much of the ambient air quality focus exists in urban areas (e.g. the vast majority of regulatory monitors are stationed in urban areas [3–5] and a number of policy initiatives have been urban centric [6, 7]), despite
two-thirds of the population residing in rural areas and recent studies concluding that ambient air pollution is a regional phenomenon [8–10].

The National Clean Air Program (NCAP) was launched in 2019 to address urban air quality, setting out an initial goal of reducing PM<sub>2.5</sub> concentrations by 20%–30% in 2024 from 2017 levels across 122 cities initially [6]. As part of NCAP, participating cities were required to submit action plans that outlined emission reduction strategies. Domestic biomass burning, a mostly rural activity [11] for cooking, heating, lighting, and waste reduction, etc [12]., is understood to be the leading anthropogenic source contributor to nationwide PM<sub>2.5</sub> levels [13]. Given this, in addition to the large volume of emissions from other sectors that occur outside of cities, it suggests that a potentially large fraction of urban PM<sub>2.5</sub> concentrations may be from sources that are outside of the city itself, i.e. transboundary.

Prior NCAP studies have focused on emission and monitoring summaries in each of the study cities [6, 14], city-specific assessments of PM<sub>2.5</sub> and PM<sub>10</sub> trends [15, 16] and evaluation of progress and expenditures during the implementation period [17]. These studies, which have addressed various aspects of NCAP, have found that there has been progress on certain critical components for the scheme, including an increase in institutional monitoring and inventory development capital, identifying and outlining city-specific emission reduction strategies, and implementing interventions for specific sources (e.g. mobile sources).

Here, we add to the existing body of NCAP literature by assessing the source-specific, transboundary contributions to urban PM<sub>2.5</sub> in 143 Indian cities (122 NCAP and 67 million plus (MP, cities with population >1 million) among which 46 are also NCAP cities) using top-down and bottom-up modeling approaches. We couple a new, spatially-resolved (5 km × 5 km) anthropogenic emission inventory (Tibrewal *et al* in revision [18]) with existing back-and-forward modeling frameworks to quantify the impact of transboundary emissions on PM<sub>2.5</sub> in cities in the year 2019. We then identify which emission sectors contribute to the transboundary impact across the 143 study cities. Finally, we discuss the important policy implications of this work.

## 2. Methods

### 2.1. Defining urban boundaries for 143 NCAP and MP cities

To ultimately quantify the transboundary contribution to the 143 cities studied here (figure 1, SI figure 1, and SI table 1), we first defined the geographic boundaries of each urban domain, which has been an ongoing point of discord in studies of urban areas

globally [14, 19–21]. Here, we utilize Google and Open Street Maps to define urban boundaries for each of the 143 cities, consistent with the approach outlined in previous work defining urban boundaries [22]. This approach resulted in a strong agreement between the modeled city areas (km<sup>2</sup>) and population with that reported in local/government handbooks and census data (SI figure 2 for a map of each city's boundary & SI figure 3 for modeled and reported area and population evaluation).

### 2.2. Speciated Multipollutant Generator-India (SMoG-India) fine-scale (5 km × 5 km) emission inventory

SMoG-India is a detailed inventory of monthly emissions in India for 2019 that covers eight anthropogenic emission sectors: agricultural waste burning, domestic, energy, industry, shipping, solvent use, transportation, and urban fugitive dust, while agriculture [23], forest fire and grassland fire [24], and waste burning [25] emissions are introduced from global inventories. SMoG-India provides nationwide, high-resolution (5 km × 5 km) emission data for BC, CH<sub>4</sub>, CO, CO<sub>2</sub>, anthropogenic mineral matter (MM), N<sub>2</sub>O, NH<sub>3</sub>, NO<sub>x</sub>, OC, and SO<sub>2</sub>. Detailed inventory methods, underlying data, and evaluation are available in Tibrewal *et al* (in revision) [18]. Briefly, this inventory builds on an existing engineering, technology-linked, energy emission modeling approach that includes technology parameters for process and emissions control technologies, including technology type, efficiency, specific fuel consumption, and technology-linked emission factors (g pollutant kg<sup>-1</sup> fuel) [12, 26–28]. Natural dust emissions, which are understood to be a meaningful contributor to the atmospheric aerosol burden in India [29, 30], from Meng *et al* [31] and surface emissions outside India from the Community Emissions Data System (CEDS; 0.5 × 0.5°) [23] are used for the modeling effort outlined below.

### 2.3. Top-down and bottom-up PM<sub>2.5</sub>-air quality modeling and evaluation for transboundary emission contribution estimates

We leverage top-down and bottom-up modeling approaches to most comprehensively characterize and assess transboundary emission contributions on the study cities. Traditional assessments of transboundary impacts are on the national or continental boundary scale; in this study, transboundary refers to transport across the city-boundary scale. Top-down, back-trajectory modeling was performed using the Stochastic Time Inverted Lagrangian Transport (STILT) model version 2 [32, 33] that we coupled with a first-order aerosol module to estimate PM<sub>2.5</sub> concentrations and spatially and temporally resolved source contributions. STILT is generally used for
<figure><img src="image_5.png" /></figure>

long-lived, inert tracer species, but has previously been used in studies of PM<sub>2.5</sub> and its components following similar first-order aerosol parameterizations as used here [34, 35]. Our aerosol module incorporates first-order chemical growth (e.g. SO<sub>2</sub> to SO<sub>4</sub><sup>2-</sup>, NH<sub>3</sub> to NH<sub>4</sub><sup>+</sup>, NO<sub>x</sub> to NO<sub>3</sub><sup>-</sup>), and wet and dry deposition schemes; the modeling framework is herewith referred as STILT-PM<sub>2.5</sub>. We refer the readers to SI section 1 for a detailed overview of these approaches, including an explanation of the deposition tuning methodology used to optimize model performance. STILT outputs a surface sensitivity footprint (μmol/m<sup>2</sup> - sec)

surface emission fluxes (kg/m<sup>2</sup>-sec), e.g. from SMoG-India, can generate fine-scale, upstream PM<sub>2.5</sub> source contributions by:

$$ 
\begin{aligned}
\text{PM}_{2.5}\text{source contribution} & \left(\frac{\mu\text{g}}{\text{m}^3}\right) \\
= & \text{Footprint} \left(\frac{\text{ppm}}{\mu\text{mol}/\text{m}^2 - \text{sec}}\right) \\
& \ast \text{Emission Rate} \left(\frac{\text{kg}}{\text{m}^2 - \text{sec}}\right) \ast \text{UCF}(T)
\end{aligned}
$$

where UCF (T) is a unit conversion factor derived from the ideal gas law as a function of ambient temperature, T (SI section 2).

We released 400 air parcels from the geographic center of each of the 143 cities, 10 m above ground level, and tracked them for 5 d back in time (roughly the lifetime of PM<sub>2.5</sub> in the atmosphere)—consistent with parameterizations used in other trajectory studies of urban/India PM<sub>2.5</sub> [36, 37]. The
modeling domain consists of a fine-resolution grid (0.05° × 0.05°) over India (6–39°N; 67–99°E) and a coarse-resolution grid (0.5° × 0.5°; 0–60°N; 0–150°E) that was used to capture regional influences and covered areas outside of India in the Northern Hemisphere (SI figure 4). STILT-PM₂.₅ was driven with meteorological inputs from Global Data Assimilation System (GDAS) 0.5° × 0.5° resolution product (January 2019–June 2019) and Global Forecast System (GFS) 0.25° × 0.25° resolution (June 2019–December 2019) product. The higher resolution GFS meteorological product was launched in June 2019 and GDAS was retired concurrently, explaining why we had to utilize two separate meteorological products for this modeling effort. From each receptor we released parcels at a random time each day of the year and then generated monthly-averaged surface sensitivity footprints to align with the monthly resolution of SMoG-India (SI figure 5 for sample monthly-averaged, 5 d back-in-time footprint for Aligarh, UP in January & SI figure 6 for emissions from each SMoG-India sector within the same domain). The rationale for using a random release time is to capture the temporal variability of atmospheric conditions to most representatively assess and reconstruct PM₂.₅ concentrations and source contributions.

The focus of this reduced-complexity, top-down modeling approach is to create representative PM₂.₅ profiles and sectoral contributions using a computationally efficient framework to address policy-relevant questions, consistent with the approach outlined in a suite of recent reduced-complexity modeling application studies [38–40]. To quantify the fraction of modeled urban PM₂.₅ that is transboundary, we calculate the within-city and out-of-city-origin PM₂.₅ source contributions. We further assess these contributions based on the emission sector and additionally quantify the fraction of the transboundary contribution that occurs from other cities. The source apportionment results presented in this work should be treated as representative source profiles due to the complexity of PM₂.₅ modeling in a highly polluted environment with distinct chemical pathways and the simplifications used in this modeling approach. Since SMoG-India does not include VOC emissions, we are unable to use a first-order approach to capture potential SOA loadings/contributions to the PM₂.₅ profiles generated here, which would have inherent restrictions due to the highly non-linear nature of SOA formation. Observational estimates of SOA's contribution to PM₁.₂.₅ budgets vary considerably across urban areas: 20%–35% in Delhi [41, 42], ~20%–40% in Kanpur [43, 44], ~30% in Ahmedabad [45], ~20% in Chennai [46], and ~15% in Mumbai [47]. On the other hand, most nationwide modeling studies that use state-of-the-science chemical pathways show that SOA is often a minor (<10%) contributor

to PM₂.₅ budgets [2, 48–50], suggesting that modeling of primary and SNA species can be used to generate representative source profiles across these study cities.

The deposition tuning used to improve the modeled PM₂.₅ concentration performance against observations will also influence the source profiles of cities—a higher deposition velocity would minimize the influence of transboundary emissions and amplify the influence of near-field emissions. We conducted a first-order sensitivity assessment and found that there were marginal differences between source contributions across cities between the endpoints of the range of deposition velocities (0.1 and 1.0 cm s⁻¹) used in this study (SI table 2). The source profiles reported in this paper are the findings associated with the deposition velocities that optimized PM₂.₅ model performance.

The WRF-Chem model version 3.9.1 was used for one base case and one all city zero-out scenario simulation to assess transboundary contributions to urban PM₂.₅. Model runs were performed between 12° S–40° N and 39° E–121° E at a horizontal resolution of 27 km × 27 km (225 grids × 327 grids) with 48 vertical layers from the surface to 50hPa. We used the Regional Acid Deposition Model version 2 (RADM2) [51] and the Modal Aerosol Dynamics model [52] for gas-phase and aerosol chemistry, respectively, and implemented cyclic data assimilation and nudging techniques as detailed in Sharma et al [48] and Venkataraman et al [49]. Following the approach introduced by Mogno et al [53] to reduce computational demands in their WRF-Chem modeling campaign over the Indo-Gangetic Plain, we simulated 1 month of each season (Winter–January; Pre-Monsoon–April; Monsoon–July; Post-Monsoon–October) with a 1 month spin-up. SMoG-India with emissions from CEDS and GFED described above was coupled with anthropogenic emissions from CEDS outside of India and BVOC emissions from the Model of Emissions of Gases and Aerosols from Nature [54] for baseline model simulations. In the all city zero-out scenario simulation, all anthropogenic emissions within the 143 city boundaries were set to zero. We then compared the difference in modeled PM₂.₅ concentrations at each city's centroid from the zero-out simulation with the baseline simulation to estimate the PM₂.₅ transboundary emission contribution. By zeroing out the anthropogenic emissions within each city's boundary, only emissions that occur outside of the city, i.e. transboundary, are contributing to each city's PM₂.₅ load, allowing us to estimate the transboundary PM₂.₅ contribution. This first-order approach assumes that each urban area is uninfluenced by emissions from other urban areas; our results from STILT-PM₂.₅ found ~2.4% of average urban PM₂.₅ originates from other cities' emissions.
Monthly modeled PM<sub>2.5</sub> concentrations from both STILT-PM<sub>2.5</sub> and WRF-Chem modeling frameworks were evaluated with monthly-averaged Central Pollution Control Board (CPCB) monitored data (n = 52 cities) and the same satellite-derived, surface-level PM<sub>2.5</sub> product used by the Global Burden of Disease in their global health assessments [55]. We additionally compare our STILT-PM<sub>2.5</sub> modeled source apportionment and PM<sub>2.5</sub> species composition with observations from nine cities where source apportionment studies have been performed [56–67]

## 3. Results

### 3.1. Transboundary contribution to urban PM<sub>2.5</sub>

Across all 143 cities we find, on average, ~85% (STILT-PM<sub>2.5</sub>: 82% [95% CI: 80%–85%; IQR: 77%–94%] and WRF-Chem: 89% [95% CI: 87%–91%; IQR: 88%–96%]) of urban PM<sub>2.5</sub> is from transboundary emissions (figures 2 and 3). 107 of 122 NCAP cities had annual transboundary contributions >80% in both modeling approaches—these cities will be unable to achieve the most conservative NCAP target under an entire mitigation of in-boundary anthropogenic emissions. Further, only 10 NCAP cities can achieve the ambitious NCAP target of 30% reduction in PM<sub>2.5</sub> concentrations from in-boundary actions alone. The 122 NCAP cities generally had a higher transboundary contribution than the 67 MP cities (87% (average of STILT-PM<sub>2.5</sub> and WRF-Chem results) compared to 81%), expected given that MP cities are generally larger and more populated than NCAP cities (SI figure 7). The largest, most populated, and often most polluted cities had some of the lowest transboundary PM<sub>2.5</sub>-contributions of the study cities (e.g. Delhi (65%), Mumbai (46%), Kolkata (47%), and Bangalore (60%)).

WRF-Chem modeling resulted in a slightly higher, on average, transboundary contribution compared to the STILT-PM<sub>2.5</sub> modeled contribution across cities (figures 2, SI 8 and 9). This is likely due to the coarser grid resolution of WRF-Chem (27 km × 27 km) than that of STILT-PM<sub>2.5</sub> (5 km × 5 km), which would dampen the contribution of immediate, near-field PM<sub>2.5</sub> impacts at the city scale. In addition, WRF-Chem includes SOA modeling while STILT-PM<sub>2.5</sub> does not, which will further amplify the influence of transboundary contributions to urban PM<sub>2.5</sub> across longer spatial scales. There was no clear regional trend in the transboundary performance bias between the two modeling frameworks (SI figure 8).

Domestic biomass burning (32% [95% CI: 27%–37%; IQR: 18%–43%]), energy generation (16% [95% CI: 13%–19%; IQR: 5%–17%]), and industry (15% [95% CI: 12%–18%; IQR: 13%–28%]) are the leading source contributors to the transboundary urban PM<sub>2.5</sub> burden nationwide (figure 3 and SI

table 3). Seasonally, the highest transboundary contributions occurred during non-monsoon periods (SI table 3 and SI figures 10 and 12) as the monsoons will limit long-range transport potential and accordingly reduce transboundary influences. During the monsoon months, consequently, we see a larger fraction of urban sources contributing to average PM<sub>2.5</sub> source profiles; the contribution from urban fugitive dust and industry increased from 6% and 20% non-monsoon periods to 9% and 24% during the monsoon, respectively (SI table 4). Low mixing layer heights, particularly in the mountainous regions and during wintertime, were expected to result in a lower transboundary contribution; however, this was not observed in either modeling framework (SI figure 8 and SI table 3). This was again likely attributed to the generally small size of the cities in these regions relative to the modeling grid size. Jamshedpur, Jharkhand and Korba, Chhattisgarh had the lowest modeled annual-average PM<sub>2.5</sub> transboundary contribution of all cities, at 31 and 34%, respectively. In both of the NCAP cities, a high-volume power generation point source is in-boundary, likely driving the high in-boundary PM<sub>2.5</sub> contribution (SI figure 13). Across cities, only 2.4% [95% CI: 2%–3%; IQR: 0%–3%] of the average urban PM<sub>2.5</sub> transboundary contribution originated from other cities’ emissions, despite the relatively close proximity of many of the study cities and higher emission intensities in urban areas compared to their rural counterparts (table 1).

### 3.2. Model evaluation

We found reasonable agreement between both STILT-PM<sub>2.5</sub> and WRF-Chem PM<sub>2.5</sub> modeling results with the observed PM<sub>2.5</sub> concentrations (figure 4), although a seasonal bias exists in the performance (see SI table 5). Across model-observation combination evaluations, we found that both models, on average, over-predicted observations (figures 4 and SI 14). Spatially, we observe a noticeable over-prediction in South India and under-prediction throughout Central and East India from both STILT-PM<sub>2.5</sub> and WRF-Chem. STILT-PM<sub>2.5</sub> under-predicted observations in North India, while WRF-Chem over-predicted those levels (SI figure 15). A number of factors may be contributing to these biases, including unresolved emissions, unresolved chemical/deposition loss mechanisms, or potentially systemic biases with the observations. The STILT-PM<sub>2.5</sub> results showed slightly better performance than the WRF-Chem model results; in STILT-PM<sub>2.5</sub> the deposition tuning approach was specifically employed to reduce bias against PM<sub>2.5</sub> observations, which explains why we see better performance compared to the WRF-Chem model outputs. We found better agreement from both models with the satellite-retrieved PM<sub>2.5</sub> data compared to the in-situ monitoring data. This may be the result of the high heterogeneity of PM<sub>2.5</sub>
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Transboundary emission contribution distribution to urban PM<sub>2.5</sub> across 143 cities (122 National Clean Air Program (NCAP) and 67 million plus (MP)) from (top left) top-down, STILT-PM<sub>2.5</sub> modeling, (top right) bottom-up, WRF-Chem modeling, and (bottom) box plots of both modeling approaches. The red diamonds on the box plots indicate the average transboundary contribution across cities. Shown on all figures are the NCAP conservative (20%) and ambitious (30%) PM<sub>2.5</sub> reduction targets.</figcaption></figure>

concentrations in cities nationwide—a single model-
ing grid could have orders of magnitude variances in
PM<sub>2.5</sub> levels—and the known uncertainties of CPCB-
reported monitoring data [3].

Deposition tuning within the range of reported
urban deposition velocities, which improved STILT-
PM<sub>2.5</sub> performance, had little impact on the average
transboundary PM<sub>2.5</sub> contributions—the primary
focus of this paper (SI section 3 and SI figure 16).
Additionally, we find modest agreement of STILT-
PM<sub>2.5</sub> sectoral profiles with recent source apportion-
ment results across nine cities (SI table 6), suggesting
our reduced-complexity, back-trajectory modeling
framework can provide representative PM<sub>2.5</sub> source
profiles that align with observations. In each of the
nine cities where source apportionment results were
available, while most of the sectors showed consistent
observed apportionment with our modeling results,
there was a large variation in mobile source contribu-
tions across each of the cities. While this might be due
to speciation profiles or how mobile source impacts
are quantified in source apportionment modeling,
SMOG-India and its predecessor version [12] have
both received feedback that mobile source PM<sub>2.5</sub>
and precursor emissions may be underestimated.
However, after updating methodologies and activ-
ity data to generate the former, the contribution of
mobile sources to the nationwide PM<sub>2.5</sub> emissions

budget was still found to be minor. A comprehens-
ive evaluation of baseline WRF-Chem PM2.5 fields is
available in Venkataraman et al [49]. Furthermore,
given similar transboundary contribution estimates
across cities between the reduced-form top-down and
state-of-the-science bottom-up modeling approach,
it suggests this reduced-form approach can be used
in future studies to assess transboundary PM2.5 con-
tributions in cities.

4. Discussion

Improving ambient air quality has been a focal
point of recent nationwide environmental policies.
Specifically targeting urban air quality, NCAP was
introduced and participating cities were required to
submit action plans outlining policies and emission
mitigation strategies. While the focus of emission
reduction strategies has largely been on in-boundary
strategies, as determined here, emissions from out-
side of cities, i.e. transboundary, are driving urban
PM2.5 budgets nationwide. The limited prior lit-
erature on nationwide transboundary air pollution
assessments—which have either been at coarse (e.g.
state/regional) scales [68], of a small subset of cit-
ies using reduced complexity modeling only [69], or
from single sources (e.g. power plants) [70]—have
also implicated the importance of transboundary
<figure><img src="image_7.png" /></figure>

emissions and regional actions to improving PM<sub>2.5</sub> concentrations. Globally, the impact of transboundary emissions, long-range transport, and the formation of secondary aerosols has been more studied; such studies, which have spanned from the city to the continental scale, have also implicated the important role of cross-boundary impacts to PM<sub>2.5</sub> concentrations [71–73].

To achieve urban PM<sub>2.5</sub> reduction targets—including those outlined by NCAP—control of trans-boundary emissions and multi-scale, regional action planning, in addition to urban emission mitigation actions, is necessary. Despite little evidence that cities have made progress toward the initial NCAP targets, the program recently announced an updated

goal of 40% PM<sub>10</sub> concentration reductions in these cities over an additional 2 years following the initial 7 year outline, further emphasizing the important role that addressing transboundary emissions will play in achieving proposed urban air quality targets. City-specific action planning, taking into account the source profiles generated in this study, and identifying specific transboundary emission sources will be a necessary strategy for city planners and urban air quality managers when developing clean air action plans.

Although there has historically been an urban-centric focus in air quality management, the findings from this study add support to the growing body of literature that air pollution is a regional
<table><caption>**Table 1.** Nationwide anthropogenic and dust primary PM<sub>2.5</sub> species and precursor emissions (Mt yr<sup>-1</sup>) and intensities (ton km<sup>-2</sup> yr<sup>-1</sup> and kg capita<sup>-1</sup> yr<sup>-1</sup>) for urban and rural India.</caption><thead><tr><th rowspan="3">Emission Sector</th><th colspan="12">Nationwide Emissions (Mt yr<sup>-1</sup>)</th></tr><tr><th colspan="6">Urban</th><th colspan="6">Rural</th></tr><tr><th>BC</th><th>MM</th><th>OC</th><th>NO<sub>x</sub></th><th>NH<sub>3</sub></th><th>SO<sub>2</sub></th><th>BC</th><th>MM</th><th>OC</th><th>NO<sub>x</sub></th><th>NH<sub>3</sub></th><th>SO<sub>2</sub></th></tr></thead><tbody><tr><td>Agriculture</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.07</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.34</td><td>7.15</td><td>0</td></tr><tr><td>Agricultural waste burning</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.04</td><td>0.21</td><td>0.22</td><td>0.15</td><td>0.02</td><td>0.08</td></tr><tr><td>Domestic</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.02</td><td>0</td><td>0.01</td><td>0.40</td><td>0.26</td><td>1.77</td><td>0.82</td><td>0.21</td><td>0.09</td></tr><tr><td>Dust</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.00</td><td>0</td><td>3.04</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Energy</td><td>0.01</td><td>0.22</td><td>0.01</td><td>0.27</td><td>0</td><td>0.64</td><td>0.05</td><td>1.01</td><td>0.05</td><td>1.91</td><td>0.07</td><td>3.83</td></tr><tr><td>Forest fire</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.06</td><td>0.02</td><td>0.01</td><td>0.01</td></tr><tr><td>Grassland fire</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.02</td><td>0.03</td><td>0</td><td>0</td></tr><tr><td>Industrial</td><td>0.02</td><td>0.08</td><td>0.02</td><td>0.26</td><td>0</td><td>0.52</td><td>0.18</td><td>0.73</td><td>0.11</td><td>1.36</td><td>0.03</td><td>2.93</td></tr><tr><td>Shipping</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.03</td><td>0</td><td>0.02</td></tr><tr><td>Solvent</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Transportation</td><td>0.01</td><td>0</td><td>0.01</td><td>0.27</td><td>0</td><td>0.01</td><td>0.13</td><td>0.02</td><td>0.05</td><td>2.96</td><td>0.01</td><td>0.07</td></tr><tr><td>Urban fugitive dust</td><td>0</td><td>0.05</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.38</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Waste burning</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.02</td><td>0</td><td>0.03</td><td>0</td><td>0.19</td><td>0.27</td><td>1.49</td><td>0.02</td></tr><tr><td>Total</td><td>0.05</td><td>0.36</td><td>0.04</td><td>0.82</td><td>0.09</td><td>1.18</td><td>0.84</td><td>5.64</td><td>2.48</td><td>7.88</td><td>8.99</td><td>7.06</td></tr></tbody></table><table><thead><tr><th rowspan="3">Emission Sector</th><th colspan="12">Nationwide average emission intensity (ton km<sup>-2</sup> yr<sup>-1</sup>)</th></tr><tr><th colspan="6">Urban</th><th colspan="6">Rural</th></tr><tr><th>BC</th><th>MM</th><th>OC</th><th>NO<sub>x</sub></th><th>NH<sub>3</sub></th><th>SO<sub>2</sub></th><th>BC</th><th>MM</th><th>OC</th><th>NO<sub>x</sub></th><th>NH<sub>3</sub></th><th>SO<sub>2</sub></th></tr></thead><tbody><tr><td>Agriculture</td><td>0</td><td>0</td><td>0</td><td>0.13</td><td>2.88</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.12</td><td>2.45</td><td>0</td></tr><tr><td>Agricultural waste burning</td><td>0.01</td><td>0.07</td><td>0.07</td><td>0.05</td><td>0.01</td><td>0.03</td><td>0.02</td><td>0.13</td><td>0.14</td><td>0.09</td><td>0.02</td><td>0.05</td></tr><tr><td>Domestic</td><td>0.29</td><td>0.35</td><td>0.37</td><td>1.04</td><td>0.10</td><td>0.35</td><td>0.14</td><td>0.10</td><td>0.61</td><td>0.28</td><td>0.07</td><td>0.03</td></tr><tr><td>Dust</td><td>0</td><td>0.31</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>11.41</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Energy</td><td>0.60</td><td>13.15</td><td>6.80</td><td>14.53</td><td>0.09</td><td>35.09</td><td>0.08</td><td>2.20</td><td>0.33</td><td>3.22</td><td>0.02</td><td>6.59</td></tr><tr><td>Forest fire</td><td>0.01</td><td>0</td><td>0.10</td><td>0.02</td><td>0.01</td><td>0.01</td><td>0.02</td><td>0</td><td>0.33</td><td>0.08</td><td>0.04</td><td>0.04</td></tr><tr><td>Grassland fire</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.01</td><td>0.02</td><td>0</td><td>0</td></tr><tr><td>Industrial</td><td>1.34</td><td>4.46</td><td>0.89</td><td>13.95</td><td>0.05</td><td>28.80</td><td>0.10</td><td>0.82</td><td>0.10</td><td>1.25</td><td>0.01</td><td>2.47</td></tr><tr><td>Shipping</td><td>0</td><td>0</td><td>0</td><td>0.09</td><td>0</td><td>0.05</td><td>0</td><td>0</td><td>0</td><td>0.05</td><td>0</td><td>0.03</td></tr><tr><td>Solvent</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Transportation</td><td>0.49</td><td>0.12</td><td>0.28</td><td>12.79</td><td>0.01</td><td>0.28</td><td>0.05</td><td>0.01</td><td>0.02</td><td>1.14</td><td>0</td><td>0.03</td></tr><tr><td>Urban fugitive dust</td><td>0</td><td>2.31</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.16</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Waste burning</td><td>0.02</td><td>0</td><td>0.10</td><td>0.14</td><td>0.75</td><td>0.01</td><td>0.01</td><td>0</td><td>0.07</td><td>0.09</td><td>0.51</td><td>0.01</td></tr><tr><td>Total</td><td>2.76</td><td>20.8</td><td>8.61</td><td>42.7</td><td>3.91</td><td>64.6</td><td>0.43</td><td>14.8</td><td>1.60</td><td>6.35</td><td>3.13</td><td>9.24</td></tr></tbody></table>

(Continued.)
<table><caption>Table 1. (Continued.)</caption><thead><tr><th rowspan="3">Emission Sector</th><th colspan="10">Nationwide average emission intensity (kg person<sup>-1</sup> yr<sup>-1</sup>)</th></tr><tr><th colspan="6">Urban</th><th colspan="5">Rural</th></tr><tr><th>BC</th><th>MM</th><th>OC</th><th>NO<sub>x</sub></th><th>NH<sub>3</sub></th><th>SO<sub>2</sub></th><th>BC</th><th>MM</th><th>OC</th><th>NO<sub>x</sub></th><th>NH<sub>3</sub></th><th>SO<sub>2</sub></th></tr></thead><tbody><tr><td>Agriculture</td><td>0</td><td>0</td><td>0</td><td>0.03</td><td>0.74</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.27</td><td>5.71</td><td>0</td></tr><tr><td>Agricultural waste burning</td><td>0</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0</td><td>0</td><td>0.03</td><td>0.17</td><td>0.18</td><td>0.12</td><td>0.01</td><td>0.06</td></tr><tr><td>Domestic</td><td>0.07</td><td>0.08</td><td>0.09</td><td>0.24</td><td>0.03</td><td>0.08</td><td>0.32</td><td>0.21</td><td>1.42</td><td>0.65</td><td>0.16</td><td>0.07</td></tr><tr><td>Dust</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2.42</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Energy</td><td>0.12</td><td>2.69</td><td>0.09</td><td>3.16</td><td>0.02</td><td>7.43</td><td>0.04</td><td>0.80</td><td>0.04</td><td>1.52</td><td>0.05</td><td>3.06</td></tr><tr><td>Forest fire</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.05</td><td>0.01</td><td>0.01</td><td>0.01</td></tr><tr><td>Grassland fire</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.01</td><td>0.02</td><td>0</td><td>0</td></tr><tr><td>Industrial</td><td>0.27</td><td>0.92</td><td>0.18</td><td>2.95</td><td>0.01</td><td>6.08</td><td>0.15</td><td>0.58</td><td>0.09</td><td>1.09</td><td>0.03</td><td>2.34</td></tr><tr><td>Shipping</td><td>0</td><td>0</td><td>0</td><td>0.01</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.03</td><td>0</td><td>0.02</td></tr><tr><td>Solvent</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Transportation</td><td>0.12</td><td>0.03</td><td>0.06</td><td>2.98</td><td>0</td><td>0.06</td><td>0.10</td><td>0.02</td><td>0.04</td><td>2.37</td><td>0.01</td><td>0.05</td></tr><tr><td>Urban fugitive dust</td><td>0</td><td>0.53</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.30</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Waste burning</td><td>0</td><td>0</td><td>0.02</td><td>0.03</td><td>0.19</td><td>0</td><td>0.02</td><td>0</td><td>0.15</td><td>0.22</td><td>1.19</td><td>0.02</td></tr><tr><td>Total</td><td>0.58</td><td>4.26</td><td>0.47</td><td>9.41</td><td>0.99</td><td>13.7</td><td>0.67</td><td>4.51</td><td>1.98</td><td>6.30</td><td>7.18</td><td>5.63</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Evaluation of STILT-PM<sub>2.5</sub> and WRF-Chem modeled annual PM<sub>2.5</sub> concentrations in the 143 study cities with Central Pollution Control Board (CPCB)-monitored and satellite-derived surface-level PM<sub>2.5</sub> concentrations. See SI figures 14 and 15 for individual evaluation and spatial evaluation, respectively, plots of each of the four model-observation combinations.</figcaption></figure>

phenomenon nationwide [9, 11]. As such, rural emission mitigation efforts to improve urban air quality will have an added co-benefit of improving rural air quality, leading to potential health and well-being outcomes. Domestic biomass burning is understood to be the leading contributor to nationwide ambient PM<sub>2.5</sub> emissions, despite being an indoor activity. It was also the leading source contributor, on average, to transboundary PM<sub>2.5</sub> contributions across cities. Targeting these emissions for urban air quality purposes will improve household PM<sub>2.5</sub> levels in rural, mostly low-income homes that rely on biomass fuels for cooking and heating, which can result in large health benefits from reduced household PM<sub>2.5</sub> exposures. It is suggested that there maintains continued emphasis on clean cooking interventions in rural areas. Finally, combating rural emissions for urban air quality improvements offers large mitigation potential of rural climate-forcing gas emissions, which is particularly relevant given India is the fastest growing greenhouse-gas emitting country in the world.

Addressing and combatting ambient air pollution has been a growing point of emphasis in India. From recent increases in monitoring capacity to higher investments in air quality management to the proposal and implementation of emission reduction policies, strong nationwide efforts are being made to improve air quality. With a specific focus on urban air quality, NCAP sets out a series of ambitious targets to reduce PM<sub>2.5</sub> levels in cities. Here, we show that transboundary emissions are often a driving contributor to urban PM<sub>2.5</sub> levels, which will prevent these cities from achieving NCAP targets from within-boundary actions alone. The findings from this study underscore the importance of city-specific, targeted multi-scale, regional action planning to achieve nationwide PM<sub>2.5</sub>-air quality targets.

## Data availability statement

All data that support the findings of this study are included within the article (and any supplementary files).

## Acknowledgments

This work was supported by a Fulbright KalamClimate Fellowship and the Indian MoEFCC under the NCAP-COALESCE Project Grant# 14/10/2014-CC(Vol.II). We thank Eric Kort and Natasha Dacic at the University of Michigan, Kaushik Reddy of IIT-Bombay, and Sarath Guttikunda of Urban Emissions for providing resources and feedback to the manuscript pre-submission. The SMoG emission inventory datasets are the research outcome of the NCAP-COALESCE consortium (www.ncapcoalesce.iitb.ac.in).

## Author contributions

RML conceptualized, designed, and performed the research and wrote the manuscript. A S N, C V, and K K T advised the research. K T, G G, and C V developed SMoG-India and prepared model ready emission files and A A, S M, and C V performed WRF-Chem modeling.

## Conflict of interest

The authors declare no conflicts of interest.

## ORCID iDs

Raj M Lal https://orcid.org/0000-0001-6024-0629
Ajay S Nagpure https://orcid.org/0000-0002-4817-7329
Abhinav Anand https://orcid.org/0009-0003-8117-2882

Sujit Maji https://orcid.org/0000-0001-9175-3339

Kushal Tibrewal https://orcid.org/0000-0003-1009-4250

Chandra Venkataraman https://orcid.org/0000-0002-2280-3360

Kangkang Tong https://orcid.org/0000-0002-8515-4812

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