Strong reduction in near-surface turbulence due to aerosols in South and East Asia

manuscript version 2025-02-24 1 1 Strong reduction in near-surface turbulence due to aerosols in South and East 2 Asia 3 4 Øivind Hodnebrog1, Kristin Aunan1, Sourangsu Chowdhury1, Louis Marelle2, Gunnar 5 Myhre1, Camilla W. Stjern1, and Shuxiao Wang3 6 1Center for International Climate Research (CICERO), Oslo, Norway 7 2Sorbonne Université, UVSQ, CNRS, LATMOS, Paris, France 8 3State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of 9 Environment, Tsinghua University, Beijing, China 10 11 Corresponding author: Øivind Hodnebrog ([email protected]) 12 13 Abstract 14 Absorbing and scattering aerosols influence the vertical temperature profile and can lead to a 15 shallower boundary layer and potentially enhanced surface air pollution. Using a regional chemistry-16 climate model, validated against >1,000 air quality monitoring stations, we show that absorbing 17 black carbon (BC) aerosols reduce near-surface turbulence and boundary layer height through 18 aerosol-radiation interactions (ARI). While ARI due to total aerosols lead to enhanced PM2.5 surface 19 concentrations and 25,000-27,000 annual excess deaths in each of Northern India and Eastern China, 20 ARI due to BC have a modest impact on near-surface PM2.5 because of its ability for self-lofting and 21 enhanced precipitation affecting wet scavenging. However, over India BC ARI strongly increase the 22 number of days with combined high temperature and relative humidity, dangerous for human 23 health. These results highlight that the multitude of indirect impacts from individual aerosol species 24 need to be considered to achieve efficient mitigation of air pollution. 25 26 27 Introduction 28 Large parts of South and East Asia suffer from poor air quality, mainly due to high concentrations of 29 PM2.5 (fine particulate matter with a diameter <2.5 µm) leading to millions of excess deaths every 30 year1. Both through short-term and long-term exposure, PM2.5 can cause a number of detrimental 31 effects on human health, such as respiratory and cardiovascular diseases, and other pollutants, such 32 as ozone (O3), add to the problem2. 33 Local anthropogenic emissions are the most important factor influencing air quality in the 34 highly polluted Eastern China and Indo-Gangetic plains of India, but meteorological conditions also 35 play a key role3-5. Absorption and scattering of solar radiation by particles, i.e., aerosol-radiation 36 interactions (ARI), have been found to enhance near-surface pollution by modifying meteorology in 37 the planetary boundary layer (PBL)6-9, leading to an increased number of PM2.5 related deaths in 38 China10 and India11. 39 manuscript version 2025-02-24 2 The enhancement of surface air pollution through ARI is a consequence of reduced 40 turbulence and lowering of the PBL height, leading to reduced sink of surface air pollution from 41 turbulent transport to higher altitudes12. This is particularly important for black carbon (BC) 42 aerosols12-14, due to their ability to absorb solar radiation and thereby heat the atmosphere on a 43 short time scale15. BC in the PBL also leads to higher relative humidity, enhancing cloud development 44 and potentially affecting climate12. Elevated relative humidity combined with high temperatures can 45 also have negative health impacts16,17. The impact on PBL suppression is, however, dependent on the 46 vertical distribution of BC concentrations18-20. Aerosols that scatter solar radiation, cool the surface 47 and therefore also modify the vertical temperature profile and stability, although the aerosol-PBL 48 feedback has been found to be small for sulfate (SO4) aerosols9. In addition to ARI, aerosols can alter 49 the Earth’s radiation budget through aerosol-cloud interactions where aerosols act as cloud 50 condensation nuclei21. 51 Aerosol-radiation interactions have been found to influence tropospheric ozone through two 52 mechanisms: meteorological changes by aerosol-radiation feedback, and changes in the photolysis 53 rates by aerosol-photolysis interactions22. The net effect is a reduction of near-surface ozone, mainly 54 due to aerosol-photolysis interactions23, but the daily maximum ozone response is dependent on the 55 season24. In addition to detrimental health effects, elevated near-surface ozone concentrations have 56 negative impacts on agricultural crop production25. 57 Aerosol abundances over China have been reduced in recent years due to air quality 58 regulations while India has seen an increase26-29. However, regional trends in anthropogenic 59 emissions vary between individual aerosols and aerosol precursors30, and there are uncertainties in 60 how natural emissions, such as dust aerosols, respond to climate change31. Thus, it is important to 61 gain knowledge on aerosol-PBL feedbacks due to individual aerosol pollutants. While BC has, in 62 previous studies, been emphasized as particularly important for triggering aerosol-PBL feedbacks 63 and exacerbating air pollution9,12,13,32, there is a lack of knowledge on the quantification of its role 64 compared to other aerosols. Additionally, most previous studies have focused on relatively short 65 haze episodes12-14,32, but in terms of health effects it is important to also investigate long-term 66 exposure of air pollution2. 67 In this study, we investigate ARI (including adjustments) due to total aerosols and separately 68 ARI due to BC based on multi-year simulations, spanning the first two decades of this century, using 69 the Weather Research and Forecasting model with chemistry (WRF-Chem33; see Methods). The 70 simulations cover a relatively large region in Asia, but we focus particularly on regions with large 71 anthropogenic aerosol emissions; Northern India and Eastern China. Additional single-year 72 simulations are performed to investigate ARI due to individual aerosol compounds. We focus on 73 aerosol-PBL feedbacks and resulting health impacts. Modelled near-surface concentrations of PM2.5 74 and gas pollutants are evaluated against >1,000 air quality monitoring stations, and aerosol optical 75 depth (AOD) is evaluated against satellite observations. 76 77 78 79 80 81 82 83 84 manuscript version 2025-02-24 3 Results and Discussion 85 Model evaluation 86 The WRF-Chem model is able to reproduce satellite-retrieved annual and monthly mean AOD values 87 over South and East Asia relatively well (Figure 1a-c). While the modelled values are generally lower 88 than MODIS-Aqua in Northern India and, especially, in Eastern China, the model overestimates AOD 89 over these regions when compared against the MISR satellite-retrieval (Supplementary Figure 1). 90 This confirms that there are substantial uncertainties in AOD from individual satellite products34. It 91 should be noted, however, that the modeled AOD is based on diurnal means for all-sky conditions 92 while the satellite-retrieved AOD are for a given overpass time and clear-sky conditions. 93 Comparisons of modelled annual mean near-surface PM2.5 against >1,000 air quality stations 94 in China show generally good correspondence (Figure 1d-e). It is worth noting that despite extensive 95 policies to improve air quality in China since 201335, near-surface PM2.5 in Eastern China was still high 96 in 2019 (Figure 1d) (to put it in perspective, an air quality guideline of 5 μg m-3 has been set by the 97 World Health Organization36). Probability density functions of hourly PM2.5 measurements are also 98 well reproduced by WRF-Chem, especially for the year 2019, and a shift towards lower 99 concentrations between 2015 and 2019 is seen in both the measurements and WRF-Chem (Figure 100 1f). Annual mean PM2.5 concentrations are better reproduced by the model in countries that are 101 outside of China but within the model domain (red dots in Figure 1e; Supplementary Figure 2). 102 Comparisons between measured and modelled near-surface gas concentrations (SO2, CO, 103 NO2 and O3) over China show more variable results (Supplementary Figures 2-3), but this can be 104 expected given that the model grid spacing is 45 km while the measurements often take place in 105 urban areas and are influenced by local emission sources37. It should also be noted that the model 106 has been run only with spectral nudging of horizontal winds outside of the boundary layer (see 107 Methods), and therefore it cannot be expected that the model perfectly reproduces day-by-day 108 historical conditions for such a large domain. Nevertheless, the model is able to reasonably well 109 reproduce the seasonal and diurnal cycle of the gas-phase compounds, but with an underestimation 110 for CO and NO2, and too high nighttime concentrations of O3 (Supplementary Figure 3). Modelled 111 annual and monthly mean near-surface temperature and diurnal temperature range compares well 112 against CRU observations (Supplementary Figure 4). 113 114 Regional distribution of ARI 115 The modelled pattern of changes in near-surface turbulent kinetic energy due to total ARI largely 116 reflects the distribution of near-surface PM2.5, which has its highest values over India, Eastern China, 117 and the Gobi and Taklamakan deserts in Central Asia (Figure 2a-b). ARI reduces turbulence in nearly 118 the whole region, with substantial decreases of around 30% in Northern India, and exceeding 10% 119 reductions in the rest of India and most of Eastern China. Changes in PBL height show a very similar 120 pattern as changes in turbulence but with somewhat smaller magnitude, e.g., with around 20% 121 reduction in PBL height over N. India (Figure 2c). The reduced turbulence and PBL height due to ARI 122 imply that near-surface air pollution will be subject to less mixing and dilution. In fact, the model 123 shows that near-surface PM2.5 concentrations are enhanced due to ARI by up to approx. 15% in 124 regions that are already heavily polluted (Figure 2d). The desert areas are an exception, showing 125 almost no change in PM2.5, indicating that dust aerosols may be less efficient in enhancing air 126 pollution through ARI. Also, ARI due to total aerosols change the mix of different PM2.5 aerosol 127 pollutants, e.g., with dust aerosols mostly decreasing and carbonaceous aerosols (BC/OA), which are 128 suspected of being more toxic than other aerosols1,38, showing a relative increase that is larger than 129 for total PM2.5 (Supplementary Figure 5). Nitrate aerosols increase due to ARI by 50% or more in 130 large parts of India. The change due to ARI in the mix of different PM2.5 species is likely linked to their 131 height distribution, for instance with nitrate particles mainly formed from agricultural ammonia 132 (NH3) emissions that are emitted at the surface level. 133 manuscript version 2025-02-24 4 ARI due to BC also lead to widespread reductions in turbulence and PBL height, although 134 with a smaller magnitude than due to ARI from all aerosols (Figure 2e-g). Interestingly, BC has been 135 considered particularly efficient in enhancing air pollution through ARI9,13 but here it induces only 136 modest changes in PM2.5 (Figure 2h). There are likely several factors explaining these different 137 conclusions. For instance, Ding et al.13 also found strong influence of ARI due to BC on weakened 138 turbulence and lowered PBL height using WRF-Chem, but the modelled influence on PM2.5 139 concentrations was only shown explicitly for ARI due to total aerosols, and not BC specifically; a 140 direct comparison with our results is therefore difficult. In addition, their model study was limited to 141 one winter month (December 2013) while our results are averaged over five years. In Stjern et al.9, 142 different methods are likely to explain the different conclusions. They used a global climate model to 143 simulate a tenfold increase in BC emissions, and were therefore unable to directly quantify the 144 influence of ARI due to BC on PM2.5 concentrations. When averaged over the whole land region 145 shown in Figure 2, the BC share of total ARI is 40% of the reduction in turbulent kinetic energy, 41% 146 of the reduction in PBL height, but only 13% of the increase in PM2.5. The two regions with strongest 147 enhancement of PM2.5 due to total ARI, Northern India and Eastern China, are densely populated and 148 with high near-surface BC concentrations (Figure 2d-e), and will be given particular emphasis in the 149 further analysis to better understand the processes involved. 150 151 Vertical distribution of ARI 152 Figure 3 shows that impacts of ARI due to BC are substantial in both Northern India and Eastern 153 China, despite BC mass mixing ratios being small compared to other aerosol compounds. BC leads to 154 strong atmospheric heating due to solar absorption (Figure 3b,j), up to around 0.5 K at 800 hPa over 155 N. India and around half of that over E. China (Figure 3c,k). Aerosols other than BC are mainly 156 scattering solar radiation and therefore cooling the surface, and both BC and non-BC ARI act to 157 decrease the lapse rate and thereby strengthen lower atmospheric stability. This stabilization effect 158 weakens turbulence (Figure 3f,n), and increases relative humidity near the surface (Figure 3d,l). A 159 possible explanation for higher relative humidity near the surface in polluted conditions is that the 160 decrease in turbulence leads to weakened transport of moist and polluted air out of the surface 161 mixed layer and weakened transport of dry and clean air into the mixed layer from above12. The 162 modelled increase in cloud fraction throughout the atmospheric column for N. India, and the small 163 change in cloud fraction for E. China (Figure 3e,m), agree with a former multi-model study of BC 164 impacts (ref.39, their Figure 7). 165 Over both regions, ARI lead to increases in PM2.5 in the PBL, with >5% near the surface, and 166 decreases in the middle troposphere, but BC ARI alone shows the opposite effect: decrease in PM2.5 167 in the PBL (except near the surface) and increase above (Figure 3h,p). The self-lofting ability of BC 168 has been shown to increase ascent over southern Asia40, and this is indeed the case here as well 169 (Figure 3g,o), largely explaining how BC ARI influence the PM2.5 vertical profile. In addition, the 170 model shows that BC ARI increases precipitation in N. India, and partly also E. China, likely leading to 171 stronger removal of aerosols through wet scavenging due to BC compared to the effect of ARI due to 172 total aerosols (Supplementary Figure 6). Several studies have tried to isolate the role of BC to 173 precipitation over India and China and results are somewhat contrasting. While some find that 174 Indian BC emissions cause local drying42-44, other find insignificant precipitation change45,46. 175 However, monsoon season precipitation over India is found to increase in response to BC aerosols 176 over China, or when perturbing BC globally or at least over both China and India simultaneously41177 44,47 – consistent with our findings. In the same studies, precipitation over China was found to 178 decrease locally following an increase in BC, although comparisons to this study is more difficult due 179 to larger differences in the East China domain. 180 Figure 3 also shows that ARI due to all individual aerosol types lead to increased PM2.5 181 concentrations in the PBL in the two regions, with organic aerosols being the largest contributor. 182 Dust aerosols have a different vertical profile than other aerosols, with relatively large 183 manuscript version 2025-02-24 5 concentrations in the middle troposphere in the two regions, likely because dust is transported into 184 the region while other aerosols have more local sources. This leads to a strong stabilization effect 185 and reduction of turbulence due to ARI from dust-only, and subsequent effects on the vertical 186 profile of relative humidity and clouds. 187 188 Seasonal changes in ARI 189 Figure 4 shows that the effects of ARI are unevenly distributed throughout the year for N. India, but 190 less so for E. China. ARI from all aerosols lead to an increase in relative humidity and a decrease in 191 turbulence throughout the year for both regions, and a large part is due to BC ARI (Figure 4e-f,i-j). 192 Over N. India, the decreased turbulence and increased PM2.5 due to ARI from all aerosols are 193 particularly strong in the post-monsoon (SON) and winter (DJF) season, when the concentrations of 194 anthropogenic aerosols are high (Figure 4a,i,k). The PM2.5 enhancement reaches 25% in November 195 and December 2015 with 7% contribution from BC ARI, and approximately the same contribution 196 from ARI due to organic aerosols (Figure 4k). In the monsoon season (JJA), both ARI due to all 197 aerosols and ARI due to BC lead to enhanced precipitation, and PM2.5 concentrations are reduced 198 due to more wet scavenging (Figure 4g,k). Over E. China, the PM2.5 enhancement due to ARI from all 199 aerosols is within 12% for 2015, and that due to ARI from BC is near-zero, throughout the year 200 (Figure 4l). In both regions, most effects of ARI are fairly constant between the simulated years 201 (ranging from 2003 to 2019), except that PM2.5 enhancements over N. India have increased since the 202 beginning of the time period (Supplementary Figure 7). 203 204 Impacts of ARI on health-related metrics 205 Figure 5a shows that the change in near-surface concentrations of PM2.5 due to ARI from all aerosols 206 leads to excess mortality in several regions, with total changes in annual excess deaths due to PM2.5 207 of +27,200, +24,900, and +76,900 in N. India, E. China, and the whole domain, respectively. 208 Corresponding numbers for ARI due to BC are +1,930, +426, and -8,190, respectively, and the 209 distribution is shown in Figure 5e. For comparison, the estimated number of annual excess deaths 210 due to PM2.5 in the BASE simulation is 0.613 [0.478-0.792; 95% CI], 0.665 [0.531-0.834], and 2.74 211 [2.11-3.59] million in N. India, E. China, and the whole domain, respectively (Supplementary Figure 212 8). This means that ARI constitute 4% of the annual excess deaths due to PM2.5 in each of the two 213 regions, and 3% when aggregated over the whole domain. While ARI due to all aerosols affect excess 214 deaths of elderly people the most in the domain as a whole, a relatively large share (31%) of excess 215 deaths in N. India are among middle adulthood and younger populations (age <60 years) 216 (Supplementary Figure 9), partly due to a younger population. In this region, excess mortality due to 217 ARI from BC has increased substantially in the later years (2015 and 2019). In both N. India and E. 218 China, ARI due to all aerosols, and especially due to BC, lead to a particularly large increase in deaths 219 due to chronic obstructive pulmonary disease. It is worth noting that while excess deaths of adults in 220 the domain as a whole are reduced due to ARI from BC, the BC still leads to an increase in excess 221 deaths caused by lower respiratory infection among children. 222 The above health impact calculations are based on annual concentrations of PM2.5, but short-223 term exposure of elevated air pollution are also associated with a number of detrimental health 224 effects2. Results show that the number of haze days, defined here as PM2.5 daily mean concentration 225 higher than the WHO interim target 1 of 75 µg m-3 36, increases by more than 30 days in large parts 226 of N. India due to ARI from all aerosols, with smaller but non-negligible contribution from BC ARI 227 (Figure 5b,f). 228 The effect of ARI on near-surface ozone is to reduce the concentrations throughout the domain, 229 and BC is the main cause (Figure 5c,g). The amount of solar radiation reaching the surface is reduced 230 by >5% in large parts of India and E. China due to ARI from BC (Supplementary Figure 10), and this 231 would reduce ozone formation due to photolysis attenuation. However, dynamical changes could 232 manuscript version 2025-02-24 6 also influence ozone levels24, including the impact of relative humidity changes on ozone dry 233 deposition48. 234 Heat index, or apparent temperature, is a measure of the temperature felt by the human body 235 when accounting for relative humidity49. A heat index >41°C is categorized as dangerous and involves 236 risk of sunstroke and heatstroke50. Results show that ARI from BC substantially increase the number 237 of days with maximum heat index exceeding 41°C throughout India and part of E. China, with more 238 than 10 days increase in parts of N. India (Figure 5h). This occurs because BC enhances both surface 239 temperature and relative humidity (Figure 4c,e; Supplementary Figure 11). In contrast, ARI from 240 total aerosols cool the surface and show only small influences on the number of days with maximum 241 heat index exceeding 41°C (Figure 5d), implying that scattering aerosols reduce this number of days 242 considerably. For heat indices with lower thresholds, the number of days with exceedance is 243 reduced due to ARI from total aerosols but increased considerably when analyzing ARI due to BC 244 (Supplementary Figure 12). 245 Diurnal temperature range is the difference between the daily maximum and minimum 246 temperature, and its variation could influence crop yields51 and human health, mostly with greater 247 diurnal temperature range being associated with increased mortality52. Results show that ARI from 248 all aerosols reduce the diurnal temperature range by 0.5-1°C in nearly all of India, largely due to a 249 decrease in daily maximum temperature caused by scattering aerosols (Supplementary Figure 13). 250 ARI due to BC lead to an increase in daily maximum temperature in most of India, and an even 251 stronger increase in daily minimum temperature. Some studies indicate that enhanced night-time 252 temperatures are of particular concern regarding mortality and morbidity risk, including humid-hot 253 nights53,54. 254 In conclusion, our results highlight the multitude of impacts from individual aerosol species on 255 meteorology and air pollution. Most notably, BC is particularly efficient in reducing near-surface 256 turbulence through aerosol-radiation interactions but, in contrast to ARI from non-BC aerosols, 257 reduces the number of excess deaths caused by long-term PM2.5 exposure in southern and eastern 258 Asia as a whole. At the same time, ARI from BC lead to increases in both near-surface temperature 259 and relative humidity over India, in contrast to non-BC aerosols that cool the surface. Consequently, 260 if potential future air pollution mitigation over India would involve reductions of scattering aerosols 261 without simultaneous reductions in black carbon, as has partly been the case in China (ref.30, their 262 Figure 8), this could cause a substantial increase in the number of days exceeding heat index levels 263 that are dangerous to human health. 264 265 266 267 268 Methods 269 Air pollution monitoring data 270 China’s air pollution monitoring data was from the national monitoring sites 271 (https://quotsoft.net/air/). Air pollution measurements of annual mean PM2.5 and NO2 in countries 272 other than China have been taken from the WHO database v6.155. 273 274 Aerosol Optical Depth (AOD) satellite observations 275 Satellite observations of AOD at 550 nm (level 3) have been taken from the Moderate Resolution 276 Imaging Spectroradiometer (MODIS) instrument on board the Aqua (dataset MYD08_M3) and Terra 277 (dataset MOD08_M3) platforms56, and the Multi-Angle Imaging Spectroradiometer (MISR) 278 instrument on board the Terra platform (dataset MIL3MAEN_004)57. 279 280 manuscript version 2025-02-24 7 Surface temperature observations 281 Monthly-mean observations of surface temperature and diurnal temperature range have been taken 282 from the Climatic Research Unit gridded Time Series (CRU TS) version 4.0858. 283 284 WRF-Chem model simulations 285 The WRF-Chem model33 version 3.9.1.1 has been used at a horizontal resolution of 45 km x 45 km 286 covering a large part of Asia. The extent of the model domain is shown in Figure 1a, except that an 287 outer boundary of 10 grid boxes have been removed in the analysis to avoid spurious boundary 288 effects. In the vertical, 50 layers were used, extending from the surface and up to 50 hPa. 289 Meteorological initial and 6-hourly boundary conditions, including sea-surface temperatures, were 290 taken from the ERA5 reanalysis data59. Spectral nudging of horizontal winds were applied outside of 291 the PBL. Physics schemes applied include the RRTMG radiation scheme60, Morrison 2-moment 292 microphysics scheme61, MYNN PBL and surface layer scheme62, Unified Noah land surface model63, 293 and Grell 3D ensemble cumulus parameterization scheme64. Although the WRF-Chem model version 294 is 3.9.1.1, relevant bug fixes have been applied up until version 4.4. The cloud fraction diagnosis was 295 modified to follow Xu and Randall 65. 296 Gas-phase chemistry is represented by the MOZART mechanism66 and aerosols by the 297 MOSAIC 4-bin sectional aerosol module67 including aqueous chemistry and aerosol-cloud 298 interactions. Chemical initial and boundary conditions are from CESM2.1/CAM-Chem68,69. 299 Anthropogenic emissions are from the Community Emissions Data System (CEDS) version of April 300 2021, which goes until 2019 and builds upon the CEDS system described in ref.30. Sector-dependent 301 factors have been applied to crudely account for vertical distribution70 and temporal (weekly and 302 diurnal variations) profiles71. Biomass burning emissions are from the CMIP6 inventory, which goes 303 until 201572, and 2015 emissions are assumed also for 2019. The biomass burning emissions have 304 been distributed evenly between the surface and 1.2 km height in the vertical, and a diurnal cycle 305 has been assumed as in ref.73 (their Table 9). The calculation of biogenic emissions is done online 306 using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.0474. Natural 307 dust and sea salt emissions are also calculated online, while dimethyl sulfide (DMS) emissions use 308 the sea-air exchange fluxes of ref.75 and ocean climatology of ref.76. 309 The three core simulations, BASE, noARI and noBCARI, are performed for five years spanning 310 the first two decades of the 21st century: 2003, 2007, 2011, 2015, 2019, each with two months of 311 spin-up. BASE is the reference simulation while in noARI, aerosol-radiation interactions are switched 312 off by setting aer_ra_feedback=0 in the namelist. In noBCARI, aerosol-radiation interactions for BC 313 are switched off by setting the mass of BC to zero in the file module_optical_averaging.F. Similarly, 314 in simulations for year 2015 (+two months of spin-up) only, aerosol-radiation interactions have been 315 switched off for organic aerosols (noOAARI), sulphate (noSO4ARI), nitrate (noNO3ARI), ammonium 316 (noNH4ARI), sea salt (noSSARI), and dust (noDUSTARI). When showing map plots of differences 317 between simulations (in Figures 2 and 5), stippling indicates where at least 4 of the 5 simulation 318 years agree on the sign of change. However, a stricter requirement of all 5 years agreeing on the sign 319 of change does not substantially change the area of stippling in the plots (not shown). 320 321 322 Health impact calculations 323 In combination with the modelled data on ambient PM2.5 exposure and sensitivity ARI experiments, 324 we apply the MR-BRT (meta-regression-Bayesian, regularized, trimmed) exposure-response 325 function77,78, which was also used in our previous studies1,79 and the most recent iteration of the 326 Global Burden of Disease study80. Using the cause-specific exposure-response function, we estimate 327 excess deaths from ischemic heart disease (IHD), stroke (both ischemic and hemorrhagic), chronic 328 obstructive pulmonary disease (COPD), lung cancer (LC), and Type II diabetes (T2DM) among adults 329 (aged 25 and above), as well as acute lower respiratory tract infections (ALRI) among children (under 330 manuscript version 2025-02-24 8 5 years old). The excess death burden was calculated at a 5 × 5 km spatial resolution by interpolating 331 the modeled exposure data, and the estimates were stratified by age and disease category, following 332 the approach of our previous studies: 333 𝑀𝑀𝑐𝑐,𝑎𝑎,𝑑𝑑=𝐵𝐵𝑀𝑀𝑎𝑎,𝑑𝑑×𝑃𝑃 𝑎𝑎× 𝑅𝑅𝑅𝑅𝑐𝑐,𝑎𝑎,𝑑𝑑−1 𝑅𝑅𝑅𝑅𝑐𝑐,𝑎𝑎,𝑑𝑑 (1) 334 335 Excess deaths were estimated separately for adults and children at each 5 year intervals. RR(c,a,d) 336 were derived using MR-BRT functions for all diseases by age, where c,a,d denotes concentration of 337 PM2.5, population age and disease respectively. Age specific RRs (Relative Risks) for IHD and stroke, 338 are obtained using MR-BRT. For LC, T2DM, and COPD uniform RR(c,d) were used across all age 339 groups among adults. BM(a,d) is the baseline mortality rate per 100,000 population, obtained from 340 the GBD (http://ghdx.healthdata.org/gbd-results-tool) for India and China for respective years. We 341 considered BM to remain uniform within a country at 5 × 5 km resolution by age and disease. P(a) is 342 the exposed population in a grid by age; the age distributions at 5-year intervals (adults > 25 years), 343 and <5 years for children were obtained from the GBD which are then merged with the gridded 344 population data at about 5 × 5 km horizontal resolution from Global Human Settlement Layer 345 (https://human-settlement.emergency.copernicus.eu/) to obtain the age-specific population (P(a)) 346 at each 5 × 5 km grid. 347 348 Acknowledgments 349 ØH, GM and CWS have received support from the project GREenhouse gases, Aerosols and lower 350 atmospheric Turbulence (GREAT; Grant No. 275589), funded by the Research Council of Norway 351 (RCN). LM, SC, and KA have received support from the EU Horizon 2020 project EXHAUSTION (grant 352 no. 820655). ØH further acknowledges support by European Union’s Horizon Europe project 353 “CleanCloud” (grant agreement no. 101137639). Computing resources from NOTUR (NN9188K) are 354 acknowledged. We further acknowledge the use of the WRF-Chem preprocessor tools mozbc and 355 bio_emiss provided by the Atmospheric Chemistry Observations and Modeling Lab (ACOM) of NCAR. 356 357 Data availability 358 The WHO Ambient Air quality database was downloaded from 359 https://www.who.int/data/gho/data/themes/air-pollution/who-air-quality-database. MODIS data 360 were obtained from the Giovanni online data system81, developed and maintained by the NASA 361 Goddard Earth Sciences Data and Information Services Center, while MISR data were obtained from 362 the NASA Langley Research Center Atmospheric Science Data Center82. CRU TS data were obtained 363 from https://crudata.uea.ac.uk/cru/data/hrg/. WRF-Chem model data will be made available on the 364 NIRD research data archive, https://archive.sigma2.no/, upon publication. 365 366 Author contributions 367 ØH carried out model simulations and LM helped with the model setup. KA and SC did health impact 368 calculations. 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