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

Øivind Hodnebrog, Kristin Aunan, Sourangsu Chowdhury, Louis Marelle,

Gunnar Myhre, Camilla W Stjern, Shuxiao Wang

► To cite this version:

Øivind Hodnebrog, Kristin Aunan, Sourangsu Chowdhury, Louis Marelle, Gunnar Myhre, et al. Strong reduction in near-surface turbulence due to aerosols in South and East Asia. npj Clean Air, 2025, 1 (9), 10.1038/s44407-025-00009-6. [hal-05056700](https://hal.science/hal-05056700v1)

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

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Øivind Hodnebrog<fnref n="1" />✉, Kristin Aunan<fnref n="1" />, Sourangsu Chowdhury<fnref n="1" />, Louis Marelle², Gunnar Myhre<fnref n="1" />, Camilla W. Stjern<fnref n="1" /> & Shuxiao Wang³

Absorbing and scattering aerosols influence the vertical temperature profile and can lead to a shallower boundary layer and potentially enhanced surface air pollution. Using a regional chemistry-climate model validated against >1000 air quality monitoring stations, we show that absorbing black carbon (BC) aerosols reduce near-surface turbulence and boundary layer height through aerosol-radiation interactions (ARI). While ARI due to total aerosols lead to enhanced PM₂.₅ surface concentrations and 25,000–27,000 annual excess deaths in each of Northern India and Eastern China, ARI due to BC have a modest impact on near-surface PM₂.₅ because of its ability for self-lofting and enhanced precipitation affecting wet scavenging. However, over India, BC ARI strongly increase the number of days with combined high temperature and relative humidity, which is dangerous for human health. These results highlight that the multitude of indirect impacts from individual aerosol species need to be considered to achieve efficient mitigation of air pollution.

Large parts of South and East Asia suffer from poor air quality, mainly due to high concentrations of PM₂.₅ (fine particulate matter with a diameter <2.5 µm), leading to millions of excess deaths every year<fnref n="1" />. Both through short-term and long-term exposure, PM₂.₅ can cause a number of detrimental effects on human health, such as respiratory and cardiovascular diseases, and other pollutants, such as ozone (O₃), add to the problem².

Local anthropogenic emissions are the most important factor influencing air quality in the highly polluted Eastern China and Indo-Gangetic plains of India, but meteorological conditions also play a key role³⁻⁵. Absorption and scattering of solar radiation by particles, i.e., aerosol-radiation interactions (ARI), have been found to enhance near-surface pollution by modifying meteorology in the planetary boundary layer (PBL)⁶⁻⁹, leading to an increased number of PM₂.₅ related deaths in China¹⁰ and India¹¹.

The enhancement of surface air pollution through ARI is a consequence of reduced turbulence and lowering of the PBL height, leading to the reduced sink of surface air pollution from turbulent transport to higher altitudes¹². This is particularly important for black carbon (BC) aerosols¹²⁻¹⁴, due to their ability to absorb solar radiation and thereby heat the atmosphere on a short time scale¹⁵. BC in the PBL also leads to higher relative humidity, enhancing cloud development and potentially affecting climate¹². Elevated relative humidity combined with high temperatures can also have negative health impacts¹⁶,¹⁷. The impact on PBL suppression is, however, dependent

on the vertical distribution of BC concentrations¹⁸⁻²⁰. Aerosols that scatter solar radiation, cool the surface and, therefore, also modify the vertical temperature profile and stability, although the aerosol-PBL feedback has been found to be small for sulfate (SO₄) aerosols⁹. In addition to ARI, aerosols can alter the Earth's radiation budget through aerosol-cloud interactions where aerosols act as cloud condensation nuclei²¹.

ARI have been found to influence tropospheric ozone through two mechanisms: meteorological changes by aerosol-radiation feedback, and changes in the photolysis rates by aerosol-photolysis interactions²². The net effect is a reduction of near-surface ozone, mainly due to aerosol-photolysis interactions²³, but the daily maximum ozone response is dependent on the season²⁴. In addition to detrimental health effects, elevated near-surface ozone concentrations have negative impacts on agricultural crop production²⁵.

Aerosol abundances over China have been reduced in recent years due to air quality regulations while India has seen an increase²⁶⁻²⁹. However, regional trends in anthropogenic emissions vary between individual aerosols and aerosol precursors³⁰, and there are uncertainties in how natural emissions, such as dust aerosols, respond to climate change³¹. Thus, it is important to gain knowledge on aerosol-PBL feedback due to individual aerosol pollutants. While BC has, in previous studies, been emphasized as particularly important for triggering aerosol-PBL feedback and exacerbating air pollution⁹,¹²,¹³,³², there is a lack of knowledge on the quantification of its

<footnote n="1">¹Center for International Climate Research (CICERO), Oslo, Norway. ²Sorbonne Université, UVSQ, CNRS, LATMOS, Paris, France. ³State Key Laboratory of Regional Environment and Sustainability, Tsinghua University, Beijing, China. ✉e-mail: oivind.hodnebrog@cicero.oslo.no</footnote>
role compared to other aerosols. Additionally, most previous studies have focused on relatively short haze episodes¹²⁻¹⁴,³², but in terms of health effects it is important to also investigate long-term exposure to air pollution².

In this study, we investigate ARI (including adjustments) due to total aerosols and separately ARI due to BC based on multi-year simulations, spanning the first two decades of this century, using the Weather Research and Forecasting model with chemistry (WRF-Chem³³; see Methods). The simulations cover a relatively large region in Asia, but we focus particularly on regions with large anthropogenic aerosol emissions; Northern India and Eastern China. Additional single-year simulations are performed to investigate ARI due to individual aerosol compounds. We focus on aerosol-PBL feedback and resulting health impacts. Modeled near-surface concentrations of PM₂.₅ and gas pollutants are evaluated against >1000 air quality monitoring stations, and aerosol optical depth (AOD) is evaluated against satellite observations.

# Results and discussion

## Model evaluation

The WRF-Chem model is able to reproduce satellite-retrieved annual and monthly mean AOD values over South and East Asia relatively well (Fig. 1a–c). While the modeled values are generally lower than Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) in Northern India and, especially, in Eastern China, the model overestimates AOD over these regions when compared against the Multi-Angle Imaging Spectroradiometer (MISR) satellite retrieval (Supplementary Fig. 1). This confirms that there are substantial uncertainties in AOD from individual satellite products³⁴. It should be noted, however, that the modeled AOD is based on diurnal means for all-sky conditions, while the satellite-retrieved AOD is for a given overpass time and clear-sky conditions.

Comparisons of modeled annual mean near-surface PM₂.₅ against >1000 air quality stations in China show generally good correspondence (Fig. 1d, e). It is worth noting that despite extensive policies to improve air

quality in China since 2013³⁵, near-surface PM₂.₅ in Eastern China was still high in 2019 (Fig. 1d) (to put it in perspective, an air quality guideline of 5 µg m⁻³ has been set by the World Health Organization³⁶). Probability density functions of hourly PM₂.₅ measurements are also well reproduced by WRF-Chem, especially for the year 2019, and a shift towards lower concentrations between 2015 and 2019 is seen in both the measurements and WRF-Chem (Fig. 1f). Annual mean PM₂.₅ concentrations are better reproduced by the model in countries that are outside of China but within the model domain (red dots in Fig. 1e; Supplementary Fig. 2).

Comparisons between measured and modeled near-surface gas concentrations (SO₂, CO, NO₂, and O₃) over China show more variable results (Supplementary Figs. 2 and 3), but this can be expected given that the model grid spacing is 45 km while the measurements often take place in urban areas and are influenced by local emission sources³⁷. It should also be noted that the model has been run only with spectral nudging of horizontal winds outside of the boundary layer (see Methods), and therefore it cannot be expected that the model perfectly reproduces day-by-day historical conditions for such a large domain. Nevertheless, the model is able to reasonably well reproduce the seasonal and diurnal cycle of the gas-phase compounds, but with an underestimation for CO and NO₂, and too high night-time concentrations of O₃ (Supplementary Fig. 3). Modeled annual and monthly mean near-surface temperature and diurnal temperature range compares well against CRU observations (Supplementary Fig. 4).

## Regional distribution of ARI

The modeled pattern of changes in near-surface turbulent kinetic energy due to total ARI largely reflects the distribution of near-surface PM₂.₅, which has its highest values over India, Eastern China, and the Gobi and Taklimakan deserts in Central Asia (Fig. 2a, b). ARI reduce turbulence in nearly the whole region, with substantial decreases of around 30% in Northern India, and exceeding 10% reductions in the rest of India and most of Eastern China. Changes in PBL height show a very similar pattern as changes in

<figure><img src="image_4.png" /><figcaption>**Fig. 1** | Evaluation of AOD and PM₂.₅. Annual mean AOD from WRF-Chem (a) and MODIS-Aqua (b), monthly mean AOD density scatter plot between MODIS-Aqua and WRF-Chem (c), annual mean PM₂.₅ from WRF-Chem and air quality</figcaption></figure>

monitoring stations (d, e), and probability density function of hourly PM₂.₅ for all stations shown in d (f). The WHO dataset in e includes stations that are outside of China but within the model domain (see Supplementary Fig. 2).
<figure><img src="image_5.png" /><figcaption>**Fig. 2 | Regional distributions of PM<sub>2.5</sub> and turbulence.** 5-year averages of near-surface total PM<sub>2.5</sub> and BC-only PM<sub>2.5</sub> in BASE (a, e), and relative changes in near-surface turbulent kinetic energy (b, f), PBL height (c, g), and near-surface total PM<sub>2.5</sub></figcaption></figure>

(d, h) due to ARI from all aerosols (BASE-noARI) and due to ARI from BC-only (BASE-noBCARI). Stippling indicates where at least 4 of the 5 years agree on the sign of change.

turbulence but with a somewhat smaller magnitude, e.g., with ~20% reduction in PBL height over N. India (Fig. 2c). The reduced turbulence and PBL height due to ARI imply that near-surface air pollution will be subject to less mixing and dilution. In fact, the model shows that near-surface PM<sub>2.5</sub> concentrations are enhanced due to ARI by up to approx. 15% in regions that are already heavily polluted (Fig. 2d). The desert areas are an exception, showing almost no change in PM<sub>2.5</sub>, indicating that dust aerosols may be less efficient in enhancing air pollution through ARI. Also, ARI due to total aerosols change the mix of different PM<sub>2.5</sub> aerosol pollutants, e.g., with dust aerosols mostly decreasing and carbonaceous aerosols (BC/OA), which are suspected of being more toxic than other aerosols<sup>1,38</sup>, showing a relative increase that is larger than for total PM<sub>2.5</sub> (Supplementary Fig. 5). Nitrate aerosols increase due to ARI by 50% or more in large parts of India. The change due to ARI in the mix of different PM<sub>2.5</sub> species is likely linked to their height distribution, for instance, with nitrate particles mainly formed from agricultural ammonia (NH<sub>3</sub>) emissions that are emitted at the surface level.

ARI due to BC also lead to widespread reductions in turbulence and PBL height, although with a smaller magnitude than due to ARI from all aerosols (Fig. 2e–g). Interestingly, BC has been considered particularly efficient in enhancing air pollution through ARI<sup>9,13</sup>, but here, it induces only modest changes in PM<sub>2.5</sub> (Fig. 2h). There are likely several factors explaining these different conclusions. For instance, Ding et al.<sup>13</sup> also found strong influence of ARI due to BC on weakened turbulence and lowered PBL height using WRF-Chem, but the modeled influence on PM<sub>2.5</sub> concentrations was only shown explicitly for ARI due to total aerosols, and not BC specifically; a direct comparison with our results is therefore difficult. In addition, their model study was limited to one winter month (December 2013), while our results are averaged over five years. In Stjern et al.<sup>9</sup>, different methods are likely to explain the different conclusions. They used a global climate model to simulate a tenfold increase in BC emissions, and were therefore unable to directly quantify the influence of ARI due to BC on PM<sub>2.5</sub> concentrations. When averaged over the whole land region shown in Fig. 2, the BC share of total ARI is 40% of the reduction in turbulent kinetic energy, 41% of the reduction in PBL height, but only 13% of the increase in PM<sub>2.5</sub>. The two regions with strongest enhancement of PM<sub>2.5</sub> due to total ARI, Northern India and Eastern China, are densely populated and with high near-surface BC concentrations (Fig. 2d, e), and will be given particular emphasis in the further analysis to better understand the processes involved.

## Vertical distribution of ARI

Figure 3 shows that impacts of ARI due to BC are substantial in both Northern India and Eastern China, despite BC mass mixing ratios being

small compared to other aerosol compounds. BC leads to strong atmospheric heating due to solar absorption (Fig. 3b, j), up to around 0.5 K at 800 hPa over N. India and around half of that over E. China (Fig. 3c, k). Aerosols other than BC mainly scatter solar radiation and, therefore, cool the surface, and both BC and non-BC ARI act to decrease the lapse rate and thereby strengthen lower atmospheric stability. This stabilization effect weakens turbulence (Fig. 3f, n), and increases relative humidity near the surface (Fig. 3d, l). A possible explanation for higher relative humidity near the surface in polluted conditions is that the decrease in turbulence leads to weakened transport of moist and polluted air out of the surface mixed layer and weakened transport of dry and clean air into the mixed layer from above<sup>12</sup>. The modeled increase in cloud fraction throughout the atmospheric column for N. India, and the small change in cloud fraction for E. China (Fig. 3e, m), agree with a former multi-model study of BC impacts (ref. 39, their Fig. 7).

Over both regions, ARI lead to increases in PM<sub>2.5</sub> in the PBL, with >5% near the surface, and decreases in the middle troposphere, but BC ARI alone show the opposite effect: decrease in PM<sub>2.5</sub> in the PBL (except near the surface) and increase above (Fig. 3h, p). The self-lofting ability of BC has been shown to increase ascent over southern Asia<sup>40</sup>, and this is indeed the case here as well (Fig. 3g, o), largely explaining how BC ARI influence the PM<sub>2.5</sub> vertical profile. In addition, the model shows that BC ARI increase precipitation in N. India, and partly also E. China, likely leading to stronger removal of aerosols through wet scavenging due to BC compared to the effect of ARI due to total aerosols (Supplementary Fig. 6). Several studies have tried to isolate the role of BC to precipitation over India and China and results are somewhat contrasting. While some find that Indian BC emissions cause local drying<sup>41–43</sup>, others find insignificant precipitation change<sup>44,45</sup>. However, monsoon season precipitation over India is found to increase in response to BC aerosols over China, or when perturbing BC globally or at least over both China and India simultaneously<sup>41–43,46,47</sup>—consistent with our findings. In the same studies, precipitation over China was found to decrease locally following an increase in BC, although comparisons to this study are more difficult due to larger differences in the East China domain.

Figure 3 also shows that ARI, due to all individual aerosol types, lead to increased PM<sub>2.5</sub> concentrations in the PBL in the two regions, with organic aerosols being the largest contributor. Dust aerosols have a different vertical profile than other aerosols, with relatively large concentrations in the middle troposphere in the two regions, likely because dust is transported into the region while other aerosols have more local sources. This leads to a strong stabilization effect and reduction of turbulence due to ARI from dust only, and subsequent effects on the vertical profile of relative humidity and clouds.
<figure><img src="image_6.png" /><figcaption>**Fig. 3 | Regionally averaged vertical profiles.** Mass mixing ratios of PM<sub>2.5</sub> aerosol compounds in BASE (a, i) and changes in meteorological variables (b–g and j–o) and total PM<sub>2.5</sub> (h, p) due to ARI from black carbon only (BCARI: BASE-noBCARI), organic aerosols only (OAARI: BASE-noOAARI), sulfate only (SO4ARI: BASE-noSO4ARI), nitrate only (NO3ARI: BASE-noNO3ARI), ammonium only</figcaption></figure>

(NH4ARI: BASE-noNH4ARI), sea salt only (SSARI: BASE-noSSARI), dust only (DUSTARI: BASE-noDUSTARI), and all aerosols (ARI: BASE-noARI) for year 2015 over the two regions indicated by the black rectangles in Fig. 2a. Variable “W” is vertical velocity. Shadings for BCARI and ARI represent the range of all 5 simulated years.

## Seasonal changes in ARI

Figure 4 shows that the effects of ARI are unevenly distributed throughout the year for N. India, but less so for E. China. ARI from all aerosols lead to an increase in relative humidity and a decrease in turbulence throughout the year for both regions, and a large part is due to BC ARI (Fig. 4e, f, i, j). Over N. India, the decreased turbulence and increased PM<sub>2.5</sub> due to ARI from all aerosols are particularly strong in the post-monsoon (SON) and winter (DJF) seasons, when the concentrations of anthropogenic aerosols are high (Fig. 4a, i, k). The PM<sub>2.5</sub> enhancement reached 25% in November and December 2015 with 7% contribution from BC ARI, and approximately the same contribution from ARI due to organic aerosols (Fig. 4k). In the monsoon season (JJA), both ARI due to all aerosols and ARI due to BC lead to enhanced precipitation, and PM<sub>2.5</sub> concentrations are reduced due to more wet scavenging (Fig. 4g, k). Over E. China, the PM<sub>2.5</sub> enhancement due to ARI from all aerosols is within 12% for 2015, and that due to ARI from BC is near-zero, throughout the year (Fig. 4l). In both regions, most effects of ARI are fairly constant between the simulated years (ranging from 2003 to 2019), except that PM<sub>2.5</sub> enhancements over N. India has increased since the beginning of the time period (Supplementary Fig. 7).

## Impacts of ARI on health-related metrics

Figure 5a shows that the change in near-surface concentrations of PM<sub>2.5</sub> due to ARI from all aerosols leads to excess mortality in several regions, with total changes in annual excess deaths due to PM<sub>2.5</sub> of +27,200, +24,900, and +76,900 in N. India, E. China, and the whole domain, respectively. Corresponding numbers for ARI due to BC are +1930, +426, and −8190, respectively, and the distribution is shown in Fig. 5e. For comparison, the estimated number of annual excess deaths due to PM<sub>2.5</sub> in the BASE simulation is 0.613 [0.478–0.792; 95% CI], 0.665 [0.531–0.834], and 2.74 [2.11–3.59] million in N. India, E. China, and the whole domain,

respectively (Supplementary Fig. 8). This means that ARI constitute 4% of the annual excess deaths due to PM<sub>2.5</sub> in each of the two regions and 3% when aggregated over the whole domain. While ARI, due to all aerosols, affect excess deaths of elderly people the most in the domain as a whole, a relatively large share (31%) of excess deaths in N. India is among middle adulthood and younger populations (age <60 years) (Supplementary Fig. 9), partly due to a younger population. In this region, excess mortality due to ARI from BC has increased substantially in the later years (2015 and 2019). In both N. India and E. China, ARI due to all aerosols, and especially due to BC, lead to a particularly large increase in deaths due to chronic obstructive pulmonary disease. It is worth noting that while excess deaths of adults in the domain as a whole are reduced due to ARI from BC, the BC still leads to an increase in excess deaths caused by lower respiratory infection among children.

The above health impact calculations are based on annual concentrations of PM<sub>2.5</sub>, but short-term exposure to elevated air pollution is also associated with a number of detrimental health effects<sup>2</sup>. Results show that the number of haze days, defined here as PM<sub>2.5</sub> daily mean concentration higher than the WHO interim target 1 of 75 µg m<sup>-3</sup><sup>36</sup>, increases by >30 days in large parts of N. India due to ARI from all aerosols, with smaller but non-negligible contribution from BC ARI (Fig. 5b, f).

The effect of ARI on near-surface ozone is to reduce the concentrations throughout the domain, and BC is the main cause (Fig. 5c, g). The amount of solar radiation reaching the surface is reduced by >5% in large parts of India and E. China due to ARI from BC (Supplementary Fig. 10), and this would reduce ozone formation due to photolysis attenuation. However, dynamical changes could also influence ozone levels<sup>24</sup>, including the impact of relative humidity changes on ozone dry deposition<sup>48</sup>.

Heat index, or apparent temperature, is a measure of the temperature felt by the human body when accounting for relative humidity<sup>49</sup>. A heat
Northern India

<figure><img src="image_7.png" /><figcaption>**Fig. 4** | Regionally averaged seasonal cycle. PM<sub>2.5</sub> aerosol compounds in BASE (a, b) and changes in meteorological variables (c–j) and total PM<sub>2.5</sub> (k, l) due to ARI from black carbon only (BCARI: BASE-noBCARI), organic aerosols only (OAARI: BASE-noOAARI), sulfate only (SO4ARI: BASE-noSO4ARI), nitrate only (NO3ARI: BASE-noNO3ARI), ammonium only (NH4ARI: BASE-noNH4ARI), sea salt only</figcaption></figure>

Eastern China

<figure><img src="image_8.png" /></figure>

(SSARI: BASE-noSSARI), dust only (DUSTARI: BASE-noDUSTARI), and all aerosols (ARI: BASE-noARI) for year 2015 over the two regions indicated by the black rectangles in Fig. 2a. Bars show annual means. Shadings for BCARI and ARI represent the range of all 5 simulated years.

index >41 °C is categorized as dangerous and involves the risk of sunstroke and heatstroke⁵⁰. Results show that ARI from BC substantially increased the number of days with maximum heat index exceeding 41 °C throughout India and part of E. China, with more than 10 days increase in parts of N. India (Fig. 5h). This occurs because BC enhances both surface temperature and relative humidity (Fig. 4c, e; Supplementary Fig. 11). In contrast, ARI from total aerosols cool the surface and show only small influences on the number of days with maximum heat index exceeding 41 °C (Fig. 5d), implying that scattering aerosols reduce this number of days considerably. For heat indices with lower thresholds, the number of days with exceedance is reduced due to ARI from total aerosols but increased considerably when analyzing ARI due to BC (Supplementary Fig. 12).

Diurnal temperature range is the difference between the daily maximum and minimum temperature, and its variation could influence crop yields⁵¹ and human health, mostly with greater diurnal temperature range

being associated with increased mortality⁵². Results show that ARI from all aerosols reduce the diurnal temperature range by 0.5–1 °C in nearly all of India, largely due to a decrease in daily maximum temperature caused by scattering aerosols (Supplementary Fig. 13). ARI due to BC lead to an increase in daily maximum temperature in most of India, and an even stronger increase in daily minimum temperature. Some studies indicate that enhanced night-time temperatures are of particular concern regarding mortality and morbidity risk, including humid-hot nights⁵³,⁵⁴.

In conclusion, our results highlight the multitude of impacts from individual aerosol species on meteorology and air pollution. Most notably, BC is particularly efficient in reducing near-surface turbulence through ARI but, in contrast to ARI from non-BC aerosols, reduces the number of excess deaths caused by long-term PM<sub>2.5</sub> exposure in southern and eastern Asia as a whole. At the same time, ARI from BC lead to increases in both near-surface temperature and relative humidity over India, in contrast to non-BC
<figure><img src="image_9.png" /><figcaption>**Fig. 5 | Regional distributions of health-related metrics.** 5-year average of the change in excess deaths due to long-term exposure to PM<sub>2.5</sub> (a, e), change in the number of haze days (PM<sub>2.5</sub> > 75 µg m<sup>-3</sup>) (b, f), the relative change in annual mean ozone (c, g), and change in days with a maximum hourly heat index exceeding 41 °C</figcaption></figure>

(d, h) due to ARI from all aerosols (BASE-noARI; top) and ARI from BC-only (BASE-noBCARI; bottom). Stippling indicates where at least 4 of the 5 years agree on the sign of change.

aerosols that cool the surface. Consequently, if potential future air pollution mitigation over India would involve reductions of scattering aerosols without simultaneous reductions in black carbon, as has partly been the case in China (ref. 30, their Figure 8), this could cause a substantial increase in the number of days exceeding heat index levels that are dangerous to human health.

## Methods

### Air pollution monitoring data

China's air pollution monitoring data was from the national monitoring sites (https://quotsoft.net/air/). Air pollution measurements of annual mean PM<sub>2.5</sub> and NO<sub>2</sub> in countries other than China have been taken from the WHO database v6.1<sup>55</sup>.

### AOD satellite observations

Satellite observations of AOD at 550 nm (level 3) have been taken from the MODIS instrument on board the Aqua (dataset MYD08_M3) and Terra (dataset MOD08_M3) platforms<sup>56</sup>, and the MISR instrument on board the Terra platform (dataset MIL3MAEN_004)<sup>57</sup>.

### Surface temperature observations

Monthly mean observations of surface temperature and diurnal temperature range have been taken from the Climatic Research Unit gridded Time Series version 4.08<sup>58</sup>.

### WRF-Chem model simulations

The WRF-Chem model<sup>33</sup> version 3.9.1.1 has been used at a horizontal resolution of 45 km × 45 km, covering a large part of Asia. The extent of the model domain is shown in Fig. 1a, except that an outer boundary of 10 grid boxes has been removed in the analysis to avoid spurious boundary effects. In the vertical, 50 layers were used, extending from the surface and up to 50 hPa. Meteorological initial and 6-hourly boundary conditions, including sea-surface temperatures, were taken from the ERA5 reanalysis data<sup>59</sup>. Spectral nudging of horizontal winds was applied outside of the PBL. Physics schemes applied include the RRTMG radiation scheme<sup>60</sup>, Morrison 2-moment microphysics scheme<sup>61</sup>, MYNN PBL and surface layer scheme<sup>62</sup>, Unified Noah land surface model<sup>63</sup>, and Grell 3D ensemble cumulus parameterization scheme<sup>64</sup>. Although the WRF-Chem model version is 3.9.1.1, relevant bug fixes have been applied up until version 4.4. The cloud fraction diagnosis was modified to follow Xu and Randall<sup>65</sup>.

Gas-phase chemistry is represented by the MOZART mechanism<sup>66</sup> and aerosols by the MOSAIC 4-bin sectional aerosol module<sup>67</sup> including

aqueous chemistry and aerosol-cloud interactions. Chemical initial and boundary conditions are from CESM2.1/CAM-Chem<sup>68,69</sup>. Anthropogenic emissions are from the Community Emissions Data System (CEDS) version of April 2021, which goes until 2019 and builds upon the CEDS system described in ref. 30. Sector-dependent factors have been applied to crudely account for vertical distribution<sup>70</sup> and temporal (weekly and diurnal variations) profiles<sup>71</sup>. Biomass burning emissions are from the CMIP6 inventory, which goes until 2015<sup>72</sup>, and 2015 emissions are assumed also for 2019. The biomass burning emissions have been distributed evenly between the surface and 1.2 km height in the vertical, and a diurnal cycle has been assumed as in ref. 73. (their Table 9). The calculation of biogenic emissions is done online using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.04<sup>74</sup>. Natural dust and sea salt emissions are also calculated online, while dimethyl sulfide emissions use the sea-air exchange fluxes of ref. 75. and ocean climatology of ref. 76.

The three core simulations, BASE, noARI, and noBCARI, are performed for five years spanning the first two decades of the 21st century: 2003, 2007, 2011, 2015, and 2019, each with two months of spin-up. BASE is the reference simulation, while in noARI, ARI are switched off by setting aer ra feedback=0 in the namelist. In noBCARI, ARI for BC are switched off by setting the mass of BC to zero in the file module optical averaging.F. Similarly, in simulations for the year 2015 (+ 2 months of spin-up) only, ARI have been switched off for organic aerosols (noOAARI), sulfate (noSO4ARI), nitrate (noNO3ARI), ammonium (noNH4ARI), sea salt (noSSARI), and dust (noDUSTARI). When showing map plots of differences between simulations (in Figs. 2 and 5), stippling indicates where at least 4 of the 5 simulation years agree on the sign of change. However, a stricter requirement of all 5 years agreeing on the sign of change does not substantially change the area of stippling in the plots (not shown).

### Health impact calculations

In combination with the modeled data on ambient PM<sub>2.5</sub> exposure and sensitivity ARI experiments, we apply the MR-BRT (meta-regression-Bayesian, regularized, trimmed) exposure-response function<sup>77,78</sup>, which was also used in our previous studies<sup>1,79</sup> and the most recent iteration of the Global Burden of Disease study<sup>80</sup>. Using the cause-specific exposure-response function, we estimate excess deaths from ischemic heart disease (IHD), stroke (both ischemic and hemorrhagic), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and Type II diabetes (T2DM) among adults (aged 25 and above), as well as acute lower respiratory tract infections among children (under 5 years old). The excess death burden was calculated at a 5 × 5 km spatial resolution by interpolating the modeled
exposure data, and the estimates were stratified by age and disease category, following the approach of our previous studies:

$$M_{c,a,d} = BM_{a,d} \times P_a \times \frac{RR_{c,a,d} - 1}{RR_{c,a,d}} \quad (1)$$

Excess deaths were estimated separately for adults and children at each 5-year interval. RR (c, a, d) were derived using MR-BRT functions for all diseases by age, where c, a, d denotes the concentration of PM₂.₅, population age, and disease, respectively. Age-specific RR (Relative Risks) for IHD and stroke, are obtained using MR-BRT. For LC, T2DM, and COPD, uniform RR (c, d) were used across all age groups among adults. BM (a, d) is the baseline mortality rate per 100,000 population, obtained from the GBD (http://ghdx.healthdata.org/gbd-results-tool) for India and China for respective years. We considered BM to remain uniform within a country at 5 × 5 km resolution by age and disease. P(a) is the exposed population in a grid by age; the age distributions at 5-year intervals (adults > 25 years), and <5 years for children were obtained from the GBD, which are then merged with the gridded population data at about 5 × 5 km horizontal resolution from Global Human-Settlement Layer (https://human-settlement.emergency.copernicus.eu/) to obtain the age-specific population (P(a)) at each 5 × 5 km grid.

## Data availability

The WHO Ambient Air quality database was downloaded from https://www.who.int/data/gho/data/themes/air-pollution/who-air-quality-database. MODIS data were obtained from the Giovanni online data system⁸¹, developed and maintained by the NASA Goddard Earth Sciences Data and Information Services Center, while MISR data were obtained from the NASA Langley Research Center Atmospheric Science Data Center⁸². CRU TS data were obtained from https://cran.uea.ac.uk/cru/data/hrg/. WRF-Chem model data have been made available in the NIRD research data archive: https://doi.org/10.11582/2025.00037.

Received: 8 November 2024; Accepted: 7 March 2025;

Published online: 30 April 2025

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## Acknowledgements

Ø.H., G.M., and C.W.S. have received support from the project GREenhouse gases, Aerosols, and lower atmospheric Turbulence (GREAT; grant no. 275589), funded by the Research Council of Norway (RCN). L.M., S.C., and K.A. have received support from the EU Horizon 2020 project EXHAUSTION (grant no. 820655). Ø.H. further acknowledges support by the European Union's Horizon Europe project "CleanCloud" (grant agreement no. 101137639). Computing resources from NOTUR (NN9188K) are acknowledged. We further acknowledge the use of the WRF-Chem preprocessor tools mozbc and

bio_emiss provided by the Atmospheric Chemistry Observations and Modeling Lab (ACOM) of NCAR.

## Author contributions

Ø.H. carried out model simulations, and L.M. helped with the model setup. K.A. and S.C. did health impact calculations. S.W. provided air pollution measurements. Ø.H., G.M., and C.W.S. designed the study. Ø.H. made the figures and wrote the manuscript with input from all authors.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s44407-025-00009-6.

**Correspondence** and requests for materials should be addressed to Øivind Hodnebrog.

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