<mark>constructed by combining Aerosol Optical Depth (AOD) satellite retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrating to global ground-based observations using a geographically weighted regression. The resolution of 0.01 degree (equivalent to about 1.1 kilometer at the equator) is well suited to capture regional variation in</mark> concentrations, though not granular local variations.

<mark>As a global modeled data set, some uncertainty is to be expected, though sensitivity tests suggest good agreement with ground measurements (van Donkelaar et al, 2021). For more spatially nuanced analyses, for instance at the neighborhood or street level, alternative data based on local measures would be required. Moreover, it should be noted that the chemical composition of PM2.5 particles can differ by pollution source (Thurston, et al 2022). PM2.5 particles associated with the combustion of fossil fuels are more toxic due to higher acidity levels (e.g. sulfuric particulate matter from coal burning). The global PM2.5 can inform on total particle concentration, but not the spatial variation in the chemical composition (i.e. acidity) of particles.</mark>

**Population density:** We estimate the location of people using the WorldPop Global High Resolution Population data set (WPGP), produced by the University of Southampton, the World Bank, and other partners. It offers global coverage and is available yearly from 2000 to 2020. While WorldPop provides several data sets (including poverty, demographics, and urban change mapping), we use the population density map (WorldPop-PPP-2020). In a raster format, this data set provides the number of inhabitants per cell, with a resolution of 3 arcseconds, thus specifying the distribution of population. This information is based on administrative or census-based population data, disaggregated to grid cells based on distribution and density of built-up area, which is derived from satellite imagery (Freire et al., 2016, 2020).

The choice of population density map is important for estimating people’s exposure to natural hazards. Smith et al. (2019) provide a sensitivity analysis for flood exposure assessments using different population density maps, including WorldPop (3 arcsecond). They show that high-resolution population density maps perform best in capturing local exposure distribution, particularly the High-Resolution Settlement Layer (HRSL) with 1-arcsecond, or ~30 m resolution, produced jointly by Facebook, Columbia University and the World Bank. While HRSL is only available for a limited number of countries, WorldPop is shown to perform better than alternatives with global coverage, such as LandScan data (30-arcsecond, ~900 m resolution) (Bright et al., 2015).

**Subnational poverty rates:** For 1,755 of the 2,227 subnational units, the World Bank’s Global Subnational Poverty Atlas offers several poverty estimates, which are all derived from the latest available Living Standards Measurement Survey (LSMS) for the respective country (World Bank, 2021). Areas where no poverty estimates are available tend to be high-income countries and small island states. For the purpose of this study, the standard World Bank definitions of poverty are used to determine the number of poor people in a given subnational administrative unit. Specifically, poverty is defined by the daily expenditure thresholds of $1.90, $3.20, and $5.50.

**Administrative boundaries:** The definition of national administrative boundaries follows the standard World Bank global administrative map. However, national boundaries are further disaggregated into subnational units for all countries where World Bank household surveys are available with subnational representativeness. These subnational units are typically provinces or states (i.e. admin1) but can also include custom groupings of subnational regions determined by the sampling strategy of household surveys. Overall, this study covers 211 countries, which are disaggregated into 2,183 subnational units.

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