try based on the sum of the contributions of that broad category at each grid-cell within the country.

**Urbanization data–** We use data measuring urban and rural catchment areas from ref.<sup>37</sup> This data provides a pixel level estimate of the travel time to the nearest urban area (URCA). Pixels within an urban areas are assigned values that indicate they are within an urban area of various sizes. These values range from 1, indicating the pixel is in a city of at least 5 million people, to 30, indicating the pixel is in a rural hinterland. Values in between 1 and 30 indicate the pixel is in an urban area of increasingly smaller sizes (e.g. 20,000-50,000 people) or is less than 1 hours travel, 1-2 hours travel, or more than 2 hours travel time from a city of various sizes (e.g. 1 hour from a city of 20,000-50,000 people). We define pixels that fall within urban areas of any size as urban, those that are less than 1 hours travel from a city of any size as peri-urban, and those that are more than an hour’s travel from a city as rural. These classifications are only for explanatory purposes, in all analysis we use the assigned score from 1-30. To classify RWI pixels we assign it the URCA value of the nearest URCA pixel. The plurality of grid-cells in our sample are in the peri-urban category (Figure SI-6).

**Economic concentration–** We use data from the Global Human Settlement Layer (GHSL) data product BUILT-V that measures the built-up volume of a pixel as the number of cubic meters of built volume in that pixel. We use a pixel size of 100m. We collected data on the NRES version of the BUILT-V product that only include non-residential built up area in the calculation of the built volume. For each city in our data we calculate the standard deviation in the BUILT-V-NRES value across the BUILT-V-NRES cells within that city. We normalize this value by dividing by the sum of the BUILT-V-NRES values within the city.

**Country-level income–** We use data from the World Bank World Development indicators to measure country level GDP per capita (constant 2015 US$) when we examine the correlation between pollution averaged across the entire country and income averaged across the entire country. We also use the Urban population (% of total population) at the country level in the same analysis. Data available here: `https://data.worldbank.org/` .

**Data for additional heterogeneity analyses–** In our analysis of heterogeneity by primary source of pollution we use data from the EU EDGAR database on the amount of PM2 _._ 5 emitted from various different sources on a global grid. We assign grid points to countries (or cities) and calculate the total PM2 _._ 5 emissions from each category of source in the EDGAR data (e.g. air transportation). We then aggregate into broad categories (e.g. transportation, industry, etc.). We then determine the primary source of emissions by summing all the categories and choosing the category that accounts for the largest share of the total. Data available here: `https:// edgar.jrc.ec.europa.eu/` . For heterogeneity by history of colonization and equatorial location we assemble indicators for each country based on a review of the country’s history of colonization

14