regression specification, weighting grid-cells jointly by country population and the inverse of the error on our wealth measure, indicate that a 1 standard deviation increase in the wealth index is associated with a 0.04 standard deviation increase in ambient PM2 _._ 5. This result is robust to a variety of alternative weighting schemes (Table SI-1) and the relationship is further strengthened by trimming our wealth data at the 5<sup>_th_</sup> and 95<sup>_th_</sup> percentile (Table SI-2).

This positive correlation appears to be driven by urban-rural differences in both income and pollution. The shading in Figure 1b-c highlights that both grid-cells with the highest pollution levels and grid cells with the highest wealth levels are the most urban in our sample. More generally, being in or near larger urban areas is associated with higher levels of wealth (Figure SI-2). Wealth increases with urbanization both within and across categories (urban, peri-urban, rural) and the differences are stark enough that on average the wealthiest rural areas are poorer than the poorest urban areas. Exposure to ambient air pollution follows similar patterns albeit less dramatically (Figure 1c). Nonetheless, average pollution in urban areas is 12% ( _t-stat:_ 47.7) higher than in peri-urban areas and 50% ( _t-stat:>_ 100) higher than in rural areas.

Our pooled estimate hides substantial heterogeneity across countries. Country specific estimates indicate that ambient pollution increases with wealth in the majority of LMICs. However, a substantial minority of countries demonstrate the opposite relationship. While wealthier individuals live in more polluted locations in countries like Nepal, pollution decreases strongly with wealth in other countries like Nigeria (Figure 2a). When we map the country level relationship (Figure 2b-c) one immediate pattern becomes clear: countries with strong negative correlations between ambient pollution and wealth are highly concentrated in West or Central Africa. In most of the rest of the world, particularly Asia, wealthier areas have higher ambient pollution.

One reason the wealth-pollution relationship may differ in West and Central Africa is that a substantial portion of the ambient particulate pollution in these regions is driven by dust storms off the Sahel and Sahara.<sup>30,31</sup> Pollution from these sources does not generate the same correlation with urbanization (and hence wealth) as anthropogenic sources and is more severe in rural locations which are often in closer proximity to the dust sources (Figure 3). Across the sample dust and sea salt makeup 3.5% of PM2 _._ 5 concentrations in urban areas but more than 30% of PM2 _._ 5 in rural areas (Methods). Panels **A** and **B** of Figure 3 show the different spatial distributions of total PM2 _._ 5 and PM2 _._ 5 with dust and sea salt removed (an imperfect proxy for anthropogenic emissions) in West Africa. Total PM2 _._ 5 is concentrated inland, near the Sahel in particular, while anthropogenic PM2 _._ 5 is heavily concentrated along the coast, where urbanization and wealth are highest (Panels **C** and **D** ). When we recalculate the correlation between wealth and pollution using PM2 _._ 5 measures that remove dust and sea salt most of the correlations between wealth and total ambient pollution that were negative become positive suggesting that even in these settings anthropogenic emissions increase with wealth (Figure 2c,d 4