While comparisons at the annual 10km level show a relatively high correlation between ground-based PM2.5 and AOD with an _R_<sup>2</sup> of 0.61 (Gendron-Carrier _et al._ , 2022), others find a weak correlation between AOD and PM2.5 at the daily 1km x 1km level (Handschuh _et al._ , 2023). In general, it is necessary to model the relationship between AOD and ground-based PM2.5 including other covariates to obtain good predictions.

In addition to AOD, other instruments on satellites can measure the levels of specific gases and aerosols. For example, the TROPOMI (TROPOspheric Monitoring Instrument) instrument on the Sentinel-5P satellite is capable of measuring levels of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, formaldehyde and methane (Zeng _et al._ , 2020). These raw data from different satellites and instruments have also been processed and integrated to generate assimilations, where data is integrated into atmospheric models. Examples of assimilations include the Copernicus Atmosphere Monitoring Service (CAMS) and NASA’s MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, version 2), which provide usable atmospheric information in one system (Buchard _et al._ , 2017; Flemming _et al._ , 2017).<sup>10</sup> In preliminary comparisons, AOD captured from MODIS does not correlate well with the data from regulatory-grade monitors in Dakar at the daily level, which aligns with findings in the literature for other contexts (Handschuh _et al._ , 2023). This is likely due to several important limitations of the AOD measure. AOD has trouble distinguishing water vapor from other particles, while the ground-based monitors capture only dry particles. In a context like Dakar, which is surrounded by water, this can have important consequences (Gendron-Carrier _et al._ , 2022). Additionally, particles that are present in the atmosphere, such as smoke or dust from long-range transport, can lead to no (or poor) correlation between satellite measures and PM from ground-monitors, which measure particles only at the surface (Kumar _et al._ , 2007).

Given this, we focus on an estimate of PM2.5 that we generate using data from the MERRA-2 assimilation. This assimilation provides processed data on individual aerosols at the surface rather than column level, helping to address the issue of water vapor and additional particles in the atmosphere. The MERRA-2 data is accessed via Google Earth Engine, and it is available hourly at the 55 x 69km2 spatial level (Gelaro _et al._ , 2017). MERRA-2 provides assimilation data on individual aerosols (dust, organic carbon, black carbon, sulfate, and sea salt). To generate a measure of PM2.5, we combine these aerosols using a formula provided by NASA (2023): The MERRA-2 data has numerous benefits. It is freely available. It provides a measure of pollution that can be compared across the world, and there is high temporal granularity, so we are able to produce PM2.5 estimates at the hourly level. It has data from the time period of 1980 to the present day. However, an important limitation is that due to the low level of spatial granularity, the MERRA-2 data in effect provides only a single value of pollution for Dakar at any given time.

> 10See Appendix Table A1 for a description of different satellite products.

10