the level of air pollution at ground level. Satellites produce images that can be used to create gridded data sets of aerosol optical depth (AOD) as well as individual aerosols that are comparable across the world and can be used to estimate levels of particulate matter. However, satellite data does not directly measure particulate matter at ground level and provides data at a lower temporal and spatial frequency. Third, low-cost monitors that citizens can use to track their own air pollution exposure can be deployed by governments to create a network of air pollution monitors at low-cost. However, low-cost monitors do not record pollution measurements at the same level of accuracy as regulatory-grade monitors. Because each of these data sources has advantages and disadvantages, evaluating their performance in measuring air pollution levels and in estimating the effects of policies to reduce air pollution is critical for researchers and policy makers. This is especially important for LMIC settings that can face additional challenges implementing these data sources, where there have been few studies comparing their performance, and where effective environmental policies are most needed.

Dakar, Senegal provides an ideal context for this study for three reasons. First, Dakar is a large and growing city in a lower middle-income country with large industrial plants and recurrent construction works. This implies that our results are informative for other fast-growing Sub-Saharan African cities that are similarly experiencing high levels of congestion and pollution. Second, in addition to the air pollution generated by congestion and economic activity, Dakar experiences Saharan desert storms, especially during the first few months of the year. These environmental conditions differ from those typical of the high-income contexts where air pollution data is usually calibrated and analyzed. Third, Dakar currently has seven regulatory-grade monitors, which allows for comparison and calibration. But in contrast to high-income settings, the density of the monitoring network is low, and the data available is sparse and of uncertain accuracy due to challenges in maintaining the monitors. While this amplifies the challenges in comparing data sources and calibrating the data from low-cost monitors, this reflects the realities of air quality monitoring in lower-income settings.<sup>2</sup> The dearth of high-quality data at fine intervals of time and space combined with high levels of air pollution highlight the importance of studying ways to utilize the existing data and increase the data available for rigorous policy analysis, which could then be applied to other settings in the region.

To collect the data for this study, we set up a network of low-cost air pollution monitors in 28 locations across Dakar, and we worked closely with the local government agency in charge of air quality data to access the data available from their regulatory-grade monitors. We test several different sources of satellite air pollution measures to identify the one that generates the values most aligned with the ground-based measures.

Armed with these three sets of air pollution data, we compare the level of PM2.5 at various temporal frequencies as measured by each data source. The satellite data is closely aligned with the data from the regulatory-grade monitors at the daily, weekly, and monthly levels but less so at the hourly level. There are substantial differences between the PM2.5 estimates from the regulatory-grade and the lowcost monitors in terms of level. Similarly, the low-cost monitors substantially underestimate PM2.5

> 2We use calibration to refer to the calibration of the data collected by low-cost monitors, not the calibration of the instrument (Jaffe _et al._ , 2023). In engineering or science literature, this is often called "data correction".

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