the low-cost values and the regulatory monitor (Table 2 Panel A), the figure shows that there remains a large underestimation when using the low-cost monitor data adjusted using the Jaffe calibration, which is confirmed by a larger RMSE when comparing the regulatory and Jaffe-adjusted data (Table 2 Panel B).

We find that aggregating the data temporally improves alignment, as it did with the satellite and regulatory data comparison. This can be seen both visually in Figure 6 Panel D, where the data is aggregated weekly, and also in the higher correlation of 0.89 at the weekly level (Table 2 Panel A). Comparing at a higher temporal frequency at the hourly level leads to lower alignment (Figure 5). Nevertheless, the locally calibrated data performs much better at the hourly level (correlation of 0.74) as compared to the satellite data (correlation of 0.54) (Table 2 Panel A).

Third, we compare the PM2.5 values from the low-cost monitors to the PM2.5 estimates produced using the satellite MERRA-2 data. In a similar result to the comparison between the regulatory-grade and low-cost monitors, we find that the low-cost monitors record much lower levels of pollution compared to the satellite data (Figure 7). This underestimation was particularly severe in early 2020 and early 2021, when dust storms were frequent, where the unadjusted Purple Air PM2.5 values do not increase substantially despite peaks in PM2.5 in the satellite data (Figure 7A).

To confirm that the substantial divergence between the PM2.5 measurements from the two data sources is due to an underestimation of dust by the Purple Air monitors in our context, we generate a modified PM2.5 estimate using all of the MERRA-2 components except dust.<sup>20</sup> Comparing the MERRA2 estimates of PM2.5 with and without dust suggests that much of the PM2.5 pollution in Dakar is due to dust (Figure A2). The PM2.5 estimate excluding dust from MERRA-2 and the unadjusted PM2.5 values from the low-cost monitors are more closely aligned (Figure 7B). However, the Purple Air values tend to be higher, especially during peaks in PM2.5, than the satellite-derived estimates that exclude dust since some dust pollution is captured by the Purple Air monitors. Furthermore, while the closer alignment between these two pollution series provides evidence that the low-cost monitors underestimate PM2.5 in Dakar due to incomplete measurement of dust pollution, the closer alignment does not reflect a better measurement of PM2.5 in Dakar.

As with the comparison between the data from the regulatory-grade monitors and the low-cost monitors, we compare the satellite estimates of PM2.5 to two adjustments of the PM2.5 data from the low-cost monitors. Again, the adjustment using the Jaffe calibration does not improve the underestimation of values (Figure 7C), but there is an increase in the correlation between the adjusted and satellite data (Table 2). The local calibration that we develop improves the alignment of the data from the low-cost monitor with the satellite estimates, both in terms of levels and the correlation (Figure 7D and Table 2).

Considering alignment with both the PM2.5 measurements from the regulatory-grade monitors and the satellite estimates, calibrating the data from the low-cost monitor appears to partially compensate for its underestimation of PM2.5 due to dust. Furthermore, the results suggest that local calibration improves alignment relative to off-the-shelf adjustment of the data from the low-cost monitors.

> 20To generate the MERRA-2 PM2.5 estimate without dust, we use the same equation to combine the different components except dust is not included.

17