levels relative to the satellite measures. This relative underestimation is largely due to the low-cost monitors’ under-measurement of dust. Adjusting the data from the low-cost monitors, especially using a context-specific formula, improves the alignment of the data with both the data from the regulatory-grade monitors and the estimates from the satellite.

Next, we use the three data sets to evaluate pollution externalities of the set of mobility-reducing policies enacted during the COVID-19 pandemic. We use a difference-in-differences identification strategy to analyze the impact of the policies on the level of air pollution. Overall, we find the policies lead to a positive externality in the form of a reduction in air pollution, similar to research conducted in highincome settings (Brodeur _et al._ , 2021). Importantly, we find that all three data sources display similar patterns of PM2.5 reductions due to the policy. Using PM2.5 data at the hourly level, the impact of the policy in levels is smaller when using the data from the low-cost monitors than the other data sources because the low-cost monitors tend to underestimate PM2.5 levels. However, the impact of the policy in percent is similar across all data sources.

Finally, we demonstrate the value of co-locating at least one regulatory-grade monitor in a network of low-cost monitors. We demonstrate that for a given day, relying on only a regulatory-grade monitor or only the satellite data will provide no spatial variation. However, relying on only the network of low-cost monitors will provide spatial variation but severely underestimate the level of PM2.5. Using a co-located regulatory-grade monitor to develop a context-specific calibration formula to be applied to the data from the network of low-cost monitors improves the alignment in the level of PM2.5 while preserving spatial variation. Given the tight budget constraints that policy makers in LMICs face, investing in one well-maintained regulatory-grade monitor complemented with a dense network of low-cost monitors may strike the right balance. Barring any regulatory-grade monitors, calibration of PM2.5 measurements from low-cost monitors using satellite based estimates of PM2.5 can serve as an alternative. Given the better performance of satellite data at a lower temporal frequency, we also show that this alternative calibration should be applied at a daily level (rather than hourly), to achieve results that are better aligned with what would be expected from regulatory measurements.

We make three contributions to the literature. First, we contribute unique evidence comparing the three principal sources of air pollution data in a LMIC context. Fowlie _et al._ (2019) contrasts air pollution levels from satellite data with those derived from regulatory-grade monitors in the U.S. and concludes that differences imply potential misclassifications of E.P.A. attainment status. In Greece, Stavroulas _et al._ (2020) find a moderate to strong correlation between PM2.5 levels measured with lowcost monitors and with regulatory-grade monitors. However, these studies do not compare all three data sources and the results of these studies may not translate directly to contexts in LMICs because the accuracy and precision of the air pollution data can vary across low- and high-income contexts. The precision and accuracy of air pollution data from low-cost monitors is affected by environmental conditions (e.g. relative humidity) and the type of particulate matter, such as dust events (Jaffe _et al._ , 2023). The availability, accuracy, and precision of air pollution data from regulatory-grade monitors depends on a dense network of monitors and regular investments in maintaining and replacing monitors.

Second, we extend this literature beyond a comparison of the air pollution levels captured by the three 4