Designing Air Quality Measurement Systems in Data-Scarce Settings

While populations in low- and middle-income countries are exposed to some of the highest levels of air pollution and its consequences, the majority of economics research on the topic is focused on high-income settings where there is greater data availability. This paper compares and evaluates the three principal sources of air pollution data (regulatory-grade monitors, satellites, and low-cost monitors) in a Sub-Saharan African context in terms of the accuracy of measurements of inhalable fine particulate matter across spatial and temporal frequencies and their performance when studying policy impacts. Satellite data is closely aligned with data from the regulatory-grade monitors at lower temporal frequencies. The low-cost monitors underestimate the amount of fine particulate matter relative to the other data sources. Calibration, especially context-specific calibration, of the low-cost monitors' data improves its alignment with other data sources. The paper uses each data source to evaluate the air pollution externality of mobility reduction policies using a difference-in-differences design and finds similar results, especially in terms of percent reduction. The paper considers policy makers' constraints to air pollution monitoring in low-income settings and demonstrates that co-locating one regulatory-grade monitor in a network of low-cost monitors can capture the spatial variation of pollution across an urban area and achieve better accuracy than either of these data sources alone. This provides a framework for policy makers to generate the data needed to evaluate environmental policies and externalities.