Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth Measurements to Surface Concentrations in North America and Africa

Abstract. Low-cost particulate mass sensors provide opportunities to assess air quality at unprecedented spatial and temporal resolutions. Established traditional monitoring networks have limited spatial resolution and are frequently absent in less-developed countries (e.g. in sub-Saharan Africa). Satellites provide snapshots of regional air pollution, but require ground-truthing. Low-cost monitors can supplement and extend data coverage from these sources worldwide, providing a better overall air quality picture. We demonstrate such a multi-source data integration using two case studies. First, in Pittsburgh, Pennsylvania, both traditional monitoring and dense low-cost sensor networks are present, and are compared with satellite aerosol optical depth (AOD) data from NASA's MODIS system. We assess the performance of linear conversion factors for AOD to surface PM2.5 using both networks, and identify relative benefits provided by the denser low-cost sensor network. In particular, with 10 or more ground monitors in the city, there is a two-fold reduction in worst-case surface PM2.5 estimation mean absolute error compared to using only a single ground monitor. Second, in Rwanda, Malawi, and the Democratic Republic of the Congo, traditional ground-based monitoring is lacking and must be substituted with low-cost sensor data. Here, we assess the ability of regional-scale satellite retrievals and local-scale low-cost sensor measurements to complement each other. In Rwanda, we find that combining local ground monitoring information with satellite data provides a 40 % improvement (in terms of surface PM2.5 estimation accuracy) with respect to using ground monitoring data alone. Overall, we find that combining ground-based low-cost sensor and satellite data can improve and expand spatio-temporal air quality data coverage in both well-monitored and data-sparse regions.