Application of low-cost fine particulate mass monitors to convert satellite aerosol optical depth to surface concentrations in North America and Africa

Abstract

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 simply absent in many major cities across sub-Saharan Africa (SSA). 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 investigate the utility of such a multi-source data integration approach using two case studies. First, in Pittsburgh, Pennsylvania, both traditional monitoring and dense low-cost sensor networks are compared with satellite aerosol optical depth (AOD) data from NASA's MODIS system, and a linear conversion factor is developed to convert AOD to surface fine particulate matter mass concentration (as PM2.5). With 10 or more ground monitors in Pittsburgh, there is a 2-fold reduction in surface PM2.5 estimation mean absolute error compared to using only a single ground monitor. Second, we assess the ability of combined regional-scale satellite retrievals and local-scale low-cost sensor measurements to improve surface PM2.5 estimation at several urban sites in SSA. In Rwanda, we find that combining local ground monitoring information with satellite data provides a 40 % improvement in surface PM2.5 estimation accuracy with respect to using low-cost ground monitoring data alone. A linear AOD-to-surface-PM2.5 conversion factor developed in Kigali, Rwanda, did not generalize well to other parts of SSA and varied seasonally for the same location, emphasizing the need for ongoing and localized ground-based monitoring, which can be facilitated by low-cost sensors. Overall, we find that combining ground-based low-cost sensor and satellite data, even without including additional meteorological or land use information, can improve and expand spatiotemporal air quality data coverage, especially in data-sparse regions.

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

“…Presently, comparisons between satellite and surface aerosol observations in Africa report moderate to poor agreement (Hersey et al, 2015;Malings et al, 2020).…”

“…Remote sensing is a valuable tool to address these data gaps, but satellite observations alone have various shortcomings relative to in situ measurements (Martin et al, 2019). Additional validation with reliable surface measurements is required, particularly in SSA (Malings et al, 2020). In the meantime, low-cost gas and particulate sensor packages provide an affordable, compact, and easily transportable approach to supplement air quality networks in regions where reference grade instrumentation is not accessible.…”

Commentary: AfriqAir’s Mission Towards Cleaner Air for Africa and a Call to Action

“…In Western Africa, there is approximately 1 reference-grade PM 2.5 (PM with an aerodynamic diameter less than 2.5 micrometers) monitor per 10 million people. In Eastern Africa, the problem is even worse with approximately 1 reference-grade monitor per 100 million people (Malings et al, 2020). Second, this lack of monitoring is compounded by the fact that the sheer diversity of sources of PM and other atmospheric pollutants (e.g., ozone precursors, NO x , SO x ) is much greater than the sources in most of the OECD or Global North (Kiesewetter, 2016).…”

Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources

“…Use of low‐cost sensor data to calibrate statistical models (incorporating land use and meteorological information to estimate AQ) also improved accuracy with respect to the use of the same methods with regulatory monitoring sites in Imperial Valley because of the larger number of sites available through the low‐cost sensor network (Bi et al., 2020 ). Using low‐cost sensors to provide ground‐based information to relate satellite‐derived quantities (especially AOD) to surface concentrations has also shown promise, especially where low‐cost sensors can provide broader spatial data coverage as compared to more sparsely distributed regulatory monitoring sites (Gupta et al., 2018 ; Malings et al., 2020 ). A recent project combining satellite AOD, regulatory monitoring station data, and an extensive network of thousands of low‐cost sensors has facilitated regional‐scale air pollution mapping over the island of Taiwan (Li et al., 2020 ).…”