Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors

Abstract

We estimated global fine particulate matter (PM₂.₅) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically based satellite-derived PM₂.₅ estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998–2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM₂.₅ estimates were highly consistent (R² = 0.81) with out-of-sample cross-validated PM₂.₅ concentrations from monitors. The global population-weighted annual average PM₂.₅concentrations were 3-fold higher than the 10 μg/m³ WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM₂.₅ data sources can yield valuable improvements to PM₂.₅ characterization on a global scale.