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

• Published in: Environmental science & technology Vol. 50; no. 7; pp. 3762 - 3772
• Main Authors: van Donkelaar, Aaron, Martin, Randall V, Brauer, Michael, Hsu, N. Christina, Kahn, Ralph A, Levy, Robert C, Lyapustin, Alexei, Sayer, Andrew M, Winker, David M
• Format: Journal Article
• Language: English
• Published: Goddard Space Flight Center American Chemical Society 05.04.2016
• Subjects: Aerosols; Aerosols - analysis; Airborne particulates; Algorithms; Dust; Earth Resources And Remote Sensing; Environment Pollution; Environmental Monitoring - instrumentation; Environmental Monitoring - methods; Environmental Monitoring - statistics & numerical data; Environmental science; Geological Phenomena; Geophysics; governmental programs and projects; guidelines; Land use; Meteorology And Climatology; Models, Statistical; Models, Theoretical; moderate resolution imaging spectroradiometer; monitoring; Particulate Matter - analysis; particulates; photometers; Satellite Communications; Satellites; Simulation; uncertainty; World Health Organization; Africa; Satellite Remote Sensing; Aerosols; Aeronet
• ISSN: 0013-936X; 1520-5851; 1520-5851
• DOI: 10.1021/acs.est.5b05833

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 (R 2 = 0.81) with out-of-sample cross-validated PM2.5 concentrations from monitors. The global population-weighted annual average PM2.5 concentrations 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.