Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors
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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 (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/m(3) 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.
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The record
- Venue
- Environmental Science & Technology
- Topic
- Air Quality and Health Impacts
- Field
- Environmental Science
- Canadian institutions
- University of British ColumbiaDalhousie University
- Funders
- Natural Sciences and Engineering Research Council of CanadaGovernment of CanadaNational Aeronautics and Space Administration
- Keywords
- AERONETEnvironmental scienceSatelliteSeaWiFSParticulatesAerosolMineral dustRemote sensingMeteorologySun photometerGeostationary orbitGeography
- Has abstract in OpenAlex
- yes