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Environ Sci Technol. 2016 Apr 5;50(7):3762-72. doi: 10.1021/acs.est.5b05833. Epub 2016 Mar 24.

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

Author information

1
Department of Physics and Atmospheric Science, Dalhousie University , Halifax, N.S. Canada.
2
Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts 02138, United States.
3
School of Population and Public Health, The University of British Columbia , 2206 East Mall, Vancouver, British Columbia V6T1Z3, Canada.
4
NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States.
5
Goddard Earth Sciences Technology and Research, Universities Space Research Association , Greenbelt, Maryland 20771, United States.
6
NASA Langley Research Center, Hampton, Virginia 23665, United States.

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.

PMID:
26953851
DOI:
10.1021/acs.est.5b05833
[Indexed for MEDLINE]

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