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Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors
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    Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors
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    Department of Physics and Atmospheric Science, Dalhousie University, Halifax, N.S. Canada
    Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts 02138, United States
    § School of Population and Public Health, The University of British Columbia, 2206 East Mall, Vancouver, British Columbia V6T1Z3, Canada
    NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States
    Goddard Earth Sciences Technology and Research, Universities Space Research Association, Greenbelt, Maryland 20771, United States
    # NASA Langley Research Center, Hampton, Virginia 23665, United States
    *Phone: (902) 494-1820; e-mail: [email protected]
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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2016, 50, 7, 3762–3772
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    https://doi.org/10.1021/acs.est.5b05833
    Published March 8, 2016
    Copyright © 2016 American Chemical Society

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    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 PM2.5 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.

    Copyright © 2016 American Chemical Society

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    Cite this: Environ. Sci. Technol. 2016, 50, 7, 3762–3772
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    Published March 8, 2016
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