title: 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 (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.
authors.affiliation: Department of Physics and Atmospheric Science, Dalhousie University , Halifax, N.S. Canada.
authors.forename: Aaron
authors.initials: A
authors.lastname: van Donkelaar
authors.affiliation: Department of Physics and Atmospheric Science, Dalhousie University , Halifax, N.S. Canada.|Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts 02138, United States.
authors.forename: Randall V
authors.initials: RV
authors.lastname: Martin
authors.affiliation: School of Population and Public Health, The University of British Columbia , 2206 East Mall, Vancouver, British Columbia V6T1Z3, Canada.
authors.forename: Michael
authors.initials: M
authors.lastname: Brauer
authors.affiliation: NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States.
authors.forename: N Christina
authors.initials: NC
authors.lastname: Hsu
authors.affiliation: NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States.
authors.forename: Ralph A
authors.initials: RA
authors.lastname: Kahn
authors.affiliation: NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States.
authors.forename: Robert C
authors.initials: RC
authors.lastname: Levy
authors.affiliation: 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.
authors.forename: Alexei
authors.initials: A
authors.lastname: Lyapustin
authors.affiliation: 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.
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Sayer
authors.affiliation: NASA Langley Research Center, Hampton, Virginia 23665, United States.
authors.forename: David M
authors.initials: DM
authors.lastname: Winker
mesh_terms: D000336:Aerosols / Q000032:analysis; D000465:Algorithms; D004391:Dust; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D055593:Geological Phenomena; D015233:Models, Statistical; D008962:Models, Theoretical; D052638:Particulate Matter / Q000032:analysis*; D017215:Satellite Communications
chemical_list: D000336:Aerosols; D004391:Dust; D052638:Particulate Matter