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Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors.
Environ Sci Technol. 2016 Apr 05; 50(7):3762-72.ES

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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Publisher Full Text (DOI)

Authors+Show Affiliations

van Donkelaar A
Department of Physics and Atmospheric Science, Dalhousie University , Halifax, N.S. Canada.
Martin RV
Department of Physics and Atmospheric Science, Dalhousie University , Halifax, N.S. Canada. Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts 02138, United States.
Brauer M
School of Population and Public Health, The University of British Columbia , 2206 East Mall, Vancouver, British Columbia V6T1Z3, Canada.
Hsu NC
NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States.
Kahn RA
NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States.
Levy RC
NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States.
Lyapustin A
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.
Sayer AM
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.
Winker DM
NASA Langley Research Center, Hampton, Virginia 23665, United States.

MeSH

AerosolsAlgorithmsDustEnvironmental MonitoringGeological PhenomenaModels, StatisticalModels, TheoreticalParticulate MatterSatellite Communications

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

26953851

Citation

van Donkelaar, Aaron, et al. "Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method With Information From Satellites, Models, and Monitors." Environmental Science & Technology, vol. 50, no. 7, 2016, pp. 3762-72.
van Donkelaar A, Martin RV, Brauer M, et al. Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors. Environ Sci Technol. 2016;50(7):3762-72.
van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C., Lyapustin, A., Sayer, A. M., & Winker, D. M. (2016). Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors. Environmental Science & Technology, 50(7), 3762-72. https://doi.org/10.1021/acs.est.5b05833
van Donkelaar A, et al. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method With Information From Satellites, Models, and Monitors. Environ Sci Technol. 2016 Apr 5;50(7):3762-72. PubMed PMID: 26953851.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors. AU - van Donkelaar,Aaron, AU - Martin,Randall V, AU - Brauer,Michael, AU - Hsu,N Christina, AU - Kahn,Ralph A, AU - Levy,Robert C, AU - Lyapustin,Alexei, AU - Sayer,Andrew M, AU - Winker,David M, Y1 - 2016/03/24/ PY - 2016/3/9/entrez PY - 2016/3/10/pubmed PY - 2016/12/15/medline SP - 3762 EP - 72 JF - Environmental science & technology JO - Environ Sci Technol VL - 50 IS - 7 N2 - 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. SN - 1520-5851 UR - https://www.unboundmedicine.com/medline/citation/26953851/Global_Estimates_of_Fine_Particulate_Matter_using_a_Combined_Geophysical_Statistical_Method_with_Information_from_Satellites_Models_and_Monitors_ DB - PRIME DP - Unbound Medicine ER -