1. Title, Journal and Authors
Title: Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors
Journal: Environmental science & Technology
Authors: Aaron van Donkelaar*†, Randall V. Martin†‡, Michael Brauer§, N. Christina Hsu∥, Ralph A. Kahn∥, Robert C. Levy∥, Alexei Lyapustin∥, Andrew M. Sayer∥⊥, and David M. Winker#
†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 Fighter Center, Greenbelt, Maryland 20771, United States
⊥Goddard Earth Sciences Technology and Research, Universities Space Association, Greenbelt, Maryland 20771, United states
# NASA Langley Research Center, Hampton, Virginia 23665, United States
The objective of this paper is to estimate the global PM2.5 concentrations with a combination of various information from satellite, simulation, and monitoring by applying a GWR (Geographically Weighted Regression).
AOD (Aerosol Optical Depth) was calibrated by using the simulation results and the satellite observations based on ground-based monitoring. Then, GWR adjusted satellite data (R2=0.81) was developed with the simulation results.
The satellite data were obtained by adapting the adequate retrieval algorithms. The simulation was done by GEOS-Chem, a transport model. Ground-based monitoring was carried out with AERONET data.
The GWR adjusted satellite data and GBD (Global Burden Disease) data were compared to estimate the global PM2.5 concentrations. As a result, it was verified that the global population-weighted annual mean PM2.5 concentration of 32.6 μg/m3 was three times higher than the WHO guideline of 10 μg/m3. Especially, the concentrations of Asia and Africa were estimated very highly.
There were too many areas to affect the uncertainties of the simulation, resulting from both sparse ground-based monitoring and challenging conditions for retrieval and simulation.
Dae Seong Jeong / Intern student
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