Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model
2015
Geng, Guannan | Zhang, Qiang | Martin, Randall V. | van Donkelaar, Aaron | Huo, Hong | Che, Huizheng | Lin, Jintai | He, Kebin
Epidemiological and health impact studies of fine particulate matter (PM2.5) have been limited in China because of the lack of spatially and temporally continuous PM2.5 monitoring data. Satellite remote sensing of aerosol optical depth (AOD) is widely used in estimating ground-level PM2.5 concentrations. We improved the method for estimating long-term surface PM2.5 concentrations using satellite remote sensing and a chemical transport model, and derived PM2.5 concentrations over China for 2006–2012. We generated a map of surface PM2.5 concentrations at 0.1°×0.1° over China using the nested-grid GEOS-Chem model, most recent bottom-up emission inventory, and satellite observations from the MODIS and MISR instruments. Aerosol vertical profiles from the space-based CALIOP lidar were used to adjust the climatological drivers of the bias in the simulated results, and corrections were made for incomplete sampling. We found significant spatial agreement between the satellite-derived PM2.5 concentrations and the ground-level PM2.5 measurements collected from literatures (r=0.74, slope=0.77, intercept=11.21μg/m3). The population-weighted mean of PM2.5 concentrations in China is 71μg/m3 and more than one billion people live in locations where PM2.5 concentrations exceed the World Health Organization Air Quality Interim Target-1 of 35μg/m3. The results from our work are substantially higher than previous work, especially in heavily polluted regions. The overall population-weighted mean uncertainty over China is 17.2μg/m3, as estimated using ground-level AOD measurements and vertical profiles observed from CALIOP.
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