Mapping annual mean ground-level PM 2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States
MetadataShow full item record
CitationLiu, Yang, Rokjin J. Park, Daniel J. Jacob, Qinbin Li, Vasu Kilaru, and Jeremy A. Sarnat. 2004. “Mapping Annual Mean Ground-Level PM 2.5 Concentrations Using Multiangle Imaging Spectroradiometer Aerosol Optical Thickness over the Contiguous United States.” Journal of Geophysical Research 109 (D22). doi:10.1029/2004jd005025.
AbstractWe present a simple approach to estimating ground-level fine particulate matter (PM2.5, particles smaller than 2.5 μm in diameter) concentrations by applying local scaling factors from a global atmospheric chemistry model (GEOS-CHEM with GOCART dust and sea salt data) to aerosol optical thickness (AOT) retrieved by the Multiangle Imaging Spectroradiometer (MISR). The resulting MISR PM2.5 concentrations are compared with measurements from the U.S. Environmental Protection Agency's (EPA) PM2.5 compliance network for the year 2001. Regression analyses show that the annual mean MISR PM2.5 concentration is strongly correlated with EPA PM2.5 concentration (correlation coefficient r = 0.81), with an estimated slope of 1.00 and an insignificant intercept, when three potential outliers from Southern California are excluded. The MISR PM2.5 concentrations have a root mean square error (RMSE) of 2.20 μg/m3, which corresponds to a relative error (RMSE over mean EPA PM2.5 concentration) of approximately 20%. Using simulated aerosol vertical profiles generated by the global models helps to reduce the uncertainty in estimated PM2.5 concentrations due to the changing correlation between lower and upper tropospheric aerosols and therefore to improve the capability of MISR AOT in estimating surface-level PM2.5 concentrations. The estimated seasonal mean PM2.5 concentrations exhibited substantial uncertainty, particularly in the west. With improved MISR cloud screening algorithms and the dust simulation of global models, as well as a higher model spatial resolution, we expect that this approach will be able to make reliable estimation of seasonal average surface-level PM2.5 concentration at higher temporal and spatial resolution.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:14121847
- FAS Scholarly Articles