Estimating Ground-Level PM2.5 in the Eastern United States Using Satellite Remote Sensing
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CitationLiu, Yang, Jeremy A. Sarnat, Vasu Kilaru, Daniel J. Jacob, and Petros Koutrakis. 2005. Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing. Environmental Science and Technology 39(9): 3269-3278.
AbstractAn empirical model based on the regression between daily PM2.5 (particles with aerodynamic diameters of less than 2.5 μm) concentrations and aerosol optical thickness (AOT) measurements from the multiangle imaging spectroradiometer (MISR) was developed and tested using data from the eastern United States during the period of 2001. Overall, the empirical model explained 48% of the variability in PM2.5 concentrations. The root-mean-square error of the model was 6.2 μg/m3 with a corresponding average PM2.5 concentration of 13.8 μg/m3. When PM2.5 concentrations greater than 40 μg/m3 were removed, model results were shown to be unbiased estimators of observations. Several factors, such as planetary boundary layer height, relative humidity, season, and other geographical attributes of monitoring sites, were found to influence the association between PM2.5 and AOT. The findings of this study illustrate the strong potential of satellite remote sensing in regional ambient air quality monitoring as an extension to ground networks. With the continual advancement of remote sensing technology and global data assimilation systems, AOT measurements derived from satellite remote sensors may provide a cost-effective approach as a supplemental source of information for determining ground-level particle concentrations.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:3988783
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