Mapping annual mean ground‐level PM2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States

Abstract

We present a simple approach to estimating ground‐level fine particulate matter (PM 2.5, 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 PM 2.5 concentrations are compared with measurements from the U.S. Environmental Protection Agency's (EPA) PM 2.5 compliance network for the year 2001. Regression analyses show that the annual mean MISR PM 2.5 concentration is strongly correlated with EPA PM 2.5 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 PM 2.5 concentrations have a root mean square error (RMSE) of 2.20 μg/m 3, which corresponds to a relative error (RMSE over mean EPA PM 2.5 concentration) of approximately 20%. Using simulated aerosol vertical profiles generated by the global models helps to reduce the uncertainty in estimated PM 2.5 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 PM 2.5 concentrations. The estimated seasonal mean PM 2.5 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 PM 2.5 concentration at higher temporal and spatial resolution.