Open AccessMapping annual mean ground‐level PM 2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States
Author(s) -
Liu Yang,
Park Rokjin J.,
Jacob Daniel J.,
Li Qinbin,
Kilaru Vasu,
Sarnat Jeremy A.
Publication year - 2004
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2004jd005025
Subject(s) - aerosol , spectroradiometer , environmental science , atmospheric sciences , troposphere , mean squared error , moderate resolution imaging spectroradiometer , correlation coefficient , particulates , linear regression , meteorology , satellite , chemistry , mathematics , statistics , physics , organic chemistry , astronomy , optics , reflectivity
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.