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Volume 14, issue 12
Atmos. Chem. Phys., 14, 6301-6314, 2014
https://doi.org/10.5194/acp-14-6301-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 14, 6301-6314, 2014
https://doi.org/10.5194/acp-14-6301-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 25 Jun 2014

Research article | 25 Jun 2014

10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data

X. Hu1, L. A. Waller2, A. Lyapustin3, Y. Wang3,4, and Y. Liu1 X. Hu et al.
  • 1Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
  • 2Department of Biostatistics & Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
  • 3NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 4University of Maryland Baltimore County, Baltimore, MD, USA

Abstract. Long-term PM2.5 exposure has been associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of the spatiotemporally continuous distribution of PM2.5 concentrations are important. Satellite-retrieved aerosol optical depth (AOD) has been increasingly used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, previous studies indicated that an inherent disadvantage of many AOD products is their coarse spatial resolution. For instance, the available spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) AOD products are 10 and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm based on MODIS measurements was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5–AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US centered at the Atlanta metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted annually, and we obtained model fitting R2 ranging from 0.71 to 0.85, mean prediction error (MPE) from 1.73 to 2.50 μg m−3, and root mean squared prediction error (RMSPE) from 2.75 to 4.10 μg m−3. In addition, we found cross-validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 μg m−3, and RMSPE from 3.12 to 5.00 μg m−3, indicating a good agreement between the estimated and observed values. Spatial trends showed that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. Our time-series analysis showed that, for the 10-year study period, the PM2.5 levels in the southeastern US have decreased by ~20%. The annual decrease has been relatively steady from 2001 to 2007 and from 2008 to 2010 while a significant drop occurred between 2007 and 2008. An observed increase in PM2.5 levels in year 2005 is attributed to elevated sulfate concentrations in the study area in warm months of 2005.

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