Development and evaluation of alternative approaches for exposure assessment of multiple air pollutants in Atlanta, Georgia

Measurements from central site (CS) monitors are often used as estimates of exposure in air pollution epidemiological studies. As these measurements are typically limited in their spatiotemporal resolution, true exposure variability within a population is often obscured, leading to potential measurement errors. To fully examine this limitation, we developed a set of alternative daily exposure metrics for each of the 169 ZIP codes in the Atlanta, GA, metropolitan area, from 1999 to 2002, for PM2.5 and its components (elemental carbon (EC), SO4), O3, carbon monoxide (CO), and nitrogen oxides (NOx). Metrics were applied in a study investigating the respiratory health effects of these pollutants. The metrics included: (i) CS measurements (one CS per pollutant); (ii) air quality model results for regional background pollution; (iii) local-scale AERMOD air quality model results; (iv) hybrid air quality model estimates (a combination of (ii) and (iii)); and (iv) population exposure model predictions (SHEDS and APEX). Differences in estimated spatial and temporal variability were compared by exposure metric and pollutant. Comparisons showed that: (i) both hybrid and exposure model estimates exhibited high spatial variability for traffic-related pollutants (CO, NOx, and EC), but little spatial variability among ZIP code centroids for regional pollutants (PM2.5, SO4, and O3); (ii) for all pollutants except NOx, temporal variability was consistent across metrics; (iii) daily hybrid-to-exposure model correlations were strong (r>0.82) for all pollutants, suggesting that when temporal variability of pollutant concentrations is of main interest in an epidemiological application, the use of estimates from either model may yield similar results; (iv) exposure models incorporating infiltration parameters, time-location-activity budgets, and other exposure factors affect the magnitude and spatiotemporal distribution of exposure, especially for local pollutants. The results of this analysis can inform the development of more appropriate exposure metrics for future epidemiological studies of the short-term effects of particulate and gaseous ambient pollutant exposure in a community.

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Acknowledgements

We thank James Crooks of the US EPA’s National Health and Environmental Effects Research Laboratory for contributions to the modeling portion of this work, David Brzezinski of the US EPA’s Office of Transportation and Air Quality, Sarav Arunachalam of the University of North Carolina Institute for the Environment, Jon Morton of the Georgia Department of Natural Resources, and Joe Touma formerly of the US EPA’s National Exposure Research Laboratory for their efforts related to air quality modeling, a refined local emissions inventory, and traffic data support. We thank EPA internal reviewers for their comments on the manuscript. The US EPA through its Office of Research and Development, National Exposure Research Laboratory, funded and collaborated in the research described here under cooperative agreement number CR-83407301-1 to Emory University, and a US EPA Clean Air Research Center grant to Emory University and the Georgia Institute of Technology (RD83479901). It has been subjected to Agency review and approved for publication.

Author information

Authors and Affiliations

  1. National Exposure Research Laboratory, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA Kathie L Dionisio, Vlad Isakov, Lisa K Baxter, Janet Burke & Halûk Özkaynak
  2. Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA Jeremy A Sarnat & Stefanie Ebelt Sarnat
  3. ICF International, Rohnert Park, California, USA Arlene Rosenbaum
  4. Office of Air Quality Planning and Standards, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA Stephen E Graham
  5. Office of Transportation and Air Quality, US Environmental Protection Agency, Ann Arbor, Michigan, USA Rich Cook
  6. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA James Mulholland
  1. Kathie L Dionisio