Abstract

Introduction: There is increasing evidence of an association between individual long-term PM2.5 exposure and human health. Mortality and morbidity data collected at the area-level are valuable resources for investigating corresponding population-level health effects. However, PM2.5 monitoring data are available for limited periods of time and locations, and are not adequate for estimating area-level concentrations. We developed a general approach to estimate county-average concentrations representative of population exposures for 1980-2010 in the continental U.S.

Methods: We predicted annual average PM2.5 concentrations at about 70,000 census tract centroids, using a point prediction model previously developed for estimating annual average PM2.5 concentrations in the continental U.S. for 1980-2010. We then averaged these predicted PM2.5 concentrations in all counties weighted by census tract population. In sensitivity analyses, we compared the resulting estimates to four alternative county average estimates using MSE-based R2 in order to capture both systematic and random differences in estimates. These estimates included crude aggregates of regulatory monitoring data, averages of predictions at residential addresses in Southern California, and two sets of averages of census tract centroid predictions unweighted by population and interpolated from predictions at 25-km national grid coordinates.

Results: The county-average mean PM2.5 was 14.40 (standard deviation=3.94) µg/m3 in 1980 and decreased to 12.24 (3.24), 10.42 (3.30), and 8.06 (2.06) µg/m3 in 1990, 2000, and 2010, respectively. These estimates were moderately related with crude averages in 2000 and 2010 when monitoring data were available (R2= 0.70-0.82) and almost identical to the unweighted averages in all four decennial years. County averages were also consistent with the county averages derived from residential estimates in Southern California (0.95-0.96). We found grid-based estimates of county-average PM2.5 were more consistent with our estimates when we also included monitoring data (0.95-0.98) than grid-only estimates (0.91-0.96); both had slightly lower concentrations than census tract-based estimates.

Conclusions: Our approach to estimating population representative area-level PM2.5 concentrations is consistent with averages across residences. These exposure estimates will allow us to assess health impacts of ambient PM2.5 concentration in datasets with area-level health data.

Disciplines

Epidemiology

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