Estimating Population Exposure to Fine Particulate Matter in the Conterminous U.S. using Shape Function-based Spatiotemporal Interpolation Method: A County Level Analysis.

Abstract

This paper investigates spatiotemporal interpolation methods for the application of air pollution assessment. The air pollutant of interest in this paper is fine particulate matter PM_2.5. The choice of the time scale is investigated when applying the shape function-based method. It is found that the measurement scale of the time dimension has an impact on the quality of interpolation results. Based upon the result of 10-fold cross validation, the most effective time scale out of four experimental ones was selected for the PM_2.5 interpolation. The paper also estimates the population exposure to the ambient air pollution of PM_2.5 at the county-level in the contiguous U.S. in 2009. The interpolated county-level PM_2.5 has been linked to 2009 population data and the population with a risky PM_2.5 exposure has been estimated. The risky PM_2.5 exposure means the PM_2.5 concentration exceeding the National Ambient Air Quality Standards. The geographic distribution of the counties with a risky PM_2.5 exposure is visualized. This work is essential to understanding the associations between ambient air pollution exposure and population health outcomes.