Spatiotemporal Interpolation Methods for the Application of Estimating Population Exposure to Fine Particulate Matter in the Contiguous U.S. and a Real-Time Web Application

Lixin Li, Xiaolu Zhou, Marc Kalo and Reinhard Piltner
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Lixin Li: Department of Computer Sciences, Georgia Southern University, Statesboro, GA 30460, USA
Xiaolu Zhou: Department of Geology and Geography, Georgia Southern University, Statesboro, GA 30460, USA
Marc Kalo: Department of Computer Sciences, Georgia Southern University, Statesboro, GA 30460, USA
Reinhard Piltner: Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA 30460, USA

IJERPH, 2016, vol. 13, issue 8, 1-20

Abstract: Appropriate spatiotemporal interpolation is critical to the assessment of relationships between environmental exposures and health outcomes. A powerful assessment of human exposure to environmental agents would incorporate spatial and temporal dimensions simultaneously. This paper compares shape function (SF)-based and inverse distance weighting (IDW)-based spatiotemporal interpolation methods on a data set of PM 2.5 data in the contiguous U.S. Particle pollution, also known as particulate matter (PM), is composed of microscopic solids or liquid droplets that are so small that they can get deep into the lungs and cause serious health problems. PM 2.5 refers to particles with a mean aerodynamic diameter less than or equal to 2.5 micrometers. Based on the error statistics results of k-fold cross validation, the SF-based method performed better overall than the IDW-based method. The interpolation results generated by the SF-based method are combined with population data to estimate the population exposure to PM 2.5 in the contiguous U.S. We investigated the seasonal variations, identified areas where annual and daily PM 2.5 were above the standards, and calculated the population size in these areas. Finally, a web application is developed to interpolate and visualize in real time the spatiotemporal variation of ambient air pollution across the contiguous U.S. using air pollution data from the U.S. Environmental Protection Agency (EPA)’s AirNow program.

Keywords: fine particulate matter (PM 2.5 ); spatiotemporal interpolation; shape function; Inverse Distance Weighting (IDW); cross validation; population exposure; web application; visualization; real-time air pollution (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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