title: 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.
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 PM2.5 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. PM2.5 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 PM2.5 in the contiguous U.S. We investigated the seasonal variations, identified areas where annual and daily PM2.5 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.
authors.affiliation: Department of Computer Sciences, Georgia Southern University, Statesboro, GA 30460, USA. lli@georgiasouthern.edu.
authors.forename: Lixin
authors.initials: L
authors.lastname: Li
authors.affiliation: Department of Geology and Geography, Georgia Southern University, Statesboro, GA 30460, USA. xzhou@georgiasouthern.edu.
authors.forename: Xiaolu
authors.initials: X
authors.lastname: Zhou
authors.affiliation: Department of Computer Sciences, Georgia Southern University, Statesboro, GA 30460, USA. marc.kalo@gmail.com.
authors.forename: Marc
authors.initials: M
authors.lastname: Kalo
authors.affiliation: Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA 30460, USA. rpiltner@georgiasouthern.edu.
authors.forename: Reinhard
authors.initials: R
authors.lastname: Piltner
mesh_terms: D000393:Air Pollutants / Q000737:chemistry*; D000397:Air Pollution / Q000032:analysis; D003710:Demography; D004781:Environmental Exposure / Q000032:analysis; D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D020407:Internet; D052638:Particulate Matter / Q000737:chemistry*; D012621:Seasons; D012984:Software; D062211:Spatio-Temporal Analysis; D014481:United States; D014484:United States Environmental Protection Agency
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: Inverse Distance Weighting (IDW); cross validation; fine particulate matter (PM2.5); population exposure; real-time air pollution; shape function; spatiotemporal interpolation; visualization; web application