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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, Reinhard E. Piltner
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, Reinhard E. Piltner
Department of Mathematical Sciences Faculty Publications
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 …