Keywords: Air Pollutant, Spatial-temporal
The adverse effects of air pollution on human health have generated increasing demand in understanding pollutant exposure. Air pollutant estimation at a national level is used to support policy and regulatory decisions, and at a fine-scale spatial temporal resolution to make informed decisions on personal activities. A wide range of data are analyzed by the environmental health group at SAMSI to provide air pollutant estimation at different scales. These include, for example, data from monitoring stations that are reliable but sparse in space and time, data from a numerical model that provide a complete but coarse spatial coverage, and data from mobile devices that provide very fine-scale measurements by the second. In this talk, I will provide a brief introduction to the data and present a statistical method to analyze high resolution spatial temporal pollutant data.