Developing spatiotemporally resolved air pollution concentrations for epidemiologic study of multiple pollutants in lightly populated low-to-moderate pollution environments

Background: Investigation of air pollution health effects in areas with lower populations have historically been hindered by limited availability of monitoring data and/or lack of modeled data. Objective: Develop spatiotemporally resolved air pollution predictions by fusing data available from ambient air monitoring networks with air quality model outputs. Method: Daily air pollution data was obtained from US Environmental Protection Agency (EPA) monitoring networks across the state of South Carolina and from Community Multi-scale Air Quality (CMAQ) outputs for multiple pollutants during 2003 to 2009. Pollutants include carbon monoxide, oxides of nitrogen, ozone, sulfur dioxide, particulate matter (PM2.5), and PM2.5 components: elemental carbon, ammonium, and sulfate. Air pollution predictions are developed using a temporal kriging approach that 'fuses' the data from the ambient monitoring network with CMAQ. Results: Availability of monitoring data varies substantially by day by pollutant with greatest co...