Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Therefore, estimating the health effects of ambient air pollution require exposure assessments that accurately describe the expected variability in the pollutant of interest. These data were used to build on previous air pollution exposure modeling efforts in California using spatiotemporal models at the national scale to predict ambient particular matter (PM2.5) for the purpose of estimating exposure at residential locations for health effects analyses. These data helped researchers to predict finer temporal scales than typically available through land use regression (LUR) or remote sensing methods alone.
The raw particulate matter PM2.5 data were acquired from the US Environmental Protection Agency (EPA)'s Air Quality System (AQS) and included only measurements from Federal Reference Method (FRM) monitors over the period January 1999 through December 2008. The PM2.5 dataset included 104,172 monthly observations at 1,464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R2 values for LUR were .062=3 and .11 with and without remote sensing, respectively; suggesting remote sensing is a strong predictor of ground-level concentrations. Models are available in tabular format. They are freely available through http://www.acs.org. Download and extract the file called LURBME.zip.
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Department of Environmental Health Sciences