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Hierarchical Bayesian Model (HBM)-Derived Estimates of Air Quality for 2006 - Annual Report
Citation:
DIMMICK, F., ERICS HALL, AND J. TIKVART. Hierarchical Bayesian Model (HBM)-Derived Estimates of Air Quality for 2006 - Annual Report. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-10/021 (NTIS PB2010-107596), 2010.
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
This report describes EPA's Hierarchical Bayesian model-generated (HBM) estimates of O3 and PM2.5 concentrations throughout the continental United States during the 2006 calendar year. HBM estimates provide the spatial and temporal variance of O3 and PM2.5, allowing estimation of their concentration values across the US., independent of where air quality monitors are physically located. HBM estimates are generated through the statistical 'fusion' of measured air quality monitor concentration values and air quality model predicted concentration values from EPA's Community Multiscale Air Quality (CMAQ) computer model. Information on EPA's air quality monitors, CMAQ model, and HBM model is included to provide the background and context for understanding the data output presented in this report.