Hierarchical Bayesian Modeling
Hierarchical Bayesian modeling is a statistical method that combines air quality monitoring data (measurements) with modeled data (estimates or predictions). The US Environmental Protection Agency and CDC used this method to provide estimates of fine particle and ozone concentrations in air for areas that do not have air quality monitors and for times when monitors may not be recording data.
In 2012, the EPA improved the methods used to estimate county-level air quality results. The new model, called "downscaler," uses a statistical approach to fuse monitoring data with results from the Community Multiscale Air Quality (CMAQ) model to provide air pollution estimates across the United States. Downscaler model results are available from 2001 to 2008.
For more information about Hierarchical Bayesian modeling, contact us or see Hierarchical Bayesian Model-Derived Estimates of Air Quality for 2006 (Annual Report).