The moving-window Bayesian maximum entropy framework: estimation of PM(2.5) yearly average concentration across the contiguous United States.

ABSTRACT: Geostatistical methods are widely used in estimating long-term exposures for epidemiological studies on air pollution, despite their limited capabilities to handle spatial non-stationarity over large geographic domains and the uncertainty associated with missing monitoring data. We developed a moving-window (MW) Bayesian maximum entropy (BME) method and applied this framework to estimate fine particulate matter (PM(2.5)) yearly average concentrations over the contiguous US. The MW approach accounts for the spatial non-stationarity, while the BME method rigorously processes the uncertainty associated with data missingness in the air-monitoring system. In the cross-validation analyses conducted on a set of randomly selected complete PM(2.5) data in 2003 and on simulated data with different d

SUBMITTER: Akita Y 

PROVIDER: S-EPMC3601029 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature