Citation:

SAHU, S., A. GELFAND, AND D. M. HOLLAND. SPATIO-TEMPORAL MODELING OF FINE PARTICULATE MATTER. JOURNAL OF AGRICULTURAL, BIOLOGICAL, AND ENVIRONMENTAL STATISTICS. American Statistical Association, Alexandria, VA, 11(1):1-26, (2006).

Impact/Purpose:

Our main objective is to assess the exposure of selected ecosystems to specific atmospheric stressors. More precisely, we will analyze and interpret environmental quality (primarily atmospheric) data to document observable changes in environmental stressors that may be associated with legislatively-mandated emissions reductions.

Description:

Studies indicate that even short-term exposure to high concentrations of fine atmospheric particulate matter (PM_2.5) can lead to long-term health effects. In this paper, we propose a random effects model for PM_2.5 concentrations. In particular, we anticipate urban/rural differences with regard to both mean levels and variability. Hence, we introduce two random effects components, one for rural or background levels and the other as a supplement for urban areas. These are specified in the form of spatio-temporal processes. Weighting these processes through a population density surface results in nonstationarity in space. We analyze daily PM_2.5 concentrations in three Midwestern U.S. states for the year 2001. A fully Bayesian model is implemented, using MCMC techniques, which enables full inference with regard to process unknowns as well as predictions in time and space.

Record Details: