Predicting chronic fine and coarse particulate exposures using spatiotemporal models for the Northeastern and Midwestern United States.

BACKGROUND:Chronic epidemiologic studies of particulate matter (PM) are limited by the lack of monitoring data, relying instead on citywide ambient concentrations to estimate exposures. This method ignores within-city spatial gradients and restricts studies to areas with nearby monitoring data. This lack of data is particularly restrictive for fine particles (PM with aerodynamic diameter < 2.5 microm; PM(2.5)) and coarse particles (PM with aerodynamic diameter 2.5-10 microm; PM(10-2.5)), for which monitoring is limited before 1999. To address these limitations, we developed spatiotemporal models to predict monthly outdoor PM(2.5) and PM(10-2.5) concentrations for the northeastern and midwestern United States. METHODS:For PM(2.5), we developed models for two periods: 1988-1998 and 1999-2002. Both models included smooth spatial and regression terms of geographic information system-based and meteorologic predictors. To compensate for sparse monitoring data, the pre-1999 model also included predicted PM(10) (PM with aerodynamic diameter < 10 microm) and extinction coefficients (km(-1)). PM(10-2.5) levels were estimated as the difference in monthly predicted PM(10) and PM(2.5), with predicted PM(10) from our previously developed PM(10) model. RESULTS:Predictive performance for PM(2.5) was strong (cross-validation R2 = 0.77 and 0.69 for post-1999 and pre-1999 PM(2.5) models, respectively) with high precision (2.2 and 2.7 microg/m3, respectively). Models performed well irrespective of population density and season. Predictive performance for PM(10-2.5) was weaker (cross-validation R2 = 0.39) with lower precision (5.5 microg/m3). PM(10-2.5) levels exhibited greater local spatial variability than PM(10) or PM(2.5), suggesting that PM(2.5) measurements at ambient monitoring sites are more representative for surrounding populations than for PM(10) and especially PM(10-2.5). CONCLUSIONS:We provide semiempirical models to predict spatially and temporally resolved long-term average outdoor concentrations of PM(2.5) and PM(10-2.5) for estimating exposures of populations living in the northeastern and midwestern United States.