Spatial Modeling of PM 10 and NO 2 in the Continental United States, 1985–2000

Abstract

Background Epidemiologic studies of air pollution have demonstrated a link between long-term air pollution exposures and mortality. However, many have been limited to city-specific average pollution measures or spatial or land-use regression exposure models in small geographic areas. Objectives Our objective was to develop nationwide models of annual exposure to particulate matter < 10 μm in diameter (PM 10) and nitrogen dioxide during 1985–2000. Methods We used generalized additive models (GAMs) to predict annual levels of the pollutants using smooth spatial surfaces of available monitoring data and geographic information system–derived covariates. Model performance was determined using a cross-validation (CV) procedure with 10% of the data. We also compared the results of these models with a commonly used spatial interpolation, inverse distance weighting. Results For PM 10, distance to road, elevation, proportion of low-intensity residential, high-intensity residential, and industrial, commercial, or transportation land use within 1 km were all statistically significant predictors of measured PM 10 (model R 2 = 0.49, CV R 2 = 0.55). Distance to road, population density, elevation, land use, and distance to and emissions of the nearest nitrogen oxides–emitting power plant were all statistically significant predictors of measured NO 2 (model R 2 = 0.88, CV R 2 = 0.90). The GAMs performed better overall than the inverse distance models, with higher CV R 2 and higher precision. Conclusions These models provide reasonably accurate and unbiased estimates of annual exposures for PM 10 and NO 2. This approach provides the spatial and temporal variability necessary to describe exposure in studies assessing the health effects of chronic air pollution.