Spatial modeling of PM₁₀ and NO₂ in the continental United States, 1985-2000

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 (PM₁₀) 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 PM₁₀, 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 PM₁₀ (model R² = 0.49, CV R² = 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 NO₂ (model R² = 0.88, CV R² = 0.90). The GAMs performed better overall than the inverse distance models, with higher CV R² and higher precision. CONCLUSIONS: These models provide reasonably accurate and unbiased estimates of annual exposures for PM₁₀ and NO₂. This approach provides the spatial and temporal variability necessary to describe exposure in studies assessing the health effects of chronic air pollution.