| Spatial Modeling of PM10 and NO2 in the Continental United States, 1985–2000 Jaime E. Hart,1,2,3 Jeff D. Yanosky,1 Robin C. Puett,1,4,5,6 Louise Ryan,7 Douglas W. Dockery,1,3 Thomas J. Smith,1 Eric Garshick,2,8 and Francine Laden1,2,3 1Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; 2Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA; 3Department of Epidemiology, Harvard School of Public Health, Harvard School of Public Health, Boston, Massachusetts, USA; 4South Carolina Cancer Prevention and Control Program, University of South Carolina, Columbia, South Carolina, USA; 5Department of Environmental Health Sciences, and 6Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA; 7Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA; 8Pulmonary and Critical Care Medicine Section, Medical Service, VA Boston Healthcare System, Boston, Massachusetts, USA 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 (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. Key words: GIS, nitrogen dioxide, outdoor air pollution, particulate matter. Environ Health Perspect 117:1690–1696 (2009) . doi:10.1289/ehp.0900840 available via http://dx.doi.org/ [Online 29 June 2009]
Address correspondence to J.E. Hart, 181 Longwood Ave., Boston, MA 02115 USA. Telephone: (617) 525-2289. Fax: (617) 525-2578. E-mail: Jaime.hart@channing.harvard.edu Supplemental Material is available online (doi:10.1289/ehp.0900840.S1 via http://dx.doi.org/) . We thank C. Paciorek for helpful statistical advice and M. Jacobson Canner for programming assistance. This study was supported by grant R01 CA90792 from the National Institutes of Health/National Cancer Institute, National Institute of Environmental Health Sciences (NIEHS) Center grant ES00002, and NIEHS T32 ES007069-29 (J.E.H.) . The authors declare they have no competing financial interests. Received 26 March 2009 ; accepted 29 June 2009.
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