title: Spatial modeling of PM10 and NO2 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.
  
  
  OBJECTIVE
  Our objective was to develop nationwide models of annual exposure to particulate matter < 10 microm 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.
authors.affiliation: Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA. Jaime.hart@channing.harvard.edu
authors.forename: Jaime E
authors.initials: JE
authors.lastname: Hart
authors.forename: Jeff D
authors.initials: JD
authors.lastname: Yanosky
authors.forename: Robin C
authors.initials: RC
authors.lastname: Puett
authors.forename: Louise
authors.initials: L
authors.lastname: Ryan
authors.forename: Douglas W
authors.initials: DW
authors.lastname: Dockery
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Smith
authors.forename: Eric
authors.initials: E
authors.lastname: Garshick
authors.forename: Francine
authors.initials: F
authors.lastname: Laden
mesh_terms: D000393:Air Pollutants / Q000032:analysis* / Q000737:chemistry; D000397:Air Pollution / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods; D062665:Epidemiological Monitoring; D006801:Humans; D019570:Inhalation Exposure / Q000706:statistics & numerical data*; D015233:Models, Statistical; D009585:Nitrogen Dioxide / Q000032:analysis; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis; D011210:Power Plants; D012044:Regression Analysis; D012189:Retrospective Studies; D013997:Time Factors; D014481:United States / Q000453:epidemiology; D001335:Vehicle Emissions / Q000032:analysis
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter; D001335:Vehicle Emissions; D009585:Nitrogen Dioxide