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

Jaime E. Hart, Jeffrey Yanosky, Robin C. Puett, Louise Ryan, Douglas W. Dockery, Thomas J. Smith, Eric Garshick, Francine Laden

Research output: Contribution to journalArticle

45 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1690-1696
Number of pages7
JournalEnvironmental health perspectives
Volume117
Issue number11
DOIs
StatePublished - Nov 1 2009

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Air Pollution
Nitrogen Oxides
Power Plants
Nitrogen Dioxide
Geographic Information Systems
Spatial Analysis
Particulate Matter
Population Density
Epidemiologic Studies
Mortality
Health

All Science Journal Classification (ASJC) codes

  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

Cite this

Hart, J. E., Yanosky, J., Puett, R. C., Ryan, L., Dockery, D. W., Smith, T. J., ... Laden, F. (2009). Spatial modeling of PM10 and NO2 in the continental United States, 1985-2000. Environmental health perspectives, 117(11), 1690-1696. https://doi.org/10.1289/ehp.0900840
Hart, Jaime E. ; Yanosky, Jeffrey ; Puett, Robin C. ; Ryan, Louise ; Dockery, Douglas W. ; Smith, Thomas J. ; Garshick, Eric ; Laden, Francine. / Spatial modeling of PM10 and NO2 in the continental United States, 1985-2000. In: Environmental health perspectives. 2009 ; Vol. 117, No. 11. pp. 1690-1696.
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Hart, JE, Yanosky, J, Puett, RC, Ryan, L, Dockery, DW, Smith, TJ, Garshick, E & Laden, F 2009, 'Spatial modeling of PM10 and NO2 in the continental United States, 1985-2000', Environmental health perspectives, vol. 117, no. 11, pp. 1690-1696. https://doi.org/10.1289/ehp.0900840

Spatial modeling of PM10 and NO2 in the continental United States, 1985-2000. / Hart, Jaime E.; Yanosky, Jeffrey; Puett, Robin C.; Ryan, Louise; Dockery, Douglas W.; Smith, Thomas J.; Garshick, Eric; Laden, Francine.

In: Environmental health perspectives, Vol. 117, No. 11, 01.11.2009, p. 1690-1696.

Research output: Contribution to journalArticle

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T1 - Spatial modeling of PM10 and NO2 in the continental United States, 1985-2000

AU - Hart, Jaime E.

AU - Yanosky, Jeffrey

AU - Puett, Robin C.

AU - Ryan, Louise

AU - Dockery, Douglas W.

AU - Smith, Thomas J.

AU - Garshick, Eric

AU - Laden, Francine

PY - 2009/11/1

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N2 - 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.

AB - 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.

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