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

Hart JE, Yanosky JD, Puett RC, Ryan L, Dockery DW, Smith TJ, Garshick E, Laden F - Environ. Health Perspect. (2009)

Bottom Line: 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.The GAMs performed better overall than the inverse distance models, with higher CV R(2) and higher precision.

View Article: PubMed Central - PubMed

Affiliation: Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA. Jaime.hart@channing.harvard.edu

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 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.

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Related in: MedlinePlus

TrIPS cohort members and monitoring locations for PM10 and NO2.
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f1-ehp-117-1690: TrIPS cohort members and monitoring locations for PM10 and NO2.

Mentions: The number of monitors used in the models and annual distributions of pollutant levels are shown in Table 1. The levels of both pollutants decreased over time. The median value of PM10 in 1985 was 38.2 μg/m3, and it fell to 23.0 μg/m3 by 2000 (a 40% decrease). The median NO2 level decreased 23% over the same period, from 19.0 ppb to 14.6 ppb. The distributions of the GIS-derived covariates at the monitor locations considered in the GAM models are shown in Table 2. The covariate distributions were quite similar for both sets of monitors. As shown in Figure 1, the cohort participants are located throughout the continental U.S., and most live close to the monitoring locations. Specifically, the cohort members lived a median distance of 10.2 km from PM10 monitoring sites and 16.6 km from NO2 sites. Seventy-five percent of the cohort was no more than 21.1 km from a PM10 monitor included in the model and 35.6 km from an NO2 monitor included in the model.


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

Hart JE, Yanosky JD, Puett RC, Ryan L, Dockery DW, Smith TJ, Garshick E, Laden F - Environ. Health Perspect. (2009)

TrIPS cohort members and monitoring locations for PM10 and NO2.
© Copyright Policy - public-domain
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2801201&req=5

f1-ehp-117-1690: TrIPS cohort members and monitoring locations for PM10 and NO2.
Mentions: The number of monitors used in the models and annual distributions of pollutant levels are shown in Table 1. The levels of both pollutants decreased over time. The median value of PM10 in 1985 was 38.2 μg/m3, and it fell to 23.0 μg/m3 by 2000 (a 40% decrease). The median NO2 level decreased 23% over the same period, from 19.0 ppb to 14.6 ppb. The distributions of the GIS-derived covariates at the monitor locations considered in the GAM models are shown in Table 2. The covariate distributions were quite similar for both sets of monitors. As shown in Figure 1, the cohort participants are located throughout the continental U.S., and most live close to the monitoring locations. Specifically, the cohort members lived a median distance of 10.2 km from PM10 monitoring sites and 16.6 km from NO2 sites. Seventy-five percent of the cohort was no more than 21.1 km from a PM10 monitor included in the model and 35.6 km from an NO2 monitor included in the model.

Bottom Line: 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.The GAMs performed better overall than the inverse distance models, with higher CV R(2) and higher precision.

View Article: PubMed Central - PubMed

Affiliation: Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA. Jaime.hart@channing.harvard.edu

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 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.

Show MeSH
Related in: MedlinePlus