Dataset Information

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors.

ABSTRACT:

Background

Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations.

Methods

We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM(2.5-10)) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV).

Results

The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988-1998 and 1999-2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988-1998 and 1999-2007) and PM(2.5-10) (CV R2=0.46 and 0.52 for 1988-1998 and 1999-2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999-2007).

Conclusions

Our models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM(2.5-10) with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007.

SUBMITTER: Yanosky JD

PROVIDER: S-EPMC4137272 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Publications

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors.

Yanosky Jeff D JD Paciorek Christopher J CJ Laden Francine F Hart Jaime E JE Puett Robin C RC Liao Duanping D Suh Helen H HH

Environmental health : a global access science source 20140805

<h4>Background</h4>Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations.<h4>Methods</h4>We devel ...[more]

PMID: 25097007

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Dataset Information

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors.

Background

Methods

Results

Conclusions

Publications

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors.

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