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Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors
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 spat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137272/ https://www.ncbi.nlm.nih.gov/pubmed/25097007 http://dx.doi.org/10.1186/1476-069X-13-63 |
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author | Yanosky, Jeff D Paciorek, Christopher J Laden, Francine Hart, Jaime E Puett, Robin C Liao, Duanping Suh, Helen H |
author_facet | Yanosky, Jeff D Paciorek, Christopher J Laden, Francine Hart, Jaime E Puett, Robin C Liao, Duanping Suh, Helen H |
author_sort | Yanosky, Jeff D |
collection | PubMed |
description | 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 (PM(2.5)), inhalable (PM(10)), 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 PM(2.5) models had high predictive accuracy (CV R(2)=0.77 for both 1988–1998 and 1999–2007). While model performance remained strong, the predictive ability of models for PM(10) (CV R(2)=0.58 for both 1988–1998 and 1999–2007) and PM(2.5–10) (CV R(2)=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 R(2)=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 PM(2.5) from 1999–2007). CONCLUSIONS: Our models provide estimates of monthly-average outdoor concentrations of PM(2.5), PM(10), 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. |
format | Online Article Text |
id | pubmed-4137272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41372722014-08-28 Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors Yanosky, Jeff D Paciorek, Christopher J Laden, Francine Hart, Jaime E Puett, Robin C Liao, Duanping Suh, Helen H Environ Health Research 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 (PM(2.5)), inhalable (PM(10)), 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 PM(2.5) models had high predictive accuracy (CV R(2)=0.77 for both 1988–1998 and 1999–2007). While model performance remained strong, the predictive ability of models for PM(10) (CV R(2)=0.58 for both 1988–1998 and 1999–2007) and PM(2.5–10) (CV R(2)=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 R(2)=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 PM(2.5) from 1999–2007). CONCLUSIONS: Our models provide estimates of monthly-average outdoor concentrations of PM(2.5), PM(10), 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. BioMed Central 2014-08-05 /pmc/articles/PMC4137272/ /pubmed/25097007 http://dx.doi.org/10.1186/1476-069X-13-63 Text en Copyright © 2014 Yanosky et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Yanosky, Jeff D Paciorek, Christopher J Laden, Francine Hart, Jaime E Puett, Robin C Liao, Duanping Suh, Helen H Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors |
title | Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors |
title_full | Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors |
title_fullStr | Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors |
title_full_unstemmed | Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors |
title_short | Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors |
title_sort | spatio-temporal modeling of particulate air pollution in the conterminous united states using geographic and meteorological predictors |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137272/ https://www.ncbi.nlm.nih.gov/pubmed/25097007 http://dx.doi.org/10.1186/1476-069X-13-63 |
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