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dc.contributor.authorYanosky, Jeff Den_US
dc.contributor.authorPaciorek, Christopher Jen_US
dc.contributor.authorLaden, Francineen_US
dc.contributor.authorHart, Jaime Een_US
dc.contributor.authorPuett, Robin Cen_US
dc.contributor.authorLiao, Duanpingen_US
dc.contributor.authorSuh, Helen Hen_US
dc.date.accessioned2014-09-08T15:35:50Z
dc.date.issued2014en_US
dc.identifier.citationYanosky, Jeff D, Christopher J Paciorek, Francine Laden, Jaime E Hart, Robin C Puett, Duanping Liao, and Helen H Suh. 2014. “Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors.” Environmental Health 13 (1): 63. doi:10.1186/1476-069X-13-63. http://dx.doi.org/10.1186/1476-069X-13-63.en
dc.identifier.issn1476-069Xen
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12785804
dc.description.abstractBackground: 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 (PM2.5–10) 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 PM2.5–10 (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 PM2.5–10 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.en
dc.language.isoen_USen
dc.publisherBioMed Centralen
dc.relation.isversionofdoi:10.1186/1476-069X-13-63en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137272/pdf/en
dash.licenseLAAen_US
dc.subjectParticulate matteren
dc.subjectSpatio-temporal modelsen
dc.subjectLand use regressionen
dc.subjectSpatial smoothingen
dc.subjectPenalized splinesen
dc.subjectGeneralized additive mixed modelen
dc.titleSpatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictorsen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalEnvironmental Healthen
dash.depositing.authorLaden, Francineen_US
dc.date.available2014-09-08T15:35:50Z
dc.identifier.doi10.1186/1476-069X-13-63*
dash.contributor.affiliatedHart, Jaime
dash.contributor.affiliatedLaden, Francine
dc.identifier.orcid0000-0002-2813-2174


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