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A Unified Spatiotemporal Modeling Approach for Predicting Concentrations of Multiple Air Pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution

Keller, Joshua P. (författare)
Olives, Casey (författare)
Kim, Sun-Young (författare)
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Sheppard, Lianne (författare)
Sampson, Paul D. (författare)
Szpiro, Adam A. (författare)
Oron, Assaf P. (författare)
Lindström, Johan (författare)
Lund University,Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
Vedal, Sverre (författare)
Kaufman, Joel D. (författare)
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 (creator_code:org_t)
Environmental Health Perspectives, 2015
2015
Engelska.
Ingår i: Environmental Health Perspectives. - : Environmental Health Perspectives. - 1552-9924 .- 0091-6765. ; 123:4, s. 301-309
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Background: Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time. Objectives: We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Methods: We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and participants' homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations. Results: Prediction accuracy was high for most models, with cross-validation R-2 (R-CV(2)) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R-CV(2) ranged from 0.45 to 0.92, and temporally adjusted R-CV(2) ranged from 0.23 to 0.92. Conclusions: This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated participant-specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Arbetsmedicin och miljömedicin (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Occupational Health and Environmental Health (hsv//eng)

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