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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.; Olives, Casey; Kim, Sun-Young; Sheppard, Lianne; Sampson, Paul D.; Szpiro, Adam A.; Oron, Assaf P.; Lindström, Johan LU ; Vedal, Sverre and Kaufman, Joel D. (2015) In Environmental Health Perspectives 123(4). p.301-309
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Environmental Health Perspectives
volume
123
issue
4
pages
301 - 309
publisher
National Institute of Environmental Health Science
external identifiers
  • wos:000352168000014
  • scopus:84961306016
ISSN
1552-9924
DOI
10.1289/ehp.1408145
language
English
LU publication?
yes
id
494deeb9-ba1f-4c5d-a83b-fb57e2f63b5a (old id 5402885)
date added to LUP
2015-06-01 09:22:41
date last changed
2017-10-22 04:16:28
@article{494deeb9-ba1f-4c5d-a83b-fb57e2f63b5a,
  abstract     = {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.},
  author       = {Keller, Joshua P. and Olives, Casey and Kim, Sun-Young and Sheppard, Lianne and Sampson, Paul D. and Szpiro, Adam A. and Oron, Assaf P. and Lindström, Johan and Vedal, Sverre and Kaufman, Joel D.},
  issn         = {1552-9924},
  language     = {eng},
  number       = {4},
  pages        = {301--309},
  publisher    = {National Institute of Environmental Health Science},
  series       = {Environmental Health Perspectives},
  title        = {A Unified Spatiotemporal Modeling Approach for Predicting Concentrations of Multiple Air Pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution},
  url          = {http://dx.doi.org/10.1289/ehp.1408145},
  volume       = {123},
  year         = {2015},
}