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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5402885
- author
- 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.
- organization
- publishing date
- 2015
- 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 Sciences
- external identifiers
-
- wos:000352168000014
- scopus:84961306016
- pmid:25398188
- 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
- 2016-04-01 14:10:41
- date last changed
- 2022-04-22 01:45:08
@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 Sciences}},
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}},
doi = {{10.1289/ehp.1408145}},
volume = {{123}},
year = {{2015}},
}