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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.

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  • معلومة اضافية
    • المصدر:
      Publisher: National Institute of Environmental Health Sciences Country of Publication: United States NLM ID: 0330411 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-9924 (Electronic) Linking ISSN: 00916765 NLM ISO Abbreviation: Environ Health Perspect Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: Research Triangle Park, N. C. National Institute of Environmental Health Sciences.
    • الموضوع:
      Air Pollutants/*analysis ; Air Pollution/*analysis ; Atherosclerosis/*metabolism ; Environmental Monitoring/*methods; Atherosclerosis/ethnology ; Carbon/analysis ; Ethnicity ; Humans ; Models, Theoretical ; Nitrogen Oxides/analysis ; Particulate Matter/analysis ; Spatio-Temporal Analysis ; United States
    • نبذة مختصرة :
      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 R2 (R2CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R2CV ranged from 0.45 to 0.92, and temporally adjusted R2CV 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.
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    • Grant Information:
      K24 ES013195 United States ES NIEHS NIH HHS; K24ES013195 United States ES NIEHS NIH HHS; T32 ES015459 United States ES NIEHS NIH HHS; T32ES015459 United States ES NIEHS NIH HHS; P30ES007033 United States ES NIEHS NIH HHS; P30 ES007033 United States ES NIEHS NIH HHS
    • الرقم المعرف:
      0 (Air Pollutants)
      0 (Nitrogen Oxides)
      0 (Particulate Matter)
      7440-44-0 (Carbon)
    • الموضوع:
      Date Created: 20141115 Date Completed: 20160127 Latest Revision: 20220408
    • الموضوع:
      20240829
    • الرقم المعرف:
      PMC4384200
    • الرقم المعرف:
      10.1289/ehp.1408145
    • الرقم المعرف:
      25398188