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Prediction of fine particulate matter chemical components with a spatio-temporal model for the Multi-Ethnic Study of Atherosclerosis cohort

Journal of Exposure Science & Environmental Epidemiology volume 26, pages 520–528 (2016)Cite this article

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Abstract

Although cohort studies of the health effects of PM2.5 have developed exposure prediction models to represent spatial variability across participant residences, few models exist for PM2.5 components. We aimed to develop a city-specific spatio-temporal prediction approach to estimate long-term average concentrations of four PM2.5 components including sulfur, silicon, and elemental and organic carbon for the Multi-Ethnic Study of Atherosclerosis cohort, and to compare predictions to those from a national spatial model. Using 2-week average measurements from a cohort-focused monitoring campaign, the spatio-temporal model employed selected geographic covariates in a universal kriging framework with the data-driven temporal trend. Relying on long-term means of daily measurements from regulatory monitoring networks, the national spatial model employed dimension-reduced predictors using universal kriging. For the spatio-temporal model, the cross-validated and temporally-adjusted R2 was relatively higher for EC and OC, and in the Los Angeles and Baltimore areas. The cross-validated R2s for both models across the six areas were reasonably high for all components except silicon. Predicted long-term concentrations at participant homes from the two models were generally highly correlated across cities but poorly correlated within cities. The spatio-temporal model may be preferred for city-specific health analyses, whereas both models could be used for multi-city studies.

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Acknowledgements

This work was primarily supported by the National Particle Component Toxicity (NPACT) initiative funded by the Health Effects Institute (HEI) (Health Effects Institute 4749-RFA05), along with the Multi-Ethnic Study of Atherosclerosis and Air Pollution by the U.S. Environmental Protection Agency (EPA) Science to Achieve Results program (STAR) research assistance agreement (RD 831697). This publication has not been formally reviewed by the EPA. The views expressed in this document are solely those of the University of Washington and the EPA does not endorse any products or commercial services mentioned in this publication. Additional support was provided by the National Institute of Environmental Health Sciences (NIEHS) (T32ES015459 and P50 ES015915), the U.S. EPA (RD 83479601 and CR-834077101-0), and the National Research Foundation of Korea (Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education: 2013R1A6A3A04059017).

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Authors and Affiliations

  1. Institute of Health and Environment, Seoul National University, Seoul, Korea

    Sun-Young Kim

  2. Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA

    Sun-Young Kim, Lianne Sheppard, Joel D Kaufman & Sverre Vedal

  3. Department of Biostatistics, University of Washington, Seattle, Washington, USA

    Lianne Sheppard, Silas Bergen & Adam A Szpiro

  4. Department of Mathematics and Statistics, Winona State University, Winona, Minnesota, USA

    Silas Bergen

  5. Department of Statistics, University of Washington, Seattle, Washington, USA

    Paul D Sampson

  6. Department of Medicine, University of Washington, Seattle, Washington, USA

    Joel D Kaufman

  7. Department of Epidemiology, University of Washington, Seattle, Washington, USA

    Joel D Kaufman

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  1. Sun-Young Kim
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  2. Lianne Sheppard
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  4. Adam A Szpiro
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Correspondence to Sun-Young Kim.

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Kim, SY., Sheppard, L., Bergen, S. et al. Prediction of fine particulate matter chemical components with a spatio-temporal model for the Multi-Ethnic Study of Atherosclerosis cohort. J Expo Sci Environ Epidemiol 26, 520–528 (2016). https://doi.org/10.1038/jes.2016.29

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