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A study of health effect estimates using competing methods to model personal exposures to ambient PM2.5

Journal of Exposure Science & Environmental Epidemiology volume 17, pages 549–558 (2007)Cite this article

Abstract

Various methods have been developed recently to estimate personal exposures to ambient particulate matter less than 2.5 μm in diameter (PM2.5) using fixed outdoor monitors as well as personal exposure monitors. One class of estimators involves extrapolating values using ambient-source components of PM2.5, such as sulfate and iron. A key step in extrapolating these values is to correct for differences in infiltration characteristics of the component used in extrapolation (such as sulfate within PM2.5) and PM2.5. When this is not done, resulting health effect estimates will be biased. Another class of approaches involves factor analysis methods such as positive matrix factorization (PMF). Using either an extrapolation or a factor analysis method in conjunction with regression calibration allows one to estimate the direct effects of ambient PM2.5 on health, eliminating bias caused by using fixed outdoor monitors and estimated personal ambient PM2.5 concentrations. Several forms of the extrapolation method are defined, including some new ones. Health effect estimates that result from the use of these methods are compared with those from an expanded PMF analysis using data collected from a health study of asthmatic children conducted in Denver, Colorado. Examining differences in health effect estimates among the various methods using a measure of lung function (forced expiratory volume in 1 s) as the health indicator demonstrated the importance of the correction factor(s) in the extrapolation methods and that PMF yielded results comparable with the extrapolation methods that incorporated correction factors.

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Abbreviations

FEV1:

forced expiratory volume in 1 s

F PEX :

fraction of ambient PM2.5 that a subject is exposed to

EPA:

Environmental Protection Agency

PM:

particulate matter

PM2.5:

particulate matter less than 2.5 μm in diameter

SD:

standard deviation

PMF:

positive matrix factorization

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Acknowledgements

This work was funded in part by grants EPA R 825702 and Thrasher Research Fund 02816-8. We thank RTI International for their valuable help in providing the personal monitors, advice on implementation and interpretation of the personal monitoring protocol and gravimetric analysis. We also thank the Desert Research Institute for performing the XRF and IC analyses. Last but not least, we thank the children from the Kunsberg School for participating in the study and making these analyses possible.

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Authors and Affiliations

  1. Division of Biostatistics, National Jewish Medical and Research Center, Denver, Colorado, USA

    Matthew Strand

  2. Center for Air Resources Engineering and Science, Clarkson University, Potsdam, New York, USA

    Philip K Hopke

  3. Department of Mechanical and Aeronautical Engineering, University of California, Davis, California, USA

    Weixiang Zhao

  4. Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health and Community Medicine, Seattle, Washington, USA

    Sverre Vedal

  5. Department of Pediatrics, National Jewish Medical and Research Center, Denver, Colorado, USA

    Erwin Gelfand & Nathan Rabinovitch

Authors
  1. Matthew Strand
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  2. Philip K Hopke
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  3. Weixiang Zhao
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  4. Sverre Vedal
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  5. Erwin Gelfand
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  6. Nathan Rabinovitch
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Corresponding author

Correspondence to Matthew Strand.

Appendix

Appendix

Steps in estimating λ

Perform a mixed model regression of total personal PM2.5 on ambient PM2.5, where fixed and subject-specific random terms for both the y-intercept and slope of ambient PM2.5 are included in the model. If nonambient and ambient PM2.5 exposures are approximately independent of each other, which our data suggested (also see Wilson and Suh, 1997), then the personal slope (fixed slope plus subject-specific random slope) indicates the average fraction of ambient PM2.5 that the subject is exposed to within the study period, whereas the y-intercept indicates average exposure to nonambient sources.

Repeat previous step, with the PM2.5 component of interest (e.g., sulfate).

The estimate of λ for a given subject is then obtained by dividing the personal PM2.5 slope by the personal PM2.5 component of interest (e.g., sulfate) slope.

The common λ can be obtained by averaging subject-specific estimates. A total of 21 subjects with low environmental tobacco smoke exposure were used to estimate λ.

Further detail is given in Strand et al. (2006), including a description of subjects used in estimating λ.

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Strand, M., Hopke, P., Zhao, W. et al. A study of health effect estimates using competing methods to model personal exposures to ambient PM2.5. J Expo Sci Environ Epidemiol 17, 549–558 (2007). https://doi.org/10.1038/sj.jes.7500568

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