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Exposure prediction approaches used in air pollution epidemiology studies: Key findings and future recommendations

Journal of Exposure Science & Environmental Epidemiology volume 23, pages 654–659 (2013)Cite this article

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Abstract

Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or “hybrid” models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NOx). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.

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Acknowledgements

We thank the Natural Environment Research Council, Medical Research Council, Economic and Social Research Council, Department of Environment, Food and Rural Affairs and Department of Health for the funding received for the Traffic Pollution and Health in London project (NE/I008039/1), funded through the Environmental Exposures & Health Initiative (EEHI). The research was also supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. Related research was funded, in part, by the U.S. Environmental Protection Agency (Cooperative Agreement CR-83407201-0), NIEHS-sponsored UMDNJ Center for Environmental Exposures and Disease (NIEHS P30ES005022), and the New Jersey Agricultural Experiment Station. Natasha Hodas was supported by a Graduate Assistance in Areas of National Need Fellowship and an EPA STAR Fellowship. Related publications were made possible by a cooperative agreement between Emory University and the US Environmental Protection Agency (USEPA) (CR-83407301-1) and a USEPA Clean Air Research Center grant to Emory University and the Georgia Institute of Technology (RD83479901) Related research was funded by the NIH (ES014004) and the EPA (R833865).

DISCLAIMER

Although this work was reviewed by EPA and approved for publications, it may not necessarily reflect official Agency policy. The views expressed are those of the authors (s) and not necessarily those of the NHS, the NIHR or the Department of Health.

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Authors and Affiliations

  1. National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA

    Lisa K Baxter, Kathie L Dionisio, Janet Burke & Halûk Özkaynak

  2. Department of Environmental Health, Emory University, Atlanta, Georgia, USA

    Stefanie Ebelt Sarnat & Jeremy A Sarnat

  3. Department of Environmental Sciences, Rutgers University, New Brunswick, New Jersey, USA

    Natasha Hodas & Barbara J Turpin

  4. Department of Public Health Sciences, University of Rochester, Rochester, New York, USA

    David Q Rich

  5. New York State Department of Health, Albany, New York, USA

    Rena R Jones

  6. Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA

    Elizabeth Mannshardt

  7. Department of Epidemiology and Public Health, University of Miami, Miami, Florida, USA

    Naresh Kumar

  8. MRC-HPA Centre for Environment and Health, Kings’ College London, London, UK

    Sean D Beevers

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  1. Lisa K Baxter
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  2. Kathie L Dionisio
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Correspondence to Halûk Özkaynak.

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Baxter, L., Dionisio, K., Burke, J. et al. Exposure prediction approaches used in air pollution epidemiology studies: Key findings and future recommendations. J Expo Sci Environ Epidemiol 23, 654–659 (2013). https://doi.org/10.1038/jes.2013.62

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