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

Lisa K. Baxter, Kathie L. Dionisio, Janet Burke, Stefanie Ebelt Sarnat, Jeremy A. Sarnat, Natasha Hodas, David Q. Rich, Barbara J. Turpin, Rena R. Jones, Elizabeth Mannshardt, Naresh Kumar, Sean D. Beevers, Halûk Özkaynak

Research output: Contribution to journalArticle

56 Scopus citations

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 NO x). 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.

Original languageEnglish (US)
Pages (from-to)654-659
Number of pages6
JournalJournal of Exposure Science and Environmental Epidemiology
Volume23
Issue number6
DOIs
StatePublished - Oct 3 2013

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Keywords

  • air exchange rate
  • ambient pollution
  • epidemiology
  • exposure metrics
  • exposure models
  • PM

ASJC Scopus subject areas

  • Pollution
  • Public Health, Environmental and Occupational Health
  • Toxicology
  • Epidemiology

Cite this

Baxter, L. K., Dionisio, K. L., Burke, J., Ebelt Sarnat, S., Sarnat, J. A., Hodas, N., Rich, D. Q., Turpin, B. J., Jones, R. R., Mannshardt, E., Kumar, N., Beevers, S. D., & Özkaynak, H. (2013). Exposure prediction approaches used in air pollution epidemiology studies: Key findings and future recommendations. Journal of Exposure Science and Environmental Epidemiology, 23(6), 654-659. https://doi.org/10.1038/jes.2013.62