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Exposure prediction approaches used in air pollution epidemiology studies: Keyfindings and future recommendations
Citation:
Baxter, L., K. Dionisio, J. Burke, S. Sarnat, J. Sarnat, N. Hodas, D. Rich, B. Turpin, R. Jones, E. Mannshardt, N. Kumar, S. Beevers, AND H. Ozkaynak. Exposure prediction approaches used in air pollution epidemiology studies: Keyfindings and future recommendations. Journal of Exposure Science and Environmental Epidemiology . Nature Publishing Group, London, Uk, 23(6):654-659, (2013).
Impact/Purpose:
The National Exposure Research Laboratory’s (NERL’s) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA’s mission to protect human health and the environment. HEASD’s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA’s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
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.
URLs/Downloads:
FINAL FINAL BAXTER LESSONS LEARNED - AFTER INTERNAL REVIEW.PDF (PDF, NA pp, 193.794 KB, about PDF)Journal of Exposure Science and Environmental Epidemiology
