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National Institute of Environmental Health Sciences

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Your Environment. Your Health.

Publication Detail

Title: Estimating ambient-origin PM2.5 exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis.

Authors: Miller, Kristin A; Spalt, Elizabeth W; Gassett, Amanda J; Curl, Cynthia L; Larson, Timothy V; Avol, Ed; Allen, Ryan W; Vedal, Sverre; Szpiro, Adam A; Kaufman, Joel D

Published In J Expo Sci Environ Epidemiol, (2019 03)

Abstract: OBJECTIVES: We aim to characterize the qualities of estimation approaches for individual exposure to ambient-origin fine particulate matter (PM2.5), for use in epidemiological studies. METHODS: The analysis incorporates personal, home indoor, and home outdoor air monitoring data and spatio-temporal model predictions for 60 participants from the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). We compared measurement-based personal PM2.5 exposure with several measured or predicted estimates of outdoor, indoor, and personal exposures. RESULTS: The mean personal 2-week exposure was 7.6 (standard deviation 3.7) µg/m3. Outdoor model predictions performed far better than outdoor concentrations estimated using a nearest-monitor approach (R = 0.63 versus R = 0.43). Incorporating infiltration indoors of ambient-derived PM2.5 provided better estimates of the measurement-based personal exposures than outdoor concentration predictions (R = 0.81 versus R = 0.63) and better scaling of estimated exposure (mean difference 0.4 versus 5.4 µg/m3 higher than measurements), suggesting there is value to collecting data regarding home infiltration. Incorporating individual-level time-location information into exposure predictions did not increase correlations with measurement-based personal exposures (R = 0.80) in our sample consisting primarily of retired persons. CONCLUSIONS: This analysis demonstrates the importance of incorporating infiltration when estimating individual exposure to ambient air pollution. Spatio-temporal models provide substantial improvement in exposure estimation over a nearest monitor approach.

PubMed ID: 30166581 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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