Journal
JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY
Volume 29, Issue 2, Pages 227-237Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41370-018-0053-x
Keywords
Air pollution; Epidemiology; Exposure modeling; Personal exposure; Population-based studies
Funding
- U.S Environmental Protection Agency [RD831697]
- EPA [RD-83479601-0, RD-83830001]
- National Heart, Lung, and Blood Institute [N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC95166, N01-HC-95167, N01-HC-95168, N01-HC-95169]
- National Center for Research Resources (NCRR) [UL1-TR-000040, UL1-RR-025005]
- National Institute of Environmental Health Sciences [P30ES007033]
- NCRR [UL1RR033176]
- National Center for Advancing Translational Sciences [UL1TR000124]
- [R01-HL-071051]
- [R01-HL-071205]
- [R01-HL-071250]
- [R01-HL-071251]
- [R01-HL-071252]
- [R01-HL-071258]
- [R01-HL071259]
- [UL1-TR-001079]
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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) mu g/m(3). 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 mu g/m(3) 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.
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