Ozone Exposure Assessment in a Southern California Community

• Published in: Environmental health perspectives Vol. 105; no. 1; pp. 58 - 65
• Main Authors: Liu, Lee-Jane Sally, Delfino, Ralph, Koutrakis, Petros
• Format: Journal Article
• Language: English
• Published: United States National Institute of Environmental Health Sciences. National Institutes of Health. Department of Health, Education and Welfare 01.01.1997
• Subjects: Adolescent; Adult; Air pollution; California; Child; Environmental health; Environmental Monitoring; Female; Humans; Male; Middle Aged; Modeling; Models, Statistical; Oxidants, Photochemical - analysis; Ozone; Ozone - analysis; Predictive modeling; Regression analysis; Spatial models; Statistical variance; Traffic; Waste management; California
• ISSN: 0091-6765; 1552-9924
• DOI: 10.1289/ehp.9710558

An ozone exposure assessment study was conducted in a Southern California community. The Harvard ozone passive sampler was used to monitor cohorts of 22 and 18 subjects for 8 weeks during the spring and fall of 1994, respectively. Ozone exposure variables included 12-hr personal O3 measurements, stationary outdoor O3 measurements from a continuous UV photometer and from 12-hr Harvard active monitors, and time-activity information. Results showed that personal O3 exposure levels averaged one-fourth of outdoor stationary O3 levels, attributable to high percentages of time spent indoors. Personal O3 levels were not predicted well by outdoor measurements. A random-effect general linear model analysis indicated that variance in personal exposure measurements was largely accounted for by random error (59-82%), followed by inter-subject (9-18%) and between-day (9-23%) random effects. The microenvironmental model performs differently by season, with the regression model for spring cohorts exhibiting two times the R2 of the fall cohorts (R2=0.21 vs. 0.09). When distance from the stationary monitoring site, elevation, and traffic are taken into account in the microenvironmental models, the adjusted R2 increased almost twofold for the fall personal exposure data. The low predictive power is due primarily to the apparent spatial variation of outdoor O3 and errors in O3 measurements and in time-activity records (particularly in recording the use of air conditioning). This study highlights the magnitude of O3 exposure misclassification in epidemiological settings and proposes an approach to reduce exposure uncertainties in assessing air pollution health effects.