Liu L J, Delfino R, Koutrakis P
Department of Environmental Health Sciences, University of South Carolina, Columbia 29208, USA.
Environ Health Perspect. 1997 Jan;105(1):58-65. 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.
在南加州的一个社区开展了一项臭氧暴露评估研究。1994年春季和秋季分别使用哈佛臭氧被动采样器对22名和18名受试者组成的队列进行了为期8周的监测。臭氧暴露变量包括12小时个人臭氧测量值、连续紫外线光度计和12小时哈佛主动监测仪的室外固定臭氧测量值以及时间活动信息。结果显示,个人臭氧暴露水平平均为室外固定臭氧水平的四分之一,这归因于大部分时间待在室内。室外测量值并不能很好地预测个人臭氧水平。随机效应一般线性模型分析表明,个人暴露测量值的方差在很大程度上由随机误差(59 - 82%)导致,其次是个体间随机效应(9 - 18%)和日间随机效应(9 - 23%)。微环境模型在不同季节表现不同,春季队列的回归模型的决定系数(R²)是秋季队列的两倍(R² = 0.21对0.09)。在微环境模型中考虑到与固定监测点的距离、海拔和交通情况后,秋季个人暴露数据的调整后R²几乎增加了一倍。预测能力较低主要是由于室外臭氧明显的空间变化以及臭氧测量和时间活动记录中的误差(特别是在记录空调使用情况方面)。这项研究突出了在流行病学环境中臭氧暴露错误分类的程度,并提出了一种方法来减少评估空气污染对健康影响时的暴露不确定性。