Yeh Sonia, Small Mitchell J
Department of Engineering and Public Policy, Baker Hall 129, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890, USA.
J Expo Anal Environ Epidemiol. 2002 Nov;12(6):389-403. doi: 10.1038/sj.jea.7500240.
This paper examines the link between the ambient level of particulate pollution and subsequent human health effects and various sources of uncertainty when total exposure is taken into consideration. The exposure simulation model statistically simulates daily personal total exposure to ambient PM and nonambient PM generated from indoor sources. It incorporates outdoor-indoor penetration of PM, contributions of PM from indoor sources, and time-activity patterns for target groups of the population. The model is illustrated for Los Angeles County using recent 1997 monitoring data for both PM(10) and PM(2.5). The results indicate that, on average, outdoor-source PM contributes about 20-25% of the total PM exposure to Los Angeles County individuals not exposed to environmental tobacco smoking (ETS), and about 15% for those who are exposed to ETS. The model computes both the fractional contribution of outdoor concentrations to total exposure and the effect of exposure uncertainties on the estimated slope of the (linear) concentration-response curve in time-series studies for PM health effects. The latter considers the effects of measurement and misclassification error on PM epidemiological time-series studies. The paper compares the predictions of a conventional PM epidemiological model, based solely on ambient concentration measurements at a central monitoring station, and an exposure simulation model, which considers the quantitative relationship between central-monitoring PM concentrations and total individual exposures to particulate matter. The results show that the effects of adjusting from outdoor concentrations to personal exposures and correcting dose-response bias are nearly equal, so that roughly the same premature mortalities associated with short-term exposure to both ambient PM(2.5) and PM(10) in Los Angeles County are predicted with both models. The uncertainty in the slope of the concentration-response curve in the time-series studies is the single most important source of uncertainty in both the ambient- and the exposure-health model.
本文研究了颗粒物污染的环境水平与后续人体健康影响之间的联系，以及在考虑总暴露量时的各种不确定性来源。暴露模拟模型通过统计方法模拟个人每日对环境颗粒物（PM）和室内源产生的非环境颗粒物的总暴露量。该模型纳入了颗粒物的室外-室内穿透率、室内源颗粒物的贡献以及目标人群的时间-活动模式。利用1997年洛杉矶县最近的PM(10)和PM(2.5)监测数据对该模型进行了说明。结果表明，平均而言，对于未接触环境烟草烟雾（ETS）的洛杉矶县居民，室外源颗粒物对总PM暴露量的贡献约为20%-25%，而对于接触ETS的居民，这一比例约为15%。该模型计算了室外浓度对总暴露量的分数贡献，以及暴露不确定性对PM健康影响时间序列研究中（线性）浓度-反应曲线估计斜率的影响。后者考虑了测量和错误分类误差对PM流行病学时间序列研究的影响。本文比较了仅基于中央监测站环境浓度测量的传统PM流行病学模型和考虑中央监测PM浓度与个体对颗粒物总暴露量之间定量关系的暴露模拟模型的预测结果。结果表明，从室外浓度调整到个人暴露量以及校正剂量-反应偏差的效果几乎相同，因此两种模型预测的与洛杉矶县短期暴露于环境PM(2.5)和PM(10)相关的过早死亡率大致相同。时间序列研究中浓度-反应曲线斜率的不确定性是环境模型和暴露-健康模型中最重要的单一不确定性来源。
