Ozkaynak Halûk, Frey H Christopher, Burke Janet, Pinder Robert W
U.S. Environmental Protection Agency, National Exposure Research Laboratory (E205-01), Research Triangle Park, NC 27711, USA.
Atmos Environ (1994). 2009 Mar 1;43(9):1641-1649. doi: 10.1016/j.atmosenv.2008.12.008.
Quantitative assessment of human exposures and health effects due to air pollution involve detailed characterization of impacts of air quality on exposure and dose. A key challenge is to integrate these three components on a consistent spatial and temporal basis taking into account linkages and feedbacks. The current state-of-practice for such assessments is to exercise emission, meteorology, air quality, exposure, and dose models separately, and to link them together by using the output of one model as input to the subsequent downstream model. Quantification of variability and uncertainty has been an important topic in the exposure assessment community for a number of years. Variability refers to differences in the value of a quantity (e.g., exposure) over time, space, or among individuals. Uncertainty refers to lack of knowledge regarding the true value of a quantity. An emerging challenge is how to quantify variability and uncertainty in integrated assessments over the source-to-dose continuum by considering contributions from individual as well as linked components. For a case study of fine particulate matter (PM(2.5)) in North Carolina during July 2002, we characterize variability and uncertainty associated with each of the individual concentration, exposure and dose models that are linked, and use a conceptual framework to quantify and evaluate the implications of coupled model uncertainties. We find that the resulting overall uncertainties due to combined effects of both variability and uncertainty are smaller (usually by a factor of 3-4) than the crudely multiplied model-specific overall uncertainty ratios. Future research will need to examine the impact of potential dependencies among the model components by conducting a truly coupled modeling analysis.
对空气污染导致的人体暴露和健康影响进行定量评估，涉及对空气质量对暴露和剂量影响的详细表征。一个关键挑战是在考虑到各种联系和反馈的情况下，在一致的时空基础上整合这三个组成部分。目前此类评估的实际做法是分别运行排放、气象、空气质量、暴露和剂量模型，并通过将一个模型的输出作为后续下游模型的输入来将它们联系起来。多年来，变异性和不确定性的量化一直是暴露评估领域的一个重要课题。变异性是指一个量（如暴露）在时间、空间或个体之间的值的差异。不确定性是指对一个量的真实值缺乏了解。一个新出现的挑战是如何通过考虑各个组成部分以及相互关联的组成部分的贡献，在从源到剂量的连续过程中量化综合评估中的变异性和不确定性。对于2002年7月北卡罗来纳州细颗粒物（PM(2.5)）的一个案例研究，我们表征了与相互关联的各个浓度、暴露和剂量模型相关的变异性和不确定性，并使用一个概念框架来量化和评估耦合模型不确定性的影响。我们发现，由于变异性和不确定性的综合影响而产生的总体不确定性比粗略相乘的特定模型总体不确定性比率要小（通常小3至4倍）。未来的研究需要通过进行真正的耦合建模分析来研究模型组件之间潜在依赖性的影响。
