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Evaluation and recommendation of sensitivity analysis methods for application to Stochastic Human Exposure and Dose Simulation models.适用于随机人类暴露和剂量模拟模型的敏感性分析方法的评估与推荐。
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8
A modeling framework for estimating children's residential exposure and dose to chlorpyrifos via dermal residue contact and nondietary ingestion.一种用于估算儿童通过皮肤接触残留和非饮食摄入途径接触毒死蜱及其剂量的建模框架。
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使用索伯尔方法对人体暴露模型进行的敏感性分析。

A sensitivity analysis of a human exposure model using the Sobol method.

作者信息

Langstaff John, Glen Graham, Holder Chris, Graham Stephen, Isaacs Kristin

机构信息

Office of Air Quality Planning and Standards (OAQPS), U.S. EPA, 109 T.W. Alexander Drive, Research Triangle Park, Durham, NC 27711, USA.

ICF, Inc., Durham, NC 27713, USA.

出版信息

Stoch Environ Res Risk Assess. 2022 May 4;36:3945-3960. doi: 10.1007/s00477-022-02238-7.

DOI:10.1007/s00477-022-02238-7
PMID:36733914
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9888025/
Abstract

The Air Pollutants Exposure Model (APEX) is a stochastic population-based inhalation exposure model which (along with its earlier version called pNEM) has been used by the U.S. Environmental Protection Agency (EPA) for over 30 years for assessment of human exposure to airborne pollutants. This study describes the application of a variance decomposition-based sensitivity analysis using the Sobol method to elucidate the key APEX inputs and processes that affect variability in exposure and dose for the simulated population. Understanding APEX's sensitivities to these inputs helps not only the model user but also the EPA in prioritizing limited resources towards data-collection and analysis efforts for the most influential variables, in order to maintain the quality and defensibility of the simulation results. This analysis examines exposure to ozone of children ages 5-18 years. The results show that selection of activity diaries and microenvironmental parameters (including air-exchange rate and decay rate) are the most influential to estimated exposure and dose, their aggregate main-effect indices (MEIs) equaling 0.818 (out of a maximum of 1.0) for daily-average ozone exposure and 0.469 for daily-average inhaled ozone dose. The modeled person's home location, sampled from national Census data, has a modest influence on exposure (MEI = 0.079 for daily averages), while age, sex, and body mass, also sampled from Census and other survey data, have modest influences on inhaled dose (aggregate MEI = 0.307). The sensitivity analysis also plays a quality-assurance role by evaluating the sensitivities against our knowledge of the physical properties of the model.

摘要

空气污染物暴露模型(APEX)是一种基于随机人群的吸入暴露模型,美国环境保护局(EPA)已使用该模型(及其早期版本pNEM)30多年来评估人类对空气传播污染物的暴露情况。本研究描述了使用索博尔方法基于方差分解的敏感性分析的应用,以阐明影响模拟人群暴露和剂量变异性的关键APEX输入和过程。了解APEX对这些输入的敏感性不仅有助于模型用户,也有助于EPA将有限资源优先用于最具影响力变量的数据收集和分析工作,以保持模拟结果的质量和可信度。该分析考察了5至18岁儿童的臭氧暴露情况。结果表明,活动日记和微环境参数(包括空气交换率和衰减率)的选择对估计的暴露和剂量影响最大,它们的总主效应指数(MEI)在日平均臭氧暴露中为0.818(最大值为1.0),在日平均吸入臭氧剂量中为0.469。从全国人口普查数据中抽样得到的建模者的家庭位置对暴露有适度影响(日平均值的MEI = 0.079),而同样从人口普查和其他调查数据中抽样得到的年龄、性别和体重对吸入剂量有适度影响(总MEI = 0.307)。敏感性分析还通过根据我们对模型物理特性的了解评估敏感性来发挥质量保证作用。