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Enhancing the Characterization of Epistemic Uncertainties in PM2.5 Risk Analyses

机译：在PM2.5风险分析中增强认知不确定性的表征

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摘要

The Environmental Benefits Mapping and Analysis Program (BenMAP) is a software tool developed by the U.S. Environmental Protection Agency (EPA) that is widely used inside and outside of EPA to produce quantitative estimates of public health risks from fine particulate matter (PM2.5). This article discusses the purpose and appropriate role of a risk analysis tool to support risk management deliberations, and evaluates the functions of BenMAP in this context. It highlights the importance in quantitative risk analyses of characterization of epistemic uncertainty, or outright lack of knowledge, about the true risk relationships being quantified. This article describes and quantitatively illustrates sensitivities of PM2.5 risk estimates to several key forms of epistemic uncertainty that pervade those calculations: the risk coefficient, shape of the risk function, and the relative toxicity of individual PM2.5 constituents. It also summarizes findings from a review of U.S.-based epidemiological evidence regarding the PM2.5 risk coefficient for mortality from long-term exposure. That review shows that the set of risk coefficients embedded in BenMAP substantially understates the range in the literature. We conclude that BenMAP would more usefully fulfill its role as a risk analysis support tool if its functions were extended to better enable and prompt its users to characterize the epistemic uncertainties in their risk calculations. This requires expanded automatic sensitivity analysis functions and more recognition of the full range of uncertainty in risk coefficients.
机译：环境效益图和分析程序（BenMAP）是由美国环境保护署（EPA）开发的软件工具，在EPA内外广泛使用，以产生细颗粒物（PM2.5）对公共健康风险的定量估计。 。本文讨论了支持风险管理审议的风险分析工具的目的和适当角色，并在此背景下评估了BenMAP的功能。它强调了对定量不确定性或完全缺乏对真实风险关系进行量化的认知不确定性表征的定量风险分析的重要性。本文描述并定量说明了PM2.5风险估计值对贯穿这些计算的几种关键形式的认知不确定性的敏感性：风险系数，风险函数的形状以及单个PM2.5成分的相对毒性。它还总结了来自美国的流行病学证据回顾中有关长期暴露致死的PM2.5风险系数的发现。该评论表明，嵌入BenMAP中的风险系数集大大低估了文献中的范围。我们得出的结论是，如果BenMAP的功能得到扩展，以更好地启用和提示其用户在其风险计算中表征认知不确定性，它将更加有用地发挥其作为风险分析支持工具的作用。这要求扩展自动灵敏度分析功能，并更多地认识到风险系数不确定性的全部范围。

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