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Enhancing the Characterization of Epistemic Uncertainties in PM 2.5 Risk Analyses
Author(s) -
Smith Anne E.,
Gans Will
Publication year - 2015
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.12236
Subject(s) - risk assessment , context (archaeology) , risk analysis (engineering) , agency (philosophy) , uncertainty quantification , risk management , range (aeronautics) , set (abstract data type) , function (biology) , uncertainty analysis , term (time) , computer science , management science , epistemology , engineering , business , geography , machine learning , physics , philosophy , computer security , archaeology , finance , quantum mechanics , aerospace engineering , evolutionary biology , biology , programming language , simulation
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 (PM 2.5 ). 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 PM 2.5 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 PM 2.5 constituents. It also summarizes findings from a review of U.S.‐based epidemiological evidence regarding the PM 2.5 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.