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

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  • Anne E. Smith
  • Will Gans

Abstract

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.

Suggested Citation

  • Anne E. Smith & Will Gans, 2015. "Enhancing the Characterization of Epistemic Uncertainties in PM2.5 Risk Analyses," Risk Analysis, John Wiley & Sons, vol. 35(3), pages 361-378, March.
  • Handle: RePEc:wly:riskan:v:35:y:2015:i:3:p:361-378
    DOI: 10.1111/risa.12236
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    References listed on IDEAS

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    1. Greven, Sonja & Dominici, Francesca & Zeger, Scott, 2011. "An Approach to the Estimation of Chronic Air Pollution Effects Using Spatio-Temporal Information," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 396-406.
    2. Neal Fann & Amy D. Lamson & Susan C. Anenberg & Karen Wesson & David Risley & Bryan J. Hubbell, 2012. "Estimating the National Public Health Burden Associated with Exposure to Ambient PM2.5 and Ozone," Risk Analysis, John Wiley & Sons, vol. 32(1), pages 81-95, January.
    3. Art Fraas & Randall Lutter, 2013. "Uncertain Benefits Estimates for Reductions in Fine Particle Concentrations," Risk Analysis, John Wiley & Sons, vol. 33(3), pages 434-449, March.
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    Cited by:

    1. Isadora Antoniano‐Villalobos & Emanuele Borgonovo & Sumeda Siriwardena, 2018. "Which Parameters Are Important? Differential Importance Under Uncertainty," Risk Analysis, John Wiley & Sons, vol. 38(11), pages 2459-2477, November.
    2. Anne E. Smith, 2018. "Setting Air Quality Standards for PM2.5: A Role for Subjective Uncertainty in NAAQS Quantitative Risk Assessments?," Risk Analysis, John Wiley & Sons, vol. 38(11), pages 2318-2339, November.
    3. Anne E. Smith & Garrett Glasgow, 2018. "Integrated Uncertainty Analysis for Ambient Pollutant Health Risk Assessment: A Case Study of Ozone Mortality Risk," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 163-176, January.

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