Integrating Uncertainty and Interindividual Variability in Environmental Risk Assessment

Kenneth T. Bogen · Robert C. Spear

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Risk and Safety Analysis Risk Perception and Management Statistical Methods in Epidemiology

Abstract

An integrated, quantitative approach to incorporating both uncertainty and interindividual variability into risk prediction models is described. Individual risk R is treated as a variable distributed in both an uncertainty dimension and a variability dimension, whereas population risk I (the number of additional cases caused by R) is purely uncertain. I is shown to follow a compound Poisson-binomial distribution, which in low-level risk contexts can often be approximated well by a corresponding compound Poisson distribution. The proposed analytic framework is illustrated with an application to cancer risk assessment for a California population exposed to 1,2-dibromo-3-chloropropane from ground water.

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1983Risk Assessment in the Federal Government: Managing the Process

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1994Recommended Distributions for Exposure Factors Frequently Used in Health Risk Assessment 1994The Benefits of Probabilistic Exposure Assessment: Three Case Studies Involving Contaminated Air, Water, and Soil1 1999A new methodology for the estimation of total uncertainty in computational simulation 1992The Effect of Neglecting Correlations When Propagating Uncertainty and Estimating the Population Distribution of Risk 1996Characterizing, simulating, and analyzing variability and uncertainty: An illustration of methods using an air toxics emissions example 2009A Comparative Approach for Ranking Contaminated Sites Based on the Risk Assessment Paradigm Using Fuzzy PROMETHEE 1995Updating Uncertainty in an Integrated Risk Assessment: Conceptual Framework and Methods 2003Multi-criteria decision analysis with probabilistic risk assessment for the management of contaminated ground water

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