Most public health risk assessments assume and combine a series of average, conservative, and worst-case values to derive a conservative point estimate of risk. This procedure has major limitations. This paper demonstrates a new methodology for extended uncertainty analyses in public health risk assessments using Monte Carlo techniques. The extended method begins as do some conventional methods--with the preparation of a spreadsheet to estimate exposure and risk. This method, however, continues by modeling key inputs as random variables described by probability density functions (PDFs). Overall, the technique provides a quantitative way to estimate the probability distributions for exposure and health risks within the validity of the model used. As an example, this paper presents a simplified case study for children playing in soils contaminated with benzene and benzo(a)pyrene (BaP).

Risk assessment is presented as a way of examining risks so that they may be better avoided, reduced, or otherwise managed. Risk implies uncertainty, so that risk assessment is largely concerned with uncertainty and hence with a concept of probability that is hard to grasp. The results of even the simplest risk assessments need to be compared with similar assessments of commonplace situations to give them some meaning. We compare and contrast some risk estimates to display their similarities and differences.

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For guidance in decisions on how to safeguard humans from carcinogens, it is necessary to use data on carcinogenesis in animals. This paper disucsses how such data, combined with human experience, may be used quantitatively in such decisions. It is demonstrated empirically that good correlations exist between different species for suitably defined carcinogenic potencies for various chemicals. This allows sufficient accuracy in extrapolating form animal data to human risk to support a logical scheme for the evaluation of such risks. Some recommendations for future research are given.

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