Probabilistic Sensitivity Analysis of Public Health Risk Assessment from Contaminated Soil

Abstract

Recently, there has been a growing trend toward using stochastic (probabilistic) methods in ecological and public health risk assessment. These methods are favored because they overcome the problem of compounded conservatism and allow the systematic consideration of uncertainty and variability typically encountered in risk assessment. This article demonstrates a new methodology for the analysis of uncertainty in risk assessment using the first-order reliability method (FORM). The reliability method is formulated such that the probability that incremental lifetime cancer risk exceeds a predefined threshold level is calculated. Furthermore, the stochastic sensitivity of this probability with respect to the random variables is provided. The emphasis is on exploring the different types of probabilistic sensitivity obtained through the reliability analysis. The method is applied to a case study given by Thompson et al. (1992) on cancer risk resulting from dermal contact with benzo(a)pyrene (BaP)-contaminated soils. The reliability results matched those of the Monte Carlo simulation method. On average, the Monte Carlo simulation method required about 35 times as many function evaluations as that of FORM to calculate the probability of exceeding the target risk level. The analysis emphasizes the significant impact that the uncertainty in cancer potency factor has on the probabilistic modeling results compared with other parameters.

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