Probabilistic-fuzzy health risk modeling

Abstract

Health risk analysis of multi-pathway exposure to contaminated water involves the use of mechanistic models that include many uncertain and highly variable parameters. Currently, the uncertainties in these models are treated using statistical approaches. However, not all uncertainties in data or model parameters are due to randomness. Other sources of imprecision that may lead to uncertainty include scarce or incomplete data, measurement error, data obtained from expert judgment, or subjective interpretation of available information. These kinds of uncertainties and also the non-random uncertainty cannot be treated solely by statistical methods. In this paper we propose the use of fuzzy set theory together with probability theory to incorporate uncertainties into the health risk analysis. We identify this approach as probabilistic-fuzzy risk assessment (PFRA).Based on the form of available information, fuzzy set theory, probability theory, or a combination of both can be used to incorporate parameter uncertainty and variability into mechanistic risk assessment models. In this study, tap water concentration is used as the source of contamination in the human exposure model. Ingestion, inhalation and dermal contact are considered as multiple exposure pathways. The tap water concentration of the contaminant and cancer potency factors for ingestion, inhalation and dermal contact are treated as fuzzy variables while the remaining model parameters are treated using probability density functions. Combined utilization of fuzzy and random variables produces membership functions of risk to individuals at different fractiles of risk as well as probability distributions of risk for various alpha-cut levels of the membership function.The proposed method provides a robust approach in evaluating human health risk to exposure when there is both uncertainty and variability in model parameters. PFRA allows utilization of certain types of information which have not been used directly in existing risk assessment methods.

Can Fuzzy Logic Bring Complex Environmental Problems into Focus?

“…Ferson has noted that fuzzy arithmetic may not be appropriate for routine use in risk assessments concerned primarily with variability, because it can fail to capture the range of values in complex data sets and the correlations among parameters (9). However, recent efforts treat both variability and uncertainty in a fuzzy-logic approach to risk assessment (10,11). In considering health-based groundwater remediation, Ozbek and Pinder used statements and preferences of practicing toxicologists to construct fuzzy rules that relate a benzene exposure pattern to carcinogenic effects (10).…”

“…Intake variability is typically represented by variability distributions in other risk assessments, but they used fuzzy-logic methods to describe the variation of both intake and toxicological susceptibility among exposed individuals. More recently, Kentel and Aral combined fuzzy set theory with standard probabilistic methods into a procedure they call probabilistic-fuzzy risk assessment, which addresses both uncertainty and variability in their model of health risks from tap water contaminated by tetrachloroethylene (11). Although probabilistic assessments based on tools such as Monte Carlo methods are analogous to assessments based on fuzzy logic, these two techniques differ significantly both in approach and in interpretation of results.…”

A Hybrid Method to Deal with Aleatory and Epistemic Uncertainty in Risk Assessment

“…The aim of risk assessment [10] is to estimate the severity and likelihood of harm to human health from exposure to a substance or activity that under plausible circumstances can cause to human health. The assessment is performed using model and a model is a function of parameters which are usually affected by aleatory and epistemic uncertainty.…”

“…A few researchers have addressed the issue of combining probabilistic and possibilistic representation of aleatory and epistemic uncertainty respectively within the same computation of risk. For example, Baudrit, Dubois, Fargier [2], [3], Baudrit, Dubois [4], Guyonnet, Bourgine, Dubois, Fargier, Côme and Chilès [8], Guyonnet, Côme, Perrochet, Parriaux [9], kentel and Aral [10] have proposed hybrid method for join handling of probability and possibility distributions. The hybrid method proposed in [8] combines the random sampling of probability distribution functions (PDFs) with fuzzy interval analysis on the α-cuts.…”

“…In [5] authors showed how the theory of evidence, also called theory of Dempster-Shafer (or theory of belief functions; Shafer, 1976) could provide a simple and rigorous answer to the problem of summarizing the results of the hybrid computation for comparison with a tolerance threshold. In the hybrid approach proposed in [10] combined utilization of fuzzy and random variables produces membership functions of risk to individuals at different fractiles of risk as well as probability distributions of risk for various alpha-cut levels of the membership function.…”