An intuitionistic fuzzy framework for human health risk assessment incorporating Monte Carlo uncertainty analysis

Vollständiger Abstract

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Human health risk assessment of environmental contamination is commonly based on deterministic or semi-probabilistic indices such as the hazard quotient (HQ) and hazard index (HI). Although widely used, these indicators have limited capacity to explicitly represent uncertainty and variability in exposure and toxicological parameters. Monte Carlo simulation is often employed to address this limitation by propagating uncertainty; however, probabilistic outputs alone do not fully support decision-making when multiple contaminants must be aggregated into a single interpretable metric. In this study, we propose a Monte Carlo-based intuitionistic fuzzy health risk assessment framework that integrates probabilistic exposure modeling with intuitionistic fuzzy set (IFS) theory. Hazard quotients obtained from Monte Carlo simulations are mapped to intuitionistic fuzzy membership, non-membership, and hesitation degrees using transparent threshold-based functions. Aggregation across multiple contaminants is performed using a component-wise weighted averaging scheme for the intuitionistic fuzzy components, yielding a site-level risk representation that preserves both risk magnitude and associated uncertainty. An entropy-based measure is further introduced to quantify uncertainty in the aggregated intuitionistic fuzzy assessment. The proposed framework enables enhanced interpretability and robust decision support under uncertainty, as demonstrated through a numerical example. By explicitly combining probabilistic uncertainty propagation with intuitionistic fuzzy modeling, this approach advances classical health risk assessment methodologies and provides a structured basis for risk-informed environmental decision-making.