| Title of paper:
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An intuitionistic fuzzy framework for human health risk assessment incorporating Monte Carlo uncertainty analysis
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| Author(s):
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Toncho Ivanov Boyukov  0000-0002-1349-4893
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| Faculty of Technical Sciences, Burgas State University "Prof. Dr. Asen Zlatarov", 1 Prof. Yakimov Str., 8010 Burgas, Bulgaria
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| toncho_b@abv.bg
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| Stefka Fidanova 0000-0002-8484-5849
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Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria
Centre of Excellence in Informatics and Information and Communication Technologies, Sofia, Bulgaria
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| stefka.fidanova@iict.bas.bg
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| Published in:
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Notes on Intuitionistic Fuzzy Sets, Volume 32 (2026), Number 2, pages 144–162
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| DOI:
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https://doi.org/10.7546/nifs.2026.32.2.144-162
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| Download:
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 PDF (272 Kb, File info)
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| 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.
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| Keywords:
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Health risk assessment, Monte Carlo simulation, Intuitionistic fuzzy sets, Uncertainty analysis, Multi-contaminant exposure, Hazard quotient.
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| AMS Classification:
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03E72.
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