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A Probabilistic Framework for Multimedia Dioxin Risk Assessment in Susceptible Populations
Kuan-Yi Chen1, Chien-Cheng Jung1, Ken-Hui Chang2
1Department of Public Health, China Medical University, Taichung 406040, Taiwan.
Toxics
|July 28, 2026
Summary
A new framework reduces uncertainty in environmental risk assessments for vulnerable groups by accounting for population variability. This probabilistic approach, using dioxin emissions as an example, provides more accurate cancer risk evaluations for susceptible populations.
Area of Science:
- Environmental Health
- Risk Assessment
- Toxicology
Background:
- Multimedia risk assessment faces challenges in addressing uncertainty and population variability.
- Susceptible populations, particularly children, require specialized consideration due to early-life susceptibility.
- Dioxin emissions from municipal solid waste incinerators present a relevant case study for environmental exposure assessment.
Purpose of the Study:
- To develop a probabilistic framework for multimedia risk assessment that addresses uncertainty and captures population variability.
- To evaluate cancer risks for susceptible populations, incorporating age-dependent adjustment factors (ADAFs).
- To compare deterministic and probabilistic assessment approaches for quantifying risk.
Main Methods:
- Integrated emission estimation, AERMOD dispersion modeling, and MEPAS multimedia modeling.
- Utilized dose-based slope factors and ADAFs for early-life susceptibility.
- Employed bivariate Monte Carlo simulation to incorporate inter-individual variability (IR/BW, CR/BW).
Main Results:
- Probabilistic risk assessment with ADAF adjustment significantly increased P95 risks compared to deterministic approaches.
- For the general population, risks increased 2.54-fold after ADAF adjustment and an additional 4.71-fold with Monte Carlo simulation.
- For school children, risks increased 4.35-fold after ADAF adjustment and an additional 2.1-fold with Monte Carlo simulation.
- Analyzed uncertainty in gas-particle partitioning, ADAF adjustment, and inhalation slope factor extrapolation.
Conclusions:
- The proposed probabilistic framework effectively reduces uncertainty and quantifies variability in multimedia risk assessments.
- Incorporating ADAFs and inter-individual variability is crucial for accurate risk evaluation, especially for susceptible populations.
- The stepwise increase in risk across assessment approaches highlights the framework's utility in refining exposure and health impact analyses.
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