Probabilistic human health risk assessment of PCDD/Fs near municipal solid-waste incinerator using Monte Carlo analysis coupled with triangular fuzzy numbers

文献类型： 外文期刊

第一作者： Fan, Qing-fang

作者： Fan, Qing-fang;Lin, Bi-gui;Fan, Qing-fang;Liu, Fang;Wei, Chao-xian;Liu, Bei-bei;Lin, Bi-gui;Liu, Li -jun;Zhang, Zong-yao;Chen, Xi-chao;Liu, Fang;Xie, Yi;Gao, Zhi-qiang;Lin, Bi-gui;Chen, Xi-chao

作者机构：

关键词： PCDD/Fs; Probabilistic risk assessment; Monte Carlo stochastic simulation; Triangular fuzzy numbers; Municipal solid -waste incinerator

期刊名称：ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY （ 影响因子：6.8； 五年影响因子：6.9 ）

ISSN： 0147-6513

年卷期： 2024 年 274 卷

页码：

收录情况： SCI

摘要： PCDD/Fs are dioxins produced by waste incineration and pose risks to human health. We aimed to detail the health risks of airborne and soil PCDD/Fs near a municipal solid-waste incinerator (MSWI) for the surrounding population and develop a new model that improves upon existing methods. Thus, we conducted field sampling and then investigated a MSWI in the Pearl River Delta (2016-2018). Our results showed that the carcinogenic and non-carcinogenic risk values of PCDD/Fs exposed to residents in nearby areas were acceptable, with hazard index (HI) values lower than 1.0 and a total carcinogenic risk lower than 1.0E-6. Notably, the results raised concerns regarding higher non-carcinogenic risks in children than in adults. Comparative analysis of the frequency accumulation diagram, accumulated probability risk, and the absolute value of error (delta) between the 95% confidence interval (CI) and the 90% CI of the Monte Carlo stochastic simulation-triangular fuzzy number (MCSS-TFN) and the MCSS model, respectively, demonstrated that the MCSS-TFN exhibited less uncertainty than the MCSS model, regardless of the health risk value of PCDD/Fs in ambient air or in soil. This observation underscores the superiority of the MCSS-TFN model over other models in assessing the health risks associated with PCDD/Fs in situations with limited data. Our new method overcomes the limited dataset size and high uncertainty in assessing the health risks of dioxin substances, providing a more comprehensive understanding of their associated health risks than MCSS models.

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