Bayesian inference of heavy metals exposure in crayfish for assessing human non–carcinogenic health risk. (March 2023)

• Record Type: Journal Article
• Authors: Chu, Qi; Li, Ying; Wang, Xueli
• Is Part Of: Food and chemical toxicology. Volume 173(2023)
• Publication Date: 2023-03
• Subjects: Crayfish -- Heavy metals -- Bayesian models -- Probabilistic risk assessment; Toxicology -- Periodicals; Food poisoning -- Periodicals; Food Poisoning -- Periodicals; Toxicology -- Periodicals; Toxicologie -- Périodiques; Intoxications alimentaires -- Périodiques; Food poisoning; Toxicology; Periodicals; Electronic journals; 615.9
• DOI: 10.1016/j.fct.2022.113595
• Languages: English
• ISSNs: 0278-6915

Abstract

Abstract: Contaminant concentrations often presented left censorship that below the limit of detection (LOD), which may contain true zero values because of no residue. In this study, we analyzed the concentrations of lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), and chromium (Cr) in 391 crayfish samples collected from 24 provinces of China, modelled the concentrations with two different models in a Bayesian framework, and evaluated health risk by estimating the target hazard quotient (THQ). The highest concentration was associated with Cr in Northwest China (0.1753 ± 0.1520 mg/kg w. w.), and the minimum concentration was Cd in Southwest China (0.0052 ± 0.0144 mg/kg w. w.), all heavy metal concentrations were below their safety limits. The posterior means of not detect rates P 0 of Pb, Cd, Hg, As, and Cr obtained with two models were both nearly equal to the observed not detect rates (51.15%, 36.83%, 27.37%, 64.71%, and 43.73%, respectively). The posterior probabilistic density lines for Pb, Hg, and Cd concentrations obtained with two models were similar, and fitted the empirical distributions well. The posterior density lines of THQs showed that the non–carcinogenic risk of As and Hg were significant high. Moreover, Bayesian approach presented a better understanding of the percentage of population exposed to potential risk. Graphical abstract: Image 1 Highlights: Bayesian approach with two different mixture models was used to evaluate health risk. As and Hg posed significant health risk to consumers according to Bayesian approach. Censored log–normal model described health risk of As more appropriately. Probabilities of THQ for Hg and As exceeding 1 were more than for other metals.