Dataset-based assessment of heavy metal contamination in freshwater fishes and their health risks.
Xiao-Bo LiuCongtian LinYang-Yu WuHai-Ning HuangLi-Ting ZhuRu JiangQian-Sheng HuangPublished in: Environmental science and pollution research international (2022)
The ecological risks and health hazards of heavy metals pollution in Taihu Lake have received widespread concern. This study has developed a species-pollution dataset which includes a large amount of data on heavy metal pollution in Taihu fish. The heavy metal contamination poses a significant threat to human consumption, but no studies have been conducted to assess the risk of exposure to consumption of these fish and to make recommendations for their consumption. In this study, we systematically integrated the relevant data in the dataset, analyzed its contamination level using PI (single pollution index) and MPI (metal pollution index) models, and assessed health hazards of fish consumption using THQ (target hazard quotient) and ILCR (incremental lifetime cancer risk) models. Results showed that the contamination levels of heavy metals in fish varied in a feeding habit and living habit dependent manner. The risk of non-cancer health is the highest from consuming omnivorous fish, then from carnivorous and herbivorous fish. The ILCR model predicted that the long-term Taihu consumption of omnivorous fish may pose a potential carcinogenic risk, especially for children. In all, our study provided a comprehensive understanding on the risk of heavy metals in Taihu. Accordingly, it is recommended that children should try to choose herbivorous fish when consuming fish from Taihu Lake while avoiding long-term consumption of omnivorous fish.
Keyphrases
- heavy metals
- risk assessment
- health risk
- human health
- health risk assessment
- sewage sludge
- healthcare
- public health
- drinking water
- squamous cell carcinoma
- mental health
- young adults
- electronic health record
- endothelial cells
- climate change
- particulate matter
- air pollution
- lymph node metastasis
- health promotion
- data analysis
- social media