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Zhang Z, Lou S, Liu S, Zhou X, Zhou F, Yang Z, Chen S, Zou Y, Radnaeva LD, Nikitina E, Fedorova IV. Potential risk assessment and occurrence characteristic of heavy metals based on artificial neural network model along the Yangtze River Estuary, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:32091-32110. [PMID: 38648002 DOI: 10.1007/s11356-024-33400-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 04/16/2024] [Indexed: 04/25/2024]
Abstract
Pollution from heavy metals in estuaries poses potential risks to the aquatic environment and public health. The complexity of the estuarine water environment limits the accurate understanding of its pollution prediction. Field observations were conducted at seven sampling sites along the Yangtze River Estuary (YRE) during summer, autumn, and winter 2021 to analyze the concentrations of seven heavy metals (As, Cd, Cr, Pb, Cu, Ni, Zn) in water and surface sediments. The order of heavy metal concentrations in water samples from highest to lowest was Zn > As > Cu > Ni > Cr > Pb > Cd, while that in surface sediments samples was Zn > Cr > As > Ni > Pb > Cu > Cd. Human health risk assessment of the heavy metals in water samples indicated a chronic and carcinogenic risk associated with As. The risks of heavy metals in surface sediments were evaluated using the geo-accumulation index (Igeo) and potential ecological risk index (RI). Among the seven heavy metals, As and Cd were highly polluted, with Cd being the main contributor to potential ecological risks. Principal component analysis (PCA) was employed to identify the sources of the different heavy metals, revealing that As originated primarily from anthropogenic emissions, while Cd was primarily from atmospheric deposition. To further analyze the influence of water quality indicators on heavy metal pollution, an artificial neural network (ANN) model was utilized. A modified model was proposed, incorporating biochemical parameters to predict the level of heavy metal pollution, achieving an accuracy of 95.1%. This accuracy was 22.5% higher than that of the traditional model and particularly effective in predicting the maximum 20% of values. Results in this paper highlight the pollution of As and Cd along the YRE, and the proposed model provides valuable information for estimating heavy metal pollution in estuarine water environments, facilitating pollution prevention efforts.
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Affiliation(s)
- Zhirui Zhang
- Department of Hydraulic Engineering, Tongji University, Shanghai, 200092, China
| | - Sha Lou
- Department of Hydraulic Engineering, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, Shanghai, 200092, China.
| | - Shuguang Liu
- Department of Hydraulic Engineering, Tongji University, Shanghai, 200092, China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, Shanghai, 200092, China
| | - Xiaosheng Zhou
- Department of Hydraulic Engineering, Tongji University, Shanghai, 200092, China
| | - Feng Zhou
- Department of Hydraulic Engineering, Tongji University, Shanghai, 200092, China
| | - Zhongyuan Yang
- Department of Hydraulic Engineering, Tongji University, Shanghai, 200092, China
| | - Shizhe Chen
- Department of Hydraulic Engineering, Tongji University, Shanghai, 200092, China
| | - Yuwen Zou
- Department of Hydraulic Engineering, Tongji University, Shanghai, 200092, China
| | - Larisa Dorzhievna Radnaeva
- Laboratory of Chemistry of Natural Systems, Baikal Institute of Nature Management of Siberian Branch of the Russian Academy of Sciences, Ulan-Ude, Republic of Buryatia, Russia
| | - Elena Nikitina
- Laboratory of Chemistry of Natural Systems, Baikal Institute of Nature Management of Siberian Branch of the Russian Academy of Sciences, Ulan-Ude, Republic of Buryatia, Russia
| | - Irina Viktorovna Fedorova
- Institute of Earth Sciences, Saint Petersburg State University, 7-9 Universitetskaya Embankment, 199034, St Petersburg, Russia
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Wang Q, Xu H, Yin J, Du S, Liu C, Li JY. Significance of the great protection of the Yangtze River: Riverine input contributes primarily to the presence of PAHs and HMs in its estuary and the adjacent sea. MARINE POLLUTION BULLETIN 2023; 186:114366. [PMID: 36436271 DOI: 10.1016/j.marpolbul.2022.114366] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 06/16/2023]
Abstract
The Yangtze River protection strategies are expected to improve the water quality and ecological function of the Yangtze River Estuary (YRE). The concentrations of 16 polycyclic aromatic hydrocarbons (PAHs) and 6 heavy metals (HMs) in the YRE were measured and the riverine fluxes were calculated subsequently. In particular, the concentrations of low molecular weight PAHs (LMW-PAHs), arsenic (As) and mercury (Hg) in seawater decreased over time, while those of other studied pollutants did not change a lot. In sediments, the concentration changes for all the pollutants were insignificant. For the present pollutants, the river input is the dominant source, and the flux decreased after the protection. The contribution of the discharge from wastewater treatment plants (WWTPs) was quantified. Its influence cannot be ignored. The seafood quality remained stable and the risk via diet was insignificant. Long-term monitoring is necessary, and the positive impact of the Protection Strategy is gradually emerging.
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Affiliation(s)
- Qian Wang
- College of Marine Ecology and Environment, Shanghai Ocean University, Pudong, Shanghai, China
| | - Hanwen Xu
- College of Marine Ecology and Environment, Shanghai Ocean University, Pudong, Shanghai, China
| | - Jie Yin
- College of Marine Ecology and Environment, Shanghai Ocean University, Pudong, Shanghai, China
| | - Shengnan Du
- College of Marine Ecology and Environment, Shanghai Ocean University, Pudong, Shanghai, China
| | - Caicai Liu
- Key Laboratory of Marine Ecological Monitoring and Restoration Technologies, The Ministry of Nature Resources, Pudong, Shanghai, China
| | - Juan-Ying Li
- College of Marine Ecology and Environment, Shanghai Ocean University, Pudong, Shanghai, China.
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