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Potential risk assessment and occurrence characteristic of heavy metals based on artificial neural network model along the Yangtze River Estuary, China

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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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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This study was funded by the National Natural Science Foundation of China (42072281, 41602244), Shanghai Science and Technology Innovation Projects (22230712900, 22ZR1464200, 20230742500), the Fundamental Research Funds for the Central Universities (22120210576), Top Discipline Plan of Shanghai Universities-Class I (2022–3-YB-03), and Interdisciplinary Project in Ocean Research of Tongji University (2022–2-YB-01).

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Authors and Affiliations

  1. Department of Hydraulic Engineering, Tongji University, Shanghai, 200092, China

    Zhirui Zhang, Sha Lou, Shuguang Liu, Xiaosheng Zhou, Feng Zhou, Zhongyuan Yang, Shizhe Chen & Yuwen Zou

  2. Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, Shanghai, 200092, China

    Sha Lou & Shuguang Liu

  3. 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

    Larisa Dorzhievna Radnaeva & Elena Nikitina

  4. Institute of Earth Sciences, Saint Petersburg State University, 7-9 Universitetskaya Embankment, 199034, St Petersburg, Russia

    Irina Viktorovna Fedorova

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  1. Zhirui Zhang
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  2. Sha Lou
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Contributions

Zhirui Zhang: data curation, writing—original draft, investigation, software. Sha Lou: methodology, writing—review and editing. Shuguang Liu: conceptualization, methodology, writing—review and editing. Xiaosheng Zhou: investigation. Feng Zhou: investigation. Zhongyuan Yang: investigation. Shizhe Chen: investigation. Yuwen Zou: investigation. Larisa Dorzhievna Radnaeva: methodology, writing—reviewing and editing. Elena Nikitina: writing—reviewing and editing. Irina Viktorovna Fedorova: writing—reviewing and editing.

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Correspondence to Sha Lou.

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Zhang, Z., Lou, S., Liu, S. et al. Potential risk assessment and occurrence characteristic of heavy metals based on artificial neural network model along the Yangtze River Estuary, China. Environ Sci Pollut Res 31, 32091–32110 (2024). https://doi.org/10.1007/s11356-024-33400-z

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