Spatiotemporal dynamics and machine learning-based risk assessment of heavy metal contamination in surface waters and Nile Tilapia in Egypt
Author's Department
Institute of Global Health & Human Ecology
Second Author's Department
Institute of Global Health & Human Ecology
Third Author's Department
Center for Applied Research on the Environment & Sustainability
Fourth Author's Department
Chemistry Department
Fifth Author's Department
Institute of Global Health & Human Ecology
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https://doi.org/10.1016/j.envc.2025.101209
Document Type
Research Article
Publication Title
Environmental Challenges
Publication Date
9-1-2025
doi
10.1016/j.envc.2025.101209
Abstract
Heavy metals are persistent pollutants that can devastate human health and ecosystems. In this study, we collected surface water and fish samples from various locations across five governorates in Egypt. We examined the spatiotemporal distribution of 13 heavy metals in the surface water and Nile tilapia and assessed the ecological and human health risks associated with these metals. Moreover, we utilized statistical and machine learning approaches, including principal component analysis (PCA) and linear discriminant analysis (LDA), to explore the relationships between the metals themselves and between the metals and specific governorates or seasons. Our spatiotemporal analysis revealed that aluminum (Al) and iron (Fe) are more concentrated across all governorates every season than other metals. The ecological risk assessment indicates a higher risk for Al and a moderate risk for Fe. Our findings suggest that concentrations of Fe, Cd, Pb, Mn, Al, Ni, and Hg in surface water from aquaculture exceeded national and international standards, posing risks to aquatic ecosystems. In fish, Cd levels surpassed the thresholds set by global standards but remain below the Egyptian limits, indicating a need for ongoing monitoring. Furthermore, Mn concentrations significantly exceed the limits established by Egyptian regulations and the FAO, necessitating ongoing monitoring. The human health risk assessment reveals no health risks associated with dermal exposure to metals in surface water. However, there is a moderate carcinogenic risk associated with ingesting Nile tilapia due to the presence of cadmium, barium, nickel, and chromium. PCA and LDA results provide insights into the interactions among metals, allowing us to identify which metal is unique in a particular governorate as well as the potentials of co-occurrences of various metals, which opened avenues for a deeper investigation into potential sources of metal and the cumulative effects of clustered metals on human health and the consequences of simultaneous exposure. Collectively, our study highlighted the foreseeable risk of heavy metals to human health and advocated for examining potential sources of pollution, implementing monitoring programs, imposing strict regulations, applying safety interventions, conducting public awareness campaigns, and screening programs to reduce the hazardous effects on human health and the environmental system.
Recommended Citation
APA Citation
Hussein, M.
Shamma, S.
Sewilam, H.
Shoeib, T.
&
Abdelnaser, A.
(2025). Spatiotemporal dynamics and machine learning-based risk assessment of heavy metal contamination in surface waters and Nile Tilapia in Egypt. Environmental Challenges, 20,
https://doi.org/10.1016/j.envc.2025.101209
MLA Citation
Hussein, Mohamed Ali, et al.
"Spatiotemporal dynamics and machine learning-based risk assessment of heavy metal contamination in surface waters and Nile Tilapia in Egypt." Environmental Challenges, vol. 20, 2025
https://doi.org/10.1016/j.envc.2025.101209
