Integrating unsupervised machine learning, statistical analysis, and Monte Carlo simulation to assess toxic metal contamination and salinization in non-rechargeable aquifers
This study presents the first comprehensive evaluation of groundwater quality in Siwa Oasis, Egypt, integrating advanced machine learning and statistical approaches to assess contamination, health risks, and industrial suitability. Thirty samples from the Nubian Sandstone Aquifer (NSAS) and karst sp...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025010655 |
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| author | Mohamed Hamdy Eid Omar Saeed András Székács Mostafa R. Abukhadra Haifa A. Alqhtani Attila Kovács Péter Szűcs |
| author_facet | Mohamed Hamdy Eid Omar Saeed András Székács Mostafa R. Abukhadra Haifa A. Alqhtani Attila Kovács Péter Szűcs |
| author_sort | Mohamed Hamdy Eid |
| collection | DOAJ |
| description | This study presents the first comprehensive evaluation of groundwater quality in Siwa Oasis, Egypt, integrating advanced machine learning and statistical approaches to assess contamination, health risks, and industrial suitability. Thirty samples from the Nubian Sandstone Aquifer (NSAS) and karst springs were analyzed using Self-Organizing Maps (SOM), Principal Component Analysis (PCA), and Canadian Water Quality Index (CCME WQI). SOM clustering revealed three distinct water types: (1) hypersaline springs (TDS >10,000 mg/L) near Siwa Lake, (2) moderately saline springs (4,551–8,885 mg/L), and (3) freshwater NSAS samples (<1,000 mg/L). PCA identified salinity (45.5% variance), carbonate equilibrium (21.3%), and anthropogenic inputs (11.5%) as dominant controls. The CCME WQI classified 28% of samples as ''Poor/Marginal,'' with localized heavy metal (Ba, V) contamination confirmed by MPI and NCI indices. Monte Carlo-based health risk assessment revealed severe non-carcinogenic risks for children (HI >1), primarily from Co (HQ up to 105.5) and V (HQ up to 416.9) via ingestion. Industrial indices (LSI, RSI, CSMR) highlighted scaling potential in freshwater zones (LSI >1.5) and corrosion risks in saline areas (RSI >8). As the first study to: (1) quantify emerging contaminants (V, Co, Mo) in NSAS, (2) apply SOM-PCA-Monte Carlo integration in arid aquifers, and (3) concurrently evaluate health and industrial risks, this work provides a replicable framework for non-renewable aquifer management. Immediate actions targeted remediation, infrastructure protection, and agricultural regulation are recommended in Siwa Oasis. The methodologies and gaps identified including unassessed carcinogenic metals and isotopic tracing set a roadmap for future research in vulnerable aquifer systems. |
| format | Article |
| id | doaj-art-55c78744a9394aa095d81ea0f25a2ccb |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-55c78744a9394aa095d81ea0f25a2ccb2025-08-20T02:12:15ZengElsevierResults in Engineering2590-12302025-06-012610498910.1016/j.rineng.2025.104989Integrating unsupervised machine learning, statistical analysis, and Monte Carlo simulation to assess toxic metal contamination and salinization in non-rechargeable aquifersMohamed Hamdy Eid0Omar Saeed1András Székács2Mostafa R. Abukhadra3Haifa A. Alqhtani4Attila Kovács5Péter Szűcs6Institute of Environmental Management, Faculty of Earth Science, University of Miskolc, 3515 Miskolc-Egyetemváros, Hungary; Geology Department, Faculty of Science, Beni-Suef University, Beni-Suef 65211, Egypt; Corresponding authors.Doctoral School of Environmental Science, Hungarian University of Agriculture and Life Sciences (MATE), Páter Károly u. 1, 2100 Gödöllő, Hungary; Corresponding authors.Doctoral School of Environmental Science, Hungarian University of Agriculture and Life Sciences (MATE), Páter Károly u. 1, 2100 Gödöllő, HungaryApplied Science Research Center, Applied Science Private University, Amman, Jordan; Materials Technologies and their applications Lab, Faculty of Science, Beni-Suef University, Beni Suef city, EgyptDepartment of Biology, college of Science, Princess Nourah bint Abdulrahman University, P.O. BOX 84428, Riyadh 11671, Saudi ArabiaInstitute of Environmental Management, Faculty of Earth Science, University of Miskolc, 3515 Miskolc-Egyetemváros, HungaryInstitute of Environmental Management, Faculty of Earth Science, University of Miskolc, 3515 Miskolc-Egyetemváros, HungaryThis study presents the first comprehensive evaluation of groundwater quality in Siwa Oasis, Egypt, integrating advanced machine learning and statistical approaches to assess contamination, health risks, and industrial suitability. Thirty samples from the Nubian Sandstone Aquifer (NSAS) and karst springs were analyzed using Self-Organizing Maps (SOM), Principal Component Analysis (PCA), and Canadian Water Quality Index (CCME WQI). SOM clustering revealed three distinct water types: (1) hypersaline springs (TDS >10,000 mg/L) near Siwa Lake, (2) moderately saline springs (4,551–8,885 mg/L), and (3) freshwater NSAS samples (<1,000 mg/L). PCA identified salinity (45.5% variance), carbonate equilibrium (21.3%), and anthropogenic inputs (11.5%) as dominant controls. The CCME WQI classified 28% of samples as ''Poor/Marginal,'' with localized heavy metal (Ba, V) contamination confirmed by MPI and NCI indices. Monte Carlo-based health risk assessment revealed severe non-carcinogenic risks for children (HI >1), primarily from Co (HQ up to 105.5) and V (HQ up to 416.9) via ingestion. Industrial indices (LSI, RSI, CSMR) highlighted scaling potential in freshwater zones (LSI >1.5) and corrosion risks in saline areas (RSI >8). As the first study to: (1) quantify emerging contaminants (V, Co, Mo) in NSAS, (2) apply SOM-PCA-Monte Carlo integration in arid aquifers, and (3) concurrently evaluate health and industrial risks, this work provides a replicable framework for non-renewable aquifer management. Immediate actions targeted remediation, infrastructure protection, and agricultural regulation are recommended in Siwa Oasis. The methodologies and gaps identified including unassessed carcinogenic metals and isotopic tracing set a roadmap for future research in vulnerable aquifer systems.http://www.sciencedirect.com/science/article/pii/S2590123025010655Water salinizationSOMPCACWQIToxic metals: Risk simulation |
| spellingShingle | Mohamed Hamdy Eid Omar Saeed András Székács Mostafa R. Abukhadra Haifa A. Alqhtani Attila Kovács Péter Szűcs Integrating unsupervised machine learning, statistical analysis, and Monte Carlo simulation to assess toxic metal contamination and salinization in non-rechargeable aquifers Results in Engineering Water salinization SOM PCA CWQI Toxic metals: Risk simulation |
| title | Integrating unsupervised machine learning, statistical analysis, and Monte Carlo simulation to assess toxic metal contamination and salinization in non-rechargeable aquifers |
| title_full | Integrating unsupervised machine learning, statistical analysis, and Monte Carlo simulation to assess toxic metal contamination and salinization in non-rechargeable aquifers |
| title_fullStr | Integrating unsupervised machine learning, statistical analysis, and Monte Carlo simulation to assess toxic metal contamination and salinization in non-rechargeable aquifers |
| title_full_unstemmed | Integrating unsupervised machine learning, statistical analysis, and Monte Carlo simulation to assess toxic metal contamination and salinization in non-rechargeable aquifers |
| title_short | Integrating unsupervised machine learning, statistical analysis, and Monte Carlo simulation to assess toxic metal contamination and salinization in non-rechargeable aquifers |
| title_sort | integrating unsupervised machine learning statistical analysis and monte carlo simulation to assess toxic metal contamination and salinization in non rechargeable aquifers |
| topic | Water salinization SOM PCA CWQI Toxic metals: Risk simulation |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025010655 |
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