article · Water
An evaluation of groundwater from 140 wells in Egypt's El Kharga Oasis assessed water quality and health risks using indices, multivariate statistics, artificial neural networks, and geographical information systems. The groundwater showed several distinct chemical facies shaped by silicate weathering, rock-water interactions, and ion exchange. Heavy metals, particularly iron and manganese, exceeded World Health Organization limits for drinking water across the sampled area. According to drinking water quality indices, most samples were poor to very poor, making them unsuitable for drinking without treatment, although some were categorised as good. Health risk calculations revealed potential ingestion hazards, especially for children in one location, while dermal risks remained low. Machine learning models demonstrated high accuracy in predicting water quality and health indices, offering dependable tools for monitoring aquifers and identifying contamination patterns.
Assessing groundwater safety is critical in arid regions where communities rely heavily on aquifers for drinking water. Elevated concentrations of heavy metals such as iron and manganese pose serious health risks if consumed untreated. Combining chemical analysis with predictive machine learning enables environmental authorities to identify contamination early, prioritise interventions, and protect local populations from water-borne health hazards.
The tested artificial neural network models could serve water utilities, environmental consultancies, and municipal authorities seeking to streamline water quality monitoring. The approach sits at an applied research stage, having been validated on local field samples. Commercial application would require integrating these predictive algorithms into commercial environmental monitoring platforms or digital decision-support dashboards used for regional aquifer management.
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The assessment and prediction of water quality are important aspects of water resource management. Therefore, the groundwater (GW) quality of the Nubian Sandstone Aquifer (NSSA) in El Kharga Oasis was evaluated using indexing approaches, such as the drinking water quality index (DWQI) and health index (HI), supported with multivariate analysis, artificial neural network (ANN) models, and geographic information system (GIS) techniques. For this, physical and chemical parameters were measured for 140 GW wells, which indicated Ca–Mg–SO4, mixed Ca–Mg–Cl–SO4, Na–Cl, Ca–Mg–HCO3, and mixed Na–Ca–HCO3 water facies under the influence of silicate weathering, rock–water interactions, and ion exchange processes. The GW in El Kharga Oasis had high levels of heavy metals, particularly iron (Fe) and manganese (Mn), with average concentrations above the limits recommended by the World Health Organization (WHO) for drinking water. The DWQI categorized most of the samples as not suitable for drinking (poor to very poor class), while some samples fell in the good water class. The results of the HI indicated a potential health risk due to the ingestion of water, with the risk being higher for children in only one location. However, for both children and adults, there was a low risk of dermal and ingestion exposure to the water in all locations. The contaminants could be from natural sources, such as minerals leaching from rocks and soil, or from human activities. Based on the results of ANN modeling, ANN-SC-13 was the most accurate prediction model, since it demonstrated the strongest correlation between the best characteristics and the DWQI. For example, this model’s thirteen characteristics were extremely important for predicting DWQI. The R2 value for the training, cross-validation (CV), and test data was 0.99. The ANN-SC-2 model was the best in measuring HI ingestion in adults. The R2 value for the training, CV, and test data was 1.00 for all models. The ANN-SC-2 model was the most accurate at detecting HI dermal in adults (R2 = 0.99, 0.99, and 0.99 for the training, CV, and test data sets, respectively). Finally, the integration of physicochemical parameters, water quality indices (WQIs), and ANN models can help us to understand the quality of GW and its controlling factors, and to implement the necessary measures that prevent outbreaks of various water-borne diseases that are detrimental to human health.
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DOI: 10.3390/w15061216
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