Groundwater Quality and Health Risk Assessment Using Indexing Approaches, Multivariate Statistical Analysis, Artificial Neural Networks, and GIS Techniques in El Kharga Oasis, Egypt

Abstract

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.

Modeling of Distributed Control System for Network of Mineral Water Wells

“…The solution of the system of Equation ( 9) is that K = 4.9522 × 10 −4 m −2 ; B = 9.007 × 10 −3 [-]. Thus, the static transfer coefficient of an object can be written as Equation (10):…”

“…The static transfer coefficient of the object is written by Equation (10). The effect of production wells pumping rate on lowering of the level in i-th production well in accordance with [42] can be represented as Equation (12):…”

Origin and Salinization Processes of Groundwater in the Semi-Arid Area of Zagora Graben, Southeast Morocco

“…A principal component analysis was performed using nine parameters: conductivity, pH, Cl − , Mg 2+ , Ca 2+ , Na + , SO 4 2− , K + , and HCO 3 − [34,35]. The results (Figure 5a) showed that the first two axes, F1 and F2, explained 80.94% of the overall variance in the water samples.…”

New approach into human health risk assessment associated with heavy metals in surface water and groundwater using Monte Carlo Method

“…Heavy metals can enter the human body through various pathways, including oral consumption, dermal contact, and inhalation 11 – 13 . These contaminants can be found in drinking water sources, such as surface water and groundwater 14 , 15 , vegetables, and air 16 . Metals in the environment can be attributed to industrial, agricultural, domestic, medical, and technological activities.…”

“…Effective methods for evaluating the environmental and human health risks associated with heavy metals include the heavy metal pollution index (HPI), metal index (MI), hazard quotient (HQ), hazard index (HI), and carcinogenic risk (CR), integrated with Monte Carlo simulation 2 – 4 , 9 , 16 – 20 . Furthermore, cluster analysis and principal component analysis (PCA) are valuable tools for classifying the sources of heavy metals and understanding hydrochemical processes in surface water and groundwater 14 , 21 – 23 . Contaminated water resources pose severe threats to humans and animals, giving rise to major biological and chemical concerns.…”