Unraveling the hydrogeochemical evolution and pollution sources of shallow aquifer using multivariate statistical analysis and hydrogeochemical techniques: a case study of the Quaternary aquifer in Beni Suef area, Egypt.

This study integrates multivariate statistical analysis and hydrogeochemical modeling to investigate the processes controlling the groundwater composition of a shallow aquifer where increased pumping rates and anthropogenic impacts were prevalent. Eighteen groundwater samples were collected and analyzed for major elements and selected heavy metals. The data were classified on the basis of multivariate statistical analysis into three clusters: C1 (Na-Cl facies), C2 (Ca-SO4 facies), and C3 (Ca-HCO3 facies). The application of factor analysis gave four factors affecting the groundwater chemistry, namely the salinization factor, anthropogenic/secondary enrichment factor, the secondary and the micro-nutrient fertilizers, and the aluminum fertilizer factor. The hydrogeochemical study of the groundwater revealed that the processes controlling the groundwater chemistry in the study area are mainly affected by the groundwater occurrence either to the east or to the west of Bahr Youssef Canal. Generally, the dominant hydrogeochemical processes affecting the groundwater are silicate weathering, ion exchange, irrigation return flow, gypsum applications in soil, and evaporation. The groundwater quality evaluation shows that water quality varies from fair to excellent for drinking purposes, where the best water is located in the northern and central parts of the study area. The suitability of groundwater for irrigation was evaluated using several indices indicating that groundwater is suitable for irrigation in the northwest and western parts of the study area. As some groundwater samples lie in high salinity classes on the US Salinity diagram, it is recommended to use this water for plants with good salt tolerance under good drainage conditions. The integration between the statistical and geochemical tools helps reveal the dominant processes through data reduction and classification.

Fabrication of Novel Bentonite-Anthracite@Zetag (BT-An@Zetag) Composite for the Removal of Arsenic (V) from an Aqueous Solution.

The arsenic (As) pollution of water has been eliminated via intensive scientific efforts, with the purpose of giving safe drinking water to millions of people across the world. In this study, the adsorption of As(V) from a synthetic aqueous solution was verified using a Bentonite-Anthracite@Zetag (BT-An@Zetag) composite. The SEM, FT-IR, XRD, DSC, TGA, and SBET techniques were used to characterize the (BT-An@Zetag) composite. The adsorption of As(V) was explored using batch adsorption under varied operating scenarios. Five kinetic modelswere used to investigate kinetic data, whereas three isotherms had been used to fit empirical equilibrium data. According to the findings, the adsorption mechanism of As(V) was best described by the Freundlich isotherm with a maximum monolayer coverage of 38.6 mg/g showing pseudo-second-order mode. The estimated enthalpy (H°) indicates that the adsorption process is both chemical and endothermic.The calculated free energy (G°) indicates that the reaction is nonspontaneous. After four sequential adsorption cycles, the produced BT-An@Zetag composite demonstrated good reusability and a greater adsorption affinity for As(V) ions. Overall, the BT-An@Zetag composite is suited for removing arsenic from wastewater using adsorption as a cost-effective and efficient technique.

Predictive model for progressive salinization in a coastal aquifer using artificial intelligence and hydrogeochemical techniques: a case study of the Nile Delta aquifer, Egypt.

To monitor groundwater salinization due to seawater intrusion (SWI) in the aquifer of the eastern Nile Delta, Egypt, we developed a predictive regression model based on an innovative approach using SWI indicators and artificial intelligence (AI) methodologies. Hydrogeological and hydrogeochemical data of the groundwater wells in three periods (1996, 2007, and 2018) were used as input data for the AI methods. All the studied indicators were enrolled in feature extraction process where the most significant inputs were determined, including the studied year, the distance from the shoreline, the aquifer type, and the hydraulic head. These inputs were used to build four basic AI models to get the optimal prediction results of the used indicators (the base exchange index (BEX), the groundwater quality index for seawater intrusion (GQISWI), and water quality). The machine learning models utilized in this study are logistic regression, Gaussian process regression, feedforward backpropagation neural networks (FFBPN), and deep learning-based long-short-term memory. The FFBPN model achieved higher evaluation results than other models in terms of root mean square error (RMSE) and R2 values in the testing phase, with R2 values of 0.9667, 0.9316, and 0.9259 for BEX, GQISWI, and water quality, respectively. Accordingly, the FFBPN was used to build a predictive model for electrical conductivity for the years 2020 and 2030. Reasonable results were attained despite the imbalanced nature of the dataset for different times and sample sizes. The results show that the 1000 µS/cm boundary is expected to move inland ~9.5 km (eastern part) to ~10 km (western part) to ~12.4 km (central part) between 2018 and 2030. This encroachment would be hazardous to water resources and agriculture unless action plans are taken.

Hydrogeochemical evolution of inland lakes' water: A study of major element geochemistry in the Wadi El Raiyan depression, Egypt.

Wadi El Raiyan is a great depression located southwest of Cairo in the Western Desert of Egypt. Lake Qarun, located north of the study area, is a closed basin with a high evaporation rate. The source of water in the lake is agricultural and municipal drainage from the El Faiyum province. In 1973, Wadi El Raiyan was connected with the agricultural wastewater drainage system of the Faiyum province and received water that exceeded the capacity of Lake Qarun. Two hydrogeological regimes have been established in the area: (i) higher cultivated land and (ii) lower Wadi El Raiyan depression lakes. The agricultural drainage water of the cultivated land has been collected in one main drain (El Wadi Drain) and directed toward the Wadi El Raiyan depression, forming two lakes at different elevations (upper and lower). In the summer of 2012, the major chemical components were studied using data from 36 stations distributed over both hydrogeological regimes in addition to one water sample collected from Bahr Youssef, the main source of freshwater for the Faiyum province. Chemical analyses were made collaboratively. The major ion geochemical evolution of the drainage water recharging the El Raiyan depression was examined. Geochemically, the Bahr Youssef sample is considered the starting point in the geochemical evolution of the studied surface water. In the cultivated area, major-ion chemistry is generally influenced by chemical weathering of rocks and minerals that are associated with anthropogenic inputs, as well as diffuse urban and/or agricultural drainage. In the depression lakes, the water chemistry generally exhibits an evaporation-dependent evolutionary trend that is further modified by cation exchange and precipitation of carbonate minerals.