Due to water shortages and the potential impact of Ethiopia’s new dam on the Nile River, Egypt is seeking new water resources. This study assesses the drinking water quality and associated risks from potentially toxic elements (PTEs) in the Quaternary aquifer (QA) in Beni-Suef, Egypt. Using a comprehensive approach, including PHREEQC geochemical modeling, ionic ratios, multivariate statistical analyses, and the integrated weight water quality index (WQI), the study evaluated the sources of ion contamination and the mixing of Nile water with QA. Various indices, such as the Heavy Metal Pollution Index (HPI), ecological Risk Index (RI), Hazard Quotient (HQ), and Hazard Index (HI), were used to assess ecological and health risks. Monte Carlo simulations provided probabilistic assessments of non-carcinogenic risks for adults and children. GIS tools were used to map risk indices, identifying the most deteriorated locations for sustainable management. The hydrochemical analysis revealed water facies including Na–Cl, Ca–Mg–HCO₃, and mixed types, influenced by carbonate dissolution, ion exchange, and silicate weathering. Contamination sources, particularly in the north and south, were linked to agricultural activities, irrigation return flow, municipal waste, and evaporation. The WQI indicated that 10.14% of samples were extremely poor, 21.7% were poor, 26% were medium, and 42% were good to excellent. PTE contamination varied, with HPI values indicating good water quality in the central area in 53.6% of the collected samples (HPI < 30), but contamination in the north and south is high (HPI > 51). Ecological Risk Index values were below the threshold in 100% of samples (RI < 30), confirming water safety regarding PTEs. In comparison, for hazard index (HI) through oral/ingestion, adults exhibited HI values ranging from 0.012 to 2.16, while children showed higher values, ranging from 0.045 to 8.25. However, the hazard index for oral/ingestion exceeded safe limits in the north and south (HI oral > 1), posing non-carcinogenic risks. Monte Carlo simulations revealed significant risks from oral exposure to manganese (HQ oral > 1), particularly in El-Wasta and El-Fashn, necessitating further treatment and management.

Manganese–containing mica (Mn–mica) was synthesized at 200 °C/96 h using Mn–carbonate, Al–nitrate, silicic acid, and high KOH concentration under hydrothermal conditions. Mn–mica was characterized and tested as a new adsorbent for the removal of methyl orange (MO) dye from aqueous solutions. Compared to naturally occurring mica, the Mn–mica with manganese in the octahedral sheet resulted in enhanced MO uptake by four times at pH 3.0 and 25 °C. The pseudo–second order equation for kinetics and Freundlich equation for adsorption isotherm fitted well to the experimental data at all adsorption temperatures (i.e., 25, 40 and 55 °C). The decrease of Langmuir uptake capacity from 107.3 to 92.76 mg·g⁻¹ within the temperature range of 25–55 °C suggested that MO adsorption is an exothermic process. The role of manganese in MO selectivity and the adsorption mechanism was analyzed via the physicochemical parameters of a multilayer adsorption model. The aggregated number of MO ions per Mn–mica active site ( $n$ ) was superior to unity at all temperatures signifying a vertical geometry and a mechanism of multi–interactions. The active sites number (D_M) of Mn–mica and the total removed MO layers (N_t) slightly changed with temperature. The decrease in the MO adsorption capacities (Q_sat = n·D_M·N_t) from 190.44 to 140.33 mg·g⁻¹ in the temperature range of 25–55 °C was mainly controlled by the n parameter. The results of adsorption energies revealed that MO uptake was an exothermic (i.e., negative ΔE values) and a physisorption process (ΔE < 40 kJ mol ⁻¹). Accordingly, the adsorption of MO onto Mn–mica was governed by the number of active sites and the adsorption energy. This study offers insights into the manganese control of the interactions between MO ions and Mn–mica active sites.

This research evaluates the environmental and health risks linked to potentially toxic elements (PTEs) and PAHs along the western coast of the Gulf of Suez, Egypt. This study investigated the concentration of 16 PAH compounds in the Suez Gulf, revealing significantly higher levels than the EU (0.20 µg/L) and US (0.030 µg/L) standards. The average total PAH concentration across eight locations was significantly higher, with the Suez area having the highest concentration at 479 µg/L. Pyrene (Pyr) was the dominant PAH with a concentration of 443 µg/L in Suez, while acenaphthylene (Ace) had the lowest concentration at 0.120 µg/L in Northern Zaafarana. Carcinogenic PAHs (CAR) ranged from 8.67 µg/L at Ras Gharib to 29.62 µg/L at Suez, highlighting the urgent need for regulatory measures. Confirmatory ratios pointed to industrial and shipping influences as petrogenic sources. Elevated total organic carbon (TOC) levels in Suez Bay indicated aggravated organic pollution, exacerbated by oil rigs and refineries. The ecological risk assessment highlighted substantial risks, particularly in Suez, necessitating immediate interventions to combat PAH contamination and preserve the environmental balance of the Red Sea. The dominant metals in water samples were arranged in descending order as follows: Pb > Fe > Cr > Cu > Zn > Mn > Cd > Ni. The study evaluated environmental and human health risks using a multifaceted approach, including cluster analysis, principal component analysis, and various indices (HPI, RI, MI, HQ, HI, and CR). Most water samples exhibited high pollution risks, surpassing permissible limits for HPI (> 100) and MI (> 6). Notably, HI oral values indicated significant non-carcinogenic risks for adults and children. While HI values for adults suggested low-risk dermal contact, those for children showed a substantial proportion in the high-risk category. Most water samples displayed CR values exceeding 1 × 10^–4 for Cd, Cr, and Pb, indicating vulnerability to carcinogenic effects in both age groups. Monte Carlo simulations reinforced these findings, revealing a significant carcinogenic impact on children and adults. The identified clusters, reflective of industrial, petroleum-related, and urban runoff contamination sources, were consistently validated and clarified through PCA, enhancing the reliability of the findings. In light of these results, urgent and comprehensive water treatment measures are imperative to mitigate carcinogenic and non-carcinogenic health risks. These insights provide a foundation for implementing targeted management strategies to effectively address the challenges of heavy metal contamination in the Red Sea.

The current study integrates remote sensing, machine learning, and physicochemical parameters to detect hydrodynamic conditions and groundwater quality deterioration in non-rechargeable aquifer systems. Fifty-two water samples were collected from all water resources in Siwa Oasis and analyzed for physical (pH, T°C, EC, and TDS) chemical (SO₄ ^2-, HCO₃ ^-, NO₃ ^-, Cl^-, CO₃ ^2-, SiO₂, Mg²⁺, Na⁺, Ca²⁺, and K⁺), and trace metals (AL, Fe, Sr, Ba, B, and Mn). A digital elevation model supported by machine learning was used to predict the change in the land cover (surface lake area, soil salinity, and water logging) and its effect on water quality deterioration. The groundwater circulation and interaction between the deep aquifer (NSSA) and shallow aquifer (TCA) were detected from the pressure-depth profile of 27 production wells penetrating NSSA. The chemical facies evolution in the aquifer systems were (Ca-Mg-HCO₃) in the first stage (freshwater of NSSA) and changed to (Na-Cl) type in the last stage (brackish water of TCA and springs). Support vector machine successfully predicted the rapid increase of the hypersaline lake area from 22.6 km² to 60.6 km² within 30 years, which deteriorated a large part of the cultivated land, reflecting the environmental risk of over-extraction of water for irrigation of agricultural land by flooding technique and lack of suitable drainage network. The waterlogging in the study was due to a reduction in the infiltration rate (low permeability) of the soil and quaternary aquifer. The cause of this issue could be a complete saturation of agricultural water with chrysotile, calcite, talc, dolomite, gibbsite, chlorite, Ca-montmorillonite, illite, hematite, kaolinite and K-mica (saturation index >1), giving the chance of these minerals to precipitate in the pore spaces of the soil and decrease the infiltration rate. The NSSA is appropriate for irrigation, whereas TCA is inappropriate due to potential salinity and magnesium risks. The best way to manage water resources in Siwa Oasis could be to use underground drip irrigation and combine water with TCA and NSSA.

The natural Egyptian bentonite, collected from south El Hammam area, was modified at three different temperatures 100°C, 200°C and 300°C for 1 h. The raw and modified bentonite samples were characterized by powder X-ray diffraction (XRD), scanning electron microscope (SEM) and BET surface area. The bentonite modified at 100°C exhibited more flaky grains with smooth surface and high surface area as compared to the two other modified types. Response surface methodology in conjunction with central composite rotatable design was used in optimizing and modeling the effect of different parameters such as contact time, initial concentration and dose on the removal of iron ions. Second order quadratic polynomial model was selected to represent the removal process. The mathematical equations of quadratic polynomial model were derived from Design Expert Software (Version 6.0.5). The predicted values from the mathematical equations were highly correlated with the experimental results (R² above 0.9) for the required responses in untreated and modified bentonite at 100°C for 1 h. 3D and linear graphs were used to understand the effect of the studied variable parameters and the interaction between them. Under the predicted conditions suggested by the quadratic programming, the modified bentonite at 100°C is more promising and the removal efficiency could be enhanced to 100%. The quadratic polynomial model could be efficiently applied for the modeling of iron removal from aqueous solutions by bentonite.