Evaluation of Groundwater Resources in the Qeft Area of Egypt: A Geophysical and Geochemical Perspective

Abstract

This study focuses on the critical issue of access to clean water in water-stressed regions like the Middle East and North Africa (MENA). To address the challenges of water stress, the study proposes an integrated approach involving geographical, statistical, and geophysical analysis. The objectives are to assess the distribution of pollutants such as heavy metals, salts, and water turbidity near industrial facilities; identify their sources and pathways; evaluate water quality and its impact on human health; and improve environmental classification using geophysical and geochemical methods. The study area, located southeast of Qena city, is characterized by an arid climate with minimal rainfall and is primarily covered by Upper Cretaceous and Lower Eocene rocks. The third layer in the study area is considered a shallow aquifer of Quaternary alluvial deposits; it deepens from 20 m to 93 m, displaying resistivity from 18 Ω∙m to 120 Ω∙m, with thickness increasing downstream to approximately 90 m. Understanding groundwater flow from northeast to southeast is crucial for understanding pollutant distribution in the region. The research reveals variations in groundwater quality, including high total dissolved solids (TDS) ranging from 240 to 531 mg/L and electrical conductivity (EC) values ranging from 376–802 μS/cm, as well as the presence of heavy metals. Some water samples exceeded the recommended limits for certain parameters set by the World Health Organization (WHO). Spatial distribution analysis showed higher mineralization toward the northeast of the study area. Overall, the integrated approach proposed in this study can contribute to effective water-management strategies to ensure sustainable water resources and protect public health in water-stressed regions like Egypt.

1. Introduction

Clean water is a fundamental necessity of life, but billions of people worldwide do not have proper access to it. Water stress can harm public health, economic growth, and international trade [1,2]. The Middle East and North Africa (MENA) are the worst off in terms of water stress. Despite housing 6.3% of the world’s population, the area has access to only 1.4% of the world’s renewable freshwater resources [3,4]. Water availability is only 1200 m³/person/year in the MENA region, compared to more than 7000 m³/person/year in other geographical regions [5,6]. The region currently uses more than 75% of its renewable water resources and has the highest per capita freshwater-extraction rates (804 m³/year) in the world [7]. The situation is anticipated to worsen due to global warming, which is predicted to result in a 20% decrease in rainfall and high evaporation rates, which will worsen the region’s water stress. Additionally, there is a greater tendency for groundwater to replace surface water as the climate becomes drier due to the effects of human impacts. Some regions of MENA, such as Egypt and Iraq, depend on the Nile, Tigris, and Euphrates rivers, but now, these surface water flows do not reach the ocean. In light of this, groundwater aquifer serves as a significant source of water for human requirements, especially given the continual expansion of development activities [8,9]. Salinity dangers, problems with rock–water interactions, industrial and agricultural consequences, and other problems were frequently encountered as deeper extraction proceeded. To prevent toxins from poisoning the groundwater, it is essential to determine their sources and pathways.

Potential groundwater-contamination sources are diverse and include natural saline groundwater, dissolution, paleo-brackish water, and sea water intrusion [10,11,12]. Contamination sources also include domestic, agricultural, and industrial effluents [13,14,15]. In Egypt and other developed countries, debris from unmanaged wastewater-disposal systems can seep into shallow groundwater aquifers, causing severe environmental and hydrological deterioration. The raw wastewater is then deposited into nearby waterways [16,17]. In such contexts, understanding and managing chemical and biological contaminants in shallow groundwater aquifers has become a priority. Several approaches can be used to evaluate the origin of dissolved salts and pollutants in groundwater. Hydrochemical, isotope-based, and multivariate statistical-analysis methods have been successfully employed to explore the origin, control, and influence of salts and contaminants [11,18,19]. In areas with no data, geospatial and remote-sensing techniques with machine learning and modeling can offer complementary information that enables hydrogeologic correlation and predictions [20,21,22,23]. Additionally, indirect geophysical methods like electrical resistivity tomography (ERT) and vertical electrical surveys (VES) have been used to generate continuous data throughout a given profile [24]. Application of these methods aids in understanding the spatial interactions between contaminated and uncontaminated water, which typically coexist in shallow aquifers [25,26,27]. VES and time-delay electromagnetics (TDEM) geophysical tools are commonly combined in the assessment of pollution in shallow groundwater aquifers due to their ability to provide information about the subsurface geology and hydrogeology, including the location and extent of aquifers, aquitards, and contaminants [28,29]. This present study aimed to develop an integrated approach based on geospatial, statistical, and geophysical analysis to investigate the spatial distribution characteristics of dissolved heavy metals, dissolved salts, and water turbidity in the groundwater near industrial facilities; identify the sources and pathways of these metals; understand the water-quality and the hazard impacts on human health; and apply exploratory geophysics methods to enhance environmental classification and clarify the strategy for the distribution of heavy metals in surface and groundwater.

Although there is another deep-water reservoir in the study area in the Nubian sandstone at greater depths, this study focused on the shallow Quaternary aquifer. The primary objective of this study is to examine the shallow aquifer, as the geophysical techniques applied in this investigation have limited penetration capabilities. Moreover, the scarcity of deep aquifer wells, primarily due to the expensive drilling expenses, poses a significant challenge for local farmers who heavily rely on groundwater for essential activities such as drinking, agriculture, and industry within the study area. in the study area are based on drilling. Significant recharge of well water into the shallow aquifer from the River Nile further encourages decision-makers, land reclaimers, and local farmers to drill in the shallow aquifer.

This research attempts to integrate geophysical and geochemical examination of the groundwater aquifer in the Qeft area to address potential challenges with problems related to aquifer management and use. These include issues such as groundwater pollution, overexploitation, and the need for sustainable management practices.

2. Study Area

The study area (Figure 1) is located between 25°59′ and 26°02′ N and 32°50′ and 32°53′ E. The area is part of the Qeft administrative area, which extends into the eastern desert and constitutes the natural and urban extension for the governorate. The topography of the study area declines from the northeast (508 m) toward the River Nile (88 m) in the west. The climate is arid, with high temperatures (average 41 °C in July) and low average rainfall (3.2 mm in summer). From a geological perspective, the research area is mostly covered by Upper Cretaceous and Lower Eocene rocks, which are unconformably overlain by Pliocene and Quaternary deposits to the west, while Precambrian basement rocks protrude to the eastern parts of the area. This study focused on the aquifer, which is the upper unconfined Quaternary hydrogeological aquifer [30,31]. This aquifer is composed of alluvial deposits (sand, gravel, and boulders) with variable texture, composition, and water chemistry. In general, the thickness increases downstream of the wadi and reaches 100 m downstream [30].

3. Materials and Methods

3.1. Geophysical Exploration

Identifying areas where uncertainty prevails in geological interpretation is certainly crucial in geophysical exploration endeavors. However, it is essential to acknowledge that while geophysical measurements are intrinsically linked to the geological column, their direct relationship with geological interpretations might not always be straightforward. Geophysical measurements provide comprehensive insights into the physical properties of soil layers, encompassing parameters like resistivity, conductivity, and magnetic susceptibility. While these measurements offer valuable data, their interpretation often relies on sophisticated algorithms and models, which may not always directly reflect geological features.

The interpretation process in geophysics is distinct, characterized by the classification of subsurface materials based on their geophysical properties rather than solely geological attributes. This classification includes identifying anomalies in resistivity, conductivity, or magnetic susceptibility, which may indicate the presence of features such as water reservoirs, mineral deposits, or geological structures. While these anomalies can provide indirect information about geological formations, they do not always offer a direct representation of lithological units or stratigraphic sequences. Thus, while geophysical investigations contribute significantly to the elucidation of subsurface characteristics, their interpretations should be integrated with geological knowledge and field observations for a comprehensive understanding of the geological setting.

Although the number of wells may appear limited, the wells selected represent hydrogeological diversity within the study area. It should be made clear that the decision to select ten shallow wells for research was based on a variety of factors and constraints. The most important of these considerations are that these wells are located within the boundaries of the (civilian area) as a study area and that they are also linked to the spatial distribution of wells across the study area.

The estimation of geological properties involves a multi-faceted approach that integrates interpretation of geophysical data, geological knowledge, and calibration/validation with ground-truth data from well logs and field measurements. Local porosity, aquifer depths, water-table levels, and permeability are estimated by analyzing geophysical data outputs, such as resistivity models from VES and TDEM inversions, in conjunction with geological and hydrogeological concepts. To address uncertainty, the study considers various sources, including geological heterogeneity, measurement-related uncertainties, and model-parameter uncertainties. The presentation of the results acknowledges the importance of quantifying uncertainty in permeability estimates, for example, by establishing confidence intervals based on geological facies variations or measurement-related uncertainties, as suggested by [32,33,34].

In essence, while there exists a connection between geophysical measurements and the geological column, the interpretation of geophysical data encompasses a broader spectrum of materials and properties beyond traditional geological descriptions. Integrating both geological and geophysical interpretations enables a more holistic understanding of subsurface dynamics, facilitating informed decision-making in various applications, including groundwater exploration, mineral prospecting, and environmental assessment.

The VES method is useful for identifying the depth and thickness of a shallow aquifer and determining the extent of groundwater contamination. Using a Schlumberger array for electrode arrangement, with 1 km and 200 m as the largest distances between similar electrodes of current (I₁ and I₂) and potential (V₁ and V₂), respectively, 60 VES were conducted in the study area and distributed spatially in a grid pattern (Figure 1d). Syscal Pro devices from IRIS Instruments were used to measure the apparent resistance of the earth’s layers vertically from the top down at each site from February 2022 to October 2022 at two field camps. In the Schlumberger configuration, the two current electrodes are moved symmetrically around the midpoint of the array against each desired depth point, while the two potential electrodes are moved periodically, not at each depth point [35,36]. The plotted apparent resistivity was calculated by the given mathematical relationships (Equations (1) and (2)), as follows:

$${\mathsf{\rho }}_{\mathrm{a}(\mathrm{a}\mathrm{p}\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{r}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{y})}={\mathrm{G}}_{\mathrm{a}\mathrm{f}(\mathrm{a}\mathrm{r}\mathrm{r}\mathrm{a}\mathrm{y} \mathrm{g}\mathrm{e}\mathrm{o}\mathrm{m}\mathrm{e}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{c} \mathrm{f}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r})}\times {\mathrm{R}}_{\mathrm{m}\left(\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{e}\mathrm{d} \mathrm{r}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e}\right)}$$

(1)

$${\mathrm{G}}_{\mathrm{a}\mathrm{f}}=\mathrm{\pi }\left(\frac{{{\mathrm{I}}_1{\mathrm{V}}_1}_{\mathrm{S}\mathrm{D}}\times {{\mathrm{I}}_1{\mathrm{V}}_2}_{\mathrm{S}\mathrm{D}}}{{{\mathrm{V}}_1{\mathrm{V}}_2}_{\mathrm{S}\mathrm{D}}}\right)$$

(2)

where SD represents the distance separating the mentioned electrodes. The geoelectrical model parameters (number of subsurface geoelectrical layers, layer resistivity, and layer thickness) were gained using Res1D v.1 software by applying the least-squares optimization inversion method [37]. Thus, it was fed with an initial model prepared by using the program developed by [38].

TDEM is another useful geophysical method for identifying the depth and thickness of an aquifer and locating and characterizing subsurface conductive anomalies, such as groundwater-contamination plumes. TDEM values were measured at the same locations used for the VES sampling and at the same time using a t-AIE-2 TDEM instrument, applying the coincident loop method with two different geometries: 50 × 50 and 100 × 100. The transmission loop (R_t) and receiver loop (R_x) are within the same cable [39].

Equation (1) provides the calculation for apparent resistivity (ρ_a), a crucial parameter in geophysical surveys. However, the interpretation of apparent resistivity is subject to uncertainty due to several factors. One significant source of uncertainty lies in the estimation of the array geometric factor (G_af), as it depends on the precise positioning of the current and potential electrodes. Small errors in electrode placement or irregularities in subsurface conditions can lead to inaccuracies in the calculated apparent resistivity. Additionally, variations in the measured resistance (R_m) due to noise, instrument limitations, or environmental interference contribute to uncertainty in ρ_a.

Equation (2) defines the array geometric factor (G_af), which is fundamental for calculating apparent resistivity. However, uncertainties in G_af arise from the geometric configuration of the electrodes and the accuracy of distance measurements (represented by SD). Any deviations from the idealized assumptions of the electrode array setup, such as non-uniform soil properties or electrode misalignment, introduce uncertainties in G_af and subsequently affect the reliability of apparent resistivity estimations.

Mathematical equations and models are used to interpret the data. Faraday’s Law relates the rate of change in magnetic flux to the induced electromotive force (EMF) (Equation (3)), as follows:

$$\mathrm{E}\mathrm{M}\mathrm{F}=-\mathrm{d}\Phi /\mathrm{d}\mathrm{t}$$

(3)

Electromotive Force (EMF), which is measured in volts (V), represents the induced voltage or potential difference generated in a conductor due to a changing magnetic field. dΦ/dt represents the negative rate-change of magnetic flux with respect to derivative time (dt). The magnetic flux Φ is a measure of the total magnetic field passing through a given area and is measured in Webers (Wb). Maxwell’s Equation is combined with Ampere’s Law to describe the behaviors of electromagnetic waves and their interactions with charges and currents (Equation (4)), as follows:

$$\nabla \times \mathrm{B}=\mathsf{\mu }₀\mathrm{J}+\mathsf{\mu }₀\mathsf{\epsilon }₀\partial \mathrm{E}/\partial \mathrm{t}$$

(4)

where ∇ × B is the curve of the magnetic field vector (B); μ₀ is the permeability of free space, a fundamental constant; J is the electric-current density; and ∂E/∂t is the rate of change of the electric field vector (E) with respect to time. This equation relates magnetic fields to electric currents and the displacement current (the term involving ∂E/∂t), which accounts for the changing electric field inducing a magnetic field.

The Cole–Cole model describes the electrical conductivity of a material using a complex number composed of a real part (σ′) and an imaginary part (σ″). The real part represents the material’s resistance, while the imaginary part represents its capacitive or inductive behavior. The complex conductivity (σ*) as a function of the angular frequency (ω) is given by the following equation:

$${\sigma }^{\ast }\left(\mathsf{\omega }\right)=\mathsf{\sigma }\mathrm{’}\left(\mathsf{\omega }\right)-\mathrm{j}\mathsf{\sigma }\mathrm{’}\mathrm{’}\left(\mathsf{\omega }\right),\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} \mathrm{j} \mathrm{i}\mathrm{s} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{i}\mathrm{m}\mathrm{a}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{y} \mathrm{u}\mathrm{n}\mathrm{i}\mathrm{t}(\surd (-1\left)\right)$$

(5)

In addition to the conductivity components, the Cole–Cole model introduces the Cole–Cole parameter (α), which influences the shape of the complex conductivity spectrum. The value of α determines the degree of dispersion or relaxation in the material. A smaller α indicates a more dispersive or relaxed behavior, while a larger α indicates a more resistive behavior. By fitting the Cole–Cole model to experimental data, it is possible to determine the values of σ′, σ″, and α, which provide valuable insights into the electrical properties of subsurface materials. This information finds applications in groundwater exploration, mineral exploration, and the characterization of geological formations.

Inversion algorithms are employed to interpret the TDEM data and create a resistivity model of the subsurface by finding the best-fitting distribution of resistivity. Inversion algorithms play a crucial role in interpreting TDEM data and constructing a resistivity model of the subsurface. These algorithms aim to find the optimal distribution of resistivity values that best fit the measured TDEM data. By inverting the data, the algorithms can estimate the spatial variation of resistivity in the subsurface. The process of inversion involves iteratively adjusting the resistivity distribution until the predicted response from the resistivity model and the observed TDEM data are matched. This is typically done by minimizing the misfit between the observed and predicted data through an optimization process. Various numerical techniques, such as least-squares, regularization, or Bayesian methods, can be employed to perform the inversion. Inversion algorithms use the forward-modeling process, which simulates the response of the subsurface to an electromagnetic source, to generate predicted TDEM data for a given resistivity distribution. The inversion process compares these predicted data with the measured data and adjusts the resistivity model accordingly to minimize the misfit. The iterations continue until a satisfactory match is achieved, indicating the best-fitting resistivity distribution (Figure 2). These sites were located as close as possible to the already-drilled wells (Figure 1d). Using the known lithostratigraphy and data from nearby wells, we calibrated and compared the probing results with actual data and created an accurate geological model with which to interpret the geophysical results.

Equations (3)–(5) involve mathematical formulations for interpreting time domain electromagnetic (TDEM) data. These equations relate to the measurement of electromagnetic properties of the subsurface, which are crucial for understanding groundwater dynamics. However, uncertainties arise in estimating parameters such as the induced electromotive force (EMF), magnetic flux, and complex conductivity (σ*) due to noise in the measured data, instrument-calibration errors, and assumptions made in model-fitting procedures. These uncertainties can impact the accuracy of TDEM interpretations and subsequent efforts in geological modeling.

The shallow aquifer geophysical-based protectivity index (GBPI) (Equations (6) and (7)) is long-established [40]. The expressions for longitudinal conductance in the case of a single layer that overlies the water-bearing layer or total longitudinal conductance in the multilayer case have been applied recently by many authors [41], as follows:

$$GBPI=\frac{h (layer thickness)}{\rho (layer resistivity)} in case of one layer$$

(6)

$$GBPI=\sum _i^n\frac{h_i}{{\rho }_i} in case of multi\text{-}layer overlying the aquifer$$

(7)

Equations (6) and (7) describe the calculation of the geophysical-based protectivity index (GBPI) for the water-bearing layer. While these equations provide a quantitative measure of aquifer protectivity, uncertainties arise from inaccuracies in determining layer thickness ($h_i$) and resistivity (${\rho }_i$) parameters. Variability in lithological properties, measurement errors, and assumptions about layer homogeneity contribute to uncertainties in GBPI calculations, affecting the reliability of groundwater-vulnerability assessments based on geophysical data.

In this work, the protectivity of the water-bearing layer is calculated using the parameters extracted from the VES and TDEM inversions.

3.2. Water Sampling and Analytical Techniques

Ten groundwater samples were collected from local groundwater wells, considering land use patterns, population, industrial activity, and the heavy-mineral deposit area in December 2023 (Figure 1). The water wells range in depth from 50 m to 90 m (

Table 1. Descriptive statistics of the analyzed groundwater-quality parameters.

	Parameter	Mean	Minimum	Maximum	World Health Organization (2011) Standards
Major Anions and Cations (mg/L)	pH	7.7	7.5	7.9	6.5–8.5
	EC	567.9	375.5	803.5	300
	TDS	373.0	240.00	532.00	1000
	Tur.	14.8	8.0	26.5	0.2
	DO	4.7	1.7	6.8	5.0
	BOD	11.9	5.0	52.5	1.5
	Cl−	53.5	21.8	81.2	5
	NO3−	20.8	5.7	37.2	50
	SO42-	52.4	30.4	83.8	250
	CO32-	0.0	0.0	0.0	-
	HCO3−	193.8	151.0	236.0	-
	Ca2+	40.9	32.1	52.6	100–300
	K+	8.6	6.5	11.0	-
	Mg2+	18.1	16.0	21.5	50
	Na+	50.7	30.0	79.5	200
Heavy Metals (µg/L)	Ba	33.2	2.8	77.4	700.0
	Cr	0.1	0.0	0.2	50.0
	Fe	18.1	4.8	31.2	300
	Mn	3.2	0.0	5.7	80.0
	Ni	1.2	0.2	1.6	70.0
	Sb	7.0	0.1	33.1	20.0
	Al	1.8	1.0	2.6	100.0
	As	4.5	0.4	8.4	10.0
	Cd	0.3	0.0	0.6	3.0
	Co	0.9	0.8	1.1	10.0
	Cu	0.3	0.0	0.7	2000.0
	V	9.9	3.0	23.0	-
	Zn	1.1	0.0	2.6	3000.0

TDS = total dissolved solids, Tur. = turbidity, DO = dissolved oxygen, BOD = biochemical oxygen demand.

). In accordance with the procedures of the U.S. Geological Survey (2006), each well was pumped for ten minutes to purge out double the well volume before sampling. Samples were collected using a water sampler and stored in clean high-density polyethylene (HDPE) plastic bottles. A portable YSI water-quality-monitoring meter (Xylem Inc., Yellow Springs, OH, USA) was used to measure various physical and chemical characteristics on site, including temperature, pH, and electrical conductivity (EC). On-site water samples were filtered using a 0.22-µm membrane in preparation for the measurement of dissolved metals. Before elemental analysis, the filtrate was acidified with ultrapure concentrated nitric acid to a pH of less than or equal to 2, then kept at approximately 4 °C before transporting to laboratory. The major metals calcium (Ca²⁺), sodium (Na⁺), potassium (K⁺), and magnesium (Mg²⁺) in the water samples were measured using an inductively coupled plasma optical emission spectrophotometer (ICP-OES) in the Water Engineering Corporation (WEC) laboratory, and the results were confirmed using an inductively coupled plasma mass spectrophotometer (ICP-MS, Elan 9000, Perkin Elmer Optima, Waltham, MA, USA) in METER Group Co. (Pullman, WA, USA). The instrument was calibrated using a multielement standard IV for ICP The instrument was standardized with a multi-element calibration standard IV for ICP for copper (Cu), manganese (Mn), iron (Fe), chromium (Cr), cadmium (Cd), arsenic (As), nickel (Ni), zinc (Zn), lead (Pb) and cobalt (Co). Analytical precision was confirmed using the standard reference material (Multi-elemental calibration standard, serial number GSB 04-1767-2004) [42], which has an uncertainty level of 0.7% or less. To maintain accuracy, blanks were measured in between each batch of 10 water samples. Method precision was controlled by retesting randomly selected samples between each batch of 10 water samples.

3.3. Assessment and Evaluation

To investigate the spatial-distribution characteristics of dissolved metals, the concentrations were interpolated using ordinary kriging in ArcGIS version 10.8. Kriging offers some advantages over other interpolation techniques. It interpolates using weights independent of the data; hence, the weights after the first estimation can be used for all datasets. Also, it is an exact interpolator, i.e., the estimate at any observational point is the observation itself [43]. The output raster maps were set to the exact extent of the location points.

Four different water-quality indices were calculated, including the Water Quality Index (WQI), Pollution Index of Groundwater (PIG), Heavy Metal Pollution Index (HPI), and Heavy Metal Evaluation Index (HEI). Tables S1 and S2 contain all explanations, calculations, and ratings for these indices.

Using SPSS (Version 25.0) software, correlation, principal component analysis (PCA), and cluster analyses were conducted to determine the toxicity and contamination levels of heavy metals in the groundwater within the study area. The Pearson correlation coefficient was used to assess the degree of correlation between the components (Table S3). PCA was used to extract the principal components by examining correlations between the observable variables from the studied data to assess changes and pinpoint pollution sources in the groundwater. Exploratory factor analysis was carried out using the varimax rotation technique; the variables were condensed to only those with high loading to assist in the interpretation of the PCA results. In this study, factors with Eigen values > 1 were considered.

In this study, the exposure of humans to metals through two main pathways, water consumption (ingestion) or skin contact (dermal absorption), were computed based on equations developed by [44]. Firstly, the Hazard Quotient (HQ) and Hazard Index (HI) values were used to assess the possible risks to human health, including the possibility of cancer, from ingesting heavy metals (Equation (8)) or from skin absorption of those metals (Equation (9)):

$${\mathrm{H}\mathrm{Q}}_{\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{e}\mathrm{s}\mathrm{t}}=\frac{{\mathrm{C}}_{\mathrm{W}}\times \mathrm{I}\mathrm{R}\mathrm{W}\mathrm{r}\mathrm{e}\mathrm{s}\times \mathrm{E}\mathrm{F}\mathrm{r}\mathrm{e}\mathrm{s}\times \mathrm{E}\mathrm{D}}{\mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{s}\times \mathrm{R}\mathrm{f}\mathrm{D}\mathrm{O}\times {10}^3}$$

(8)

$${\mathrm{H}\mathrm{Q}}_{\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}}=\frac{{\mathrm{C}}_{\mathrm{w}}\times \mathrm{S}\mathrm{A}\times \mathrm{K}\mathrm{p}\times \mathrm{E}\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{s}\times \mathrm{E}\mathrm{V}\times \mathrm{E}\mathrm{F}\mathrm{r}\mathrm{e}\mathrm{s}\times \mathrm{E}\mathrm{D}}{\mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{s}\times \mathrm{R}\mathrm{f}\mathrm{D}\mathrm{o}\times \mathrm{G}\mathrm{I}\mathrm{A}\mathrm{B}\mathrm{S}\times {10}^6}$$

(9)

Descriptions of the applied parameters are presented in Table S4. Using HI, the total noncarcinogenic risk (THI) from exposure to all metals in each pathway was calculated (Equation (10)). The total hazard index (THI) was computed as follows by adding the HI values for the ingestion and skin-absorption paths:

$$\mathrm{T}\mathrm{H}\mathrm{I}=\sum HQs$$

(10)

The calculated THI is compared to standard values: if the computed THI is greater than 1, residents may experience noncarcinogenic effects; if it is lower than 1, the exposed individual may unexpectedly experience clearly negative health effects.

Secondly, the Probable Cancer Risk (PCR) associated with exposure to a specific amount of a heavy metal in drinking water is also calculated. A person is said to have a higher chance of developing any type of cancer during their lifetime if they spend twenty-four hours a day in contact with a certain level of a carcinogenic substance over a period of seventy years. The lifetime cancer risk was determined using the following relationships (Equations (11)–(15)):

$${\mathrm{P}\mathrm{C}\mathrm{R}}_{ingest}=\frac{C_w\times {\mathrm{I}\mathrm{F}\mathrm{W}}_{res}\times {\mathrm{C}\mathrm{S}\mathrm{F}}_o}{AT\times {10}^3}$$

(11)

$${\mathrm{I}\mathrm{F}\mathrm{W}}_{res}=\frac{{EF}_{res}\times {\mathrm{E}\mathrm{D}}_a\times {\mathrm{I}\mathrm{R}\mathrm{W}}_{res-a}}{{BW}_a}+\frac{{EF}_{res}\times {\mathrm{E}\mathrm{D}}_c\times {\mathrm{I}\mathrm{R}\mathrm{W}}_{res-c}}{{BW}_c}$$

(12)

$${\mathrm{C}\mathrm{R}}_{dermal}=\frac{C_w\times {\mathrm{K}}_p\times {0.001\times \mathrm{E}\mathrm{T}}_{event-res}\times {DFW}_{res}\times {CSF}_O}{AT\times {GIABS\times 10}^3}$$

(13)

$${ET}_{event-res}=\frac{{ET}_{res-a}\times {\mathrm{E}\mathrm{D}}_a+{\mathrm{E}\mathrm{T}}_{res-c}\times {ED}_c}{ED}$$

(14)

$${DFW}_{res}=\frac{{EV}_a\times {\mathrm{E}\mathrm{F}}_{res}\times {\mathrm{E}\mathrm{D}}_a\times {SA}_{ca}}{{BW}_a}+\frac{{EV}_c\times {\mathrm{E}\mathrm{F}}_{res}\times {\mathrm{E}\mathrm{D}}_c\times {SA}_c}{{BW}_c}$$

(15)

where CSF is the cancer slope factor, the risk generated by a lifetime average quantity of 1 mg/kg/day of carcinogenic chemicals. The accepted limits are 10⁻⁶ and <10⁻⁴ for a single carcinogenic element and multielement carcinogens, respectively. Descriptions of the applied parameters are presented in Table S4.

4. Results

4.1. Geophysical Exploration

4.1.1. Subsurface Layer Sequance

The final interpretations of VES and TDEM results (Table S5) provide insight into the shallow subsurface sequence of the area in the form of geoelectric layers with different thicknesses and resistivities. Geoelectrical cross-sections (Figure S1) and maps were also created from these results to provide a better understanding of the subsurface geologic and hydrogeologic situations in the study area. The sequence of subsurface layers in terms of their electrical resistivity or conductivity properties was determined to be as follows:

• The first and topmost layer covers the entire study area. The surface altitude (Figure 3a) decreases from 127 m above sea level in the northeast to 72 in the west near the Nile, representing the natural topographic gradient of this area. This layer is divided based on its resistivity values into sublayers a and b. Layer 1a has resistivity values ranging from 16 Ω∙m to 64 Ω∙m and covers the western portion of the study area. Layer 1a consists of Nile silt and fine sand and was formed by successive annual floods of the Nile, with thicknesses ranging from 7 m to 41 m. The resistivity values of layer 1b in the eastern part of the study area vary from 333 Ω∙m to 711 Ω∙m. (Figure 3b). The relatively high resistivity values represent layers of unreclaimed soil with thicknesses ranging from 5 m to 42 m (Figure 3c).
• The second layer reaches its greatest depth, 42 m, in the northern and northeastern parts of the study area, as well as below VES station No. 18, and its shallowest depth, 4 m, in the southeastern part below VES station No. 47 (Figure 3d) because the thickness of the surface layer is less in the stream valleys. The recorded resistivity values of this layer oscillate between 738 Ω∙m and 1112 Ω∙m (Figure 3e), and its thickness gradually decreases from 51 m in the eastern part to 7 m in the western part of the study area (Figure 3f). This layer consists of a mixture of weathering sediments and valley sediments as gravelly sand.
• The third geoelectric layer is the water-bearing layer, consisting of a shallow aquifer formed by Quaternary alluvial deposits. It increases in depth from 20 m in the west to 93 m in the east (Figure 3g). Resistivity ranges from 18 Ω∙m to 120 Ω∙m (Figure 3h). The resistivity of this layer varies due to the diversity of its components, which include sand, gravel, boulders, and some clay lenses, as recorded through piezometers. The thickness of this water-bearing layer increases from 42 m upstream (in the east) to approximately 90 m downstream (in the west), with an average thickness of 60 m (Figure 3i).
• The maximum separation between two current electrodes helped to determine the depth of the upper surface of the fourth (last) layer, and this depth ranges from 94 m to 145 m (Figure 3j), with altitudes 7 m to 43 m below sea level (Figure 3k). However, this separation distance was not sufficient to reach the bottom surface of this layer and, therefore, it was not possible to determine its thickness. The fourth layer is interpreted as being composed of different lithologies ranging from Pliocene lacustrine deposits to the Lower Eocene Thebes Formation, with resistivity values that are limited to the range between 1250 and 1450 Ω∙m (Figure 3l).

4.1.2. Geophysical Based Protectivity Assessment

The geoelectrical results were used to calculate the longitudinal conductance of layers above the aquifer using Equations (6) and (7), through which the aquifer protectivity can be assessed. The longitudinal conductance of the first geoelectric layer (Figure 4a) ranges between 0.014 Ω⁻¹ and 0.66 Ω⁻¹; that of the second geoelectric layer is very small and ranges between 0.009 Ω⁻¹ and 0.05 Ω⁻¹ (Figure 4b); and the total longitudinal conductance ranges between 0.06 Ω⁻¹ and 0.7 Ω⁻¹ (Figure 4c).

4.2. Geochemical Evaluation

4.2.1. Descriptive Statistics and Spatial Distribution

The physicochemical analyses of the water from the study area indicated that the pH values ranged from 7.5 to 7.9 (Table 1). Sampling site L4 had the highest pH value, while sites L6 and L8 had the lowest pH values. The samples had substantial variations in the total dissolved solids (TDS) levels, with values ranging from 240 mg/L to 532 mg/L. Site L1 had the highest TDS value, whereas site L10 had the lowest value. Similarly, the EC values varied from 375.5 to 803.5 μS/cm throughout the study area (Table 1).

The results showed that groundwater turbidity ranged from 8 NTU to 26.5 NTU, with a mean value of 14.8, indicating the effect of inorganic particle matter and insoluble metal oxides (Table 1). The concentration ranges for chloride, nitrate, sulfate, and bicarbonate were 21.8–81.2 mg/L 5.7–37.2 mg/L, 30.4–83.8 mg/L, and 151.0–236.0 mg/L, with mean values of 53.5 mg/L, 20.8 mg/L, 52.4 mg/L, and 193.8 mg/L, respectively. The anion concentration ranges of calcium, potassium, magnesium, and sodium were 32.1–52.6 mg/L, 6.5–11.0 mg/L, 16.0–21.5 mg/L, and 30.0–79.5 mg/L with mean values of 40.9 mg/L, 8.6 mg/L, 18.1 mg/L, and 50.7 mg/L, respectively. In general, the average values of the major ion concentrations and heavy metal concentrations were below the recommended standard levels determined by the World Health Organization (WHO 2011). Moreover, the spatial distributions of EC and TDS (Figure 5b,c) showed that mineralization rises toward the northeast of the study area. Major ions and heavy metals had similar spatial distributions, with concentrations that increased in the central and northeastern parts of the study area (Figures S1 and S2).

4.2.2. Pollution and Suitability Indices

Significant differences were found among the WQI values, with declines observed between the L1 and L10 sampling points (Figure 6). The WQI values for the water samples varied from 53 to >100, with an average of 75, reflecting the large variations among the groundwater samples. Additionally, these results indicate that the water quality was good at L10 and bad at L1 (Figure 6).

Like the WQI results, the PIG values decreased from the eastern regions (L1) toward the western areas (L10). Values ranged from 0.93 to 1.87, with a mean value of 1.29. Based on the PIG classification, three (30%) of the collected groundwater samples fell into the moderate-pollution category; six (60%) of the samples fall into the low-pollution category, and one sample was slightly polluted. Thus, most of the study area is in zones of low-to-moderate pollution (Figure 6a). Likewise, HPI and HEI (Figure 6b,c) were evidently lower toward the northeast, where L1 is located. The HPI values were over 80 at three sampling points (L1, L2, and L3), indicating high levels of heavy-metal pollution, while the HPI values were below 20 at L9 and L10, suggesting low levels of heavy-metal pollution at those sampling points.

4.2.3. Multivariate Statistical Analysis

To identify the sources of the studied elements, correlation analysis was used to demonstrate linkages between pairs of elements and to ascertain the relationships between variables and influencing factors (Table S3). When there is a positive correlation, the trace elements may have the same source, whereas a negative correlation suggests they came from distinct sources. The correlations between the measured parameters and the chemical-characteristic results (Table S3) reveal several notable positive and negative associations. For instance, substantial positive correlations were found between most of the main ions and heavy metals and physicochemical characteristics like EC and TDS (r = 0.8–0.9). Additionally, there are considerable positive links between SO₄ and HCO₃ and Ca, Mg, Na, Cr, Fe, Al, Cd, Sb, As, Co, and Zn. Similar pairings exist between B, Cr, and Fe with Ni, Sb, Al, As, Cd, Co, V, and Zn. As, Cd, Co, and Zn are also present, along with Ni, Sb, and Al. Such strong connections imply that these components are transported by the same geogenic or anthropogenic pathways through leaching and/or movement. In contrast, there are slight associations (r = 0.3–0.4) between NO₃ and Ca, Mn, Sb, and V, as well as between K and Mg, Sb, and V, which indicate different inputs into the groundwater system. With the aid of principal component analysis (PCA), the significant elements related to the various metal sources that contributed to the water samples were found (

Table 2. Varimax rotated principal component analysis for the water samples from the study area.

Parameter	PC1	PC2	PC3	PC4	PC5
pH	−0.77	−0.52	−0.01	0.14	0.29
EC	0.68	0.24	0.33	0.42	0.38
TDS	0.80	0.19	0.31	0.27	0.29
Tur.	0.93	0.07	−0.01	0.13	0.17
NH4	0.26	0.86	−0.33	0.09	−0.02
DO	0.06	−0.73	−0.51	−0.36	−0.08
BOD	0.03	0.97	0.11	0.04	0.06
Cl−	0.53	0.57	0.36	0.16	0.39
NO3−	0.32	0.64	0.56	0.21	0.25
SO42-	0.71	0.08	0.35	0.55	0.24
HCO3−	0.64	−0.01	0.29	0.41	0.56
Ca2+	0.61	−0.22	0.36	0.57	0.23
K+	0.39	0.06	0.90	0.05	−0.07
Na+	0.67	0.41	0.19	0.32	0.36
Mg2+	0.67	0.12	0.03	0.54	0.29
B	0.95	0.22	0.11	0.08	0.15
Cr	0.92	0.20	0.22	0.07	0.17
Fe	0.91	0.12	0.26	0.03	0.25
Mn	0.48	0.13	−0.09	0.06	0.84
Ni	0.71	0.07	0.44	−0.19	0.40
Sb	0.90	0.04	−0.08	0.31	0.08
Al	0.92	0.12	0.27	0.04	0.23
As	0.91	0.12	0.30	0.02	0.24
Cd	0.94	0.12	0.22	0.05	0.17
Co	0.92	0.12	0.28	0.03	0.23
Cu	0.11	−0.34	0.03	−0.87	0.04
V	0.96	0.01	−0.01	0.11	0.10
Zn	0.94	0.12	0.22	0.05	0.18
Eigenvalues	14.74	3.88	2.91	2.60	2.43
% of Variance	52.63	13.85	10.39	9.28	8.69
Cumulative %	52.63	66.48	76.87	86.15	94.85

). Varimax rotation is employed to maximize the sum of variances of the factor coefficients, which better explains the potential influences on water chemistry (Table 2). In this study, the five principal components, PC1, PC2, PC3, PC4, and PC5, explain more than 52.63%, 13.85%, 10.39%, 9.28%, and 8.69% of variance, respectively (Figure 7 and Table 2). The first principal component, PC1, was found to be highly loaded (>0.8) with Tur., TDS, Fe, Sb, Al, As, Cd, Co, V, and Zn; to be moderately loaded (0.5–0.7) with EC, Cl⁻, SO₄⁻², HCO₃⁻, Ca⁺², Na⁺, Mg⁺², and Ni; and to have relatively low loading value with NH₄, NO₃, K, and Cu. PC1 could be used as an indication of the general water quality.

The second principal component, PC2, was loaded with BOD, NH₃, NO₃ and Cl. The third principal component, PC3, was loaded with K, NO₃, Ni, SO₄, EC, and As. The fourth component PC4 was dominantly loaded with SO₄, NO₃, Mg, EC, HCO₃, and Sb. The dominant loaded elements of the fifth component, PC5, were Mn, SO₄, Ni, Cl, and EC.

Furthermore, chemical-concentration data were subjected to cluster analysis to determine the similarities and dissimilarities among the different chemical parameters. Cluster analysis for the water-sampling locations indicated four clusters within the study area. Cluster 1 constitutes 50% of the investigated samples (L8, L9, L6, L7, and L5), which were located in the central study area; cluster 2 includes samples 3, 4, and 2, which were located in the western part; and clusters 3 and 4 include samples 1 and 10, which were located in the far west and east of the study area, respectively. Three clusters were obtained based on the chemical concentrations of the water samples, suggesting that these groups may have different sources. Cluster 1 encompasses most of the major ions and heavy metals; cluster 2 includes HCO₃; and cluster 3 comprises EC and TDS (Figure 8).

4.2.4. Health-Risk Assessment

Reference doses (RfD) were used to determine child and adult noncarcinogenic risks of exposure to heavy metals (Table S6). The Cd, Cr, and Cu HQ values range from insignificant (0.01) to low (0.03) chronic risk for both adults and children. The maximum HQ value for children and adults was 1.4 and 0.96 (As) via ingestion and 0.03 and 0.0237 (Cd) via skin contact, suggesting a potential noncarcinogenic risk from daily oral and skin ingestion (

Table 3. Descriptive statistics for noncarcinogenic human-health risks [hazard quotient (HQ) and hazard index (HI)] caused by heavy-metal content in water via different pathways.

Ingestion	Metal	Adult			Child
		Minimum	Maximum	Average	Minimum	Maximum	Average
	Cr	0.0000	0.0019	0.0009	0.0000	0.0028	0.0013
	Mn	0.0000	0.0081	0.0046	0.0000	0.0118	0.0067
	Ni	0.0004	0.0028	0.0020	0.0006	0.0041	0.0030
	Cu	0.0000	0.0006	0.0002	0.0000	0.0008	0.0003
	Zn	0.0000	0.0029	0.0013	0.0000	0.0004	0.0002
	As	0.0468	0.9600	0.5106	0.0681	1.3978	0.7435
	Cd	0.0000	0.2123	0.0932	0.0000	0.3092	0.1356
	B	0.0005	0.0133	0.0057	0.0007	0.0193	0.0083
	HQ	0.0490	1.2004	0.6185	0.0713	1.7440	0.8988
Dermal	Cr	0.0000	0.0009	0.0004	0.0000	0.0010	0.0005
	Mn	0.0000	0.0011	0.0006	0.0000	0.0013	0.0007
	Ni	0.0000	0.0001	0.0001	0.0000	0.0001	0.0001
	Cu	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
	Zn	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
	As	0.0001	0.0016	0.0009	0.0003	0.0062	0.0033
	Cd	0.0000	0.0237	0.0104	0.0000	0.0272	0.0120
	B	0.0000	0.0001	0.0000	0.0000	0.0001	0.0000
	HQ	0.0003	0.0273	0.0124	0.0005	0.0357	0.0165
	HI	0.0492	1.2277	0.6309	0.0718	1.7797	0.9153

). In addition, the HI values range from 0.049 to 1.23, indicating a wide range of potential health hazards related to heavy-metal exposure.

There was a significant risk to human health at five sampling sites for both adults and children (Figure 9 and Table S6). The danger to human health from the remaining samples was low to moderate. Overall, greater HQ and HI values were seen for children than for adults, indicating that children are more vulnerable to heavy metals in water. These findings emphasize the urgency of taking decisive action to effectively address and minimize the potential negative health impacts associated with exposure to heavy metals.

Children are more vulnerable to carcinogenic risks than are adults. As a result, numerous malignancies may develop after prolonged exposure to low concentrations of hazardous metals. The overall exposures at water-sampling locations were calculated using As and Cd as example carcinogens (

Table 4. The cancer-risk values for carcinogenic human-health risks via total exposure to the water of the study area.

	Cr—CRing	As—CRing	Sum 1–2	Cr—CRderm	As—CRderm	TCRCr-As	Sum
L1	3.58 × 10−6	5.90 × 10−13	3.59 × 10−6	3.58 × 10−6	1.77 × 10−4	1.81 × 10−4	1.85 × 10−4
L2	3.16 × 10−6	4.65 × 10−13	3.16 × 10−6	3.16 × 10−6	1.58 × 10−4	1.62 × 10−4	1.65 × 10−4
L3	2.74 × 10−6	3.56 × 10−13	2.74 × 10−6	2.74 × 10−6	1.40 × 10−4	1.43 × 10−4	1.45 × 10−4
L4	2.53 × 10−6	2.88 × 10−13	2.53 × 10−6	2.53 × 10−6	1.23 × 10−4	1.25 × 10−4	1.28 × 10−4
L5	1.69 × 10−6	1.66 × 10−13	1.69 × 10−6	1.69 × 10−6	1.06 × 10−4	1.08 × 10−4	1.10 × 10−4
L6	1.27 × 10−6	1.03 × 10−13	1.27 × 10−6	1.27 × 10−6	8.73 × 10−5	8.86 × 10−5	8.98 × 10−5
L7	6.33 × 10−6	3.95 × 10−14	6.33 × 10−7	6.33 × 10−7	6.73 × 10−5	6.79 × 10−5	6.85 × 10−5
L8	4.22 × 10−7	1.87 × 10−14	4.22 × 10−7	4.22 × 10−7	4.77 × 10−5	4.81 × 10−5	4.85 × 10−5
L9	2.11 × 10−7	5.45 × 10−15	2.11 × 10−7	2.11 × 10−7	2.78 × 10−5	2.80 × 10−5	2.83 × 10−5
L10	2.11 × 10−7	5.45 × 10−15	2.11 × 10−7	2.11 × 10−7	8.65 × 10−6	8.86 × 10−6	9.07 × 10−6
Minimum	2.11 × 10−7	5.45 × 10−15	2.11 × 10−7	2.11 × 10−7	8.65 × 10−6	8.86 × 10−6	9.07 × 10−6
Maximum	3.58 × 10−6	5.90 × 10−13	3.59 × 10−6	3.58 × 10−6	1.77 × 10−4	1.81 × 10−4	1.85 × 10−4
Average	1.64 × 10−6	2.04 × 10−13	1.64 × 10−6	1.64 × 10−6	9.43 × 10−5	9.60 × 10−5	9.76 × 10−5

). CR values are highest for sampling points from the western areas and lowest for those in the central and eastern areas. The pollutants in the study area’s drinking water pose a cancer risk to locals through skin contact and cumulative consumption.

5. Discussion

In the evaluation of groundwater dynamics in the Qeft area, a comprehensive assessment of the horizontal and vertical extents of the water-bearing layer, alongside the characterization of overlying sediments, was imperative. This study involved the precise determination of layer thicknesses, types, and aquifer-protection rates derived from geophysical surveys. Notably, the water-bearing layer, predominantly comprising Quaternary sediments, was found to be overlain by Nile silt, sand, and gravel deposits [41]. Throughout the study area, the water-bearing layer exhibited variable thickness, with a notable decrease in thickness towards the east. Despite its high groundwater electrical potential, the aquifer is susceptible to contamination from surface sources; its free-type nature, coupled with its low longitudinal conductance in the overlying layers, result in a classification of very weak to weak protectivity.

This study embarked on an extensive investigation to ascertain the viability of water-management strategies aimed at mitigating hazardous contamination, combining geophysical methods with geochemical analyses of water samples. Against the backdrop of increased agricultural productivity and burgeoning population growth over the past three decades, the study placed paramount importance on preserving human health by probing the presence of harmful compounds in a groundwater aquifer [45]. However, to effectively implement targeted drinking-water-management measures, a nuanced understanding of the principal factors contributing to health risks remains indispensable. Thus, while geochemical studies have provided critical insights, further analyses are warranted to comprehensively evaluate community health risks associated with groundwater.

Although there are two known main aquifers in the area, the upper shallow aquifer was detected by a geophysical survey. This upper aquifer is an unconfined quaternary aquifer comprised primarily of alluvial deposits such as sand, gravel, and boulders. The upper unconfined Quaternary aquifer exhibits variable texture, composition, and water chemistry. Downstream of the wadi, its thickness increases progressively, reaching up to 100 m downstream, as noted in previous studies [30]. This aquifer serves as a vital source of groundwater for various purposes due to its relatively shallow depth and accessibility. This aquifer is characterized by its alluvial deposits and offers relatively easy access for groundwater extraction. Its variable composition allows for efficient filtration and storage of groundwater, making it suitable for a wide range of practical applications, including irrigation, domestic use, and industrial purposes.

The shallow aquifer is particularly important for supporting agricultural activities, industrial development, and ensuring water security in the region. The upper unconfined Quaternary aquifer offers substantial potential for groundwater extraction to support irrigation practices. This can contribute significantly to agricultural productivity and food security in the region. The reliable and abundant groundwater resources provided by this aquifer are crucial for sustaining industrial activities such as manufacturing, mining, and energy production. Groundwater from this aquifer can be utilized for various industrial processes, ensuring continued economic development. Access to clean and potable groundwater from the aquifer is essential for meeting the domestic water needs of communities residing in the study area. Proper management and utilization of this aquifer can ensure a reliable supply of safe drinking water for residential use, promoting public health and well-being. By elucidating the characteristics and practical applications of the aquifer in our study area, our findings contribute to informed decision-making regarding groundwater-resource management and utilization.

In this pursuit, several water-quality indices, including HEI, WQI, PIG, and HPI, were employed to assess contamination levels, with key parameters such as pH, turbidity, Na⁺, HCO₃⁻, SO₄⁻², NH₄, Cd, and As emerging as primary contributors, particularly in the northeastern region of the study area [46,47,48,49]. The presence of contaminants in the groundwater was found to stem from diverse sources, including industrial pollutants and weathered rocks and minerals [50].

Principal component analysis (PCA) further elucidated the dominant factors influencing water quality, with PC1 explaining a significant variance of 52.63% [51]. Notably, high loadings of various constituents, including Tu, TDS, Fe, Sb, Al, As, Cd, Co, V, and Zn, underscored the multifaceted nature of contamination sources, which encompassed both geogenic and anthropogenic origins [52]. Furthermore, the observed correlations between SO₄⁻², HCO₃⁻, and several cations provided additional insights into the complex interplay between different contaminants.

Of particular concern are the adverse health implications associated with elevated concentrations of heavy metals, such as Cd and As, especially in the northeastern regions [53]. Sources of contamination, including excessive fertilizer use, sewage disposal, and industrial activities, underscore the urgency for stringent monitoring and regulatory measures [54,55]. Notably, the study highlights the need for ongoing surveillance to avert potential health risks arising from heavy-metal exposure, necessitating proactive measures to curtail pollution sources and regulate agricultural practices.

This study underscores the imperative to use integrated approaches combining geophysical techniques with robust geochemical analyses to comprehensively evaluate groundwater quality and associated health risks. By elucidating the complex dynamics of contamination sources and their potential health impacts, the findings advocate for proactive measures to safeguard public health and ensure sustainable groundwater-management practices in the Qeft area and beyond.

6. Conclusions

The study focused on evaluating groundwater conditions in the Qeft area, conducting a thorough analysis of both geophysical and geochemical attributes of the local aquifer system. It sheds light on the area’s water quality and the crucial implications of those findings for human health. Recommendations to preserve groundwater quality are provided, emphasizing proactive management strategies. Geophysical methods are employed to delineate subsurface layers, enhancing our understanding of the region’s hydrogeological conditions. The water-bearing layer, part of the quaternary sediments, spans the entire study area, with variable thickness and composition, diminishing from west to east. Despite its considerable groundwater potential, it remains susceptible to pollution from agricultural and industrial sources due to its open structure and limited protection. Evaluation of groundwater quality is vital for safeguarding human health, especially given population growth and increased agricultural activities. Elevated water-quality indices and the presence of contaminants, particularly heavy metals, in the northeastern zones suggest potential health hazards associated with consuming groundwater. Various factors contribute to compromised water quality, including the weathering of rock-forming minerals, agricultural runoff, and industrial operations. These factors result in heightened concentrations of substances like sulfate (SO₄), ammonia (NH₄), cadmium (Cd), and arsenic (As). Geophysical techniques were used to characterize subsurface layers, categorizing them into four distinct layers based on electrical resistivity or conductivity: natural topographical features, un-reclaimed soil, a water-bearing aquifer, and deeper geological strata. To assess the aquifer’s protective capacity, longitudinal conductance was calculated and revealed that the first layer exhibits greater conductance compared to the second. The overall longitudinal conductance value offers insights into the aquifer’s overall protective capabilities. The study concludes by recommending precautions and control measures to mitigate health risks from contaminated groundwater, including judicious management of agricultural inputs, regulation of industrial discharges, and ongoing monitoring of heavy-metal levels.

7. Limitations and Recommendations

The sustainable development of groundwater resources in this area should be ensured by necessary additional investigations. These include a comprehensive study of the various aquifers to thoroughly elucidate groundwater occurrences and evolution. Additionally, the involvement of experts in geophysics, hydrology, hydrochemistry, and soil is essential to confirm the primary sources of heavy metals and the protection of water resources. Furthermore, updated data on groundwater wells is crucial to enhance accuracy and efficiency. The findings of this study will serve as a valuable foundation for hydrogeological research. Lastly, the implementation of a well-structured drilling program to access deep aquifers and consistently sample and monitor groundwater sources is imperative.