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Article

Integrated Multivariate and Spatial Assessment of Groundwater Quality for Sustainable Human Consumption in Arid Moroccan Regions

1
Laboratory of Physical Chemistry of Materials, Natural Substances and Environment (LAMSE), Chemistry Department, Faculty of Science and Technology of Tangier, Abdelmalek Essaadi University, Tangier 90090, Morocco
2
Institute for Systems and Robotics (ISR), Insituto Superior Technico, 1049-001 Lisbon, Portugal
3
High Institute of Marine Fisheries (ISPM), Fisheries Department, Agadir 80000, Morocco
4
Laboratory of Organic and Physical Chemistry, Faculty of Science, Ibn Zohr University, Agadir 80000, Morocco
5
Environmental Management and Civil Engineering Team, Laboratory of Applied Sciences, National School of Applied Sciences, Abdelmalek Essaadi University, Al Hoceima 32200, Morocco
6
Chemistry Research Unit (CIQUP), Institute of Molecular Sciences (IMS), Department of Geosciences, Environment and Spatial Plannings, Faculty of Sciences, University of Porto, Rua do Campo Alegre s/n, 4169-007 Porto, Portugal
*
Author to whom correspondence should be addressed.
Water 2025, 17(16), 2393; https://doi.org/10.3390/w17162393
Submission received: 10 July 2025 / Revised: 6 August 2025 / Accepted: 9 August 2025 / Published: 13 August 2025
(This article belongs to the Section Water Quality and Contamination)

Abstract

Groundwater quality in arid and semi-arid regions of Morocco is under increasing pressure due to both anthropogenic influences and climatic variability. This study investigates the physicochemical and heavy metal characteristics of groundwater across four Moroccan regions (Tangier-Tetouan-Al Hoceima, Oriental, Souss-Massa, and Marrakech-Safi) known for being argan tree habitats. Thirteen groundwater samples were analyzed for twenty-five parameters, including major ions, nutrients, and trace metals. Elevated levels of ammonium, turbidity, electrical conductivity, and dissolved oxygen were observed in multiple samples, surpassing Moroccan water quality standards and indicating significant quality deterioration. Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) detected arsenic concentrations exceeding permissible limits in sample AW11 alongside widespread lead contamination in most samples except AW5 and AW9. Spatial patterns of contamination were characterized using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), K-means clustering, and GIS-based Inverse Distance Weighted (IDW) interpolation. These multivariate approaches revealed marked spatial heterogeneity and highlighted the dual influence of geogenic processes and anthropogenic activities on groundwater quality. To assess consumption suitability, a Water Quality Index (WQI) and Human Health Risk Assessment were applied. As a result, 31% of samples were rated “Fair” and 69% as “Good”, but with notable non-carcinogenic risks, particularly to children, attributable to nitrate, lead, and arsenic. The findings underscore the urgent need for systematic groundwater monitoring and management strategies to safeguard water resources in Morocco’s vulnerable dryland ecosystems, particularly in regions where groundwater sustains vital socio-ecological species such as argan forests.

1. Introduction

Groundwater is a key freshwater resource, particularly in arid and semi-arid regions where surface water is not available or is highly seasonal. It meets the drinking water needs of about half the world’s population and supports up to 40% of irrigated agriculture, besides playing a vital role in industrial development [1]. In climatic variability and prolonged droughts, where surface water does not bring sufficient sustenance, groundwater serves as a buffer against scarcity in many instances wherein such supply is the only source that can be relied upon [2,3]. This dependency becomes very imperative for countries in the Mediterranean region. Morocco, for instance, has recently been impacted significantly by climate change, as evidenced by reduced rainfall, rising temperatures, and increasing water scarcity. The country has faced increasing challenges over the past few decades. Water scarcity, population growth, and the expansion of agricultural and industrial activities have caused a sustained demand for groundwater resources in the country’s arid and semi-arid areas [4,5].
Given the increasing reliance on groundwater for various uses, understanding its quality is imperative for sustainable management. The quality of groundwater is influenced by several physiochemical parameters, such as the potential of hydrogen (pH), electrical conductivity (EC), and major ions, including chloride ( Cl ) , magnesium ( Mg 2 + ) , calcium ( Ca 2 + ) , and bicarbonate ( HCO 3 ) [6]. The concentration of ions in groundwater plays a vital role in supporting plant development and ensuring the sustainability of agricultural practices over time [7]. In addition to common ions, the presence of trace elements like iron (Fe), chromium (Cr), and molybdenum (Mo), along with hazardous heavy metals such as arsenic (As) and lead (Pb), complicate quality evaluations [8]. Conventional statistical methods, including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and K-means clustering, have been extensively utilized to categorize and analyze complex hydrochemical data. They effectively discriminate between natural geochemical signatures and anthropogenic influences, while Pearson correlation matrices elucidate inter-parameter relationships [6,9,10].
Another very common metric for groundwater quality assessment is the Water Quality Index (WQI), which aggregates the values of quite a number of physicochemical parameters into one dimensionless value that gives a general indication of the status of water quality. It is a good method for evaluating the suitability of groundwater, especially regarding its influence on soil health and crop productivity, and gives insight into the extent of groundwater pollution [7,10]. Introduced first by Horton in 1965, the WQI has since been refined through various models such as the CCME-WQI, which gained broad acceptance for assessing the quality of diverse aquatic environments [11]. Geographic Information System (GIS) techniques enable spatial interpolation of Water Quality Indices (WQIs), thus producing comprehensive maps of groundwater suitability [12,13].
Despite numerous studies on groundwater availability, there remains a noticeable gap concerning its quality within ecologically sensitive zones such as argan-growing regions. The argan tree (Argania spinosa L. Skeels) stands out as an ecologically significant species endemic to the arid and semi-arid landscapes of southwestern Morocco, the Beni Snassen mountains, and the eastern Rif of northeastern Morocco [14,15,16,17]. The presence of this keystone species in southwestern Morocco is a testament to its ecological importance by stabilizing the soil, preventing erosion, and maintaining soil fertility and moisture. Additionally, it regulates the water flow through a combination of its deep root systems and interaction with the local hydrological cycle [18,19,20].
While previous Moroccan studies have investigated various aspects of the argan trees’ relationship with water, they have focused mainly on water availability rather than water quality [21,22,23,24]. They investigated water sources and water-use strategies amidst severe summer water deficits [22], physiological responses to water stress [23,25,26], the role of humidity through dew and fog as alternative water sources, especially in sublittoral zones [24,27,28], and severe drought adaptation mechanisms like leaf shedding to reduce transpirative water loss [29,30]. Several related works emphasize the importance of water characteristics. For instance, some studies have reported that high salinity in controlled tests resulted in decreasing growth and increasing ionic toxicity symptoms and reduced survival [31,32,33,34], and have noticed changes in growth under different watering regimes at the nursery stage [21]. In addition, a study found that mature argan trees irrigated with brackish well water did not significantly reduce oil yield, but it altered the oil composition (significantly higher total polyphenol and flavonoid contents, along with two saturated acids) [34]. Nonetheless, few investigations have evaluated whether groundwater in these argan habitats meets Moroccan and international quality standards. Existing research in other Moroccan basins has revealed issues such as salinization, nitrate pollution, and heavy metal enrichment, highlighting the urgent need to characterize groundwater quality before its use for drinking and irrigation [35,36,37,38]. Thus, the question of groundwater quality within the argan tree ecosystem remains largely unexplored. To the best of our knowledge, no study has been found to date that has characterized water quality across the different habitats of argan trees.
In light of this, this research, the first of its kind, aims to (1) assess the physicochemical properties of groundwater in the selected argan-growing regions, (2) detect and quantify heavy metals using Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES), (3) ensure their compliance with Moroccan water quality standards, as well as (4) determine the spatiotemporal distribution of argan tree’s water quality using Water Quality Indices (WQIs), and Geographic Information System (GIS) techniques, and (5) evaluate the water’s safety and compatibility for consumption uses by applying multivariate statistical analyses to identify key factors influencing groundwater quality and regional variations.
Being a concern for decision makers and a topic of both societal and ecological importance, assessing groundwater quality in the argan regions is a must, mainly due to the lack of previous studies in the area. By comparing groundwater quality in Southwest and Northeast Morocco, such knowledge provided through this study will provide valuable insights to support sustainable water use in these regions, especially in the context of a future decline in argan woodlands in the face of climate change.

2. Materials and Methods

2.1. Study Area

Thirteen water samples were collected from four administrative regions surrounding argan tree habitats: Tangier-Tetouan-Al Hoceima, Oriental, Souss-Massa, and Marrakech-Safi. The first two are located in northeastern Morocco, where argan trees grow in isolated areas, while the latter two represent the main distribution areas of the species in the southwest.
The northern region was selected for its considerable distance from the main argan tree distribution area. It corresponds to three sampling sites: the rural commune of Snada, located in the peripheral zone of the National Park of Al Hoceima (NPAH), Kariat Arekmane in the province of Nador, and the rural commune of Chouihia near the hills of Beni Snassen in the province of Berkane. This region is distinguished by a Mediterranean climate with hot summers (Köppen Classification: Csa). In contrast, the southern region, the main distribution area of the endemic argan tree, includes ten sampling sites: Ait Melloul, Biougra, Aït Baha, Tajabert, and the commune center Sidi Ahmed Ou Hamed (between Agadir and Essaouira city), as well as Taghzout, Tafraout, Tiznit, Taroudant, and Amskroud. The climate of these areas varies from dry and hot semi-arid (Köppen Classification: BSh) to dry and cold semi-arid (Köppen classification: BSk).
Figure 1 illustrates the study areas’ locations in Morocco (a) and pinpoints all the sampling sites within the northern (c) and the southern (d) regions. The geographic coordinates (latitude, longitude, and altitude) and climatic classification of all sampling sites are provided in Table S1 (supplementary materials). As depicted in Figure 2, the selected sites also differ in their geological origin.

2.2. Climatic Conditions and Rainfall Variability

The rainfall maps (Figure 3) were generated using rainfall data (in mm) from the CHRS Data Portal [39]. They illustrate high variability of rainfall patterns in Morocco between 2012 and 2022, with some regions showing a noticeable decline, especially in southern areas where the argan tree is prevalent. This variability has potential effects on both the availability of groundwater resources and the water quality in the argan regions.
In recent years, Morocco has also experienced an increase in the intensity and frequency of extreme weather events due to climate change, such as heavy rainfall in some regions and prolonged droughts and heat waves in others [40]. These climatic shifts have significant risks to groundwater sustainability. While droughts reduce recharge rates, leading to water scarcity, heavy rainfall accelerates runoff, reducing effective infiltration and potentially deteriorating water quality through erosion and contamination.
The Atlas Mountains play a crucial role in recharging Morocco’s aquifers through snowmelt and precipitation. However, climate change-induced reductions in snowfall and increased drought frequency are diminishing this natural recharge process, leading to declining groundwater levels across the country [41]. The presence of the Atlas Mountains also heightens the diminishing gradient of rainfall from north to south, a pattern resulting from the introduction of colder air and increasing cloud cover by the incursion of extratropical weather systems originating from Europe and the Atlantic Ocean [40,42]. To illustrate this, a cloud cover map of the sampling period (Figure S1) was generated with data from the NASA Earth Observations [43]. The escalating frequency, severity, and prolonged duration of drought also pose a continuous and substantial concern for the country [43]. The ongoing seven-year drought, the longest since 1980, has significantly impacted groundwater reserves, exacerbating water scarcity issues in Morocco.
In addition, temperature (°C), relative humidity (%), and dew/frost point (°C) data presented in the climatographs (Figure S2) were collected from the NASA Prediction of Worldwide Energy Resources [44].

2.3. Sampling, Laboratory Analysis, and Analytical Method

Water samples were collected from thirteen wells surrounding the argan trees’ habitat in the northern and southern sites of Morocco during January and February 2023.
Prior to sampling, each polyethylene bottle was rinsed with distilled water, then rinsed three times with the local samples’ water in order to prevent cross-contamination. Temperature (T°), potential of hydrogen (pH), total dissolved solids (TDS), dissolved oxygen (DO), turbidity (TUR), redox potential (ORP), and salinity (SAL) were measured in situ.
After collection, the samples were filtered through a 0.45 µm Millipore membrane for ICP analysis and preserved following Rodier’s Method [45]. They were then transported in a cooler and stored at a temperature of 4 °C until laboratory analysis.
The physicochemical parameters were analyzed in the “Laboratory of Physical Chemistry of Materials, Natural Substances and Environment, Chemistry Department, Sciences, and Technology Faculty of Tangier, Abdelmalek Essaadi University, Morocco”, while the Inductively Coupled Plasma Spectroscopy (ICP) used for the determination of arsenic (As), chromium (Cr), cadmium (Cd), lead (Pb), nickel (Ni), molybdenum (Mo), and copper (Cu) was conducted at the National Center for Scientific and Technical Research (CNRST) Laboratory.
Ion concentrations, including chloride ( Cl ) , magnesium ( Mg 2 + ) , calcium ( Ca 2 + ) , total hardness (TH), and bicarbonate ( HCO 3 ) , were determined using titrimetric methods. Ammonium ( N H 4 + ), nitrate ( N O 3 ) , iron ( F e 2 + ) , phosphate ( P O 4 3 ) , and sulfate ( S O 4 2 ) were measured using a UV−2005 spectrophotometer (ISMS, Baghdad, Iraq). All analyses were performed in triplicate to ensure accuracy.

2.4. Spatial Analysis

Geographic Information System (GIS) techniques, complemented by the Inverse Distance Weighting (IDW) approach, were implemented using ArcGIS 10.8’s Spatial Analyst Extension for spatial characterization of the samples over the study area. Spatial distribution maps for water quality parameters and the Water Quality Index were generated. These maps can greatly improve the visualization of the obtained results and are essential tools for decision-making and water resource management.

2.5. Data Treatment by Statistical Method

To assess the physicochemical properties of groundwater in argan tree regions, we used Microsoft Excel to compute the mean and standard deviation for each parameter, which facilitated trend analysis across various concentrations at each sampling point. For deeper insights into spatial trends within these different sampling points, we employed multivariate techniques, including a Pearson correlation matrix using R4.3.1 software, whereas the Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and K-means clustering were plotted using Origin (OriginPro 2024 10.1.0.178) and Python 3.11.5.
In order to deepen our understanding of the water properties and water quality, the application of multivariate statistical methods greatly helps in unraveling intricate data matrices [46]. First, the Pearson correlation matrix was used, which enabled us to determine the degree of association between two parameters, with the strength and significance of the correlation reflecting the nature of their relationship. Likewise, PCA and HCA stand as exemplary multivariate statistical techniques known for their efficacy in analyzing and interpreting extensive and complex water quality datasets [47]. Not only that, these methods serve to identify pollution sources, classify sampling sites, and quantify spatial variations in water quality [48].

2.6. K-Means Clustering

The K-means clustering algorithm, first introduced by Mac Queen in 1967 and later developed by Lloyd in 1982 [49,50], is one of the classical and most widely used clustering algorithms in numerous fields [51]. It represents a simple iterative method to partition a certain dataset into a user-specified number of clusters ‘k’, with the mean value in each cluster called a centroid [51]. The K-means algorithm calculates its center iteratively [52].
Let {xiRd}i = 1,…,n be a dataset with k clusters and C1,…,kRd be the cluster center; the K-means algorithm minimizes the following functions:
m i n q ( x i ) = r C r x i 2
where function q(x) returns the closest centroid for sample xi [53].
To calculate the centroid, the K-means algorithm starts by creating initial k points as centroids CK [53]. Then, it makes a comparison among all n data using the Euclidean distance and assigns each sample to the nearest adjoining centroid [49,53]. These two steps are repeated and recomputed until convergence, where the centroids do not change between the two consecutive rounds. This process is known as the Lloyd iterative procedure [53,54]. The K-means algorithm was made using the Scikit learn library, sklearn cluster function, and the definition of the centroids by the kmeans cluster_centers function and calculation of the Euclidean distance with the sklearn.

2.7. Water Quality Index (WQI)

The Water Quality Index (WQI) is widely used and is regarded as one of the most efficient techniques for evaluating water quality, being applied to both surface water and groundwater [55,56,57,58].
The Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) provides a straightforward approach that simplifies intricate water quality data and is renowned for its adaptability in choosing input variables. It has accuracy in assessing water quality across diverse water types in specific regions and high tolerance for missing data [57,59,60].
This index consists of three key components: scope (F1), which indicates the number of parameters falling short of set water quality standards; frequency (F2), which denotes the rate at which these standards are not met; and amplitude (F3), which pertains to the degree to which the standards are not attained [57,60].
The CCME WQI can be expressed mathematically in three steps as follows [60,61]:
Step 1: Compute the scope, where F1 (scope) = the number of variables whose objectives are not met:
F 1 = N u m b e r   o f   f a i l e d   v a r i a b l e s T o t a l   n u m b e r   o f   v a r i a b l e s × 100
Step 2: Compute the frequency, F2 (frequency) = the number of individual tests that do not meet the objectives:
F 2 = N u m b e r   o f   f a i l e d   t e s t s T o t a l   n u m b e r   o f   t e s t s × 100
Step 3: Compute the amplitude, F3 (amplitude) = amount by which failed test values do not meet the Moroccan standard, and is calculated in the different steps.
The number of times a person’s concentration exceeds (or falls short of, if the target is a minimum) the objective is expressed as follows:
When the test value cannot be greater than the objective:
e x c u r s i o n   i = F a i l e d   T e s t   V a l u e   i O b j e c t i v e   j 1
When the test value must not be lower than the recommended guidelines values:
e x c u r s i o n   i = O b j e c t i v e   j F a i l e d   T e s t V a l u e   i 1
The normalized sum of excursions, or nse, is a variable that is calculated as follows:
n s e = n = 1 i e x c u r s i o n   i T o t a l   n u m b e r   o f   t e s t s
The calculation of F3 involves the utilization of an asymptotic function that scales the normalized sum of the excursion from objectives (nse) to produce a numerical range that spans from 0 to 100 and can be represented as:
F 3 = n s e 0.01 n s e + 0.01
The final step involves combining all factors to calculate the index using the CCME WQI equation into one score:
C C M E   W Q I = 100 F 1 2 + F 2 2 + F 3 2 1.732
The resulting WQI scores are categorized into five classes (Table 1) that are easy to interpret with a numerical scale ranging from zero (poor quality) to one hundred (excellent quality) [57].

2.8. Health Risk Assessment

Human health risk assessment is a technique for determining if exposure to any pollutant, at any level, increases the likelihood of unfavorable health consequences on residents [55]. Thus, in this investigation, hazard quotients (HQs) and hazard indices (HIs) were utilized to estimate the non-carcinogenic risk associated with each water sample. The following equations [57] were used to assess the danger presented by Fe, Pb, NO 3 , and NH 4 + , Cr, As, Ni, and Mo via the drinking water route.
The average daily dose (ADD) via drinking pathway was used according to the following equations (Equation (4)) [62].
A D D m g K g d a y =   C w × I R ×   E F ×   E D     B W × A T
H Q =   A D D R f D
H I = H Q
In this calculation, the Average Daily Dose (ADD) represents the daily exposure level for each element, expressed as (mg/kg-day)−1. Cw refers to the concentration of the element in the water (mg/L), while the ingestion rate (IR) refers to daily water consumption—2 L/day for adults and 1 L/day for children. Exposure frequency (EF) is set at 365 days/year, with exposure duration (ED) defined as 30 years for adults and 6 years for children. The body weight (BW) is assumed to be 70 kg for adults and 17 kg for children, and the average lifespan (AT) is calculated as 30 × 365 days for adults and 6 × 365 days for children. The hazard quotient (HQ) measures risk, with the oral reference dose (RfD) specific to each element: Pb (0.0035), Fe (0.7), NO 3 (1.6), NH 4 + ,   (0.97), Cr (0.003), As (0.0003), Ni (0.02), and Mo (0.005) mg/kg/day [61,63,64,65].
To assess exposure risks for both adults and children through drinking water, the daily intake was calculated based on the U.S. Environmental Protection Agency (EPA) guidelines [62]. Water samples with HQ values below 1 are considered safe, while HQ values above 1 indicate potential health hazards.

3. Results and Discussion

3.1. Physicochemical Analysis

Table 2 provides the results on the twenty-five physical–chemical parameters measured in the studied water samples (AW1 to AW13). Interpreting the results obtained involves comparing the values to the Moroccan Standard to assess the quality of the water samples around argan trees and comparing the two regions.
Upon data analysis, variations in the measured parameters were observed across the samples. Figure 4 represents the spatial distribution maps of some parameters. Water temperature ranged from 17.9 °C (AW10) to 29 °C (AW12). pH levels range from 6.23 to 8.16, indicating a slightly acidic to slightly alkaline condition, with all samples within the range specified by the Moroccan standard, except for AW9, which falls below the permissible range. Electrical conductivity (EC) varies between 233 µS/cm and 3760 µS/cm, exceeding the range prescribed by the Moroccan Standard at three locations: AW1, AW3, and AW7. This indicates the high concentrations of dissolved salts and ions in groundwater that could be attributed to water–rock interactions and seawater intrusion [66]. In coastal locations, EC concentrations in groundwater are higher because of seawater intrusion [36]. Such elevations can impede water absorption by plants, which results in diminished productivity and yield reductions, particularly in sensitive crop species like argan trees [67,68]. In fact, TH and pH could also fluctuate due to corrosiveness in the water that results from high concentrations of EC. Although this does not affect health forthright, it can alter the taste of water and cause some health issues like skin and gut problems [68,69].
In addition, dissolved oxygen (DO) varies between 2.3 and 13.6 mg/L. Only five samples are within the limit recommended by the Moroccan standard (5 mg/L to 8 mg/L), with sample AW8 displaying the highest concentration, indicating a higher degree of oxygenation. A regional comparison (Figure 4c) reveals that the northern samples (AW1, AW2, and AW3) exhibit the lowest DO values. Oxidation–reduction potential varies from 136 mV to 213 mV as shown in Figure 4d, indicating the tendency of water to either gain or lose electrons. Turbidity, which reflects water clarity, ranges from 0.22 NTU to 7.61 NTU. Higher turbidity values above the permissible limit of 5 NTU are recorded in samples AW3 and AW6. This suggests the presence of suspended particles, colloidal matter, or disease-causing organisms that find a favorable environment for microbial growth. This could lead to health issues like nausea, diarrhea, and cramps, especially in vulnerable people such as infants, the elderly, and those with weak immunity [69,70].
Salinity ranged from 0.22% (AW12) to 2.63% (AW7) across the samples. Indeed, the Moroccan standard does not specify a precise salinity range, but typical values for freshwater bodies are generally below 0.5% [71]. This indicates that some samples may have higher salinity levels, potentially due to factors like saline intrusion or anthropogenic activities [72]. Bicarbonate levels ranged from 192.15 mg/L to 484.95 mg/L, with the highest value observed in sample AW1, while the lowest was in sample AW2. The Moroccan standard does not define a specific limit for bicarbonate, suggesting that these values may not pose immediate concerns within the context of the standard.
Total hardness, calcium, and magnesium concentrations varied considerably among the samples. Total hardness ranged from 53.86 mg/L to 154.33 mg/L, calcium from 21.55 mg/L to 61.73 mg/L, and magnesium from 13.52 mg/L to 60.42 mg/L, with no significant difference observed between the northern and southern regions. In addition, nitrate concentrations varied in a range of 2.69 mg/L–32.51 mg/L, with the highest value recorded in sample AW4 and the lowest in sample AW8. Sulfate levels varied widely, with concentrations ranging from 25.22 mg/L (AW7) to 221.04 mg/L (AW2). It is worth noting that the northern region samples (AW1, AW2, and AW3) have all recorded high concentrations of sulfate. As for phosphate, the samples’ readings vary between 0.00065 and 0.001 mg/L, with AW4 and AW7 showing the lowest values and AW6 representing the highest. The distribution pattern of phosphate was similar across samples, with no significant differences observed.
Alarmingly, in this study, all samples had ammonium concentrations above the Moroccan standard threshold of 0.5 mg/L, with values ranging from 0.619 mg/L to 4.281 mg/L (AW1). This, in fact, suggests potential contamination from sewage or agricultural activities [66]. Moreover, chloride concentrations varied greatly, ranging from 33.725 mg/L (AW12) to 899.925 mg/L (AW1). Like ammonium, AW1 recorded the highest chloride concentration, being the only value surpassing the Moroccan standard limit of 750 mg/L. Chloride in groundwater may originate from pollution sources such as seawater intrusion or industrial discharge, as this element exhibits high mobility while not adhering to geological formations nor participating in chemical precipitation [73,74]. Chemical Oxygen Demand (COD) concentrations ranged from 7.9 mg/L to 899.92 mg/L (Figure S3).
To sum up, the physicochemical analysis of the groundwater samples in the argan tree areas revealed remarkable variations, with some exceeding the Moroccan standards for EC, DO, turbidity, ammonium, and chloride. These variations indicate spatial heterogeneity in water quality, likely due to natural and human-related factors. Notably, the northern samples showed lower DO and higher sulfate concentrations, but elevated EC and salinity levels, suggesting potential seawater intrusion or contamination. Such water quality fluctuations could affect plant and human health, underlining the influence of local hydrogeological and environmental factors.

3.2. Heavy Metals

Pollutants such as arsenic, chromium, cadmium, lead, nickel, molybdenum, copper, and iron can pose risks to human health and the environment if present in elevated concentrations. Comparing the measured concentrations to relevant regulatory limits or guidelines can help assess potential risks and determine the need for remediation or treatment measures, as well as assess the adjustment of argan trees in an environment containing such elements. In this regard, the concentrations of these pollutants in our water samples are presented in Table 2.
Arsenic concentrations range from below the detection limit to 0.0104 mg/L across the samples, as illustrated in Figure 5. Arsenic is a highly toxic metalloid that can occur naturally in groundwater, posing significant health risks even at low concentrations [75,76]. Sample AW11 exhibited the highest concentration, which slightly exceeded the limit set at 0.01 mg/L by the Moroccan standard, indicating potential health risks. Chromium, cadmium, and copper were barely detected, if at all, in some samples. Iron levels ranged from 0.016 mg/L to 0.022 mg/L, with sample AW8 recording the highest concentration. Although these values do not exceed the Moroccan standard’s limit of 0.3 mg/L, they may not pose immediate health risks but could contribute to aesthetic issues like discoloration or staining [77,78].
Lead is a notorious heavy metal with detrimental effects on neurological development, particularly in children, and can also harm aquatic ecosystems [78,79]. In our samples, the concentrations of this heavy metal ranged from below the detection limit to 0.065 mg/L, with all the samples presenting values exceeding the permissible value for lead, except for samples AW5 and AW9, where there was none. The concentrations of nickel ranged from 0.0001 mg/L to 0.0044 mg/L, while molybdenum concentrations fluctuated from 0 mg/L to 0.01 mg/L. The concentrations of these two heavy metals in the samples all fall within the normal range.
These heavy metals are of particular concern due to their persistence in the environment, potential bioaccumulation in the food chain, and adverse effects on human health and ecosystems [80]. Therefore, even when their concentrations remain below the Moroccan regulatory limits, continuous monitoring and control are essential to ensure long-term protection of both human health and the environment.

3.3. The Correlation Matrix

The correlation matrix provides valuable insights into the complex interplay between various water quality parameters, facilitating a better understanding of the underlying processes, potential sources of contamination, and environmental consequences.
The correlation matrix provided in Figure 6 encompasses a plethora of parameters associated with water quality analysis. The variables include temperature and various chemical compositions and pollutant concentrations such as pH, EC, DO, redox potential, TUR, SAL, H C O 3 , TH, Ca2+, Mg2+, N O 3 , S O 4 2 ,   PO 4 3 , N H 4 + , Cl, Fe, COD, As, Cr, Cd, Pb, Ni, Mo, and Cu. Each cell in the matrix represents the correlation coefficient between two parameters, ranging from −1 to 1. A perfect positive correlation coefficient is indicated by a correlation coefficient of 1, suggesting that as one variable increases, the other also increases. Oppositely, a perfect negative correlation is indicated by a correlation coefficient of −1, suggesting that as one variable increases, the other decreases. No correlation is indicated by 0.
Upon examining the correlations, several patterns emerge. One of the noticeable correlations is a negative one between temperature (°C) and several parameters such as pH, EC, DO, redox potential, TUR, SAL, and Fe concentrations. This suggests that as the temperature increases, these other parameters tend to decrease. For example, the negative correlation with Fe (−0.544) concentrations implies the precipitation or dissolution of iron compounds affecting its concentration [81,82]. Conversely, parameters like bicarbonate, nitrate, total hardness, Ca2+, and Mg2+ show positive correlations with temperature, indicating an increase in these parameters with rising temperature.
There is a strong negative correlation (−0.927) between pH and redox potential. This relationship is expected as pH influences the speciation of various chemical species in the water, including those involved in redox reactions. Lower pH values generally indicate more acidic conditions, which can enhance the oxidation of certain elements and compounds, thereby affecting redox potential. Conversely, higher pH values promote reducing conditions. In addition, pH exhibits a negative correlation with several ions like calcium, magnesium, and sulfate, but a positive correlation with nickel.
Dissolved oxygen shows, on one hand, negative correlations with TH, SO 4 2 , Cl, and salinity, implying that as dissolved oxygen decreases, these parameters tend to increase. On the other hand, it shows a positive correlation with iron concentrations. Dissolved oxygen is crucial for the ecosystem dynamics. Low DO levels can result from pollution sources like agricultural runoff, leaching, industrial waste, dissolved gases, and elevated temperatures, all of which contribute to ecosystem degradation and alteration [83]. This could be caused by some anthropogenic factors that increase temperature, which in turn, increase microbial activity [69].
For EC, we depict strong positive correlations with parameters like bicarbonate concentration, magnesium, ammonium, lead, salinity, and chloride with values of 0.5764, 0.5267, 0.5546, 0.543, 0.9817, and 0.9317, respectively. These correlations reflect the conductive properties of ions in water, and are an indicator of its mineralization and its salinity [36]. When the values of EC increase, it is an indicator of pollutants being carried in the groundwater as a consequence of anthropogenic activities [68,84]. Since there is a noticeable presence of nutrients like N O 3 and N H 4 + , we can confirm that EC could have been impacted by agricultural activities [66], as we know about these sampling points from fieldwork. The main salts contributing to conductivity are sodium chloride, magnesium chloride, and calcium chloride. Therefore, chloride concentration shows a strong positive correlation with conductivity and salinity with values of 0.9317 and 0.9202, respectively. The consequence of this correlation is significant for water treatment and agricultural practices, as high chloride levels can affect the palatability of drinking water and impact soil salinity, potentially reducing crop yields and affecting argan trees’ growth. Additionally, this correlation also suggests the influence of seawater intrusion in the coastal sampling sites [36,66,73].
Salinity refers to the concentration of dissolved salts in water, which directly contributes to its conductivity, hence the strong positive correlation (0.982) between salinity and electrical conductivity. Consequently, areas with high salinity tend to have higher electrical conductivity, like the sampling sites of AW, AW3, and AW7. The consequence of this correlation is particularly important in assessing the salinity of water bodies, especially in regions where saltwater intrusion or anthropogenic activities affect freshwater resources and agricultural practices [85].
Additionally, the positive correlation between Ca2+ and Mg2+ concentrations and TH indicates that these ions primarily define the water hardness and may share common sources or undergo similar geochemical processes, indicating that their common origin is likely from carbonate minerals such as calcite and dolomite [66,86]. The presence of another positive correlation between calcium and sulfate means that there might be a dissolution of gypsum or anhydride in groundwater [61].
Furthermore, both sulfate and ammonium have also shown direct correlations with lead and chloride ions. Comparably, chloride has demonstrated a strong positive correlation with magnesium, which could explain the impact of natural systems and agricultural undertakings, which, in turn, helps the process of oxidation/dissolution of sulfate minerals in clay and silt deposits and therefore increases sulfate concentrations [87].
Nickel shows strong positive correlations with arsenic and phosphate, with values of 0.472 and 0.725, respectively. The geochemical co-occurrence of nickel and arsenic in sulfide minerals like pyrite makes them more prone to being mobilized in groundwater as a result of weathering and dissolution of these minerals. Higher concentrations of both these heavy metals are more likely to be found in groundwater systems with low oxygen concentrations, as the mobility of these metals increases in such reducing conditions [88]. As for nickel correlation with phosphate, it confirms the effect of agricultural runoff in the sampling points. Phosphate is commonly found in agricultural fertilizers; that is why locations with high agricultural activity may exhibit elevated phosphate levels in groundwater in these areas, and nickel may also be coupled with these inputs or decaying organic materials [89]. Contrariwise, nickel shows negative correlations with ORP, chromium, and bicarbonate. As aforementioned, nickel tends to be more mobile under reducing conditions. When ORP increases, the water becomes more oxidizing, causing nickel to precipitate out of solution and reduce its concentration [88]. However, chromium is more soluble under oxidizing conditions. This opposing behavior explains the negative correlation of these two heavy metals [90].
These correlations are important for assessing the overall contamination level, as they offer insights into potential contaminant sources or processes affecting water quality within the argan trees’ habitat.

3.4. Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a statistical method that provides insights into the key characteristics that shape the interpretation of a dataset. It aids in data reduction and reveals statistical relationships among water constituents while preserving the essential information [46,91,92]. It has been employed to discern both human-made and natural influences on surface water quality [70,93]. Factor analysis is a method for reducing a large number of linked variables to a smaller number of super variables while simultaneously defining the links between observable variables and potentially unknown factors [94,95].
Factor loading is important as it shows how strongly a variable and a factor are related [70]. On one hand, the absolute loading values of more than 0.75, 0.50–0.75, and 0.30–0.50, respectively, were used to classify factor loadings as “strong,” “moderate,” and “weak” [96]. On the other hand, factors that have eigenvalues larger than 1 are rotated using the Varimax rotation technique and are regarded as important components [97].
In order to assess the differences in compositional patterns across the examined water samples and determine the variables affecting each one, the PCA loading for all the studied parameters was analyzed. It allowed the extraction of eight principal components with eigenvalues greater than 1 (Figure 7b), explaining 93.2% of the overall variance of the data, as provided in Table S2 in the supplementary materials. The first three components (PC1, PC2, and PC3) together account for over 56.9% of the total variance in the dataset of the study area (Figure 7a).
On one hand, based on the extracted variable loadings from the rotation component, the first component (PC1) accounts for 27.85% of the total variation. PC1 is indicative of water mineralization and salinity levels, exhibiting weak positive loadings for association with variables such as salinity, EC, TH, Ca, and Cl. On the other hand, PC2 contributes to approximately 15.49% of the total variance and displays distinct weak loadings. It exhibits negative loadings for N O 3 and temperature with −0.342 and 0.343, respectively, and a positive loading for Fe with 0.364, suggesting that it may capture fluctuations in nutrient levels and temperature.
PC3 is notably influenced by parameters such as pH, ORP, P O 4 3 , COD, As, and Ni. It is to be highlighted that the only significant negative loading of this PC is with ORP. Hence, it is indicative of mixed anthropogenic and natural sources of acidity as well as possible phosphate, arsenic, and nickel contamination.
PC4 demonstrates stronger loadings for ORP, pH, and SO 4 2 . The significant contribution of ORP implies variations in the oxidizing or reducing conditions of the water, which can affect various chemical reactions and the overall quality of the water. Additionally, the negative loading with pH indicates that when ORP and SO 4 2 are high, the acidity levels in the water are low. The presence of a positive loading of sulfate in PC4 suggests variations in sulfate concentrations, which may be associated with industrial activities or natural geological factors, which confirms what the correlations suggested.
Moving on to the PC5 component, which contributes to around 8.48% of the total variance, a distinct pattern emerges; several variables show negative loadings, including DO, N O 3 , Fe, and Mo. Conversely, there is a positive loading with P O 4 3 characterizing PC5. This combination suggests that PC5 may represent variations in nutrient levels and dissolved oxygen content, possibly reflecting the influence of anthropogenic activities or natural processes on water quality parameters. The presence of phosphate has been related to agricultural practices, namely fertilization and sewage contamination [66].
In PC6, which contributes to approximately 7.15% of the total variance, we observe positive loadings for TUR, Mg, and N O 3 . Overall, PC6 appears to represent the influence of factors such as sedimentation, nutrient inputs, and anthropogenic activities in the water samples of the study.
Furthermore, PC7, which contributes to approximately 5.28% of the total variance, displays moderate positive loadings for DO and weak positive loadings for H C O 3 and PO 4 3 - . These loadings suggest that PC7 captures variations in dissolved oxygen content, with minor contributions from bicarbonate and phosphate levels. These variations could be influenced by factors such as microbial activity, nutrient inputs, and geochemical processes in the water bodies under study.
In PC8, we observe negative loadings for temperature and arsenic, indicating an inverse relationship between these variables and the principal component. Temperature variations might reflect seasonal changes or local environmental conditions, whereas arsenic levels may be altered by geological forces or human activity. Conversely, there are positive moderate loadings for COD and Cr. Elevated COD levels typically indicate higher organic pollution levels in the water, potentially originating from industrial or domestic sources. Similarly, increased Cr concentrations might indicate pollution from industrial activities such as metal processing or wastewater discharges. Overall, PC8 captures variations related to temperature, arsenic concentrations, organic pollution (COD), and chromium contamination, providing insights into the factors influencing water quality dynamics in the dataset.
The PCA results indicate that even though agricultural practices and urban development are the main observed activities in the study area, the impact of natural processes cannot be ignored, as they play a significant role in water quality [98,99].

3.5. Hierarchical Cluster Analysis

Hierarchical Cluster Analysis was conducted to classify the thirteen analyzed samples into distinct groups based on their water quality characteristics, as shown in Figure 8. The resulting dendrogram indicates that the parameters evaluated divided the samples into two important clusters or groups. The Euclidean distances are revealed by the comparatively significant linkage distance between these two groups [60,100].
We can infer the similarities in patterns or profiles among the samples grouped within the first cluster (AW1, AW3, AW7, and AW11). These samples share common characteristics, such as relatively higher temperatures, moderately alkaline pH levels, elevated electrical conductivity (up to 3760 µS/cm), and dissolved oxygen concentrations. Additionally, these samples exhibit similar trends in other parameters, including ORP, TUR, SAL, H C O 3 , TH, Ca 2 + ,   Mg 2 + , N O 3 , and PO 4 3 among others. Conversely, the samples in the second cluster (AW2, AW4, AW5, AW6, AW8, AW9, AW10, AW12, and AW13) demonstrate different profiles characterized by lower temperatures, a wider range of pH values, lower electrical conductivity, and dissolved oxygen concentrations compared to those in the first cluster. These samples also exhibit variations in other parameters.
In general, hierarchical clustering makes it possible to identify sets of samples that have similar traits related to the quality of the water, which facilitates the identification of underlying patterns and trends in the dataset. Based on the clustering results, it can be concluded that Argan trees tend to thrive in environments with moderately alkaline water and moderate conductivity levels.

3.6. K-Means Clustering

The unsupervised K-means clustering identified two groups around centroids as a function of DO and EC, as visualized in the scatter plot in Figure 9. The first cluster, plotted in blue, gathers the following samples: AW1, AW3, AW7, and AW11. The remaining samples (AW2, AW4, AW5, AW6, AW8, AW9, AW10, AW12, and AW13) were assembled in the second cluster, plotted in red. The K-means clustering method gave us the same resulting clusters as the Hierarchical Cluster Analysis. It is worth mentioning that there might be a similarity between the samples of the first cluster due to their proximity to the coast.

3.7. Water Quality Index

The Water Quality Index was used to determine the level of contamination at the different sampling locations. The assessment sheds light on thirteen parameters: pH, DO, turbidity, conductivity, Cl, Pb, Cd,   N H 4 + , N O 3 , F e 2 + ,   SO 4 2 As, Cr, Pb, and Ni, each adhering to the respective limit values set by the Moroccan standard.
The CCME WQI scores range from 73.43 to 87.09, indicating varying levels of water quality (Figure S4). Samples AW2, AW5, AW6, AW7, AW8, AW9, AW10, AW12, and AW13 achieved scores categorizing them as “Good”, with scores falling within the range of 80–94. Conversely, samples AW1, AW3, AW4, and AW11 received lower scores, classifying them as “Fair”, with scores within 65–79. Notably, sample AW9 achieved the highest score of 87.09, while AW4 attained the lowest score of 73.43, indicating comparatively poorer water quality. The distribution of classifications illustrates that the majority of samples (69%) fall into the “Good” category, followed by “Fair” (31%), and no samples fall into the “Excellent”, “Marginal”, or “Poor” categories, as shown in Figure 10.
The CCME WQI applied in this study serves as an integrated tool to evaluate the overall suitability of groundwater for domestic and agricultural use in the study regions. While it does not directly measure the impact of water quality on Argania spinosa, it provides a useful indication of the environmental conditions that support or may influence argan growth and cultivation.

3.8. Human Health Risk Assessment

The human health risk assessment serves as a pivotal tool to gauge potential health risks associated with exposure to contaminants through drinking water. Table S3 exhibited the highest non-carcinogenic risk values of the eight investigated variables (Pb, Fe, NO 3 , NH 4 + , Cr, As, Ni, and Mo) that are suspected to be the cause of some diseases. Found in groundwater, some of these variables are considered to be poisonous and toxic heavy metals, including chromium, copper, cadmium, lead, zinc, and iron [101].
Upon meticulous examination, the majority of individual contaminant HQ values for adults across water samples remain below the safety threshold, suggesting a generally low risk in drinking water consumption. However, Pb and NO 3 contributed to the HI exceeding the limit in AW4, indicating potential health risks linked to their presence. Conversely, except for AW3, AW5, and AW8, all the remaining samples exhibited HI values surpassing unity for the health risk assessment of children, as illustrated in the spatial distribution map (Figure 11). Furthermore, because the hazard quotient of Pb, NO 3 , and As reached 1.1, 1.2, and 2, respectively, in different water samples, children who consume water from these water sampling sites may suffer adverse health effects, as children are more vulnerable to pollution than adults. It is worth mentioning that even though heavy metals were exhibited only as trace elements, their presence at more or less of the limit values in drinking water could lead to toxicity of the human body. This could cause people to suffer from various effects ranging from reduced to even damaged mental frontal nervous function, kidney and liver failure, to diseases such as Alzheimer’s, Parkinson’s, multiple sclerosis, and muscular dystrophy if exposed to such contaminated water for a long period of time [101,102]. Lead is a notorious heavy metal for being the most widespread but toxic even in small amounts, as it accumulates and builds up over time, especially in children [103,104]. Due to their similar charge and size, lead can replace calcium in the bone. But when the latter is later ingested in high levels for skeletal system development, especially in children, it replaces the lead that becomes free, which then could cause nephrotoxicity, neurotoxicity, and hypertension. Aside from hindering the child’s growth, lead poisoning lies behind damage to the nervous system, learning disabilities, as well as crime and anti-social behavior in children. Hence, it is the number one health threat to children with effects lasting a lifetime [79,105].
It is worth mentioning that these sampling sites were deemed of rather “Fair” and “Good” quality according to the WQI assessment. However, in evaluating the non-carcinogenic health risk, we came to the conclusion that exposure to drinking water from human activities may be linked to nitrate-driven and lead-following and arsenic hazards, resulting in changes to the water sites that may affect exposed children’s cognitive abilities and cause certain diseases.

4. Conclusions

This study aims to assess the groundwater quality in argan trees surroundings through a comparative assessment of the groundwater in the southwest and northeast regions of Morocco, where argan trees are known to grow unlike any other location. The results of the physicochemical analysis show that, even though some parameters such as ammonium, turbidity, electrical conductivity, and dissolved oxygen are not within the permissible limits of the Moroccan Standard for all the samples, there is a notable spatial variability in groundwater quality in argan-growing areas. It also differs between the northern and southern argan regions, with the north showing lower dissolved oxygen and higher sulfate, EC, and salinity levels, suggesting possible seawater intrusion or contamination. In contrast, the southern region generally shows better water quality, indicating less influence from such factors. These fluctuations, likely driven by natural and anthropogenic factors, may impact both ecosystems and human health. ICP-AES also revealed concentrations exceeding the permissible limits of arsenic in sample AW11, and of lead in all the samples except for AW5 and AW9.
In light of this, the WQI has been performed to evaluate the water’s quality and safety for consumption purposes. The findings show that the sampling sites AW1, AW3, AW4, and AW11 were deemed of fair quality according to the WQI. All the other sampling sites were classified as of “Good” quality. However, these sites represented non-carcinogenic health risks for children with the human health risk assessment related to nitrate, lead, and arsenic contamination. The apparent discrepancy between water quality status and high hazard index values in certain areas (Figure 10 and Figure 11) is primarily due to the different objectives and calculation bases of the two indices. The WQI reflects the general suitability of water for irrigation or domestic use based on general parameters such as EC, pH, and major ions, where some zones exhibit good water quality, as shown in Figure 10. However, in Figure 11, the HI is a health risk assessment that focuses specifically on the potential non-carcinogenic effects of heavy metal exposure through ingestion and dermal contact. This explains why some areas with acceptable overall water quality still present elevated health risks. This highlights the critical necessity for continual monitoring and management of water quality to mitigate health risks, particularly for vulnerable populations like children.
The Hazard Index (HI) map provides spatial insight into potential non-carcinogenic health risks from groundwater consumption. This information can support authorities in identifying high-risk zones where targeted interventions such as installing filtration units and treatment systems, restricting drinking water use for affected sources, and monitoring public health are imperative. The map can also guide further investigations of pollution sources, such as agricultural runoff or industrial discharges, and enforce protective regulations. By prioritizing interventions in areas with heightened HQ and HI values as well as WQI to complement the assessment, decision-makers can better protect both human health and groundwater resources in argan-growing regions.
Combining PCA, HCA, and K-means clustering with the distribution maps of argan tree’s water quality created using GIS-Based Inverse Distance Weighted Interpolation (IDW) has proven to be crucial to understand the similarities and differences of the various sampling locations, and the influence of anthropogenic activities or natural processes behind the elevated concentrations of physicochemical parameters and heavy metal exposure. For instance, high correlations between certain ions and metals have indicated anthropogenic inputs, necessitating regulatory measures to mitigate pollution and protect aquatic ecosystems. Additionally, all the results obtained attest to the complex nature of the argan trees and their astounding ability to adapt and acclimatize to the harsh conditions of arid and semi-arid regions, further supporting their polymorphic character that allowed them to thrive in littoral, sublittoral, mountainous, and desert habitats. Therefore, regular monitoring of key parameters (e.g., EC, pH, nitrates, heavy metals) over time would provide valuable insights into water quality, ecological dynamics, and potential environmental impacts, especially on endangered species like argan trees and in regions showing early signs of anthropogenic pressure. Future research focusing on different seasons and on long-term trends can help track changes in water quality and give a better insight into the characteristics of argan trees’ water quality. Management strategies such as the protection of recharge zones in these arid and semi-arid regions, promoting water-saving irrigation practices such as drip irrigation, regulating agricultural inputs such as fertilizers and pesticides, and raising awareness among local stakeholders should also be implemented. The findings of this study provide baseline data necessary for adopting strategies that will assist in informed decision-making concerning water and argan trees’ management in the study area, and anticipate future environmental challenges such as climate change impacts and/or land-use alterations.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17162393/s1, Figure S1: Cloud coverage maps of Morocco during sampling season in January 2023 (a) and February 2023 (b); Figure S2: Climatographs of temperature (°C), relative humidity (%), and dew/frost point (°C) data of the samples for each month of 2023; Figure S3: Bar charts showing the spatial distribution of the remaining physicochemical parameters; Figure S4: Score of the WQI for the studied samples; Table S1: Geographic positions and climate classification of all the samples; Table S2: Loading factor of variables on significant principal components for the studied parameters; Table S3: Highest non-carcinogenic risk values of lead, iron, nitrate, ammonia, chromium, arsenic, nickel and molybdenum in the studied area.

Author Contributions

Y.T.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing—original draft, Writing—review and editing. W.L.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing—original draft, Writing—review and editing. T.L.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing—original draft, Writing—review and editing. A.L.: Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing—review and editing. N.N.: Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing—review and editing. E.M.A.: Data curation, Formal analysis, Validation, Writing—review and editing. F.S. and E.K.C.: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing—review and editing. J.C.G.E.d.S.: Writing—review and editing, Project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

All authors have read, understood, and complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Acknowledgments

The author would like to extend her heartfelt thanks to Laila Belmekki, Mohamed El Hassan Tligui, and Nabil Chatt for their collaboration and support.

Conflicts of Interest

The authors declare no conflicts of interest. The authors have no relevant financial or non-financial interests to disclose.

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Figure 1. Study area’s location in Morocco (a), the four administrative regions of sampling (b), and sampling sites in the northern region (c) and the southern region (d).
Figure 1. Study area’s location in Morocco (a), the four administrative regions of sampling (b), and sampling sites in the northern region (c) and the southern region (d).
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Figure 2. Geological map of the study area.
Figure 2. Geological map of the study area.
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Figure 3. Rainfall maps of Morocco of the last decade in 2012 (a) and 2022 (b).
Figure 3. Rainfall maps of Morocco of the last decade in 2012 (a) and 2022 (b).
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Figure 4. Spatial distribution maps of (a) potential of hydrogen, (b) electrical conductivity, (c) the degree of oxidation, (d) the oxidation–reduction potential, (e) turbidity, (f) salinity.
Figure 4. Spatial distribution maps of (a) potential of hydrogen, (b) electrical conductivity, (c) the degree of oxidation, (d) the oxidation–reduction potential, (e) turbidity, (f) salinity.
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Figure 5. Spatial distribution maps of (a) arsenic, (b) chromium, (c) lead, (d) nickel, (e) molybdenum.
Figure 5. Spatial distribution maps of (a) arsenic, (b) chromium, (c) lead, (d) nickel, (e) molybdenum.
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Figure 6. Correlogram of physicochemical variables and heavy metals: from dark red indicating negative correlations to dark blue indicating positive correlations.
Figure 6. Correlogram of physicochemical variables and heavy metals: from dark red indicating negative correlations to dark blue indicating positive correlations.
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Figure 7. The classification of water quality based on the Principal Component Analysis (a), and the variance ratio of each principal component (b).
Figure 7. The classification of water quality based on the Principal Component Analysis (a), and the variance ratio of each principal component (b).
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Figure 8. Hierarchical clustering dendrogram.
Figure 8. Hierarchical clustering dendrogram.
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Figure 9. Scatter plot of the two K-means clusters.
Figure 9. Scatter plot of the two K-means clusters.
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Figure 10. Spatial distribution maps of the CCME water quality of the northern and southern regions of the study area.
Figure 10. Spatial distribution maps of the CCME water quality of the northern and southern regions of the study area.
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Figure 11. Spatial distribution maps of HI for adults and children.
Figure 11. Spatial distribution maps of HI for adults and children.
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Table 1. Water quality classes as defined by the CCME WQI [57].
Table 1. Water quality classes as defined by the CCME WQI [57].
CategorizationScoreWater Quality Interpretation
Excellent95–100Water quality protected, near natural or pristine levels, virtually without threat or impairment.
Good80–94Water quality protected with minimal threats, rarely deteriorating.
Fair65–79Water quality is generally protected, but can be threatened or impaired.
Marginal45–64Water quality frequently deviates from natural or desirable levels due to risks and deterioration.
Poor0–44Water quality is often threatened or impaired, deviating from natural or desirable levels.
Table 2. Mean results of physicochemical parameters and heavy metals of the 13 samples in January–February 2023.
Table 2. Mean results of physicochemical parameters and heavy metals of the 13 samples in January–February 2023.
Sample NameT
(°C)
pHEC
(µS/cm)
DO
(mg/L)
ORP (mVH)TUR
(NTU)
SAL
(%)
HCO 3
(mg/L)
TH
(mg/L)
Ca2+
(mg/L)
Mg2+
(mg/L)
NO 3
(mg/L)
SO 4 2
(mg/L)
PO 4 3
(mg/L)
NH 4 +
(mg/L)
Cl
(mg/L)
Fe2+
(mg/L)
COD
(mg/L)
As
(mg/L)
Cr
(mg/L)
Cd
(mg/L)
Pb
(mg/L)
Ni
(mg/L)
Mo
(mg/L)
Cu
(mg/L)
AW122.47.0237604.52080.232.2484.95138.5255.4132.3115.69136.020.000794.28899.9250.0188.50.00930.000400.064600.00290
AW220.27.189704.51942.620.8192.15139.8055.9222.404.13221.050.000810.62159.750.01990.0029000.05290.0020.00440
AW321.67.0628502.32087.611.6372.10118.8547.5460.4216.05122.550.000730.71736.6250.0198.450000.020900.00810
AW421.87.348239.11870.250.4399.5585.0834.0329.9932.5155.550.000651.20124.250.0199.350.00020.000300.057100.0040
AW521.47.496958.81711.010.3372.1057.7223.0927.7715.6125.220.000691.0953.250.0189.20.00200000.00250
AW6227.26126891925.750.6381.2580.8032.3225.0812.6282.400.000981.14131.350.0179.30.0081000.02910.00390.00230
AW720.27.6533306.31702.631.6405.6569.2627.7025.7013.66125.110.000650.80749.050.0188.250.0003000.0520.00040.00120
AW819.77.16115313.61984.080.6344.6594.9137.9636.962.69123.870.000720.83523.6250.0227.90.0023000.023600.00610
AW921.96.232338.42130.240.1219.6087.2134.8922.4019.8890.210.000670.81108.2750.0188.30.00490000.00110.00010
AW1017.98.015098.11470.360.2247.0553.8721.5513.524.7433.730.000720.7586.9750.0188.60.0043000.0130.00130.0010
AW1122.28.16220081360.671.1308.05154.3361.7353.2617.8961.970.000801.00649.650.0198.80.0104000.05650.00440.00970
AW12287.355976.51820.220.3329.4055.1522.0620.5012.0134.350.000750.9133.7250.0178.950.0024000.02370.0020.00110
AW13296.9611137.92060.90.5442.25150.4960.1916.6524.00108.940.000680.7499.40.0178.70.0098000.055500.00460
Min17.96.232332.31360.220.1192.1553.8721.5513.522.6925.220.000650.61933.7250.0167.9000000.00010
Max298.16376013.6213.0007.6102.20484.95154.3361.73460.42232.506221.040.0014.281899.9250.0229.3500.01040.0000.0000.0650.0040.0100.000
Mean22.1777.2981500.0777.462185.5382.0440.725346.05898.92239.56929.76614.72993.9210.0011.142335.0650.0188.7150.0040.0000.0000.0350.0010.0040.000
STDEV3.080.4861150.4722.78023.9052.4110.6286.00337.15114.86013.6108.22554.2970.0000.960321.6680.0010.4380.0040.0000.0000.0230.0020.0030.000
Moroccan Standard 6.5–8.527005–8 5 50400 0.57500.3300.010.050.0030.010.02 2
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MDPI and ACS Style

Tligui, Y.; Cherif, E.K.; Lechhab, W.; Lechhab, T.; Laghzal, A.; Nouayti, N.; Azzirgue, E.M.; Silva, J.C.G.E.d.; Salmoun, F. Integrated Multivariate and Spatial Assessment of Groundwater Quality for Sustainable Human Consumption in Arid Moroccan Regions. Water 2025, 17, 2393. https://doi.org/10.3390/w17162393

AMA Style

Tligui Y, Cherif EK, Lechhab W, Lechhab T, Laghzal A, Nouayti N, Azzirgue EM, Silva JCGEd, Salmoun F. Integrated Multivariate and Spatial Assessment of Groundwater Quality for Sustainable Human Consumption in Arid Moroccan Regions. Water. 2025; 17(16):2393. https://doi.org/10.3390/w17162393

Chicago/Turabian Style

Tligui, Yousra, El Khalil Cherif, Wafae Lechhab, Touria Lechhab, Ali Laghzal, Nordine Nouayti, El Mustapha Azzirgue, Joaquim C. G. Esteves da Silva, and Farida Salmoun. 2025. "Integrated Multivariate and Spatial Assessment of Groundwater Quality for Sustainable Human Consumption in Arid Moroccan Regions" Water 17, no. 16: 2393. https://doi.org/10.3390/w17162393

APA Style

Tligui, Y., Cherif, E. K., Lechhab, W., Lechhab, T., Laghzal, A., Nouayti, N., Azzirgue, E. M., Silva, J. C. G. E. d., & Salmoun, F. (2025). Integrated Multivariate and Spatial Assessment of Groundwater Quality for Sustainable Human Consumption in Arid Moroccan Regions. Water, 17(16), 2393. https://doi.org/10.3390/w17162393

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