From Contamination to Conservation: A Hydrochemical and Isotopic Evaluation of Groundwater Quality in the Semi-Arid Guire Basin (Morocco)

Abstract

Groundwater is a critical resource in semi-arid regions like Morocco’s Guire Basin, yet pollution and overexploitation threaten its sustainability. This study evaluates the groundwater quality of the Guire aquifer (Eastern High Atlas) using an integrated approach combining hydrochemical, isotopic (δ¹⁸O, δ²H, δ¹³C), multivariate statistical, and Geographic Information System (GIS) analyses alongside the Water Quality Index (WQI). Sixteen wells were monitored for physicochemical parameters (pH: 7–7.9; EC: 480–3004 μS/cm; BOD5: 1.03–30.5 mg/L; COD: 10.2–45.75 mg/L) and major ions, revealing widespread exceedances of Moroccan standards for Cl⁻, HCO₃⁻, Mg²⁺, Ca²⁺, and NH₄⁺. WQI classified 81% of samples as “Poor” to “Unsuitable for drinking” (WQI: 51–537), driven by elevated Cl⁻, Na⁺, and SO₄²⁻ from Triassic evaporite dissolution and NO₃⁻ (up to 45 mg/L) from agricultural runoff. Stable isotopes (δ¹⁸O: −7.73‰ to −5.08‰; δ²H: −66.14‰ to −44.20‰) indicate Atlantic-influenced recharge at 900–2200 m altitudes, with a δ¹⁸O-δ²H slope of 5.93 reflecting evaporation during infiltration. Strontium (Sr²⁺/Ca²⁺: 0.0024–0.0236) and bromide (Br/Cl: 8.47 × 10⁻⁵–9.88 × 10⁻⁴) ratios further confirm evaporitic dominance over anthropogenic contamination. This work provides actionable insights for policymakers, advocating for targeted restrictions on fertilizers, enhanced monitoring near evaporite zones, and artificial recharge initiatives. By linking geogenic/anthropogenic contamination to governance strategies, this study advances sustainable groundwater management in semi-arid regions.

1. Introduction

Groundwater constitutes a vital resource for drinking water supply, agricultural irrigation, and industrial activities, playing a pivotal role in sustaining public health and socioeconomic development globally [1,2]. However, increasing anthropogenic pressures—such as urbanization, intensive agriculture, and industrial discharges—coupled with climate change-induced variability in precipitation patterns threaten both the quantity and quality of groundwater resources [3,4,5]. In semi-arid and arid regions, where surface water scarcity exacerbates reliance on groundwater, these challenges are particularly acute. Understanding the interplay between geogenic processes, anthropogenic contamination, and climatic factors is thus critical for ensuring sustainable groundwater management.

Water quality assessment requires a multidisciplinary approach integrating hydrochemical analyses [6], isotopic tracers [7,8], advanced statistical techniques [9], and GIS [10,11,12].

According to global statistics, approximately half of the world’s population lacks access to adequate sanitation facilities, a critical issue linked to the spread of waterborne diseases and over 5 million annual deaths [13]. Effective water quality assessment and monitoring are essential for sustainable water resource management, enabling policymakers to address challenges such as nutrient enrichment and eutrophication [14,15]. Given water’s fundamental role in sustaining life and ecological balance, researchers have prioritized developing robust methodologies to safeguard water quality and ensure equitable access [16].

To assess and monitor groundwater quality, researchers have employed various methods and techniques, including the Water Quality Index (WQI) [17], fuzzy logic [17], and graphical representations such as Wilcox, Riverside, Stiff, Schöeller-Berkaloff, Piper, and Durov plots [18]. Additional approaches, such as environmental tracers, irrigation indices [19], machine learning models [20,21,22], and artificial intelligence (AI) [21,23,24,25], have been utilized to identify and quantify exchange fluxes. Among these, environmental tracer methods are particularly advantageous due to their capacity to deliver both localized point measurements and broad-scale assessments [26].

Groundwater vulnerability assesses the ability of contaminants to penetrate the aquifer. Vulnerability studies can provide valuable information to stakeholders working to prevent further environmental degradation [3]. Many arid and semi-arid regions of North Africa suffer from limited water resources due to scarce rainfall and high evaporation rates, exacerbated by climate change [17]. The management of water resources across many parts of Africa is facing significant challenges in quantitative and qualitative terms [18].

Many studies conducted in Morocco, in different regions and areas, investigate the groundwater recharge process and its residence time using hydrochemical and isotopic tracers in the Errachidia basin (Central-Eastern Morocco) [5], Hydrochemical and isotopic assessment for characterizing groundwater quality and recharge processes in Haouz plain aquifer (Central Morocco) [19], Hydro-chemical study of water quality in the Oued Fez watershed (Saïs Basin, Morocco) [20], Appraising seawater intrusion in the Moroccan Ghiss-Nekor coastal aquifer: Hydrochemical analysis coupled with GIS-based overlay approach [21], Mapping the spatiotemporal evolution of seawater intrusion in the Moroccan coastal aquifer of Ghiss-Nekor using GIS-based modeling [22], Identifying the origin of groundwater salinization in the Bokoya massif (central Rif, northern Morocco) using hydrogeochemical and isotopic tools [23], Assessment of groundwater mineralization of alluvial coastal aquifer of Essaouira basin (Morocco) using the hydrochemical facies evolution diagram (HFE-Diagram) [24], Identifying the effects of human pressure on groundwater quality to support water management strategies in coastal regions: A multi-tracer and statistical approach (Bou-Areg region, Morocco) [25], Multiple stable isotopes and geochemical approaches to elucidate groundwater salinity and contamination in the critical coastal zone: A case from the Bou-areg and Gareb aquifers (North-Eastern Morocco) [26], Characterization of groundwater in the arid Zenaga plain: Hydrochemical and environmental isotopes approaches [27], A comprehensive overview of groundwater salinization and recharge processes in a semi-arid coastal aquifer (Essaouira, Morocco) [28], The salinity origin and hydrogeochemical evolution of groundwater in the Oued Kert basin, north-eastern of Morocco [29].

Geochemical processes controlling groundwater quality under semi-arid environment: A case study in central Morocco [30], Index-based groundwater vulnerability and water quality assessment in the arid region of Tata city (Morocco) [31], Multi-tracer approach for assessing complex aquifer systems under arid climate: A case study of the River Tata catchment in the Moroccan Anti-Atlas Mountains [32], Geochemical and isotopic (oxygen, hydrogen, carbon, strontium) constraints for the origin, salinity, and residence time of groundwater from a carbonate aquifer in the Western Anti-Atlas Mountains, Morocco [33], Origin and residence time of groundwater in the Tadla basin (Morocco) using multiple isotopic and geochemical tools [34], Seawater intrusion assessment in the Bir Guendouz-Boulanoire coastal transboundary aquifers of Morocco and Mauritania [35]. Hydrochemical studies of groundwater in the Oum Er Rbia region [30,36].

Other studies conducted in different regions and areas under semi-arid environments, in developing countries, Groundwater Sustainability in Arid Regions Using Environmental Isotopes Hydrology Cursors [37], for example in Algeria, the authors studied groundwater quality in semi-arid areas of Algeria and Impacts on potable water supply and agricultural sustainability [38], Integrated assessment of groundwater quality in Souk Ahras region [39], using Geo-chemical and isotopic relationships between groundwater and rainwater in the Zaccar karst system (Northern Algeria) [40]. In Tunisia, the authors studied the Groundwater recharge estimation under a semi-arid climate in the Northern Gafsa watershed [41], via Geo-statistical Analyses and Groundwater Modelling [42], Groundwater salinity in a semi-arid region of central-eastern Tunisia: insights from multivariate statistical techniques and geostatistical modeling [43], Groundwater quality assessment for drinking and irrigation purposes by utilizing integrated water quality indexes in a semi-arid region, SE Sfax area, [44] and Combined geophysical approach as a tool to identify spatial groundwater aquifer distribution in structurally complex area. Case study of the Kasserine aquifer system (central Tunisia) [45].

Few studies in Libya, evaluate Challenges and Potentials Irrigation Water Resources [46]. In Egypt, the researchers integrate conventional and remote sensing to map groundwater potential areas using the analytical hierarchy process method, North Wadi Diit, https://doi.org/10.1038/s41598-025-94598-7 (accessed on 28 May 2025), Potentiality Delineation of Groundwater Recharge in Arid Regions Using Multi-Criteria Analysis https://doi.org/10.3390/w17050766 (accessed on 28 May 2025), Combining Hydro-Geochemistry and Environ-mental Isotope Methods to Evaluate Groundwater Quality and Health Risk (Middle Nile Delta) https://doi.org/10.3390/hydrology12040072 (accessed on 28 May 2025), Groundwater of the eastern Egyptian desert: Age and salinity patterns https://doi.org/10.1016/j.apgeochem.2025.106367 (accessed on 28 May 2025) Innovative Machine Learning, Iso-topic, and Hydrogeochemical Techniques for Groundwater Analysis in Arid Land-scapes in Egypt’s Eastern Desert https://doi.org/10.1007/s41748-025-00628-9 (accessed on 28 May 2025)and the hydrogeochemical processes and its impact on the quality of groundwater in the area between Abu Simbil and Tushka, Western Desert, Egypt https://doi.org/10.1038/s41598-025-90669-x (accessed on 28 May 2025).

Stable isotopes ¹⁸O and ²H, integral components of the water molecule, are widely recognized environmental tracers. They provide critical insights into groundwater origin, flow pathways, and chemical evolution. Similarly, dissolved inorganic carbon (DIC) isotopes, such as ¹³C, serve as indicators of biogeochemical processes affecting groundwater [30]. Strontium isotope ratios (⁸⁷Sr/⁸⁶Sr) are employed to assess the lithological composition of aquifer formations and the degree of water-rock interaction [33].

Precipitation typically falls along the meteoric water line (MWL), which has a slope of 8 and is defined by the equation δ²H (‰) = 8δ¹⁸O (‰) + 10, known as the Global Meteoric Water Line (GMWL). However, in semi-arid and arid environments, meteoric water isotopic signatures often deviate from this line due to evaporative enrichment either before or after infiltration, resulting in a slope lower than 8 in the δ²H versus δ¹⁸O relationship. This deviation reflects a relative enrichment in heavy isotopes. In other cases, a shift away from the GMWL can also be caused by the mixing of meteoric waters with ocean-derived waters, such as those from the Atlantic Ocean, which possess distinct isotopic signatures. Such mixing processes can alter both the slope and intercept of the δ²H–δ¹⁸O relationship, indicating the influence of multiple water sources [34]. Isotope hydrology methodologies are increasingly vital in groundwater studies for characterizing water resources, both by analyzing the water molecule (δ²H, δ³H, and δ¹⁸O) and the isotopic composition of dissolved compounds (e.g., δ¹³C, ¹⁴C, δ¹⁵N, and δ³⁴S), as well as for assessing groundwater contamination and refining resource characterization [35].

Naturally occurring stable and radioactive isotopes in water enable the identification of their origin, average residence time, and recharge rates in groundwater systems, ensuring their long-term sustainability as reliable resources for human consumption, agriculture, irrigation, and industrial activities [35]. To safeguard groundwater and promote sustainable local economic development, a comprehensive assessment of water quality in the Guire aquifer is imperative [36]. This study focuses on evaluating groundwater quality in southern Morocco’s Guire aquifer, with results from a hydrochemical and multi-isotopic approach presented herein.

A multidisciplinary approach combining hydrochemistry, stable nitrate isotopes (δ¹⁵N_NO₃), multivariate statistical techniques, and GIS was employed to assess groundwater quality in the study area. This article underscores the role of environmental isotopic tracers in advancing the understanding of groundwater resources in southern Morocco.

Despite the growing body of research on groundwater in Morocco, the upper Guir Basin remains largely unexplored from a hydrogeological perspective. This study represents the first detailed investigation combining hydrochemical and isotopic characterization in this area, making it both exploratory and original. The selection of this zone was guided by its scientific, environmental, and socio-economic relevance. From a hydrological standpoint, aquifers in the region play a key role in recharging downstream basins, especially under conditions of increasing water stress in eastern Morocco. A thorough assessment of water quality and aquifer dynamics is thus essential for informed and sustainable groundwater resource management. Moreover, the area shows significant potential for future investment in irrigated agriculture, rural development, and ecotourism, all of which require a deep understanding of available water resources. This study therefore fills a critical knowledge gap while providing a foundation for future development and water governance strategies in the region.

2. Materials and Methods

2.1. Study Area

The study area lies within the Guir Basin, geographically situated between Lambert coordinates X (615,000–625,000 m) and Y (190,000–207,000 m) [37] (Figure 1). Administratively, it is located near Guerrama in the Errachidia Province.

The selection of this area is justified by several scientific and socio-economic factors. Hydrogeologically, the region remains poorly known, and this study constitutes the first detailed investigation of its groundwater characteristics, giving it a pioneering value. Moreover, the aquifers in this area play a key role in recharging downstream basins, especially under increasing water stress conditions in eastern Morocco.

In addition, the area holds significant potential for future investment, particularly in irrigated agriculture, rural development, and ecotourism. Understanding the quality and behavior of groundwater resources is essential for any future planning or sustainable development initiative in the region.

2.2. Geological Framework

The geological formations in the study area are predominantly Jurassic deposits influenced by faulting and folding during the Hercynian orogeny. As illustrated in Figure 1, the Guir Basin comprises Triassic strata composed of detrital deposits and doleritic basalt interbedded with evaporite layers. These strata lie in angular unconformity above the deformed Paleozoic basement, which was structured by multiple tectonic phases, and are further characterized by Cambro-Ordovician paleothresholds and boutonnière structures (Mougger). The Jurassic series rests conformably on the Triassic-Lower Lias red formations. Their lithology consists primarily of dolomite, limestone, calcareous marl alternations, and siliciclastic detritus (Figure 1).

2.3. Hydrological and Hydrogeological Frame

The upper Guir basin is located in a semi-desert bioclimatic zone. Temperatures there show significant seasonal variations, ranging from approximately −2 °C to 46 °C. Summer (June to September) is marked by very high temperatures, with maximums reaching 46 °C in July and August, while winter (December to February) experiences significantly lower temperatures, with minimums falling below 0 °C, particularly in January. These significant differences reflect a continental climate, accentuated by the aridity and continentality of the region. (Figure 2).

The aquifer systems in the Rheris watershed are: (1) Plio-Quaternary alluvium aquifers containing essentially, conglomerates, gravels, and pebbles, located along the valleys; (2) Cretaceous Aquifer generally containing limestone with a karst behavior, sand, and sandstone; (3) Lias-Domerian lower aquifer with limestone and dolomite, often fractured and sometimes karstified; (4) Aalenian-Dogger aquifer with the limestonemarl with cracked and karst networks.

2.4. Study Objective

• Characterize groundwater quality using conventional hydrochemical analyses and synthetic indices such as the Water Quality Index (WQI).
• Identify the dominant geochemical processes (such as evaporite dissolution or ion exchange) controlling the chemical composition of the water.
• Use stable isotope tracers (δ¹⁸O, δ²H) to trace the origin of groundwater and associated phenomena (evaporation or water mixing).
• Apply multivariate statistical tools (PCA) and GIS techniques for spatialization and data interpretation, with a view to identifying vulnerable areas and potential sources of contamination.
• Provide a solid scientific basis for the sustainable management of water resources in this region, with a view to territorial development and planning potential agricultural, tourism, or rural investments.

2.5. Methodology

2.5.1. Groundwater Sampling and Chemical Analysis

Groundwater samples were analyzed both in situ and in the laboratory for physicochemical and isotopic characterization. Field measurements of pH, temperature (T), turbidity, and electrical conductivity (EC) were conducted using a multiparameter probe (Hanna HI 9811-5, Hanna Instruments Inc., Woonsocket, RI, USA). Total dissolved solids (TDS) were calculated from EC values using the standard formula: TDS = EC × 0.65. Major ions—including calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), potassium (K⁺), ammonium (NH₄⁺), bicarbonate (HCO₃⁻), chloride (Cl⁻), sulfate (SO₄²⁻), nitrate (NO₃⁻), and nitrite (NO₂⁻)—were quantified following standard water quality protocols outlined by APHA (2012).

Calcium (Ca²⁺) and total hardness (TH) were determined via titration with a standardized EDTA solution (Merck KGaA, Darmstadt, Germany). Magnesium (Mg²⁺) concentrations were derived by subtracting Ca²⁺ values from TH. Bicarbonate (HCO₃⁻) and chloride (Cl⁻) ions were analyzed using sulfuric acid (H₂SO₄, Sigma-Aldrich, St. Louis, MO, USA) and silver nitrate (AgNO₃, Sigma-Aldrich, St. Louis, MO, USA) titration, respectively.

Sodium (Na⁺) and potassium (K⁺) levels were measured with a flame photometer (Model 130, Systronics, Ahmedabad, India). Sulfate (SO₄²⁻) concentrations were assessed via UV-visible spectrophotometry (Model 135, Systronics, Ahmedabad, India), while nitrate (NO₃⁻) was quantified using a colorimeter (Model 112, Systronics, Ahmedabad, India).

The analytical methods and corresponding Moroccan standard limits for each parameter are summarized in

Table 1. Moroccan standard.

Parameters	Analytical Method	Unit	Moroccan Standard
pH	pH meter (HI 2211, Hanna Instruments Inc., Woonsocket, RI, USA)	---	6.5 < pH < 9
T	Thermometer (Testo 110, Testo SE & Co. KGaA, Lenzkirch, Germany)	°C	T°< 30
EC	Conductimeter (Consort C833, Consort bvba, Turnhout, Belgium)	µS/cm	2700
DO	Oximeter (Hach HQ40d, Hach Company, Loveland, CO, USA)	mg/L	5 < O2 < 8
TURB	Turbidimetry (Hach 2100Q, Hach Company, Loveland, CO, USA)	NFU	5
Ca2+	Titrimetric technique	mg/L	75
Mg2+	Complexometry with E.D.T.A. (0.02 N)	mg/L	50
Na+	Flame photometer (Model 130, Systronics, Ahmedabad, India)	mg/L	200
K+	Photometer (Model 130, Systronics, Ahmedabad, India)	mg/L	50
Cl−	Mohr’s method	mg/L	300
HCO3−	Acido-basic titration (HCl 0.05 N)	mg/L	120
NO32−	Steam distillation	mg/L	50
SO42−	Nephelometric method	mg/L	200

.

2.5.2. The Ionic Balance Error (IBE)

The Ionic Balance Error (IBE) was calculated to assess the accuracy of the chemical analyses by comparing the total concentrations of cations (∑[cations]) and anions (∑[anions]) in each groundwater sample, expressed in meq/L. The IBE was computed using the following equation:

IBE = ((∑[cations] − ∑[anions])/(∑[cations] + ∑[anions])) × 100

(1)

All samples exhibited an IBE of less than 5%, indicating acceptable analytical precision in the measured ionic composition.

2.5.3. Calculation of Chloro-Alkaline Indices

To differentiate between cation exchange and reverse ion exchange processes in the groundwater system, chloro-alkaline indices (CAI-I and CAI-II) were calculated using Equations (2) and (3). These indices determine the dominant ionic exchange mechanism: Cation exchange occurs when Ca²⁺ and Mg²⁺ ions in groundwater are replaced by Na⁺ and K⁺ ions from aquifer materials. Reverse ion exchange refers to the replacement of Na⁺ and K⁺ ions in groundwater by Ca²⁺ and Mg²⁺ ions from aquifer materials [38].

CAI-I = (Cl⁻ − (Na⁺ + K⁺))/Cl⁻

(2)

CAI-II = (Cl⁻ − (Na⁺ + K⁺))/(SO₄²⁻ + HCO₃⁻ + CO₃²⁻ + NO₃⁻)

(3)

2.5.4. Stable Isotope Analyses

Oxygen-18 and deuterium, tracers internal to the water molecule, offer the greatest probability of behaving like water in a given environment, thanks to their dimensions and peripheral electron densities close to those of the water molecule. Therefore, the reliability of isotope studies will be greater the wider the variation ranges of these tracers.

Taking into account the causes of variations in isotope contents in water, such as temperature, altitude, or the effect of continentality, regions characterized by very distinct climatic conditions, such as arid and semi-arid zones, are ideal study sites. Indeed, the study of stable isotopes of the water molecule will provide additional information on the origin of water masses as well as the history of their surface and subsurface movements, which are difficult to obtain using conventional methods.

2.5.5. Isotopic Content of Precipitation

Estimating aquifer recharge altitudes is particularly interesting for the Atlas Mountains, where recharge zones are poorly understood. Generally, isotopes of the water molecule (Oxygen-18, Deuterium) are among the most widely used in aquifer investigations. They are expressed using delta notation, defined by Craig (1961) as:

δ(‰) = [(Rsample/Rreference) − 1] × 1000

(4)

The isotopic abundance ratio (R) is defined as ¹⁸O/¹⁶º or ²H/¹H. Positive δ-values denote enrichment of the sample relative to the reference standard, while negative values indicate depletion. For δ¹⁸O and δ²H (Deuterium), the reference standard is the Standard Mean Ocean Water (SMOW), where oceanic water is assigned a value of 0‰ by definition. Globally, the δ¹⁸O and δ²H composition of precipitation unaffected by evaporation follows a linear relationship, termed the Global Meteoric Water Line (GMWL). The GMWL is expressed as:

δ‰ (²H) = 8 δ‰ (¹⁸O) + 10

(5)

The intercept of the Global Meteoric Water Line (GMWL) represents the deuterium excess (d-excess), which is 10‰ for precipitation of oceanic origin. The slope (8) reflects equilibrium conditions during condensation at saturation. While no rainwater was sampled in this study, δ¹⁸O and δ²H values from precipitation in neighboring regions were analyzed:

-

Middle Atlas (Fez-Meknes plain, >600 m altitude):

δ¹⁸O of precipitation: −5‰ vs. SMOW.

Recharge zone of the Liassic aquifer (limestone plateau): δ¹⁸O ≈ −7.3‰ vs. SMOW.

-

Oulmes region (1260 m altitude, 100 km SE of Rabat):

δ¹⁸O of non-evaporated water: −6‰ to −7‰ vs. SMOW.

-

Souss Valley (1000 m altitude):

δ¹⁸O of precipitation: −5.6‰ vs. SMOW.

-

Western High Atlas (Agadir region):

δ¹⁸O of rainwater: −7‰ to −3.5‰ vs. SMOW.

The most isotopically depleted waters (δ¹⁸O: −7‰ to −8‰ vs. SMOW) occur in high-altitude areas of the High and Middle Atlas ranges. This depletion aligns with the altitude effect, where δ¹⁸O decreases by −0.3‰ per 100 m in the Beni Mellal Atlas and −0.26‰ per 100 m in other Atlas regions, as documented in prior studies.

Morocco’s precipitation isotopic data closely follow the GMWL (δ²H = 8δ¹⁸O + 10), confirming its utility as a regional reference for isotopic hydrology. Precipitation in the Atlas Mountains (>1000 m altitude), predominantly of oceanic origin, exhibits δ¹⁸O values of −7‰ to −8‰ vs. SMOW, consistent with high-altitude isotopic fractionation.

2.6. Water Quality Index (WQI)

WQI is one of the most widely used tools for assessing water quality. It provides policymakers and stakeholders with a clear, comprehensive overview of a water body’s pollution status. The standard development process for a WQI involves four steps: (1) parameter selection, (2) weight assignment, (3) sub-index function development, and (4) aggregation of weighted sub-index values [39].

Over the years, numerous WQI models have been developed to consolidate water quality datasets into a single cumulative value, enabling straightforward comparisons between different water sources and tracking changes over time or under varying conditions. This indexing system generates actionable insights for water managers, policymakers, and the public, aligning with frameworks like the EU Water Framework Directive (WFD). However, most WQIs focus narrowly on physicochemical parameters (e.g., nutrients, organic pollutants, dissolved oxygen levels), limiting their scope [40].

While specific methodologies vary, WQI development generally follows four sequential steps:

Selection of water quality parameters

Generation of parameter sub-indices

Assignment of parameter weights

Aggregation of weighted sub-indices

Step-by-Step Development

WQI models typically include 8–11 parameters, though the total may range from 4 to 26.

Measured parameter values are converted into unitless sub-indices using methods such as linear interpolation, rating curves, or direct concentration ratios. While most models apply these techniques, some omit this step.

Parameters are assigned weights based on their environmental or health impacts, often using unequal weighting (e.g., Analytic Hierarchy Process [AHP] or House Index methods). Adjusting weights for specific applications enhances accuracy. Proper weighting improves model robustness by reducing uncertainty, whereas inappropriate weights compromise results.

Weighted sub-indices are combined via functions (additive, multiplicative, harmonic mean, etc.) to calculate a final WQI score. This score is categorized using model-specific rating scales.

The WQI has been widely adopted for freshwater monitoring, including in the Suquía River (Argentina), Bagmati River (Nepal), Sapanca Lake (Turkey), and Terengganu River (Malaysia). It effectively identifies pollution trends and facilitates spatial-temporal comparisons. However, methodologies vary: for example, the Bagmati River WQI uses 18 parameters, while the Suquía River model employs 20. Streamlined WQIs are increasingly favored to reduce redundancy (e.g., correlated variables) and analytical costs. Customized indices must also account for local factors like land use changes or anthropogenic activities [41].

The WQI was first introduced by Horton in 1965, using 10 parameters to rate water quality. Brown later refined this into the NSF-WQI with support from the National Sanitation Foundation [13]. A panel of 142 experts established its parameter selection and weighting criteria, paving the way for subsequent models [13].

As a mathematical tool, the WQI simplifies complex water quality data into a single numeric value [42,43]. In Morocco, it has been applied to assess groundwater [1,17] and surface water [45], including proposals for a “global pollution index” [46,47].

This study evaluates drinking water quality in 16 wells from the Guire aquifer using the WQI. Results were compared against Moroccan drinking water standards to identify contaminants and inform sustainable management strategies for local stakeholders.

The Weighted Arithmetic Water Quality Index (WQI) was applied to assess water quality in this study. This method assigns parameter-specific weights (Wi) based on expert judgment of their relative health and environmental impacts [47]. The WQI calculation followed five sequential steps:

-

Relative Weight (Wi):

Each parameter’s relative weight was determined using Equation (6):

$$Wi=Wi/{\sum }_{i=1}^{n}Wi$$

(6)

where Wi = assigned weight for parameter $i$, and $n$ = total number of parameters.

-

Quality Score (qi):

The quality score for each parameter was computed via Equation (7):

qi = (Si/C_i) × 100

(7)

where C_i = measured concentration and Si = permissible limit (WHO/EPA standards).

-

pH Adjustment (Q_pH):

For pH, a modified quality score (Q_pH ) was calculated using Equation (8):

Q_pH = (C_pH − 8.5)/(6.5 − 8.5)

(8)

where C_pH = measured pH value.

-

Sub-index (SIi):

Sub-indices for each parameter were derived from Equation (9):

SIi = Wi × qi

(9)

$$WQI={\sum }_{k=1}^{n}Wi\times Qi={\sum }_1^{n}SI$$

(10)

Based on the computed Groundwater WQI (GWQI) and Surface Water WQI (SWQI), water quality was categorized as per

Table 2. The WQI categories.

Classes	Classification
0 to 25	Excellent
26 to 50	Good
51 to 75	Poor
76 to 100	Very poor
>100	Unsuitable for drinking

.

2.7. Irrigation Water Quality (IWQI)

Water quality is thus an important element in the sustainable use of water for irrigation, particularly in cases where salinity development is expected to be a problem in an irrigated agricultural area. The hydrochemical characteristics of the main GW and SW variables are used in this evaluation to determine the suitability for irrigation. The next paragraph provides a number of calculations that may assist in establishing the suitability of irrigation water [1].

Equation (6) is used to determine the sodium adsorption rate SAR [47], which is defined as the salt risk associated with calcium and magnesium concentrations.

$$\mathrm{S}\mathrm{A}\mathrm{R}={\displaystyle \frac{{\mathrm{N}\mathrm{a}}^{+}}{\sqrt{\frac{{\mathrm{C}\mathrm{a}}^{2+}+{\mathrm{M}\mathrm{g}}^{2+}}{2}}}}$$

(11)

$$\mathrm{R}\mathrm{S}\mathrm{C}=\left[\left({{\mathrm{H}\mathrm{C}\mathrm{O}}_3}^{-}+{{\mathrm{C}\mathrm{O}}_3}^{-}\right)-\left({\mathrm{C}\mathrm{a}}^{2+}+{\mathrm{M}\mathrm{g}}^{2+}\right)\right]$$

(12)

$$\mathrm{N}\mathrm{a}\%={\displaystyle \frac{\left({\mathrm{N}\mathrm{a}}^{+}+{\mathrm{K}}^{+}\right)\ast 100}{({\mathrm{C}\mathrm{a}}^{2+}+{\mathrm{M}\mathrm{g}}^{2+}+{\mathrm{N}\mathrm{a}}^{+}+{\mathrm{K}}^{+})}}$$

(13)

$$\mathrm{M}\mathrm{H}={\displaystyle \frac{({\mathrm{M}\mathrm{g}}^{2+}\ast 100)}{({\mathrm{C}\mathrm{a}}^{2+}+{\mathrm{M}\mathrm{g}}^{2+})}}$$

(14)

$$\mathrm{P}\mathrm{I}={\displaystyle \frac{\left({\mathrm{N}\mathrm{a}}^{+}+\sqrt{{{\mathrm{H}\mathrm{C}\mathrm{O}}_3}^{-}}\right)\ast 100}{({\mathrm{C}\mathrm{a}}^{2+}+{\mathrm{M}\mathrm{g}}^{2+}+{\mathrm{N}\mathrm{a}}^{+})}}$$

(15)

$$\mathrm{K}\mathrm{I}={\displaystyle \frac{{\mathrm{N}\mathrm{a}}^{+}}{{\mathrm{C}\mathrm{a}}^{2+}+{\mathrm{M}\mathrm{g}}^{2+}}}$$

(16)

$$\mathrm{P}\mathrm{S}={\mathrm{C}\mathrm{l}}^{-}+{\displaystyle \frac{1}{2}}{{\mathrm{S}\mathrm{O}}_4}^{2-}$$

(17)

$$\mathrm{R}\mathrm{S}\mathrm{B}\mathrm{C}={{\mathrm{H}\mathrm{C}\mathrm{O}}_3}^{-}-{\mathrm{C}\mathrm{a}}^{2+}$$

(18)

Equation (7) is used to calculate the RSC, that plays an important part of irrigation water. In addition, there is another approach, which is widely used to understand the effects of excess calcium and magnesium on soil and is calculated by the equation below [1].

The risk of magnesium (MH) Equation (9) and the percentage of sodium (%Na) Equation (8) are important parameters that can be used to evaluate the quality of GW and its appropriateness for irrigation purposes. A well-known classification was developed by Wilcox which was documented and used in the literature for a long time. The GW and SW were classified into five classes of Equations (8) and (9). PI is an index for the permeability of water in soil (Equation (10)) [1].

Sodium, compared to Ca²⁺ and Mg²⁺, KI > 1 indicates an excess of salt, whereas a KI < 2 indicates a shortfall in water (Equation (11)). Salinity Potential (PS) (Equation (12)) refers to the quantity of salt that builds up in the soil, which is constantly dissolved in irrigation water, increasing salinity as determined by the formula below. Since most natural waters do not have substantial levels of carbonate ions and bicarbonate ions do not precipitate magnesium ions, the alkalinity hazard will be measured by an indicator known as residual sodium bicarbonate (RSBC) and calculated using Equation (13). The calculation methods and classification criteria for irrigation quality parameters are provided in

Table 3. Calculating irrigation quality parameters and Classification of water [48].

Parameters	Classification
SAR	Excellent, Good, Permissible, Doubtful
RSC	Good, Medium, Bad
Na%	Excellent, Good, Permissible, Doubtful, Unsuitable
PI	Excellent, Good, Unsuitable
KI	Permissible, Non- Permissible
PS	Excellent, Good,
RSBC	Excellent, Good,
MH	Suitable, Unsuitable,

. General hydrochemical characteristics of groundwater are summarized in

Table 4. Hydrochemical Characteristics of groundwater and General groundwater quality description in the study area.

Variable	Mini	Max	Mean	Standard Deviation
EC	480.000	3004.000	1286.688	694.442
Cl−	70.400	1092.655	429.575	268.304
pH	7.000	7.900	7.504	0.214
BOD5	1.030	30.500	3.911	7.128
COD	10.200	45.750	20.466	12.525
Turbidity	13.980	87.000	61.318	23.478
SM	0.010	0.090	0.028	0.024
T°	17.000	24.240	19.878	1.886
SO4	78.550	980.530	229.078	238.910
NH4+	0.080	12.834	2.345	3.248
NO3−	0.600	45.000	23.767	14.746
NO2−	0.050	10.300	1.451	2.573
Na+	0.000	850.200	148.601	219.179
K+	1.080	16.820	4.294	3.982
Ca2+	72.640	357.880	105.876	69.083
Mg2+	24.300	77.060	51.655	18.730
HCO3−	155.550	347.700	246.859	58.957

Note(s): EC: electrical conductivity; BOD5: Biological oxygen demand; COD: Chemical Oxygen Demand; SM: Suspended matter.

) [1].

2.8. Multivariate Statistical Analysis

The water quality index provides a clear and comprehensive picture of groundwater quality. Principal component analysis provides a statistical basis for parameter reduction using a minimal number of parameters. Parameter selection is based on data availability [49].

Principal components analysis (PCA) has been used to group together individual parameters to form a composite indicator with the motive to use the entire data set to capture maximum variance with a minimum number of parameters. This also requires individual indicators to have a common unit hence they have been normalized (by using the z-score in this study) prior to PCA. The factors estimated (using PCA) form clusters of indicators having the strongest associations amongst themselves. Therefore, the final aggregate is based on the “statistical” dimensions of the normalized dataset and is independent of the dimensionality of the original data set [49].

In the final study after the parameter reduction step comes the important step of estimating weights in which some researchers prefer equal weights to all parameters, while others opt for either subjective or objective methods Following this sub-index is developed by using rating curves and other methods described in the literature [49].

Multivariate statistical analyses such as principal component analysis (PCA) and correlation analysis were carried out by the XLSTAT (Version 2016, Addinsoft SARL, Paris, France). Generally, the PCA method in hydrogeochemical studies condenses a big collection of data into a few components that have comparable properties. In order to show relationships between data set variables and the influence of certain chemical parameters, a correlation analysis was also carried out [50].

2.9. Geographic Information System (GIS)

The Geographic Information System (GIS) is a software-based technology to determine the spatial distribution of groundwater chemical quality. ArcGIS (Version 10.7, Esri Inc., Redlands, CA, USA) was used for the spatial distribution of parameter zones, using the inverse distance weighted interpolation technique [51].

3. Results

3.1. Hydrochemical Characteristics of Groundwater and General Groundwater Quality Description

To evaluate groundwater chemistry and its suitability for consumption, groundwater quality must be assessed against established standards [1,51]. Table 4 summarizes the physicochemical parameters of groundwater samples. Key ranges include pH (6.74–9.25; unitless), electrical conductivity (SEC: 320–5689 μS/cm), total dissolved solids (TDS: 208–3698 mg/L), and major ion concentrations. Cations ranged as follows: Ca²⁺ (26–281 mg/L), Mg²⁺ (5–248 mg/L), Na⁺ (22–700 mg/L), and K⁺ (1–30 mg/L). Anions included HCO₃⁻ (159–1129 mg/L), Cl⁻ (21–766 mg/L), SO₄²⁻ (3–376 mg/L), NO₃⁻ (2–700 mg/L), and F⁻ (0.27–4.73 mg/L. Average values were: pH (7.62; unitless), SEC (1938.32 μS/cm), TDS (1318 mg/L), cations (Ca²⁺: 134 mg/L; Mg²⁺: 66 mg/L; Na⁺: 223 mg/L; K⁺: 7.3 mg/L), and anions (HCO₃⁻: 535 mg/L; Cl⁻: 256 mg/L; SO₄²⁻: 93 mg/L; NO₃⁻: 200 mg/L; F⁻: 1.72 mg/L). The ionic dominance order was Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ for cations and HCO₃⁻ > Cl⁻ > NO₃⁻ > SO₄²⁻ for anions. Elevated Na⁺ and HCO₃⁻ concentrations suggest rock weathering and dissolution processes dominate aquifer hydrogeochemistry [52].

Figure 3 shows that the pH, suspended solids, NH⁴⁺, NO³⁻, and K⁺ parameters are within the Moroccan standard for drinking water. While the Cl⁻, Turbidity, Mg²⁺, and HCO³⁻ parameters exceeded the Moroccan standard in all wells.

For COD, wells P1, P2, P15, and P16 exceeded the Moroccan standard. For SO₄²⁻, wells P4, P2, P14, and P15 exceeded the Moroccan standard. For NO²⁻, wells P2, P4, P13, P14, and P16 exceeded the Moroccan standard. For BOD5, well 1 exceeded the Moroccan standard. This shows that most wells exceeded the Moroccan standard for drinking water.

The analysis revealed groundwater temperatures ranging from 17 °C (P5) to 24.5 °C (P2), with an average of 21.6 °C. These values mirror seasonal air temperatures due to the shallow aquifer depth. The pH values (7.0–7.9) indicate neutral to slightly alkaline groundwater at all sampling points (Table 4).

Electrical conductivity (EC) ranged from 664 µS/cm (P7) to 3004 µS/cm (P4) (Table 4), increasing spatially from south to north (Figure 4). Elevated EC values stem from aquifer material leaching and potential marine intrusion.

• Sodium (Na⁺): Values ranged from 10.15 mg/L (P5) to 850 mg/L (P4), Moroccan (300 mg/L) standards at sites P1, P4, and P6 (Table 4).
• Chloride (Cl⁻): Concentrations (386–489.01 mg/L) surpassed Moroccan thresholds (300 mg/L) at all sites except P2 (Table 4), likely due to Triassic evaporite dissolution.
• Sulfate (SO₄²⁻): Levels (78.55–980.53 mg/L) exceeded Moroccan standards (250 mg/L) (Table 4), driven by gypsum-rich geological formations.
• Nitrate (NO₃⁻): Peaked at 42.38 mg/L (P5), indicating agricultural runoff (Table 4).
• Calcium (Ca²⁺): Concentrations (72.64–357.88 mg/L) exceeded the 160 mg/L potability standard at most sites (Table 4), reflecting calcite/dolomite dissolution.

Total dissolved solids (TDS: 366–1400 mg/L) were highest at P7, P5, and P6.

3.2. Piper Diagrams of the Ionic Composition of Groundwater

The Piper diagram (Figure 5) reveals three dominant hydrochemical facies: (1) hyperchloride-calcium sulfate, (2) chlorurea and calcium-magnesium sulfate, and (3) sodium-potassium chloride, indicating evaporitic and carbonate dissolution influences.

3.3. Isotopic Composition of Water in the Study Area

Stable isotopes (δ¹⁸O: −7.73‰ to −5.08‰; δ²H: −66.14‰ to −44.20‰) plot below the Global Meteoric Water Line (GMWL) with a regression slope of 5.93 (Figure 6), indicating evaporation during recharge. Recharge elevations (900–2200 m) correlate with δ¹⁸O altitudinal gradients (Figure 6 and

Table 5. Recharge altitude of sampled wells.

Sampling	The Recharge Zone
1	1420
2	1900
3	1020
4	1180
5	1600
6	2200
7	900

).

Isotopic characterization of groundwater was performed at water points located along the Guire Basin–Eastern High Atlas (Figure 1). Twelve groundwater samples were collected in 2021, and distributed throughout the study area (Figure 3). These points are located at varying altitudes, ranging from 800 m (P7) to 1529 m (P8), allowing assessment of altitude effects on stable isotope contents.

The results show variations in δ¹⁸O (−7.73‰ at P6 to −5.08‰ at P3 vs. SMOW; average: −6.38‰) and δ²H (−66.14‰ at P6 to −44.20‰ at P1 vs. VSMOW; average: −53.36‰). Plotting δ²H vs. δ¹⁸O (Figure 5) reveals alignment below the Western Mediterranean Meteoric Water Line (WMMWL), with a regression line: δ²H = 5.93δ¹⁸O − 12.671 (R² = 0.23).

The slope (5.93 < 8) indicates evaporation during recharge, except for points 9 and 10, which plot above the GMWL, reflecting Atlantic-influenced precipitation. These findings align with Abdelfadel et al. (2020) [53], who reported δ¹⁸O (−9.14‰ to −6.67‰) and δ²H (−65.21‰ to −45.82‰) in similar settings.

Using the δ¹⁸O altitudinal gradient (−0.26‰ per 100 m), recharge elevations were estimated (Figure 7 and Table 5), ranging from 900 m (P7) to 2200 m (P6). High-altitude zones (~400 mm annual rainfall; Figure 2) dominate aquifer recharge, consistent with Bouchaou et al. (1995) [54], who reported a −0.27‰/100 m gradient and recharge elevations of 450–1770 m in the Beni Mellal Atlas.

3.4. Carbon Origin

Dissolved inorganic carbon (DIC) in groundwater serves as a tracer of hydrological pathways and biogeochemical processes. Total dissolved inorganic carbon (TDIC) quantifies the sum of CO₂, HCO₃⁻, and CO₃²⁻ in water. The origin of TDIC is complex, reflecting contributions from:

• Organic matter decomposition in aquifer sediments (δ¹³C ≈ −28‰ to −25‰),
• Carbonate mineral dissolution (e.g., calcite, δ¹³C ≈ 0‰ to +2‰),
• Atmospheric CO₂ diffusion (δ¹³C ≈ −8‰ to −6‰).

In the Guire Basin, TDIC concentrations range from 366 mg/L (P4) to 1400 mg/L (P7), with δ¹³C values between −13.12‰ (P7) and −3.58‰ (P6) (Figure 8). These δ¹³C values indicate a predominance of carbonate mineral dissolution (e.g., calcite) rather than organic matter degradation, consistent with low biological activity in the soil.

A linear relationship between TDIC and δ¹³C (Figure 8) suggests that increasing TDIC correlates with enhanced carbonate dissolution, likely driven by CO₂ influx from soil or atmospheric sources.

3.5. Strontium (Sr²⁺/Ca²⁺) Raio

Strontium is strongly associated with evaporitic deposits. Unlike carbonate minerals, Sr²⁺ is poorly integrated into carbonate lattices and exhibits limited adsorption onto clays. Elevated Sr²⁺ concentrations in groundwater are primarily attributed to the dissolution of celestite (SrSO₄), a mineral often associated with gypsum. This makes Sr²⁺ a robust tracer for evaporite influence.

In the study area, the Sr²⁺/Ca²⁺ ratio ranges from 0.0024 (point 4) to 0.0236 (point 1) (Figure 9a), while sulfate (SO₄²⁻) concentrations vary between 78.55 mg/L (point 6) and 980.53 mg/L (point 4) (Figure 9b). These ratios and sulfate levels classify the groundwater as Triassic evaporite-influenced, consistent with waters circulating through gypsum-rich formations. High Sr²⁺/Ca²⁺ ratios, coupled with elevated SO₄²⁻, Cl⁻, and Na⁺ concentrations, confirm the dominance of evaporitic dissolution over sulfide oxidation.

3.6. Bromide/Chloride (Br⁻/Cl⁻) Ratio

The Br⁻/Cl⁻ molar ratio in sampled wells ranges from 8.47 × 10⁻⁵ (point 3) to 9.88 × 10⁻⁴ (point 6) (Figure 10). These ratios deviate from seawater (Br⁻/Cl⁻ ≈ 1.5 × 10⁻³), ruling out marine intrusion. Chloride concentrations (70.4–489.01 mg/L) further highlight evaporitic contributions, as Cl⁻ enrichment typically originates from halite (NaCl) dissolution rather than anthropogenic sources (e.g., wastewater).

The sulfate-dominant hydrochemical facies (Figure 4) and low Br⁻/Cl⁻ ratios distinguish evaporite-derived salinity (gypsum/halite dissolution) from anthropogenic contamination. This underscores water-rock interactions as the primary salinity mechanism.

3.7. Principal Component Analysis

Principal Component Analysis (PCA) of hydrochemical parameters revealed four principal components (F1–F4) explaining 48.06% of the total variance (Figure 11). The first component (F1) accounted for 30.17% of the variance, with strong positive loadings from electrical conductivity (EC), total dissolved solids (TDS), Ca²⁺, Mg²⁺, and total hardness (TH), alongside moderate loadings from pH and HCO₃⁻. This component reflects groundwater mineralization driven by carbonate dissolution (calcite and dolomite) and anthropogenic inputs, where elevated Ca²⁺ over Mg²⁺ highlights preferential calcite weathering. The presence of Mg²⁺ suggests interactions between groundwater and dolomitic limestone. HCO₃⁻ and CO₃²⁻ loadings indicate alkalinity contributions from carbonate dissolution and organic matter decomposition. Stations R4, R5, R8, R9, R10, R11, P6, P7, P8, and P9 exhibited the strongest influence on F1.

The second component (F2) explained 23.18% of the variance, characterized by moderate loadings of temperature (T), CO₃²⁻, K⁺, SO₄²⁻, and turbidity (TURB). SO₄²⁻ primarily originates from inorganic salts, while CO₃²⁻ and K⁺ suggest silicate weathering processes. Temperature variations correlate with well depth, influencing ionic mobility. Stations P1, P2, P3, R3, R6, and R7 dominated F2 scores.

F3 (11.04% variance) showed significant loadings for Na⁺ and Cl⁻, indicating salinity from halite dissolution, particularly at station P4. The fourth component (F4, 7.63% variance) correlated with NO₃⁻ and dissolved oxygen (DO), linking nitrate pollution to agricultural fertilizers and wastewater. Stations R1, R2, and P5 strongly influenced F4.

The Pearson correlation matrix (

Table 6. Spearman’s correlation matrix for the eighteen physicochemical parameters analyzed in the study area. Values are rounded to two decimal places; │r│ ≥ 0.50 are shown in bold.

Variables	EC__µS_cm	Cl_	pH	BOD5	COD	Turbidity	MES__mg_l_	T°	Residi_sec	SO4__	NH4_	NO3_	NO2_	Na_	K_	Ca__	Mg__	HCO3_
EC__µS_cm	1
Cl_	−0.068	1
pH	0.103	−0.236	1
BOD5	0.002	0.050	0.451	1
COD	−0.282	0.557	0.180	0.527	1
Turbidity	−0.286	−0.205	−0.197	0.348	−0.137	1
MES__mg_l_	0.030	0.066	0.118	−0.099	0.259	−0.646	1
T°	−0.526	0.054	0.277	0.207	0.437	0.357	−0.357	1
Residi_sec	−0.512	−0.104	0.113	−0.125	−0.129	0.062	0.357	0.001	1	−
SO4__	0.596	0.072	−0.053	−0.027	−0.030	−0.280	0.225	−0.365	−0.074	1
NH4_	−0.113	0.467	−0.168	−0.191	0.481	−0.495	0.613	−0.185	0.079	−0.050	1
NO3_	−0.442	0.515	−0.054	0.334	0.485	−0.137	0.161	−0.002	0.159	−0.207	0.333	1
NO2_	0.635	−0.159	−0.036	−0.092	−0.149	−0.079	0.054	−0.185	−0.261	0.845	−0.129	−0.514	1
Na_	0.853	−0.092	0.136	0.102	−0.222	0.054	−0.121	−0.276	−0.317	0.659	−0.204	−0.604	0.787	1
K_	0.650	0.355	−0.132	0.007	0.118	−0.319	0.166	−0.417	−0.242	0.902	0.136	−0.129	0.763	0.683	1
Ca__	0.672	0.037	−0.129	0.105	−0.154	0.091	−0.075	−0.343	−0.231	0.815	−0.131	−0.311	0.884	0.826	0.833	1
Mg__	−0.130	0.371	−0.312	0.200	0.249	0.261	−0.286	0.027	0.052	0.370	0.060	0.367	0.161	0.054	0.430	0.375	1
HCO3_	−0.052	−0.237	0.257	0.429	0.203	0.049	0.026	0.257	−0.215	−0.130	−0.307	−0.048	−0.041	−0.051	−0.235	−0.164	−0.398	1

) revealed strong relationships between EC and TDS (r > 0.9r > 0.9), confirming conductivity as a proxy for mineralization. Ca²⁺ and Mg²⁺ showed moderate correlation (r > 0.5r > 0.5), defining total hardness (TH). Geogenic processes dominated through Ca²⁺-HCO₃⁻ (r = 0.703 r = 0.703) and Ca²⁺-SO₄²⁻ (r = 0.815 r = 0.815) associations, reflecting carbonate and evaporite dissolution. Anthropogenic impacts were evident in NO₃⁻-Cl⁻ (r = 0.515 r = 0.515) and Na⁺-SO₄²⁻ (r = 0.902 r = 0.902) correlations, signaling fertilizer use and wastewater infiltration. pH exhibited a negative correlation with K⁺ (r = −0.450 r = −0.450), suggesting pH-dependent ion exchange. The squared-cosine (cos²) values for each variable on the first five principal components are summarized in

Table 7. Summary of squared-cosine (cos²) values for each variable on the first five principal components (F1–F5). Only positive values are shown; negative placeholders (if present in original) have been removed for clarity.

	F1	F2	F3	F4	F5
EC	0.850	−0.100	−0.129	0.243	−0.264
Cl−	−0.031	0.726	0.348	−0.117	−0.306
pH	−0.085	−0.199	0.069	0.724	0.341
BOD5	−0.085	−0.046	0.722	0.480	0.199
COD	−0.252	0.582	0.550	0.428	−0.068
Turbidity	−0.163	−0.558	0.539	−0.415	0.146
MES	0.028	0.583	−0.514	0.438	0.298
T°	−0.473	−0.243	0.496	0.133	0.008
Residi_sec	−0.325	0.129	−0.270	−0.192	0.825
SO42−	0.869	0.238	0.079	0.040	0.269
NH4+	−0.107	0.808	−0.243	0.070	−0.136
NO3−	−0.473	0.622	0.303	−0.031	0.059
NO2−	0.901	−0.078	0.062	0.065	0.118
Na+	0.891	−0.228	0.114	0.138	−0.003
K+	0.874	0.410	0.169	0.005	0.024
Ca2+	0.917	0.010	0.251	−0.100	0.133
Mg2+	0.196	0.371	0.613	−0.522	0.228
HCO3−	−0.176	−0.343	0.184	0.646	−0.111

.

These results underscore mixed geogenic (rock-water interactions) and anthropogenic (agricultural/industrial) influences on groundwater quality, consistent with regional hydrogeological and land-use conditions.

The PCA results reveal a correlation between conductivity and the dissolved ions Na⁺, NO²⁻, as well as K⁺, SO₄²⁻, and Ca²⁺. It should be noted that conductivity describes the inorganic salts present in solution in the water. These correlations reflect the influence of each parameter on the mineralization of the groundwater in the Guire aquifer. This correlation of conductivity with these major ions reflects the mineralization or hydrolysis of minerals. The correlation between electrical conductivity and NO²⁻ is 0.64, with Na⁺ being 0.85, with K⁺ being 0.65, and with Ca2+ being 0.67 (Table 6 and Figure 12).

4. Conclusions

This study employed a multidisciplinary approach combining hydrochemical analysis, isotopic tracers, and the WQI to evaluate groundwater quality, recharge mechanisms, and contamination sources in the Guire aquifer (Eastern High Atlas, Morocco).

In this study, the Isotopic signatures (δ¹⁸O: −7.73‰ to −5.08‰ vs. SMOW; δ²H: −66.14‰ to −31.89‰ vs. VSMOW) indicate recharge waters undergo evaporation prior to infiltration and derive from Atlantic-influenced precipitation. Recharge zones are localized at altitudes of 900–2200 m, aligning with regional orographic controls.

Also, δ¹³C values (−13.12‰ to −3.58‰) confirm carbonate mineral dissolution (e.g., calcite) as the primary carbon source, reflecting aquifer-rock interactions. Br/Cl and Sr/Ca ratios distinguish salinity sources; Evaporitic dissolution (salt-bearing formations) dominates in areas with elevated Br/Cl and anthropogenic inputs (e.g., wastewater) correlate with distinct Sr/Ca trends.

The WQI highlighted significant spatial variability in groundwater quality, necessitating targeted remediation in contaminated zones. Findings provide actionable insights for policymakers to mitigate anthropogenic impacts and ensure sustainable resource use.

This work underscores the value of integrating isotopic and hydrochemical tools to unravel complex groundwater systems, offering a robust framework for similar arid-region aquifers globally.