Groundwater Suitability for Irrigation in the Hennaya Region, Northwest Algeria: A Hydrochemical and GIS-Based Multi-Criteria Assessment

Abstract

This study investigated groundwater suitability for irrigation in the Hennaya Irrigated region of Northwest Algeria. The research pursued two primary objectives: first, to establish the hydrochemical origin of the groundwater through comprehensive analyses including hydrochemical parameters, diagrams, and hierarchical clustering; and second, to assess its suitability for irrigation based on key criteria such as the Water Quality Index (WQI), Wilcox, and US Salinity diagrams. The analysis revealed a high level of groundwater suitability for irrigation, as indicated by various indices: Sodium Adsorption Ratio (SAR) values ranged from 1.69 to 2.55 (Excellent), Sodium Percentage (Na%) ranged from 24.22% to 36.98% (Good), and the Residual Sodium Carbonate (RSC) was negative, falling between −8.91 to −1.70 meq/L (Safe). Kelly’s Ratio (KR) ranged from 0.32 to 0.59 (Good), and the Permeability Index (PI) was between 62% and 99% (Moderate). Supported by the Analytic Hierarchy Process (AHP) and spatial analysis, the Water Quality Index (WQI) values ranged from 69.25 to 88.71, categorizing the groundwater in the study area as ‘Good’ quality. While suitable for irrigation, the groundwater showed slight salinity (EC 1247–2010 μS/cm) and alkalinity (pH 7.09–8.02), with elevated total dissolved solids (TDSs) ranging from 990 to 1930 mg/L, approaching the permissible limits for optimal agricultural use. The dominant ion concentrations (Ca²⁺ > Na⁺ > Mg²⁺ > K⁺; HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻) indicate a mixed hydrochemical facies influenced by both water–rock interactions and evaporative processes. Although these findings are promising, they highlight the necessity for preventive measures. Ongoing proactive management and continuous monitoring are essential to ensure the long-term sustainability and protection of groundwater resources in the region.

1. Introduction

Groundwater plays a crucial role in global water resource management, constituting approximately 99% of the world’s liquid freshwater reserves [1]. On a global scale, about 31% of extracted groundwater is utilized by commercial, industrial, and municipal sectors, while the remaining 69% is allocated for agricultural use [2]. In semi-arid regions, where precipitation levels are low and irregular (ranging from 200 to 400 mm annually), groundwater becomes an essential resource for meeting agricultural, industrial, ecological, and domestic demands, thereby ensuring water and food security [3,4,5]. Furthermore, the increasing variability in surface water availability due to climate change is likely to heighten reliance on groundwater, a trend further exacerbated by rising demand.

This growing dependence on groundwater raises significant threats from anthropogenic factors, including aquifer overuse and agricultural pollution [1]. In semi-arid regions, these challenges are intensified by aquifer overexploitation to meet escalating water demands [2]. Factors such as population growth, agricultural intensification, and urbanization further strain water resources, exacerbating quality degradation and sustainability concerns [3,4]. As an illustration, excessive use of nitrogen-based fertilizers, pesticides, and agricultural chemicals has led to notable contamination of aquifers with nitrates, sulfates, and trace metals [5].

Moreover, the overexploitation of aquifers to meet increasing water demands has exacerbated issues of salinization and seawater intrusion in many coastal areas worldwide [6,7] These issues, combined with the artificial recharge of aquifers using treated wastewater, highlight the urgent need for a systematic investigation of groundwater quality [8]. This is particularly critical, as this practice can introduce residual contaminants into local hydrological systems, potentially altering the chemical composition of groundwater [9].

Groundwater quality hinges on physicochemical parameters (e.g., pH, electrical conductivity, major ions: Na⁺, Ca²⁺, Cl⁻, HCO₃⁻; pollution indicators: nitrates, sulfates) and biological factors, quantified and assessed via hydrochemical analysis [10]. Hydrochemistry proves to be a powerful tool for gaining an understanding of the complex interactions between natural and anthropogenic factors that influence groundwater composition. By analyzing physicochemical parameters and pollution indicators, it is possible to assess the suitability of water for various uses, including irrigation and human consumption [6,11].

Recently, studies have emphasized the importance of integrating Water Quality Indices (WQIs) to synthesize complex hydrochemical data into a simple and interpretable indicator [7,12]. WQI, pioneered by [13] and refined through weighted parameter models, consolidates complex hydrochemical data into a single metric, enabling rapid assessment of groundwater suitability for drinking and irrigation [14].

These indices not only enable the evaluation of overall water quality but also assist in prioritizing intervention measures for the sustainable management of water resources [15]. Studies analyzing water quality indices across northern Algeria have highlighted significant concerns regarding groundwater quality. The results indicate the presence of contamination in several regions, emphasizing the need for corrective measures to ensure the sustainable use of groundwater resources [16,17,18,19].

Coupling WQI with Multi-Criteria Decision Analysis (MCDA), particularly the Analytic Hierarchy Process (AHP), enhances resource management by prioritizing interventions based on hydrochemical, environmental, and socio-economic criteria [20].

Developed by [21], AHP has been widely applied in groundwater studies to evaluate aquifer vulnerability and optimize monitoring strategies, especially in data-scarce semi-arid regions [22]. In addition, Geographic Information Systems (GISs) further amplify WQI utility by mapping spatial variations in groundwater quality using interpolation techniques such as Inverse Distance Weighting [23].

This integrated approach, combining hydrochemical analysis with WQI-AHP-GIS methods for spatial mapping, has been successfully applied in numerous groundwater studies [24,25,26,27,28,29,30,31,32,33,34,35].

The mapping of quality parameters and the Irrigation Water Quality Index (IWQI) intends to provide a clear understanding of irrigation water adequacy. By creating an IWQI map using GIS-based Analytic Hierarchy Process (AHP), decision-makers, water distributors, and managers can achieve easy, rapid interpretation and a better understanding of water quality for various uses [27].

In the Hennaya Irrigated region of semi-arid northwestern Algeria, limited surface water resources drive a heavy dependence on groundwater for irrigated agriculture. Extracting water from wells is essential for maintaining agricultural productivity and supporting the livelihoods of the local population [36].

The Hennaya agricultural area, which spans approximately 1900 hectares and is vital for irrigated farming, uses a dual water source system. It primarily relies on groundwater from wells, supplemented by treated wastewater that is delivered through a municipal treatment plant network [37]. However, this region faces significant challenges in the sustainable management of its water resources and soils.

The practice of mixed irrigation—combining groundwater and treated wastewater—has been in place for about 15 years. While this approach helps alleviate water stress, it risks the gradual deterioration of groundwater and soil quality over time due to the potential accumulation of contaminants and dissolved salts [38]. These cumulative effects highlight the need for a comprehensive analysis to ensure the sustainable and balanced management of these critical resources [39].

Therefore, this study aims to understand the hydrochemical origin of groundwater by analyzing hydrochemical parameters, hydrochemical diagrams, and hierarchical clustering. Additionally, it assesses groundwater suitability for irrigation using irrigation-specific indicators. The study applies the Water Quality Index (WQI) for groundwater quality evaluation and employs the Analytic Hierarchy Process (AHP) in conjunction with Geographic Information System (GIS) techniques to develop detailed groundwater quality maps, thereby providing a comprehensive framework for assessment. This investigation is part of a scientific effort to promote the sustainability of local water resources while addressing the socio-economic needs of the region.

2. Materials and Methods

2.1. Study Area

The Irrigated region of Hennaya, located in the north of Tlemcen province in Algeria, lies between 34°57′ to 35°30′ N latitude and 1°19′ to 1°24′ E longitude (Figure 1), with an altitude of 200 to 400 m above mean sea level (msl). It is a rural area with some village settlements where agriculture is the main activity and the predominant agricultural activities are arboriculture (olive trees, citrus fruits and various fruit trees) and cereal cultivation.

The region is characterized by a semi-arid climate with an average annual rainfall of 395.5 mm, with the highest precipitation occurring in November. Notably, the winter season contributes significantly to the total rainfall. The average winter temperatures tend to be around 9.9 °C, while in the summer they average around 24.7 °C, resulting in an annual average temperature of 16.7 °C for the period between 2003 and 2023.

The study area spans a total of 1900 hectares. Of this, 912 hectares are irrigated using a collective network that relies solely on treated wastewater supplied by the Ain Houtz municipal wastewater treatment plant, located 11 km upstream. This facility provides an annual volume of 2 million cubic meters, which is distributed through a gravity-fed irrigation system managed by the National Office for Irrigation and Drainage (see Figure 1). Within the Hennaya Irrigated region, some farmers enhance their irrigation by supplementing the treated wastewater with groundwater extracted from wells or boreholes, utilizing their own independent irrigation systems. In areas outside this collective irrigation zone, irrigation is conducted exclusively through the autonomous use of groundwater resources.

The Hennaya aquifer covers an area of approximately 29 km². This aquifer is of Plio-Quaternary age, featuring a Tortonian horizon in the north and alluvial deposits in the south. Its thickness reaches a maximum of 17.8 m. The marl substratum varies in depth, from approximately 400 m in the south to 200 m in the north, and extends toward the outlet [40], which consists of a series of emerging springs that flow toward the Sikkak dam and the associated watercourse. At this outlet, a lithological contact facilitates groundwater flow, enhancing connectivity between the aquifer and surface water systems [41,42,43].

Geological studies show the presence of Tortonian sandstones and Quaternary sediments. The impermeable bedrock formations are represented by Helvetian marl with thin layers of sandstone (Figure 2a,b), which outcrop on the surface to the east and west, encouraging runoff [40].

This south-to-north cross-section illustrates Miocene formations (marl and sandstone) with a gentle northward dip, overlain by varied Quaternary deposits (conglomerates, travertines, clays, and gravels) [44] (Figure 3).

The soils in the region are predominantly clay and silt, hence the clayey-silty to clayey tendency. Limestone is also well-represented in the region [38].

2.2. Methodology Framework

The applied method is structured in the methodology flow diagram (Figure 4) and discussed in detail in the specific sections below.

In general, the methodological approach consists of the following:

Step 1 details the initial measurements and characterization (Data Acquisition), where data for 15 physicochemical parameters were collected from the Hennaya Irrigated region. These acquired data were subsequently utilized to generate spatial distribution maps for each parameter.

In Step 2, the complete set of these 15 measured parameters (including pH, CE, TDS, major cations, anions, and nutrients) was used for the calculation of the Water Quality Index (WQI).

Step 3 focused on plotting key hydrochemical diagrams. Parameters such as TDS, Ca²⁺, Mg²⁺, K⁺, Na⁺, HCO₃⁻, SO₄⁻, and Cl⁻ were used to construct Piper, Gibbs, Wilcox, and US salinity diagrams to discern water types and their suitability.

Finally, Step 4 constituted the comprehensive analysis phase. This involved utilizing all measured physicochemical parameters along with the derived indices (WQI, SAR, RSC, Na%, KR, PI, MH). The Water Quality Index (WQI), which was calculated based on the Analytic Hierarchy Process (AHP), was further elucidated by generating a WQI map using spatial interpolation. This visual representation is crucial as maps offer an intuitive and readily understandable means of communicating the spatial variations in groundwater quality and the overall findings.

2.3. Groundwater Data Acquisition and Analysis

To assess groundwater quality, water samples were collected from twenty-five (25) wells and boreholes and two (2) springs representing the aquifer system’s outlet in the study area (Figure 1). The sampling points were selected based on a previous survey conducted with local irrigation area managers and complemented by a field trip to identify operational and equipped water points, ensuring representative coverage of active sources used for irrigation. Sampling was carried out once during the first 10 days of October 2022, at the end of the irrigation campaign and just before the rainy season. This timing minimized the influence of rainfall on groundwater quality, capturing baseline conditions unaffected by seasonal recharge. Due to the absence of prior water quality analysis reports for these locations, sample collection was conducted as a single event without parallel replicates or repeated collections. Fifteen (15) water quality parameters were analyzed. Polypropylene (PP) bottles were used for sample collection; these were rinsed several times with sampling water before collecting representative samples, which were then stored at 4 °C in portable coolers. Subsequently, all samples were sent to the Open LAB laboratory [45], the service provider, for analysis. This methodology reflects a practical compromise given resource constraints and the limited availability of historical data, while providing an initial comprehensive snapshot of groundwater quality relevant to irrigation in the Hennaya region. Future monitoring campaigns should consider sampling repetitions and quality control replicates to confirm temporal variability and ensure data reliability.

This research delves into the comprehensive analysis of groundwater samples, examining a range of physicochemical parameters to assess their quality and uncover the interplay of natural and anthropogenic influences on the studied aquifer. Key parameters include hydrogen potential (pH), electrical conductivity (EC), total dissolved solids (TDSs), Complete alkalimetric title (CAT), major cations such as calcium (Ca²⁺), magnesium (Mg²⁺), potassium (K⁺), and sodium (Na⁺), major anions like bicarbonate (HCO₃⁻), sulfate (SO₄²⁻), and chloride (Cl⁻), total hardness (TH), as well as nitrogen and phosphorus compounds including nitrate (NO₃⁻), nitrite (NO₂⁻), and phosphorus (P). These parameters were carefully chosen due to their critical role in evaluating groundwater quality and their ability to reflect both environmental and human-induced impacts on the aquifer system.

The determination of the concentrations of physicochemical elements is done according to the international methods and standards (

Table 1. Overview of International Water Quality Standards and Analytical Methods used in this Study [45].

Parameters	Unit	Analysis Protocol	Desirable Limits (FAO, WHO)
Hydrogen Potential (pH)	-	ISO 10523:2008 [45]	6.5–8.5
Electrical Conductivity (EC)	µS/cm	ISO 7888:1985 [45]	0–2800
Total Dissolved Solids (TDSs)	mg/L	Rodier [45]	0–2000
Complete Alkalimetric Title (CAT)	mg as CaCO3	ISO 9963-2/1994 [45]	0–500
Calcium (Ca2+)	mg/L	NFT 90-016 [45]	0–200
Magnesium (Mg2+)	mg/L	NFT 90-005 [45]	0–50
Potassium (K+)	mg/L	NFT 90-020 [45]	0–12
Sodium (Na+)	mg/L	NFT 90-019 [45]	0–200
Bicarbonates (HCO3−)	mg/L	Gravimetric Method [45]	0–125
Sulphates (SO42−)	mg/L	NFT 90-009 [45]	0–400
Chlorides (Cl−)	mg/L	NFT 90-014 [45]	0–500
Total Hardness (TH)	mg as CaCO3	ISO 6059:1984 [45]	0–200
Nitrates (NO3−)	mg/L	NFT 90-012 [45]	0–50
Nitrites (NO2−)	mg/L	NFT 90-012 [45]	0–3
Phosphorus (P)	mg/L	ISO 6878:2004 [45]	0–5

).

To obtain the spatial distribution of physicochemical parameters, fifteen groundwater quality maps were generated using the ArcGIS 10.8.2 tool. For intuitive and accurate visualization of groundwater hydrochemical parameters, spatial interpolation was performed using Kriging as a geostatistical interpolation. This approach enabled the identification of spatial variability and potential hotspots of groundwater contamination or suitability across the study area.

2.3.1. Gibbs Diagram

The Gibbs diagram is used to infer the mechanisms controlling groundwater chemistry, distinguishing between three primary processes: precipitation dominance, rock (lithological) dominance, and evaporation–crystallization dominance [46].

The Gibbs diagram illustrates the relationship between the lithological properties of aquifers and the composition of water. It consists of three main domains: the interaction between water and rock, evaporation, and precipitation [47]. However, this diagram does not explicitly account for the effects of human activities on hydrochemical components. The Gibbs diagram deals with the relationship between the lithological properties of aquifers and the water composition [48].

In a Gibbs diagram, scatter plots were represented on the abscissa, while Total Dissolved Solids (TDSs) were on the ordinate. Gibb’s ratios are calculated using the following equations (Equations (1) and (2)):

$$Ratio for cations={\displaystyle \frac{({Na}^{+}+K^{+})}{({Na}^{+}+K^{+}+{Ca}^{++})}}$$

(1)

$$Ratio for anions={\displaystyle \frac{{Cl}^{-}}{({Cl}^{-}+{HCO}_3^{-})}}$$

(2)

Note that the units of representation for all ionic concentrations are meq/L [47].

Unlike most surface waters, groundwater has the ability to span the entire range of Na⁺/(Na⁺ + Ca²⁺) values (i.e., from <0.1 to >0.9) at mid-range TDS levels [49].

2.3.2. Piper Diagram

Understanding groundwater geochemical dynamics was achieved through the use of the Piper plot or diagram. Various groundwater facies were classified using this Piper diagram, which is a widely recognized trilinear graphical representation [50,51]. The Piper diagram categorizes and interprets water types based on the relative concentrations of major cations (such as Ca²⁺, Mg²⁺, Na⁺, and K⁺) and anions (such as Cl⁻, SO₄²⁻, and HCO₃⁻, CO₃²⁻).

This diagram is composed of two triangular sections and a field with a diamond shape. The cations are represented as a single point in the left triangle based on their total cations, while the anions are positioned in the opposite triangle [19]. For each parameter, a parallel line is projected into the top field. The intersection of this parallel with the upper border demonstrates the interaction between cations and anions, which characterizes the water. Piper diagrams can be used to illustrate the general chemical properties of water samples as well as the proportion of different ions. Furthermore, by combining Piper diagrams with geological and hydrogeological data from the study area, it is possible to analyze the evolution of the chemical composition of groundwater [52].

All hydrochemical diagrams, including Piper, Gibbs, Wilcox, and US Salinity diagrams, were constructed by plotting the relative concentrations of major cations and anions using DIAGRAMMES software (version 6). The Piper diagram combines cation and anion ternary plots into a diamond-shaped field to classify hydrochemical facies. The Gibbs diagram distinguishes water formation mechanisms based on ion ratios and total dissolved solids. Wilcox and US Salinity diagrams assess irrigation suitability using established plot criteria. This software enabled precise visualization and standardized classification of groundwater quality and irrigation suitability.

2.4. Hierarchical Clustering Analysis

Hierarchical clustering analysis is an unsupervised pattern identification method that reveals inherent structures within a dataset without prior assumptions, categorizing objects into clusters based on their similarity [53]. This multivariate statistical method is very useful for figuring out the quality of water because it shows how samples and factors naturally group together. In this study, we used Ward’s method and Euclidean distances to find the most similar items in the normalized dataset and then did hierarchical agglomerative clustering on them. The Euclidean distance quantifies the similarity between two samples. The ‘distance’ represents the ‘difference’ between analytical values from both samples [54]. Ward’s method uses an analysis of variance approach to assess distances between clusters, seeking to minimize the sum of squares of any two clusters generated at each phase, resulting in compact, spherical clusters [55]. To discover regional variability patterns, we used this approach with our water quality dataset, which included 15 physicochemical characteristics collected from multiple distinct sample sites. In the resulting dendrogram, the linkage distance is displayed as Dlink/Dmax, representing the quotient of the linkage distance for a specific example divided by the maximal distance, multiplied by 100 to standardize the linkage distance represented on the y-axis [56,57]. The hierarchical clustering results were visualized through dendrograms, heatmaps, and radar charts to facilitate the interpretation of the identified water quality patterns and their potential relationships with hydrogeological processes and anthropogenic influences.

2.5. Groundwater Quality Evaluation for Irrigation

2.5.1. Groundwater Quality Criteria for Irrigation Purpose

(1) The Sodium Adsorption Ratio (SAR): developed by the U.S. Salinity Laboratory Staff [58], and can be calculated using Equation (3):

$$SAR ({\displaystyle \frac{meq}{L}})={\displaystyle \frac{{Na}^{+}}{\sqrt{\frac{({\mathit{Ca}}^{2+} + {\mathit{Mg}}^{2+})}{2}}}}$$

(3)

Purpose of using SAR is to assess the potential damage to soil structure, specifically permeability reduction, caused by excessive sodium relative to calcium and magnesium in irrigation water [58].

(2) Residual Sodium Carbonate (RSC): proposed by [59], uses Equation (4).

$$RSC ({\displaystyle \frac{meq}{L}})=({{\mathit{CO}}_3}^{2-} + {{\mathit{HCO}}_3}^{-})-({\mathit{Ca}}^{2+} + {\mathit{Mg}}^{2+})$$

(4)

This parameter evaluates the hazard posed by high carbonate and bicarbonate levels which can precipitate calcium and magnesium, effectively increasing the relative sodium concentration [59]. Ref. [60] noted that CO₃²⁻ concentrations are generally low at pH below 8.3.

(3) Sodium Percentage (Na%): calculated as:

$$Na\%={\displaystyle \frac{{Na}^{+}}{{Na}^{+}+K^{+}+{Ca}^{2+}+{Mg}^{2+}}} \ast 100$$

(5)

It is a widely used index in irrigation water assessment, often used in classifications such as that by [61]. It indicates the proportion of sodium relative to total cations to evaluate potential sodium hazards [58].

(4) Kelly’s Ratio (KR): this index serves as a key metric for evaluating the suitability of groundwater for irrigation purposes. A KR value below one signifies low salinity, making the water safe for irrigation use. Conversely, groundwater with a KR value exceeding one is deemed unsuitable for irrigation [62]. KR can be estimated using the following Equation (6)

$$KR={\displaystyle \frac{{Na}^{+}}{{Ca}^{2+}+{Mg}^{2+}}}$$

(6)

(5) Permeability Index (PI): serves as an important indicator for assessing water quality and its potential impacts on agricultural soil degradation [63]. Following the classification system established by [64], groundwater samples were categorized into three suitability classes: Class I (suitable), Class II (doubtful), and Class III (unsuitable). This parameter is calculated using:

$$PI={\displaystyle \frac{{Na}^{+}+\sqrt{{HCO}_3^{-}}}{{Ca}^{2+}+{Mg}^{2+}+{Na}^{+}}} \times 100$$

(7)

(6) Magnesium Hazard (MH): calcium (Ca²⁺) and magnesium (Mg²⁺) typically exist in equilibrium in groundwater. While both ions contribute to soil structure and serve as essential plant nutrients, elevated concentrations can adversely affect soil properties. Excessive Ca²⁺ and Mg²⁺ levels may raise soil pH, potentially leading to soil salinization [65]. Notably, agricultural studies demonstrate that high Mg²⁺ concentrations particularly degrade soil quality and reduce crop yields [66]. This phenomenon, known as the magnesium hazard (MH), serves as a critical water quality parameter for irrigation. When present in excess, magnesium can alkalinize soil and diminish agricultural productivity [67]. The magnesium hazard is quantitatively assessed using Equation (8).

$$MH={\displaystyle \frac{{Mg}^{2+}}{{Ca}^{2+}+{Mg}^{2+}}}\times 100$$

(8)

2.5.2. Water Quality Index

The Water Quality Index (WQI), a critical tool for environmental management [68,69,70], integrates multiple physicochemical parameters into a single value to assess and compare groundwater quality [71,72], enabling decision-makers to evaluate the combined impact of water quality criteria and support effective policy-making in the study area.

WQIs may have become more popular recently, but the concept in its major basic form was first introduced more than 150 years ago, in 1848 in Germany, where the presence or absence of certain organisms in water was used as an indicator of the quality of a water source [73]. WQIs are used extensively in research assessing water quality and are becoming more crucial in the management of water resources [74].

Suitability for irrigation has been a central focus of numerous studies. These investigations thoroughly assess water quality, relying on quality indices that incorporate various diagrams and specific parameters of irrigation water to determine if it is appropriate for agricultural use [75,76,77,78].

Horton formulated an early Water Quality Index (WQI) model in the 1960s, based on ten water quality criteria considered essential for most aquatic environments [13], a new WQI akin to Horton’s index was also created in 1970 by the Brown group [79], Following Horton’s initial work, Brown et al., supported by the National Sanitation Foundation, developed the influential NSF-WQI by employing a large expert panel to refine parameter selection and weighting. This model became a foundation for subsequent indices [80]. The Scottish Research Development Department (SRDD) developed the SRDD-WQI. This model led to derivatives such as the Bascaron Index, House Index, and Dalmatian Index [81]. In 1982, the Environmental Quality Index was created for the Great Lakes [82], and in the mid-1990s, the British Columbia Water Quality Index (BCWQI) was established to evaluate water quality in British Columbia [83]. The Canadian Council of Ministers of the Environment later introduced the CCME WQI in 2001, based on the BCWQI model [83]. Since then, numerous other models, including the Liou Index, Malaysian Index, and Almeida Index, have been developed, resulting in over 35 WQI models worldwide for assessing surface water quality [80].

The construction of any WQI typically involves the following four processes [73,84,85,86]:

• Selecting a parameter and allocating a weight:

Grasping the applications of WQIs relies on comprehending the parameters. There might be a lot of components in a sample of water [87]. If the WQIs had information on every possible ingredient, it would get complicated. Rather, one ought to choose a set of standards that represent the whole WQIs [73]. An index’s defined parameters might range from 4 to 26, and selecting parameters is an essential step in the index-building process [73,88]. Three distinct systems exist for choosing parameters: fixed, open, and mixed systems [87].

The researcher’s individual experiences and knowledge backgrounds influence the weight determination process, which may have some subjective consequences [89]. Therefore, to create a more realistic WQI model, the weights should be chosen and modified based on a thorough review of the literature, the measured data, and the background of the object [90].

Since SAR, %Na, EC, and TDS have a significant impact on irrigation groundwater quality, they were given the highest weighting rates. Due to its minimal impact on assessing the quality of water [19], the other parameters have the lowest weighting rate.

• Proportional weight calculation. Equation (9) was applied to calculate the proportional weight (Wi) in this phase.

$$W_i={\displaystyle \frac{w_i}{\sum _{i=1}^n{w}_i}}$$

(9)

W_i is the proportional weight, n is the total number of parameters, and w_i represents the weight of each parameter [91].

• Rating scale for quality assignment quality. Equation (10) was used to establish the quality rating scale (qi) for each parameter.

$$q_i={\displaystyle \frac{C_i}{S_i}}\times 100$$

(10)

where qi represents the quality rating scale, and for each water sample, the concentration of each parameter (Ci) is divided by its respective standard or guideline value (Si) to calculate the quality rating scale (Table 1).

• Sub-index value determination WQI calculation. In this phase, the sub-index values (SI_i) for all chemical properties are calculated by multiplying the proportional weight, as given in Equation (11) [72], then summing all the sub-indices from all samples of the study area together to get the final WQI (Equation (4)).

$${SI}_i=W_i\times q_i$$

(11)

where SI_i, i is the parameter’s subindex [91]

$$WQI=\sum {SI}_i$$

(12)

The quality of groundwater can be classified according to the values obtained from the WQI.

2.5.3. The Analytic Hierarchy Process (AHP)

AHP, which [21] proposed and developed, is one of the most widely used Multi-Criteria Decision Analysis (MCDA) techniques. Pairwise parameter comparisons and expert opinions serve as the foundation for this methodology [92]

To make a decision in a guided way, Saaty has created four steps [93,94,95]

• Defining and elucidating the context of the problem;
• Dividing the decision-making problem into levels, starting at the top with the decision’s objective, moving down to specify criteria and options, and ending with a collection of alternatives;
• Making pairwise comparison matrices and determining the relative weights;
• Assessing the accuracy of the pairwise comparisons can be done by computing the consistency index (CI) and the consistency ratio (CR).

AHP is based on pairwise comparisons to determine each criterion’s relative weight. Decision-makers can use a verbal scale and pairwise comparisons to communicate their judgments on the scope of criteria. In multi-criteria situations, decision makers make subjective decisions [20].

To assess the extent of water quality for irrigation purposes within the Hennaya Irrigated region, the Analytic Hierarchy Process (AHP) was employed to establish the weights of the contributing water quality factors. The determination of these weights was conducted through a systematic process:

The pairwise comparison matrix necessitates a decision-making phase. A scale ranging from 1 to 9 is utilized to create the pairwise comparisons, where 1 indicates equal significance and 9 represents the utmost importance [21] according to the following table (

Table 2. Evaluation scale for the Analytic Hierarchy Process [21].

Significance Values	Value Definitions
1	Both factors have equal deduction
3	Factor 1 is more important than factor 2
5	Factor 1 is very important than factor 2
7	Factor 1 has a stronger proposition than factor 2
9	Factor 1 has absolute superiority over factor 2
2, 4, 6, 8	Intermediate values

):

3. Results and Discussion

3.1. Hydrochemical Analysis Results

The data collected in the study area concern 15 physicochemical parameters that were determined in the laboratory. The table provides a statistical description of the analysis results (

Table 3. Statistical summary of groundwater quality parameters.

Parameter	Unit	Max	Min	Mean	Standard Deviation	Coefficient of Variation
pH	-	8.02	7.09	7.73	0.15	2%
EC	µS/cm	2010	1247	1622.07	125.04	8%
TDS	mg/L	1930	990	1504.07	282.14	19%
CAT	mg/L as CaCO3	369	321	344.89	11.29	3%
Ca2+	mg/L	188.78	105.26	138.38	14.79	11%
Mg2+	mg/L	75.64	46.48	61.39	6.20	10%
K+	mg/L	4.52	1.17	2.04	0.54	27%
Na+	mg/L	141.26	95.24	116.13	9.47	8%
HCO3−	mg/L	470	390	430.78	23.81	6%
SO42−	mg/L	153.54	69.43	98.16	15.12	15%
Cl−	mg/L	349.54	154.25	213.85	28.06	13%
TH	mg/L as CaCO3	550	334	432.37	58.21	13%
NO3−	mg/L	145.38	46.72	85.45	20.70	24%
NO2−	mg/L	1.45	0.41	0.77	0.22	28%
P	mg/L	0.4	0.02	0.08	0.09	118%

).

3.2. Hydrogeochemical Evaluation

Groundwater experimental analysis conducted in the Hennaya irrigated region, in accordance with international standards, generated valuable data for the characterization of the aquifer and the evaluation of regional groundwater quality.

The spatial distribution of physico-chemical parameters was delineated using Kriging interpolation, a geostatistical method implemented in ArcGIS software. Figure 5 illustrates these distributions.

The case study is an alkaline zone, defined as having a potential of hydrogen (pH) greater than 7.0. It has an average pH of 7.73 and a minimum and a maximum of 7.09 and 8.02, respectively. These results are in line with those of the Hennaya Irrigated region research [43], which found a mean pH of 7.47.

Alkalinity is the groundwater’s capacity to neutralize acids, which represents a buffer capacity to maintain pH balance. This characteristic is associated with the presence of essential compounds in the water, such as carbonates, bicarbonates, and hydroxides. Alkalinity is expressed in CAT (Complete alkalimetric title). All values range from 321 to 369 mg/L as CaCO₃. Excessive alkalinity in irrigation water causes carbonates to precipitate out and degrade soil structure, blocking nutrient uptake by plants, and leading to the risk of scaling up irrigation systems [96], which requires management strategies to prevent soil pH increases, especially since the FAO recommends a threshold of 200 mg/L as CaCO₃ for irrigation water [97].

Total dissolved solids (TDSs) are the residue left behind from the evaporation of a filtered water sample. TDS in natural water is made up of significant ions such as Ca²⁺, Mg²⁺, K⁺, Na⁺, HCO₃⁻, SO₄²⁻, Cl⁻, and other minerals and nutrients that have dissolved in the water [67].

For irrigation use, the FAO recommends 2000 mg/L as an acceptable threshold. Admissible [98], because dissolved salts increase the osmotic pressure of the soil, requiring more energy for plant absorption, leading to increased respiration and a gradual decrease in plant growth and yield.

The TDS concentrations in the samples from our research area vary from 990 to 1930 mg/L. All TDS values obtained are adequate for agricultural use (<2000 mg/L according to [97]). For human consumption, the TDS values remain far from the preferred value of 1000 [99] except for one sample (S26; 990 mg/L), and this situation can be explained by the use of irrigation with treated and sometimes raw sewage water. This is due to the leaching of salts from the soil and also domestic sewage that may percolate into groundwater, leading to an increase in TDS values [100].

The samples from the study area have an EC ranging from 1247 to 2010 (μS/cm), with an average value of 1622 (μS/cm). According to the results of [101], the EC values are below 3000 (μS/cm). These results allow us to classify the groundwater in the study region as slightly saline water according to the FAO classification [97].

Calcium concentrations range from 105.2 to 188.8 mg/L, which falls within the range allowed by Algerian standards, setting a maximum value of 200 mg/L [102]. These values are significantly lower than those observed in the Ain Sefra region, located 300 km south of our study area, where [19] reported a range between 12 and 427 mg/L. [103] reports an average of 80 mg/L in the Maghnia region (60 km to the west). The values obtained in our study confirm the extent of the limestone environment, where the most common concentrations range between 70 and 120 mg/L [104].

The study area exhibits magnesium concentrations ranging from 46.48 mg/L to 75.64 mg/L. 89% of the magnesium results exceed the standard set by the [105] (with the exception of samples S7, S8, S9) are within the standard limits (around 50 mg/L [99]). In reality, these values are strongly correlated with the flow of water through geological formations, as there is a predominance of carbonate rocks (such as calcite and dolomite) across the study area [106].

Sodium is a widely occurring element in nature. The values at the water sampling points in our study area range from 95.24 to 141.26 mg/L (the highest permissible concentration in drinking water being 200 mg/L). These values differ from those of the groundwater in the Ain Oussera plain (center of the country), which range from 19 mg/L to 528 mg/L [16,17] found in their study a range of sodium between 218.5 and 330.3 mg/L. Moving south from our study area, elevated sodium levels were reported in the Naama region [107], where sodium ranged from 5 to 1114 mg/L. Similar values (below the Algerian standard of 200 mg/L) were found in Tunisia [108]. The risk of stroke increases linearly with the increase in dietary sodium intake; according to [109], 20% of all stroke deaths are attributable to high-sodium diets.

The average potassium concentration ranges from 1.17 mg/L in sample S19 to 4.52 mg/L in sample S1, with all samples remaining below the standard recommended by Algerian law, which sets the threshold at 12 mg/L [110].

Situated between previous studies, we find that the Bouhanifia plain records a range between 22.31 and 44.22 [17], the Adrar region between 7 and 28.6 [111], the Ouessara region between 2 and 52 [16], and in Tunisia [108] a potassium range between 3 and 90.2 mg/L.

Potassium concentrations in irrigation water typically pose minimal risk, as potassium is both an essential plant nutrient and rarely exceeds levels harmful to soils or crops. Furthermore, health-based limits for potassium in drinking water are unnecessary, given that common beverages naturally contain significantly higher potassium levels than groundwater. Establishing stringent regulatory thresholds based on beverage standards would be nutritionally unsound [112].

The order of cation concentration is descending in the following order: Ca²⁺ > Na⁺ > Mg²⁺ > K⁺, with average concentrations of 188.78, 141.26, 75.64, and 4.52 mg/L, respectively.

The average chloride values in this study area ranged from 154.25 mg/L in sample S3 to 349.54 mg/L in sample S1. All water points comply with Algerian standards (500 mg/L) [110]. According to [113], the western region of Algeria (Oranie Chott Chergui hydrographic basin) shows chloride concentrations up to 3600 mg/L. In the same region, [17] found concentrations between 258 and 344 mg/L. For the Naama region, the chloride range is 9.92–2815.3 mg/L [107].

The presence of bicarbonates in water is due to the dissolution of carbonate formations. The concentration of bicarbonates in water depends on the lithological nature of the traversed terrains [114]. Bicarbonate concentrations range from 390 to 470 mg/L. These values are higher compared to the bicarbonate levels found in the Naama watershed, where they ranged from 190 to 272 mg/L [115].

The toxicity of nitrate to aquatic animals increases with rising nitrate concentrations and exposure times. However, nitrate toxicity may decrease with larger body size, water salinity, and environmental adaptation. The main sources of nitrates in groundwater are chemical fertilizers and treated wastewater in the irrigated area [116]. Our study area encompasses approximately 900 hectares irrigated with treated wastewater from the sewage treatment plant of Tlemcen city, in addition to residential domestic waste and chemical fertilizers, leading to nitrate excess [116]. Despite nitrogen pollution decreasing with groundwater depth [117].

The nitrate concentration in the groundwater of the study ranges from 46.72 mg/L at sampling point S8 to 145.38 mg/L at sampling point S1, with an average value of 85.45 mg/L. The Algerian standard sets the limit at 50 mg/L as the threshold [110]. Only 3 samples met this standard (S7, S8, and S23).

These values significantly differ from results reported in other regions: Adrar with 15–29.28 mg/L [111]. Djelfa region with 0.07–72.9 mg/L [118], and Bouhnifia with 0.47–0.96 mg/L [17]. This notably reflects the impact of irrigation with treated or raw wastewater on groundwater in terms of nitrogen-related pollution.

Nitrites, like nitrates, should be considered of anthropogenic origin [119]. Nitrites formed by the reduction of nitrates are likely to bind to hemoglobin [120], reducing the red blood cells’ ability to transport oxygen [121]. The nitrate values expressed previously justify the nitrite values detected in the analyzed samples; an average of 0.77 mg/L was found with a maximum of 1.45 mg/L and a minimum of 0.41 mg/L, all wells exceeded the threshold defined by Algerian standards (0.2 mg/L) [110] and remain around the limit set by the World Health Organization [99].

Sulfates, the most common form of dissolved sulfur in natural waters, mainly originate from two sources: geochemical and atmospheric [122]. Due to the high solubility of sulfates, groundwater can contain up to 1.5 g/L under normal conditions [123]. The groundwater samples show an average sulfate concentration ranging from 69.43 to 153.54 mg/L, with an average of 98.16 mg/L. These levels remain below the threshold recommended by Algerian standards (400 mg/L) [102]. These sulfate levels are significantly lower than those obtained in the study conducted by [18] in the Ouargla Basin, in southern Algeria, where the minimum value of sulfates is 476.00 mg/L, or in the Merdja plain (Far East of the country) [124], where sulfate levels range between 204 and 900 mg/L.

The average concentration of anions follows a descending order: HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻, with respective values of 430.78, 213.85, 98.16, and 85.45 mg/L.

The study of the occurrence and fate of phosphorus has often been limited to surface water rather than groundwater [125]. P in groundwater has been increasingly recognized in recent years, and it is increasingly accepted that groundwater can have phosphorus concentrations exceeding thresholds of ecological relevance [126].

In their study, [127,128] focus on mass transfer between groundwater and surface water by quantifying the amounts of phosphorus transported by groundwater to a lake.

In our study area, phosphorus values are relatively high due to excessive irrigation with treated wastewater, but remain largely below the Algerian standard (5 mg/L) [110]. The laboratory values obtained oscillate between 0.02 and 0.4 mg/L.

3.3. Origin of Hydrogeochemistry

3.3.1. Gibbs Plot

Figure 6a,b present the Gibbs distribution plots—specifically the Na⁺/(Na⁺ + Ca²⁺) versus TDS and Cl⁻/(Cl⁻ + HCO₃⁻) versus TDS diagrams. Analyzing these plots enables us to reveal several noteworthy hydrogeochemical characteristics:

(a)

Position and Distribution of Samples

All samples are located in the upper parts of both diagrams, aligned with the central part where the geochemical process is controlled by water–rock exchange. The samples cluster with TDS values ranging between 1000 and 2000 mg/L, and Na⁺/(Na⁺ + Ca²⁺) ratios between 0.4 and 0.6. This distribution is characteristic of rock dominance with a trend toward evaporation processes.

(b)

Dominant Geochemical Processes

Rock–Water Interaction: The central positioning of the groundwater samples indicates that the weathering of minerals and ionic exchanges with the aquifer’s rock matrix are the dominant processes governing the chemical composition of the groundwater. This interaction leads to the dissolution of silicate and carbonate minerals, releasing calcium, sodium, and bicarbonate ions into the water.

Evaporation Influence: The upper position of the samples in the diagram represents the domain of evaporation. This can be explained by the high drainage characteristics of the aquifer, particularly evidenced by samples S26 and S27, which are two discharge sources of the aquifer characterized by perpetual flow. The relatively high TDS values further support this evaporation influence.

Minimal Precipitation Influence: The absence of samples in the lower regions of the diagram (low TDS) indicates that direct precipitation has only a minor influence on the chemical composition of these groundwater samples.

(c)

Hydrogeological Implications: as outlined below:

i.

Aquifer Characteristics: The wells and boreholes studied are at medium depths (between 35 to 110 m), which contributes to the observed water–rock interaction patterns. The intermediate Na⁺/(Na⁺+ Ca²⁺) ratios (around 0.5) likely indicate a mixed aquifer, composed of both carbonate formations (calcium source) and silicate formations containing sodium feldspars (sodium source).

ii.

Residence Time and Flow Dynamics: The dominance of rock–water interaction processes suggests a moderate to long residence time of water in the aquifer. The perpetual flow characteristics of discharge sources (S26 and S27) indicate active groundwater circulation within the system, which influences the overall hydrochemistry.

iii.

Climatic and Drainage Influence: The evaporation signature in the upper part of the diagram, combined with the high drainage characteristics of the aquifer, suggests a complex interplay between climatic conditions and hydrogeological factors in determining the final water chemistry.

iv.

Relative Homogeneity: The clustering of samples in a relatively restricted area of the diagram suggests a certain homogeneity in the geochemical processes controlling water chemistry across the studied aquifer, despite variations in well depths.

The consistency between both diagrams (cation and anion ratios), Figure 6a,b, respectively, strengthens the validity of this interpretation and confirms the significant mineralization of groundwater samples, resulting from substantial water–rock interaction and evaporative processes within this medium-depth aquifer system with its characteristic drainage patterns.

3.3.2. Piper Plot

The Piper diagram analysis reveals that groundwater samples in the study area predominantly plot in the “Chloride and sulfated calcium and magnesium” facies (Figure 7), indicating a mixed water type with no single dominant ion. The samples cluster near the calcium corner in the cation triangle with moderate magnesium content, while showing a mixed distribution between bicarbonate and chloride + Nitrate in the anion triangle. This composition of chloride, sulfate, calcium, magnesium, and bicarbonate, without abundant anions and cations, reflects the significant influence of the lithological factor in the region. The tight clustering of samples indicates a relatively homogeneous hydrochemical signature across the aquifer, confirming that water–rock interaction is the dominant process controlling groundwater chemistry, as also evidenced in the Gibbs diagram analysis. This mixed water type further supports the interpretation that the groundwater chemistry is primarily shaped by interactions with the local geological formations rather than by precipitation or extensive evaporation processes.

The analysis of groundwater in the study area reveals a composition of chloride, sulfate, calcium, magnesium, and bicarbonate, without abundant anions and cations. This classification indicates a mixed type of water, once again reflecting the effect of the lithological factor in the region.

3.4. Hierarchical Clustering Analysis Results

3.4.1. Parameter Clustering

The hierarchical clustering analysis of water quality parameters revealed distinct groupings based on their behavior across sampling sites (Figure 8). The dendrogram shows that the 15 parameters clustered into several meaningful groups with varying degrees of similarity. The first major cluster consists of EC, Ca²⁺, SO₄²⁻, and Cl⁻, indicating strong correlations among these parameters. This association suggests that these parameters are influenced by similar hydrogeochemical processes, primarily related to water–rock interactions and dissolution of evaporitic minerals. The second notable cluster comprises Mg²⁺, NO₃⁻, and NO₂⁻, which points to a potential anthropogenic influence, particularly from agricultural activities in the watershed. The clustering of nitrogen compounds (NO₃⁻, NO₂⁻) with Mg²⁺ suggests possible co-contamination from agricultural fertilizers and soil leaching processes.

Parameters Na⁺, HCO₃⁻, and TH (total hardness) formed separate clusters, reflecting their distinct behavior in the hydrochemical system. The pH parameter formed an independent cluster, demonstrating its unique variability pattern compared to other parameters. This separation is expected as pH is controlled by multiple equilibrium processes and can be influenced by both natural and anthropogenic factors. The TDS (total dissolved solids) and CAT (complete alkalimetric title) parameters also formed individual clusters, indicating their composite nature that integrates multiple ionic contributions.

3.4.2. Sample Clustering

The hierarchical clustering of water samples identified 10 distinct clusters (Figure 9), revealing spatial patterns in water quality across the study area. Cluster 1, comprising samples S17 and S21, exhibited the highest concentrations of Ca²⁺ (184.00 ± 6.76 mg/L), Cl⁻ (282.88 ± 20.00 mg/L), and electrical conductivity (1947.50 ± 88.39 μS/cm), suggesting a strong influence of evaporitic mineral dissolution. This cluster also showed elevated nitrate levels (118.53 ± 15.73 mg/L), indicating potential anthropogenic contamination superimposed on the natural hydrogeochemical signature.

Cluster 2 (samples S18, S26, S27) displayed moderately high concentrations of major ions but was distinguished by a more balanced ionic composition compared to Cluster 1. The mean electrical conductivity (1764.00 ± 38.04 μS/cm) and calcium content (154.28 ± 10.90 mg/L) suggest intermediate mineralization, possibly reflecting a transition zone between different hydrogeological units.

Clusters 3 through 10 showed progressively decreasing mineralization, with Cluster 10 (samples S2, S3) exhibiting the lowest electrical conductivity (1309.50 ± 88.39 μS/cm) and chloride content (168.18 ± 19.69 mg/L). Interestingly, Cluster 7 (samples S7, S8) showed the lowest nitrate concentrations (47.93 ± 1.71 mg/L) despite moderate mineralization, suggesting these sampling points may be less affected by agricultural pollution compared to other sites with similar overall mineralization.

The hydrochemical interpretation identified distinct groundwater quality patterns across the study area, with some clusters exhibiting high mineralization and elevated concentrations of calcium, chloride, sulfate, and nitrates, likely due to evaporite deposits, water–rock interactions, and agricultural activities [129]. Other clusters showed more balanced or lower levels of mineralization and contamination, reflecting areas with greater recharge or less anthropogenic influence. These differences highlight the need for targeted management: highly mineralized and nitrate-rich zones require priority pollution control and monitoring, while less impacted areas should focus on preventive measures to maintain their current water quality [130].

3.5. Groundwater Quality Evaluation for Irrigation

Table 4. Values of SAR, Na%, RSC, KR, PI and MH in the groundwater samples.

	SAR	Na%	RSC	KR	PI	MH
MIN	1.692	24.221	−8.910	0.3208	99%	48%
MAX	2.550	36.976	−1.701	0.5912	62%	39%
MEAN	2.074	29.732	−4.909	0.4278	79%	42%
SD	0.173	2.410	1.375	0.050	0.071	1.877

summarizes the main groundwater quality indices for irrigation in the study area. The results show moderate variability across parameters, with SAR and KR displaying the greatest consistency, while RSC and MH exhibit higher variability. Mean values for all indices are generally centered between their minimum and maximum, indicating a balanced distribution of groundwater quality characteristics throughout the region.

3.5.1. Irrigation-Specific Indicators

Sodium Adsorption Ratio (SAR)

The SAR values range from 1.69 to 2.55 across the samples. As these values are all significantly below the threshold of 10, the groundwater is classified as having an ‘Excellent’ quality or ‘Low Sodium Hazard’ regarding SAR (

Table 5. Standard Values for Irrigation Water Quality Parameters [96].

Parameter	Range	Suitability
SAR	<10	Excellent
	10–18	Good
	18–26	Doubtful
	>26	Unsuitable
RSC	<1.25	Safe
	1.25–2.5	Marginal
	>2.5	Unsuitable
Na%	<20	Excellent
	20–40	Good
	40–60	Permissible
	60–80	Doubtful
	>80	Unsuitable
KR	<1	Suitable
	1–2	Marginal
	>2	Unsuitable
PI	>75	Suitable
	25–75	Marginal
	<25	Unsuitable
MH	<50	Suitable
	50–60	Marginal
	>60	Unsuitable

). This suggests a minimal risk of soil permeability reduction or structural degradation due to sodium when using this water for irrigation. These results are confirmed after projection onto the Wilcox and US salinity diagram (Figure 10 and Figure 11).

Residual Sodium Carbonate (RSC)

All calculated RSC values are negative, ranging from −8.91 meq/L to −1.70 meq/L. Negative RSC values indicate an excess of Ca²⁺ + Mg²⁺ over carbonates and bicarbonates, falling well within the ‘Safe’ or ‘Good’ category (<1.25 meq/L). Therefore, there is no hazard associated with carbonate precipitation leading to increased sodium problems in the soil (Table 5).

Sodium Percentage (Na%)

The Na% varies between 24.22% and 36.98% for the groundwater samples analyzed. These percentages fall entirely within the 20–40% range, which is generally considered ‘Good’ for irrigation water quality. This classification suggests the proportion of sodium relative to other major cations is acceptable for most irrigation purposes without causing significant harm; these findings are validated by plotting on the Wilcox and US salinity diagrams (Figure 10 and Figure 11).

Kelly’s Ratio (KR)

The assessment using Kelly’s Index (KI) revealed that all groundwater samples were unsuitable for irrigation purposes. The recorded KI values, which ranged from 0.3208 to 0.5912 (Table 4), fell significantly below the permissible threshold of 1, reflecting low to moderate salinity conditions.

Permeability Index (PI)

The Permeability Index (PI) values for groundwater in the Hennaya irrigation zone ranged from 62% to 99% (Table 4), with all samples classified as suitable. These results indicate optimal water quality for maintaining soil permeability, showing no signs of potential soil degradation.

Magnesium Hazard (MH)

Elevated magnesium concentrations in soil can impair water infiltration and reduce crop yields [67]. While irrigation water with a Magnesium Hazard (MH) value >50% is considered unsuitable, the current study recorded MH values ranging from 38% to 48% (Table 4). Although these values remain below the threshold, they approach critical levels—particularly notable as 16 samples exceeded 42%, nearing the permissible limit.

3.5.2. AHP-Based Water Quality Index

In the Analytic Hierarchy Process (AHP), expert opinion is a fundamental component for assigning weights to the criteria used in decision-making. The pairwise comparison matrix is built based on expert judgments regarding the relative importance of each water quality parameter. These experts, typically professionals in hydrogeology, environmental science, water resources, or agriculture, contribute domain-specific knowledge that allows for a more contextualized and informed evaluation. Several previous studies have similarly prioritized parameters like Electrical Conductivity (EC), Total Dissolved Solids (TDSs), Sodium Adsorption Ratio (SAR), for evaluating groundwater suitability for irrigation using AHP [92,131,132,133].

The matrix proposed, which yielded 66 pairwise comparisons (

Table 6. AHP Criteria Pairwise Comparison Matrix.

	pH	EC	TDS	HCO3−	Cl	NO3−	SAR	%Na	RSC	KR	PI	MH
pH	1.00	0.20	0.25	0.50	0.33	1.00	0.20	0.33	0.33	0.50	0.50	1.00
EC	5.00	1.00	2.00	4.00	3.00	5.00	1.00	3.00	3.00	4.00	4.00	4.00
TDS	4.00	0.50	1.00	3.00	2.00	4.00	0.50	2.00	2.00	3.00	3.00	3.00
HCO3−	2.00	0.25	0.33	1.00	0.50	2.00	0.25	0.50	0.50	1.00	1.00	1.00
Cl	3.00	0.33	0.50	2.00	1.00	2.00	0.33	1.00	1.00	2.00	2.00	2.00
NO3−	1.00	0.20	0.25	0.50	0.50	1.00	0.33	0.50	1.00	1.00	1.00	1.00
SAR	5.00	1.00	2.00	4.00	3.00	3.00	1.00	1.00	1.00	2.00	2.00	2.00
%Na	3.00	0.33	0.50	2.00	1.00	2.00	1.00	1.00	1.00	1.00	1.00	1.00
RSC	3.00	0.33	0.50	2.00	1.00	1.00	1.00	1.00	1.00	2.00	2.00	2.00
KR	2.00	0.25	0.33	1.00	0.50	1.00	0.50	1.00	0.50	1.00	1.00	1.00
PI	2.00	0.25	0.33	1.00	0.50	1.00	0.50	1.00	0.50	1.00	1.00	1.00
MH	1.00	0.25	0.33	1.00	0.50	1.00	0.50	1.00	0.50	1.00	1.00	1.00

), is presented in the table below.

Derive Weights: The weights for each criterion were derived from the pairwise comparison matrix. For each criterion, the relative importance is calculated based on the comparisons made. The following steps are typically involved:

• Normalize the pairwise comparison matrix by dividing each element by the sum of its column.
• Calculate the average of each row to derive the weight for each criterion.
• Ensure that the weights sum to 1.

After determining the criterion weights, we assess the consistency of the pairwise comparisons used to derive them. This begins with calculating the Consistency Index (CI) using the following equation:

$$CI={\displaystyle \frac{\lambda -n}{n-1}}$$

(13)

where λ = the mean value of the consistency vector and n = the number of criteria.

Subsequently, the Consistency Ratio (CR) is computed (CR = CI/RI) to validate the overall consistency of the judgments. A consistency ratio (CR) below 0.1 signifies an acceptable level of consistency, while a CR of 0.1 or higher suggests inconsistency (

Table 7. Random Index (RI) Values for AHP Consistency Assessment [21].

n	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
RI	0	0	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49	1.51	1.48	1.56	1.57	1.58

) [134].

The consistency of the comparisons was verified with a Consistency Ratio (CR) of 2.4%, which, being below the 0.1 threshold, indicates an acceptable level of consistency in the weighting process.

The average of each row is calculated to determine the corresponding criterion weight (

Table 8. Criteria weight values of water quality parameters.

Criterion	pH	EC	TDS	HCO3−	Cl−	NO3−
Weight	3.10%	20.22%	13.62%	4.87%	8.14%	4.34%
Criterion	SAR	%Na	RSC	KR	PI	MH
Weight	14.38%	7.65%	8.57%	5.13%	5.13%	4.87%

).

Table 9. Classification of each sample using the Water Quality Index (WQI).

S. No.	WQI Value	Water Quality Classification	S. No.	WQI Value	Water Quality Classification
1	81.463	Good	15	74.322	Good
2	75.701	Good	16	79.084	Good
3	69.628	Good	17	84.243	Good
4	82.215	Good	18	81.025	Good
5	79.883	Good	19	80.665	Good
6	76.342	Good	20	85.044	Good
7	69.259	Good	21	86.479	Good
8	80.509	Good	22	82.261	Good
9	83.469	Good	23	76.876	Good
10	80.557	Good	24	79.515	Good
11	79.118	Good	25	82.952	Good
12	79.353	Good	26	81.910	Good
13	79.385	Good	27	88.713	Good
14	82.101	Good

presents the Water Quality Index (WQI) values of the groundwater samples, calculated using weights derived from the AHP method in accordance with the methodology described in Section 2.5.2.

To assess the irrigation suitability of groundwater, the Water Quality Index (WQI) scores were systematically categorized employing a framework derived from analogous studies on groundwater quality evaluation in semi-arid environments. This structured classification schema, delineated in

Table 10. Classification of Water Quality Based on WQI scores [135].

GWQI Range	Water Quality Class	Suitability for Irrigation
<50	Excellent	Highly suitable; no restrictions
50–100	Good	Suitable; minor precautions
100–200	Poor	Marginally suitable; requires management (e.g., leaching, crop selection)
>300	Unsuitable for drinking	Highly unsuitable

, organizes WQI scores into discrete intervals, each corresponding to a specific water quality category and its respective implications for irrigation applicability, thus effectively elucidating the spectrum of WQI scores.

The Water Quality Index (WQI) scores for the study area, which range from 69.26 to 88.71, classify the groundwater as ‘Good’ (notably, the samples from the southern region stand out, as they exhibit the highest values within the ‘Good’ range, approaching the upper limit of the ‘Excellent’ category, e.g., S3: 69,628, S7: 69,259) (Figure 12). The consistent WQI values across all sampling locations demonstrate uniform and reliable water quality throughout the study area. These findings confirm that the irrigation water maintains excellent overall quality, with all parameters meeting optimal agricultural requirements, characterized by optimal values for Electrical Conductivity (EC), Total Dissolved Solids (TDSs), Sodium Adsorption Ratio (SAR), Kelly’s Ratio (KR), Permeability Index (PI), and Magnesium Hazard (MH). This ensures low salinity, good soil infiltration, and structural stability. The balanced pH, along with controlled levels of bicarbonate (HCO₃⁻) and chloride (Cl⁻), minimizes the risk of plant toxicity and prevents damage to irrigation systems. These properties make this water highly suitable for sustainable irrigation across most crop types. However, regular monitoring of nitrate (NO₃⁻) levels, which range between 46.72 and 145.38 mg/L, is recommended to mitigate potential risks of over-fertilization for plants and groundwater contamination. Only minor adjustments to fertilization and periodic soil monitoring are needed to sustain this favorable balance over the long term.

The Water Quality Index (WQI) spatial distribution was modeled using geostatistical interpolation in the ArcGIS tool (Figure 12). The Ordinary Kriging method was applied to predict WQI values across the study area, optimizing spatial autocorrelation based on sampled point data, with the southern sector exhibiting superior quality. This spatial pattern, particularly pronounced in the southern sector of the study area, reflects (1) effective crop rotation mitigating soil depletion despite intermittent cultivation intensification in arboriculture zones; (2) naturally efficient drainage from the region’s highly permeable aquifer, preventing waterlogging and salt accumulation; and (3) balanced cation ratios (Na⁺/Ca²⁺/Mg²⁺) that optimize soil permeability. The tiered classification pinpoints areas where water quality approaches ideal conditions (84–87), enabling strategic crop planning (e.g., salt-sensitive crops in high-WQI zones), while marginally suitable areas (75–78) could benefit from adopting the south’s integrated soil-water management practices to sustain long-term agricultural productivity.

Northward progression through the study area reveals a consistent increase in WQI scores across most sampling locations, culminating in a maximum value of 88.71 at Sample 27. This peak measurement, recorded at the aquifer outlet, corroborates the earlier hypothesis of efficient drainage characteristics within the system.

Although Total Dissolved Solids (TDSs) (990–1930 mg/L) and Electrical Conductivity (EC) (1247–2010 μS/cm) are within FAO limits (<2000 mg/L and <3000 μS/cm, respectively), intensive irrigation with these waters may still lead to progressive soil salinization and structural degradation unless managed through corrective measures such as drainage or leaching. While the region’s carbonate-rich lithology naturally contributes to elevated calcium, magnesium, and bicarbonate levels, anthropogenic activities remain the primary driver of water quality variability.

4. Conclusions

This study provides a comprehensive assessment of groundwater suitability for irrigation in the Hennaya irrigated region in northwestern Algeria. It examines the quality parameters and spatial variations in groundwater using an integrated methodology that combines hydrochemical analysis, the Water Quality Index (WQI), the Analytic Hierarchy Process (AHP), and Geographic Information Systems (GIS). The hydrochemical assessment reveals a groundwater profile marked by slight salinity and high alkalinity (CAT), with Total Dissolved Solids (TDSs) approaching the threshold for optimal agricultural use. Despite encouraging irrigation indicators—such as a Sodium Adsorption Ratio (SAR) (excellent), a Sodium Percentage (Na%) of (rated good), and a Residual Sodium Carbonate (RSC), the Kelly’s Index and the Permeability Index values for the groundwater samples fall within a very acceptable range (considered secure)—the groundwater grapples with substantial challenges posed by elevated nitrate and nitrite levels, primarily attributable to treated wastewater, fertilizers, and pesticides. The prevailing ion sequences (Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ for cations; HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻ for anions), corroborated by Piper and Gibbs diagrams, delineate a blended water type shaped by the region’s carbonate-rich geology, water–rock interactions, evaporation, and human-induced pollution, with the latter emerging as the predominant force behind quality decline.

The WQI scores, oscillating between 69.25 and 88.71, categorize the groundwater as ‘Good’. Spatial analysis reveals a clear gradient across the study area: notably, groundwater samples collected from the southern region demonstrated the most favorable quality within this category. While these results are informative, they do not preclude the necessity for corrective action. Considering the anthropogenic activities that have contributed to abnormal nitrate concentrations, it is imperative to adopt comprehensive measures to mitigate and reverse this contamination.

This research has markedly enriched the comprehension of the physicochemical characteristics of water within the Hennaya irrigated region. The integration of WQI and GIS has proven instrumental in generating detailed maps that depict the spatial distribution of water quality parameters. These maps serve as an invaluable resource for identifying pollution hotspots and guiding strategic management efforts. The findings of this study furnish actionable guidance for policymakers, local, and regional authorities to bolster groundwater management and ensure the secure utilization of irrigation water, fostering sustainable agricultural practices and long-term resource preservation to meet the socio-economic demands in the study region. Thereby, the findings contribute to increasing and enlarging the scale to raise water productivity for coping with expected water scarcity situations, conserve groundwater resources and contribute to protecting the population against health risks caused by water pollution impacting drinking water provision and food chains.