Hydrogeochemical Characterization and Water Quality Index-Based Evaluation of Groundwater for Drinking, Livestock, and Irrigation Use in the Arid Ewaso Ng’iro–Lagh Dera Basin, Kenya

Abstract

Groundwater is the main source of water for both domestic and agricultural use in arid regions. This study assessed the hydrogeochemical characteristics and suitability of groundwater for drinking and irrigation in Kenya’s Ewaso Ng’iro–Lagh Dera Basin. A total of 129 borehole groundwater samples were collected and analyzed for pH, electrical conductivity (EC), total hardness, and major ions. The groundwater was found to be mostly neutral to slightly alkaline and ranged from marginal to brackish in salinity. The dominant water type is Na-HCO₃, with the ionic order Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ and HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻. Mineral saturation indices indicate that the water is undersaturated with gypsum and anhydrite but is saturated with calcite, dolomite, and aragonite. Groundwater chemistry is primarily influenced by ion exchange, the mixing of fresh and paleo-saline water, and rock weathering processes. The water quality index (WQI) reveals that 80.5% of groundwater is suitable for drinking. The rest have high levels of sodium, EC, and bicarbonate. Thus, they are not suitable. The irrigation water quality index (IWQI) places most samples in the moderate-to-severe restriction category due to high salinity and sodicity. These findings highlight the importance of properly treating groundwater before use.

1. Introduction

Water is one of the vital resources for human survival in this world. Approximately 97.5% of the global water is saline/saltwater, while the remaining 2.5% comprises freshwater, of which 30% of these global freshwater resources are groundwater [1]. In arid regions, groundwater is the main water source for domestic, agricultural, and industrial purposes.

Numerous global studies highlight the critical role of understanding hydrogeochemistry in assessing groundwater quality [2,3,4,5,6]. The chemistry of groundwater is generally diverse [7]. However, groundwater quality has been observed to range from brackish to saline in most arid areas [2,8]. Therefore, assessing hydrogeochemistry to understand the suitability of groundwater in arid regions is very important [9,10].

Generally, as groundwater flows from recharge to discharge areas, it chemically evolves as it interacts with aquifer materials or mixes with different groundwater bodies along the flow path [11]. This chemical evolution is driven by processes such as dissolution, ion exchange, evaporation, and saline water intrusion [6,11,12]. Additionally, the type of groundwater flow—whether local, intermediate, or regional—can influence the hydrogeochemical signature by affecting the reaction kinetics during water–rock interactions. The concentration and distribution of major ions may serve as indicators of flow regimes and can aid in understanding aquifer dynamics [13,14]. The interaction of rocks with groundwater leads to the saturation of various mineral species at equilibrium conditions. Changes in the saturation states are helpful in the identification of the different geochemical reactions that control the groundwater and decipher the various stages of the hydrochemical evolution of the groundwater [3,5]. The calculation of the saturation index (SI) assesses the chemical equilibrium between groundwater and a specific mineral.

Major ion chemistry determinations, combined with the application of statistical methods, have been used in groundwater studies to provide insight into the hydrogeochemical processes affecting the groundwater chemistry in an aquifer [7,15,16]. These statistical methods include bivariate regression analysis, correlation, the calculation of the ion ratio [12,15,17,18,19], and multivariate statistical methods.

Multivariate statistical techniques have become essential in hydrogeochemical studies for analyzing large and complex datasets [20]. Methods such as principal component analysis (PCA), cluster analysis, and factor analysis help to decipher patterns in groundwater chemistry by identifying the relationships between variables and grouping samples with similar characteristics [4,21,22,23]. These approaches are particularly useful in distinguishing between processes, such as mineral dissolution, ion exchange, or evaporation, and the anthropogenic influences that have affected hydrogeochemistry. By simplifying the data interpretation, multivariate analysis enhances our understanding of the key factors influencing groundwater quality. PCA analysis is instrumental in helping to simplify diverse and complex relationships among various observed variables [24,25,26,27]. It is also helpful in identifying relationships and patterns in each dataset while highlighting similarities and differences [28,29,30]. Statistical methods complement the classical standard graphical methods, such as Piper and Scholler diagrams, used to classify water facies and identify factors affecting groundwater quality in an aquifer [17,31].

The suitability of groundwater for drinking and agricultural purposes can be determined by applying the water quality indices (WQI). The WQI is a single, dimensionless numeric score that reports the water quality as a single score [32]. This numeric score was first used by Horton [33]. The calculation and the mode of deriving the WQI have been updated and applied successfully in various hydrogeological contexts and for many different purposes [34,35,36,37]. The water quality data is often extensive and has many parameters to consider, such as electrical conductivity (EC), TDS, and the concentration of various analyzed ions. The WQI is an essential and effective tool that helps minimize the data into a single numeric value that is easy to understand, evaluate, and monitor [37].

The suitability of water for irrigation purposes is commonly assessed using the irrigation water quality index (IWQI) [38,39,40,41]. This tool combines various physical and chemical water quality variables into a single value representing the water quality level [38,39]. The commonly used parameters in calculating the IWQI are those that measure salinity, sodicity, and toxicity, e.g., EC, sodium adsorption ratio (SAR), and the concentration of ions [42,43].

International and national standards typically guide the assessment of groundwater quality for domestic and agricultural uses to ensure public health and environmental safety. The World Health Organization (WHO) guidelines [44] provide globally accepted thresholds for drinking water quality. This offers a comprehensive framework for evaluating the potential health risks associated with chemical and microbiological contaminants that have been applied globally [45,46]. Complementing these, the Water Services Regulatory Board (WASREB) of Kenya has established localized standards that account for regional water use patterns, environmental conditions, and infrastructural capacities [47]. Integrating both WHO and WASREB standards in water quality assessments enables a more context-specific evaluation, ensuring that water resources meet both international safety benchmarks and the practical needs of local populations. The Food and Agriculture Organization (FAO) also provides guidelines for various agricultural applications [42,43,48].

Ewaso Ng’iro–Lagh Dera basin, on which this study is based, is a water-stressed, arid region of Kenya where groundwater is the primary water source. The demand for groundwater is high due to the dry climatic conditions and limited surface water. This demand is expected to persist, especially in Sub-Saharan Africa, where the projected population is anticipated to increase drastically [49]. Specifically, in Kenya, the population is expected to increase from approximately 48.8 million in 2020 to about 70.2 million by 2045, representing approximately a 43.85% increase [50]. The combined effects of climate change, pollution, population increase, and land use changes are causing an alarming decline in groundwater level and quality [51,52,53].

Agriculture plays a significant role in Kenya’s economic growth [54]. The agricultural sector is largely carried out by small-to-medium-scale farmers who rely on the rainfall seasons [55]. Unfortunately, the study area lies in an arid region with low and unpredictable rainfall [56]. This leads to farmers concentrating their activities along riverbanks [57]. It has been proposed that precision irrigated farming is needed to expand farming activities and food production sustainably [58]. Given that the rivers in the region are ephemeral, groundwater serves as a viable alternative to support agricultural activities.

The study area is a key region for animal husbandry in Kenya, where pastoralism is the primary livelihood activity [57]. Since the area experiences unreliable rainfall, livestock production relies heavily on groundwater resources. Studies show that residents of the arid regions of Northern Kenya, in areas such as Ewaso Ngi’ro basin, rely heavily on groundwater for domestic and farming purposes, especially in the dry season [59,60]. Therefore, groundwater is a critical resource in the region, necessitating sustainable utilization and management. One of the critical steps in the management and sustainable utilization involves understanding the hydrogeochemical and hydrogeological properties of the aquifer [61].

Several studies have been conducted on groundwater resources within the Ewaso Ngi’iro–Lagh Dera basin [62,63,64,65,66,67,68,69,70,71,72]. These studies have assessed the extent of the aquifers, established the spatial and temporal variation of various geochemical and geophysical parameters, modeled the groundwater flow, and investigated the groundwater recharge and discharge dynamics. The overextraction of groundwater resources and the possibility of a decline in water quality through up-coning have been expounded by Leudeling et al. [71]. Recharge in the study area has been noted to occur during the sporadic and episodic rainfall within inundation along the ephemeral rivers and the swamp [67,69]. The possibility of recent episodic recharge has been established using tritium and carbon isotope analysis carried out by [64,73,74,75]. However, these studies have not provided a detailed analysis of the ion chemistry necessary to interpret and identify the various hydrogeochemical processes affecting groundwater quality. Identifying these processes can substantially contribute to ensuring the sustainable management of groundwater resources. Therefore, this study’s main objective was to identify and characterize the hydrogeochemical processes that control groundwater chemistry and assess the groundwater’s suitability for human and livestock consumption and irrigation purposes.

2. Materials and Methods

2.1. The Study Area

The Ewaso Ng’iro–Lagh Dera basin is located in the northeastern part of Kenya. It is bounded by longitudes 38°10″ E to 40°60″ E and latitudes 0°05″ S to 1°45″ N (Figure 1), covering an approximate area of 42,997 km². The study area is part of the large Ewaso Ng’iro basin (Figure 1) that covers parts of Kenya, Ethiopia, and Somalia. In Kenya, the Ewaso Ng’iro River, which drains the basin, originates from the central parts of the country as a perennial river. The river flows into the northeastern part of the country as the ephemeral Lagh Dera river, which extends into Somalia and drains into the Indian Ocean. Climatically, the study area is an arid region, with temperatures ranging between 22 °C and 33.4 °C and an average rainfall of about 250 mm per year. The rainy seasons occur biannually, one from March to May and another from October to December, with the latter being more significant [56]. Shrubs and grassland largely cover the study area. Thus, nomadic pastoralism is the main land use activity [71]. Farming activities are carried out along the Lagh Dera river. The towns are mainly low-density urban centers, apart from Hagadera, which has a moderately high population density. Hagadera hosts Dadaab refugee camp, the largest in East and Central Africa. Topographically, the area is mainly flat, with a few elevated grounds. The altitude ranges from 45 m to 1543 m above the mean sea level.

2.2. Geological and Structural Setting

The Ewaso Ng’iro–Lagh Dera basin is located mainly within the Anza Basin, Kenya. The Anza basin is an inactive continental rift system formed in the Mesozoic Era. The basin stretches inland from the coast of Kenya with a northwest–southeast trend [76,77,78]. The Anza basin is filled with very deep (>3 Km) sediments. In the Mesozoic era, the basin experienced a period of marine incursion followed by a long period of uplift and erosion, which was later followed by the paleo-Tethyan transgression of the Trans-Eritrean trough. Later in the Cenozoic era, extensive continental erosion led to leveling in most parts of the study area, followed by periods of deposition in the Miocene and continued deposition and peneplanation in the Pliocene [79].

The exposed lithologies of the Anza basin are Pliocene sediments consisting of sandstone, conglomerates, limestone, siltstone, sand, grits, gravel, and clays. The Holocene colluvium consists of gravel, silts, and sands, whereas the Holocene alluvium consists of clays, sand, and silts. The cross-section in Figure 2 indicates that deeper formations include the Miocene limestone, Miocene sand and clay, and further below, at 500 m below the surface, are the Miocene marine shales [69,80]. The metamorphic rocks consist of metamorphosed components of calcareous rocks, semi-pelitic sedimentary rocks, intrusive rocks, and psammitic sediments [80]. Lithologies that do not belong to the Anza basin are the Precambrian metamorphic and the Quaternary volcanic rocks occurring in the western and southwestern part of the study area, respectively. The Quaternary volcanic rock prevalent in the study area is the olivine basalt of the Pleistocene Epoch (Figure 2).

Deep-seated faults are the main structural features, since the study area occurs within the Anza basin trough [79]. Most of these faults are deep structures (>0.8 Km below ground level) and are therefore concealed by various superficial unconsolidated sediments and volcanic rocks of the Quaternary age [77,81]. However, the overlying formation mimics the deep structures, forming thick layers as the sediment accumulates in the depressions. It has, therefore, been hypothesized that they impact the occurrence and flow of groundwater in the area. In addition, there is a postulation that these structures influence the recharge and discharge of the freshwater lens of the aquifers in the study area [72].

2.3. Hydrogeology

Hydrogeologically, the principal aquifer in the study area is the Merti aquifer. The aquifer is comprised of fresh water along the Lagh Dera river axis. Salinity increases away from the river axis [66]. This is especially evident at the fresh lens of the Merti aquifer stretches from Habaswein to Liboi [71] (on the Kenya–Somalia border), with an aerial geographical extent of approximately 20,000 square kilometers. The Merti aquifer is multilayered [65,71], with a shallow unconfined aquifer occurring at an average depth of 20 m, constituted mainly of alluvial and colluvial deposits. A deeper aquifer is largely confined and occurs at depths greater than 80 m below ground level, primarily comprising weathered sandstone, gravel, sand, clay, dolomite, and limestones [67]. The transmissivity is high in the alluvium and colluvium and low in the sandstone and metamorphic zones [62,69,72].

Figure 2. The geology of the study area was adopted from the Geological Map, Geostructural, and Bouguer gravity map of Kenya (National Oil Corporation Kenya (NOCK) [82]). Geological cross-section A–A’ representing the subsurface formation adopted from [69,79].

Figure 2. The geology of the study area was adopted from the Geological Map, Geostructural, and Bouguer gravity map of Kenya (National Oil Corporation Kenya (NOCK) [82]). Geological cross-section A–A’ representing the subsurface formation adopted from [69,79].

2.4. Sampling and Analysis

One hundred and twenty-nine water samples were collected from the aquifer in the study area to analyze the major ion chemistry in December 2021. Two groundwater samples were collected from the shallow wells (Figure 1) at depths less than 20 m, whereas the rest of the samples were collected from boreholes at depths greater than or equal to 80 m below ground level. The groundwater samples were taken from the boreholes using already installed pumps. Standard sampling procedures were followed according to the local authority (WASREB) [47] guidelines and the APHA [83] guidelines. After pre-rinsing the bottles with the water sample, 500 mL polyethylene bottles were used to collect the final water samples.

The water samples were collected in duplicates. One sample was used to analyze major anions, while the other was treated with nitric acid (HNO₃) to a pH of less than 2 for analysis of major cations. The cations analyzed were Na⁺, K⁺, Ca²⁺, and Mg²⁺ while the anions were HCO₃⁻, SO₄²⁻, NO₃⁻, and Cl⁻. The pH and electrical conductivity (EC) were measured in the field using a portable conductivity, salinity, and pH meter, and the location of the sample was recorded using a Garmin GPS. To ensure quality sampling, the collected samples were stored and transported in cooler boxes at 4 °C and then taken to the laboratory for analysis. The major cations were analyzed using inductively coupled plasma optical emission spectroscopy (ICP-OES) methods using the Thermo Scientific Model—iCAP 6000 Series (ISO 11885:2007) [84]. The Cl⁻ concentration was determined colorimetrically by forming a red/brown iron(III) thiocyanate complex, with its intensity measured at 480 nm using spectrophotometry (EPA Method 325.1). SO₄²⁻ was analyzed colorimetrically using the barium chloride–glycerol method. The HCO₃⁻ was determined using the titration method with hydrochloric acid to a pH endpoint (around 4.5), where the volume of acid required reflects the bicarbonate concentration.

For quality assurance and control, all the laboratory analyses were performed in duplicate. To ensure accuracy, the charge balance error (CBE) was calculated as indicated in Equation (1), and the dataset used in this study had a charge balance error range of ±10%, which is an acceptable standard limit [18,85,86]. The error was computed based on the analyzed ions expressed in milliequivalent per liter (meq/L).

$$\mathrm{C}\mathrm{B}\mathrm{E}={\displaystyle \frac{\sum \mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}-\sum \mathrm{a}\mathrm{n}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}}{\sum \mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}+\sum \mathrm{a}\mathrm{n}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}}}\times 100$$

(1)

The analyzed ions in the groundwater were plotted on a Piper diagram [87] to characterize the groundwater into the different water types. Interpretations of the potential interactions between the groundwater and host rocks were conducted and illustrated using geochemical plots. The saturation index (SI) was calculated using PHREEQC (Version 3) geochemical modeling software. The saturation index (SI) indicates the status of a mineral phase reaction, whether it is dissolving or precipitating [7]. It is, therefore, used to determine the equilibrium state of the groundwater with respect to the mineral phase using Equation (2).

SI = log (IAP/Ksp(T))

(2)

where the ion activity product (IAP) is the product of the dissociated chemical species in solution, and Ksp(T) is the equilibrium constant at sample temperature T [88].

The saturation index quantitatively indicates the sampled water’s deviation from the equilibrium [89]. If the groundwater is at equilibrium, SI = 0. If the groundwater is under saturated, SI < 0, and if SI > 0, then the water is supersaturated with respect to the mineral facies and may represent the precipitation phase [90].

Statistical techniques such as bivariate analysis were used to understand the relationship between the parameters. Bivariate analysis reveals the existence of an association, strength, or the presence of differences between the two variables [91,92]. This study used bivariate regression and correlation using EXCEL and XLSTAT 2016 to carry out the analysis. A correlation matrix was used to examine the statistical relationships between the hydrogeochemical parameters, and this helped to identify the common sources or geochemical processes influencing groundwater composition [93,94]. By revealing strong positive or negative correlations, it can point to processes such as mineral dissolution, ion exchange, or anthropogenic inputs. Correlation analysis, therefore, supports the interpretation of groundwater chemistry and further helps in parameter selection for further multivariate techniques like PCA, as well as helps distinguish between natural and human-induced influences on water quality.

PCA, a multivariate statistical method, was applied to identify the most influential variables and underlying geochemical processes [24]. The dataset was first standardized and converted into a correlation matrix with normally distributed variables. Eigenvalues and eigenvectors were then extracted, and only components with eigenvalues greater than one, indicating significant contributions to total variance, were retained [22]. To better interpret the influence of each variable, a varimax rotation was applied to the factor loadings, aligning them closer to +1, 0, or −1 to indicate strong positive, negligible, or strong negative contributions, respectively. These results were used to compute factor scores, revealing dominant variables and associated geochemical processes. The PCA analysis was carried out using XLSTAT 2016 software.

The suitability of the groundwater for human consumption was assessed by comparing the sample-measured parameter with the World Health Organization (WHO) 2017 guideline values [44] and the Water Services Regulatory Board of Kenya (WASREB) [47]. The drinking suitability for livestock was compared with the Food and Agriculture Organization (FAO) 2016 guidelines [48].

Drinking water suitability was also analyzed using the water quality index (WQI) obtained using the parameters assessed in this study. The tool used in this analysis was a weighted arithmetic water quality index whose procedure involved four main steps.

• Selection of the parameters. In this study, all the analysed parameters were used; these are EC, TDS, pH, TH, Na⁺, Ca²⁺, Mg²⁺, K⁺, HCO₃⁻, Cl⁻, SO₄²⁻, and NO₃⁻;
• Assigning weights to the parameters (Wi). The assigning of the weight took into consideration the influence of the parameter on water quality and the weight rating of the studies carried out in similar settings [95,96]. The weighting ranged from 2 to 5. The highest weight of 5 was awarded TDS, and NO₃⁻. EC, pH, and SO₄²⁻ were awarded a weight of 4. HCO₃⁻ and Cl⁻ were awarded a weight of 3, whereas Na⁺, Ca²⁺, Mg²⁺, K⁺, and TH were awarded a weight of 2. Relative weights (wi) for the ith parameter and n number of parameters were then calculated using the formulae (Equation (3)).

$$\mathrm{W}\mathrm{i}=\sum {\displaystyle \frac{\mathrm{w}\mathrm{i}}{\sum _{\mathrm{i}=1}^{\mathrm{n}}\mathrm{w}\mathrm{i}}}$$

(3)

wi represents the relative weight of the parameter, and Wi is the assigned weight of each parameter;

3.

Calculate the quality rating (Qi) of each of the parameters using the formulae (Equation (4)).

$$\mathrm{Q}\mathrm{i}={\displaystyle \frac{\mathrm{C}\mathrm{i}}{\mathrm{S}\mathrm{i}}}\times 100$$

(4)

where Ci is the measured concentration of each of the parameters, and Si is the permissible standard guideline value for the parameter. For this study, the WHO (2017) standards were considered;

4.

Calculate the overall water quality index using Equation (5).

$$\mathrm{W}\mathrm{Q}\mathrm{I}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{S}\mathrm{I}\mathrm{i}$$

(5)

where SIi is given using Equation (6).

$$\mathrm{S}\mathrm{I}\mathrm{i}=\mathrm{w}\mathrm{i}\times \mathrm{Q}\mathrm{i}$$

(6)

The calculated WQI values range from 0 to values greater than 300. These values are then categorized into five rating classes, with values close to zero having excellent water quality and values greater than 300 being the unsuitable water quality [97].

The irrigation water quality index (IWQI) was used to determine the suitability of the groundwater for irrigation. The procedure for calculating the IWQI is similar to that used in WQI. Here, the parameters influencing salinity, sodium hazard, and toxicity were considered: EC, sodium adsorption ratio (SAR), and ion (Na⁺, Cl⁻, and HCO₃⁻) concentration.

The EC helps evaluate the salinity, while the Cl⁻ and HCO₃⁻ ion concentrations help evaluate the toxicity. The concentration of Na⁺ and SAR measures the hazard of sodium. SAR can be determined using Equation (7).

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

(7)

where Na⁺, Ca²⁺, and Mg²⁺ are expressed in meq/L

After identifying the parameters to consider, relative weights, wi, were assigned to each depending on the parameter’s impact on irrigation. The weights assigned to the parameters were according to [42] (

Table 1. Weights for the IWQI parameters according to [42].

Parameter	Weight (wi)
EC	0.211
Na+	0.204
HCO3−	0.202
Cl−	0.194
SAR	0.189
Total	1

).

The next step was calculating the quality rating (Qi) for each parameter according to [43]. (

Table 2. Limiting values of qi calculations [43,98].

HCO3− (meq/L)	Cl− (meq/L)	Na+ (meq/L)	SAR (meq/L) 0.5	EC (μS/cm)	Qi
1 ≤ HCO3− <1.5	1 ≤ Cl− < 4	2 ≤ Na+ < 3	2 ≤ SAR < 3	200 ≤ EC < 750	85–100
1.5 ≤ HCO3− < 4.5	4 ≤ Cl− < 7	3 ≤ Na+ < 6	3 ≤ SAR < 6	750 ≤ EC < 1500	60–85
4.5 ≤ HCO3− < 8.5	7 ≤ Cl− < 10	6 ≤ Na+ < 9	6 ≤ SAR < 12	1500 ≤ EC < 3000	35–60
HCO3− < 1 or HCO3− ≥ 8.5	1 < Cl− ≥ 10	Na+ < 2 or Na+ ≥ 9	2 < SAR ≥ 12	EC > 200 or EC ≥ 3000	0–35

) by applying Equation (8).

$$\mathrm{Q}\mathrm{i}={\mathrm{q}}_{\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{x}}-{\displaystyle \frac{[\left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}\mathrm{n}\mathrm{f}}\right)\times {\mathrm{q}}_{\mathrm{i}\mathrm{a}\mathrm{m}\mathrm{p}}}{{\mathrm{x}}_{\mathrm{a}\mathrm{m}\mathrm{p}}}}$$

(8)

where ${\mathrm{q}}_{\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{x}}$ is an upper limit of the last class of each parameter, ${\mathrm{x}}_{\mathrm{i}\mathrm{j}}$ is the observed value of chemical parameters, ${\mathrm{x}}_{\mathrm{i}\mathrm{n}\mathrm{f}}$ is the minimal limit of the class to which each parameter belongs, ${\mathrm{q}}_{\mathrm{i}\mathrm{a}\mathrm{m}\mathrm{p}}$ is the class amplitude, and ${\mathrm{x}}_{\mathrm{a}\mathrm{m}\mathrm{p}}$ is an upper limit of the last class of each parameter.

The irrigation water quality index is calculated using Equation (9).

$$\mathrm{I}\mathrm{W}\mathrm{Q}\mathrm{I}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{Q}\mathrm{i}\times \mathrm{w}\mathrm{i}$$

(9)

where $\mathrm{Q}\mathrm{i}$ represents the parameter’s quality measurement, ith is the number between 1 and 100 representing the concentration of the parameter, and wi is the normalized weight of the ith parameter. The results of IWQI are values ranging from 0–100 and are categorized into five categories.

3. Results

3.1. Evaluation of Geochemistry

The pH level of the water samples analyzed ranged between 7.1 and 8.9, indicating that the groundwater is neutral to slightly alkaline. The electrical conductivity (EC) of the groundwater ranges from 419.8 to 18,000.0 μS/cm (

Table 3. Summary statistics of the measured parameters in the study area.

Parameter	Units	Min	Max	Median	Average	Maximum Allowable Limit (WHO 2017)	Maximum Allowable Limit (WASREB-KENYA 2016)	Percentage Number of Samples Above the WHO Acceptable Limit
pH	-	7.1	8.9	7.7	7.9	8.5	8.5	13.3
EC	μS/cm	419.8	18,000.0	1232.2	1937.0	1500	1500	36.0
TDS	mg/L	313.8	8366.0	912.0	1422.9	1000	1500	25.4
HCO3−	mg/L	109.2	4979.2	560.5	767.7	300	500	85.3
Cl−	mg/L	2.0	4300.0	60.0	217.1	600	250	5.3
SO42−	mg/L	3.0	1230.0	35.3	89.5	400	400	5.3
NO3−	mg/L	0	129.9	1.1	5.5	50	10	1.4
Na+	mg/L	10.4	3410.0	221.0	416.9	200	200	49.3
K+	mg/L	2.0	83.0	11.0	12.7	20	20	10.7
Ca2+	mg/L	2.8	233.0	20.0	26.5	200	250	2.7
Mg2+	mg/L	1.0	151.0	11.1	18.2	150	100	2.7
TH	mg/L	11.6	1125.4	11.6	120.7	500	500	1.4
SI Calcite	mg/L	−0.4	1.6	0.4	0.4	N/A	N/A	N/A
SI dolomite	mg/L	−1.0	3.2	0.9	0.9	N/A	N/A	N/A
SI aragonite	mg/L	−0.5	1.4	0.3	0.3	N/A	N/A	N/A
SI Anhydrite	mg/L	−3.9	−1.8	−2.9	−2.9	N/A	N/A	N/A
SI gypsum	mg/L	−3.7	−1.6	−2.7	−2.7	N/A	N/A	N/A
SI Halite	mg/L	−8.8	−4.4	−6.9	−6.9	N/A	N/A	N/A
Na+/Cl− ratio		1.0	306.6	5.6	22.7	N/A	N/A	N/A
CAI 1		−306.7	−0.01	−4.9	−22.2	N/A	N/A	N/A
CAI 2		−1.7	−0.06	−0.7	−0.7	N/A	N/A	N/A

N/A represents not applicable

). The average EC of the collected samples in the study area is 1937.0 μS/cm. Based on the EC values, the groundwater system can be classified into three groups: freshwater (<1500 μS/cm), brackish water (1500–3000 μS/cm), and saline water (>3000 μS/cm) [99]. Of 129 samples analyzed, 62.0% are freshwater, 25.6% are brackish water, and 12.4% are saline water. Therefore, the groundwater in the study area is primarily fresh and brackish. The fresh groundwater with relatively lower EC occurs in the zone adjoining and close to the Lagh Dera River (downstream from Habaswein) (Figure 3), and the salinity increases with increasing distance from the river [69]. A higher EC is evident in the northeastern part of the study area along the Lagh Dima River. The low-transmissive sandstone dominates this zone. Further, high EC values occur in the central part of the study, around Modagashe, and this zone is dominated by thick clay formation in areas close to Lagh Dera, surrounded by the Pliocene sandstone. The total dissolved solids (TDS) range from 314 to 8366 mg/L, with an average of 1423 mg/L (Table 3).

Na⁺ is the most abundant cation in the groundwater, and the sequence of the relative abundance of the cations is Na⁺ > Ca²⁺ > Mg²⁺ > K⁺. The most abundant anion is HCO₃⁻, and the sequence of the relative abundance of the anions in the basin is HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻. The Na⁺ concentration ranges between 10.4 and 3410.0 mg/L, with a median of 221.0 mg/L. The concentration of Ca²⁺ in the groundwater varies from 2.8 to 233.0 mg/L, with a median of 20.0 mg/L. The Mg²⁺ concentrations ranged from 1.0 to 151.0 mg/L, with a median concentration of 11.1 mg/L. The K⁺ concentration in the groundwater ranges from 2.0 to 83.0 mg/L, with a median of 11.0 mg/L. The concentration of HCO₃⁻ ranges from 109.2 to 4779.2 mg/L, with a median of 560.5 mg/L. The Cl⁻ ion concentration ranges from 2.0 to 4300.0 mg/L, with a median of 60.0 mg/L. The SO₄²⁻ concentration varies from 3.0 to 1230.0 mg/L, with a median of 35.3 mg/L. The NO₃⁻ concentration in the study area ranges from 0.0 to 129.9 mg/L, with a median of 1.1 mg/L (Table 3).

The major ions were illustrated graphically using the Piper diagram [87] (Figure 4). The diagram shows that the alkaline earth elements dominate, i.e., Na⁺ + K⁺ > Ca²⁺ + Mg²⁺, and weak acidic anions exceed strong acidic anions, i.e., HCO₃⁻ > Cl⁻ + SO₄²⁻. Most of the groundwater samples are Na⁺ + K⁺ and bicarbonate types. Therefore, the dominant water facies are Na-HCO₃, which accounts for 45.74% of the collected samples. Other facies present are Na-HCO₃-Cl, Na-Mg-HCO₃, Na-Ca-HCO₃, Na-Cl-HCO₃, Na-Cl, Na-HCO₃-SO₄, Na-Mg-Cl-HCO₃, Na-Ca-Mg-HCO₃-Cl, Na-Ca-HCO₃-Cl, and Na-Mg-HCO₃-Cl, with percentage abundances of 17.83%, 11.63%, 3.88%, 3.88%, 2.33%, 2.33%, 2.33%, 1.55%, 1.55%, and 1.55%, respectively. Ca-Na-HCO₃-NO₃, Na-Ca-Cl-HCO₃, Ca-Na-HCO₃, Ca-K-HCO₃, Na-Ca-Mg-HCO₃, Mg-Na-Ca-HCO₃-Cl, and Mg-Na-HCO₃ were all represented by 0.78%. The Ca²⁺ and Mg²⁺-dominated facies were from the shallow wells, and areas dominated by bicarbonates.

3.2. Processes Controlling the Groundwater Chemistry

Chemical changes occur to groundwater as it moves within/through the aquifers. These changes occur because of various processes, such as rock interactions, groundwater residence time in the aquifer, mixing of various groundwater bodies, and ion exchange processes. The processes usually lead to solute acquisition by the groundwater, which generally reflects an increase in the TDS and EC. A p × p correlation matrix of the 11 analyzed parameters was carried out (

Table 4. Correlation matrix between analyzed parameters in groundwater.

Variables	EC	TDS	Cl−	SO4−	NO3−	HCO3−	Na+	K+	Ca2+	Mg2+	pH
EC	1	0.96	0.76	0.78	−0.05	0.60	0.93	0.14	−0.05	−0.02	0.14
TDS	0.96	1	0.66	0.83	−0.06	0.77	0.93	0.143	−0.08	−0.06	0.19
Cl−	0.76	0.66	1	0.80	0.33	0.15	0.86	0.20	0.31	0.26	−0.01
SO42−	0.78	0.83	0.80	1	0.17	0.51	0.90	0.22	0.11	0.08	0.09
NO3−	−0.05	−0.06	0.33	0.17	1	−0.11	0.17	0.12	0.56	0.42	−0.03
HCO3−	0.60	0.77	0.15	0.51	−0.11	1	0.63	0.05	−0.13	−0.13	0.31
Na+	0.93	0.93	0.86	0.90	0.17	0.63	1	0.13	0.098	0.06	0.17
K+	0.14	0.14	0.20	0.22	0.12	0.05	0.13	1	0.41	0.61	−0.20
Ca2+	−0.05	−0.08	0.31	0.11	0.56	−0.13	0.10	0.41	1	0.66	−0.25
Mg2+	−0.02	−0.06	0.26	0.08	0.42	−0.13	0.06	0.61	0.66	1	−0.19
pH	0.14	0.20	−0.01	0.09	−0.03	0.31	0.17	−0.20	−0.25	−0.19	1

Values in bold are different from 0, with a significance level of alpha = 0.05.

) to decipher and identify the various processes. The good correlation between TDS with sodium (r = 0.93), sulphate (r = 0.83), bicarbonate (r = 0.77), and chloride (r = 0.66) indicates that these ions contribute to groundwater chemical enrichment. The low correlation between TDS with potassium r = 0.14), calcium (r = −0.08), magnesium (r = −0.06), and nitrate (r = −0.06) indicates that the ions do not have a very high impact on groundwater TDS.

The high correlation between Cl⁻ and SO₄²⁻ (r = 0.80), Na⁺ and Cl⁻ (r = 0.86), SO₄²⁻ and HCO₃⁻ (r = 0.51), and Na⁺ and SO₄²⁻ (r = 0.9) suggests increased salinization of the groundwater through processes such as cation exchange and mixing of groundwater with old saline connate water. The moderately good correlation between K⁺ and Mg²⁺ (r = 0.61) suggests silicate weathering. The correlation between Ca²⁺ and Mg²⁺ indicates that the ions may have originated from carbonate dissolution. The moderately good correlation between Ca²⁺ and NO₃⁻ suggests groundwater anthropogenic contamination from agricultural activities.

3.2.1. Ion Exchange

The ion exchange between the aquifer rocks and the groundwater is one of the processes that controls the relative concentration of ions in the groundwater [89]. If Na⁺ + K⁺ replaces Ca²⁺ + Mg²⁺, the ion exchange process is referred to as a cation exchange, and if Ca²⁺ + Mg²⁺ replaces Na⁺ + K⁺, the process is referred to as a reverse ion exchange [100].

A major indicator of ion exchange is the Chloralkaline indices, as described by Schoeller [101]. The Chloralkaline indices 1 and 2 (CAI 1 and 2) help to understand the ion exchange process between the groundwater and the host environment. These indices are calculated using equations 10 and 11, and the results are as in Table 3:

$$\mathrm{CAI} 1={\displaystyle \frac{{\mathrm{C}\mathrm{l}}^{-}-({\mathrm{N}\mathrm{a}}^{+}+{\mathrm{K}}^{+})}{{\mathrm{C}\mathrm{l}}^{-}}}$$

(10)

$$\mathrm{CAI} 2={\displaystyle \frac{{\mathrm{C}\mathrm{l}}^{-}-({\mathrm{N}\mathrm{a}}^{+}+{\mathrm{K}}^{+})}{{\mathrm{S}\mathrm{O}}_4^{2-}+{\mathrm{H}\mathrm{C}\mathrm{O}}_3^{-}+{\mathrm{C}\mathrm{O}}_3^{2-}+{\mathrm{N}\mathrm{O}}_3^{-}}}$$

(11)

(These indices are expressed or calculated in meq/L.) The CAI1 and CAI2 are negative when cation exchange occurs. In such cases, the Ca²⁺/Mg²⁺ in the water is exchanged with Na⁺/K⁺ in the host environment, creating a chloro-alkaline disequilibrium [89]. Conversely, a positive index indicates a reverse ion exchange between Na⁺ or K⁺ with Mg²⁺ or Ca²⁺ in the groundwater [4,27,88,102]. All the samples have negative CAI 1 and CAI 2 indices (Figure 5 and Figure 6, respectively), implying that cation exchange is the dominant process. These results show that Na⁺ and K⁺ are being added into the groundwater, whereas Ca2+ and Mg²⁺ ions are removed through adsorption processes. CAI 1 indices had a very large data range, from −306.7 to −0.01, with a median of −4.9, and thus, due to the scale factor, most samples plot close to the zero line (Figure 5). Values very close to zero indicate slight ion exchange processes and vice versa. CAI 2 ranges from −1.7 to −0.06, with a median of −0.7, corroborating the results of CAI 1, that sodium enrichment of groundwater is due to the cation exchange process.

The bivariate plot of (Ca²⁺ + Mg²⁺) − (HCO₃⁻ + SO₄²⁻) as a function of (Na⁺ + K⁺ − Cl⁻) provides evidence of the cation exchange (Figure 7). Data would be plotted at the origin if the ion exchange were not a significant process in the aquifer system. In cases where the cation exchange is a major controlling process, the two parameters plot linearly, with a slope of −1 [27,88]. Figure 7 shows that the analyzed samples have a linear relationship, with a slope of −0.94. Therefore, an increase in Na⁺ + K⁺ is related to a decrease in Ca²⁺ + Mg²⁺ or an increase in HCO₃⁻ + SO₄²⁻ [103]. Therefore, the cation exchange process is a possible cause of increasing Na⁺ concentration and reduction of Ca²⁺ and Mg²⁺ in the groundwater in the study area.

3.2.2. Silicate Weathering

Silicate weathering is also a probable cause of the abundance of sodium (Na⁺) in the study area. The higher concentrations of sodium support this conclusion compared to chloride ions in the groundwater (Table 3). A good indicator of the silicate weathering process is the Na⁺/Cl⁻ ion ratio [18,19]. A ratio greater than one suggests that sodium in the groundwater originates from silicate minerals.

The analyzed groundwater samples exhibited Na⁺/Cl⁻ ratios ranging from 1.0 to 306.6, with an average of 22.7 (Table 3). Notably, all samples had a ratio exceeding one, indicating silicate weathering as an important process. The reaction of feldspar minerals with carbonic acid in the presence of water releases bicarbonate (HCO₃⁻) [12,104], thus contributing to the concentration of HCO₃⁻ in the groundwater. While halite dissolution could also introduce Na⁺ and Cl⁻ ions, no halite deposits have been identified in the study area, making this an unlikely source. The significant enrichment of Cl⁻ in the study area is probably because Cl⁻ is readily transported within the soil. Further Cl⁻ ions could result from a prolonged interaction of groundwater with the host rocks due to the long residence time of groundwater in the aquifers, and also paleo-marine brine connate water.

3.2.3. Carbonate Precipitation and Dissolution

Carbonate rocks such as limestone and dolomite are abundant in the study area [62,64]. Therefore, carbonate mineral dissolution is expected to contribute to the concentrations of HCO₃⁻, Ca²⁺, and Mg²⁺ in the groundwater. However, only a few samples plot along the carbonate dissolution line (Figure 8). Furthermore, the weak correlations among Ca²⁺, Mg²⁺, and HCO₃⁻ (Table 4) suggest that these ions are being removed from the solution. The reduced concentrations of Ca²⁺ and Mg²⁺ may be attributed to two main processes: (1) cation exchange reactions and (2) precipitation of carbonate minerals under supersaturated conditions.

Positive saturation indices of calcite, dolomite, and aragonite mineral phases in most samples (Figure 9) indicate supersaturation, suggesting that excess carbonates are likely precipitating. This further indicates that the groundwater has significantly interacted with the carbonates and reached geochemical equilibrium with respect to the calcite, dolomite, and aragonite mineral phases. The widespread occurrence of calcrete in the study area [62] supports the hypothesis that carbonate precipitation processes play a role in the evolution of groundwater chemistry.

Carbonate dissolution is only evidenced in a few samples that fall on the carbonate dissolution trend line (Figure 8) and in several groundwater samples that exhibited negative saturation indices (Figure 9). The negative SI does imply that, in some aquifer sections, there is an insufficient concentration of the dissolved ions required to form the carbonate mineral phase, and thus, the system is undersaturated and has the chemical potential for dissolution. These samples are mainly the Ca-HCO₃ and Mg-HCO₃ water types from the shallow wells and from the zones dominated by carbonate rocks. The dissolution of carbonate minerals, such as calcite, dolomite, and aragonite, occurs when rainwater comes into contact with carbonate rocks, releasing Ca²⁺, Mg²⁺, and HCO₃⁻ ions in the groundwater. The chemical reactions shown in Equations (12) and (13) can describe these processes.

Calcite dissolution: −CaCO₃ + H₂O + CO₂ → Ca²⁺ + 2HCO₃⁻

(12)

Dolomite dissolution: −CaMg(CO₃)₂ + 2H₂O + 2CO₂ → Ca²⁺ + Mg²⁺ + 4HCO₃⁻

(13)

3.2.4. Fresh–Saline Water Mixing

The principal aquifer in the study area is the fresh–brackish Merti aquifer, bounded laterally and vertically by saline water aquifers [62]. The movement and interaction of the groundwater in the Merti aquifer is facilitated by stratigraphic heterogeneity and possible structural controls, such as buried faults or paleo-depositional features [105]. At greater depths, the Paleo saline aquifer, characterized by Na-Cl water facies, may result from prolonged residence time within the aquifer [64,67]. Laterally, the observed increase in salinity is primarily due to the low transmissivity of the fine-grained geological formations, which slows groundwater movement and promotes prolonged water–rock interaction. This leads to a gradual enrichment of anions—particularly Cl⁻ and SO₄²⁺—as groundwater migrates slowly from the recharge zone along the Lagh Dera River, which is consistent with the geochemical evolution described by the Chebotarev sequence (Equation (14)) [7,106].

Travel along the flow path:

HCO₃⁻ → HCO₃⁻ + SO₄²⁻ → SO₄²⁻ + HCO₃⁻ → SO₄²⁻ + C1⁻ → C1⁻ + SO₄²⁻ → C1⁻

(14)

Increasing age.

Luedeling et al. [71] postulated that groundwaters in the study area continually become saline through fresh–saline water mixing. This study evaluated the extent of salinization using the Cl⁻/HCO₃⁻ ratio. The ratio classifies the water into five classes: good quality (<0.5), slightly contaminated (0.5–1.3), moderately contaminated (1.3–2.8), highly contaminated (2.8–6.6), and extremely contaminated (6.6–15.5) [107,108]. This ratio was considered because the chloride ion is stable and a good proxy for TDS [109]. The results show that 81.4% of the groundwater is of good quality, with a ratio of less than 0.5. 11.6%, 5.4%, 0.7%, and 0.9% representing slightly, moderately, highly, and extremely contaminated groundwater, respectively (

Table 5. Classification of groundwater using Cl⁻/HCO₃⁻ ion ratio.

Classification Scheme	Categories	Ranges	Percent of Samples
Cl−/HCO3−	good quality	<0.5	81.4
	slightly contaminated	0.5–1.3	11.6
	moderately contaminated	1.3–2.8	5.4
	highly contaminated	2.8–6.6	0.7
	extremely contaminated	6.6–15.5	0.9

). The extremely contaminated reflect the Na-Cl and Na-HCO₃-SO₄ facies located in the saline zones, which are located in low transmissivity formations, such as clays and Pliocene sandstone.

3.3. Principal Component Analysis (PCA)

In analyzing the groundwater of a large basin, one is faced with the complexity associated with many measured variables. Therefore, in this study, PCA was used to recognize the pattern that explains the variance in the intercorrelated variables. PCA helps to show an association between variables, thus reducing the dimensionality of the dataset. This analysis, therefore, reduced the measured parameters into four factors. Among these factors, three factors with eigenvalues > 1 were extracted. These three factors accounted for 78.08% of the total variance of the dataset. Factor F1 accounted for 44.06%, F2, 24.38%, and F3 accounted for 9.63% of the total variance in the original dataset (

Table 6. Factor loading of principal component analysis.

Variable	Factor Loadings
	F1	F2	F3
EC	0.879	0.027	0.009
TDS	0.908	0.052	0.015
Cl−	0.683	0.087	0.032
SO42−	0.838	0.002	0.001
NO3−	0.021	0.410	0.390
HCO3−	0.434	0.130	0.018
Na+	0.974	0.001	0.007
K+	0.054	0.364	0.327
Ca2+	0.014	0.724	0.017
Mg2+	0.013	0.713	0.018
pH	0.030	0.171	0.227
Eigenvalue	4.847	2.682	1.060
Variability (%)	44.062	24.380	9.634
Cumulative %	44.062	68.442	78.077

Values in bold are variables with high influence on each factor.

).

The results indicate that Factor 1 (F1) is highly influenced by electrical conductivity (EC), total dissolved solids (TDS), and the concentrations of Na⁺, Cl⁻, and SO₄²⁻, and is moderately influenced by the concentration of HCO₃⁻, suggesting a strong interrelationship among these variables. Therefore, F1 is primarily governed by geogenic processes that contribute to the salinization of the groundwater. That is, the processes that cause the abundance of Na⁺, HCO₃⁻, SO₄²⁻, and Cl⁻, which correlate highly with EC and the TDS. These processes are cation exchange, silicate weathering, and mixing between the saline and fresh groundwater. Additionally, the prolonged residence time of groundwater within the aquifer contributes to the elevated ion concentrations.

Factor 2 (F2) is primarily influenced by the concentration of Ca²⁺ and Mg²⁺ (Table 6). Therefore, this factor represents the precipitation process of the carbonate mineral facies. Factor 3 is moderately influenced by K⁺ and NO₃⁻, which indicates contamination of the aquifer through agricultural practices, though it is not prevalent in the study area.

3.4. Drinking Water Quality

The suitability of the groundwater for drinking was evaluated by comparing it with the drinking water guidelines provided by the World Health Organization (WHO) [44] and the Water Services Regulatory Board of Kenya (WASREB KENYA 2016) [47] (Table 3). The maximum recommended limit in the WASREB recommendations is less than or equal to that of the WHO recommendations, apart from the concentrations Cl⁻, Mg²⁺, and NO₃⁻. This study, therefore, applied the WHO standard to assess the limits of the parameters because it is universally acceptable. Only the HCO₃⁻ and Na parameters had a high percentage of samples exceeding the WHO maximum allowable limit, with 85.5% and 49.3%, respectively.

• Water Quality Index (WQI)

Since assessing drinking water suitability involves many parameters, the drinking water quality index (WQI) was used to simplify the evaluation. According to the results in

Table 7. Drinking water index rating class and percent sample representation.

WQI	Rating Class	Percent of Samples
<50	Excellent	20.31
50–100	Good	60.16
100–200	Poor	13.27
200–300	Very poor	3.13
>300	Unsuitable	3.13

, only 20.31% of the samples are of excellent quality, 60.16% are of good quality, and are suitable for drinking, and 13.27% of the samples have a poor rating, with 6.26% being poor and unsuitable for drinking.

Excellent-to-good drinking water quality is primarily found downstream along the Lagh Dera River (Figure 10), where alluvial and colluvial deposits dominate the aquifer. In the western part of the study area, good water quality is also observed within volcanic formations, particularly west of Garba Tula. Poor-to-very poor water quality is prevalent primarily in the northeastern region, where the aquifer is composed predominantly of Pliocene sandstone. Unsuitable water quality is found in the central part of the study area, which is characterized by thick clay formations with low transmissivity.

The economic activity practiced by the residents in the study area is mainly pastoralism [56]. Therefore, it is vital to assess the suitability of the water for livestock consumption to prevent poisoning from toxic solutes, salt imbalances, and even diseases [110]. Unlike human drinking water standards, livestock water quality standards allow for higher salinity levels and total dissolved solids (TDS) [111]. TDS is the main parameter considered when assessing the suitability of water for livestock farming. Based on the FAO livestock drinking water standard, values less than 4000 mg/L and between 4000 and 5000 mg/L are very satisfactory and satisfactory for most livestock, apart from poultry, in which values greater than 3000 mg/L are unfit [48]. The analyzed TDS values in the study area ranged from 313.8 mg/L to 8366.0 mg/L, and 93.7% of the analyzed water had values less than 4000 within the permissible range for most livestock. Only 7.8% of the analyzed samples had a TDS value greater than 3000 mg/L. Therefore, they were unfit for poultry consumption. Overall, groundwater in the study area is largely suitable for livestock consumption.

3.5. Irrigation Water Quality

Groundwater is the primary source of irrigation water in arid areas. Therefore, careful assessment of the water’s suitability for irrigation projects is critical as it influences the mineral availability to the soils and plants, soil structure, and permeability of the soils [63] suitability of groundwater for agriculture is evaluated using the sodium adsorption ratio (SAR).

The calculated SAR values in the study area are as displayed in the standard SAR value table according to Richards [112] (

Table 8. Showing the classification of groundwater in the study area using irrigation parameters.

Classification Scheme	Categories	Ranges	Percent of Samples
SAR (Richards 1954)	Excellent	<10	39.54
	Good	10–18	17.83
	Doubtful	18–26	9.30
	Unsuitable	>26	33.33

). From the analysis, only 39.54% and 17.83% of the analyzed groundwater samples can be used for irrigation as they fall into the excellent and good categories, respectively. The excellent and the good groundwater samples plot within the low (S1) and medium (S2) sodium hazard sections in the Wilcox diagram [113] (Figure 11). However, 9.33% and 33.33% of the sampled groundwater cannot be applied for irrigation, as this water is categorized as doubtful and unsuitable, respectively (Table 8). These samples are plotted in high (S3) to very high (S4) sodium (alkali hazard section, Figure 11). However, approximately more than 80% of the analyzed samples plotted in the C3 and C4 indicated a high salinity (EC) hazard.

• Irrigation Water Quality Index (IWQI)

The irrigation water index was used to assess the suitability of groundwater in the study area for irrigation purposes. The results indicate that most of the groundwater in the study area is largely not suitable for irrigation, with only 12.4% having moderate restrictions (

Table 9. Irrigation water index rating class and percent sample representation [42].

Water Use Restrictions	IWQI	Percent of Samples
No restriction (NR)	85–100	0
Low restriction (LR)	70–85	0
Moderate restriction (MR)	55–70	12.4
High restriction (HR)	40–55	45.7
Severe restriction (SR)	0–40	41.9

). The rest of the groundwater has high-to-severe restrictions, with 45.7% and 41.9%, respectively. Most of the groundwater with a severe restriction for irrigation is in the north–northeastern part of the study area (Figure 12), whereas those with moderate and high restrictions occur along the Lagh Dera River. Water classified as having a moderate irrigation water quality index (IWQI) can be used on soils with high permeability and moderate salt leaching [43,98]. Similarly, water with a high restriction rating is suitable for highly permeable soils without compact layers. Under normal soil conditions, high restriction water should be avoided [42]

4. Discussion

The principal aquifer in the study area, known as the Merti Aquifer, exhibits unique groundwater chemistry influenced by geological and hydrological processes. Groundwater in this aquifer predominantly has a neutral-to-alkaline pH, which is consistent with findings from prior studies [64,67,69,114]. Elevated electrical conductivity (EC) and total dissolved solids (TDS) levels suggest significant mineralization, classifying the groundwater as fresh to brackish. The aquifer’s multilayered lithology includes weathered sandstone, limestone, sand, silt, gravel, and clay-rich materials, contributing to its diverse water chemistry [63,69].

The study area’s groundwater chemistry is significantly influenced by rock weathering. The silicate weathering provides Na⁺ and Mg²⁺ ions. Carbonate weathering occurring in the shallow aquifer and in zones dominated by carbonate rocks further adds Ca²⁺, Mg²⁺, and HCO₃⁻ ions. Shallow wells in the southwestern study area and the downstream sections of the Lagh Dera River also display Ca/Mg-HCO₃ water facies, which is indicative of young, fresh recharge water [64,67]. In addition, positive saturation indices for calcite, dolomite, and aragonite mineral phases indicate supersaturation, supporting the role of precipitation in regulating Ca²⁺, Mg²⁺, and HCO₃⁻ abundance [100].

Cation exchange also plays a critical role, as evidenced by the abundance of Na⁺ and HCO₃⁻ ions and the dominance of Na-HCO₃ water facies in the aquifer. This process occurs when fresh recharge water interacts with paleo-sodium-rich brine, replacing Ca²⁺ and Mg²⁺ with Na⁺ [115]. It was also noted that the pH was relatively stable in the range between 7.1 and 8.9 in all the samples. Since the study area’s aquifer largely contains clay and carbonates, the carbonate equilibrium and cation exchange processes provide adequate buffering capacity for pH. Additionally, the incongruent weathering of silicate minerals can further contribute to pH buffering, leading to an increase in sodium (Na⁺) and bicarbonate (HCO₃⁻) ions in the groundwater [116]. Moreover, the observed Na⁺ and HCO₃⁻ concentrations are relatively high and strongly correlate with electrical conductivity (EC) and total dissolved solids (TDS). In contrast, magnesium (Mg²⁺) and calcium (Ca²⁺) exhibit a weaker correlation with EC and TDS, suggesting that precipitation and ion exchange processes are actively influencing the groundwater system.

Overexploitation of the aquifer can lead to the upconing or intrusion of deep-seated, naturally occurring saline water, as observed by Luedeling et al. [71]. Still, this phenomenon remains minimal, with only 18.6% of groundwater samples indicating saline contamination (Table 5).

The underlying geology strongly influences the water quality. Excellent-to-good groundwater quality is found along the Lagh Dera River downstream of Habaswein, likely due to its proximity to a recharge zone via alluvial deposits [72] and highly transmissive aquifer materials [69]. Transmissivity is highest along the Lagh Dera River and decreases laterally away from it [64,67,69], which may explain the decline in water quality in those areas. Clay material with low transmissivity dominates the central part of the study area along Lagh Dera. These clay formations slow down the groundwater flow, causing clogging and swampy conditions in the section. This causes surface water to evaporate, leading to the accumulation of salt that slowly recharges the deeper aquifer. In this zone, the groundwater is typically older [64] and saline, leading to poorer drinking water quality. Given that groundwater is the main source of potable water in the region, appropriate treatment methods such as desalination are essential to protect public health.

The dwellers in the study area are nomadic pastoralists, and most groundwater is suitable for livestock consumption according to FAO (2016) standards. Concerning irrigation, most of the groundwater had high salinity and alkalinity, as exemplified by the high SAR indices, and therefore, was unsuitable for irrigation purposes. Moreover, with regard to the irrigation water quality index, most of the groundwaters were classified into the high and severe restriction categories. The severity pattern is similar to that of the drinking water quality index, with good-to-moderate quality groundwater occurring along the Lagh Dera. This poor quality was interpreted to be a result of long groundwater residence time, poor transmissivity of the aquifer materials, and increasing distances to zones of groundwater recharge [64,69,72]. Prolonged application of saline water for irrigation can cause poor soil permeability. Furthermore, saline water may affect osmotic activities, inhibiting crop germination and growth [45,46]. Hence, water treatment, such as desalination, and soil treatment, such as the addition of gypsum, are essential if irrigation is carried out. Adding gypsum may help prevent sodium from displacing the adsorbed calcium and magnesium in the soil, which causes damage to the soil structure [117,118].

5. Conclusions

This study successfully achieved its objectives by identifying the primary geochemical processes influencing groundwater in the Ewaso Ngi’iro–Lagh Dera basin. The groundwater is predominantly fresh to brackish, with Na-HCO₃ as the dominant water type. Sodium (Na⁺) is the most abundant cation, while bicarbonate (HCO₃⁻) is the most abundant anion. The groundwater chemistry in the study area is shaped by natural processes, such as ion exchange, mixing fresh and paleo-saline water, silicate weathering, and calcite and dolomite precipitation and dissolution.

The findings of this study enhance our understanding of how hydrogeochemistry affects groundwater quality in the basin. An assessment of drinking water quality based on WHO (2017) standards indicates that the average levels of TDS, Cl⁻, SO₄²⁻, Ca²⁺, Mg²⁺, and total hardness (TH) are within permissible limits. Thus, most groundwater samples have good WQI. However, elevated EC, HCO₃⁻, and Na⁺ values render some groundwater to have a poor-to-unsuitable drinking water quality index (WQI) rating.

Irrigation suitability, assessed using SAR, reveals moderate-to-high salinity and alkalinity hazards in the groundwater. The IWQI rating of the groundwater, calculated using the EC, SAR, Cl⁻, HCO₃⁻, and Na⁺ concentration, showed that groundwater is mostly in the mostly in the moderate—severe restrictions categories. Consequently, most groundwater samples are not suitable for irrigation without proper management. This study recommends that (1) careful management and treatment are necessary to utilize groundwater for domestic and agricultural purposes safely. (2) Farming practices should focus on crops that tolerate high salinity levels. (3) Regular groundwater quality monitoring is essential to understand spatial and temporal variations, enabling sustainable groundwater management in the basin. We also recommend further studies investigating groundwater salinization processes in the aquifer using advanced geochemical and isotopic techniques. This will provide deeper insights into salinization mechanisms and support long-term groundwater sustainability in the region.