Geochemical characteristics, mechanisms, and suitability of groundwater resource for sustainable water supply in Quetta valley Open Access

Groundwater (GW) is a crucial resource for various human activities, particularly for drinking and irrigation. The quality of GW is essential to ensure the health and sustainability of ecosystems and communities that depend on it. GW scarcity and the associated concerns over quality remain pressing issues in numerous countries (Debnath et al. 2022; Hao et al. 2023; Yang et al. 2024).

The World Health Organisation (WHO) declaration (WHO 2017) highlights the need to provide abundant, safe, and accessible water to ensure the sustainability of communities worldwide. On the other hand, GW degradation continues to increase, primarily due to the combined effects of human intervention and natural phenomena. Regular assessment of GW quality is essential to ensure its suitability for drinking and irrigation (Yang et al. 2023), and thus its importance for current use and future conservation efforts to promote sustainable development of the GW resources. The sustainable use of GW for human consumption requires a thorough assessment of its chemical and physical factors (Asadi et al. 2019). However, addressing the increasing demand for GW in terms of quality requires the implementation of comprehensive monitoring measures for GW, a necessity underscored by various studies (Zhang et al. 2022; Yang et al. 2023).

GW composition is affected by aquifer geology, geochemical processes, rock–water interactions, mineral dissolution, chemical weathering (Yang et al. 2023), and human activities like industrial discharge and agricultural runoff, precipitation, and water infiltration characteristics, infiltrating the earth's crust into the aquifer, further shaping the chemical composition of GW (Zhang et al. 2024). The evaluation of water through hydrochemical assessment beneath the earth's surface relies on these factors accessibility (Xiao et al. 2022a), and the study of these factors provides crucial insights into aquifer mineralogy and the suitability of GW for domestic, industrial, and agricultural purposes.

In recent academic efforts, a growing amount of research has been dedicated to studying GW. Notably, there has been a concerted focus on assessing and comprehending hydrochemical characteristics and GW quality, employing diverse practical tools. These tools encompass geochemical modelling, geographic information system (GIS), and sophisticated statistical approaches such as multivariate statistical analysis (cluster analysis, discriminant analysis) and mechanic learning models (Xiao et al. 2018; Abbasnia et al. 2019; Liang et al. 2020; Zhang et al. 2024). This collective effort is crucial for gaining a nuanced understanding of the intricate dynamics governing GW hydrochemistry, quality, and its sustainable management (Wang et al. 2023).

However, assessing the quality and contamination of GW relies on using indices that are derived from a variety of physical–chemical parameters. Despite water's profound impact on socioeconomic growth and the preservation of robust ecosystems, GW reservoirs persistently encounter quality challenges arising from both anthropogenic and natural factors (Abanyie et al. 2023). This issue is particularly acute in arid and semi-arid regions, where water scarcity is nearly endemic. Regrettably, there has been a lack of historical focus on regulating and monitoring GW based on its physical and chemical properties using reliable techniques.

In Pakistan, GW is the primary drinking water source (Jamil et al. 2019; Khan et al. 2020). Multiple research studies on GW quality have shown that Pakistan faces significant water pollution challenges (Noor et al. 2023). This issue is exacerbated by water shortages and the growing demand for water for different uses (Ali et al. 2023). In addition, Pakistan's annual average GW potential is 67,841m³, while only 51,189m³ of GW is used annually. Mainly, people get their water via dug wells and tube wells, which are susceptible to contamination from natural and human-induced sources (Rashid et al. 2020).

The water table in the study region (Quetta Valley) is experiencing a significant decline (Iqbal et al. 2023). Agricultural activities and the rapid increase in population primarily drive this decline. As a result, the aquifer is under severe stress (Khan et al. 2020; Xiao et al. 2022c). The extensive use of GW for agricultural irrigation, along with chemical fertilizers, has resulted in various issues, such as salinization, crop toxicity, and soil degradation. Previous studies have indicated that the GW quality in the Quetta valley is unsafe for drinking (Liu et al. 2021; Tanzeel et al. 2022). This emphasizes the need to assess overall GW quality to ensure sustainable GW practices. Existing studies on the Quetta Valley have been scarce, with a notable absence of thorough investigations into GW hydrogeochemistry. Existing research often utilizes single-method approaches (Durrani & Farooqi 2021), failing to provide a holistic understanding of the complex hydrological dynamics at play. Moreover, historical data on GW hydrogeochemistry and quality in the Quetta Valley need to be included, leaving uncertainties regarding its suitability for drinking and agricultural purposes.

Addressing these gaps is crucial for informed decision-making and sustainable water management. This study integrates hydrochemical, irrigation water quality index (IWQI), entropy-weighted water quality index (EWQI), multivariate statistical, and GIS approaches to understand GW hydrogeochemistry and quality, ensuring its suitability for human consumption and promoting sustainable agriculture. Therefore, the main objective of this study is to assess GW quality and determine its suitability for both domestic and agricultural purposes with the following aspects: (1) investigate the hydrogeochemical processes, major ions, and water type system of GW; (2) assess GW suitability for drinking water and irrigation: Identifying the contamination zones with spatial analysis; (3) reveal the mechanisms controlling GW hydrochemistry in a sedimentary basin in the Quetta Valley. This study can enhance the understanding of sedimentary basin hydrochemistry and establish a baseline for managing GW in Quetta Valley and similar regions over the world.

The geological composition of the study area comprises sedimentary rocks. Weathering of these host rocks results in the production of sand, silt, and clay, forming semi-consolidated structures like clay-stone, sandstone, and subordinate conglomerate bedded with calcareous and carbonaceous strata. In addition, a significant portion of the basin rocks is covered with quaternary deposits (Rahman et al. 2022). Jurassic rocks are prevalent in the study area, including light to dark grey limestone and brownish to bluish-grey massive limestone, while tertiary limestone exists in certain parts of the basin (Sagintayev et al. 2012). The Quetta district has two main kinds of aquifers: unconsolidated alluvial aquifers and hard bedrock aquifers (Qureshi et al. 2022). The main origin of GW is derived from the Quaternary alluvial deposits, which have a variable thickness ranging from 30 to 900m. The deposits demonstrate a combination of litho formations, including gravel, sand, and silt. Conversely, the bedrock aquifer composed of Jurassic deposits has a smaller quantity of GW sources and is subjected to less use for extraction.

Key indicators of GW physicochemical properties encompass electrical conductivity (EC), pH, total dissolved solids (TDS), and total hardness (TH). Hydrochemical constituents including calcium, magnesium, chlorine, sodium, potassium, sulphate, and bicarbonate ions are pivotal in defining GW hydrochemical types. The sample was collected in the year 2021 using random sampling testing technique, amounting to a total of 29 from the monitoring wells located in the Quetta Valley. While collecting samples, each sample was filtered using a 0.22-mm filter. During sampling, in situ physical parameters such as pH, TDS, EC, and temperature were assessed using a portable multiparameter device (HQ40d, HACH, USA). Subsequently, all samples were transferred to the Pakistan Council of Water Resources Research (PCRWR) laboratory, where they were stored at 4 °C until further hydrochemical analysis.

Principal component analysis (PCA) is a valuable multivariate statistical technique employed to discern the key factors influencing hydrogeochemical compositions (Subba Rao et al. 2017). By applying PCA to hydrogeochemical parameters, complex analytical metrics are simplified, reducing the dimensionality of the analysis and facilitating the primary identification of contaminant sources within GW. To ensure comparability among diverse indicators, z-score normalization was performed before the PCA. The suitability of the dataset for PCA was assessed through the Kaiser–Meyer–Olkin (KMO) and Bartlett tests.

To assess the correlation among variables and indicators, the KMO test was used with a significance level of 0.5. The Bartlett's sphericity test examines the level of correlation between each indicator variable, with a significance level (ρ) below 0.05 indicating relevant interrelationships among variables. In this study, the degree of eigenvalue greater than 1 is considered for suitable analysis for factor analysis, and PCA was performed using IBM SPSS 27 software package.

The index value ratio of the jth index of sample I is represented as P_ij, while the information entropy is denoted as e_j. In addition, the entropy weight of parameter j is denoted as w_j.

The next phase is evaluating the water's quality using the EWQI criteria listed in Table 4.

As per standard, water with an EWQI of less than 50 is of excellent quality, between 50 and 100 is of good quality, between 100 and 150 is of medium quality, between 150 and 200 is of poor quality, and over 200 is of extremely poor quality.

The GW EC varied between 260 and 2,935 μS/cm, averaging 795 μS/cm. About 20% of samples exceeded guidelines reflect mineral content variations. pH levels ranged from 6.8 to 8.4 averaging 7.6, falling within the recommended limit of WHO 6.5–8.5 for drinking water. TDS ranged from 158 to 1,901mg/L, averaging 492mg/L, mostly falling within the permissible limit of 1,000mg/L, with 3% of samples surpassing this threshold. GW hardness (CaCO₃) spanned 105 to 640mg/L (average 234mg/L). Approximately one sample of GW surpasses the hardness standard of 450mg/L, indicating relatively soft and fresh water in terms of hardness.

The cation of the sampled GW was observed in the increasing order: Ca²⁺ > Na⁺ > Mg²⁺ > K⁺, with Ca²⁺ concentrations ranging from 36 to 120mg/L (mean 54mg/L). About 10% of samples surpassed WHO limits for Ca²⁺. Also, Mg²⁺ concentrations ranged from 1 to 85mg/L (average 23mg/L). Na⁺ levels varied from 16 to 411mg/L (average 85.89mg/L), with 3% exceeding WHO standards. K⁺ concentrations spanned 1 to 6mg/L, averaging 1.7mg/L in the study area.

The anionic sequence of sampled GW was found in the following order, > Cl⁻ > > _, and the concentration of these ions indicates the variations as the concentration of Cl⁻ ranged from 20 to 390mg/L with a mean of 93mg/L, while the varying from 32 to 668mg/L with an average of 141.47mg/L, along with the ion ranging from 65 to 350mg/L with the average value of 144mg/L. Regarding anions, some ions surpass the standard limit of (WHO) by 6, 3, and 3%, which are Cl⁻ and ⁠. The ions were observed with a low range in the study area, ranging from 0 to 6.4mg/L with a mean of 1.7mg/L. Based on the ionic distribution, the most dominant cation was Ca²⁺. In contrast, the most dominant anion was in the sampled GW.

The diamond shape file of the Piper diagram implies that the primary hydrochemical type of GW was mixed HCO₃-Ca, SO₄-Mg, and Cl-Na. Overall, the Piper analysis indicates that the GW chemistry changed from Ca, HCO₃ to Na, and HCO₃ due to the enrichment of salts (Ezzeldin 2022) in the GW.

PC1 was characterized by high positive loadings from EC, TDS, TH, SO₄²⁻, Na⁺, HCO₃⁻, and Mg²⁺ (Table 6). Conversely, PC2 exhibited a distance profile with a moderate negative correlation of pH and a positive correlation of nitrate NO₃⁻, indicating a contrasting relationship between NO₃ and pH within PC2. The positive correlation of TDS, TH, Ca²⁺, Mg²⁺, Cl⁻, Na⁺, HCO₃⁻, SO₄²⁻ in PC1, and NO₃⁻ in PC2 suggests that both natural and anthropogenic activities regulate the GW chemistry. However, the majority of the GW chemistry is dominated by regular factors such as rock water interaction and minerals dissolution, as the lithology of the sedimentary aquifer contains an elevated level of sedimentary rock minerals such as calcite, dolomite, limestone, gypsum and feldspar, and these minerals favour these ions in the aquifer. Conversely, the favourable loading of NO₃⁻ in PC2 reveals that agricultural and other activities favour the NO₃⁻ concentration in the study area.

Table 6

Results of PC1 and PC2 of principle component analysis

Parameters .	PC1 .	PC2 .	Communality .
EC	0.994	/	0.988
pH	0.499	−0.328	0.357
TDS	0.993	/	0.986
TH	0.943	/	0.891
Ca2+	0.735	/	0.554
Mg2+	0.833	/	0.695
Na+	0.954	/	0.911
K+	0.653	/	0.54
Cl−	0.96	/	0.924
	0.963	0.336	0.93
	0.934	/	0.888
	/	0.948	0.899
Eigenvalue	8.444	1.149	/
Cumulative	70.032	79.694	/

Parameters .	PC1 .	PC2 .	Communality .
EC	0.994	/	0.988
pH	0.499	−0.328	0.357
TDS	0.993	/	0.986
TH	0.943	/	0.891
Ca2+	0.735	/	0.554
Mg2+	0.833	/	0.695
Na+	0.954	/	0.911
K+	0.653	/	0.54
Cl−	0.96	/	0.924
	0.963	0.336	0.93
	0.934	/	0.888
	/	0.948	0.899
Eigenvalue	8.444	1.149	/
Cumulative	70.032	79.694	/

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Similarly, the calcite and aragonite were over-saturated (Figure 6(i)), indicating that carbonate rocks predominantly dominate the aquifer lithology. Sulphate minerals, including gypsum and anhydrite, show the aquifer's over-saturation state, indicating that the GW chemistry is influenced by sulphate deposition. As previously explained, is the dominant anion of the study area and exceeded the WHO limit. The saturation of minerals indicates that sulphate sources in the aquifer come from sulphate-bearing rock minerals, such as gypsum and anhydrite (Osselin et al. 2019).

However, the halite concentration was observed (Figure 6(i)) under-saturation as the value ranged from −5.3 to −2.8, indicating the fresh hydrochemical facies of the Na-Cl in the GW. SI analysis emphasizes sedimentary rock dominance in the aquifer. Dissolving carbonates and sulphate (e.g., dolomite, calcite, gypsum) strongly influences GW chemistry. To further understand the chemical evolution of GW, as shown by the Gibbs plots, ion ratio analysis has been utilized in various studies (Elumalai et al. 2023). This method proves effective in elucidating the geochemical mechanisms underlying GW composition.

The bivariate plots between Na⁺ versus Cl⁻ (Figure 6(a)) reflect that the majority of samples are located near the 1:1 line, indicating the halite dissolution within the aquifer (Equation (20)) as the (meq/L) equivalence of the NaCl, the concentration of the NaCl is greater than 1, and this indicated that the silicate hydrolysis is a crucial contribute of sodium in GW (Haji et al. 2018). The plots also depict some Na enrichment as compared to Cl, which indicate the GW interacts with feldspar minerals in sandstone and leading to a higher Na percentage and reveals the cation exchange processes (Liu et al. 2016) during the runoff. The bivariate plots Ca²⁺ versus (Figure 6(b)) reflect a ratio greater than 1. The samples are distributed on both sides of the 1:1 line as the percentage of Ca on the lower side depicts 48%, and above the percentage of shows 44%; this indicates that both gypsum and anhydrite dissolution are responsible for the enrichment of Ca²⁺ and in the aquifer.

The bivariate plots between of Ca²⁺ versus Mg²⁺ have frequently utilized to differentiate the carbonates and silicate minerals dissolution process, and in the case of the samples positioned on the 1:1 line and 0.5 line (Figure 6(e)), Ca/Mg ration greater than 2 indicates the silicate minerals dissolution, while a ratio between 1 and 2 suggests the calcite dissolution (He et al. 2017; Nasher & Ahmed 2021), likewise the dissolution of dolomite occurs when the Ca/Mg ratio depicted as equal to 1. Similarly the plot shows that the samples are falling along the 1:1 and 0.5 line, and this indicates both carbonates and silicate mineral (Ansari & Umar 2019) dissolution.

The plots of Ca²⁺, Mg²⁺ versus ⁠, (Figure 6(f)) explain the source of calcium and magnesium, the bulk of the samples falling above the 1:1 line indicate silicate dissolution, and some samples falling below indicate calcite dissolution. Both zones reveal that the GW chemistry is primarily influenced by calcite and silicate minerals. However, the correlation between GW and aquifers is often evaluated by analysing the concentrations of Na⁺, K⁺, and Cl⁻ in relation to Ca²⁺, Mg²⁺, ⁠, and (Figure 6(g)) (Batayneh et al. 2014). The samples above the y = x line suggest cation exchange processes in the GW. Thus, to determine whether the cation exchange process is negative (forward) or positive (reverse), the chloro-alkaline indices were analysed.

The investigation into irrigation water quality for sustainable agriculture revealed crucial insights into various hydrochemical indices. EC values, ranging from 260 to 2,995 μS/cm with an average of 260 μS/cm, exhibited a concerning upward trend in GW salinity. This trend, observed in 20% (Table 1) of the samples exceeding the established irrigation threshold, poses potential risks of salt accumulation in the soil, adversely affecting the crop growth. Sodium hazard, assessed through the sodium adsorption ratio (SAR), demonstrated values ranging from 0.16 to 0.7 meq/L, categorizing the water within the excellent class for irrigation quality and utilizing SAR as the essential evaluation index suggests that GW in the study area is suitable for irrigating soil types.

The study categorized irrigation water into four unique classifications (C1, C2, C3, and C4) according to the degree of salinization. These categories were determined based on specific EC measurements, divided into four ranges: less than 250, 250–750, 750–2,250, and higher than 2,250 μS/cm. In addition, water was classified into four categories according to its sodium (alkaline) hazard, namely, S1 (low alkaline hazard), S2 (moderate alkaline hazard), S3 (high alkaline risk), and S4 (extremely high alkaline hazard). The sodium adsorption ratio was used to establish these classes, defined by the following ranges: less than 10, 10–18, 18–26, and greater than 26. The USSL classification system facilitated the breaking down irrigation water into 16 unique categories.

Analysis reveals that most of the samples were located (Figure 9(b)) in the C2–S1 and C3–S1 zones. These zones indicate medium to high salt levels with a low alkalinity level in class S1. There was just one sample found in the low salinity zone, namely, in class C1 S1. Another sample was found in the high salinity zone, specifically in class C4 S1. Overall, the USSL diagram shows that the GW in the study area has moderate to high salt and low alkalinity levels. This makes it suitable for growing crops that can tolerate moderate salt levels. Furthermore, the IWQI was utilized to classify the GW zones within the geographic location, employing a combination of spatial analysis tools. The irrigation water quality index (IWQI) was used to evaluate water quality for irrigation based on parameters such as electrical conductivity (EC), sodium adsorption ratio (SAR), sodium (Na⁺), chloride (Cl⁻), and bicarbonate (HCO₃⁻).

Notably, the no-restriction and low-restriction zones exhibit a lack of correlation with population density, anthropogenic inputs, and minimal agricultural activities, as elucidated by the spatial distribution maps within the study area. In this specific geographical expanse, characterized by the lowest values of hydrochemical constituents, including HCO₃, Na, Cl, and NO₃ percentages, it is evident that human activities are at a minimum. Natural factors and mineral dissolution primarily influence the chemical composition within these zones. Nevertheless, no samples fell in the moderate or severe restriction zones.

Remarkably, 1% of the samples fell into the high restriction zone, localized in the north-western Chilton booster area. In this specific geographical domain, both human activities and natural processes exert a substantial influence on GW chemistry (Khan et al. 2010). Being situated at the core of the basin, this zone (characterized as urban area) experiences significant effects from the weathering of rocks and wastewater, resulting in pronounced GW contamination. The water in this category exhibits stability for soils characterized by high permeability coupled with a moderate to high salt tolerance in plants.

The assessment based on IWQI categorizes the GW mostly in the no-restriction and low-restriction zones, suggesting minimal risks associated with soil salinity and sodicity problems across different soil types.

Assessing GW quality for drinking usage was critical. Nitrate contamination is evaluated using the NPI, while overall water quality is measured through the EWQI to ensure safe drinking water standards are met. Findings indicate that all GW samples yielded NPI values within acceptable limits, with all values less than 1 signifying good nitrate GW quality in the study area. However, to assess drinking water quality ranking and zoning, 12 parameters, including cations, anions, pH, TDS, and TH were analysed using the EWQI method. Results revealed that a substantial portion, specifically 75% (n = 22) of the water samples, attained the highest rank (Figure 10(a)), denoting excellent water quality. Of the analysed samples, n = 6 (20%) were ranked as having good water quality.

The present study revealed the hydrochemical genetic mechanism of GW resources along with its quality suitability for drinking and agricultural usages in the sedimentary basin of Quetta Valley in Pakistan through comprehensive hydrogeochemical evaluation. This study has drawn the following conclusion:

The GW samples exhibit a slightly alkaline nature and are characterized as soft freshwater with respect to total hardness. The hydrochemical composition is identified as the HCO₃-Na type, with a discernible order of cations and anions showing an increasing trend of Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ for cations and > > Cl⁻ > for anions. The most prevalent cation is Ca²⁺, and the dominant anion is ⁠, surpassing recommended guidelines by 10 and 6%, respectively. The primary source of GW major ions is traced back to the dissolution of carbonates and silicate minerals from the host sedimentary rocks. This mechanism is corroborated by analyses using PCA, ion ratio analysis, and SI, indicating a correlation with cation exchange in the GW.

In terms of irrigation water quality, hydrochemical indices, including SAR, MAR, PI, KR, RSC, and percentage of sodium (%Na), were employed. Overall, the GW quality is deemed suitable for irrigation. However, 20% of GW samples exceeded the irrigation threshold for electric conductivity, indicating potential soil salt accumulation, while KR revealed alkali hazards in 17% of the GW samples. The USSL analysis indicates moderate to high salt levels and low alkalinity, making the GW suitable for crops tolerant to moderate salt concentrations.

The IWQI suggests that 68% of the GW falls into the no-restriction category, while 27% falls into the low-restriction category when used for irrigation. The EWQI classifies GW in the region as excellent to good quality for drinking, accounting for 75 and 20%, with only one sample falling into the extremely poor rank. Nevertheless, the study identifies a general decline in water quality in the north-western part of the Quetta Valley, impacting both drinking and irrigation purposes, and attention should be paid to the use of GW. This research can provide insights into the categorical use and sustainable use of GW.

The authors are grateful to the editor and anonymous reviewers whose insightful comments were very helpful in improving the paper.

Conceptualization: Yong Xiao. Methodology: Muhammad Haziq Khan, Hongjie Yang, Yuqing Zhang, Feiyu Chen. Formal analysis: Muhammad Haziq Khan, Hongjie Yang, Liwei Wang, Yuqing Zhang. Investigation: Muhammad Haziq Khan, Wenxu Hu, Rohit Sherstha. Data curation: Muhammad Haziq Khan, Jie Wang, Feiyu Chen. Writing – original draft: Muhammad Haziq Khan. Writing – review and editing: Yong Xiao, Hongjie Yang. Supervision: Yong Xiao.

This research was supported by the National Natural Science Foundation of China (42007183; 4210071010); Sichuan Science and Technology Program (2022NSFSC1084); Applied Basic Research Project of Qinghai Provincial Department of Science and Technology (2017-ZJ-743); MOE Key Laboratory of Groundwater Circulation and Environmental Evolution, China University of Geosciences (2023–004); Fujian Provincial Key Laboratory of Water Cycling and Eco-Geological Processes (SK202305KF01); Key Lab of Geo-environment of Qinghai Province (2023-KJ-15); Student Research Training Program of Southwest Jiaotong University (202310613077).

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.