4.1. Major ions chemistry
The major cations observed in groundwater of the aquifer were Na+> Ca2+> Mg2+> K+, while the anions followed a declining order of HCO3‾ > SO42− > Cl‾ > NO3‾ (Table 2). The measured concentrations of Na+ and K+ in the analyzed samples exhibit significant variability, ranging from 4 mg L− 1 to 352 mg L− 1 and 2 mg L− 1 to 15.2 mg L− 1, respectively, with average values of 147.39 mg L− 1 and 4.99 mg L− 1. The measured presence of Na+ within the study area can be attributed to processes such as silicate weathering, gypsum dissolution, and contributions from anthropogenic sources (Marghade 2020). The Ca2+ and Mg2+ concentrations in the analyzed samples exhibited a range of 57.2 mg L− 1 to 232.8 mgL− 1 and 29 mg L− 1 to 188 mg L− 1, respectively, with average values of 123.8 mg L− 1 and 74.7 mg L− 1. The release of Ca2+ and Mg2+ into the groundwater is facilitated by the ion exchange mechanism during the interactions between water and rock, as well as through mineral dissolution (Ribinu et al. 2023). The prevailing anions in the groundwater were HCO3¯ and Cl¯, which exhibited concentrations ranging from 126 mg L− 1 to 480 mg L− 1 and 35 mg L− 1 to 600 mg L− 1, respectively (mean 318.94 mg L− 1 and 183.61 mg L− 1). The presence of HCO3¯ in groundwater is likely due to weathering of silicates and dissolution of carbonates from the atmosphere (Trabelsi et al. 2007). The SO42¯ concentration in the samples varied in the range of 52 mg L− 1 to 680 mg L− 1 (mean value; 300.5 mg L− 1). The primary origin of SO42¯ in the groundwater is associated with the dissolution of anhydrite and gypsum, although human activities such as improper sewage disposal and the application of fertilizers in agriculture may also contribute to its occurrence (Srinivasamoorthy et al. 2014).
NO3¯ concentration in the study area varies from 6.7 mg L− 1 to 85.7 mg L− 1 (average 45.85 mg L− 1). The eastern and southern regions of the study area exhibit the highest concentrations of NO3¯. Generally, elevated levels of NO3¯ and TDS indicate the presence of anthropogenic influences within a specific area (Sunkari et al. 2019). The higher concentrations of NO3¯ in groundwater can be attributed to agricultural practices involving the wider application of fertilizers (Ali et al. 2021)(Khan et al. 2021). Field observations and land use indicated that sewage and chemical fertilizer are more likely to be the main origin of elevated NO3¯ levels in the investigated study area.
The F¯ concentrations in several wells exceed the World Health Organization's (WHO) maximum permissible limit of 1.5 mg L− 1, with values ranging from 0.99 mg L− 1 to 5.6 mg L− 1 and an average of 2.75 mg L− 1 (Ali et al. 2023). Approximately, 56 percent of the groundwater samples have elevated F¯ level in the area. The high concentration can be attributed to the presence of fluorine-bearing rocks in the aquifer and vicinity areas. In addition, this study for the first time reported elevated F¯ in the area and also in Afghanistan which was undocumented earlier. However, an in-depth evaluation of F− sources can be taken up in the future.
Table 2
Statistics analysis of the physicochemical parameters of groundwater samples within the designated study area
Sample ID | pH | Temp. | TDS | EC | Ca2+ | Mg2+ | Na+ | K+ | HCO3̄ | Cl¯ | SO42− | NO3̄ | F¯ | Br¯ |
- | °C | mg L− 1 | µS/cm | mg L− 1 | mg L− 1 | mg L− 1 | mg L− 1 | mg L− 1 | mg L− 1 | mg L− 1 | mg L− 1 | mg L− 1 | mg L− 1 |
MZ-01 | 7.34 | 18.1 | 3240 | 4710 | 222.4 | 188 | 90 | 3.8 | 210 | 370 | 680 | 85 | 5.44 | 0.62 |
MZ-02 | 7.41 | 18.2 | 899 | 1306 | 76.8 | 55 | 193 | 3.1 | 345 | 144 | 240 | 28.88 | 4.22 | 0.79 |
MZ-03 | 7.39 | 23.3 | 896 | 1302 | 57.2 | 60 | 164 | 4.3 | 375 | 97 | 218 | 39.52 | 1.8 | 0.29 |
MZ-04 | 7.67 | 17.5 | 2745 | 3990 | 144 | 74 | 352 | 7.1 | 126 | 600 | 390 | 63.6 | 3.7 | 0.46 |
MZ-05 | 7.78 | 17.9 | 2401 | 3490 | 232.8 | 76 | 288 | 5.2 | 145 | 470 | 380 | 80.08 | 2.95 | 0.44 |
MZ-06 | 7.12 | 23.3 | 2477 | 3600 | 224 | 120 | 99 | 3.6 | 480 | 255 | 410 | 31.64 | 5.48 | 0.57 |
MZ-07 | 7.43 | 17.7 | 1555 | 2260 | 68.8 | 94 | 325 | 5 | 335 | 290 | 480 | 37.04 | 1.5 | 0.39 |
MZ-08 | 7.27 | 19.5 | 1658 | 2410 | 111.2 | 120 | 127 | 4 | 330 | 205 | 410 | 6.65 | 2.28 | 0.42 |
MZ-09 | 7.56 | 18.8 | 979 | 1423 | 97.6 | 65 | 160 | 12 | 360 | 145 | 254 | 44.38 | 1.31 | 0.81 |
MZ-10 | 7.34 | 17.6 | 833 | 1283 | 140.8 | 47 | 57 | 15.2 | 175 | 57 | 270 | 52.9 | 1.35 | 0.75 |
MZ-11 | 7.72 | 26.2 | 1085 | 1577 | 94 | 55 | 173 | 6.2 | 400 | 135 | 228 | 53.4 | 1.37 | 0.44 |
MZ-12 | 7.68 | 25.8 | 519 | 754 | 72 | 29 | 52 | 2.1 | 300 | 35 | 52 | 32 | 0.99 | 0.33 |
MZ-13 | 7.87 | 26.4 | 786 | 1142 | 80 | 44 | 123 | 2 | 480 | 40 | 97 | 22.2 | 2.6 | 0.41 |
MZ-14 | 7.65 | 26.5 | 863 | 1254 | 76 | 35 | 170 | 3.8 | 370 | 93 | 190 | 42.1 | 1.33 | 0.52 |
MZ-15 | 7.78 | 26.7 | 607 | 882 | 64 | 34 | 94 | 3.6 | 290 | 57 | 114 | 40.88 | 1.23 | 0.41 |
MZ-16 | 7.68 | 26.4 | 2229 | 3240 | 168 | 110 | 78 | 3.7 | 390 | 132 | 400 | 71.04 | 5.6 | 0.56 |
MZ-17 | 7.78 | 26.3 | 1610 | 2340 | 220 | 100 | 4 | 2.4 | 360 | 82 | 440 | 53.32 | 5.35 | 0.53 |
MZ-18 | 7.62 | 26.2 | 736 | 1070 | 78 | 38 | 104 | 2.7 | 270 | 98 | 155 | 40.7 | 1.12 | 0.36 |
Min | 7.12 | 17.50 | 519 | 754 | 57.20 | 29 | 4 | 2 | 126 | 35.00 | 52 | 6.65 | 0.99 | 0.29 |
Mean | 7.56 | 22.36 | 1451 | 2112.94 | 123.76 | 74.67 | 147.39 | 4.99 | 318.94 | 183.61 | 300.44 | 45.85 | 2.76 | 0.51 |
Max | 7.87 | 26.70 | 3240 | 4710 | 232.80 | 188 | 352.00 | 15.20 | 480 | 600 | 680 | 85.00 | 5.60 | 0.81 |
SD | 0.21 | 3.88 | 807.81 | 1171.38 | 61.24 | 39.91 | 91.56 | 3.35 | 99.26 | 153.34 | 155.29 | 19.41 | 1.69 | 0.15 |
WHO (2022) Permissible Limit | 7.5 | - | 1000 | 1500 | 200 | 150 | 200 | 12 | 500 | 250 | 250 | 50 | 1.5 | 0.5 |
4.2. Hydro-chemical facies
The identification of hydro-chemical facies within an aquifer has the potential to elucidate the mechanisms underlying the occurrence and salinization processes, and may also provide insights into the palaeo environmental history of the groundwater system (Sunkari et al. 2021). The analysis of Piper's diagram indicates that the major water types in the Mazar-e-Sharif region are predominantly Na-HCO3, Na-Ca-HCO3, Ca-Mg-SO4, and Na-Cl, arranged in descending order of occurrence (as observed in Fig. 5). The Na-HCO3 water type signifies the influence of cation exchange reactions occurring within the aquifer as well as the weathering of silicates minerals (Song et al. 2007). On the other hand, the Na-Ca-HCO3 and Ca-Mg-SO4 types of water suggest the presence of recharge water within the aquifer (Gopinath and Srinivasamoorthy 2015). Furthermore, the rural and urban samples were separately plotted in the diagram to better understand the LULC influence on the groundwater quality (Fig. 5). The Fig. 5 reveals that samples from urban areas shows its distribution towards Cl¯ field suggesting anthropogenic influences on the groundwater quality.
4.3. Factor governing water chemistry
In this study, the utilization of the Gibbs diagram is employed to quantitatively assess the primary mechanisms governing the chemistry of groundwater (Fig. 6a, and 6b) Within this diagram, three distinct mechanisms can be noted namely, rock dominance, precipitation dominance, and evaporation dominance (He and Li 2020). As depicted in Fig. 6, the prominent mechanisms influencing the chemistry of groundwater are evaporation dominance and rock weathering dominance. The influence of rock weathering dominance on groundwater chemistry arises from the fact that groundwater traverses the porous medium of the aquifer, wherein interactions between water and rock alter the chemical attributes of the groundwater. Additionally, the presence of evaporation dominance is evident in Fig. 6a, and 6b, highlighting the notable contribution of groundwater evaporation in the specific study area. In light of the arid to semi-arid characteristics of the study area, it is likely that evaporation also influences hydro-geochemical processes. To comprehend these processes, the local geological characteristics of the research area and the ionic composition of the groundwater samples are assessed. Furthermore, isotopic studies on this aspect should be taken up in the future for better understanding of salinity sources.
The bivariate diagrams depicting the relationships among HCO3−/Na+ and Ca2+/Na+, as well as Mg2+/Na+ and Ca2+/Na+, were utilized to gain deeper insights into the specific rock types that exert a substantial influence on the hydrogeochemical evolution of water (Fig. 6c, and 6d). According to Fig. 6c, and 6d, only one sample plotted between carbonate dissolution. This finding suggests that within the aquifer carbonate dissolution plays a negligible role in the hydro-chemical evolution of water. The significant portion of the groundwater samples shown in Fig. 6c, and Fig. 6d are plotted between silicate weathering predominance and evaporate rock area. These findings suggest that the hydro-chemical evolution of water within the Mazar-e-Sharif aquifer is predominantly governed by the dissolution of evaporate rock and the process of silicate weathering.
Figure 7a indicates that both cation exchange and reverse cation exchange have occurred in the aquifer. Figure 7b, implies that a considerable dissolution of calcite and partial dissolution of dolomite have happened in the aquifer. The ratios of Cl/Br are widely employed for identifying origin of groundwater salinity, because both elements are chemically conservative and can predict various hydrogeochemical processes which have occurred in the groundwater system. The Cl/Br ratios suggest that the primary origins of groundwater salinity in the Mazar-e-Sharif urban aquifer are likely to be evaporite and lacustrine deposits (Fig. 7c). Figure 7d indicates that dissolution of gypsum has remarkably occurred in the aquifer. Further, Fig. 7e suggests geogenic dominance influence by evaporation. Also, most of the samples from urban areas plotted in evaporation region. Figure 7f shows anthropogenic activities on releasing the nitrate pollution, specifically sewage waste and agricultural activities.
4.4. Geochemical modelling
Saturation indices are employed to assess the level of equilibrium between water and minerals. Alterations in saturation state serve as valuable indicators for distinguishing various stages of hydro-chemical evolution and aid in identifying the primary geochemical reactions that govern water chemistry (Coetsiers and Walraevens 2006)(Subba Rao et al. 2017)(Marghade 2020).
Figure 8 illustrates the SI values for various minerals, such as dolomite, calcite, gypsum, fluorite, and halite. According to Fig. 8, the dolomite and calcite were noted to exhibit an over-saturated condition, whereas gypsum, anhydrite, and halite were found to be dissolved in almost all water samples. The values of SI for dolomite and calcite in most water samples exceeded 0.25, suggesting their potential to precipitate due to being in a supersaturated state. Moreover, the increased values of SI for dolomite and calcite suggest the predominance of diffuse flow within the investigated region. Conversely, the values of SI for halite in all sampled sites are below − 4, indicating the significant ability of water samples to dissolve halite within the aquifer. Lower SI values for halite indicate a lesser influence on the chemical composition of water. Figure 8 demonstrates the positive correlation between values of SI for gypsum and halite with total dissolved solids (TDS) in all groundwater samples, implying that the salinity of water samples will increase over time, leading to higher TDS levels. Furthermore, the geochemical modelling of fluorite suggests that approximately half of the samples are under-saturated, indicating a likelihood of increased F¯ concentration over time (Ali et al. 2021).
4.5. Statistical correlation
In this study, descriptive statistics were calculated for the 12 hydro-chemical variables (Table 3). Analysis of Spearman's correlation coefficient matrix for the hydro-chemical parameters reveals significant associations between EC and TDS with Mg2+, Cl‾, SO42−, and Ca2+ concentrations (Table 3). This observed correlation can be attributed to the fact that EC and TDS serve as indicators of the overall dissolved solid content in groundwater. The results suggest that evaporation and irrigation return flow are the main components attributing to groundwater salinity. There is a correlation among Mg2+, Ca2+ and SO42−, which highlights the dissolution of gypsum (CaSO4·2H2O). Additionally, the correlation among Cl‾ and Na+ suggests the dissolution of halite (NaCl) as a contributing factor. Furthermore, statistical correlation shows negative correlation F¯ with NO3¯ suggesting possibility of geogenic contamination over anthropogenic.
Table 3. Correlation matrix between groundwater variables (shown as heat map for better visualization).
|
pH
|
Temp.
|
TDS
|
EC
|
Ca2+
|
Mg2+
|
Na+
|
K+
|
HCO3̄
|
Cl‾
|
SO42−
|
NO3̄
|
F−
|
pH
|
1
|
|
|
|
|
|
|
|
|
|
|
|
|
Temp.
|
0.499*
|
1
|
|
|
|
|
|
|
|
|
|
|
|
TDS
|
-0.274
|
-0.460
|
1
|
|
|
|
|
|
|
|
|
|
|
EC
|
-0.288
|
-0.491*
|
0.998*
|
1
|
|
|
|
|
|
|
|
|
|
Ca2+
|
-0.071
|
-0.301
|
0.752*
|
0.767*
|
1
|
|
|
|
|
|
|
|
|
Mg2+
|
-0.415
|
-0.388
|
0.907*
|
0.913*
|
0.678*
|
1
|
|
|
|
|
|
|
|
Na+
|
-0.024
|
-0.396
|
0.245
|
0.224
|
-0.174
|
0.030
|
1
|
|
|
|
|
|
|
K+
|
-0.304
|
-0.616*
|
0.354
|
0.372
|
0.170
|
0.229
|
0.463
|
1
|
|
|
|
|
|
HCO3̄
|
0.063
|
0.568*
|
-0.074
|
-0.095
|
-0.139
|
0.053
|
-0.069
|
-0.306
|
1
|
|
|
|
|
Cl‾
|
-0.363
|
-0.644*
|
0.841*
|
0.835*
|
0.488*
|
0.704*
|
0.599*
|
0.499*
|
-0.278
|
1
|
|
|
|
SO42−
|
-0.429
|
-0.5*
|
0.855*
|
0.865*
|
0.625*
|
0.923*
|
0.031
|
0.323
|
-0.129
|
0.683*
|
1
|
|
|
NO3̄
|
0.259
|
-0.163
|
0.453
|
0.457
|
0.507*
|
0.245
|
-0.036
|
0.446
|
-0.358
|
0.312
|
0.326
|
1
|
|
F−
|
0.311
|
0.586*
|
-0.218
|
-0.240
|
-0.106
|
-0.149
|
-0.143
|
-0.131
|
0.329
|
-0.247
|
-0.322
|
-0.067
|
1
|
*Correlation is significant at the 0.05 level
Principal Component analysis (PCA)
Principal component analysis was applied to ascertain the primary hydrogeochemical processes controlling groundwater chemistry in Mazar-e-Sharif aquifer. The varimax rotation technique and the Kaiser’s criterion was employed for PCA analysis. According to Fig. 9, PCA gathered three main principal components such as PC1, PC2, and PC3. The eigenvalues for all three PCs are greater than one, and the total cumulative variance contribution rate was 78.7%. The PC1 is composed of EC, TDS, Ca2+, Mg2+, SO42−, and Cl‾, representing the dissolution gypsum, halite, weathering of silicates, cation exchange were the greatest factors. PC2 is contained of Ca2+, Mg2+, and HCO3, which play a significant role in the dissolution of carbonates. PC3 is consisted of NO3¯, indicating aquifer has influenced by anthropogenic activities.
4.5.1 Dendrograms and heat map
For this study, two dendrograms displayed by heat map, were carried out using Euclidean distance and Word’s linkage method to categorize groundwater samples into distinguished groups based on their hydro-chemical characteristics and sampling locations. The outcome of dendrograms classification and heat map visualization are exhibited in Fig. 10. According to Fig. 10, three main hydro-chemical integrations are existed between 10 chemical parameters. R-mode dendrogram of the sampling locations is grouped into three clusters, and the clusters are shown as C1, C2, and C3. R-mode HCA displays that the majority of groundwater samples (66%) related to cluster C1. According to the heat map, the green color represents the minimum concentrations and red color depicts the highest concentrations of chemical parameters. The samples with highest EC values are classified into clusters C2 and C3.
4.6. Drinking Water Quality Evaluation
4.6.1. Groundwater quality index
The study area was assessed using the Water Quality Index (WQI), and the corresponding findings are presented in Fig. 12 and Table 4. The computed WQI values varied from 49.3 to 201.5, with an average value of 107.9. These values can be broadly classified into four categories, namely very poor, poor, good, and excellent water quality. It is noteworthy that more over 44% of the water samples were categorized as poor water quality. These findings can be attributed primarily to the combined effect of relatively elevated concentrations of F−, NO3¯, and high salt levels. Particularly, the WQI values for MS-01, MS-04, and MS-06 were calculated as 201.5, 168.2, and 156.7, respectively. The calculation results further indicated that TDS, F−, EC, and NO3‾ are the major contributors to the WQI. The distribution of WQI values, as illustrated in the Fig. 12, clearly demonstrates that the poor WQI level is predominantly concentrated in the western and central parts of the study area. Moreover, the spatial distribution of GWQI reveals that approximately 0.11% of the study area exhibits excellent water quality, primarily located in the western part of the city and Nahar Shahi district. Furthermore, around 39.9% of the study area is characterized by good water quality. Conversely, poor water quality and very poor water quality occupy 59.82% and 0.16% of the study area, respectively, and are primarily located in the central and eastern parts. These areas primary correspond to the brackish zone, characterized by high concentrations of TDS, F¯, EC, and NO3¯. These variables exhibit the strongest spatial correlation with the GWQI map (Fig. 11). Overall, the results of the GWQI analysis demonstrate that a significant portion of the study area exhibits poor and very poor water quality, suggesting the urgent need for timely management actions (Fig. 12).
Table 4
Classification of groundwater intended for consumption according to the GWQI
GWQI range | Type of water | Number samples | % Of samples | % Of area |
< 50 | Excellent | 1 | 1 | 0.11 |
50–100 | Good | 8 | 44.44 | 39.92 |
100–200 | Poor | 8 | 44.44 | 59.82 |
200–300 | Very poor | 1 | 5.56 | 0.16 |
> 300 | Unsuitable for drinking | 0 | 0 | 0 |
4.7. Hydrogeochemical Conceptual Model
A schematic conceptual model has been provided to display the existing hydro-geochemical properties of Mazar-e-Sharif area (Fig. 13). The conceptual model exhibits that the aquifer mainly consists of sand, gravel and fine to very fine sediments. Some clay lenses also present in the aquifer; however, its length and thickness are varying place to place. Groundwater extensively abstracted in urban areas and utilized for drinking, domestic and industrial purposes. This study showed that anthropogenic activities have considerably impacted the groundwater quality of the aquifer. Moreover, in the study area, the primary groundwater contaminants are NO3¯, F¯, and salinity. In addition, groundwater salinity in few areas are very high which could be attributed to the existence of lacustrine deposits. Therefore, water resources in the area should be developed sustainably to achieve the UN-Sustainable Development Goals (SDGs).