Introduction

In recent years, the rapid development of human society has caused some serious environmental problems, such as unplanned land use, energy shortage, and water pollution (Liu et al. 2020; Luque-Espinar and Chica-Olmo 2020). Groundwater contamination, which is defined as the deterioration of water quality caused by anthropogenic activities, has threatened the survival and development of human beings (He et al. 2022; Li et al. 2014; Subba Rao et al., 2022a, 2022b). Human health may be seriously affected by polluted groundwater as it serves as the primary source of drinking and irrigation water (Li and Wu 2019; Wang and Li 2022). Therefore, the prevention of groundwater pollution has become a high priority for sustainable water resources management (Hu et al. 2020; Zhang et al. 2022a). It is of great significance to carry out first the groundwater quality and pollution degree assessment, and then implement groundwater pollution control policies to reduce and mitigate groundwater pollution (Fida et al. 2022; Zhou et al. 2022).

Researchers have used numerous methods to assess water pollution worldwide, and some of them include improved fuzzy comprehensive evaluation, fuzzy mathematics, multivariate statistical analysis; Nemerow index, set pair analysis, water quality identification index, and water quality index methods (Ali et al. 2021; Maurya et al. 2021; Shankar and Sreevidya 2020; Tian et al. 2021; Tian and Wu 2019; Wang et al. 2014; Qiao et al. 2015; Su et al. 2019; Zhang et al. 2020, 2021a, 2022b). The water quality index (WQI) method has been widely used to determine the overall groundwater quality and its suitability for drinking purposes worldwide (Fadel et al. 2021; Zotou et al. 2019; Mthembu et al. 2022). Li et al. (2010) integrated entropy weights and conventional water quality index (WQI) and developed the entropy water quality index (EWQI), improving the reliability of the water quality assessment results. Since then, EWQI has been widely used for water quality assessment many scholars (Amiri et al. 2014; Ali et al. 2017; Islam et al. 2020). To comprehensively assess groundwater pollution, a large number of physicochemical and biological parameters need to be considered, many of which have been included in the calculation of WQI (Singh et al. 2019). However, the existing EWQI classification criteria vary considerably between studies. Indeed, several researchers have classified the EWQI into five classes, namely < 25, 25–50, 50–100, 100–150, and > 150 (classification criteria one hereafter), corresponding to excellent, good, moderate, poor, and very poor groundwater quality, respectively (Kumar and Augustine 2022; Liu et al. 2022; Zhang et al. 2022c). While other researchers have classified EWQI into < 50, 50–100, 100–150, 150–200, and > 200(classification criteria two hereafter), corresponding to excellent, good, moderate, poor, and very poor groundwater quality, respectively (Egbueri et al. 2020b; Masood et al. 2022; Nguyen et al. 2021; Zhang et al. 2021b). Moreover, a third water quality classification criterion has widely been adopted, namely < 50, 50–100, 100–200, 200–300, and > 300(classification criteria three hereafter), corresponding to excellent, good, moderate, poor, and very poor groundwater quality, respectively (Egbueri et al. 2020a; Ismail and Ahmed 2021; Raheja et al. 2022). These different water quality classification criteria lead to confusing and varying groundwater quality assessment results (Singh et al. 2019; Wang et al. 2020; Panneerselvam et al. 2021). Therefore, using an appropriate EWQI classification is critical for ensuring the reliability of the groundwater quality assessment results. Although heavy metals are harmful to human health, they are rarely considered in the groundwater quality assessment due to the low levels in the analyzed water samples (Ali et al. 2017). Therefore, including heavy metal elements in the EWQI may result in more reliable groundwater quality results, and will be useful for the water quality management (Li et al. 2021).

Zhouzhi County is an important county in the Guanzhong Plain, China, and provides the major water supply for Xi’an. Assessment of groundwater quality of this area is of great importance for water safety and national economic development. Indeed, Qiao et al. (2020) and Zhang et al. (2019) revealed that groundwater is polluted in the entire Guanzhong Plain, showing high nitrate (NO3−) and heavy metals concentration is exceeding Chinese groundwater quality thresholds. However, the groundwater quality status around Xi’an city has not been comprehensively assessed. Therefore, this study aims to: (1) to assess the hydrochemical characteristics of groundwater and evaluate its quality using the improved water quality classification criteria; (2) to compare the results obtained using the improved water quality classification criteria with those obtained using the original classification criteria. This study also generated the spatial distribution maps of the main water quality parameters. This study provides suggestions and a basis for local groundwater pollution prevention and control.

Study Area

Location and Geography

Zhouzhi County is located in the central part of the Shaanxi Province, China (107°39′-108°37′ E and 33°42′-34°14′ N) at 68 km from Xi’an City (Fig. 1). The study area is bounded to the west by Mei County, to the east by Huyi District (previous Hu County), to the south by the Qinling Mountains, and to the north by the Wei River, covering a total area of 2974 km2. Zhouzhi County provides important water supply source for Xi’an City. The study area has a temperate continental climate with an average annual temperature of 13.3  °C (Zhang et al. 2019). The average annual precipitation and evaporation range from 530 to 700 mm and between 1000 and 1200 mm, respectively, with uneven seasonal distribution of precipitation. Indeed, about 45% of the precipitation amount occurs during the month of July and September period (Xu et al. 2019). The overall dry and wet seasons are distinct, with high winds and sandstorms in spring, intense rainfall events occurs in early summer, and limited rainfall occurs during the autumn, winter, and spring. The main rivers in the study area are the Wei River and Hei River. The Hei River is a first-class tributary of the Wei River, characterized by significant seasonal changes in flow. Whereas the highest flow of the mainstream is observed in autumn.

Fig. 1
figure 1

Location map of the study area and distribution of sampling sites

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Geological and Hydrogeological Settings

The landform types in the study area are pre-mountain alluvial fans, loess plateau, and valley terraces from the Qinling Mountains to the Wei River, consisting mainly of Quaternary alluvium-lacustrine and loess accumulation (An et al. 2020). The primary terraces are not widely distributed on the north bank of the Wei River, while secondary terraces are continuously developed. Nevertheless, primary and secondary terraces are developed in the Xi’an depression on the south bank of the Wei River, showing narrow terraces with shallow hydraulic gradients (Li et al. 2016a). In addition, the study area is characterized by the presence of two aquifer types, namely the Quaternary sand and gravel aquifer and the Neoproterozoic to Paleoproterozoic bedrock aquifer with a thickness of about 800 m. The main lithological classes are loess, sand, and gravel layers. According to Kong et al. (2019), the average hydraulic conductivity and the infiltration coefficient of the aquifer is 25.26 m/d and 0.25, respectively. The aquifer is mainly recharged by atmospheric precipitation infiltration and surface runoff, with decreasing recharge rates along both sides of the Wei River, while groundwater discharge occurs mainly by artificial extraction, evaporation, and lateral runoff to the river (Ji et al. 2020). According to the investigation in 2021, groundwater level depth in the study area ranges generally between 8 and 45 m.

Material and Methods

Sample Collection and Analysis

In this study, 71 groundwater samples were collected in polyethylene bottles from well-distributed sampling points in and around Zhouzhi County in June 2021 in the monsoon season. Portable GPS devices were used to record the location of the sampling sites. The groundwater sampling was carried out after pumping groundwater for 3 min and cleaning polyethylene bottles three times with the groundwater to be sampled. In addition, to preserve groundwater samples and ensure the reliability of analytical analyses, concentrated nitric acid was added to the groundwater samples intended for heavy metals analyses. Afterward, groundwater samples were stored and transported according to the Chinese drinking water standards (Ministry of Health of the People’s Republic of China and Standardization Administration of the People’s Republic of China 2006). On-site measurements of groundwater physicochemical parameters (such as temperature and pH) were conducted using a portable multi-parameter meter (SX723). Whereas fluorine (F−), chloride (Cl−), nitrite (NO2−), sulfate (SO42−), nitrate (NO3−), sodium (Na+), aluminum (Al), arsenic (As), zinc (Zn), total hardness (TH), total dissolved solids (TDS), potassium (K+), calcium (Ca2+), magnesium (Mg2+), bicarbonate (HCO3−), and carbonate (CO32−) were analyzed in the laboratory of Xi’an Center for Mineral Resources Survey. In addition, the charge balance error percentage (%CBE) of each groundwater sample was checked to ensure the reliable analytical results (Eq. 1). The acceptable standard limit %CBE is set at ± 5%, and in this study all water samples are within this limit, indicating acceptable quality of physicochemical analyses for further study.

$$\%\text{CBE} = \frac{{\sum \text{Cations} - \sum \text{Anions} }}{{\sum \text{Cations} + \sum \text{Anions} }} \times 100$$
(1)

Correlation Analysis

The degree of correlation between different parameters in groundwater can be quantified by the Pearson correlation coefficient (r), which can be calculated as follows:

$$r = \frac{{\sum\limits_{i = 1}^{n} {(X_{i} - \overline{X})(Y_{i} - \overline{Y})} }}{{\sqrt {\sum\limits_{i = 1}^{n} {(X_{i} - \overline{X})^{2} } } \sqrt {\sum\limits_{i = 1}^{n} {(Y_{i} - \overline{Y})^{2} } } }}$$
(2)

where, r is the correlation coefficient between two variables (x and y). \( \overline{x}\, {\text{and}}\, \overline{y}\) are the mean values of the variables. The value of r ranges between − 1 and 1. A correlation coefficient of 0 indicates that the two variables are not correlated (Wei et al. 2022).

Introduction to EWQI

EWQI is a comprehensive method for assessing groundwater quality. This method can be used to transform a large water quality dataset into comprehensive water quality scores by assigning objective weights to the hydrochemical parameters (Li et al. 2019; Qiao et al. 2020). Indeed, EWQI has been widely used by numerous researchers (Adimalla 2021; Marghade et al. 2021; Zhang et al. 2022c) due to its calculation simplicity and the ability to integrate multiple hydrochemical parameters. However, the EWQI classification criteria vary considerably between studies, resulting in discrepancies in the results of groundwater quality of the same aquifer. Moreover, differences in the input hydrochemical parameters may result in significant impacts on the EWQI classification criteria. In this study, the main parameters influencing groundwater quality were selected to determine the classification criteria of EWQI. The calculation of EWQI was performed using two main processes (Fig. 2) according to Li et al. (2010) and Wu et al. (2015). The first process consists of calculating the rating scale (qj) of EWQI for the hydrochemical parameters of groundwater. qj of pH values was calculated based on the standard pH (SpH) range of 6.5–8.5. Whereas the second process consists of matrix normalization using two different formulae, depending on the different parameters considered (Ali et al. 2017).

Fig. 2
figure 2

EWQI calculation process

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The EWQI classification criteria were redefined by considering the 12 parameters, namely F−, Cl−, NO2−, SO42−, NO3−, Na+, Al, As, Zn, pH, TH, and TDS, which were selected based on their pollution characteristics in groundwater. All levels of water quality standard limit value of each parameter were introduced in the EWQI formulae to obtain the new classification criteria. Based on the groundwater quality standard limits of the People’s Republic of China (General Administration of Quality Supervision, Inspection & Quarantine of China, and Standardization Administration of China 2017), all hydrochemical parameters and heavy metals can be classified into grades I–V (Table 1).

Table 1 Water quality classification standard limits and allowable limits
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Results and Discussion

Hydrochemical Characteristics of Groundwater

Groundwater in the study area is mainly used for agriculture, industry and drinking purposes. Therefore, the groundwater pollution in the study area threatens seriously the health of local residents. The concentrations of Cu, Cr6+ and Ni in groundwater samples were lower than the detection limits, suggesting that their concentrations were well below the limits for grade I, which would not affect human health (Edokpayi et al. 2018). Therefore, these heavy metals are not discussed in the current study. In this study, 12 parameters were selected to evaluate the groundwater quality. These hydrochemical parameters were selected based on their significant impacts on the groundwater pollution in the study area. Hydrochemical parameter concentrations below the detection limit were considered as half of the detection limit.

The Piper diagram consists of three different zones, in which the anion and cation proportions in water samples are plotted to determine their groundwater facies types, providing a better understanding of the evolution of groundwater (Piper 1944; He and Li 2020; Su et al. 2020). As shown in Fig. 3, the groundwater sample points in the study area mainly fall in zone 4, indicating that the groundwater chemical type of groundwater is dominated by the HCO3−Ca.Mg facies type. Whereas the anions and cations of groundwater samples were mainly distributed in zones E and A, indicating that the anions and cations in groundwater of the study area are dominated by HCO3− + CO32− and Ca2+, respectively. These results suggest that carbonate mineral weathering (such as calcite and dolomite) is likely to be the main factor controlling groundwater chemistry in Zhouzhi County.

Fig. 3
figure 3

Piper diagram showing major water types of the groundwater samples

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The Gibbs diagrams include two subplots divided into three parts, representing three evolutionary mechanisms of groundwater, namely precipitation dominance, rock dominance and evaporation dominance. These diagrams have, indeed, been applied by many scholars to assess groundwater evolution (Gibbs 1970; He et al. 2021). The results showed that all groundwater samples fall under the rock dominance zone, suggesting that the formation of groundwater chemistry is dominated by water–rock interaction (Fig. 4). As shown in Fig. 4a, there are four groundwater samples falling in the middle left part of the diagram, indicating that these groundwater samples are affected by a combination of the three evolutionary mechanisms. Whereas the remaining groundwater samples suggested a dominance of rock weathering (e.g., carbonate minerals), which influence significantly the groundwater chemical characteristics in the study area. These results are consistent with those obtained using the Piper diagram.

Fig. 4
figure 4

Gibbs diagrams, a TDS versus Na/(Na + Ca), b TDS versus Cl/(Cl + HCO3)

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Chemical Compositions of Groundwater

For a clear understanding of the hydrochemical characteristics of groundwater. a statistical analysis of the main water quality parameters was carried out. As shown in Table 2, 30.99% of the groundwater samples showed NO3− concentrations exceeding the permissible limit, indicating NO3− pollution in the study area. Indeed, high NO3− concentrations in drinking water are likely to cause methemoglobinemia in infants (Adimalla and Li 2018). The TDS is the sum of inorganic salts and small amounts of dissolved organic matter. The results showed that 11.27% of the groundwater samples exceeded the permissible limit of TDS (1000 mg/L). In fact, high TDS concentrations may affect significantly human health (Ravindra et al. 2019; Tiwari et al. 2016). The TH range was 169–750 mg/L, with a mean concentration of 374.80 mg/L. The results revealed 35.21% of the groundwater samples exceeding the permissible limit of TH (450 mg/L).

Table 2 Statistics of physiochemical parameters of groundwater (Unit: mg/L, except pH)
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The results showed 5.63% of the groundwater samples exceeding the permissible limit of NO2− (1 mg/L). The NO2− concentrations ranged from 0.63 to 1.06 mg/L, with a mean value of 0.86 mg/L. In fact, high groundwater NO2− concentrations pose a high health risk to adults and children (Su et al. 2018; Li et al. 2022). Whereas, Al concentrations ranged from 0.002 to 0.802 mg/L, showing 7.04% of the groundwater samples exceeding the permissible limit of Al. High Al concentrations may cause human health diseases, such as Alzheimer’s disease and dialysis encephalopathy in patients with chronic kidney disease (Hart et al. 2021). The As and Zn concentrations in groundwater were within the permissible limits, suggesting low impacts of As and Zn on the groundwater quality in the study area (Tiwari et al. 2017).

Correlation Analysis

The Pearson correlation coefficients between the chemical parameters of groundwater are reported in Table 3. According to the obtained results, TDS was significantly correlated with SO42−, K+, Ca2+, Na+, Mg2+, Cl− and HCO3−, indicating that these ions are the main components of TDS. These ions were mainly originated from the strong weathering of the rocks in the study area (Wei et al. 2022). Na+ showed highly positive correlations with Cl− (r = 0.597), and was moderately positively correlated with SO42− (r = 0.468), which indicates that Na+ may come from the dissolution of some evaporites (e.g., halite and mirabilite) in the study area (He et al. 2019; Li et al. 2018). The above reactions can be expressed as formulas (3) and (4).

$${\text{NaCl}}\to{\text{Na}}^{+} + {\text{Cl}}^{-}$$
(3)
$$\text{Na}_{2} \text{SO}_{4} \to 2\text{Na}^{ + } + \text{SO}_{4}^{2 - }$$
(4)
Table 3 Pearson correlations of major parameters
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HCO3− showed highly positive correlations with Ca2+ (r = 0.522) and Mg2+ (r = 0.810), SO42− showed highly positive correlations with Ca2+ (r = 0.683), and Ca2+ and Mg2+ are highly positively correlated (r = 0.637), indicating that they may have the same source, such as weathering and dissolution of gypsum (CaSO4·2H2O), dolomite [CaMg (CO3)2] and calcite (CaCO3) (Li et al. 2016b; Zhang et al. 2018). The above reactions can be expressed in formulas (5)-(7).

$$\text{CaCO}_{3} + \text{H}_{2}\text{O} + \text{CO}_{2} \to \text{Ca}^{2 + } + 2\text{HCO}_{3}^{ - }$$
(5)
$$\text{CaMg(CO}_{3} )_{2} + 2\text{H}_{2} \text{O} + 2\text{CO}_{2} \to \text{Ca}^{2 + } + \text{Mg}^{2 + } + 4\text{HCO}_{3}^{ - }$$
(6)
$$\text{CaSO}_{4} \cdot 2\text{H}_{2}\text{O} \to \text{Ca}^{2 + } + \text{SO}_{4}^{2 - } + 2\text{H}_{2} \text{O}$$
(7)

F− is highly correlated with Na+ (r = 0.598), because high Na+ groundwater usually promotes F− enrichment, and fluorine in groundwater mainly comes from the dissolution of fluoride-bearing minerals (Ali et al. 2016, 2018; Liu et al. 2019; Wu et al. 2020a; Subba Rao et al. 2021). Similarly, F− and As are positively correlated (r = 0.421), because in arid and semi-arid regions, alkaline and oxidizing as well as reducing conditions are favorable for the enrichment of As and F−, and this association is well documented (Guo et al. 2014; He et al. 2020; Kumar et al. 2020; Sathe et al. 2021). Therefore, the weakly alkaline water environment and mineral dissolution in the study area may lead to the coexistence of As and F− (Li et al. 2021). As and F− are widely investigated elements in medical geological research (Li and Wu 2022). NO2− is an intermediate product of nitrification and will be converted to NO3− upon oxidation. NO2− and NO3− are negatively correlated (r = − 0.145), and it can be determined that there is a conversion between NO2− and NO3− (Wu et al. 2020b). As shown in Fig. 5, similar spatial distributions of NO3− and TH were observed, indicating that they are homologous, which is consistent with the results of the correlation analysis (r = 0.593), and this phenomenon can be related to nitrification. The nitrification process favors the dissolution of carbonates in loess sediments, increasing in TH (Hussain et al. 2019).

Fig. 5
figure 5

Spatial distributions of physicochemical parameters

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Improvement in Water Quality Classification Criteria of EWQI

To determine the optimal water quality classification criteria, the allowable water quality limits prescribed in the national groundwater quality standards (Table 1) were composited as virtual water samples to calculate the virtual EWQI. The calculation showed that the highest weight values were for F− and Cl−, while the lowest weight values were for Zn and As. The EWQI values obtained in this study showed that the classification limits for grades I, II, III, and IV of the alkaline groundwater ranged approximately within 0–26, 26–50, 50–100, and 100–243, respectively, while those of the acidic groundwater were approximately 0–26, 26–50, 50–100, and 100–258, respectively (Fig. 6). These obtained results also indicate that two factors influenced significatively the EWQI values, namely the weights of the hydrochemical parameters and the threshold limit of each hydrochemical parameter. In fact, compared to other parameters, NO2− and heavy metals had relatively higher threshold limits for the IV and V levels than for the first three levels (Fig. 7).

Fig. 6
figure 6

Proportion of each parameter in the EWQI classification

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Fig. 7
figure 7

Limits of each parameter in the groundwater quality standards

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The classification criteria calculated for the alkaline groundwater and acidic groundwater were slightly different from the traditional classification criteria (classification criteria 1 to 3 in Table 4), because the EWQI values are typically higher when considering heavy metals in the calculation process. Compared with the traditional classification criteria, the classification values for each water quality level in the obtained criteria in this study are lower, which may be due to the consideration of more hydrochemical parameters and the addition of heavy metals. Based on the calculated EWQI values, the ranges of the grades I, II, and III groundwater quality indices were set as 0–25, 25–50 and 50–100, respectively (Table 4). In addition, since the standard limits of grades IV and V for heavy metals are many times higher than those of the first three grades, the EWQI values showed higher values for grades IV and V. In order to have more strict requirements for water quality, benefiting water quality protection, the EWQI of grade IV Values are set to 100–200 for both alkaline groundwater and acidic groundwater (Table 4). Therefore, the new classification criteria are more suitable for assessing the groundwater quality, combining several hydrochemical parameters and heavy metal elements. In the study, the groundwater quality in Zhouzhi County was assessed using a total of 12 parameters to ensure a comprehensive assessment.

Table 4 Overall water quality classification criteria based on EWQI
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Groundwater Quality Assessment Using Improved EWQI

The entropy weights of hydrochemical parameters were calculated in this study to determine EWQI classes (Table 5). The higher the entropy value, the greater the influence of water chemistry parameters on groundwater quality. According to the obtained results, NO2− and pH showed the highest weights of 0.15 and 0.14, respectively, while Al and Zn revealed the lowest weight of 0.02. The EWQI results showed that 1.41, 63.38, and 35.21% were of excellent, good, and medium groundwater quality, respectively, suggesting that groundwater in the study area is suitable for drinking (Sivasankar et al. 2013).

Table 5 Entropy weight of each parameter
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Kriging interpolation of EWQI values were applied to reveal the spatial distribution of groundwater quality in the study area (Fig. 8). The results showed deteriorated groundwater quality in the Wei River Plain area compared to that in the Qinling Mountain Front. Areas closer to the Qinling Mountain Front showed relatively better groundwater quality. In addition, excellent to good groundwater quality (grades I and II) were observed near the Hei River, which may be due to significant recharge from the infiltration of water from the Hei River. Whereas medium groundwater quality (grade III) was particularly observed near the urban area (Wugong, Yangling, Mei County, and Hu County), suggesting that human activities are the main factors affecting the groundwater quality in the study area.

Fig. 8
figure 8

Groundwater quality distribution map based on EWQI. Higher value indicates worse water quality

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The improved EWQI classification results of this study were compared with those of the three water quality classification criteria mentioned in the introduction (Table 6). According to the comparison results, classification criteria 1 and the improved classification criteria indicated similar groundwater quality results, showing similar numbers of groundwater samples in all groundwater classes. Whereas classification criteria 2 and 3 showed similar groundwater quality, the reason is that the first two levels of water quality EWQI value is the same. Overall, classification criteria 1 and the improved classification criteria are slightly stricter than classification criteria 2 and 3. In addition, the improved EWQI takes heavy metal elements in the evaluation of groundwater quality, making it the most suitable method for comprehensively and accurately assessing groundwater quality.

Table 6 Number of samples falling into different grades in different classification criteria
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Recommendations for Groundwater Quality Management

Although there were high groundwater NO3− concentrations in some areas of Zhouzhi County, the EWQI results suggested that groundwater in the study area was suitable for human drinking. Nevertheless, some necessary measures must be taken to improve the quality of groundwater and prevent further potential groundwater contamination in the study area.

First, since the study area is covered by a large amount of farmland, the government should support and encourage farmers to substitute industrial fertilizer with organic fertilizers and/or rationalize industrial fertilizer uses to prevent groundwater pollution. In addition, there are piles of livestock manure in the study area, which may lead to groundwater pollution through pollutant leaching. Effective management of manure piles is, therefore, required in the study area.

Second, the presence of intense industrial activities imposes a potential threat to the groundwater resource. Therefore, the government needs to strictly monitor the industrial wastewater effluents and ensure that industrial wastewater meets effluent discharge standards. Moreover, land development activities and drilling production around the water source area need to be reduced to prevent groundwater pollution.

Third, the high numbers of private wells in the study area, the poor awareness of farmers about water quality safety, and the lack of unified government management and water quality monitoring have led to a lack of groundwater protection against pollution. Therefore, it is of great importance to ensure that private well owners and local groundwater management authorities are aware of water quality and potential contamination risks in the study area and ensure that regular maintenance of wells, regular water quality monitoring, and water treatment are carried out (Hynds et al. 2014).

Conclusions

In this study, the overall groundwater quality in Zhouzhi County was assessed using an improved EWQI. In addition, the EWQI classification criteria results were compared with those obtained using other classification criteria. The main conclusions are as follows:

(1) According to the obtained EWQI results, the standard limits of heavy metals for grade IV were higher than those for the first three grades, resulting in higher IV and V EWQI values. The updated groundwater quality classification criteria are excellent quality water (EWQI < 25), good quality water (EWQI within 25–50), moderate quality water (EWQI within 50–100), poor quality water (EWQI within 100–200), and very poor quality water (EWQI > 200).

(2) NO3− and TH were the dominant pollution parameters in the groundwater of the study area, followed by TDS, Al, and NO2−. In addition, pH, F−, Cl−, Na+, SO42−, Zn, and As revealed mean and maximum concentration values within the permissible limit standards.

(3) According to the Kriging interpolation of EWQI, excellent groundwater quality is identified in areas near the Hei River where river water recharge takes place; however, in other areas, human activities are affecting the quality of groundwater.

(4) The improved EWQI results showed that 1.41, 63.38, and 35.21% of the groundwater samples were of excellent, good, and medium groundwater, respectively. Moreover, no groundwater sample showed poor and very poor groundwater quality, suggesting that groundwater is suitable for drinking. Compared with other classification criteria, the improved EWQI is more suitable and comprehensive for assessing the groundwater quality.