Chemical Characteristics and Suitability Assessment of Surface Water in the Area Surrounding the Nansi Lake

Abstract

Surface water quality, serving as a key link between domestic water use and agricultural production, impacts both the drinking water safety of local residents and the sustainable use of irrigated soil. To better protect water resources and enhance their sustainable value, this study collected 50 water samples from the areas surrounding Nansi Lake. Using the Piper trilinear diagram, Gibbs model, and ion ratio analysis, the main hydrochemical types were identified. Based on this, the entropy-weighted water quality index (EWQI) was used to evaluate the water’s suitability for drinking, while irrigation water quality indicators were applied to assess its suitability for irrigation. The results indicate that during both dry and rainy seasons, Na⁺ and SO₄²⁻ dominate the water, with average total dissolved solids (TDS) of 1279 mg/L and 1163 mg/L, respectively, indicating moderately elevated salinity. The ion concentrations follow the order: SO₄²⁻ > HCO₃⁻ > Cl⁻ > NO₃⁻ > F⁻ and Na⁺ > Ca²⁺ > Mg²⁺ > K⁺. From a hydrochemical perspective, mixed-type and Cl-Na-type waters prevailed in both seasons. The chemical composition of surface water in the study area is largely governed by rock weathering, with ions primarily originating from the dissolution of silicate and evaporite minerals. Furthermore, cation exchange processes play a significant role in shaping the evolution of the water chemistry. The water quality evaluation indicates that surface water in the study area is generally Class II, representing good water quality. However, Class IV and Class V water exist in some areas, where the primary exceedance parameter is SO₄²⁻, which is a key factor influencing water quality. Irrigation suitability is generally good. Systematic investigation of surface water hydrochemistry and quality is of great practical significance for ensuring safe drinking and irrigation water and promoting sustainable socio-economic development.

1. Introduction

Surface water is an indispensable natural resource in coal mining areas and their surrounding regions, fulfilling the dual role of ensuring domestic water security and supporting agricultural irrigation [1,2]. However, coal mining activities inevitably pose potential risks to the water quality of both mining and neighbouring areas [1]. Where surface water bodies serve as sources of drinking water, elevated concentrations of pollutants such as sulphates and fluorides can directly threaten human health [3]. When used for irrigation, excessive salt content and specific ion concentrations may contribute to soil salinisation, structural degradation, and reduced crop yields [3,4]. In this context, systematic assessments of both drinking and irrigation water quality have become an urgent necessity—essential for safeguarding water resources, protecting public health, and ensuring the sustainable development of agriculture in mining regions.

Previous approaches to water quality assessment have included single-factor evaluation, fuzzy comprehensive evaluation, and the analytic hierarchy process. Although these methods are computationally straightforward and easy to understand, they are limited in their ability to quantitatively capture changes in water quality [5,6]. In response, scholars have developed a range of novel assessment techniques. Among these, the Water Quality Index (WQI) has attracted considerable attention due to its capacity to reflect overall water quality status [7]. Nevertheless, the determination of weighting coefficients in WQI relies on expert judgement, which introduces uncertainties and potential informational bias. Even minor variations in assigned weights can lead to inconsistencies in water quality classification [8,9]. In contrast, the Entropy Water Quality Index (EWQI), which is based on standard permissible limits, offers a more objective and reliable alternative and has been widely adopted for assessing the suitability of drinking water resources [10,11,12].

In assessing irrigation water quality, scholars have commonly employed a range of established indicators, including the sodium content method (%Na) [13], Sodium Adsorption Ratio (SAR) [14], Residual Sodium Carbonate (RSC) [15], Permeability Index (PI) [16], and Magnesium Hazard (MH) [17]. These specialised parameters enable a comprehensive evaluation of water suitability for agricultural use by taking into account salinity, alkalinity, and ionic toxicity. For example, Wei et al. [18] used the EWQI together with local topographic features to quantitatively evaluate groundwater quality in the Dawen River basin, thereby uncovering anthropogenic impacts on the region’s groundwater. In the context of irrigation suitability, SAR is effective in assessing the risk of reduced soil permeability caused by sodium, while RSC helps determine the potential hazard of excess carbonate to crop growth. MH reflects the risk of soil structural degradation resulting from high magnesium concentrations, and PI indicates whether a water source may lead to surface crusting and diminished infiltration capacity [19,20]. Together, these complementary indicators substantially enhance the accuracy and specificity of irrigation water quality evaluations.

Jining City is rich in coal resources and serves as a major coal energy base in Shandong Province, as well as one of China’s eight largest coal production bases. Nevertheless, long-term coal mining has greatly changed the hydrogeological conditions of the area, causing sulfates and potentially toxic elements to seep into both groundwater and surface water. Consequently, local residents face health risks due to water pollution. Moreover, while most researchers have focused on mine water, relatively few studies have examined surface water. As a vital water source for the region, surface water plays a crucial role in residents’ daily lives, particularly for domestic drinking water and agricultural irrigation. To enrich the research in this region, this study utilizes data from both the dry and wet seasons to investigate surface water near the South Four Lakes, systematically analyzing the hydrochemical characteristics and evolutionary patterns under different hydrological conditions. The specific objectives of this study are as follows: (1) to identify and quantify the factors that control surface water characteristics, and to evaluate their contributions to hydrochemical processes; (2) to determine the suitability of surface water for drinking and agricultural uses; and (3) to evaluate the quality of water for both drinking and irrigation purposes by applying the EWQI and the Irrigation Water Quality Index. The results of this study serve as a valuable reference for research on surface water environments in analogous regions across the globe, and provide strong support for the development of water resource protection strategies and pollution control policies.

2. Study Area

The research area is located in Jining City, Shandong Province, with geographical coordinates of 116°25′42″–116°58′41″ E and 35°02′52″–35°41′51″ N, with a total area of approximately 2192 km². The study area is located in a warm temperate, semi-humid monsoon climate zone, featuring four distinct seasons. Hot summers bring concentrated rainfall and high humidity, while cold winters are marked by low temperatures, dryness, and minimal snowfall. The mean annual precipitation over the long term is 668.1 mm, with most rain falling in July and August. The region also has abundant sunlight and thermal resources, reflected in a long-term average annual temperature of 13.6 °C.

The main landforms in the research area include erosion hills, intermountain alluvial-floodplain, piedmont alluvial-floodplain, and alluvial-floodplain. From the east of Nansi Lake to the eastern foothills, there is a sub area of alluvial plain in front of the mountain, which slopes from east to west, with a ground elevation of 60–35 m and a ground slope of 1/1000–1/3000. The ground undulates slightly; To the west of Nansi Lake is a relatively flat alluvial and flood plain area, sloping from west to east with a ground elevation of 39–34 m and a ground slope of 1/5000–1/10,000. The terrain is relatively undulating. In addition, the research area exhibits a diverse landscape, including towns, farmland, lakes, and green spaces, with agriculture occupying a significant portion of land use. Residential buildings are mainly concentrated in the mountainous alluvial floodplain in the central and northeastern regions (Figure 1c).

The study area has a relatively well-developed water system, which is part of the Nansi Lake system within the Huai River Basin. The Beijing-Hangzhou Grand Canal traverses the area from north to south, whereas other rivers flow radially from the surrounding regions toward both the Grand Canal and the Nansi Lake, shaped by the local topography. The main rivers distributed include Sihe River, Guangfu River, Baima River, etc. The lakes in the area consist of Nansi Lake and Xiaobei Lake, while the water conservancy projects are primarily represented by the South-to-North Water Diversion Project. Tectonically, the study area lies within the Jining Stratigraphic Subdistrict of the western Shandong Stratigraphic Division. From oldest to youngest, the strata exposed in the region include the Neoarchean Mount Taishan Group, the Paleoproterozoic Jining Group, the Paleozoic Cambrian–Ordovician Changqing Group, the Jiulong Group, the Majiagou Group, the Carboniferous–Permian Yuemengou Group, the Mesozoic Jurassic Zibo Group and Cretaceous Qingshan Group, as well as the Cenozoic Paleogene Guanzhuang Group and the Quaternary strata. According to the lithology of the aquifer, the occurrence conditions of groundwater, and the water physical properties, the aquifer in the working area can be divided into four aquifer rock groups, namely the loose rock pore aquifer rock group, the clastic rock pore fracture aquifer rock group, the carbonate rock fracture karst aquifer rock group, and the bedrock fracture aquifer rock group. In terms of tectonic location, it is mainly located in the North China Plate (I), the Luxi Uplift Area (II), the Southwest Shandong Subtropical Uplift Area (III), the Heze Yanzhou Subtropical Uplift (IV), the Jining Subtropical Depression, and the Yanzhou Subtropical Uplift (V). The main faults in the research area include the Fushan Fault, Jiaxiang Fault, and Sunshidian Fault. They form the basic framework of the fault structures in this region, controlling the distribution of regional strata, topography, geomorphology, and the morphology and remains of geological relics.

3. Materials and Methods

Statistical analysis of the data was performed using SPSS Statistics 27. Origin 2024 was used to draw box plots, relationship diagrams between main chemical components, Piper plots, correlation analysis plots, and other graphs. ArcGIS 10.8 was then used to create a land use type map and an overview map of the study area.

3.1. Sample Analysis

In 2020, a total of 50 surface water samples were systematically collected from various locations across the study area in different batches, with 25 samples collected during the dry season and 25 samples collected during the wet season. Figure 1 illustrates the sampling locations, where GPS positioning was used to ensure the accuracy of each sampling point. For water quality sampling, two clean, dry 500 mL plastic bottles were used. Prior to sampling, each bottle was rinsed three times with the water sample to be collected, then filled and emptied, and finally sealed. Surface water, including rivers and lakes, was sampled along the direction of incoming flow at a depth of 10 to 15 cm below the water surface. After collection, the samples were kept refrigerated and transported to the hydrochemical laboratory for quality analysis within one week. On site measurements of temperature and pH were conducted using the HANNA HI 98128 pH analyzer, which has a temperature accuracy of ±0.5 °C and a pH accuracy of ±0.05 pH units. The main chemical properties of the surface water samples, including electrical conductivity (EC), total hardness (TH), total dissolved solids (TDS), and major cations (K⁺, Na⁺, Ca²⁺, and Mg²⁺), were determined using an inductively coupled plasma emission spectrometer (ICAP 6300, Thermo Fisher, Waltham, MA, USA) and an ion chromatograph (ICS 600, Thermo Fisher, Waltham, MA, USA). The concentration of HCO₃⁻ was measured via the titration method. After completing the water quality analysis, the charge balance error (CBE) was calculated to verify data reliability. All CBE values fell within ±5%, confirming the trustworthiness of the water quality data.

3.2. Entropy-Weighted Water Quality Index (EWQI)

In this study, the EWQI model based on information entropy theory is used to assess surface water quality in the study area. According to the EWQI classification, water quality is divided into five grades: Excellent (EWQI < 50), Good (50 ≤ EWQI < 100), Moderate (100 ≤ EWQI < 150), Poor (150 ≤ EWQI < 200), and Very Poor (EWQI ≥ 200) [18]. In line with the “Standards for groundwater quality” (GB/T 14848-2017) [21] of China, the key indicators selected for this study are TDS, TH, Na⁺, Cl⁻, SO₄²⁻, NO₃⁻, and F⁻ [18]. The procedure consists of five steps: establishing an initial water quality matrix, normalizing the data, determining weights using the entropy weighting method, setting up quantitative grading criteria, and finally calculating and classifying the water quality index. The EWQI calculation process is as follows:

(1) Create the initial water quality matrix

$$\begin{array}{c}\mathrm{X}=\left(\begin{array}{ccc}{\mathrm{x}}_{11}& \cdots & {\mathrm{x}}_{1\mathrm{n}}\\ ⋮& \ddots & ⋮\\ {\mathrm{x}}_{\mathrm{m}1}& \cdots & {\mathrm{x}}_{\mathrm{mn}}\end{array}\right)\end{array}$$

(1)

In this formula, m denotes the number of water samples, and n denotes the number of water chemistry parameters.

(2) Using Formula (2), the obtained eigenvalue matrix is standardized, and the standard evaluation matrix Y is subsequently derived from Formula (3):

$$\begin{array}{c}{\mathrm{y}}_{\mathrm{ij}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{ij}}-\min \left({\mathrm{x}}_{\mathrm{j}}\right)}{\max \left({\mathrm{x}}_{\mathrm{j}}\right)-\min \left({\mathrm{x}}_{\mathrm{j}}\right)}}\in \left(0,1\right)\end{array}$$

(2)

$$\begin{array}{c}\mathrm{Y}=\left(\begin{array}{ccc}{\mathrm{y}}_{11}& \cdots & {\mathrm{y}}_{1\mathrm{n}}\\ ⋮& \ddots & ⋮\\ {\mathrm{y}}_{\mathrm{m}1}& \cdots & {\mathrm{y}}_{\mathrm{mn}}\end{array}\right)\end{array}$$

(3)

Here, max(x_j) and min(x_j) denote the maximum and minimum values of the same hydrochemical parameter across all samples, respectively, while Y_ij represents the standardisation process.

(3) Calculate the ratio P_ij and the information entropy e_ij using Formula (4) and Formula (5).

$$\begin{array}{c}{\mathrm{P}}_{\mathrm{ij}}={\displaystyle \frac{{\mathrm{y}}_{\mathrm{ij}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{y}}_{\mathrm{ij}}}}\in \left(0,1\right)\end{array}$$

(4)

$$\begin{array}{c}{\mathrm{e}}_{\mathrm{j}}=-{\displaystyle \frac{1}{\ln \mathrm{m}}}\times {\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{m}}}{\mathrm{p}}_{\mathrm{ij}}\ln {\mathrm{p}}_{\mathrm{ij}}\end{array}$$

(5)

Among these, P_ij denotes the parameter value ratio for parameter j in sample i, obtained via Formula (5).

(4) The entropy weight (w_j) for each parameter and the grading index (q_j) for indicator j are calculated using Formulas (6) and (7).

$$\begin{array}{c}{\mathrm{w}}_{\mathrm{j}}={\displaystyle \frac{1-{\mathrm{e}}_{\mathrm{j}}}{{\sum }_{\mathrm{j}=1}^{\mathrm{n}}1-{\mathrm{e}}_{\mathrm{j}}}}\in \left(0,1\right)\end{array}$$

(6)

$$\begin{array}{c}{\mathrm{q}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{c}}_{\mathrm{j}}}{{\mathrm{s}}_{\mathrm{j}}}}\times 100\end{array}$$

(7)

In this formula, c_j represents the measured concentration (in mg/L) of chemical ions in surface water, while s_j denotes the allowable limit for indicator j, which corresponds to the Class III ion concentration specified by the “Standards for groundwater quality” [21].

(5) Calculate the EWQI according to Formula (8)

$$\begin{array}{c}\mathrm{E}\mathrm{W}\mathrm{Q}\mathrm{I}={\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{n}}}{\mathrm{w}}_{\mathrm{j}}{\mathrm{q}}_{\mathrm{j}}\end{array}$$

(8)

3.3. Irrigation Water Quality Assessment

Based on the concentrations of K⁺, Na⁺, Mg²⁺, Ca²⁺, and HCO₃⁻, several irrigation water quality indicators (specifically %Na, SAR, RSC, PI, and MH) were applied to water samples in order to comprehensively assess the suitability of surface water for irrigation in the study area. The corresponding calculation formulas are provided in Equations (9)–(13). According to the five evaluation results, the classification of irrigation suitability levels is based on

Table 1. Irrigation water suitability rating scale.

Evaluation Criteria	Classification Reference Values	Applicable Grades	Evaluation Criteria	Classification Reference Values	Applicable Grades
%Na	<20	Very suitable	SAR	<10	Very suitable
	20–40	Fairly suitable		10–18	Fairly suitable
	40–60	Suitable		18–26	Suitable
	60–80	Not sure		>26	Not suitable
	>80	Not suitable
RSC	<1.25	Very suitable	PI	>75	I (Very suitable)
	1.25–2.50	Suitable		25–75	II (Suitable)
	>2.50	Not suitable		<25	III (Not suitable)
MH	<50	Very suitable
	50–75	Suitable
	>75	Not suitable

.

$$\begin{array}{c}\%\mathrm{N}\mathrm{a}={\displaystyle \frac{{\mathrm{Na}}^{+}+{\mathrm{K}}^{+}}{{\mathrm{Mg}}^{2+}+{\mathrm{Ca}}^{2+}+{\mathrm{Na}}^{+}+{\mathrm{K}}^{+}}}\times 100\end{array}$$

(9)

$$\begin{array}{c}\mathrm{S}\mathrm{A}\mathrm{R}={\displaystyle \frac{{\mathrm{Na}}^{+}}{\sqrt{\frac{\left({\mathrm{Ca}}^{2+}+{\mathrm{Mg}}^{2+}\right)}{2}}}}\end{array}$$

(10)

$$\begin{array}{c}\mathrm{R}\mathrm{S}\mathrm{C}={\mathrm{HCO}}_3^{-}+{\mathrm{CO}}_3^{2-}-\left({\mathrm{Ca}}^{2+}+{ \mathrm{Mg}}^{2+}\right)\end{array}$$

(11)

$$\begin{array}{c}\mathrm{P}\mathrm{I}={\displaystyle \frac{{\mathrm{Na}}^{+}+\sqrt{{\mathrm{HCO}}_3^{-}}}{{\mathrm{Ca}}^{2+}+{ \mathrm{Mg}}^{2+}+{ \mathrm{Na}}^{+}}}\times 100\end{array}$$

(12)

$$\begin{array}{c}\mathrm{M}\mathrm{H}={\displaystyle \frac{{\mathrm{Mg}}^{2+}}{{\mathrm{Ca}}^{2+}+{\mathrm{Mg}}^{2+}}}\times 100\end{array}$$

(13)

4. Results and Discussion

4.1. Characteristics of Surface Water Quality

Statistical analysis is the most commonly used tool for interpreting the hydrochemical characteristics of surface water. It can reveal the basic patterns in hydrochemical data and lay the foundation for a deeper understanding of the formation processes. Figure 2 presents the statistical results of the major ionic components in surface water samples collected from the study area during both the dry and wet seasons, with 25 samples obtained in each period. The main cation and anion detected in surface water during the dry season are Na⁺ and SO₄²⁻, with average values of 270 mg/L and 418 mg/L, respectively. The abundance order of cations is Na⁺ > Ca²⁺ > Mg²⁺ > K⁺, and that of anions is SO₄²⁻ > HCO₃⁻ > Cl⁻ > NO₃⁻ > F⁻. TH and TDS are two key indicators for assessing surface water quality. TH levels in surface water range from 25.4 to 1222 mg/L, while TDS levels range from 271 to 3681 mg/L, with mean values of 461 mg/L and 1279 mg/L, respectively. The average pH is 7.9, indicating that the water is generally weakly alkaline.

During the wet season, the primary cation and anion in surface water are Na⁺ and SO₄²⁻, with average concentrations of 271 mg/L and 439 mg/L, respectively. The cations rank in the order Na⁺ > Ca²⁺ > Mg²⁺ > K⁺, while the anions follow the sequence SO₄²⁻ > HCO₃⁻ > Cl⁻ > NO₃⁻ > F⁻. TH values range from 75.2 to 573 mg/L, and TDS values range from 386 to 5413 mg/L, with mean values of 342 mg/L and 1163 mg/L, respectively. The average pH during the wet season is 8.1, which is slightly higher than that in the dry season, indicating that the water remains generally weakly alkaline.

The coefficient of variation (CV) is a statistical metric used to quantify the degree of data dispersion, reflecting the relative variability of each parameter through the ratio of the standard deviation to the mean. During the dry season, surface water exhibits relatively high CVs for SO₄²⁻ and NO₃⁻, at 101% and 175%, respectively, suggesting pronounced spatial heterogeneity in their distribution. In the wet season, the CVs for Na⁺, SO₄²⁻and NO₃⁻ all exceed 100%, reaching 120%, 116%, and 128%, respectively, indicating considerable spatial variability and instability in the occurrence of these three ions in surface water.

4.2. Piper Diagram

The Piper trilinear diagram [22] is a classical tool for analyzing the evolution of hydrochemical characteristics in water bodies, as it not only offers an intuitive depiction of water chemistry but also enables the identification of hydrochemical types and the sources of chemical constituents. Figure 3 displays the Piper diagram for surface water chemistry in the study area. As shown, the hydrochemical types of surface water in both the dry and wet seasons are fairly consistent, with only slight seasonal differences. In the anion ternary diagram, most surface water samples fall within Zone B, corresponding to the “no dominant type”, followed by Zones E and F, which represent the “bicarbonate type” and “sulphate type”, respectively. The cation ternary diagram indicates that surface water samples are predominantly distributed in the Na⁺ region, followed by Zone B, corresponding to the “sodium type” and “no dominant type”, respectively. Based on the combined distribution of anions and cations, the sampling points are primarily concentrated in Zones 5 and 3 of the central diamond, with the dominant hydrochemical facies being mixed water types and Cl–Na type water.

4.3. Analysis of Water Chemistry Control Factors

Based on the Gibbs diagram model [23], the key mechanisms governing the chemical composition of natural waters can be classified into three types: rock weathering-dominated, evaporation-dominated, and atmospheric precipitation-dominated. In this semi-logarithmic diagram, TDS is plotted on the logarithmic vertical axis, while the horizontal axis shows the ionic ratios Na⁺/(Na⁺ + Ca²⁺) and Cl⁻/(Cl⁻ + HCO₃⁻).

As shown in Figure 4a,b, surface water samples collected during different periods in the study area mainly lie within the rock weathering-dominated region of the Gibbs diagram, although some samples are located near the boundary of or within the evaporation-dominated region. This pattern indicates that the chemical composition of surface water in the study area is largely controlled by rock weathering, with evaporation and crystallization playing a secondary role.

To further identify the specific lithological sources of dissolved ions in surface water, end-member diagrams of HCO₃⁻/Na⁺ versus Ca²⁺/Na⁺ and Mg²⁺/Na⁺ versus Ca²⁺/Na⁺ were used. These diagrams were proposed by Gaillardet et al. [24] through the analysis of global river hydrochemical data. The Ca²⁺/Na⁺, Mg²⁺/Na⁺, and HCO₃⁻/Na⁺ ratios for carbonate rock end-members are 50, 10, and 120, respectively, while those for silicate rock end-members are 0.35 ± 0.15, 0.24 ± 0.12, and 2 ± 1, respectively. By plotting the surface water samples on the ion ratio end-member diagrams (Figure 4c,d), it can be observed that most samples lie close to the silicate rock end-members, indicating that the hydrogeochemical composition of surface water in the study area is mainly influenced by silicate rock weathering. Additionally, some samples plot near the evaporite rock end-member, suggesting that evaporite dissolution also contributes to the surface water chemistry.

4.4. Ion Ratio Analysis

Plotting the milliequivalent ratios of various ions in surface water helps to interpret hydrogeochemical processes and the mechanisms of formation [25,26]. Theoretically, if the only source of Na⁺ and Cl⁻ in surface water is the dissolution of salt rocks, then the Na⁺/Cl⁻ ratio ought to be 1. Figure 5a shows that during both the dry and wet seasons, water samples are primarily distributed above the 1:1 line, indicating an excess of Na⁺ in the water and the presence of other anions to balance this excess. This implies that atmospheric precipitation and the weathering dissolution of evaporite rocks are not the dominant sources of ions. Instead, the main source of Na⁺ in the study area is the dissolution of salt rock minerals and silicates, and it may also be influenced by cation exchange.

The milliequivalent concentration ratio of (Ca²⁺ + Mg²⁺)/(HCO₃⁻ + SO₄²⁻) can serve as an indicator of the sources of Ca²⁺ and Mg²⁺ in water. When this ratio falls along the 1:1 line, it suggests that the dissolution of Ca²⁺, Mg²⁺, and other minerals derived from carbonates and sulfates is the dominant hydrogeochemical process in surface water systems [27,28]. As shown in Figure 5b, except for a few sampling points during the wet season that lie exactly on the 1:1 line, the majority of points plot below this line, indicating an excess of (HCO₃⁻ + SO₄²⁻). This suggests that the chemical composition of surface water is primarily influenced by the dissolution of silicate minerals or by cation exchange/adsorption processes, in addition to being controlled by carbonates [29].

Use the ratio of (SO₄²⁻ + Cl⁻)/HCO₃⁻ milligram equivalent concentration to determine whether the source of hydrochemical components is evaporite or carbonate. As shown in Figure 5c, a small proportion of the water samples from the study area fall on the 1:1 line, whilst the majority lie above it; this is particularly evident for samples taken during the dry season. The majority of surface water chemical components come from the dissolution of evaporite rocks.

Ion exchange is a very common chemical reaction in water bodies, and the milliequivalent ratio of (Na⁺-Cl⁻)/((Ca²⁺ + Mg²⁺)-(SO₄²⁻ + HCO₃⁻)) can further indicate whether cation exchange occurs in surface water [29,30]. As shown in Figure 5d, the sampling points from both the dry and wet seasons are mostly distributed around the –1:1 line, suggesting the presence of cation exchange in the water samples from the study area. To more clearly identify the direction of cation exchange, the Chlor-Alkali Index (CAI) was adopted, and its calculation is presented in Equations 14 and 15 [31,32]. When both CAI-I and CAI-II have positive values, reverse cation exchange occurs, that is, Na⁺ and K⁺ in the water are replaced by Ca²⁺ and Mg²⁺ from the rock, resulting in a reduction in Na⁺ and K⁺ concentrations and an increase in Ca²⁺ and Mg²⁺ levels in the water. Conversely, when both CAI-I and CAI-II are negative, the exchange direction is reversed. As shown in Figure 5e, the vast majority of sample points fall within the negative region for both CAI-I and CAI-II, indicating forward cation exchange, where Ca²⁺ and Mg²⁺ in the water exchange with Na⁺ and K⁺ in the rock, resulting in a decrease in Ca²⁺ and Mg²⁺ in the water.

$$\begin{array}{c}\mathrm{C}\mathrm{A}\mathrm{I}\text{-}\mathrm{I}={\displaystyle \frac{\mathrm{C}{\mathrm{l}}^{-}-\left(\mathrm{N}{\mathrm{a}}^{+}+{\mathrm{K}}^{+}\right)}{\mathrm{C}{\mathrm{l}}^{-}}}\end{array}$$

(14)

$$\begin{array}{c}\mathrm{C}\mathrm{A}\mathrm{I}\text{-}\mathrm{I}\mathrm{I}={\displaystyle \frac{\mathrm{C}{\mathrm{l}}^{-}-\left(\mathrm{N}{\mathrm{a}}^{+}+{\mathrm{K}}^{+}\right)}{{\mathrm{HCO}}_3^{-}+\mathrm{S}{\mathrm{O}}_4^{2-}+\mathrm{C}{\mathrm{O}}_3^{2-} +\mathrm{N}{\mathrm{O}}_3^{-}}}\end{array}$$

(15)

Human activities are highly dependent on surface water runoff, and this dependence inevitably affects the hydrochemical evolution of surface water. In regions with intensive human activity, concentrations of NO₃⁻ and SO₄²⁻ are often elevated [27]. As shown in Figure 5f, the plot of NO₃⁻/Ca²⁺ versus SO₄²⁻/Ca²⁺ exhibits a relatively high SO₄²⁻/Ca²⁺ ratio. This indicates that human activities, including industrial mining and domestic sewage discharge, are key factors influencing the hydrogeochemical characteristics of surface water in the study area.

4.5. Correlation Analysis

Pearson correlation analysis can quantitatively depict the linear correlation between different hydrochemical parameters, discover the co-directional or anti-directional variation relationships between variables, and further reveal the inherent laws of hydrochemical evolution.

A Pearson correlation analysis was performed, and the resulting correlation matrix is shown in Figure 6. In both the dry (Figure 6a) and wet (Figure 6b) seasons, TDS exhibited a strong positive correlation with Na⁺, Cl⁻, SO₄²⁻ and F⁻. Among these, the correlations with Na⁺ and SO₄²⁻ were the strongest, with correlation coefficients of 0.98 for both ions during the dry season, and 0.97 and 0.95, respectively, during the wet season. This suggests that Na⁺ and SO₄²⁻ are the main contributors to TDS. In both seasons, TH was strongly correlated with Ca²⁺ and Mg²⁺, confirming that these two ions are the main contributors to the hardness of the surface water. Regarding inter-ionic relationships, Na⁺ was strongly positively correlated with SO₄²⁻ in the dry season, indicating a common origin, likely from the dissolution of evaporite minerals or anthropogenic inputs. In the wet season, Na⁺ was strongly correlated with Cl⁻ and F⁻, suggesting that these ions may share similar sources during this period. In contrast, NO₃⁻ showed weak correlations with most ions in both seasons, indicating that its source is relatively independent and may be mainly influenced by anthropogenic factors, such as agricultural activities.

4.6. Principal Component Analysis (PCA)

Using principal component analysis to perform dimensionality reduction analysis on surface water data through linear transformation to simplify the data and facilitate observation of distribution and subsequent analysis. During the dry season, four principal components were extracted based on the eigenvalues (Figure 7a), with a cumulative variance explained of 87.1%. The first principal component (F1) contributed 36.3%, serving as the primary factor controlling the hydrochemistry in the study area. Highly loaded variables for F1 include Na⁺ (0.86), Cl⁻ (0.81), and SO₄²⁻ (0.82). Correlation analysis indicated a strong positive correlation between Na⁺ and Cl⁻ (correlation coefficient of 0.62), as well as between Na⁺ and SO₄²⁻ (correlation coefficient of 0.87). The ratio analysis of Na⁺ and Cl⁻, representing typical ions, indicated that their sources can be attributed to the dissolution of evaporite rocks. Additionally, anthropogenic activities also affect the concentrations of Na⁺, Cl⁻ and SO₄²⁻. Given that the study area is densely populated with industrial activities, F1 can be interpreted as a combined effect of evaporite rock dissolution and industrial wastewater discharge. The second principal component (F2) contributed 27.2%, primarily driven by HCO₃⁻ (0.79) and pH (0.74), reflecting the acidic and alkaline environments of the water body. The third principal component (F3) contributed 12.8%, with Ca²⁺ (0.66) and Mg²⁺ (0.61) as the main loadings. Considering the regional geological conditions, it can be interpreted as the dissolution of carbonate rocks. The fourth principal component (F4) contributed 10.8%, primarily controlled by NO₃⁻ (0.78), which typically originates from agricultural inputs. Therefore, F4 can be interpreted as the impact of agricultural activities.

In the wet season, four principal components were likewise extracted based on the eigenvalues (Figure 7b), together accounting for 84.2% of the cumulative variance. The first principal component (F1) explained 41.7% of the variance, serving as the dominant factor influencing the hydrochemistry of the study area. The high-loading variables of F1 included Na⁺ (0.97), Cl⁻ (0.87), and SO₄²⁻ (0.93). Correlation analysis indicated a strong positive correlation between Na⁺ and Cl⁻ (correlation coefficient 0.62) and between Na⁺ and SO₄²⁻ (correlation coefficient 0.95). The dimensionality reduction results for F1 were strikingly similar to those during the dry season, suggesting a combined effect of evaporite dissolution and industrial wastewater discharge. The second principal component (F2) contributed 18.8%, primarily driven by HCO₃⁻ (0.75) and pH (0.70), reflecting the weakly alkaline environment of the surface water. The third principal component (F3) contributed 11.9%, with Mg²⁺ (0.77) as the main loading, which, combined with the ion ratio plot, can be explained by the influence of carbonate rock weathering. The fourth principal component (F4) contributed 11.7%, primarily driven by Ca²⁺ (0.67) and NO₃⁻ (0.54). NO₃⁻ showed low correlation with other ions, indicating a relatively independent source. The two high-loading ions in F4 may originate from different input end-members, while NO₃⁻ can promote the dissolution of carbonate rocks, leading to an increase in Ca²⁺ concentration. Therefore, F4 can be explained by the combined influence of agricultural activities and carbonate rock dissolution.

4.7. Drinking Water Quality Assessment

The WQI method, which evaluates overall water quality and drinking suitability based on hydrochemical data and standards, often involves subjective weight allocation for various indicators, potentially obscuring important information. To objectively reflect each indicator’s relative importance, this study adopts the entropy weight method, which determines the entropy index from data dispersion and calculates weights, resulting in the EWQI. This method has been widely applied in drinking water quality evaluation. During the dry season, EWQI values in the study area ranged from 23.7 to 236, with a mean of 77.1; during the wet season, they ranged from 26.2 to 499, with a mean of 93.2. The average EWQI value during the dry season was slightly lower than that in the wet season. As shown in Figure 8a for the dry season, 20% of the water samples fell into the excellent category and 68% into the good category, indicating that 88% were suitable for direct consumption. Additionally, 4% were of medium quality, and another 4% were deemed unsuitable for direct consumption. In the wet season (Figure 8b), 20% were excellent and 48% were good, totaling 68% suitable for direct drinking. The proportion of medium-quality samples was 24%, and 4% were unsuitable. As illustrated in Figure 8c, d, elevated EWQI values during the dry season were mainly concentrated in the Wanglou mining area, whereas in the wet season, higher values appeared in both the Wanglou and Xinglongzhuang mining areas. In contrast, EWQI values for surface water in other parts of the study area remained relatively low. Consequently, these mining areas need to prioritize improvements in drinking water quality.

4.8. Assessment of Irrigation Water Quality

As noted above, Na⁺ concentrations in the study area are generally elevated. High salinity levels in irrigation water can lead to soil salinisation, while excessive Na⁺ may also induce soil compaction and reduce permeability, ultimately affecting plant growth and crop yield [33]. The regional climate is characterised by humid and rainy summers, in contrast to relatively dry winters and springs, with agricultural irrigation depending largely on surface water and groundwater resources. In view of this, this study employs multiple indicators (including the %Na, SAR, RSC, PI and MH) to assess the suitability of surface water for irrigation. These indicators, combined with Wilcox and USSL diagrams [13,14,34], comprehensively evaluate the agricultural suitability of water quality from different dimensions, providing a more comprehensive reference for irrigation decision-making.

During the dry season, the %Na of surface water in the study area varied between 27.4% and 94.5%, while during the wet season it ranged from 28.2% to 93.7%. About 56% of the samples collected in the dry season were deemed suitable for irrigation, and this proportion rose to 72% in the wet season (Figure 9a). A more detailed assessment of irrigation suitability can be obtained by examining the relationship between EC and %Na. As shown in Figure 10a, surface water samples from both seasons were mainly distributed within the “excellent to good” and “good to permissible” categories, indicating that the overall water quality is generally acceptable for irrigation. Nevertheless, a portion of the samples fell into the “doubtful to unsuitable” and “unsuitable” categories, suggesting that elevated salinity and sodium levels may limit their suitability for irrigation in some areas.

During the dry season, SAR values ranged from 1.52 to 27.7, while in the wet season, they varied between 1.25 and 36.4. Overall, the majority of water samples from both periods fell within the range considered suitable for irrigation, with only 12% of dry season samples and 8% of wet season samples classified as unsuitable (Figure 9b). To further evaluate the potential risk of salinisation, this study employed a USSL diagram integrating EC and SAR values to assess irrigation suitability. As illustrated in Figure 10b, most samples were concentrated within the C2–C3 and S1–S2 combination zones, indicating irrigation water with moderate salinity and low to medium sodium hazard, which is generally acceptable for most crops. However, a small number of samples fell within the C4 and S3–S4 zones, reflecting high salinity and high sodium levels. These conditions may pose risks to soil structure and crop health, suggesting that such water should either be used with caution under appropriate irrigation management or avoided altogether.

As shown in Figure 9c, 92% of the water samples from both the dry and wet seasons were suitable for irrigation; only one sample from each season had an RSC value exceeding 2.5, indicating unsuitability for agricultural use. Regarding MH, water samples with MH values below 50 are generally considered suitable for irrigation (Figure 9d). In this study, 80% of the samples fell below this threshold, suggesting that the majority are appropriate for plant irrigation. Additionally, the PI values for all samples from both seasons fell within the “excellent to good” range, further supporting their suitability for irrigation (Figure 9e). A comprehensive analysis integrating multiple indicators, including %Na, SAR, RSC, MH, and PI, indicates that the vast majority of surface water samples are suitable for agricultural irrigation, with suitability being slightly higher in the wet season than in the dry season. Nevertheless, certain samples exhibit elevated salinity and sodium levels, which warrant attention in irrigation management to mitigate potential risks to soil structure and crop growth. Overall, the surface water in the study area can be used for irrigation.

5. Limitations of the Evaluation Methods and Management Recommendations

Although the EWQI enables a relatively objective determination of the weights of various evaluation indicators, its weight allocation remains sensitive to the variability of indicator data, thereby retaining certain limitations in comprehensive water quality assessment. Meanwhile, traditional irrigation water quality indices such as %Na, SAR, RSC, PI and MH tend to focus on a single environmental risk, resulting in a relatively narrow evaluation scope. In summary, notwithstanding these limitations, the methods described above remain of considerable referential value for evaluating irrigation water quality in this study area and analogous regions. Moreover, they can serve as a foundation for developing regional policies on water resource protection and pollution control. On the basis of the foregoing analysis, the following recommendations are put forward in this paper:

(1)

Research indicates that surface waters in the study area have high salinity as well as elevated concentrations of SO₄²⁻ and Na⁺ in both the dry and wet seasons. To reduce the entry of high-salinity ions and inorganic pollutants into surface water bodies, it is recommended that oversight of mining activities near the mining area be tightened. This would help lessen the continued influence of human activities on the region’s hydrochemical environment.

(2)

Some surface water samples exhibited elevated salinity and sodium concentrations. Although the water is generally suitable for irrigation, there remains a potential risk of salinisation. It is recommended that zoned irrigation management be implemented in agricultural production, whereby high-mineralisation water is diluted or used in rotation, and water and fertiliser management measures are appropriately coordinated to prevent the deterioration of soil physicochemical properties and ensure the safety of agricultural production.

(3)

According to the drinking water quality assessment, surface water in the study area is mostly of good to excellent quality. However, the sources of NO₃⁻ are relatively independent and show considerable variability, indicating a certain level of water quality instability. It is recommended to establish a long-term dynamic water quality monitoring network to track changes in water quality, identify major pollution sources and transport pathways, and provide continuous data support for the safe use and protection of regional water resources.

6. Conclusions

Based on 50 sets of surface water sample data collected during different periods in the study area, this study employs methods such as hydrochemical diagrams and mathematical statistics to conduct an in-depth analysis of the hydrochemical characteristics and evolution patterns of surface water in the region, as well as to evaluate its current water quality status. The main conclusions are as follows:

(1)

The dominant cations and anions in the surface water of the study area during the dry and wet seasons are SO₄²⁻ and Na⁺, with average values of 418 mg/L and 270 mg/L, 439 mg/L and 271 mg/L, respectively. The average TDS values were 1279 mg/L and 1163 mg/L, respectively. The coefficient of variation of SO₄²⁻ and NO₃⁻ during the dry season is relatively high, while the coefficient of variation of Na⁺, SO₄²⁻, and NO₃⁻ during the wet season is relatively high, all exceeding 100%. The hydrochemical types of surface water are mainly mixed and Cl-Na type water.

(2)

The hydrochemical characteristics of the study area are primarily governed by rock weathering. According to hydrochemical analyses, both natural geochemical processes and anthropogenic inputs play a decisive role in shaping groundwater composition. Natural geochemistry is mainly controlled by silicate rock weathering, evaporite dissolution, and cation exchange, whereas human influences are derived chiefly from mining and agricultural activities.

(3)

Pearson correlation analysis in mathematical statistics shows that TDS is significantly positively correlated with Na⁺ and SO₄²⁻ during both dry and wet seasons, indicating that they are the main contributing ions to TDS. NO₃⁻ has a weak correlation with most ions, indicating that its source is relatively independent. Principal component analysis explained 88.6% and 88.5% of the data during the dry season and wet season, respectively. The main influencing factors of F1, which contributed the most to the data during the dry season, were evaporite rock dissolution and industrial wastewater discharge, while the influencing factors of F1 during the wet season were industrial wastewater.

(4)

An assessment of drinking water quality showed that the “good” grade accounted for the largest share of samples in both seasons, representing 68% in the dry season and 48% in the wet season. Overall, 88% of the samples from the dry season and 68% from the wet season were considered suitable for direct drinking. The majority of water samples are suitable for use as drinking water. During the dry season, higher EWQI values were mainly distributed in the Wanglou mining area, while in the wet season, elevated values were found in both the Wanglou mining area and the Xinglongzhuang mining area. According to the single-indicator analysis, 92% of surface water samples in both the dry and wet seasons are suitable for irrigation and do not pose any risk of salinization or alkalinization. Nevertheless, some samples exhibit high salinity and high sodium content, which should be given attention in irrigation management.