Hydrochemical Characteristics and Indicative Significance of Terminal Tributaries in Karst Water Systems: A Case Study of the Zhongdu River Basin in Southwest China

Abstract

The terminal tributaries of karst rivers are often under-researched, with low investigation coverage and incomplete surveys. These areas face significant human activity disturbances, fragile soil and water environments, and insufficient research on water quality conditions. Residents in their basins are confronted with urgent issues of water scarcity and deteriorating water quality. This study focused on the Zhongdu River Basin, a terminal tributary in the Pearl River system in Southwest China. By measuring the conventional hydrochemical parameters and stable isotope ratios (e.g., δ¹⁸O and δ²H), this study employed methods such as hydrological and geochemical approaches, as well as classical statistical analyses, to reveal the hydrochemical characteristics, regulatory mechanisms, and water health status in the basin. Data show that the water in the Zhongdu River Basin is generally weakly alkaline, with a pH range between 6.46 and 8.28. The highest values for electrical conductivity (EC) and total dissolved solids (TDSs) are found upstream, reaching 497 μS/cm and 324.5 mg/L, respectively. The average dissolved oxygen (DO) value is 71.3 mg/L. The hydrochemical type is primarily HCO₃⁻-Ca²⁺, with Ca²⁺ and HCO₃⁻ as the dominant ions. The surface water in the middle and lower reaches of the basin is strongly influenced by evaporation, with atmospheric precipitation as the main recharge source. Rock weathering is the primary influencing factor in the basin, with most minerals in a dissolved state. Agricultural activities are the primary pollution source in the basin, with domestic pollution having a minimal effect on water quality. Water quality was assessed using the entropy-weighted water quality index (EWQI) based on 11 parameters, indicating overall good water quality, classified as Grade I. The findings indicate that human activities have a minimal impact on the water quality in the region, and the basin is expected to maintain its healthy condition for an extended period.

1. Introduction

From a global perspective, although karst areas are not lacking in natural water resources, the structural water scarcity issue due to unique geological conditions is a common phenomenon in karst regions [1]. The groundwater system in karst areas has high heterogeneity and anisotropy characteristics, which makes the diffusion path of pollutants difficult to predict once they enter underground rivers or aquifers, and the difficulty of remediation is extremely high [2]. Once karst water is polluted, it often has the characteristics of being highly concealed and difficult to treat [3], posing a direct threat to water supply security. Therefore, how to accurately assess, sustainably develop, efficiently utilize, and scientifically protect karst water is a pressing issue to be solved [4,5,6,7,8]. Currently, research on karst water primarily focuses on hydrogeological conditions and system structures [9,10], resource evaluation and protection [1,11], water cycle processes in critical zones [12,13,14], karst geological disaster early warning and prevention technologies [15,16,17], rocky desertification control and ecological restoration [18,19,20], and the impact of human activities [21,22,23]. In terms of water resource development and utilization, in recent years, through multi-source data integration and model optimization, as well as improvements in hydrogeological models [24,25,26,27], refined characterization of karst aquifers and accurate assessment of water resource quantity have been achieved; New geophysical methods, drone and remote sensing technologies, and artificial intelligence algorithms [28,29,30] have enhanced the understanding of aquifer hydrological characteristics and maximized water utilization efficiency. Comprehensive studies on cross-border development and the utilization of karst water resources, large-scale regional hydrological processes, groundwater recharge mechanisms, and ecological restoration [31,32,33] have greatly promoted sustainable development and coordinated utilization of karst water resources. However, these studies are generally based on large basins, large river systems, and cross-border regions, while studies on small basins in the source areas of karst river systems are relatively rare. By studying the hydrochemical characteristics and hydrochemical genesis of the terminal basin, scientific evidence can be provided for estimating water resources in the middle and lower reaches of the basin, assessing water health status, and guiding the development, utilization, and protection of karst water.

The area of karst regions in China is about 3.44 million km², accounting for one-sixth of the global karst area and one-third of China’s total land area, making China one of the countries with the largest karst distribution area in the world. The southern region of China is the area where karst development is the most prominent, characterized by widespread distribution, diverse controlling factors, significant lithological variations, and ecological sensitivity [34]. For instance, in Guangxi, Guizhou, and Yunnan, the karst groundwater system is well-developed, the geological structure is complex, and the strata components are simple and easily eroded, forming a complicated underground river system, dissolution caves, and surface karst landscapes. The Luzhai area in Guangxi, due to its location at the intersection of the Pacific Tectonic Belt and the ancient Mediterranean–Himalayan Tectonic Belt, exhibits multi-phase tectonic movement characteristics and unique geological structures, with a widespread distribution of carbonate rock strata. After undergoing a long and intense karstification process, it has formed rich karst landforms. The typicality of the karst landforms in Luzhai, Guangxi, is reflected in karst geological relics, cave systems, and natural bridge clusters, and many of the karst landscapes formed have been included in the Dictionary of Karstology as examples. The Zhongdu River Basin, as a highly representative basin ecosystem in the Luzhai region, is the most downstream tributary of the Pearl River system. As an important carrier of water resources, its hydrochemical characteristics are crucial for water resource quality and sustainable utilization. However, due to the thin soil layer, low vegetation coverage, and interference of human activities such as large-scale eucalyptus planting, riverbank reinforcement, mining, and deforestation for reclamation in recent years, the soil and water environment of the Zhongdu River Basin have been severely impacted, exacerbating regional rocky desertification. Additionally, accelerated industrialization, agricultural pollution, and urbanization in Luzhai have further compromised water security for local residents.

The Zhongdu River Basin, due to its small basin regional characteristics, has deficiencies in research coverage, survey depth, and long-term monitoring. The basin ecosystem is also very fragile, and the interference from agricultural pollution and residential wastewater discharge has caused long-term impacts on its ecosystem. The lack of hydrogeochemical research has led to unclear hydrochemical indicators, concentrations, and pollution distribution in the area, making it impossible to scientifically assess the health of its ecological environment, which is detrimental to the long-term healthy development of the basin. There is an urgent need to supplement research on hydrochemical characteristics analysis, water quality assessment, and other related studies. This study selected the Zhongdu River Basin as the study area, with the research objectives as follows: (1) To clarify the basic hydrogeochemical characteristics of the region through statistical and graphical analyses. (2) To reveal the regional hydrochemical types and controlling factors using traditional hydrological methods and isotope data. (3) To establish a regional water quality evaluation framework based on multiple factor conditions and clarify the degree of human inputs and water health status.

2. Study Area

The Zhongdu River Basin is located in Luzhai County, Liuzhou City, Guangxi Autonomous Region (24°14′–24°50′ N, 109°28′–110°12′ E), as shown in Figure 1, covering a total area of 2974.8 km². There are 64 rivers of various sizes within the county, of which 21 rivers have catchment areas greater than 50 km² (excluding the Liu River). Major rivers include the Zhongdu River, Luoqing River, Shiliu River, and Lagou River. Luzhai County presents a tilted hilly basin topography that slopes from northeast to southwest. The northeast and eastern parts are mountainous, the southeastern and southern parts consist of hilly terrain, the northwestern part features large karst residual hills and a small amount of mountainous terrain, the western part is mostly high hills, and the central part is flat, with the Luoqing River flowing through, forming a river valley plain.

Luzhai County is located in a low-latitude region, transitioning from the south subtropical to the mid-subtropical zone, and is significantly influenced by monsoon circulation. The climate is mild, with an annual average temperature of around 20.4 °C, a maximum temperature of 38 °C in the summer, and a minimum temperature of 0 °C in the winter. Precipitation is abundant, with an average annual rainfall of approximately 1483.8 mm, mostly concentrated from April to August.

The county is primarily composed of Devonian and Carboniferous strata, which are well-developed, with clear and discernible contact boundaries, and rich and diverse sedimentary types. The primary aquifer is the carbonate rock aquifer, dominated by conduit and fissure-controlled karst groundwater, with hydraulic connections between different aquifers and groundwater, forming unified aquifer bodies and hydrogeological units in each relatively independent karst region.

3. Materials and Methods

3.1. Sampling

In September 2024, a total of 43 water samples were collected during a groundwater and surface water survey conducted in the Zhongdu River Basin. The sampling sites are shown in Figure 1. Based on topographical and geographical conditions, water sample types, and other factors, the 43 water samples were divided into three categories: Upstream Karst Zone (UKZ, samples ZW01-ZW16), Midstream Active Zone (MAZ, samples ZW17-ZW33), and Downstream Natural Zone (DNZ, samples ZW34-ZW43). During sampling, clean 500 mL polyvinyl chloride bottles (Shanghai ANPEL Scientific Instrument Co., Ltd., Shanghai, China) were rinsed with water samples three times before collecting the samples, which were then stored at 4 °C. Within 12 h, the CO₃²⁻ and HCO₃⁻ concentrations in the water samples were titrated using hydrochloric acid. Groundwater samples were collected directly from natural discharge points (karst springs and dissolution cavities) to avoid anthropogenic interference from pumping wells, as the study area lacks conventional monitoring wells due to the fractured karst hydrogeology. Prior to sampling, each spring was purged for 10–15 min to ensure representative aquifer conditions. Samples were filtered through 0.45 μm cellulose membranes (Shanghai ANPEL Scientific Instrument Co., Ltd.), stored in pre-cleaned HDPE bottles (Shanghai ANPEL Scientific Instrument Co., Ltd.), and refrigerated at 4 °C until laboratory analysis. The water samples were filtered through 0.45 μm polypropylene membrane (Shanghai ANPEL Scientific Instrument Co., Ltd.) and divided into three portions. One portion was acidified for cation determination, and the other two portions were directly sealed for the subsequent determination of anions, hydrogen and oxygen isotopes, and major elements.

3.2. Testing

Field measurements of pH, oxidation–reduction potential (ORP), electrical conductivity (EC), and dissolved oxygen (DO) in water samples were conducted using a portable multifunctional water quality analyzer (YSI, American YSI Group, Yellow Springs, OH, USA). Hydrochemical analyses were performed at the School of Environmental Studies, China University of Geosciences (Wuhan), where an inductively coupled plasma–optical emission spectrometer (5100 ICP-OES, Agilent Technologies (China) Co., Ltd., Beijing, China) was used to measure cations such as K⁺, Ca²⁺, Na⁺, and Mg²⁺ in the water samples. An ion chromatograph (ICS-1100, Thermo Fisher Scientific Company, Waltham, MA, USA) was used to analyze major anions such as Cl⁻, SO₄²⁻, and NO₃⁻, and an isotope analyzer (GLA431, Asea Brown Boveri of Sweden, Västerås, Sweden) was used to measure isotopic compositions (δD, δ¹⁷O, δ¹⁸O) in the water samples, with precisions of ±0.2‰, ±0.03‰, and ±0.03‰, respectively.

3.3. Analysis Methods

Based on the use of mathematical statistics to describe the basic properties of hydrochemistry, further cluster analysis and correlation analyses were conducted [35]. Methods such as the trilinear diagram, Gibbs plots, ion ratios, saturation index (SI), and chloro-alkaline index (CAI) [36] were employed to comprehensively identify the hydrochemical indicators, and the entropy weight method was used to assess water quality.

3.3.1. Saturation Index

The mineral saturation index (SI) serves to identify the processes of mineral precipitation and dissolution, and is of great significance for understanding hydrochemical characteristics and assessing water quality [37]. SI > 0, SI = 0, and SI < 0 represent the supersaturated, equilibrium, and undersaturated states of minerals, respectively. To fully consider the effects of temperature, pH, and other indicators on the SI, the PHREEQC software (version 3.7.3-15968) developed by the United States Geological Survey (USGS) was employed to comprehensively calculate the SI values for the Zhongdu River Basin samples.

3.3.2. Chloro-Alkaline Index

The chloro-alkali index (CAI) method (CAI-I and CAI-II) is applicable for analyzing cation exchange adsorption between groundwater and sediments [38]. When the CAI value is less than 0, it indicates that cation exchange adsorption has occurred, and the greater the negative value, the more significant the effect. In addition, the relationship between [γ(Ca²⁺) + γ(Mg²⁺) − γ(HCO₃⁻) − γ(SO₄²⁻)] and [γ(Na⁺) − γ(Cl⁻)] can further analyze the cation exchange process in groundwater. Ion ratios reflect residual Ca²⁺ and Mg²⁺ after the dissolution or precipitation of calcite, dolomite, and gypsum, as well as remaining Na⁺ following the dissolution or precipitation of halite.

3.3.3. Entropy-Weighted Water Quality Index

The Entropy-Weighted Water Quality Index (EWQI) utilizes entropy values to determine the weights of each evaluation indicators, and then converts the measured values of water quality indicators into a single value to assess the water quality status [39,40,41]. The entropy weight method effectively reduces subjective influences in weight calculation, enhancing its objectivity. Therefore, it is more reasonable than traditional water quality index methods. In fuzzy comprehensive evaluation, factor weights are determined based on the ratio of the measured values to the environmental quality standard limits. The steps for entropy weight assignment are as follows:

(1) After standardizing the initial concentration matrix composed of n evaluation factors and s evaluation objects, the standardized matrix can be obtained:

$$R={(r_{ip})}_{n \times s}$$

(1)

where r_ip represents the standardized value of the i-th evaluation factor for the p-th evaluation object.

For parameters that follow a smaller-is-better criterion, the calculation formula is as follows:

$$r_{ip}={\displaystyle \frac{ma{x}_p\left\{x_{ip}\right\}-x_{ip}}{ma{x}_p\left\{x_{ip}\right\}-mi{n}_p\left\{x_{ip}\right\}}}$$

(2)

For large and optimal parameters, the calculation formula is as follows:

$$r_{ip}={\displaystyle \frac{\left\{x_{ip}\right\}-mi{n}_p{x}_{ip}}{ma{x}_p{x}_{ip}-mi{n}_p\left\{x_{ip}\right\}}}$$

(3)

where r_ip ∈ [0,1]; x_ip represents the observed value of the i-th evaluation factor for the p-th evaluation object; and max_p{x_ip} and min_p{x_ip} denote the maximum and minimum values of x_ip across all evaluation objects, respectively.

(2) The entropy H_i of the i-th evaluation factor is calculated using the following formula:

$$H_i=-k{\displaystyle \sum }_{p=1}^s{f}_{ip}ln{f}_{ip} \left(i=1,2,\cdots ,n;p=1,2\cdots ,\mathrm{s}\right)$$

(4)

Among them,

$$f_{ip}={\displaystyle \frac{r_{ip}}{{{\displaystyle \sum }}_{p=1}^s{r}_{ip}}},k={\displaystyle \frac{1}{lns}}$$

(5)

When f_ip = 0, set f_iplnf_ip = 0.

(3) The entropy weight w_i of the i-th evaluation factor is calculated using the following formula:

$$w_i={\displaystyle \frac{1-H_i}{n-{{\displaystyle \sum }}_{i=1}^n{H}_i}}$$

(6)

where w_i ∈ [0,1], and ${\displaystyle \sum }_{i=1}^n{w}_i=1$.

(4) Establish quantitative scoring criteria for each hydrochemical parameter:

$$P_i={\displaystyle \frac{Q_i}{S_i}}\times 100$$

(7)

$$P_{pH}\left\{\begin{array}{c}{\displaystyle \frac{Q_{pH}-7}{8.5-7}}\times 100 Q_{pH}>7\\ {\displaystyle \frac{7-Q_{pH}}{8.5-7}}\times 100 Q_{pH}<7\end{array}\right.$$

(8)

where Q_i represents the measured concentration (mg/L) of the i-th water quality parameter, Q_pH denotes the measured pH value, and Si is the permissible limit of the i-th water quality parameter as defined by the World Health Organization.

(5) Calculate the EWQI value

$$EWQI={\displaystyle \sum }_{i=1}^n(w_{\mathrm{i}}\times P_{\mathrm{i}})$$

(9)

According to the EWQI values, the quality of groundwater is categorized. Among them, the water quality of Grades I–II is good and suitable for drinking, while the water quality of Grades Ⅲ–Ⅴ is poor and not suitable for drinking.

EWQI	Rating Level	Water Quality Status
<28	I	Excellent
28–57	II	Good
57–100	III	Moderate
100–163	IV	Poor
>163	V	Very Poor

4. Results and Discussion

4.1. Hydrochemical Characteristics

Table 1. Hydrochemical statistical characteristics of Zhongdu River basin.

Cat.	Par.	pH	TH	EC	DO	ORP	TDSs	K+	Na+	Ca2+	Mg2+	Cl−	HCO32−	SO42−	NO32−	PO42−
U K Z	Max	8.18	136.4	497.00	7.40	15.10	324.50	1.40	2.32	98.96	14.08	13.01	563.42	9.54	13.97	0.90
	Min	6.46	69.6	248.30	4.16	−39.50	162.30	0.10	0.49	32.53	1.16	0.95	292.90	4.89	1.78	0.40
	Mean	6.82	106.9	391.00	5.53	−25.30	259.25	1.06	1.04	70.49	11.40	2.91	499.35	9.40	9.75	0.55
	SD	0.61	13.9	80.36	0.97	18.56	45.22	0.39	0.52	18.01	4.12	3.79	91.14	1.61	3.82	0.15
	CV	0.09	0.13	0.22	0.18	−0.98	0.18	0.42	0.44	0.26	0.42	0.99	0.19	0.19	0.46	0.27
M N Z	Max	8.06	106.5	404.30	6.45	244.60	260.50	1.61	2.40	71.53	11.50	5.86	520.70	10.57	9.48	0.80
	Min	7.10	75.18	375.80	4.60	166.40	230.10	1.05	1.06	61.59	9.60	2.86	310.19	9.14	4.41	0.50
	Mean	7.57	93.78	392.00	5.62	188.95	251.15	1.12	1.21	68.39	11.13	3.39	469.85	9.43	8.07	0.57
	SD	0.23	9.76	7.36	0.52	22.99	9.73	0.15	0.44	3.02	0.63	0.83	76.44	0.33	1.45	0.09
	CV	0.03	0.10	0.02	0.09	0.12	0.04	0.13	0.32	0.04	0.06	0.22	0.17	0.04	0.19	0.15
D N Z	Max	8.28	98.89	381.50	6.67	232.40	231.20	2.53	4.13	63.25	9.75	6.76	435.28	10.99	6.82	0.66
	Min	7.60	37.99	188.80	4.80	162.60	109.20	1.25	1.40	24.87	3.37	5.30	154.58	9.56	5.52	0.36
	Mean	7.99	85.85	362.90	5.58	190.10	215.55	1.34	1.48	59.29	9.61	5.53	398.66	9.69	5.99	0.61
	SD	0.21	17.09	56.81	0.67	24.24	35.08	0.38	0.84	11.23	1.98	0.48	81.48	0.45	0.42	0.08
	CV	0.03	0.20	0.16	0.12	0.12	0.17	0.26	0.45	0.20	0.22	0.08	0.22	0.05	0.07	0.14
A L L	Max	8.28	136.4	497.00	7.40	244.60	324.50	2.53	4.13	98.96	14.08	13.01	563.42	11.07	13.97	0.90
	Min	6.46	37.99	188.80	4.16	−39.50	109.20	0.10	0.49	24.87	1.16	0.95	154.58	4.89	1.78	0.36
	Mean	7.58	96.48	381.50	5.58	181.45	238.80	1.16	1.38	63.76	9.75	4.58	423.07	9.52	6.82	0.59
	SD	0.43	15.53	48.05	0.75	86.35	34.50	0.46	0.65	11.21	2.20	2.71	79.30	1.03	2.64	0.10
	CV	0.06	0.16	0.13	0.13	0.57	0.15	0.35	0.41	0.18	0.23	0.51	0.19	0.11	0.37	0.16

Note: In the table, pH is dimensionless, EC is in μs/cm, and TH, DO, ORP, TDSs, K⁺, Na⁺, Ca²⁺, Mg²⁺, Cl⁻, HCO₃²⁻, SO₄²⁻, NO₃²⁻, and PO₄²⁻ are in mg/L.

presents the results of the water sample tests. The pH values in the Zhongdu River Basin range from 6.46 to 8.28, primarily demonstrating a weakly alkaline quality of water, with minimal variation and spatial heterogeneity, as evidenced by the coefficient of variation (CV) of 0.06. The trend of average water temperature is as follows: that in the upstream karst area (24 °C) is less than that in the midstream active zone (28.6 °C), which is further less than that in the downstream confluence zone (30.05 °C). In the upstream karst and midstream active zones, the average EC and TDSs stand at 391 μS/cm and 259.25 mg/L, and 392 μS/cm and 251.15 mg/L respectively. The two tributaries have average EC values of 323.2 μS/cm and 370.65 μS/cm, along with TDS values of 188.6 mg/L and 225.55 mg/L, accordingly. In the downstream natural zone, the average EC and TDSs are 362.9 μS/cm and 215.55 mg/L. Upstream zones show the highest EC values, which decrease in the midstream human activity zone and rise again downstream, indicating the variance in aquifer systems and the complexity of the karst system containing distinct groundwater systems. All water samples are classified as freshwater based on their TDSs. The average DO levels fluctuate between 4.16 and 7.4 mg/L, indicating oxygen-rich water that fosters a plethora of fish and active biota. The ORP values vary from −39.5 to 244.6 mV, with potential reducing capability exhibited by upstream groundwater and potential oxidative capability exhibited by downstream water. All regions show CVs below 0.22, signifying low variability and consistent parameter concentrations.

The CVs for major ions in water samples vary between 0.06 and 0.51, indicating low variability and minimal spatial differences. Notably, Na⁺ (0.41), K⁺ (0.35), and Cl⁻ (0.51) exhibit relatively higher CVs, with Cl⁻ in upstream karst water showing a CV of 0.99, suggesting significant variability in Cl⁻, K⁺, and Na⁺ concentrations and distinct regional differences. The average concentrations of NO₃⁻ and NO₂⁻ are below the natural threshold (3 mg/L) [42], except at sampling site ZW02 in Jiangtou Village, where NO₃⁻ reached 13.97 mg/L, indicating localized human influence with limited overall impact.

TDSs and TH serve as reliable measures of water quality. Elevated levels of TDSs may suggest a decline in water quality, while a decrease in TDS levels does not necessarily indicate an enhancement in water quality [43]. As shown in Figure 2, all the water samples were within the range of soft freshwater, devoid of any signs of hard or saline water. This indicates favorable water quality with no substantial pollution found.

4.2. Hydrochemical Type

The Durov diagram uses triangular sections and their base axes to depict the relative mass concentration percentages of cations (Na⁺, K⁺, Ca²⁺, and Mg²⁺) and anions (HCO₃⁻, SO₄²⁻, and Cl⁻), with arrows indicating increasing concentrations. The rectangular areas on the right and bottom are customized to include pH and TDSs according to the specific needs of this study. The Durov diagram effectively illustrates the primary chemical composition characteristics of groundwater, revealing hydrochemical types and their evolutionary processes [38].

The Durov diagram of water samples from the study area (Figure 3) illustrates the proportions of four key cations. The left triangle indicates that Ca²⁺ is the predominant ion in groundwater, with an average milliequivalent percentage exceeding 80%, and concentrations above 50% in most samples. Mg²⁺ was the second most prevalent, but remained below 50% across all samples, while Na⁺ and K⁺ showed the lowest levels, mostly below 30%. These results indicate that Ca²⁺-type water is the predominant chemical type in groundwater. The top triangle represents the distribution of three major anions, indicating that HCO₃⁻ is the main anion, accounting for over 90% in most samples, with an average milliequivalent percentage of 94.8%. SO₄²⁻ ranked second, with concentrations not exceeding 15%, while Cl⁻ showed the lowest levels, typically below 10% in most samples. Thus, HCO₃⁻-type water is identified as the primary groundwater hydrochemical type. Based on data from the right rectangular region, the TDS concentrations ranged from 100 to 400 mg/L, with an average value of 238.8 mg/L, classifying the water samples as freshwater. The pH values spanned from 6.46 to 8.28, with an average of 7.58, suggesting a generally mildly alkaline characteristic.

The characteristics of the hydrochemical composition were further examined using the Shukarev classification method and Piper trilinear diagram to identify the hydrochemical types [44]. The Piper trilinear diagram (Figure 4) shows the water samples from the study area and reveals a significant overlap of cations and anions in the Zhongdu River Basin, with only slight differences observed in some samples. The prevalent cation and anion are Ca²⁺ and HCO₃⁻, respectively, resulting in the primary classification of the hydrochemistry as HCO₃−Ca type, characterized by the dominance of calcium and bicarbonate. The concentrations of HCO₃⁻ dropped from upstream to downstream, while the levels of Cl⁻ and SO₄²⁻ rose. However, the hydrochemical type remained consistent due to the sustained high levels of Ca²⁺ and HCO₃⁻, indicating possible impacts from human or natural factors in the Zhongdu River Basin.

4.3. Regulatory Role of Hydrochemistry

4.3.1. Hierarchical Clustering and Correlation Analysis

Hierarchical Cluster Analysis (HCA) was applied to classify hydrochemical parameters based on their similarity in spatial–temporal variability. The analysis utilized Euclidean distance as the dissimilarity metric and Ward’s minimum variance method as the linkage algorithm to minimize intra-group heterogeneity. Prior to clustering, all variables were standardized (z-scores) to eliminate scale-dependent biases. As shown in Figure 5, Through cluster analysis [45], five groups were formed based on hydrochemical parameters: Group A (TDSs, Ca²⁺, EC, NO₃⁻, and PO₄³⁻), Group B (HCO₃⁻), Group C (Mg²⁺), Group D (K⁺, Na⁺, Cl⁻, and SO₄²⁻), and Group E (DO, ORP, and pH). The varying relationships among these parameters are indicative of the water body’s physical attributes. In Group A, TDSs and EC are influenced by Ca²⁺, NO₃⁻, and PO₄³⁻, indicating that these ions contribute significantly to conductivity and are primarily derived from dissolved sources. HCO₃⁻ and Mg²⁺ are in distinct groups, suggesting that HCO₃⁻ is the leading anion and a key component, while Mg²⁺ is relatively scarce and has a weak relation to other ions. The correlation between K⁺, Na⁺, Cl⁻, and SO₄²⁻ underscores their importance as primary indicators of regional industrial and agricultural pollution.

Through the application of multivariate statistical analysis, heatmaps of ion correlation [46] were produced for upstream, midstream, and downstream regions (Figure 6). In the upstream region, TDSs are strongly linked to the concentrations of Ca²⁺, HCO₃⁻, and NO₃⁻, pointing to prevalent dissolution processes in the underlying karst landscape where water sets ions in motion from the strata. A robust correlation exists between Ca²⁺ and HCO₃⁻, with HCO₃⁻ correlating solely with Ca²⁺, providing additional evidence of the carbonate strata characteristics. EC shows a noteworthy correlation with the majority of ions, and the upstream TDS measurements are elevated compared with those from the midstream and downstream regions, reflecting the ion-rich composition and vigorous dissolution in the subsurface karst system. In the midstream region, following the groundwater’s emergence at the surface, TDS has a connection with Ca²⁺ and Mg²⁺, while the correlation of HCO₃⁻ with other parameters weakens, suggesting shifts in ion makeup and concentration due to differing water conditions. By the time the water reaches the downstream region, K⁺, Na⁺, Ca²⁺, Mg²⁺, Cl⁻, and PO₄³⁻ emerge as the primary ions, highlighting the influence of human activity.

Principal component analysis (PCA) was conducted on the parameters of water samples collected from the study area. Three principal components (PCs) were identified for the upper, middle, and lower reaches, as shown in

Table 2. Principal component analysis correlation matrix.

Parameter	UKZ			Parameter	MAZ			Parameter	DNZ
	PC1	PC2	PC3		PC1	PC2	PC3		PC1	PC2	PC3
NO32−	0.954	−0.018	−0.201	EC	0.949	−0.256	−0.09	Mg2+	0.969	−0.099	0.186
EC	0.951	0.202	−0.13	TDS	0.941	−0.271	−0.142	Na+	−0.967	0.069	−0.184
Ca2+	0.938	−0.291	−0.076	Ca2+	0.92	−0.288	−0.174	K+	−0.956	0.156	−0.187
PO42−	0.936	0.012	0.161	SO42−	0.823	0.526	0.109	Ca2+	0.953	0.089	0.281
Cl−	0.839	−0.065	0.374	NO32−	0.783	0.466	−0.235	PO42−	0.948	0.046	0.255
Na+	0.82	0.227	0.343	Mg2+	0.708	−0.657	−0.178	Cl−	−0.689	0.162	0.689
TDS	0.787	−0.584	−0.055	PO42−	0.651	−0.259	0.577	HCO3−	0.135	0.919	0.081
K+	0.778	0.603	−0.144	HCO3−	−0.641	0.177	0.122	NO3−	0.34	0.787	−0.342
HCO3−	0.664	−0.352	−0.238	Cl−	0.026	0.977	0.074	SO42−	−0.645	0.148	0.724
Mg2+	−0.111	0.96	−0.248	K+	0.465	0.877	0.055	Eigenvalue	5.619	1.56	1.369
SO42−	0.632	0.712	−0.282	Na+	0.392	0.776	0.138	Var%	62.436	17.334	15.21
pH	−0.464	0.55	0.449	DO	0.267	−0.359	0.796	CV	62.436	79.77	94.979
DO	0.512	0.177	0.61	pH	−0.231	−0.31	−0.032
Eigenvalue	7.504	2.772	1.141	Eigenvalue	5.755	3.798	1.167
Var%	57.723	21.324	8.78	Var%	44.266	29.214	8.976
CV	57.723	79.047	87.827	CV	44.266	73.48	82.456

. In all three analyses, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy exceeds 0.5, and the significance tests yield values below 0.0001, indicating the reliability of the PCA results [47].

Through three principal component analyses, the cumulative variance contribution rates were 87.827%, 90.756%, and 94.979%, respectively. These rates effectively encapsulate the majority of parameters and clarify their interrelationships. In upstream water samples, PC1 showed a positive correlation with EC, Ca²⁺, Na⁺, NO₃⁻, PO₄³⁻, Cl⁻, and HCO₃⁻, with Ca²⁺ and HCO₃⁻ being predominant, indicating karst processes. PC2 had a positive correlation with Mg²⁺ and SO₄²⁻, but was negatively correlated with TDS and HCO₃⁻, reflecting minor dissolution of sulfate-bearing rocks and the presence of thin mineral layers, with carbonate rocks being the primary deposits. PC3 showed a positive correlation with DO, pH, Na⁺, and Cl⁻, suggesting the absence of aquatic biological activity and the predominance of mineral salt dissolution.

In midstream water samples, PC1 showed a positive correlation with EC, TDS, Ca²⁺, SO₄²⁻, Mg²⁺, and PO₄³⁻ due to the presence of iron ore layers causing dissolution, ion exchange, and mixing. Despite these processes, ion concentrations largely remain stable because of the low solubility of sulfate rocks and high upstream levels of Ca²⁺ and HCO₃⁻. PC2 exhibited a positive correlation with Na⁺, K⁺, and Cl⁻, pointing to human influences like agricultural irrigation and domestic wastewater. PC3 showed a positive correlation with DO and PO₄³⁻, which was consistent with the abundance of fish [48], algae, and aquatic plants in the midstream region.

The positive correlation between PC1 and Mg²⁺, Ca²⁺, and PO₄³⁻ in downstream water samples suggests that a decrease in human interference allows for the dissolution process and blending with adjacent rocks, leading to Ca²⁺ and Mg²⁺ becoming prominent in the water’s chemical makeup. PC2 showed a positive correlation with HCO₃⁻ and NO₃⁻, which reflected the main anionic components. PC3, on the other hand, exhibited a positive correlation with Cl⁻ and SO₄²⁻, suggesting the effect of local salt rock formations inherent in the natural geological background.

4.3.2. Isotopic Characteristics

In the study area, water samples showcased a range of δ²H, δ¹⁷O, and δ¹⁸O values spanning from −54.54‰ to −37‰, −7.36‰ to −3.06‰, and −8.89‰ to −6.12‰. Their average values were −44.56‰, −3.94‰, and −7.25‰, respectively. All water samples were collected at the same hydrostatic depth across different sampling locations, which varied in elevation, allowing for the analysis of isotopic content variations against different geological backgrounds. The isotopic composition of hydrogen and oxygen notably differed, with surface water showing an enrichment of isotopes and a noticeable depletion of isotopes as depth increased.

The δ²H and δ¹⁸O measurements from water samples within the study area were depicted on a δ²H–δ¹⁸O relationship diagram (Figure 7). The Chinese Meteoric Water Line (CMWL), which is based on nationwide precipitation samples collected in 1980, is expressed as δD = 7.9δ¹⁸O + 8.2. Considering geographical location and climatic factors, a local meteoric water line serves as a better representation of the regional conditions [49]. Due to the lack of precipitation samples from the study area, the Liuzhou Meteoric Water Line (δD = 7.19δ¹⁸O + 1.41, SMOW) was utilized as a reference point.

By examining stable isotope data from 43 water samples, evaporation line equations (LEL) [49] for the upstream and mid-lower reaches of the study area were established. Overall, the surface water in the mid-lower reaches exhibited greater isotopic enrichment and less variability compared with the upstream groundwater, which was attributed to the region’s unique geographic and tectonic characteristics. Several effects were noted, including isotopic continental, altitude, precipitation, and seasonal effects, with poor fits of regression lines. The LEL for the mid–lower-reach surface water was δD = 6.04δ18O − 1 (R² = 0.82, n = 28), which markedly deviated from both the CMWL and the Liuzhou meteoric water line. Most data points, except for a few that showed δD enrichment, were located to the left of the line, indicating a strong influence of evaporation. The LEL for the upstream groundwater was δD = 4.01δ18O − 16.76 (R² = 0.5, n = 12), which further deviated from the meteoric water line and clustered toward the lower-left side. Some sampling points from ZWXX showed significant depletion, while others indicated enrichment, suggesting close hydraulic connections with surface water. Additionally, all sampling points aligned closely with the Liuzhou meteoric water line, indicating that atmospheric precipitation is the primary recharge source for the Zhongdu River in the study area.

The average deuterium excess (d = δD − 8δ¹⁸O) [50] values in the upper, middle, and lower reaches of the study area were 15.05‰, 12.28‰, and 12.73‰(As shown in

Table 3. Isotopic characteristics of the study area.

Type	Index	δD/‰	δ18O/‰	d/‰	Fitted Equation
UKZ	Max	−37.00	−6.43	18.54	δD = 4.01δ18O − 16.76 (R2 = 0.5, n = 12)
	Min	−54.54	−8.89	11.36
	Mean	−48.35	−7.94	14.92
MAZ	Max	−37.59	−6.12	15.19	δD = 6.04δ18O − 1 (R2 = 0.82, n = 28)
	Min	−46.51	−7.64	9.30
	Mean	−39.91	−6.44	12.39
DNZ	Max	−40.43	−6.85	15.32
	Min	−45.87	−7.30	10.35
	Mean	−44.69	−7.06	13.16

), respectively. The data indicate that surface water in the middle and lower reaches underwent more intense evaporation, resulting in lower d values compared with those of upstream groundwater. The downstream decline in d-excess (15.05‰ → 12.28‰) highlights evaporation-dominated modification of surface water, contrasting with upstream groundwater preserving near-pristine isotopic signatures. This evaporation–concentration effect further promotes water–rock interactions (e.g., gypsum dissolution), as evidenced by mid–downstream SO₄²⁻ enrichment. This also suggests that surface water exhibits stronger interaction with rocks than deep groundwater.

4.4. Hydrochemical Control Mechanism

4.4.1. Hydrochemical Genesis

The Gibbs plot, which utilizes the relationship of TDS-Na⁺/(Na⁺ + Ca²⁺) and TDS-Cl-/(Cl⁻ + HCO₃⁻), pinpoints three key mechanisms that govern surface hydrochemistry: atmospheric precipitation, rock weathering, and evaporation–crystallization [51]. As shown in Figure 8, The majority of water samples from the study area are mainly located within the rock weathering control region (center–left of Figure 5), indicating that rock weathering plays a dominant role in determining the hydrochemical composition of both surface water and groundwater in this region. In contrast, the effects of atmospheric precipitation and evaporation-crystallization were relatively insignificant.

In the diagram of TDS-Cl⁻/(Cl⁺ + HCO₃⁻), there is a strong clustering of water samples, with very little variation, indicating a minimal presence of human-induced pollution in this region. Similarly, in the TDS-Na⁺/(Na⁺ + Ca²⁺) diagram, the samples are concentrated, with the exception of sample ZW41 (found at the confluence of the Luoqing and Luo Rivers), which exhibited a higher Na⁺/(Na⁺ + Ca²⁺) ratio, likely due to the blending of water from the upstream Luo River.

The SI serves as an important parameter for analyzing the factors influencing alterations in hydrochemical composition [52]. Considering the potential inaccuracies in measurements, minerals are perceived to be in a state of dissolution equilibrium when the SI values vary between −0.5 and 0.5. As shown in

Table 4. Statistical analysis of mineral saturation indices.

	CaSO4	BaSO4	CaSO4:2H2O	NaCl	Ca5(PO4)3OH	O2	KCl
max	−2.76	−0.55	−2.45	−9.06	10.03	−9.31	−8.74
min	−3.2	−2.2	−2.9	−10.87	4.41	−22.35	−11.11
mean	−2.87	−1.35	−2.59	−9.71	0.88	−20.44	−9.37

, In the study area, the majority of the water samples displayed SI values below −0.5 for minerals excluding apatite, indicating an undersaturated state that allowed for further dissolution. In contrast, apatite showed SI values that greatly surpassed 0.5, suggesting a tendency for crystallization and precipitation. Specifically, over 57% of carbonate and sulfate mineral samples had SI values below −0.5, highlighting the prevalent dissolution of these evaporite minerals in the study area, with a high predisposition to further dissolution in water.

4.4.2. Ion Exchange Mechanism

The chemical origins and evolutionary processes of surface water and groundwater in the study area were thoroughly examined using ion ratio diagrams [53]. Figure 9a shows the relationship between Na+ and Cl⁻. Most of the water samples were located below the 1:1 line, indicating that γCl⁻ in the water samples was greater than γNa⁺. This implies not only the dissolution of halite, but also the impact of human-induced pollution from both industrial and domestic sources, particularly evident in the midstream human activity zone. In contrast, the upstream karst groundwater remained relatively unaffected. The γCa²⁺ and γMg²⁺ relationship is shown in Figure 9b, where the Ca²⁺ concentrations were remarkably higher than those of Mg²⁺, clustering near the 5:1 line. This correlated with higher Cl⁻ than Na⁺ concentrations due to the high solubility of CaCl₂ and the dissolution of gypsum and calcium-rich minerals. The scarcity of Mg²⁺ was linked to magnesium-deficient minerals, like calcite and gypsum, particularly in upstream karst regions. Figure 9c shows the relationship between γ(Ca²⁺ + Mg²⁺) and γHCO₃⁻, with most of the samples below the 1:1 line, reinforcing the notion that carbonates and calcite are the primary sources of Ca²⁺ and Mg²⁺, primarily driven by gypsum dissolution. Figure 9d mirrors a similar trend, indicating the prevalence of carbonate dissolution with a minor influence from silicate minerals. In Figure 9e, the γHCO₃⁻ concentrations far exceed γ(SO₄²⁻ + Cl⁻), with all samples falling below the 1:1 line. This suggests limited dissolution of evaporites and highlights carbonate dissolution as the principal process. Figure 9f depicts the relationship between γ(Ca²⁺ + Mg²⁺ − HCO₃⁻ − SO₄²⁻) and γ(Na⁺ − Cl⁻), pointing to a cation exchange between Ca²⁺, Mg²⁺, and Na⁺ in aquifers. However, the absence of a strong linear correlation suggests that cation exchange in the study area is minimal.

The CAI provides a more effective evaluation of the extent of cation exchange between water samples and adjacent rock formations [54]. Negative CAI values (CAI-I, CAI-II) signify the exchange of Ca²⁺ and Mg²⁺ in groundwater with Na⁺ and K⁺ from the host rock or soil, whereas positive CAI values indicate the replacement of Ca²⁺ and Mg²⁺ in the rock or soil by Na⁺ and K⁺ in groundwater. Higher absolute CAI values imply stronger ion exchange activities.

As shown in Figure 10, In the study area, the values of CAI-II surpassed those of CAI-I significantly, a phenomenon attributed to the increased concentrations of HCO₃⁻, with the difference becoming more pronounced from the upstream toward the downstream. It was confirmed by the positive values of both CAIs that there was a cation exchange taking place involving Ca²⁺ and Mg²⁺ in the groundwater and Na⁺ and K⁺ in the host medium. The two tributaries showed trends consistent with the main stream: an increased exchange intensity of Na⁺ and Cl⁻ downstream, while the intensity of the exchange of other ions, such as HCO₃⁻ and SO₄²⁻, decreased. This pattern reflects the dynamic evolution in the hydrochemical composition along various sections of the river.

4.4.3. Interaction Between Surface Water and Groundwater

The relationship between groundwater and surface water was further analyzed using TDS and δ18O values from the study area [55]. As shown in Figure 11, samples from groundwater and river water exhibited a dispersed distribution pattern, with similar TDS values, but significant differences in δ18O. When combined with data from Figure 4, the δ18O and δD values of groundwater and surface water appeared to be relatively close, indicating a clear hydraulic connection between the two.

Specifically, sampling was conducted in September, a time when the flow of the river was swift and water exchange was frequent. Despite the fact that the δ18O and δD values in the river water saw some fluctuation during this time, these changes were quite minimal, which implies a robust hydraulic link between the river water and the groundwater. This further confirms the intensity of interaction between groundwater and surface water.

4.5. Human Inputs

The analysis of Gibbs plots and ion ratio relationships indicated that the water in the study area was influenced not only by water–rock interactions, but also notably by human activities. The NO₃⁻/Cl⁻ to Cl⁻ ratio is effective in identifying various nitrate sources in the water [56]. Low NO₃⁻/Cl⁻ ratios and Cl⁻ concentrations suggest minimal impact from domestic pollution sources, like wastewater discharge and manure. The denitrification processes observed in the water samples further reduced nitrate levels, mitigating the risk of eutrophication and pollution. Additionally, As shown in Figure 12, the K⁺/NO₃⁻ ratio analysis highlighted the significant impact of agricultural activities and domestic sewage discharge among human factors. All water samples fall below the 1:1 equivalence line, indicating that agricultural activities are the main factor influencing water quality in the study area.

4.6. Water Quality Assessment

Assessing water quality not only reflects the status of the aquatic environment in a specific area, but also serves as a foundation for the sustainable development and utilization of water resources. The EWQI is extensively utilized to evaluate overall water quality impacts [57]. Considering the characteristics of the study area, 11 parameters, including Ca²⁺, Mg²⁺, K⁺, Na⁺, HCO₃⁻, Cl⁻, SO₄²⁻, NO₃⁻, TDS, TH, and pH, were selected to calculate the EWQI values and assess the regional water health status.

When calculating the weights, TDS, NO3⁻, and TH were identified as negative factors based on the characteristics of the test data and the regional context, while the remaining parameters were seen as positive factors. Specifically, Cl⁻ had the highest weight (wi = 0.2149), followed by Na⁺ (wi = 0.1834). SO₄²⁻ had the lowest weight (wi = 0.0433), with Mg²⁺ (wi = 0.0558) following closely behind. This indicates that Cl⁻ and Na⁺ are crucial in assessing overall water quality, whereas SO₄²⁻ and Mg²⁺ have less influence. According to the calculated results, the EWQI values varied from 11.7147 to 27.9098, The assessment results are shown in

Table 5. EWQI calculation values and weights (w_i) of the study area.

	Cl−	Na+	TH	NO3−	K+	TDS	Ca2+	HCO3−	Mg2+	SO42−
wi	0.2149	0.1834	0.0996	0.0994	0.0873	0.0863	0.0666	0.0634	0.0558	0.0433
EWQI	UKZ	max	27.9098	MAZ	max	20.7643	DNZ	max	19.4695
		min	12.6140		min	16.8011		min	11.7148
		mean	21.5481		mean	18.7189		mean	17.2320

. In general, the water quality was good, classified as Grade I, meeting the standards for drinking water.

5. Conclusions

The Zhongdu River basin’s water displays weak alkalinity, indicative of distinct rock weathering traits. There was a noticeable spatial shift in hydrochemistry across the river’s upper, middle, and lower reaches, with the main human-caused disruption being agricultural activities. An all-inclusive assessment of 11 key water quality parameters indicated that the overall water quality could be categorized as good, deeming it fit for use as a drinking water source. The following key findings are produced from the study:

• Hydrochemical Characteristics: Predominantly, the water found in the Zhongdu River basin displays a weakly alkaline nature, with the pH scale varying between 6.46 and 8.28. The highest values of EC and TDS were observed in the upper reaches, followed by the lower ones, while the lowest values were found in the middle reaches. Despite these variations, all regions fell under the category of freshwater. High DO levels were present, which promoted active biological processes, while the ORP indicated a potential reducing capability in the upstream groundwater and an oxidation capability in the downstream surface water. The TDS-TH diagrams validated the soft, fresh nature of the water, showing no signs of salinity or hardness, thus suggesting a minimal level of pollution.
• Hydrochemical Types and Controlling Factors: The hydrochemical type that prevailed was HCO₃⁻–Ca²⁺, consisting mainly of ions Ca²⁺ and HCO₃⁻. The principal component analysis highlighted the significant part played by HCO₃⁻ in defining hydrochemical characteristics of the basin. The primary water source was atmospheric precipitation, while surface water in the middle and lower reaches was considerably influenced by evaporation and stronger rock–water interactions relative to deep groundwater. The primary factor influencing hydrochemistry was rock weathering, as opposed to atmospheric precipitation or evaporative crystallization. The SI analysis indicated that the majority of minerals, particularly carbonates and sulfates, were in a dissolved state. Ion ratio analysis and the CAI reveal varying degrees of cation exchange, influenced by both geological conditions and human activities.
• Water Quality and Human Inputs: A TDS-δ¹⁸O examination revealed a progressive δ¹⁸O rise from upstream to downstream, which implied a significant hydrological interaction between underground and surface waters. The effects of human activities, particularly farming, were evident, even though pollution from residential areas was relatively low. Denitrification processes in water serve to reduce the risks of eutrophication. The EWQI, determined by 11 parameters, rates the overall water quality as Grade I, meeting drinking water standards. This indicates that the current state of water is predominantly good and fit for drinking and various other purposes.
• Spatial Structure Characteristics of Anthropogenic Factors: Upstream samples (UKZ) showed minimal NO₃⁻ (1.78−13.97 mg/L) and stable Cl⁻/Na⁺ ratios (1.8 ± 0.4), indicating limited human impact. Midstream (MAZ) exhibited elevated NO₃⁻ (4.41–9.48 mg/L; ZW02 = 13.97 mg/L) and Cl⁻/Na⁺ variance (0.99 CV), correlating with fertilizer application and village wastewater inputs. Downstream (DNZ) showed partial recovery (NO₃⁻ = 5.52–6.82 mg/L), likely due to dilution and denitrification in hyporheic zones.

This study has three key limitations: (1) single-season sampling precludes analysis of monsoon-driven hydrochemical dynamics; (2) discharge data were unavailable, limiting flux-based weathering rate calculations; and (3) trace metal/organic pollutants were not monitored. Future work should incorporate multi-season monitoring and emerging contaminant screening, particularly given expanding agricultural/urban pressures in the basin.