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Article

Study on the Surface Water Chemical Composition and Water Quality Pollution Characteristics of the Shiyang River Basin, China

by
Haifeng Wang
1,*,
Shaoqing Wu
1,
Jihai Xu
1,
Lixia Zhang
1,
Kuijing Li
2,
Jisheng Li
3,
Heping Shu
2 and
Jihua Chu
3
1
Water Resources Utilization Center of Shiyang River Basin, Department of Water Resources of Gansu Province, Wuwei 733000, China
2
College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China
3
Wuwei Hydrology and Water Resources Survey Center, Wuwei 733000, China
*
Author to whom correspondence should be addressed.
Hydrology 2025, 12(6), 132; https://doi.org/10.3390/hydrology12060132
Submission received: 2 May 2025 / Revised: 22 May 2025 / Accepted: 27 May 2025 / Published: 29 May 2025
(This article belongs to the Special Issue Hydrodynamics and Water Quality of Rivers and Lakes)
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Abstract

The surface water quality issue in arid regions is becoming increasingly severe and has become a significant challenge for global environmental protection and water resource management. By continuously collecting surface water samples (2000~2024) and utilizing hydrochemical and principal component analysis, the changes in the chemical composition of surface water and its water quality pollution characteristics are examined in the Shiyang River Basin. The surface water anion concentrations are characterized by HCO3− > SO42− > Cl−, with average concentrations of 214.11 mg/L, 117.31 mg/L, and 21.61 mg/L, respectively. The cation concentrations follow the trend of Ca2+ > Mg2+ > Na+ > K+, with average concentrations of 56.22 mg/L, 33.75 mg/L, 22.91 mg/L, and 5.33 mg/L, respectively. The dominant water types are Ca-HCO3 and Ca (Mg)-HCO3 in the mountainous area and in the plains, respectively. The weathering of carbonates and silicates is the main controlling factor for the evolution process of surface water. Strong evaporation leads to significant differences in ion concentrations, which is manifested as low in mountainous areas and high in plain areas. In addition, the surface water quality in the plains is worse than that of the mountainous areas. The main pollution indicators include DO, CODMn, COD, BOD5, NH4+-N, TP, TN, and fecal coliforms. The surface water quality of Hongyashan Reservoir and Caiqi has improved significantly, reflecting the impact of the water diversion project. The results of this study are of great significance for improving water resource management and ensuring the sustainability of the ecological environment in arid regions.

1. Introduction

Surface water is a crucial component of the ecological environment. With the acceleration of social and economic development and industrialization, surface water quality pollution has gradually emerged, posing severe challenges to human health, ecosystems, and the sustainable use of water resources [1,2]. Especially in arid regions where precipitation is relatively scarce, surface water resources are already limited. With the increase in population and economic development, the demand keeps rising, making the contradiction between supply and demand of water resources even more prominent [3]. The increasing demand for water from agricultural irrigation, industrial water use, etc. leads to the waste of water resources and environmental degradation [4]. Therefore, conducting research on the hydrochemical composition and water quality pollution characteristics of surface water has become an important part of water resource management, environmental protection, and water ecology studies.
In recent years, foreign scholars have focused on the degree of mineralization and salinization of water bodies and their influences on farmland irrigation [5,6], the chemical composition of groundwater [7,8] and lake water [9], as well as the modern and contemporary water chemical characteristics of surface water [10]. Studies in Europe and North America have also emphasized the threat of water quality pollution to local biodiversity, particularly in river and wetland ecosystems [11,12]. In China, much of the research has concentrated on water quality changes and the identification of pollution sources under the context of water scarcity. Specifically, in the northwest regions (such as Xinjiang, Gansu, Ningxia, etc.), studies on the hydrochemical composition and water quality pollution characteristics in arid areas are primarily focused on issues such as salinization, eutrophication, and heavy metal contamination [13,14]. In recent years, as water pollution has become an increasingly serious issue, domestic research has gradually advanced to include pollution source analysis, water quality modeling, and the development of water quality protection and remediation technologies. Studies have shown that excessive agricultural irrigation, industrial and mining wastewater discharge, and urban development are the major sources of pollution in these areas [15].
The hydrochemical composition of surface water typically reflects the types and concentrations of dissolved substances in the water, making it a key factor influencing water quality. The characteristics of the hydrochemical composition usually depend on various factors, including geological conditions, climate factors, precipitation, soil properties, and the degree of watershed development [16,17,18,19]. Moreover, at present, the issue of surface water quality pollution is becoming increasingly severe, posing a significant challenge to global environmental protection and water resource management. This problem is particularly prominent during industrialization and urbanization processes, where water pollution issues are more acute. Research reports indicate that driven by climate change and socio-economic development, pollutant loads, surface water extraction, and hydrological systems have changed and sub-Saharan Africa will increasingly become a major hotspot for surface water pollution [2]. Gholizadeh et al. [20] evaluated the water quality of three major rivers in South Florida by using principal component analysis (PCA), factor analysis (FA), and absolute principal component score—multiple linear regression (APCS-MLR) receptor modeling techniques. The potential pollution sources that affect water quality have been identified and quantified. Point source pollution caused by human factors such as agricultural waste discharge and domestic and industrial wastewater discharge is the main source of river water pollution.
The Shiyang River Basin is located in a region sensitive to global changes and is one of the important water systems in China’s Hexi Corridor. However, with the increase in human activities, especially the expansion of agriculture and industry, the Shiyang River Basin faces serious issues related to hydrochemical changes and water quality pollution [21,22]. Among these, widespread agricultural activities, along with the excessive use of fertilizers and pesticides, are the main sources of water quality pollution [23]. Drainage from agricultural fields contains large amounts of nutrients such as nitrogen and phosphorus, which, upon entering the river, lead to eutrophication, causing rapid algal growth and the occurrence of algal blooms, severely affecting water quality. Industrial activities in areas along the river also contribute to water quality pollution. Some industrial wastewater is discharged directly into the river without effective treatment, containing harmful substances such as heavy metals and organic pollutants. These pollutants not only degrade water quality but also harm aquatic organisms. Additionally, with the acceleration of urbanization, the volume of urban sewage discharge in the Shiyang River Basin has increased. Domestic sewage contains organic matter, nitrogen, phosphorus, and other components, further polluting the water. Furthermore, to restore the ecological environment of the basin and achieve sustainable development, the Shiyang River Basin Comprehensive Management Plan was implemented in 2007, which increased the water diversion to the basin [24]. The studies that have been carried out in the Shiyang River Basin have mostly focused on the monitoring data of a single station for 1 to 3 hydrological years [25,26]. However, at present, the understanding of the changes in surface water quality in this basin before and after the implementation of the water diversion project is still unclear. There is a lack of exploration of the changes in the hydrochemical composition and water quality pollution characteristics of surface water in the Shiyang River Basin based on continuous observation data.
Therefore, taking the surface water of the Shiyang River Basin as the research object, by continuously collecting surface water samples (from 2000 to 2024) and using hydrochemical and principal component analysis methods, the spatio-temporal distribution characteristics of surface water chemistry in the Shiyang River Basin, the types and evolution trends of surface water chemistry, as well as the pollution characteristics and pollution sources of surface water quality were studied. This is crucial for understanding water quality changes and formulating effective water quality management strategies.

2. Study Area

The Shiyang River Basin is located in the northwest of Gansu Province, China. The river basin covers an area of approximately 41,600 km2. The Shiyang River system, with the Shiyang River as the main stream, is fed by numerous tributaries, including the Xida River, Dongda River, Xiying River, Jinta River, Zamu River, Huangyang River, Gulang River, and Dajing River, which are distributed across the Qilian Mountain region (Figure 1) [27].
The natural geographical environment of the Shiyang River Basin is characterized by a diverse landscape, ranging from high mountains and hills to plains and arid deserts. The climate in the basin is arid, with low and uneven precipitation. Most rainfall occurs in the summer months, and the region experiences high evaporation rates. It is classified as a typical temperate continental climate zone, exhibiting the characteristics of arid and semi-arid climates [28].
Water resources in the Shiyang River Basin are scarce, especially in the downstream areas where water pressure is relatively high. The river flow varies significantly across different seasons and years. During the winter and spring, the flow is relatively low, while in the summer, increased rainfall boosts river flow. However, due to the large fluctuations in precipitation in the basin, the flow remains unstable. The rivers in the basin primarily rely on upstream snowmelt and precipitation for water supply.
The hydrogeological structure of the basin is varied. The upper reaches are geologically complex, dominated by mountains and fault lines shaped by tectonic movements. The region’s bedrock consists mainly of sedimentary rocks from the Paleozoic to Mesozoic era, such as sandstone, shale, and limestone, as well as metamorphic and igneous rocks. These rocks are relatively hard, which limits water infiltration, and thus groundwater recharge is limited. However, spring water and snowmelt from the mountains are vital sources of water for the basin. The middle and lower reaches of the basin feature relatively flat geological structures, composed mainly of loess and alluvial deposits. The loess in the Loess Plateau and sandy deposits in the alluvial fan areas are the primary geological components [28].
Agricultural activities in the Shiyang River Basin are mainly concentrated in the oasis areas along the river. Irrigation for farmlands predominantly relies on water from the Shiyang River. Key agricultural activities include the cultivation of crops such as wheat, corn, and soybeans, especially in the lower reaches of the basin where agricultural production is more developed. Additionally, the basin has a certain degree of livestock farming, particularly in the grassland areas.

3. Materials and Methods

3.1. Sample Collection and Testing

Between 2000 and 2024, surface water samples were continuously collected from the Shiyang River Basin, covering the following locations: Four Dam, Caiji, Hongyashan Reservoir, Hongshui River, Jinchuan Gorge Reservoir, Jinchuan Gorge Hydrological Station, and mountainous areas including Zamu Temple, Jinta, Tuan Zhuang, Gulang, Nanying Reservoir, Jiutiao Ridge, Dajing Gorge Reservoir, Huangcheng Reservoir, Huangyanghe Reservoir, Maozang Temple, Haxi, and Xidahe Reservoir. The sampling frequency was once per month. A total of 1382 surface water samples were collected, with the specific sampling locations shown in Figure 1. Sampling was conducted on clear days, avoiding stagnant water bodies and ensuring that the samples were taken 30 cm underwater. The samples were collected in polyethylene plastic bottles, ensuring they were air-free and full. For each sampling point, three 500 mL samples were collected: one with added nitric acid to adjust the pH to <2 for cation analysis, one without any reagent for anion analysis, and one for testing water quality pollution-related indicators. On-site measurements of pH and TDS were performed using a Hach portable multiparameter water quality meter. After collection, the bottles were sealed with paraffin to avoid light exposure and preserved for further testing of other indicators. HCO3− was measured by titration, and both cations and anions were determined using ion chromatography.

3.2. Methods

ArcGIS 10.7 was used to create maps depicting the general overview of the study area and the spatial distribution of TDS (Total Dissolved Solids) concentration. The Piper ternary diagram [29,30], Gibbs diagram [31], and ion ratio coefficient methods [31,32] were employed to elucidate the hydrochemical characteristics of the surface water in the basin and the main sources of ions. Principal component analysis (PCA) was then used to calculate the contribution of pollution sources to surface water quality.
Principal component analysis (PCA) is a commonly used statistical analysis method that aims to linearly transform data from a high-dimensional space to a low-dimensional space while retaining as much variability as possible [33,34]. The main steps of PCA include the following:
Data standardization: Standardize the data to ensure that each feature has the same scale and mean (usually mean 0 and variance 1) in order to avoid disproportionate influence from features with different units of measurement.
Covariance matrix calculation: Calculate the covariance matrix of the standardized data, which describes the relationships between features. The elements of the covariance matrix reflect the linear correlations between different features.
Eigenvalue and eigenvector calculation: Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and their corresponding eigenvectors. Eigenvalues represent the extent of variability along the directions of the corresponding eigenvectors, which indicate the main directions in the data.
Selecting principal components: Based on the magnitude of the eigenvalues, select the top k eigenvectors with the largest eigenvalues as the principal components. These eigenvectors form the new axes for the data.
Data transformation: Project the original data onto the selected principal components to obtain a reduced-dimensional representation of the data. This transformation maps the data to a new coordinate system, retaining the most important variability in the data.
Interpretation and application: Analyze the reduced-dimensional data, understand the meaning of the principal components, and perform further analysis or decision-making based on the specific application.

4. Results and Discussion

4.1. Spatiotemporal Distribution Characteristics of Surface Water Chemistry

The hydrochemical spatial distribution of surface water in the Shiyang River Basin shows significant regional differences (Table 1). The TDS (Total Dissolved Solids) of surface water is relatively low, with the average TDS ranging from 330 to 645 mg/L (Figure 2). The overall trend indicates that from the mountainous areas to the mountain exit and the front oasis area, the TDS of surface water generally increases, but it significantly decreases after gathering into the Hongya Mountain Reservoir (Table 1 and Figure 2). The standard deviation (SD) of TDS in the plain area is significantly higher than that in the mountainous area (Table 1), suggesting a broader range of TDS variation in the surface water of the plain area. This indicates that agricultural activities in the fine soil plain area at the foothills may lead to an increase in TDS of surface rivers, and the evaporation and concentration of surface water are also major factors contributing to the increase in mineralization. The mixing and flow of water in the reservoir can lead to the redistribution of dissolved substances in the water. In the Hongya Mountain Reservoir, the TDS decreases due to the introduction of low-TDS water during the water transfer process (water diversion project), and some dissolved substances may precipitate due to physical and chemical changes. In the mountainous area, the TDS of surface water generally decreases from east to west, which may be caused by the relatively stronger evaporation in the eastern Qilian Mountains (Table 1, Figure 2). The lower TDS in the western mountainous area is likely due to abundant precipitation, high vegetation coverage, and minimal weathering. In addition, the hydrochemical indicators of river water also have the spatial variation characteristic of decreasing with the increase of altitude. The hydrochemical indicators of river water are prone to abnormal spatial variation phenomena due to the influence of water conservancy facilities [35]. The statistical characteristics of the hydrochemistry of surface water in the Shiyang River Basin are shown in Table 1.
The concentrations of various ions in surface water at different sampling points do not change consistently along the runoff path (Table 1, Figure 3). Na+, K+, and Cl− exhibit relatively consistent trends, showing lower concentrations in the mountainous areas and higher concentrations in the plains. In the mountains, there is an east–high, west–low enrichment pattern, while in the plains and the eastern mountainous areas of the Shiyang River Basin (such as the Dajing River and Gulang River), the concentrations of Na+, K+, and Cl- in surface water are 2.2 times, 1.6 times, and 3.1 times higher than those in the western mountainous areas of the Shiyang River Basin (west of Huangyang River), respectively. Ca2+, SO42−, and HCO3− also exhibit relatively consistent trends, with the overall distribution pattern being lower in the mountains and higher in the plains. However, the concentration differences between the mountainous and plain areas within the basin are relatively small. Notably, two sampling points in the Jinta River Basin (Jinta and Nanying Reservoirs) in the mountainous area show significantly lower values. The average concentrations of Ca2+, SO42−, and HCO3− in Jinta and Nanying Reservoirs are 40.21 mg/L, 75.37 mg/L, 161.32 mg/L and 39.16 mg/L, 71.88 mg/L, 156.92 mg/L, respectively. The differences in these ion concentrations reveal that the evolution process of surface water within the basin is not consistent. The spatial variation in Mg2+ concentration is relatively small. Except for the relatively high Mg2+ content in the Huangcheng Reservoir in the eastern Qilian Mountain region (66.18 mg/L), the concentration range of Mg2+ in other surface water sampling points is between 20.23 and 38.49 mg/L. The standard deviation characteristics of ion concentrations at different sampling points reveal that the uneven distribution of Na+, K+, and Cl− concentrations in surface water in the plains and the eastern mountainous areas of the basin is significantly higher than that in the western mountainous areas of the basin. The significant differences in the concentrations of Na+, K+, and Cl− between mountainous and plain areas highlight the impact of evaporation and reduced runoff during the flow of rivers from the upper mountainous areas to the lower plain areas on the increase in ion concentrations [36]. In addition, it is reported that the ion concentration shows an increasing trend along the flow direction. The relatively strong evaporation effect in the middle and lower reaches will cause an increase in the mineralization degree and ion concentration of the river water [37,38,39].
The surface water anion concentrations at various sampling points in the Shiyang River Basin are characterized by HCO3− > SO42− > Cl−, with average concentrations of 214.11 mg/L, 117.31 mg/L, and 21.61 mg/L, respectively. The cation concentrations follow the trend of Ca2+ > Mg2+ > Na+ > K+, with average concentrations of 56.22 mg/L, 33.75 mg/L, 22.91 mg/L, and 5.33 mg/L, respectively. Overall, over the time scale from 2000 to 2024, there has been no significant change in the water chemistry composition of the surface water in the basin (Figure 4). As the Wuwei Basin in the plain area is primarily focused on irrigated agriculture, the contradiction between the ecological environment restoration of the Minqin Oasis in the downstream of the Shiyang River Basin and the agricultural water demand in the middle reaches of the Wuwei Basin is particularly prominent. Therefore, in 2007, a comprehensive management plan was implemented for the Shiyang River Basin, which involved water diversion from both within the basin (the Xiying River in the Qilian Mountains) and across the basin (from the Yellow River, flowing into the Caiqi River) to replenish the Hongya Mountain Reservoir. This water resource allocation plan may have influenced the chemical composition of surface water in the basin to some extent. Relevant studies have shown that water diversion through water diversion projects can improve the quality of surface water to a certain extent [27,40,41]. The purpose of the Xinmeng River Water Diversion Project is to improve the water quality of Zhushan Bay in Taihu Lake by diverting water from the Yangtze River [40]. Dong et al. [41] also pointed out that one of the key measures to improve the water quality of urban rivers is to divert water from upstream reservoirs into downstream rivers to wash away pollutants. Surface water samples collected from the Si Ba sampling points from 2000 to 2024 represent the original water chemistry changes in the Shiyang River Basin, while the surface water samples from the Caiqi and Hongya Mountain Reservoir sampling points from 2000 to 2024 represent the changes in the ionic composition of mixed surface water after cross-basin water diversion. The results show that from 2000 to 2024, the concentrations of Ca2+ and HCO3− in the Si Ba surface water significantly decreased, while the concentrations of Mg2+ and SO42− increased. Similar to the Si Ba surface water, the concentrations of Ca2+, K+, and Na+ in the Caiqi surface water showed a significant decreasing trend, while HCO3− concentration also showed a certain decrease. However, from 2000 to 2024, the concentration changes of various ions in the Hongya Mountain Reservoir water differed from those in the Si Ba and Caiqi sampling points. The concentration of Ca2+ in the Hongya Mountain Reservoir water showed no significant increase or decrease, while the concentrations of Mg2+ and HCO3− gradually increased, and the concentrations of K+ and Na+ showed a significant decline. This indicates that the water diversion led to changes in the contribution of the replenished water source and the mixing process of different water sources caused changes in the original surface water chemical composition in the basin.

4.2. Surface Water Hydrochemical Types and Evolution Trends

Surface water hydrochemical types reflect the chemical composition of surface water and are a key component in the study of hydrogeochemical characteristics. Various hydrochemical types exist in nature, and Shukarev’s classification system includes 49 types. This classification is based on different combinations of major cations (sodium, calcium, and magnesium) and anions (chloride, sulfate, and bicarbonate) in water. The system primarily classifies water types according to the ratio of cations to anions, revealing the hydrochemical characteristics and sources of regional water bodies. The Piper ternary diagram reflects the chemical composition characteristics of water bodies, their chemical components, and water types. It is one of the commonly used methods to identify the hydrochemical characteristics of water bodies [30,31,32]. In the Piper ternary diagram, two triangles represent the relative mass concentrations of anions and cations, with the total mass concentrations of anions and cations both summing to 100%. The upper diamond is used for water chemistry classification. The base triangle’s limitations in water chemistry classification are supplemented in the upper diamond, and the combination of both provides a reasonable explanation of the relationships among all ions [42]. Using the anion and cation concentrations of surface water samples collected from the Shiyang River Basin between 2000 and 2024, Piper ternary diagrams for surface water in the Qilian Mountain area and the foothill plain area were plotted (Figure 5). As shown in Figure 5, the hydrochemical characteristics of surface water in the Shiyang River Basin exhibit significant regional variation. The surface water in the mountainous area is primarily characterized by the dominance of Ca2+ and HCO3−, with samples concentrated in the left region of the diamond diagram, indicating that the water chemistry type in the mountainous area is Ca-HCO3 type. In the plain area, surface water is primarily characterized by the dominance of Ca2+, Mg2+, and HCO3−, with samples located in the middle-left region of the diamond diagram, indicating that the hydrochemical type of surface water in the plain area is Ca (Mg)-HCO3 type. This is consistent with the results of Zhang [14] who analyzed the hydrochemical characteristics of surface water in the mountainous, oasis, and plain areas of the Shiyang River Basin. The hydrochemical type of water in the Hongya Mountain Reservoir is also generally consistent with the surface water hydrochemical types at other sampling points in the plain area. Studies have shown that the hydrochemical characteristics of river water in the upper reaches of the Yellow River are also Ca-HCO3 type [43]. Therefore, although the inflow of water from the Yellow River has changed the ion concentration of surface water, it has not altered the hydrochemical type of water in the Hongyashan Reservoir.
The Gibbs model is an important method for determining the sources of major chemical components in natural water bodies [44,45,46,47]. It uses the relationships between Na+/[Na+ + Ca2+], Cl−/[Cl− + HCO3−], and TDS in world rivers, lakes, and ocean waters to reflect the influence of three endmembers–rock weathering, evaporation crystallization, and atmospheric precipitation–on the chemical composition of water [44]. Therefore, the TDS of surface water in the study area is projected onto the Gibbs model, with Na+/[Na+ + Ca2+] and Cl−/[Cl− + HCO3−] (Figure 6), and the positional relationship between the two is analyzed to intuitively explain the controlling factors of surface water chemistry. As shown in Figure 6, in the TDS vs. Cl−/[Cl− + HCO3−] relationship (Figure 6a), surface water samples from the Shiyang River Basin all fall within the rock weathering control zone. In the TDS vs. Na+/[Na+ + Ca2+] relationship (Figure 6b), except for two surface water samples from the Hongshui River, all other surface water samples from the Shiyang River Basin fall within the rock weathering control zone. This confirms that rock weathering and sedimentation are major controlling factors of surface water chemistry in the Shiyang River Basin. However, a few samples from the Jiutiaoling site in the Qilian Mountain area are influenced by atmospheric precipitation. It is worth noting that the range of Na+/[Na+ + Ca2+] values for surface water in the plain area and the eastern mountainous part of the basin is between 0.5 and 1 (Figure 6b), indicating that in addition to the influence of rock weathering on the major ions in surface water, evaporation concentration also has a significant impact on the ions in the surface water of the plain and eastern mountainous areas of the basin. This suggests that the main discharge pathway of surface water in the study area is evaporation and transpiration, and thus, the water chemistry gradually transitions from rock weathering control in the mountainous area to evaporation crystallization control in the plain area.
In arid region basins, the products of water–rock interactions are the main sources of soluble ions in the water body. Calcium and magnesium ions are significantly influenced by carbonate rocks, while sodium and potassium ions mainly originate from the weathering of silicate rocks and intense evaporation–crystallization processes at the water surface. The sources of anions in the water are more complex and can be easily influenced by human activities [14]. To further identify the impact of different lithologies’ weathering on the sources of karst water chemistry, the concentrations of Na+ were normalized against Ca2+ and HCO3− and the ratio of Ca2+ to Mg2+ concentrations was used to construct endmember diagrams to illustrate the effects of weathering and dissolution of carbonate, silicate, and evaporite minerals on the surface water chemistry characteristics of the study area [47,48,49] (Figure 7).
Overall, there are certain differences in the sources of cations and anions in the surface water of the basin. As shown in Figure 7a, the surface water samples mainly fall between the control of silicates and carbonates, relatively far from evaporite rocks. This indicates that the cation sources in the surface water of the basin are relatively consistent, mainly originating from the weathering and dissolution of silicates and carbonates. This is also consistent with the geological background of the mountainous area, which lacks evaporite rocks. However, from the upstream mountainous area to the midstream plain area, the contribution from silicates increases. As shown in Figure 7b, the surface water in the upstream mountainous area also falls between the silicate and carbonate endmembers, with a small number of samples located between silicates and evaporite rocks. In contrast, surface water samples from the midstream plain area are distributed among evaporite rocks, silicates, and carbonates. This indicates that the surface water in the midstream plain area and the eastern mountainous part of the basin, in addition to being controlled by the weathering and dissolution of carbonates such as calcite and dolomite, is also significantly influenced by evaporite minerals, such as halite, potassium salts, gypsum, and nitrates, which result from intense evaporation. These evaporite minerals have a major impact on the anion concentrations in the water [50]. Overall, similar evaporation and crystallization processes exist in many arid land basins [39].

4.3. Analysis of Surface Water Quality Pollution Characteristics and Sources

Before conducting surface water quality pollution analysis, it is necessary to determine the limits for each pollution indicator. In this study, the Class III water quality standard from GB 3838-2002 was used as the threshold. A total of 29 indicators from 18 surface water sampling points in the basin between 2000 and 2024 were collected. Preliminary screening revealed that the main exceedance indicators include dissolved oxygen (DO), potassium permanganate index (CODMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (AN), total phosphorus (TP), total nitrogen (TN), and fecal coliform bacteria (FCB) (Figure 8). As shown in Figure 8, the exceedance rate of TN at all 18 surface water sampling points reached 100%, making it the primary exceedance indicator. The maximum TN exceedance factor at each sampling point ranged from 5.43 to 23 times. Furthermore, except for four sampling points in the mountainous areas, the remaining 14 sampling points in the basin also showed TP pollution, with the maximum TP exceedance factor ranging from 1.02 to 47.35 times. Additionally, surface water pollution by fecal coliform bacteria and ammonia nitrogen was observed at sampling points in the plain areas and at the mountain outlet locations. The maximum exceedance factors for fecal coliform bacteria and ammonia nitrogen were found at the Siba and Caiqi sampling points, respectively. Fecal coliform bacteria are commonly found in animal intestines, and the presence of animal waste in rivers can increase their numbers [51]. Moreover, the direct discharge of untreated domestic sewage and industrial wastewater into rivers can also lead to exceedance of fecal coliform levels [52]. These findings highlight the threat posed by agricultural pollution and intense human activities to the surface water quality in the study area. The pollution of surface water can affect the quality of groundwater, and existing studies have shown that there is significant nitrate contamination in the groundwater of this region [53]. Pollution exceedance for DO, CODMn, COD, and BOD5 was also primarily concentrated in the plain areas, with maximum exceedance factors reaching 5.8, 3.6, 89, and 225 times, respectively. At the same time, sampling points in the plain areas, including Siba, Caiqi, and Hongyashan Reservoir, all showed exceedances of the eight indicators, indicating that surface water quality pollution in the plain areas is more severe than in the mountainous areas due to intense human activities. In addition to the above pollutants, some sulfur compound pollution was observed at mountainous sampling points (Jiutiaoling and Zamusi), with exceedance factors ranging from 2.42 to 4.44, while hexavalent chromium pollution was observed at sampling points in the Hongshui River and Hongyashan Reservoir in the plain areas, with an exceedance factor of about 2.1. In terms of the temporal scale, the significant exceedance of DO, CODMn, COD, BOD5, and fecal coliform bacteria mainly occurred between 2015, while ammonia nitrogen, TP, and TN pollution issues in surface water have persisted (Figure 8).
Since dissolved oxygen pollution only occurred before 2010, the study evaluates surface water quality in the plain area based on seven exceedance indicators: potassium permanganate index (CODMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (AN), total phosphorus (TP), total nitrogen (TN), and fecal coliform bacteria (FCB). Principal component analysis (PCA) was performed on these seven indicators using SPSS 19.0 software. The principal component eigenvalue and variance contribution rate are presented in Table 2. The cumulative variance contribution of the first three principal components reached 83.983%, which essentially meets the standard for selecting principal components. Therefore, the information carried by these three principal components can reflect the information of all the indicators. According to the factor loadings, the first principal component has large loadings for CODMn, COD, BOD5, and ammonia nitrogen, so it is explained by these four indicators (Table 3). This component primarily reflects organic pollution indicators in the water and mainly represents the impact of domestic sewage and industrial wastewater discharge, as well as the effects of plant and animal decomposition entering the water. The second principal component has a large loading for total nitrogen, so it is explained by this single indicator and mainly reflects the impact of agricultural development on surface water quality. This is consistent with the research results of groundwater nitrate nitrogen pollution sources conducted by Qi et al. [54] in the Shiyang River Basin, revealing the influence of agricultural sources on the water quality of the basin. The third principal component has large loadings for fecal coliform bacteria and TP, so it is explained by these two indicators, primarily reflecting the impact of domestic sewage and aquaculture on surface water quality. This study found that organic pollution in surface water in the plain area of the basin has been effectively alleviated after 2010, especially in the surface water of the Caiqi and Hongyashan Reservoirs. This also partially reflects the dilution effect of water diversion projects on surface water bodies, which has significantly improved the water ecological environment.

5. Conclusions

In the mountainous area of the Shiyang River Basin, the main type of water is Ca-HCO3 water. Among them, the cations are mainly composed of Ca2+ and the anions are mainly composed of HCO3− and SO42−. However, in the plain areas, the chemical type of surface water is Ca (Mg)-HCO3 water. The cations are mainly composed of Ca2+ and Mg2+, and the anions are mainly composed of HCO3− and SO42−. The Yellow River water introduced by the inter-basin water transfer project has not changed the water chemical type of the Hongya Mountain Reservoir.
The Na+, K+, and Cl− concentrations in the surface water were lower in the mountainous areas and higher in the plain area, meanwhile, in the mountainous areas, there is an enrichment pattern of high in the east and low in the west. The concentrations of Ca2+, SO42−, and HCO3− generally follow the pattern of being lower in the mountainous areas and higher in the plain area, although the concentration differences between the mountainous and plain areas are relatively small. Rock weathering is the key driving factor for changes in surface water chemical characteristics in the Shiyang River Basin; moreover, evaporation also have a significant impact on the ions in the surface water of the plain area and the eastern mountainous region of the basin.
The water quality in the plain area of the Shiyang River Basin is poorer than that in the mountainous areas. The main pollution indicators include DO, CODMn, COD, BOD5, ammonia nitrogen, TP, TN, and fecal coliforms, with the main pollution sources being domestic sewage, industrial wastewater, and agricultural production. After 2010, the surface water quality pollution in Caiqi and Hongya Mountain Reservoirs showed significant improvement, which also reflects the role of the water transfer project in improving the aquatic ecological environment to some extent. This study not only helps to further understand the mechanisms of changes in the water chemistry characteristics of the Shiyang River Basin, but also provides theoretical support and practical guidance for water quality improvement, pollution control, and water resource management in this region and similar areas. In addition, based on this outcome, future research can explore the assessment of water quality improvement effects of inter-basin water diversion projects, sources of water quality pollution and pollution control in plain areas, and the spatial distribution characteristics of water ions and model predictions.

Author Contributions

Conceptualization, H.W. and H.S.; methodology, S.W. and J.X.; software, J.X. and K.L.; validation, L.Z. and J.L.; formal analysis, K.L. and J.C.; investigation, S.W.; resources, H.W. and K.L.; data curation, J.C.; writing—original draft preparation, H.W.; writing—review and editing, H.S. and S.W.; visualization, J.C.; supervision, L.Z. and J.L.; project administration, H.S.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study was partially supported by the Gansu Youth Talent Program (No. 2025QNTD33), Gansu Provincial Water Science Experimental Research and Technology Extension Project (Nos. 25GSLK103 and 25GSLK087).

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no competing interest.

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Hydrology 12 00132 g001
Figure 1. Distribution of study area and sampling points.
Figure 1. Distribution of study area and sampling points.
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Figure 2. Spatial distribution characteristics of surface water TDS.
Figure 2. Spatial distribution characteristics of surface water TDS.
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Figure 3. Spatial distribution patterns of surface water hydrochemistry.
Figure 3. Spatial distribution patterns of surface water hydrochemistry.
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Figure 4. Temporal distribution of surface water hydrochemistry at various sampling points from 2000 to 2024.
Figure 4. Temporal distribution of surface water hydrochemistry at various sampling points from 2000 to 2024.
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Figure 5. Piper ternary diagram of surface water in the mountainous and plain areas.
Figure 5. Piper ternary diagram of surface water in the mountainous and plain areas.
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Figure 6. (a,b) Gibbs diagram of surface water in the mountainous and plain areas.
Figure 6. (a,b) Gibbs diagram of surface water in the mountainous and plain areas.
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Figure 7. (a,b) Endmember diagram of rock sources distribution for surface water chemistry.
Figure 7. (a,b) Endmember diagram of rock sources distribution for surface water chemistry.
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Hydrology 12 00132 g008
Figure 8. Temporal and spatial variation characteristics of water quality pollution indicators in the watershed from 2000 to 2024.
Figure 8. Temporal and spatial variation characteristics of water quality pollution indicators in the watershed from 2000 to 2024.
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Table 1. Statistical analysis of surface water hydrochemical characteristics in the Shiyang River Basin.
Table 1. Statistical analysis of surface water hydrochemical characteristics in the Shiyang River Basin.
Samplings Na+Ca2+Mg2+K+HCO3−SO42−Cl−TDS
mg/L
SibaMean33.1370.4835.947.49269.55134.7539.06642.42
Max10812598.221.844819767.791240
Min5.2521.28.332.1611142.67.85198
SD16.2626.6713.423.2256.9337.5112.65164
CaiqiMean33.756734.2910.81250.69139.2635.51599.78
Max97.215496.786.142432964.791070
Min2.78201.561.796.743.55.97222
SD20.8827.4713.5316.5864.7849.4116.13183.92
Hongyashan ReservoirMean30.5552.8530.825.68195.1117.7633.34472.87
Max68.410059.317.228330661.99737
Plain area Min8.3315.99.171.3189.839.75.63143
SD13.7415.869.462.6542.5130.9212.85104.74
Hongshui RiverMean49.1457.9733.5813.47238.08139.0543.77597.65
Max11411571.781516312154.221270
Min13.219.55.371.3498.163.73.94368
SD22.6320.6411.4119.7554.2651.3816.99132.21
Jinchuanxia ReservoirMean32.3259.935.584.59227.09138.0130.91519.77
Max98.411663.813.7433228118.171110
Min10.616.29.070.8812869.29.04113
SD15.7316.5310.662.2446.3531.7519.55111.38
Jinchuanxia hydrographic stationMean34.8261.4937.714.47252.74142.2636.13595.05
Max63.393.367.413.641129860.39947
Min2.827.417.850.6851.3050.106.26414.00
SD11.2615.6710.342.1542.3732.9311.5686.67
Plain areaMean35.5761.7134.657.79239.06135.2436.43576.82
Max114.00154.0098.2086.10516.00329.00154.22816.67
Min2.787.411.560.678651.339.703.9441401.91
SD18.2421.8711.7511.2956.9240.6615.7181.09
ZamusiMean8.7053.1826.243.71178.2101.668.26385.69
Max25.30123.0060.9015.00333.00190.0022.53608.00
Min1.9519.405.390.4861.6043.103.18152.00
SD4.6419.8710.532.1849.633.093.2993.75
JintaMean11.8340.2220.233.6161.3275.3711.03334.44
Max31.874.248.29.6928126423.03588
Min2.8717.63.130.488.516.73.94176
SD6.0712.489.281.8244.1835.024.2677.93
TuanzhuangMean10.8156.9934.313.89191.61126.789.02455.19
Max28.110267.11433721827.54894
Min2.82186.83145.859.33.67195
SD5.4418.6911.92.3449.2437.713.24108.82
GulangMean29.7356.2935.725.13261.67119.4224.95552.04
Max5910060.212.253419362.29805
Mountainous region Min3.5521.411.62.1114664.57.85345
SD12.1717.5411.032.1249.5929.129.8989.39
Nanying ReservoirMean11.9639.1621.443.6156.9271.8810.37329.6
Max51.973.539.79.2423125018.23592
Min2.5721.24.570.7682.329.33.66188
SD8.2712.878.871.9537.5834.883.8383.28
JiutiaolingMean7.7957.6530.393.62187.76113.338.05394.92
Max2399.960.114.228820926.24574
Min1.8622.97.330.8470.855.62.4984.8
SD4.1820.0810.842.3942.6136.844.1105.07
Dajingxia ReservoirMean39.6256.4438.496.94239.78135.8550.11581
Max92.28481.412.5442300134.19874
Min5.8613.79.150.8514073.77.6367
SD20.0316.9716.592.5558.8845.1832.73141.31
Huangcheng ReservoirMean26.9863.6566.183.29201.32116.367.71450.44
Max2311905909.3233821613.92649
Min3.9721.110.80.161.655.64.64240
SD51.8936.97126.72.0349.2131.761.8979.37
Huangyanghe ReservoirMean19.0953.8433.424.53220.93115.8814.86467.4
Max53.81005915.734918152.17702
Min2.7213.17.811.1982.736.33.26181
SD8.6417.789.512.3838.2827.996.6386.77
MaozangsiMean8.5953.8525.873.3178.51100.087.3390.35
Max21.714166.18.1239022116.02575
Min2.220.74.440.4986.641.83.38174
SD5.0821.8412.881.6752.3737.152.2101.02
HaxiMean12.4353.1932.394.39230.46101.559.87456.22
Max29.310152.115.144815421.93807
Min3.0824.45.851.3612760.84.48305
SD5.4915.768.532.5354.1120.354.9183.04
Xidahe ReservoirMean10.9857.9335.113.35212.33122.288.51487.2
Max40.110794.67.8738617417.92796
Min1.0115.112.11.0263.629.94.05144
SD5.4622.8713.741.868.9229.23.61106.98
Mountainous regionMean15.6853.0532.064.06199.78107.5013.24436.55
Max231.00190.00590.0015.70534.00300.00134.19673.33
Min1.0113.103.130.0045.8016.702.49133.30
SD17.2320.6533.982.3157.4437.7812.9183.33
Table 2. Principal component eigenvalue and variance explained ratio.
Table 2. Principal component eigenvalue and variance explained ratio.
ComponentsEigenvalueVariance Explained Ratio (%)Cumulative Explained Variance Ratio (%)
14.20460.0660.06
20.90912.98573.044
30.76610.93883.983
40.5768.22692.208
50.4826.88799.095
60.0390.55899.653
70.0240.347100
Table 3. Component matrix.
Table 3. Component matrix.
Components
123
CODMn0.213−0.2970.071
COD0.204−0.3710.097
CODMn0.2070.019−0.55
AN0.2080.042−0.544
TP0.1630.2270.491
TN0.1080.9040.015
FCB0.163−0.0510.673
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Wang, H.; Wu, S.; Xu, J.; Zhang, L.; Li, K.; Li, J.; Shu, H.; Chu, J. Study on the Surface Water Chemical Composition and Water Quality Pollution Characteristics of the Shiyang River Basin, China. Hydrology 2025, 12, 132. https://doi.org/10.3390/hydrology12060132

AMA Style

Wang H, Wu S, Xu J, Zhang L, Li K, Li J, Shu H, Chu J. Study on the Surface Water Chemical Composition and Water Quality Pollution Characteristics of the Shiyang River Basin, China. Hydrology. 2025; 12(6):132. https://doi.org/10.3390/hydrology12060132

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Wang, Haifeng, Shaoqing Wu, Jihai Xu, Lixia Zhang, Kuijing Li, Jisheng Li, Heping Shu, and Jihua Chu. 2025. "Study on the Surface Water Chemical Composition and Water Quality Pollution Characteristics of the Shiyang River Basin, China" Hydrology 12, no. 6: 132. https://doi.org/10.3390/hydrology12060132

APA Style

Wang, H., Wu, S., Xu, J., Zhang, L., Li, K., Li, J., Shu, H., & Chu, J. (2025). Study on the Surface Water Chemical Composition and Water Quality Pollution Characteristics of the Shiyang River Basin, China. Hydrology, 12(6), 132. https://doi.org/10.3390/hydrology12060132

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