Key Controlling Factors and Sources of Water Quality in Agricultural Rivers: A Study on the Water Source Area for the South-to-North Water Transfer Project

Abstract

River water quality is a direct determinant of both drinking water security and regional economic vitality. However, the hydrochemical trajectories and solute provenance of agricultural streams remain only fragmentarily understood. Here, we examine the Jinqian River—a representative agricultural tributary of the Danjiangkou Reservoir source area for the South-to-North Water Diversion Project—by coupling hydrochemistry with multivariate statistics techniques. The results revealed that the pH values of the river water ranged from 7.55 to 8.30, indicating a weakly alkaline condition. During all three hydrological periods, the concentrations of total nitrogen (TN) exceeded the limits set by the Class Ⅲ surface water quality standards in China, suggesting that the agricultural river has been significantly impacted by human activities. Solute dynamics followed three rainfall-modulated patterns: (i) dilution-driven decreases in the flood season (e.g., Na⁺), (ii) concentration via flushing or evaporative concentration (e.g., SO₄²⁻), and (iii) reservoir-induced damping of seasonal contrasts (e.g., TN), the latter attributable to nitrogen retention behind upstream dams. Geochemical fingerprints reveal that Cl⁻ and Na⁺ originate predominantly from halite dissolution; Ca²⁺, Mg²⁺ and HCO₃⁻ from coupled carbonate–silicate weathering; and SO₄²⁻ from evaporite dissolution. Principal component analysis distills four dominant quality controlling factors: agricultural fertilizers, halite weathering, evaporite dissolution, and domestic effluent. These findings provide a quantitative basis for managing nutrient and salt fluxes in agricultural rivers and for safeguarding water sustainability within water-diversion source regions.

1. Introduction

Hydrochemical characterization of river systems underpins both rational water resource management and effective environmental protection [1]. Agricultural tributaries are of particular concern because they must simultaneously sustain ecological functions and support intensive human use. These channels are frequently confronted with multiple challenges, such as agricultural runoff, industrial emissions, and domestic sewage. These factors can alter the chemical composition of the water, thereby leading to water quality degradation [2,3,4]. Accurate and comprehensive investigation of river water chemistry is crucial for assessing the health of aquatic ecosystems, ensuring the safety of drinking water, and guiding sustainable agricultural practices [5,6]. Within the context of large-scale water-diversion schemes such as China’s South-to-North project, maintaining pristine source-water quality is a prerequisite for long-term engineering success and sustainability [7,8].

The composition of river water reflects a dynamic interplay between natural templates and anthropogenic forcings. Natural factors, including geological composition, hydrological conditions, and climatic variability, determine the baseline water quality and natural variations of water bodies [9]. Anthropogenic factors, such as agricultural activities, industrial processes, and urban development, introduce additional substances and pollutants into river systems. For instance, agricultural runoff can introduce nutrients, pesticides, and sediments, while industrial emissions may contain heavy metals and organic compounds [10]. Previous studies have identified a variety of factors that influence river water chemistry. However, the relative importance of these factors may vary significantly across different regions and river systems.

Preventing hydrochemical degradation hinges on the unambiguous identification of solute provenance. Accurate identification and quantification of these sources are essential steps in developing effective pollution control strategies [11,12]. Globally, researchers have exploited chemical fingerprints, stable-isotope tracers, and statistical unmixing models to reconstruct the origin of dissolved and suspended constituents [13,14,15]. However, the current understanding of the patterns of water quality variation and the sources of substances in complex agricultural river systems remains inadequate.

To fill these knowledge gaps, this study focuses on the Jinqian River, a typical agricultural river in the water source area of the South-to-North Water Diversion Project. This study investigates the characteristics and controlling factors of water chemistry in agricultural rivers by conducting periodic sampling and applying hydrochemical and multivariate statistical techniques. The primary objectives of this study were to systematically investigate the water quality characteristics and seasonal variations in agricultural rivers, identify the controlling factors influencing these variations, and elucidate the sources of substances affecting agricultural rivers. The findings of this study provide a scientific basis for understanding and managing water quality in agricultural river systems and can be applied to similar regions worldwide.

2. Materials and Methods

2.1. Description of the Study Region

The Jinqian River, a left-bank tributary of the Hanjiang, drains the southern slopes of the Qinling Mountains (109°09′–110°19′ E, 33°11′–33°52′ N). Originating at Jinjing River in Zhashui County, Shaanxi Province, the river traces a 261 km southeasterly course through Zhashui and Shanyang counties before crossing into Hubei and joining the Han River at Yunxi County (Figure 1). Its 5610 km² catchment experiences a mean annual air temperature of 13.1 °C, 752 mm of precipitation, and 889 mm of evaporation. Roughly four-fifths of the rainfall is delivered between May and October, imparting a pronounced monsoon signature to the hydrological regime.

2.2. Specimen Collection and Testing

2.2.1. Specimen Collection Strategy

Field campaigns were conducted during three hydrologic windows in 2022—dry season (January), normal season (May), and flood season (August)—with one sampling event per season. Along the Jinqian main stem and its tributaries, 19 stations were established (12 on the trunk, 7 on feeders) and sampled sequentially from headwaters to confluence. Ancillary materials comprised four bulk-rainfall collectors and eight municipal effluent grabs. At each river site, water was withdrawn 0.5 m below the mid-channel surface with a 5-L polyethylene sampler, then transferred to pre-rinsed 2-L HDPE bottles that had been triple-washed with de-ionized water and conditioned three times with on-site river water. Samples were kept in portable coolers at 4 °C, transported to the laboratory, and analyzed within seven days of collection.

2.2.2. Analysis of Water Chemistry

The hydrochemical analysis included pH, total dissolved solids (TDS), major ions (K⁺, Na⁺, Ca²⁺, Mg²⁺, NH₄⁺, Cl⁻, SO₄²⁻, NO₃⁻, HCO₃⁻, F⁻), total nitrogen (TN), and dissolved organic carbon (DOC).

In the field, pH was assessed with a portable multi-parameter analyzer (Hach-HQ40D, Hach Company, Shanghai, China). The concentrations of cations, including K⁺, Na⁺, NH₄⁺, Ca²⁺, and Mg²⁺, were quantified using atomic absorption spectrophotometry (Agilent 7500ce ICP-MS, Agilent Technologies, Tokyo, Japan). Meanwhile, anions such as Cl⁻, SO₄²⁻, F⁻, and NO₃⁻ were evaluated via ion chromatography (CIC-D180, Qingdao Shenghan Chromatography Technology Co., Ltd., Qingdao, China). HCO₃⁻ levels were determined through hydrochloric acid titration. The TDS were calculated using the drying-gravimetric technique. For TN analysis, the alkaline potassium persulfate digestion combined with UV spectrophotometry was employed (Spectrophotometer, Jingke L9, Shanghai, China). Lastly, DOC was measured by high-temperature catalytic oxidation with a TOC-VCPH analyzer (Shimadzu Corporation, Kyoto, Japan). The detection limits of the analytical methods are summarized in Supplementary Table S1.

2.3. Data Analytics

2.3.1. Water Quality Index (WQI)

The WQI condenses a suite of hydrochemical variables into a single metric that reflects the suitability of surface water for potable use. Each parameter is assigned a weight (Wi) proportional to its perceived health relevance. Thresholds follow the Chinese Grade-III surface-water standard (GB 3838-2002) [16]; analytes not regulated therein adopt guideline values from World Health Organization published in 2017 were utilized [17].

The procedural steps are as follows:

$$R{W}_i= {\displaystyle \frac{W_i}{\sum _{i=1}^n{W}_i }}$$

Q_i = C_i/S_i × 100

SI_i = W_i × Q_i

WQI = ∑SI_i

2.3.2. Statistical Analysis

In this study, we employed the Kolmogorov–Smirnov (KS) test and one-way analysis of variance (ANOVA) to examine the seasonal variations in the concentration values of water quality parameters, including pH, SO₄²⁻, Na⁺, HCO₃⁻, NO₃⁻, and TN, in river water. Initially, the KS test was utilized to evaluate the normality of the data. The results revealed that the concentration values of all six water chemistry parameters conformed to a normal distribution (p > 0.05). Thereafter, one-way ANOVA was conducted to assess the seasonal differences in these water chemistry parameters. Principal component analysis was used to identify the main controlling factors affecting the water quality of the Jinqian River. All the statistical analyses were performed using R software (version 4.4.1, New Zealand).

3. Result

3.1. The Hydrochemical Characteristics of Jinqian River

Table 1. Statistical table of characteristics of water quality variation in Jinqian River.

Parameters	Units	Standard	Dry Season			Normal Season			Flood Season
			Range	Mean	CV	Range	Mean	CV	Range	Mean	CV
pH	—	6~9	7.55–7.99	7.81	1.61	7.96–8.25	8.10	1.00	7.74–8.3	8.13	1.51
K+	mg/L	12	0.680–3.87	2.00	45.7	0.650–3.20	1.63	46.5	0.720–6.44	2.01	63.9
Na+	mg/L	200	4.47–15.9	7.93	30.9	2.93–10.6	6.24	33.1	2.68–18.0	6.02	51.9
Ca2+	mg/L	75	36.3–58.7	48.9	9.89	41.3–55.1	49.3	6.56	41.0–51.9	47.6	6.59
Mg2+	mg/L	50	8.44–16.2	11.6	18.3	7.36–13.7	10.9	14.0	5.27–14.1	10.4	26.0
Cl−	mg/L	≤250	3.86–24.9	7.63	67.0	2.52–14.6	6.51	50.1	3.86–21.7	7.45	52.5
SO42−	mg/L	≤250	18.3–61.6	39.4	26.0	24.0–70.2	50.0	21.6	20.9–80.6	56.0	25.7
HCO3−	mg/L	—	146–180	165	5.78	147–176	157	6.43	131–159	145	6.37
NO3−	mg/L	≤44.3	6.07–15.4	8.98	27.3	2.27–15.2	7.54	38.7	6.56–17.7	9.45	29.3
NH4+	mg/L	≤1.29	0.352–1.33	0.710	38.7	0.303–1.20	0.720	36.7	0.280–1.36	0.620	49.8
TN	mg/L	≤1	2.56–3.84	3.32	10.6	2.79–4.18	3.22	9.09	2.56–4.01	3.23	11.6
F−	mg/L	≤1	0.893–1.12	0.990	6.59	0.399–1.34	0.810	33.6	0.599–2.26	1.02	38.2
DOC	mg/L	—	0.765–2.38	1.14	32.0	0.740–4.00	1.24	57.5	0.700–3.23	1.31	44.9
TDS	mg/L	≤1000	160–357	275	16.6	169–349	278	15.1	160–357	249	16.6

Note: CV: Coefficient of variation; Standard: Class III standard of the national surface water quality standard, Chinese (GB 3838−2002) [16].

summarizes the hydrochemical signatures of the Jinqian River across the three hydrologic seasons. pH drifted within narrow, slightly alkaline bands: 7.55–7.99 (dry season), 7.96–8.25 (normal season), and 7.74–8.30 (flood season), with respective means of 7.81, 8.10, and 8.13, respectively. The concentrations of DOC during the three periods ranged from 0.765 to 2.38 mg/L, 0.740 to 4.00 mg/L, and 0.700 to 3.23 mg/L, with mean values of 1.14 mg/L, 1.24 mg/L, and 1.31 mg/L, respectively, exhibiting a pattern of flood season > normal season > dry season. The mean concentrations of TN in the three hydrological periods were 3.32 mg/L, 3.22 mg/L, and 3.23 mg/L, respectively, showing a trend of dry season > flood season > normal season. The concentrations of TN in all three hydrological periods exceeded the limits of Class III surface water quality standards in China, indicating a substantial impact of human activities on the water body. Cationic abundance followed the sequence Ca²⁺ > Mg²⁺ > Na⁺ > K⁺ >NH₄⁺ in all campaigns, while anions ranked HCO₃⁻ > SO₄²⁻> NO₃ >Cl⁻ > F⁻. Cl⁻ exhibited substantial spatial variability in all three periods (CV > 50%), suggesting that the river water may have been continuously influenced by localized pollution sources, which caused strong spatial heterogeneity. The mean concentrations of TDS followed the order of normal season (278 mg/L) > dry season (275 mg/L) > flood season (249 mg/L), which further indicates that precipitation has a certain diluting effect on the hydrochemical components of the water body.

3.2. Evaluation of Water Quality in the Jinqian River

Water Quality Index (WQI) screening shows that “good” sites predominate in all campaigns, but their proportion declines temporally from 100% (dry season) to 94.7% (normal season) and 89.5% (flood season). Stations classed as “poor” emerge only during normal and flood season phases (5.3% and 10.5%, respectively) (

Table 2. Water quality evaluation results of the Jinqian River in different seasons.

Water Quality Level	Comprehensive Water Quality Score	Proportion of Sites in 3 Periods (%)
		Dry Season	Normal Season	Flood Season
Excellent water	<50	0	0	0
Good water	50–100	100	94.7	89.5
Poor water	100.1–200	0	5.26	10.5
Very poor water	200.1–300	0	0	0
Water unsuitable for drinking purposes	>300	0	0	0

), underscoring the dual control of hydrologic dilution and episodic external inputs on river water quality.

3.3. The Hydrochemical Type of the Jinqian River

The Piper trilinear diagram classifies the hydrochemical types of river water based on the ionic composition and concentration ratios of the water. As shown in Figure 2, the sample points from all three hydrological periods predominantly fall within the region dominated by HCO₃–Ca·Mg type water, indicating that this type is the primary hydrochemical type in the study area. Further analysis of the hydrochemical types using the Schoeller classification method revealed that during the dry season, the water type is mainly HCO₃–Ca (accounting for 42.1%), while in the normal and flood seasons, the dominant type is HCO₃·SO₄–Ca·Mg, with respective proportions of 36.8% and 52.6%. The seasonal variation in hydrochemical types may be related to the increased dissolution of gypsum in the strata due to higher rainfall, as well as the higher evaporation intensity caused by higher temperatures during the flood and normal seasons [18].

4. Discussion

4.1. The Temporal and Spatial Variations of Water Quality in the Jinqian River

The temporal variation of river water quality is an important aspect in water environment research, as it reflects the impact of natural processes and human activities on river water quality. In this study, we examined the temporal variations of 14 water quality parameters and summarized 4 seasonal variation characteristics of agricultural rivers. The first characteristic is that the dry season has significantly lower values than the flood (p < 0.001) and normal seasons (p < 0.01), as evidenced by the representative indicators of pH and SO₄²⁻ (Figure 3a,b). This result is consistent with that reported by Esquivel-Hernández et al., who found that during the flood season, storm runoff transports a large number of alkaline substances from roads and soils into the river, thereby causing an increase in the pH of the river [19]. The sulfate exhibited similar variations to pH, mainly due to abundant precipitation and increased surface runoff during the flood and normal seasons. These runoffs carried a large amount of sulfate from the soil strata into the river, leading to an increase in sulfate concentration. As detailed in Section 4.2, the study found that sulfate in the Jinqian River primarily originates from sulfate minerals in the soil. The second variation is that the concentration of water quality parameters is significantly higher only in the dry season than in the flood season, with the representative indicator being Na⁺ (p < 0.05) (Figure 3c). This is mainly related to the discharge of domestic sewage from surrounding villages during the dry season and the dilution of river water by rainwater during the flood season [20]. The third variation characteristic is that the concentration of water quality parameters in the dry season is significantly higher than that in the flood season (p < 0.001) and normal season (p < 0.05), and the normal season is significantly higher than the flood season (p < 0.001), with the representative indicator being HCO₃⁻ (Figure 3d). This is primarily attributed to the dilution effect of rainfall and the transport of residual acidic substances (such as sulfates and nitrates) from agricultural fields into the river by storm runoff during the flood season, which further reduces the concentration of HCO₃⁻ in the river water. The fourth seasonal variation is that the concentrations of water quality parameters show no difference in the three hydrological periods (p > 0.05), with representative indicators being NO₃⁻ and TN (Figure 3e,f). This indicates that the nitrogen input and transformation processes in the region are relatively stable. In addition, there are multiple hydropower stations built on the Jinqian River, and the reservoirs have a strong buffering and retention capacity for nitrogen, which further weakens the seasonal variation of nitrogen [21].

4.2. Identification of Hydrochemical Component Sources in Agricultural Rivers

The ion ratio coefficient method is frequently employed to investigate the hydrogeochemical processes of water bodies and the origins of their hydrochemical constituents [22]. The molar ratios of (Ca²⁺/Na⁺) and (Mg²⁺/Na⁺) are commonly used to distinguish the control of water chemical composition by the weathering and dissolution of carbonate rocks, silicate rocks, and evaporite rocks. Previous studies have shown that the molar concentration ratios of Ca²⁺/Na⁺ and Mg²⁺/Na⁺ produced by the weathering of silicate rocks are 0.35 ± 0.15 and 0.24 ± 0.12, respectively. For carbonate rock weathering, these ratios are 50 ± 20 and 20 ± 8, and for evaporite rocks, they are 0.17 ± 0.09 and 0.02 ± 0.013 [23]. As shown in Figure 4a, the sample points from the three periods are generally located in the intermediate zone between the weathering dissolution of silicate rocks and carbonate rocks. In combination with the analysis of hydrochemical types mentioned earlier, it is evident that the weathering dissolution of carbonate rocks is the primary source of hydrochemical components in the river water, followed by that of silicate rocks.

The origin of Na⁺, K⁺, and Cl⁻ was evaluated via the [Cl⁻] versus [Na⁺ + K⁺] ratio. Previous studies have found that when rock salt is the primary source, the ratio of [Cl⁻] to [Na⁺ + K⁺] should be approximately equal to 1 [24]. Figure 4b shows that all sample points from the three periods are situated above the y = x line and display a significant correlation (R² = 0.570). This suggests that in addition to the Na⁺ and K⁺ contributed by the weathering dissolution of rock salt, the weathering dissolution of silicate rocks also provides a significant portion of Na⁺ and K⁺ to the water. This result is consistent with the conclusions of Ren et al., who also found that in the Yellow River, Na⁺, K⁺, and Cl⁻ are not only derived from the dissolution of rock salt but are also influenced by the dissolution of silicate rocks [20].

Previous work has established that Ca²⁺ and Mg²⁺ in natural waters originate predominantly from the dissolution of carbonate, silicate, and evaporite lithologies [25], whereas HCO₃⁻ is derived mainly from carbonate and silicate weathering with limited anthropogenic influence. When carbonate dissolution prevails, the equivalent ratio [Ca²⁺ + Mg²⁺]/[HCO₃⁻] should approximate 1:1; involvement of silicate weathering shifts the expected ratio toward [Ca²⁺ + Mg²⁺]/[HCO₃⁻ + SO₄²⁻] ≈ 1:1 [26]. In Figure 4c, all flood and dry season samples plot above the y = x line for [Ca²⁺ + Mg²⁺]/[HCO₃⁻], evidencing an additional silicate contribution. After incorporating SO₄²⁻ (Figure 4d), more than 75.0% of the sample points in all three periods fall below the y = x line, indicating that the weathering-dissolution of evaporite rocks (such as gypsum dissolution) is the primary source of SO₄²⁻ in the water.

4.3. The Impact of Human Activities on Agricultural Rivers

The concentrations of Cl⁻ and NO₃⁻ in the aquatic environment are closely related to human activities. Therefore, the Cl⁻/NO₃⁻ ratio serves as a diagnostic tracer for discriminating different human impacts on river chemistry [27]. Chloride is effectively conservative in surface waters because it is unaffected by common physical, chemical, or microbial transformations. Therefore, the NO₃⁻/Cl⁻ ratio could as a robust proxy for nitrate provenance [28]. Under normal circumstances, the primary sources of Cl⁻ contamination include domestic sewage, feces, industrial wastewater, road salts, and agricultural chemicals (such as potassium chloride or KCl) [29]. Manure and sewage are characterized by elevated Cl⁻ concentrations and low NO₃⁻/Cl⁻ ratios, whereas synthetic fertilizers and atmospheric deposition exhibit the opposite signature [30].

As shown in Figure 5, the rainwater samples (n = 4) exhibited low Cl⁻ concentrations (2.01 mg/L) and high NO₃⁻/Cl⁻ratios (4.44), whereas the sewage samples (n = 8) had high Cl⁻ concentrations (7.5 mg/L) and low NO₃⁻/Cl⁻ ratios (1.16), which is consistent with previous studies [30]. In the study area, NO₃⁻ in the river is likely primarily derived from a mixture of sewage and fertilizer pollution, as evidenced by the relatively high Cl⁻ concentration and moderate to low NO₃⁻/Cl⁻ ratio. The impact of rainfall on nitrogen in the Jinqian River is minimal, given the low NO₃⁻ concentration in precipitation (4.60 mg/L). During the flood season, approximately 58% of the samples are close to the fertilizer pollution endmember. This is a typical characteristic of agricultural rivers [31]. While, during the dry season, 50% of the samples are located at the sewage endmember, with the remaining samples positioned at the mixed endmember.

4.4. Source Apportionment of Pollution in Agricultural Rivers

Effective river-basin management requires unambiguous identification of the factors driving water quality degradation. Multivariate statistics have been extensively applied to source apportionment in aquatic systems. In this study, principal component analysis (PCA) was employed to differentiate seasonal pollution sources in the Jinqian River. Thirteen routinely monitored physicochemical parameters (TDS, TH, Na⁺, K⁺, Ca²⁺, Mg²⁺, HCO₃⁻, SO₄²⁻, NO₃⁻, Cl⁻, COD, Fe, and Mn) were selected for analysis. Prior to extraction, the data matrix was subjected to Kaiser–Meyer–Olkin (KMO) and Bartlett’s sphericity tests. The resultant KMO value of 0.604 and Bartlett’s χ² = 364 (p < 0.001) confirm the suitability of the hydrochemical data set for PCA. Four principal components (PCs) with eigenvalues > 1 were retained, collectively explaining 79.9% of the total variance and thereby preserving the information embedded in the original thirteen variables.

Principal component 1 (PC1) accounts for 27.6% of the variance and exhibits strong positive loadings on Na⁺, Cl⁻, F⁻, K⁺, and NO₃⁻, with moderate positive loadings on NH₄⁺ and TN (

Table 3. The results of principal component analysis in the Jinqian River.

Parameters	PC1	PC2	PC3	PC4
Na+	0.877	0.031	0.156	0.233
Cl−	0.843	−0.204	0.091	0.020
F−	0.809	−0.151	0.021	−0.234
K+	0.808	0.370	−0.149	−0.053
NO3−	0.803	0.434	−0.085	0.245
SO42−	0.031	0.838	0.185	−0.109
Ca2+	0.101	0.823	0.015	0.044
Mg2+	−0.053	−0.002	0.830	−0.213
TDS	0.055	0.594	0.704	0.056
HCO3−	−0.157	0.120	0.672	0.467
DOC	0.206	−0.593	−0.635	−0.003
TN	0.364	−0.136	−0.109	0.883
NH4+	0.214	−0.104	−0.180	0.692
Eigenvalue	3.59	2.52	2.18	1.50
Contribution Rate (%)	27.6	19.4	17.8	15.1
Cumulative Contribution Rate (%)	27.6	47.0	64.8	79.9

). Na⁺ and Cl⁻ exhibit the strongest correlation with PC1. As demonstrated in Section 4.2, the primary sources of Na⁺ and Cl⁻ in the Jinqian River are the weathering and dissolution of rock salt (NaCl). Therefore, PC1 primarily represents the influence of rock salt dissolution on the river water. Under normal circumstances, NO₃⁻, NH₄⁺, and TN in the water environment are unequivocally linked to anthropogenic inputs such as agricultural runoff, domestic effluent, and atmospheric deposition [32,33]. Because elevated NH₄⁺ and TN are robust indicators of sewage, and these variables are most strongly correlated with PC4. In addition, as presented in Supplementary Table S2, a notably significant positive correlation (p < 0.01) is observed between K⁺ and NO₃⁻. This finding suggests that these two ions likely stem from a common origin. Moreover, previous scholars have found that the co-enrichment of K⁺ and NO₃⁻ in the aquatic environment can indicate that the water body is affected by agricultural fertilizer pollution [34]. Since different principal components represent independent sources of pollution [35], thus, PC1 mainly represents the influence of agricultural fertilization and rock salt weathering on the water chemistry of the Jinqian River.

Principal component 2 (PC2) explains 19.4% of the variance and is dominated by SO₄²⁻ and Ca²⁺. These ions are diagnostic of gypsum (CaSO₄·2H₂O) dissolution. And in Section 4.2, it has been confirmed that the SO₄²⁻ in the Jinqian River water originates from the dissolution of gypsum. Consequently, PC2 represents the control of gypsum weathering and dissolution on the water chemistry of the Jinqian River.

Principal component 3 (PC3) accounts for 17.8% of the variance and is characterized by high positive loadings on Mg²⁺, TDS, and HCO₃⁻, together with a moderate negative loading on DOC. Mg²⁺ and HCO₃⁻ are typical products of both silicate and carbonate weathering [36]; earlier analyses demonstrated that carbonate dissolution dominates their supply in this basin. Therefore, PC3 represents the influence of carbonate weathering and dissolution on the water chemistry of the Jinqian River.

Principal component 4 (PC4) explains 15.1% of the variance and is defined by strong positive loadings on TN and NH₄⁺. As shown in Supplementary Table S2, the two parameters also exhibit a significant positive correlation (p < 0.01), indicating that they originate from a common source [37]. Riverine nitrogen is derived from domestic sewage, synthetic fertilizers, and manure [38]. Because PC1 already encapsulates agricultural fertilization, the segregation of TN and NH₄⁺ on PC4 points to domestic wastewater. Field observations confirm that untreated sewage from riverside villages is discharged directly into the Jinqian River, representing a critical cause of TN exceedances.

5. Research Limitations and Future Prospects

This study provides an in-depth investigation into the hydrochemical evolution and pollution sources of agricultural rivers, offering valuable insights for the protection of water environments in such regions. However, several limitations should be acknowledged and addressed in future research.

Firstly, this study did not include analyses of trace metals, which are often contributed by fertilizers and may have significant environmental impacts. Future research should incorporate assessments of trace metals to provide a more comprehensive understanding of pollution sources and their potential risks to river ecosystems.

Secondly, the study failed to consider emerging contaminants arising from human activities within the catchment area. These contaminants, such as pharmaceuticals, personal care products, and microplastics, can exert complex and long-term effects on water quality and aquatic life. Future investigations should focus on identifying and quantifying these emerging contaminants to better assess the overall health of the river.

Additionally, this study primarily utilized principal component analysis to qualitatively identify pollution sources in the Jinqian River. While the source apportionment results offer some reference value, their accuracy needs further improvement. In recent years, isotope tracing techniques have been widely applied and developed. For instance, using the dual δ¹⁵N and δ¹⁸O-NO₃^– in combination with source apportionment models can not only accurately identify the sources of nitrogen in water bodies but also quantify the contribution rates of each source to nitrogen pollution [39,40]. Future research should pay more attention to applying this technique in this region.

6. Research Recommendations

To address the deterioration of water quality in agricultural rivers, we propose the following evidence-based measures:

(1)

Optimize Fertilizer Management

Excessive application of synthetic fertilizers is a primary driver of nutrient pollution. Current local farming practices often involve the broadcast application of chemical fertilizers at excessively high rates. To mitigate agricultural non-point source pollution caused by over-application, it is essential to improve local farmers’ fertilization methods by promoting precision agriculture technologies. These technologies can match nutrient inputs to crop demands and soil fertility, thereby reducing surplus nutrient application. We recommend the application of well-composted organic amendments or slow-release fertilizers to enhance nutrient use efficiency and support sustainable agricultural practices.

(2)

Upgrade Domestic Wastewater Treatment

At present, the domestic wastewater treatment capacity of villages along the Jinqian River is evidently insufficient. Untreated or partially treated sewage from riverside settlements significantly increases the river’s nitrogen and other pollution loads. It is crucial to extend centralized wastewater treatment infrastructure to all villages within the riparian zone. Where connection to a centralized system is not yet feasible, compact package plants or constructed wetlands should be installed to ensure that effluents meet discharge standards before being released into the environment.

(3)

Strengthen long-term monitoring and assessment

A permanent water quality monitoring network should be established and operated across the catchment, with sampling conducted at biweekly to monthly intervals. Key parameters, including TN, NO₃⁻, NH₄⁺, chemical oxygen demand, total phosphorus, metal ions, emerging contaminants, and others, must be analyzed under strict quality assurance and quality control (QA/QC) protocols. In addition, more accurate assessments of water quality and pollution sources within the catchment are essential to ensure the sustainable use and scientific protection of water resources in the region.

7. Conclusions

This study systematically analyzed the characteristics of water quality changes, material sources, and main controlling factors of agricultural rivers and conducted a comprehensive assessment using hydrochemical ratio methods and multivariate statistical techniques. The results show that agricultural rivers are significantly affected by agricultural fertilizers and domestic sewage. Based on principal component analysis, the main factors identified that control the water quality of agricultural rivers include the use of agricultural fertilizers, the dissolution of rock salt and evaporites, and the discharge of domestic sewage. Future research should focus more on the issues of heavy metal and emerging contaminant pollution in rivers, as well as the application of more precise source apportionment techniques, such as isotope tracing technology, to accurately identify pollution sources in agricultural rivers. This will provide more accurate technical support for the prevention and control of pollution in agricultural rivers.