Spatial and temporal distribution characteristics and source apportionment of biogenic elements using APCS-MLR model in the main inlet tributary of Danjiangkou Reservoir

Spatial and temporal distribution characteristics and source apportionment of biogenic elements using APCS-MLR model in the main inlet tributary of Danjiangkou Reservoir | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial and temporal distribution characteristics and source apportionment of biogenic elements using APCS-MLR model in the main inlet tributary of Danjiangkou Reservoir Yihang Wu, Qianzhu Zhang, Yuan Luo, Ke Jin, Qian He, Yang Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4818908/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted 5 You are reading this latest preprint version Abstract Danjiangkou Reservoir has been widely concerned as the water source of the world’s longest cross basin water transfer project. Biogenic elements are the foundation of material circulation and key factors affecting water quality. However, there is no comprehensive study on the biogenic elements in tributaries of Danjiangkou Reservoir, hindering a detailed understanding of geochemical cycling characteristics of biogenic elements in this region. Guanshan River, one of the main tributaries that directly enter the Danjiangkou Reservoir, was token as the research object. Spatiotemporal distribution characteristics of basic water quality parameters and biogenic elements were studied. Water quality was comprehensively evaluated through water quality index (WQI). Absolute principal component score-multiple linear regression (APCS-MLR) model was adopted to explore the main sources of biogenic elements. Results showed that, in terms of season, the concentrations of TN, TP, and DOC were significantly higher in wet season than in dry season, while no significant differences were found for DIC and DSi. Spatially, the concentrations of DC, DIC, TN and TP in the middle and lower reaches were higher than that in the upstream. DOC concentration peaked in the middle reaches, while DSi showed higher concentrations in the upstream. WQI values indicated that the river water quality was between good and excellent, although the water quality in wet season was slightly worse than that in the dry season. PCA extracted five potential sources, which accounting for 84.12% of the total variance, including rock weathering, mixed source of sewage discharge and agricultural non-point source pollution, dissolved soil CO 2 , seasonal factor and agricultural non-point source pollution. These sources contributed 38.96%, 12.33%, 13.54%, 23.95% and 11.21% to river water quality parameters, respectively. Strengthening the monitoring of biogenic elements, controlling pollutant discharge and exploring the relationship between biogenic elements and other pollutants are important for the water environment management in this basin. biogenic elements water quality assessment source apportionment APCS-MLR Danjiangkou reservoir Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction River transports large amounts of nutrients from land to the ocean, significantly affecting the biogeochemical cycles of ecosystems, and playing an important role in maintaining overall ecological functions (Chaplot and Mutema, 2021 ; Wu et al., 2023 ). Carbon (C), nitrogen (N), phosphorus (P) and silicon (Si) are important biogenic elements in river systems and there are interactions in the circulation of these elements (Ke et al., 2022 ; Ran et al., 2022 ; Xi et al., 2022 ). Dissolved carbon (DC) is a ubiquitous component of rivers and an important part of the global carbon cycle, which can be divided into dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) depending on the composition. DOC and DIC jointly contribute about 90% of the total carbon transport from land to water environments on a global scale (Ni and Li., 2022). Nitrogen and phosphorus are essential nutrients for phytoplankton and important factors for driving the composition of large plants in rivers, playing a crucial role in the growth and reproduction of organisms (Kaijser et al., 2021 ). Si predominantly determines the distribution of diatoms, which are the main drivers of biological CO 2 sequestration in aquatic environments. Therefore, Si plays an important role in river biogeochemical processes. Researches have reported that the concentration and composition of nutrients such as C, N, P and Si directly affect the primary productivity and the species, quantity and distribution of plankton, which may in turn affect the ecological balance of the aquatic environment (Ke et al., 2022 ; Ran et al., 2022 ; Zhang et al., 2023a ). Thus, studying the concentration and distribution characteristics of biogenic elements is important for the watershed environment protection. In the past few decades, more than doubling global riverine nutrients transport to the oceans and water pollution has become a major global problem because of anthropogenic activities such as agricultural fertilizers, domestic and industrial sewage discharge (Li and Bush, 2015 ; Varol et al., 2022 ). Source apportionment is a prerequisite for control and prevention of pollution (Hu et al., 2024 ). Receptor models are common methods of source apportionment, which qualitatively identify the pollution sources at different locations within a watershed by measuring the physicochemical properties of receptors, and also allow for a quantitative assessment of the contribution of these pollution sources. Absolute principal component score multiple linear regression (APCS-MLR) has been used in recent years to study the source of pollutant in rivers, because of its advantage in not sensitive to data outliers and stable performance (Chen et al., 2022 ; Zhang et al., 2022 ; Hu et al., 2024 ). However, most of the studies on the source analysis of riverine pollutants mainly focus on heavy metals, persistent organic pollutants and other emerging pollutants in large rivers (Yu et al., 2021 ; Chen et al., 2024a ; Zhang et al., 2024a ). Studies about biogenic elements mainly focus on the chemical forms, migration fluxes, and the impact of human activities such as dam construction on biogenic elements in bays and estuaries (Song et al., 2023 ). There is a relative lack of research on the source apportionment of biogenic elements in rivers, especially in small and medium-sized mountain rivers, which limits a comprehensive understanding of the geochemical cycle characteristics of biogenic elements in rivers. The total area of small and medium-sized mountain river basin accounts for 30% of the total area of global river basins, but their solute fluxes account for about 40% of the total solute fluxes of global rivers (Yin et al., 2020 ). What’s more, due to the differences in the nature of different watersheds, there may be certain differences in the distribution characteristics and main sources of river biogenic elements in different regions, resulting the applicability of geochemical characteristics of biogenic elements in different regions is limited. Danjiangkou reservoir is important water source for the middle route of the South-to-North Water Diversion, the longest inter-basin water transfer project in the world (1273km), and provides drinking water for over 100 million people. Therefore, the water quality of Danjiangkou reservoir is widely concerned. Through long-term research, the water environment quality and pollution status of Danjiangkou reservoir have been clarified. The water quality is generally good, and the water always maintains moderate nutrition. However, the nutrient concentrations of the tributaries are higher than that of the reservoir, and the control of TN in some tributaries needs to be strengthened (Wei et al., 2020 ). The inflow tributaries are not only the main water source, but also important factors affecting water quality changes. Guanshan River, located in the upstream of Danjiangkou Reservoir, is one of the main tributaries of Danjiangkou Reservoir, and the core water source of the middle route of the South-to-North Water Diversion project. Besides, the Guanshan River basin is a typical agricultural area. Due to the agricultural production activities and wastewater discharge, there were serious water quality pollution problems and the water quality of some river sections has reached the inferior class V, which has seriously affected the water quality ecology and water quality health. Until now, the backwater area is prone to algal blooms in autumn. However, there is currently no systematic study on the distribution characteristics and main sources of biogenic elements in Guanshan River basin, which poses a threat to the water quality safety of the Danjiangkou Reservoir and is not conducive to the scientific management of the aquatic environment. The objectives of this study are to evaluate the current water quality of Guanshan River, clarify the change characteristics of biogenic elements, reveal the main sources of biogenic elements, and provide scientific basis for ensuring the water environment safety and strengthening the control of water pollution in the basin. To our knowledge, this study would be the first to comprehensively report the distribution characteristics and main sources of biogenic elements in the Guanshan River basin, contributing to support the water quality assessment, water environment management and the understanding of biogenic factors geochemical cycle in Danjiangkou Reservoir area. 2 Materials and methods 2.1 Study area Guanshan river basin belongs to the Qinba Mountain Water Source Protection Zone, originating from the southwest foot of Wudang Mountain, Shiyan City, Hubei Province. It is one of the main tributaries of the Han River, following southwest and turning eastward, and finally following into the Danjiangkou Reservoir through the eastern part of Fang County and the northern part of Danjiangkou City. The Guanshan River has a drainage area of 322km 2 , with a geographical coordinate range of latitude 32°13’16”~32°58’20” and longitude 110°48’~111°34’59” (Fig. 1 ). The basin has a subtropical sub-humid climate, with an average annual temperature of 15.9℃ and an average annual precipitation of 960mm. The elevation difference in this basin is significant and the maximum altitude difference is 1366m. The main landform types in this area are hills and mountains, and the vegetation coverage is high, with an average of 71.2%. The land use type is mainly forest land, accounting for 93% of the total basin area. In terms of geological characteristics, the Guanshan River Basin is dominated by metamorphic rocks with strong weathering, soft rocks and broken rock mass (Zhang et al., 2023b ). [ Fig. 1 goes here] 2.2 Sampling and analysis The sampling process strictly the Chinese industry standard methods “Technical Guidance for Water Quality Sampling” (HJ 494–2009), and based on the geographical location of the basin and the distribution characteristics of towns, sampling points were set up along the main stream of Guanshan River, before and after the main towns, and at the confluence of the main tributaries. A total of 52 surface water samples were collected in February (dry season) and September (wet season) of 2023. Twenty-six samples were located around the main stream of the Guanshan River, and the others were in the main tributaries. Water samples were taken 10-50cm below the water surface in 1L pre-cleaned plastic bottles. Water temperature (WT), pH, dissolved oxygen (DO), total dissolves solids (TDS), redox potential (ORP) and electrical conductivity (EC) were measured on site by portable multi-parameter water quality analyzer. All the samples were filtered using a 0.45µm membrane except for that used for the measurement of total nitrogen (TN) and total phosphorus (TP). Samples were stored in a refrigerator at 4℃ and all samples were analyzed within one week after sampling. DIC and DOC were measured by the total organic carbon analyzer based on combustion oxidation nondispersive infrared absorption method. TN was measured by Ultraviolet visible spectrophotometer based on potassium persulfate digestion UV spectrophotometric method. TP was measured by flow injection analyzer based on ammonium molybdate spectrophotometry, and DSi was measured by Ultraviolet visible spectrophotometer based on silicon molybdenum blue spectrophotometry. In order to comprehensively evaluate water quality, Cl − and SO 4 2− were measured by Ion chromatograph (Thermo, ICS-600), Ca 2+ and Mg 2+ were measured by ICP-OES (Thermo, ICAP-7200). Stringent quality control measures were taken during the elemental analysis. Equipment used for in-situ measurements were calibrated using certified standards before fieldwork. Laboratory instruments were calibrated with the check standards and repeated analysis to ensure accuracy. Additionally, blank and parallel samples were measured and the relative standard values for the analyses were less than 5%. High quality reagents were used in the analysis process to ensure high accuracy and precision. 2.3 Water quality assessment Water Quality Index (WQI), with the advantages of flexible parameter selection and weights adjustment (Nong et al., 2020 ), was widely used to assess water quality by combing several water quality variables and converting them into a single value. Eleven variables (WT, pH, EC, DO, Ca 2+ , Mg 2+ , Cl − , SO 4 2− , TP, TN and TDS), which are important parameters to characterize water quality and have been extensively applied in the water quality assessment (Varol, 2020 ; Varol et al., 2022 ; Wu et al., 2021 ), were used for calculation of the WQI based on the following formula: where n is the number of environmental variables included, and C i and P i is the normalized value and the weight of variable i, respectively (Table S1 ). The assignment of weights to these parameters primarily refers to the existing expert knowledge and relevant case studies, which have been verified in previous research (Gao et al., 2023 ; Sang et al., 2024 ). According to the WQI values, water quality status was divided into five degrees: bad (0–25), low (26–50), moderate (51–70), good (71–90), and excellent (91–100) (Varol et al., 2022 ). This model has some limitations and may lead to a parameter redundancy issue since the development is typically based on expert opinions and local guidelines (Gao et al., 2023 ; Sang et al., 2024 ). Therefore, WQI min model, which fully considered relative weight and can reduce redundant information and measurement costs associated with assessing water quality, was also developed using a few crucial parameters to conduct water quality assessment cost-efficiently. Stepwise multiple linear regression method was carried out to establish the WQI min model, and the normalized values of 11 water quality parameters were used in the regression analysis (Varol et al., 2022 ). The calculation of WQI min model was based on the above formula. 2.4 APCS-MLR The absolute principal component scores (APCS) and the multiple linear regression model (MLR) were used for source apportionment since some studies have confirmed that APCS-MLR model is suitable and effective for pollution source apportionment (Gholizadeh et al., 2016 ; Varol et al., 2022 ). APCS-MLR model is developed based on the assumption that all possible pollution sources contribute linearly to the final concentration of the contaminant of interest at the receptor site. Following steps were taken to construct the APCS-MLR model. Firstly, extracting the principal component of water parameters by the PCA/FA, which was performed on Z-transformed standardized variables. Through the analysis of the relationship among multiple variables, PCA uses fewer representative factors to illustrate the main information extracted from numerous variables, making factor variables more interpretable (Varol et al., 2011 ). Before PCA, the data were first subjected to Kaiser-Meyer-Olkin (KMO) and Bartlett’s tests to validate the applicability. The minimum value of the KMO was 0.5, and the significance level obtained using Bartlett’s test was 0.05 (Xie et al., 2023 ). Secondly, these normalized factor scores, obtained by the PCA, were rescaled and converted to un-normalized the APCS values since the obtained normalized factor scores cannot be implemented directly for quantitative source contributions. Lastly, a multiple linear regression equation was constructed using the sampled parameters and APCS values as follows: where (r 0 ) j is constant term of multiple regressions for pollutant j, r kj is coefficient of multiple regression of the source k for pollutant j, APCS k is scaled value of the rotated factor k for the considered sample. The combined term r kj ×APCS k represents the contribution of source k to C j . The source contributions were calculated as follows (Gao et al., 2023 ): where S i represents the input from the i th source and p refers to the total number of identified sources. In the APCS-MLR model, variables with inverse contribution may have inverse signs, and ignoring the negative contributions may lead to source apportionment inaccuracy when calculating source contribution estimates (Xie et al., 2023 ). Therefore, all contributions were provided as absolute values in this study. 2.5 Statistical analysis Seasonal and spatial differences in water quality parameters were tested by independent samples t-tests. Correlation analysis was conducted to describe the relationship among water quality parameters. Statistical significance was determined using the p value of less than 0.05. Multivariate statistical analysis and APCS-MLR model were conducted using Origin v2021 and IBM SPSS Statistics 27. ArcGIS v10.7 was used to visualize the spatial variation of water quality parameters. 3 Results and discussion 3.1 Temporal and spatial characteristics of water quality 3.1.1 Temporal variations of water quality parameters Descriptive statistics of all water quality parameters, averaged by wet and dry seasons, are shown in Table 1. The averages of pH in wet season and dry season were 8.47 and 8.04, respectively, indicating that the water is slightly alkaline. The average levels of WT in wet season and dry season were 26.44℃ and 9.08℃, respectively, which is significantly related to changes in air temperature. The values of EC fluctuated between 159.33 and 447 μS/cm, with averages in wet and dry season were 269.55μS/cm and 310.12μS/cm, respectively. The levels of DO in water ranged from 7.29 to 13.26mg/L, and the average concentrations in wet and dry season were 8.56 and 11.76 mg/L, respectively. Average contents of TDS in wet and dry season were 174.76mg/L and 201.61mg/L, respectively, with ranged from 103 to 289mg/L. The values of ORP fluctuated between 167.8 and 299.83mV. The mean concentrations of DC, DOC and DIC in wet season were 18.72, 4.71 and 14.01mg/L, respectively, and in dry season were 6.16, 0.16 and 5.05 mg/L, respectively. The content of DIC was significantly higher than that of DOC, indicating that the dissolved carbon was dominated by inorganic carbon, which is consisted with the dissolved carbon composition of most rivers around the world (Chaplot et al., 2021). The mean concentrations of TN in wet and dry season were 1.43 and 1.07mg/L, respectively, which were both higher than the eutrophication threshold (0.2 mg/L). This is consistent with the current situation of relatively high nitrogen pollution load in the Danjiangkou Basin. The mean TP concentration in wet and dry season were 0.027 and 0.005mg/L, respectively. According to the Chinese Environmental quality standards for surface water (GB 3838-2002), the TP concentrations of all samples were lower than Class Ⅱ standard (0.1mg/L), indicating that the risk of phosphorus pollution was low. Additionally, the low phosphorus concentration in this study is consistent with the fact that natural freshwater systems are generally considered to be phosphorus limited (Maavara et al., 2020a). DSi ranged from 3.63 to 7.26 mg/L during the wet season (average value of 5.38 mg/L) and from 2.28 to 9.22 mg/L during the dry season (average value of 5.53 mg/L). [Table 1 goes here] Based on the statistical analysis results, there were significant differences in several water quality parameters studied between the dry and wet seasons (p<0.05), except ORP, DC, DIC and DSi. As shown in Fig.2, the values of pH, WT, TN, TP and DOC in wet season were significantly higher than that in dry season, and the mean values of there parameters were found to be 5.35%, 191.19%, 33.64%, 429.41% and 262.31% higher in the wet season than in the dry season., respectively. However, the values of DO, EC and TDS were significantly lower than that in dry season. Wang et al. (2021) also found that DOC and TP were higher in the wet season than in the dry season, which is similar to this study. Heavy precipitation in the wet season enhances the leaching and erosion of nutrients accumulated in the soil, resulting in nutrients entering the river with runoff or groundwater and increasing the concentration of TN, TP and DOC (Wang et al., 2021; Wang et al., 2025). Besides, the increased inflow in the wet season facilitated the resuspension of deposited pollutants, leading to a substantial release of nutrients (Sang et al., 2024). It is well known that DO level is mainly controlled by water temperature (Varol and Tokatli, 2023), and higher temperature and oxygen-consuming factors during the wet season leading to a decrease of DO content in the water (Wang et al., 2023a). The lower EC and TDS values in the wet season may be due to the large runoff dilute the ion concentration. In general, the seasonal variations of these parameters were mainly associated with seasonal variations in water flow and temperature (Varol, 2020). [Fig. 2 goes here] 3.1.2 Spatial variations of water quality parameters From a spatial perspective, the concentrations of TP, TN, and DOC presented significant variability, with CV values >50% (Table 1). CV values can reflect the impact of human activities on the aquatic environment, and high values usually indicate severe external interference and point-source pollution may be one of the causes of these substances (Na et al., 2024). The spatial distribution characteristics of biogenic elements were shown in Fig.3. Generally, the spatial variation characteristics of biogenic elements were similar in different seasons. Both DC and DIC showed an increasing trend from the upstream to the downstream. The bulk of river DC was derived from DIC, thus the spatial variations of DC and DIC were highly consistent. Strong agricultural fertilization activities in the middle and lower reaches of the basin enhanced the chemical weathering of carbonate rocks (Yin et al., 2020), which may lead to the increase of DIC concentration. Sewage discharge and anthropogenic activities can drive the spatial distribution of DOC concentration (Yan et al., 2023). Higher DOC values were observed at the middle reaches sampling points (near Guanshan Town) in both the wet and dry seasons. Guanshan Town is densely populated (14 thousand people lived) and the discharge of domestic sewage is large. Firstly, the Guanshan Town sewage treatment plant is located near G7 section, covers an area of 1592 m 2 and with a sewage treatment rate is 800 m 3 /d. Secondly, it was found that rural domestic sewage in the basin was basically directly lost to the surrounding environment during the on-site investigation. These can pose a threat to the water quality of the river. DOC concentration in river will increase greatly when domestic sewage and waste water are imported. Therefore, the increase of DOC concentration may be related to the discharge of waste water around Guanshan Town. What’s more, the concentrations of DOC in the upstream were slightly higher than that in the downstream in wet season. This may be related to the relatively fragile geological environment in the upstream and the strong soil erosion during the wet season, leading to an increase in DOC from external sources. These spatial distribution patterns of TN and TP concentrations were relatively similar, with slightly higher concentrations in the middle and lower reaches than that in the upper reaches during both dry and wet seasons. This might be attributed to the higher forest cover, lower population density and pollution discharges in the upstream areas. The concentration of DSi gradually decreased from upstream to downstream, which may be related to the increase of DSi content by stronger rock weathering in the upstream and the obstruction of DSi transport by more dams in the downstream. Independent sample T-test results showed that there was a significant difference in DSi concentration between the main stream and tributaries (p<0.001), and the average DSi concentrations of the main stream and tributaries during the study period were 4.54mg/L and 6.37mg/L, respectively. It has also been found that the concentration of DSi in tributaries was significantly higher than that in the main stream in the Yangtze River Basin. This is usually related to the fact that the main stream is more susceptible to anthropogenic interference such as dam construction and water pollution (Yu et al., 2022). Although there was no significant difference in TN, TP, DIC, and DOC concentrations between the main stream and tributaries, the average concentration of TN in tributaries (1.38mg/L) was slightly higher than that in main stream (1.13mg/L). Nevertheless, the average concentration of TP concentration in tributaries (0.010 mg/L) was lower than that in main stream (0.022 mg/L). This may be due to the greater number of people living along the main stream and the greater impact of anthropogenic activities. [Fig. 3 goes here] 3.1.3 Comparison with other rivers The comparison of selected biogenic elements in the Guanshan River with other rivers is shown in Table S2. Liu and Wang (2022) found that the average concentration of DOC across the global rivers was 10.4 mg/L, which is significantly higher than this study (mean concentration was 3.01 mg/L). The average riverine DOC concentration in the Miyun section of Chaobai river was 30.60mg/L, ranging within 17.45~48.16 mg/L (Wang et al., 2023b). These results showed that the differences in DOC concentration may be related to the differences in the geographical environment conditions of different rivers, and also indicated that the DOC concentration in Guanshan River was at a low level. The reasons for the low DOC concentration may be related to the high vegetation coverage, weak soil erosion by water and less phytoplankton in Guanshan River basin. Generally, catchment lithology is the main controlling factor for DIC concentration, and riverine DIC concentrations vary greatly on a global scale (Wang et al., 2016). In the British, concentrations of DIC in catchments dominated by carbonate are typically over 40mg/L, while in areas with little or no carbonate, DIC concentrations are usually less than 10 mg/L (Tye et al., 2022). The Guanshan River basin is mainly exposed to sedimentary rock and metamorphic rocks such as gneiss and sandshale, with silica as the main rock component (Zeng, 2013). Therefore, the DIC concentration may be lower than in catchment dominated by carbonate rocks. Compared with other rivers (Table S2), the average TN concentration in this study was similar to that in most rivers, but was higher than the globe average concentration in natural river waters (0.38mg/L) (Li et al., 2022), indicating that the water quality was partly influenced by anthropogenic activities. The average TP concentration in the Guanshan river was significantly lower than other rivers and the maximum concentration (0.074mg/L) was found below the TP concentration (0.075mg/L) in eutrophic rivers (Varol et al., 2011), which may be related to the widespread phosphorus restriction in the Yangtze River Basin (Liang and Xian, 2018), as well as the low population density and limited human interference in the study basin. DSi contents in the Guanshan River were similar to the range of worldwide rivers (from5.6 to 12.6 mg/L) (Durr et al., 2011), indicating that the DSi contents in this study is within the normal concentration range. However, the average concentration of DSi in the Guanshan River was lower than that in Tuojiang River (12.7 mg/L), Dadu River (11.6 mg/L), which is consistent with the conclusion that the concentration DSi in the upper reaches of Yangtze River is higher than that in the middle and lower reaches (Chen et al., 2024b). The increase of dams and anthropogenic nitrogen and phosphorus in the middle and lower reaches of the Yangtze River are important reasons for the decrease of DSi concentration. 3.2 Water quality assessment based on the WQI The WQI values in the dry season and wet season ranged from 82.78 to 92.78 and 77.89 to 91.05, with an average of 89.97 and 83, respectively. According to the WQI classification standard, the percentage of samples with good and excellent water quality in dry season was 80.77% and 19.23%, respectively. The percentage of samples with good and excellent water quality in wet season was 96.15% and 3.85%, indicating that the water quality of Guanshan river was generally good and the pollution was not at a critical level. This result is similar with water quality assessment result of Zhang et al. (2023c), which reported that the annual average WQI was greater than 60 in Danjiangkou Reservoir. However, the water quality in wet season was worser than that in dry season, since the WQI in wet season was significantly lower than that in dry season (p<0.05) and 15% of the samples with the WQI values lower than 80. The water quality evaluation results of the South to North Water Diversion Project and Three Gorges reservoir also showed that the WQI values were lowest in summer (Nong et al., 2020; Sang et al., 2024), which is consistent with this study, indicating that the water quality seasonal change of Guanshan River is normal. However, other several studies have reported that the WQI value in summer is higher than that in other seasons (Tian et al., 2019; Liu et al., 2020), suggesting that the seasonal variation of river water quality may be different in different regions. Spatially, WQI decreased gradually from upstream to downstream in wet season, and the samples with low WQI mainly concentrated near Guanshan Town (G7-G10) (Fig.4). However, there was no significant spatial variation trend of WQI in dry season, and WQI of samples at different locations was mostly between 86-90. This may be related to that the larger flow in the middle and lower reaches of the wet season enhances the erosion effect on surface pollutants. The WQI of the main stream and tributaries ranged from 77.89 to 90.56 and 78.95 to 92.78, with average values of 85.27 and 85.69, respectively. This result showed that there was no significant difference in WQI between the main stream and the tributaries. Linear regressions were used to develop WQI min , and four parameters were identified as having significant effects on WQI. There was no multicollinearity between the four independent variables, and VIFs were all less than 5, indicating that the results were accurate and reliable. According to the linear regressions results, TN played the most important role in explaining WQI values (R 2 =0.530, p<0.001), followed by WT, TP and SO 4 2- (Table S3). The performances of the regression models were evaluated according to their R 2 , RMSE and MAE. Model 4 presented the highest R 2 value (0.850) and the lowest RMSE (1.42) and MAE (1.05) values, indicating that that WQI min4 consisted of TN, WT, TP and SO 4 2- presented the best performance among the WQI min models. Several studies have also found that TN, TP, WT and SO 4 2- are the major contributor to the WQI (Wu et al., 2021; Varol et al., 2022), indicating that these are usually important parameters for water quality assessment since they can affect the growth of aquatic organisms and many complex biochemical processes. However, the results of WQI min may be influenced by the selection of parameters and their corresponding weights, parameters that are suitable for local conditions should be selected as much as possible in the future research. [Fig. 4 goes here] 3.3 Correlation analysis The relations among the 12 parameters were revealed using Spearman correlation matrix (Fig.5), and an absolute value of correlation coefficient greater than 0.5 usually indicates a strong correlation between variables (Ren et al., 2023). WT had significant and positive correlations with pH, DOC, DC, TN and TP (p<0.05), significant negative correlations with ORP and DO (p<0.05). Many studies have confirmed that riverine DOC concentration is closely related to temperature change (Wang, 2023). The positive correlation between water temperature and DOC, DC, TN, and TP may be related to seasonal changes. Seasons with higher temperatures have abundant rainfall and large runoff, leading to an increase in the amounts of pollutants entering the river. It is well known that DO is mainly controlled by temperature and cold water holds more DO than warm water (Varol et al., 2022). Besides, DO had significantly negative correlations with DOC, TN and TP, and the correlation coefficient were -0.62, -0.42 and -0.67, respectively. DOC, TN and TP are usually related to organic matter input, and organic decomposition consumes oxygen. In addition, pH had significantly negative correlation with ORP, but positive correlation with DOC. It was detected that ORP also had significantly negatively correlation with EC and TDS. EC and TDS had strong positive correlations (p<0.001), and they both had significantly positive correlations with DC and DIC. EC and TDS can represent the ion concentration in water, and as the inorganic salt content in river increases, the conductivity also increases. The positive correlation between DIC and TDS indicated that DIC is an important component of dissolved matter in the river, and the DIC content increases with the increase of dissolved substances. DIC is an important component of DC, thus they were significantly positively related (p<0.001). What’s more, DIC and DSi were significantly negatively related (p<0.05). Previous study also found that DIC correlated negatively with DSi (Chaplot and Mutema, 2021). The increase in DSi concentration can promote the growth of diatoms, of which photosynthesis promotes the conversion of inorganic carbon to organic carbon (Chen et al., 2024b). As expected, DOC, TN and TP were found to have significantly positive correlations (p<0.05), indicating that these parameters may be derived from similar sources. Wang et al. (2023b) have reported that DOC affecting nitrogen and phosphorus cycling in river ecosystems. Therefore, it is normal that there are significantly correlations among these parameters. [Fig. 5 goes here] 3.4 Source apportionment using the APCS-MLR model 3.4.1 Potential pollution sources Kaiser-Meyer-Olkin (KMO) and Bartlett’s sphericity test results were 0.725 (>0.5) and 1750 (df=153, p=0.000), respectively, indicating that statistically links were present among the variables and the results of PCA were reliable. Five factors with eigenvalue >1, explaining 84.12% of the total variance, were extracted using the PCA/FA with varimax rotation (Table 2). [Table 2 goes here] Loadings of each parameter in the five PCs were shown in Fig.6 and Table 2. PC1 showed strong positive loadings (>0.75) on EC, TDS, Na + , Ca 2+ , Mg 2+ and SO 4 2- , accounting for 32.51% of the total variance. These indicators represent the composition of dissolved matter in rivers and natural sources of the ionic groups of salts (Varol, 2020). The dissolution load in rivers is generally controlled by rock weathering, atmospheric deposition, and evaporation-fractional crystallization process (Gibbs, 1970). In arid areas, evaporation is generally more intense, and riverine dissolved matter is more susceptible to the influence of evaporite, characterized by high TDS content. While this study area is located in a subtropical humid region, where evaporation-fractional crystallization process is usually weak. Additionally, the average TDS content in the river is 188.19mg/L, which is much lower than the average TDS content of rivers controlled by evaporites in arid areas (732mg/L), indicating that evaporation-fractional crystallization process has a relatively small impact on river dissolved matter (Wu, 2016). Previous studies have reported that waters in the Danjinagkou Reservoir basin are controlled by carbonate weathering (Zhang et al., 2020). What’s more, TDS content is an important indicator to measure the strength of rock weathering in a watershed, and the TDS content of water controlled by atmospheric deposition is usually low. The TDS content in this study is much higher than the average TDS content in world rivers (65mg/L) (Wu et al., 2016), suggesting relatively strong weathering in the watershed. Therefore, PC1 can be termed as rock weathering. PC2, accounting for 22.59% of the total variance, showed strong positive loadings on Cl - , WT and TP, strong negative loadings on DO, and moderate positive loadings on K + and DOC. River water chemistry characteristics is not only controlled by natural geochemical process, but also by anthropogenic perturbations (Liu et al., 2019). Chloride can be an effect indicator of several anthropogenic activities such as domestic sewage and agriculture (Gholizadeh et al., 2016). K + could reflect the influence of the fertilizers use and represent agricultural runoff (Varol et al., 2011). TP, DO and DOC are often regarded as indicators of organic pollution, and anthropogenic activities such as wastewater discharge and intensive agricultural are the main sources of surface water organic pollution (Anh et al., 2023). Low levels of DO are primarily attributed to the discharge of organic matter (Santos et al., 2024) and population growth, domestic sewage discharge, and fertilizer application can lead to the increase of DOC content in rivers (Wen et al., 2021). Extensive use of detergents containing phosphorus significantly increases the phosphorus content in domestic wastewater (Gao et al., 2023), and previous studies have reported that TP in surface water might be ascribed to the domestic sewage (Liu et al., 2020; Zhang et al., 2022). The TP content increased significantly in the monitoring section (G6-G8), which is near Guanshan Town, further proving that domestic sewage discharge is an important source of TP in the river. Existing data indicates that the rural population in the upper reaches of Danjiangkou Reservoir accounts for 60%, and the agricultural industry accounts for 46% (Zhai et al., 2023). The average fertilizer application intensity in Shiyan City, where the watershed is located, is 343.07 kg·hm -2 , which is 1.52 times of the upper limit of environmental safety standard of fertilizer application in developed countries (Gong et al., 2022). What’s more, large scale cultivation along rivers is quite common according to our field survey. Therefore, excessive phosphorus can be lost from the surrounding farmland through surface runoff in intensive agricultural areas, resulting in large-scale non-point source pollution of surface water and promoting eutrophication. Previous studies have reported that in the Danjiangkou Reservoir area, which is based on agriculture, 65.6% of the surrounding farmland is at high risk of phosphorus loss (Li et al., 2020; Zhang et al., 2019). Therefore, PC2 may be termed as mixed factor by domestic sewage discharge and agricultural non-point source pollution. PC3 showed strong positive loadings on DC and DIC, accounting for 12.45% of the total variance. Generally, riverine DIC originates mostly from mineral weathering, dissolved soil CO 2 , degradation of organic matter, and biological respiration (Ni and Li, 2022; Yin et al., 2020). When carbonate rocks are weathered with soil CO 2 , half of the carbon in the weathering product HCO 3 - is derived from the soil CO 2 , and the other half is derived from carbonate rocks, whereas all the carbon produced by silicate rock weathering is derived from the soil CO 2 (Yin et al., 2020). Considering the widespread development of silicate rocks in the Guanshan River Basin, dissolved soil CO 2 may be an important source of riverine DC and DIC. Additionally, the high forest coverage rate (93%) and large biomass in the Guanshan River Basin, leading to significantly contribution of soil CO 2 to DIC. What’s more, soil-derived CO 2 is reported to accounted for 67% of DIC in the world’s rivers (Tye et al., 2022), indicating that dissolved soil CO 2 is another important source of DIC. The concentration of DIC in the Guanshan River was obviously higher than that of DOC, and there was no significant relationship between riverine DIC and DOC, indicating that DIC released by organic matter degradation in the river contributed little to the DIC pool. To sum up, PC3 can be inferred to dissolved soil CO 2 . In the PC4 (10.04% of the total variance), there was a strong positive loading on pH and a strong negative loading on ORP. Generally, pH in natural water is mainly influenced by temperature (Chen et al., 2022) and significant correlations among pH, ORP and WT were found. Therefore, the PC4 can be interpreted as the seasonal factor, reflecting the physiochemical source of variability (Varol, 2020). PC5 showed strong positive loading on TN and strong negative loading on DSi, accounting for 6.54% of the total variance. TN concentration is usually closely connected with intensive agricultural activities in rural areas (Anh et al., 2023). In Qinba Mountain area, where this study area is located, agricultural activities are frequent and nitrogen-based fertilizer are widely used, leading to the soil nutrient content greatly increased. Fertilizers in soil may migrate into river with surface runoff, becoming one of the important sources of nitrogen (Dong et al., 2022). Additionally, free-range livestock in the Guanshan River basin is prevalent, leading rivers become home to farming wastewater. Several researches have proved that agricultural activities around the Danjiangkou Reservoir are main sources of atmospheric nitrogen deposition and have an influence on water quality (Guo et al., 2022; Zhang et al., 2024b). What’s more, agricultural activities have also been confirmed to be an anthropogenic source of silicon (Bu et al., 2015). Maavara et al. (2020b) also found that the global supply of silicon to rivers has been reduced by human activities. Therefore, DSi has a negative load on PC5 and further proves that PC5 may represent an anthropogenic source. In the whole, PC5 might present agricultural non-point source pollution. [Fig. 6 goes here] 3.4.2 Source apportionment To quantify the contributions of each pollution source to water quality indices, an APCS-MLR receptor model was established based on the identified pollution sources. Predicted/observed plots of APCA-MLR model were shown in Fig.7 and Fig.S1. The correlation coefficients (R 2 ) of all parameters were higher than 0.5, ranged from 0.63-0.96, indicating that the consistency between observed and predicted values was good and the results of source apportionment were relatively reliable (Zhang et al., 2022). [Fig. 7 goes here] Fig.8 shows the contributions of different sources to the studied water quality parameters. Rock weathering (VF1) was the primary source, with an average contribution of 38.96%. The other four sources with average contribution rates of 12.34% (VF2), 13.54% (VF3), 23.95% (VF4), and 11.21% (VF5). VF1 made significant contributions to EC (74.84%), TDS (74.67%), K + (54.21%), Na + (72.95%), Ca 2+ (77.03%), Mg 2+ (64.26%) and SO 4 2- (67.64%). The contributions of domestic sewage discharge and agricultural non-point source pollution (VF2) ranged from 0.66% (EC) to 40.12% (TP) for the 18 water quality indices. Besides, VF2 also accounted for 38.32% of DO and 33.36% of Cl - . This contribution of dissolved soil CO 2 (VF3) is manifested by the high contribution of DC (57.15%) and DIC (44.48%). Contribution of seasonal change (VF4) for different variable were between 3.43% (SO 4 2- ) and 86.06% (pH). In addition, VF4 also accounted for 75.59% of ORP, 57.56% of Cl - , and 44.53% of WT. The contribution of VF5 is manifested by the high contribution of TN (47.35%), DSi (43.52%), and TP (25.40%). On the whole, natural factors such as rock weathering and seasonal variation are the main sources of water quality indexes in Guanshan River Basin. The contributions of natural sources include rock weathering and dissolved soil CO 2 to DC and DIC were 78.26% and 77.36%, respectively. For DOC, season factor contributed the most (34.82%). Seasonal variation may affect endogenous DOC by affecting microbial activity in river. Additionally, the difference of soil erosion degree in different seasons also affected the exogenous DOC (Chaplot and Mutema, 2021). However, for nutrients such as TN and TP, anthropogenic sources such as sewage discharge and agricultural non-point source pollution should not be ignored, which accounted for 51.44% of TN, and 65.53% of TP, respectively. Agricultural non-point source contributed the most for DSi (43.52%), followed by rock weathering (29.86%). Generally, DSi is mainly derived from phytolith dissolution and silicate weathering (Ma et al., 2017), which were not comprehensively considered in this study, indicating that this receptor model has certain limitations. [Fig. 8 goes here] 3.5 Implications of water environment protection Pollution sources in the upstream basin are the main sources of nitrogen and phosphorus in Danjiangkou Reservoir. Guanshan River basin is located in the upstream of Danjiangkou Reservoir and classified as a water safety guarantee zone. Therefore, water quality safety in this basin is very important. However, several water quality sections with strong human disturbance still enriched with nutrients, highlighting reasonable management and control measures should be taken to ensure water quality safety. Firstly, this study highlighted that TN and TP are important parameters of water quality assessment. Therefore, strengthening water environment monitoring in areas with intensive activities such as Guanshan Town, and closely tracking the changes of water quality parameters such as TN and TP, are essential to avoid eutrophication. Secondly, some effective measures should be adopted to control pollutants emissions since domestic sewage discharge and agricultural non-point source pollution are the main anthropogenic sources. For example, strengthening the awareness of environmental protection of residents along rivers and increasing efforts in domestic sewage collection and treatment to avoid direct discharge of sewage. Besides, optimizing the structure of agricultural industry, improving planting pollution technology and fertilizer utilization rate are also effective measures to reduce exogenous nutrient load in rivers. Thirdly, silicon plays a crucial role in global biogeochemical cycles (Hawking et al., 2018), while it usually receives less attention than N and P (Wang et al., 2016), making limited understanding on the biogeochemical cycling mechanism of riverine silicon. In order to strength the management of nutrient elements in rivers, in the future, continuous and increased monitoring of riverine silicon, determination of silicon in other forms such as biogenic silicon and using isotope tracer can be carried out to explore the long-term trend of silicon concentration and form. Exploring the coupling biogeochemical cycling mechanisms of silicon and other biogenic elements in mountainous river is also necessary. The distribution of DSi is usually controlled by natural conditions, but anthropogenic interference should not be ignored. Fourthly, with the continuous development of urbanization and agriculture, many pollutants such as plastics, heavy metals and organochlorine pesticides exist widely in river systems and have a direct impact on the biogenic elements in the ecosystem (Chen et al., 2024c). While this study only focused on the change characteristics and main sources of biogenic elements. It is essential to explore the relationship between biogenic elements and other pollutants, in order to evaluate the geochemical characteristics of biogenic elements more accurately and comprehensively. 4 Conclusions In this study, the Guanshan River, extremely sensitive to the impact of the middle route of the South-to-North Water Diversion project, was systematically studied. Spatial and temporal distribution characteristics of the biogenic element concentrations were analyzed, the water quality was comprehensively evaluated, and the main sources of biogenic elements were identified and quantified by the multivariate statics. The conclusions can be summarized as follows: (1) The concentrations of biogenic elements in the Guanshan River basin were generally low, and most parameters showed significant seasonal differences. Spatially, the variation trends of biogenic elements concentration were similar in different seasons. The concentrations of DIC, DOC, TN and TP were all higher in the middle and lower reaches, while DSi concentration was higher in the upper reaches. (2) WQI is a useful method for comprehensive evaluation of water quality, and the water quality of Guan River was generally good based on WQI assessment results, indicating that the water environment treatment carried out in recent years are effective. But the water quality in wet season was slightly worse than that in dry season. The proposed WQI min model indicated that TN was the most important parameters for water quality assessment. (3) Five potential sources of water quality indexes were revealed by APCS-MLR model. The mean contributions of rock weathering, mixed sources of domestic sewage discharge and agricultural non-point source pollution, dissolved soil CO 2 , seasonal factors and agricultural non-point source pollution were 38.96%, 12.34%, 13.54%, 23.95% and 11.21%, respectively. Each source contributed to each water quality variable differently. Riverine DIC was mainly derived from natural sources such as rock weathering and dissolved soil CO 2 , and seasonal factor contributed the most to riverine DOC. The contribution of sewage discharge and agricultural non-point source pollution to TN and TP reached 51.44% and 65.53%, respectively. The contribution of rock weathering and agricultural non-point source pollution to DSi were 29.86% and 43.53%, respectively. (4) Strengthening the concentration monitoring of biogenic elements and controlling sewage discharge and agricultural non-point source pollution are recommended to ensure water ecological security. Exploring the relationship between biogenic elements and other pollutants is likely to be a crucial focus for future research. This study enhances the understanding of spatiotemporal distribution characteristics and main sources of biogenic elements in the Guanshan River basin, and these results are of great importance for the scientific management of river water environment and the exploration of geochemical characteristics of biogenic elements. Declarations Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Consent to participate All authors have given their consent to participate in submitting this manuscript to this journal. Consent to publish All authors were informed and consented to publish the article. Author contributions Yihang Wu: sampling, methodology, statistical analyses, writing-original draft. Qianzhu Zhang: conceptualization, sampling, data curation, writing-review and editing, funding acquisition. Yuan Luo: sampling, methodology, statistical analyses. Ke Jin: sampling, data curation. Qian He: sampling, data curation. Yang Lu: supervision, funding acquisition. Funding This work was funded by the National Natural Science Foundation of China (42407108), Knowledge Innovation Program of Wuhan-Shuguang (2022020801020245), Water Conservancy Key Scientific Research Project of Hubei Province (HBSLKY202405), the Central Public-interest Scientific Institution Basal Research Fund of China (CKSF2023299/CQ). Competing Interests The authors declare no competing interests. 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(in Chinese) Tables Table 1 Descriptive statistics of physicochemical parameters parameters mean median min max CV/% SD wet season pH 8.47 8.35 7.77 9.28 5.74 0.49 WT/(℃) 26.44 27.15 20.63 29.30 9.27 2.45 EC/(μS/cm) 269.65 271.34 159.33 419.00 19.60 52.85 DO/(mg/L) 8.56 8.63 7.29 9.9 8.18 0.70 TDS/(mg/L) 174.76 175.84 103 272 19.70 34.42 ORP/(mV) 220.31 221.37 167.8 299.83 13.53 29.80 DC/(mg/L) 18.72 18.30 10.14 32.86 25.16 4.71 DOC/(mg/L) 4.71 3.70 2.12 15.92 66.88 3.15 DIC/(mg/L) 14.01 14.55 6.83 18.30 21.41 3.00 TP/(mg/L) 0.027 0.022 0.004 0.074 74.87 0.02 TN/(mg/L) 1.43 1.3 0.37 3.33 46.85 0.67 DSi/(mg/L) 5.38 5.50 3.63 7.26 19.89 1.07 dry season pH 8.04 8.02 7.38 9.06 4.85 0.39 WT/(℃) 9.08 9.55 4.7 13.70 23.90 2.17 EC/(μS/cm) 310.12 294.50 168.00 447.00 23.11 71.66 DO/(mg/L) 11.76 11.77 10.13 13.26 5.44 0.64 TDS/(mg/L) 201.61 191.50 108.00 289.00 23.26 46.89 ORP/(mV) 225.90 227.80 181.80 260.50 8.43 19.04 DC/(mg/L) 16.36 16.74 6.16 26.16 33.07 5.41 DOC/(mg/L) 1.30 1.17 0.16 5.30 90 1.17 DIC/(mg/L) 15.06 15.65 5.05 24.32 32.74 4.93 TP/(mg/L) 0.0051 0.0037 0.0005 0.0157 92.16 0.0047 TN/(mg/L) 1.07 0.75 0.17 4.62 85.05 0.91 DSi/(mg/L) 5.53 5.51 2.28 9.22 39.42 2.18 Whole study period pH 8.26 8.17 7.38 9.28 5.93 0.49 WT/(℃) 17.76 17.17 4.70 29.30 51.01 9.06 EC/(μS/cm) 289.88 285.17 159.33 447.00 22.63 65.60 DO/(mg/L) 10.16 10.02 7.29 13.26 17.22 1.75 TDS/(mg/L) 188.19 185.00 103.00 289.00 22.81 42.92 ORP/(mV) 223.10 224.45 167.80 299.83 11.17 24.92 DC/(mg/L) 17.54 17.29 6.16 32.86 29.42 5.16 DOC/(mg/L) 3.01 2.75 0.16 15.92 96.68 2.91 DIC/(mg/L) 14.53 14.87 5.05 24.32 28.08 4.08 TP/(mg/L) 0.016 0.009 0.0005 0.074 112.50 0.018 TN/(mg/L) 1.25 1.02 0.17 4.62 64.80 0.81 DSi/(mg/L) 5.46 5.50 2.28 9.22 100.73 5.50 Table 2 Varimax rotated factor loadings based on PCA/FA Variable Components PC1 PC2 PC3 PC4 PC5 WT -0.233 0.839 0.129 0.344 0.088 pH 0.022 0.249 -0.021 0.874 0.124 DO 0.299 -0.841 -0.178 0.031 -0.004 EC 0.953 -0.025 0.205 0.077 0.086 ORP -0.316 -0.102 -0.007 -0.824 0.046 TDS 0.954 -0.030 0.206 0.078 0.086 DOC -0.181 0.671 0.400 0.261 -0.137 DC 0.203 0.289 0.909 0.060 -0.023 DIC 0.386 -0.114 0.866 -0.111 0.069 TN -0.374 0.140 -0.198 0.044 0.683 TP 0.003 0.837 -0.130 -0.095 0.223 DSi -0.390 -0.131 -0.320 -0.072 -0.709 K 0.519 0.685 -0.072 0.041 0.124 Na 0.893 -0.050 0.046 0.123 -0.119 Ca 0.939 -0.057 0.232 0.072 0.011 Mg 0.784 -0.139 0.358 0.065 0.091 Cl -0.031 0.866 -0.025 0.326 0.043 SO 4 2- 0.842 -0.229 -0.158 0.027 -0.233 Eigenvalues 6.413 4.638 1.793 1.240 1.059 % of variance 32.509 22.591 12.452 10.037 6.535 Cumulative % 32.509 55.100 67.552 77.588 84.123 Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Accept 02 Jan, 2025 Reviewers agreed at journal 16 Dec, 2024 Reviewers invited by journal 16 Dec, 2024 Editor invited by journal 05 Dec, 2024 First submitted to journal 04 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4818908","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":391005408,"identity":"e6ad0f93-c188-464c-ab7b-0156b8530ef7","order_by":0,"name":"Yihang Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACZh4Izc/efODAhx+kaJHsOZZ4cGYPUdZAtRjcyDE+zMFGhAaD47wHPxf8Opy44cyZD4eB+uX5xQ4Q0HKYL1l6Zt/hxJnHezccLrBgMJw5O4GQFh4Dad6e24l9Z85uODyDhyHB4DZhLca/QVoabuQ8OMzDRpwWM2meH7cTJ9zIYSBOiyRQizVvw3/jmT3HDICBLEHYL3znzxjf5vmTJtvP3vz4w4cfNvL80gS0KBwAEoxtcL4EfuUgIN8AIv8QVjgKRsEoGAUjGAAAK7xOu1MeE1cAAAAASUVORK5CYII=","orcid":"","institution":"Changjiang River Scientific Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Yihang","middleName":"","lastName":"Wu","suffix":""},{"id":391005409,"identity":"6693090e-ad28-4ac5-862e-165b495dd1c7","order_by":1,"name":"Qianzhu Zhang","email":"","orcid":"","institution":"Changjiang River Scientific Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Qianzhu","middleName":"","lastName":"Zhang","suffix":""},{"id":391005410,"identity":"fa8c40c3-790b-4a30-8167-541f99605693","order_by":2,"name":"Yuan Luo","email":"","orcid":"","institution":"Chongqing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Luo","suffix":""},{"id":391005411,"identity":"aa66066d-9611-43a7-be5e-b6b0ab6ed2d0","order_by":3,"name":"Ke Jin","email":"","orcid":"","institution":"Changjiang River Scientific Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Jin","suffix":""},{"id":391005412,"identity":"a73c4740-43bc-4814-b54f-90cff2c51696","order_by":4,"name":"Qian He","email":"","orcid":"","institution":"Chongqing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"He","suffix":""},{"id":391005413,"identity":"a3daf955-1970-4ce5-b497-6bb0653bfea7","order_by":5,"name":"Yang Lu","email":"","orcid":"","institution":"Changjiang River Scientific Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-07-29 04:04:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4818908/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4818908/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-025-35898-3","type":"published","date":"2025-01-20T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71817775,"identity":"8a8cc10e-97d6-4bc0-b942-fddf9ef221a6","added_by":"auto","created_at":"2024-12-18 21:25:05","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":465092,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation of the study area and distribution of the sampling points\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4818908/v1/b9171246aedcdde3cc81b5e7.jpeg"},{"id":71817223,"identity":"1be2ca65-f1ce-4d2a-8de5-ca6225132cda","added_by":"auto","created_at":"2024-12-18 21:09:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox plot of water quality parameters\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage27.png","url":"https://assets-eu.researchsquare.com/files/rs-4818908/v1/21d358afa6d864ab26540c8c.png"},{"id":71817227,"identity":"720429f8-83ca-4e43-b174-a5f837e6d0c3","added_by":"auto","created_at":"2024-12-18 21:09:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1686150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) Spatial distribution of biogenic factor concentration in dry season\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b) Spatial distribution of biogenic factor concentration in wet season\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FinalImageV20241219021956.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4818908/v1/e1cdea53b7e028593e0da962.jpg"},{"id":71817229,"identity":"c61c72a9-141e-4406-b35f-0c6efa41ce18","added_by":"auto","created_at":"2024-12-18 21:09:05","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2507334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution pattern of WQI in the dry season (a) and wet season (b)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4818908/v1/eee8ad989367ff13cfd6cac6.jpeg"},{"id":71817594,"identity":"f4ec819f-14bb-40fa-b5bc-b82c49e0dfdc","added_by":"auto","created_at":"2024-12-18 21:17:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of the 12 water quality parameters\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4818908/v1/60e3347c7f1cd2b5abc5127b.png"},{"id":71817593,"identity":"45e67393-301e-4890-ab1f-1640491415dd","added_by":"auto","created_at":"2024-12-18 21:17:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComponent loadings for variables after varimax rotation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4818908/v1/059114872b90f3e073ac5c3c.png"},{"id":71817239,"identity":"4f8088e3-3afb-43e2-ad6c-d7a26b615f54","added_by":"auto","created_at":"2024-12-18 21:09:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":84069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between observed and predicted concentrations.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4818908/v1/97d8bd07295731819458abbe.png"},{"id":71817597,"identity":"7984e8ab-1ee3-4b42-8d60-39b52a2d109c","added_by":"auto","created_at":"2024-12-18 21:17:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":234552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSource contribution information obtained from APCS-MLR model. (a) Proportional contributions of different sources to the concentration of each variable; (b) Average contributions of different sources to water quality.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4818908/v1/16f1cced433d37309ad63ef4.png"},{"id":74858430,"identity":"b93f5238-90c0-4fa4-a741-617ea5cdcc05","added_by":"auto","created_at":"2025-01-27 16:09:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6654859,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4818908/v1/61841d00-fe05-4013-97e4-6baf497bebb1.pdf"},{"id":71817231,"identity":"c71b95c0-da5f-452b-9cff-76e0d09d5d91","added_by":"auto","created_at":"2024-12-18 21:09:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":496900,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4818908/v1/066cac6b472ed1bed029165a.docx"}],"financialInterests":"","formattedTitle":"Spatial and temporal distribution characteristics and source apportionment of biogenic elements using APCS-MLR model in the main inlet tributary of Danjiangkou Reservoir","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eRiver transports large amounts of nutrients from land to the ocean, significantly affecting the biogeochemical cycles of ecosystems, and playing an important role in maintaining overall ecological functions (Chaplot and Mutema, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Carbon (C), nitrogen (N), phosphorus (P) and silicon (Si) are important biogenic elements in river systems and there are interactions in the circulation of these elements (Ke et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ran et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Dissolved carbon (DC) is a ubiquitous component of rivers and an important part of the global carbon cycle, which can be divided into dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) depending on the composition. DOC and DIC jointly contribute about 90% of the total carbon transport from land to water environments on a global scale (Ni and Li., 2022). Nitrogen and phosphorus are essential nutrients for phytoplankton and important factors for driving the composition of large plants in rivers, playing a crucial role in the growth and reproduction of organisms (Kaijser et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Si predominantly determines the distribution of diatoms, which are the main drivers of biological CO\u003csub\u003e2\u003c/sub\u003e sequestration in aquatic environments. Therefore, Si plays an important role in river biogeochemical processes. Researches have reported that the concentration and composition of nutrients such as C, N, P and Si directly affect the primary productivity and the species, quantity and distribution of plankton, which may in turn affect the ecological balance of the aquatic environment (Ke et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ran et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Thus, studying the concentration and distribution characteristics of biogenic elements is important for the watershed environment protection.\u003c/p\u003e \u003cp\u003eIn the past few decades, more than doubling global riverine nutrients transport to the oceans and water pollution has become a major global problem because of anthropogenic activities such as agricultural fertilizers, domestic and industrial sewage discharge (Li and Bush, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Varol et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Source apportionment is a prerequisite for control and prevention of pollution (Hu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Receptor models are common methods of source apportionment, which qualitatively identify the pollution sources at different locations within a watershed by measuring the physicochemical properties of receptors, and also allow for a quantitative assessment of the contribution of these pollution sources. Absolute principal component score multiple linear regression (APCS-MLR) has been used in recent years to study the source of pollutant in rivers, because of its advantage in not sensitive to data outliers and stable performance (Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, most of the studies on the source analysis of riverine pollutants mainly focus on heavy metals, persistent organic pollutants and other emerging pollutants in large rivers (Yu et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Studies about biogenic elements mainly focus on the chemical forms, migration fluxes, and the impact of human activities such as dam construction on biogenic elements in bays and estuaries (Song et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). There is a relative lack of research on the source apportionment of biogenic elements in rivers, especially in small and medium-sized mountain rivers, which limits a comprehensive understanding of the geochemical cycle characteristics of biogenic elements in rivers. The total area of small and medium-sized mountain river basin accounts for 30% of the total area of global river basins, but their solute fluxes account for about 40% of the total solute fluxes of global rivers (Yin et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). What\u0026rsquo;s more, due to the differences in the nature of different watersheds, there may be certain differences in the distribution characteristics and main sources of river biogenic elements in different regions, resulting the applicability of geochemical characteristics of biogenic elements in different regions is limited.\u003c/p\u003e \u003cp\u003eDanjiangkou reservoir is important water source for the middle route of the South-to-North Water Diversion, the longest inter-basin water transfer project in the world (1273km), and provides drinking water for over 100\u0026nbsp;million people. Therefore, the water quality of Danjiangkou reservoir is widely concerned. Through long-term research, the water environment quality and pollution status of Danjiangkou reservoir have been clarified. The water quality is generally good, and the water always maintains moderate nutrition. However, the nutrient concentrations of the tributaries are higher than that of the reservoir, and the control of TN in some tributaries needs to be strengthened (Wei et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The inflow tributaries are not only the main water source, but also important factors affecting water quality changes. Guanshan River, located in the upstream of Danjiangkou Reservoir, is one of the main tributaries of Danjiangkou Reservoir, and the core water source of the middle route of the South-to-North Water Diversion project. Besides, the Guanshan River basin is a typical agricultural area. Due to the agricultural production activities and wastewater discharge, there were serious water quality pollution problems and the water quality of some river sections has reached the inferior class V, which has seriously affected the water quality ecology and water quality health. Until now, the backwater area is prone to algal blooms in autumn. However, there is currently no systematic study on the distribution characteristics and main sources of biogenic elements in Guanshan River basin, which poses a threat to the water quality safety of the Danjiangkou Reservoir and is not conducive to the scientific management of the aquatic environment. The objectives of this study are to evaluate the current water quality of Guanshan River, clarify the change characteristics of biogenic elements, reveal the main sources of biogenic elements, and provide scientific basis for ensuring the water environment safety and strengthening the control of water pollution in the basin. To our knowledge, this study would be the first to comprehensively report the distribution characteristics and main sources of biogenic elements in the Guanshan River basin, contributing to support the water quality assessment, water environment management and the understanding of biogenic factors geochemical cycle in Danjiangkou Reservoir area.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study area\u003c/h2\u003e\n \u003cp\u003eGuanshan river basin belongs to the Qinba Mountain Water Source Protection Zone, originating from the southwest foot of Wudang Mountain, Shiyan City, Hubei Province. It is one of the main tributaries of the Han River, following southwest and turning eastward, and finally following into the Danjiangkou Reservoir through the eastern part of Fang County and the northern part of Danjiangkou City. The Guanshan River has a drainage area of 322km\u003csup\u003e2\u003c/sup\u003e, with a geographical coordinate range of latitude 32\u0026deg;13\u0026rsquo;16\u0026rdquo;~32\u0026deg;58\u0026rsquo;20\u0026rdquo; and longitude 110\u0026deg;48\u0026rsquo;~111\u0026deg;34\u0026rsquo;59\u0026rdquo; (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The basin has a subtropical sub-humid climate, with an average annual temperature of 15.9℃ and an average annual precipitation of 960mm. The elevation difference in this basin is significant and the maximum altitude difference is 1366m. The main landform types in this area are hills and mountains, and the vegetation coverage is high, with an average of 71.2%. The land use type is mainly forest land, accounting for 93% of the total basin area. In terms of geological characteristics, the Guanshan River Basin is dominated by metamorphic rocks with strong weathering, soft rocks and broken rock mass (Zhang et al., \u003cspan class=\"CitationRef\"\u003e2023b\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cstrong\u003egoes here]\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Sampling and analysis\u003c/h2\u003e\n \u003cp\u003eThe sampling process strictly the Chinese industry standard methods \u0026ldquo;Technical Guidance for Water Quality Sampling\u0026rdquo; (HJ 494\u0026ndash;2009), and based on the geographical location of the basin and the distribution characteristics of towns, sampling points were set up along the main stream of Guanshan River, before and after the main towns, and at the confluence of the main tributaries. A total of 52 surface water samples were collected in February (dry season) and September (wet season) of 2023. Twenty-six samples were located around the main stream of the Guanshan River, and the others were in the main tributaries. Water samples were taken 10-50cm below the water surface in 1L pre-cleaned plastic bottles. Water temperature (WT), pH, dissolved oxygen (DO), total dissolves solids (TDS), redox potential (ORP) and electrical conductivity (EC) were measured on site by portable multi-parameter water quality analyzer. All the samples were filtered using a 0.45\u0026micro;m membrane except for that used for the measurement of total nitrogen (TN) and total phosphorus (TP). Samples were stored in a refrigerator at 4℃ and all samples were analyzed within one week after sampling. DIC and DOC were measured by the total organic carbon analyzer based on combustion oxidation nondispersive infrared absorption method. TN was measured by Ultraviolet visible spectrophotometer based on potassium persulfate digestion UV spectrophotometric method. TP was measured by flow injection analyzer based on ammonium molybdate spectrophotometry, and DSi was measured by Ultraviolet visible spectrophotometer based on silicon molybdenum blue spectrophotometry. In order to comprehensively evaluate water quality, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e were measured by Ion chromatograph (Thermo, ICS-600), Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e were measured by ICP-OES (Thermo, ICAP-7200).\u003c/p\u003e\n \u003cp\u003eStringent quality control measures were taken during the elemental analysis. Equipment used for in-situ measurements were calibrated using certified standards before fieldwork. Laboratory instruments were calibrated with the check standards and repeated analysis to ensure accuracy. Additionally, blank and parallel samples were measured and the relative standard values for the analyses were less than 5%. High quality reagents were used in the analysis process to ensure high accuracy and precision.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Water quality assessment\u003c/h2\u003e\n \u003cp\u003eWater Quality Index (WQI), with the advantages of flexible parameter selection and weights adjustment (Nong et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), was widely used to assess water quality by combing several water quality variables and converting them into a single value. Eleven variables (WT, pH, EC, DO, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, TP, TN and TDS), which are important parameters to characterize water quality and have been extensively applied in the water quality assessment (Varol, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Varol et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), were used for calculation of the WQI based on the following formula:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1734555803.png\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere n is the number of environmental variables included, and C\u003csub\u003ei\u003c/sub\u003e and P\u003csub\u003ei\u003c/sub\u003e is the normalized value and the weight of variable i, respectively (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The assignment of weights to these parameters primarily refers to the existing expert knowledge and relevant case studies, which have been verified in previous research (Gao et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sang et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). According to the WQI values, water quality status was divided into five degrees: bad (0\u0026ndash;25), low (26\u0026ndash;50), moderate (51\u0026ndash;70), good (71\u0026ndash;90), and excellent (91\u0026ndash;100) (Varol et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This model has some limitations and may lead to a parameter redundancy issue since the development is typically based on expert opinions and local guidelines (Gao et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sang et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, WQI\u003csub\u003emin\u003c/sub\u003e model, which fully considered relative weight and can reduce redundant information and measurement costs associated with assessing water quality, was also developed using a few crucial parameters to conduct water quality assessment cost-efficiently. Stepwise multiple linear regression method was carried out to establish the WQI\u003csub\u003emin\u003c/sub\u003e model, and the normalized values of 11 water quality parameters were used in the regression analysis (Varol et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The calculation of WQI\u003csub\u003emin\u003c/sub\u003e model was based on the above formula.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 APCS-MLR\u003c/h2\u003e\n \u003cp\u003eThe absolute principal component scores (APCS) and the multiple linear regression model (MLR) were used for source apportionment since some studies have confirmed that APCS-MLR model is suitable and effective for pollution source apportionment (Gholizadeh et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Varol et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). APCS-MLR model is developed based on the assumption that all possible pollution sources contribute linearly to the final concentration of the contaminant of interest at the receptor site. Following steps were taken to construct the APCS-MLR model. Firstly, extracting the principal component of water parameters by the PCA/FA, which was performed on Z-transformed standardized variables. Through the analysis of the relationship among multiple variables, PCA uses fewer representative factors to illustrate the main information extracted from numerous variables, making factor variables more interpretable (Varol et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). Before PCA, the data were first subjected to Kaiser-Meyer-Olkin (KMO) and Bartlett\u0026rsquo;s tests to validate the applicability. The minimum value of the KMO was 0.5, and the significance level obtained using Bartlett\u0026rsquo;s test was 0.05 (Xie et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Secondly, these normalized factor scores, obtained by the PCA, were rescaled and converted to un-normalized the APCS values since the obtained normalized factor scores cannot be implemented directly for quantitative source contributions. Lastly, a multiple linear regression equation was constructed using the sampled parameters and APCS values as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img173455580379.png\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere (r\u003csub\u003e0\u003c/sub\u003e)\u003csub\u003ej\u003c/sub\u003e is constant term of multiple regressions for pollutant j, r\u003csub\u003ekj\u003c/sub\u003e is coefficient of multiple regression of the source k for pollutant j, APCS\u003csub\u003ek\u003c/sub\u003e is scaled value of the rotated factor k for the considered sample. The combined term r\u003csub\u003ekj\u003c/sub\u003e\u0026times;APCS\u003csub\u003ek\u003c/sub\u003e represents the contribution of source k to C\u003csub\u003ej\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003eThe source contributions were calculated as follows (Gao et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1734555802.png\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere S\u003csub\u003ei\u003c/sub\u003e represents the input from the i\u003csub\u003eth\u003c/sub\u003e source and p refers to the total number of identified sources. In the APCS-MLR model, variables with inverse contribution may have inverse signs, and ignoring the negative contributions may lead to source apportionment inaccuracy when calculating source contribution estimates (Xie et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, all contributions were provided as absolute values in this study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eSeasonal and spatial differences in water quality parameters were tested by independent samples t-tests. Correlation analysis was conducted to describe the relationship among water quality parameters. Statistical significance was determined using the p value of less than 0.05. Multivariate statistical analysis and APCS-MLR model were conducted using Origin v2021 and IBM SPSS Statistics 27. ArcGIS v10.7 was used to visualize the spatial variation of water quality parameters.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results and discussion","content":"\u003ch2\u003e3.1 Temporal and spatial characteristics of water quality\u003c/h2\u003e\n\u003ch3\u003e3.1.1 Temporal variations of water quality parameters\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics of all water quality parameters, averaged by wet and dry seasons, are shown in Table 1. The averages of pH in wet season and dry season were 8.47 and 8.04, respectively, indicating that the water is slightly alkaline. The average levels of WT in wet season and dry season were 26.44℃ and 9.08℃, respectively, which is significantly related to changes in air temperature. The values of EC fluctuated between 159.33 and 447 \u0026mu;S/cm, with averages in wet and dry season were 269.55\u0026mu;S/cm and 310.12\u0026mu;S/cm, respectively. The levels of DO in water ranged from 7.29 to 13.26mg/L, and the average concentrations in wet and dry season were 8.56 and 11.76 mg/L, respectively. Average contents of TDS in wet and dry season were 174.76mg/L and 201.61mg/L, respectively, with ranged from 103 to 289mg/L. The values of ORP fluctuated between 167.8 and 299.83mV. The mean concentrations of DC, DOC and DIC in wet season were 18.72, 4.71 and 14.01mg/L, respectively, and in dry season were 6.16, 0.16 and 5.05 mg/L, respectively. The content of DIC was significantly higher than that of DOC, indicating that the dissolved carbon was dominated by inorganic carbon, which is consisted with the dissolved carbon composition of most rivers around the world (Chaplot et al., 2021). The mean concentrations of TN in wet and dry season were 1.43 and 1.07mg/L, respectively, which were both higher than the eutrophication threshold (0.2 mg/L). This is consistent with the current situation of relatively high nitrogen pollution load in the Danjiangkou Basin. The mean TP concentration in wet and dry season were 0.027 and 0.005mg/L, respectively. According to the Chinese Environmental quality standards for surface water (GB 3838-2002), the TP concentrations of all samples were lower than Class Ⅱ standard (0.1mg/L), indicating that the risk of phosphorus pollution was low. Additionally, the low phosphorus concentration in this study is consistent with the fact that natural freshwater systems are generally considered to be phosphorus limited (Maavara et al., 2020a). DSi ranged from 3.63 to 7.26 mg/L during the wet season (average value of 5.38 mg/L) and from 2.28 to 9.22 mg/L during the dry season (average value of 5.53 mg/L).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Table 1 goes here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the statistical analysis results, there were significant differences in several water quality parameters studied between the dry and wet seasons (p\u0026lt;0.05), except ORP, DC, DIC and DSi. As shown in Fig.2, the values of pH, WT, TN, TP and DOC in wet season were significantly higher than that in dry season, and the mean values of there parameters were found to be 5.35%, 191.19%, 33.64%, 429.41% and 262.31% higher in the wet season than in the dry season., respectively. However, the values of DO, EC and TDS were significantly lower than that in dry season. Wang et al. (2021) also found that DOC and TP were higher in the wet season than in the dry season, which is similar to this study. Heavy precipitation in the wet season enhances the leaching and erosion of nutrients accumulated in the soil, resulting in nutrients entering the river with runoff or groundwater and increasing the concentration of TN, TP and DOC (Wang et al., 2021; Wang et al., 2025). Besides, the increased inflow in the wet season facilitated the resuspension of deposited pollutants, leading to a substantial release of nutrients (Sang et al., 2024). It is well known that DO level is mainly controlled by water temperature (Varol and Tokatli, 2023), and higher temperature and oxygen-consuming factors during the wet season leading to a decrease of DO content in the water (Wang et al., 2023a). The lower EC and TDS values in the wet season may be due to the large runoff dilute the ion concentration. In general, the seasonal variations of these parameters were mainly associated with seasonal variations in water flow and temperature (Varol, 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Fig. 2 goes here]\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e3.1.2 Spatial variations of water quality parameters\u003c/h3\u003e\n\u003cp\u003eFrom a spatial perspective, the concentrations of TP, TN, and DOC presented significant variability, with CV values \u0026gt;50% (Table 1). CV values can reflect the impact of human activities on the aquatic environment, and high values usually indicate severe external interference and point-source pollution may be one of the causes of these substances (Na et al., 2024). The spatial distribution characteristics of biogenic elements were shown in Fig.3. Generally, the spatial variation characteristics of biogenic elements were similar in different seasons. Both DC and DIC showed an increasing trend from the upstream to the downstream. The bulk of river DC was derived from DIC, thus the spatial variations of DC and DIC were highly consistent. Strong agricultural fertilization activities in the middle and lower reaches of the basin enhanced the chemical weathering of carbonate rocks (Yin et al., 2020), which may lead to the increase of DIC concentration. Sewage discharge and anthropogenic activities can drive the spatial distribution of DOC concentration (Yan et al., 2023). Higher DOC values were observed at the middle reaches sampling points (near Guanshan Town) in both the wet and dry seasons. Guanshan Town is densely populated (14 thousand people lived) and the discharge of domestic sewage is large. Firstly, the Guanshan Town sewage treatment plant is located near G7 section, covers an area of 1592 m\u003csup\u003e2\u003c/sup\u003e and with a sewage treatment rate is 800 m\u003csup\u003e3\u003c/sup\u003e/d. Secondly, it was found that rural domestic sewage in the basin was basically directly lost to the surrounding environment during the on-site investigation. These can pose a threat to the water quality of the river. DOC concentration in river will increase greatly when domestic sewage and waste water are imported. Therefore, the increase of DOC concentration may be related to the discharge of waste water around Guanshan Town. What\u0026rsquo;s more, the concentrations of DOC in the upstream were slightly higher than that in the downstream in wet season. This may be related to the relatively fragile geological environment in the upstream and the strong soil erosion during the wet season, leading to an increase in DOC from external sources.\u003c/p\u003e\n\u003cp\u003eThese spatial distribution patterns of TN and TP concentrations were relatively similar, with slightly higher concentrations in the middle and lower reaches than that in the upper reaches during both dry and wet seasons. This might be attributed to the higher forest cover, lower population density and pollution discharges in the upstream areas. The concentration of DSi gradually decreased from upstream to downstream, which may be related to the increase of DSi content by stronger rock weathering in the upstream and the obstruction of DSi transport by more dams in the downstream. Independent sample T-test results showed that there was a significant difference in DSi concentration between the main stream and tributaries (p\u0026lt;0.001), and the average DSi concentrations of the main stream and tributaries during the study period were 4.54mg/L and 6.37mg/L, respectively. It has also been found that the concentration of DSi in tributaries was significantly higher than that in the main stream in the Yangtze River Basin. This is usually related to the fact that the main stream is more susceptible to anthropogenic interference such as dam construction and water pollution (Yu et al., 2022). Although there was no significant difference in TN, TP, DIC, and DOC concentrations between the main stream and tributaries, the average concentration of TN in tributaries (1.38mg/L) was slightly higher than that in main stream (1.13mg/L). Nevertheless, the average concentration of TP concentration in tributaries (0.010 mg/L) was lower than that in main stream (0.022 mg/L). This may be due to the greater number of people living along the main stream and the greater impact of anthropogenic activities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Fig. 3 goes here]\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e3.1.3 Comparison with other rivers\u003c/h3\u003e\n\u003cp\u003eThe comparison of selected biogenic elements in the Guanshan River with other rivers is shown in Table S2. Liu and Wang (2022) found that the average concentration of DOC across the global rivers was 10.4 mg/L, which is significantly higher than this study (mean concentration was 3.01 mg/L). The average riverine DOC concentration in the Miyun section of Chaobai river was 30.60mg/L, ranging within 17.45~48.16 mg/L (Wang et al., 2023b). These results showed that the differences in DOC concentration may be related to the differences in the geographical environment conditions of different rivers, and also indicated that the DOC concentration in Guanshan River was at a low level. The reasons for the low DOC concentration may be related to the high vegetation coverage, weak soil erosion by water and less phytoplankton in Guanshan River basin. Generally, catchment lithology is the main controlling factor for DIC concentration, and riverine DIC concentrations vary greatly on a global scale (Wang et al., 2016). In the British, concentrations of DIC in catchments dominated by carbonate are typically over 40mg/L, while in areas with little or no carbonate, DIC concentrations are usually less than 10 mg/L (Tye et al., 2022). The Guanshan River basin is mainly exposed to sedimentary rock and metamorphic rocks such as gneiss and sandshale, with silica as the main rock component (Zeng, 2013). Therefore, the DIC concentration may be lower than in catchment dominated by carbonate rocks.\u003c/p\u003e\n\u003cp\u003eCompared with other rivers (Table S2), the average TN concentration in this study was similar to that in most rivers, but was higher than the globe average concentration in natural river waters (0.38mg/L) (Li et al., 2022), indicating that the water quality was partly influenced by anthropogenic activities. The average TP concentration in the Guanshan river was significantly lower than other rivers and the maximum concentration (0.074mg/L) was found below the TP concentration (0.075mg/L) in eutrophic rivers (Varol et al., 2011), which may be related to the widespread phosphorus restriction in the Yangtze River Basin (Liang and Xian, 2018), as well as the low population density and limited human interference in the study basin. DSi contents in the Guanshan River were similar to the range of worldwide rivers (from5.6 to 12.6 mg/L) (Durr et al., 2011), indicating that the DSi contents in this study is within the normal concentration range. However, the average concentration of DSi in the Guanshan River was lower than that in Tuojiang River (12.7 mg/L), Dadu River (11.6 mg/L), which is consistent with the conclusion that the concentration DSi in the upper reaches of Yangtze River is higher than that in the middle and lower reaches (Chen et al., 2024b). The increase of dams and anthropogenic nitrogen and phosphorus in the middle and lower reaches of the Yangtze River are important reasons for the decrease of DSi concentration.\u003c/p\u003e\n\u003ch2\u003e3.2 Water quality assessment based on the WQI\u003c/h2\u003e\n\u003cp\u003eThe WQI values in the dry season and wet season ranged from 82.78 to 92.78 and 77.89 to 91.05, with an average of 89.97 and 83, respectively. According to the WQI classification standard, the percentage of samples with good and excellent water quality in dry season was 80.77% and 19.23%, respectively. The percentage of samples with good and excellent water quality in wet season was 96.15% and 3.85%, indicating that the water quality of Guanshan river was generally good and the pollution was not at a critical level. This result is similar with water quality assessment result of Zhang et al. (2023c), which reported that the annual average WQI was greater than 60 in Danjiangkou Reservoir. However, the water quality in wet season was worser than that in dry season, since the WQI in wet season was significantly lower than that in dry season (p\u0026lt;0.05) and 15% of the samples with the WQI values lower than 80. The water quality evaluation results of the South to North Water Diversion Project and Three Gorges reservoir also showed that the WQI values were lowest in summer (Nong et al., 2020; Sang et al., 2024), which is consistent with this study, indicating that\u0026nbsp;the water quality seasonal change of Guanshan River is normal. However, other several studies have reported that the WQI value in summer is higher than that in other seasons (Tian et al., 2019; Liu et al., 2020), suggesting that the seasonal variation of river water quality may be different in different regions. Spatially, WQI decreased gradually from upstream to downstream in wet season, and the samples with low WQI mainly concentrated near Guanshan Town (G7-G10) (Fig.4). However, there was no significant spatial variation trend of WQI in dry season, and WQI of samples at different locations was mostly between 86-90. This may be related to that the larger flow in the middle and lower reaches of the wet season enhances the erosion effect on surface pollutants. The WQI of the main stream and tributaries ranged from 77.89 to 90.56 and 78.95 to 92.78, with average values of 85.27 and 85.69, respectively. This result showed that there was no significant difference in WQI between the main stream and the tributaries.\u003c/p\u003e\n\u003cp\u003eLinear regressions were used to develop WQI\u003csub\u003emin\u003c/sub\u003e, and four parameters were identified as having significant effects on WQI. There was no multicollinearity between the four independent variables, and VIFs were all less than 5, indicating that the results were accurate and reliable. According to the linear regressions results,\u0026nbsp;TN played the most important role in explaining WQI values (R\u003csup\u003e2\u003c/sup\u003e=0.530, p\u0026lt;0.001), followed by WT, TP and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e (Table S3). The performances of the regression models were evaluated according to their R\u003csup\u003e2\u003c/sup\u003e, RMSE and MAE. Model 4 presented the highest R\u003csup\u003e2\u003c/sup\u003e value (0.850) and the lowest RMSE (1.42) and MAE (1.05) values, indicating that that WQI\u003csub\u003emin4\u003c/sub\u003e consisted of TN, WT, TP and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e presented the best performance among the WQI\u003csub\u003emin\u003c/sub\u003e models. Several studies have also found that TN, TP, WT and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e are the major contributor to the WQI (Wu et al., 2021; Varol et al., 2022), indicating that these are usually important parameters for water quality assessment since they can affect the growth of aquatic organisms and many complex biochemical processes. However, the results of WQI\u003csub\u003emin\u003c/sub\u003e may be influenced by the selection of parameters and their corresponding weights, parameters that are suitable for local conditions should be selected as much as possible in the future research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Fig. 4 goes here]\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e3.3 Correlation analysis\u003c/h2\u003e\n\u003cp\u003eThe relations among the 12 parameters were revealed using Spearman correlation matrix (Fig.5), and an absolute value of correlation coefficient greater than 0.5 usually indicates a strong correlation between variables (Ren et al., 2023). WT had significant and positive correlations with pH, DOC, DC, TN and TP (p\u0026lt;0.05), significant negative correlations with ORP and DO (p\u0026lt;0.05). Many studies have confirmed that riverine DOC concentration is closely related to temperature change (Wang, 2023). The positive correlation between water temperature and DOC, DC, TN, and TP may be related to seasonal changes. Seasons with higher temperatures have abundant rainfall and large runoff, leading to an increase in the amounts of pollutants entering the river. It is well known that DO is mainly controlled by temperature and cold water holds more DO than warm water (Varol et al., 2022). Besides, DO had significantly negative correlations with DOC, TN and TP, and the correlation coefficient were -0.62, -0.42 and -0.67, respectively. DOC, TN and TP are usually related to organic matter input, and organic decomposition consumes oxygen.\u003c/p\u003e\n\u003cp\u003eIn addition, pH had significantly negative correlation with ORP, but positive correlation with DOC. It was detected that ORP also had significantly negatively correlation with EC and TDS. EC and TDS had strong positive correlations (p\u0026lt;0.001), and they both had significantly positive correlations with DC and DIC. EC and TDS can represent the ion concentration in water, and as the inorganic salt content in river increases, the conductivity also increases. The positive correlation between DIC and TDS indicated that DIC is an important component of dissolved matter in the river, and the DIC content increases with the increase of dissolved substances.\u003c/p\u003e\n\u003cp\u003eDIC is an important component of DC, thus they were significantly positively related (p\u0026lt;0.001). What\u0026rsquo;s more, DIC and DSi were significantly negatively related (p\u0026lt;0.05). Previous study also found that DIC correlated negatively with DSi (Chaplot and Mutema, 2021). The increase in DSi concentration can promote the growth of diatoms, of which photosynthesis promotes the conversion of inorganic carbon to organic carbon (Chen et al., 2024b). As expected, DOC, TN and TP were found to have significantly positive correlations (p\u0026lt;0.05), indicating that these parameters may be derived from similar sources. Wang et al. (2023b) have reported that DOC affecting nitrogen and phosphorus cycling in river ecosystems. Therefore, it is normal that there are significantly correlations among these parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Fig. 5 goes here]\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e3.4 Source apportionment using the APCS-MLR model\u003c/h2\u003e\n\u003ch3\u003e3.4.1 Potential pollution sources\u003c/h3\u003e\n\u003cp\u003eKaiser-Meyer-Olkin (KMO) and Bartlett\u0026rsquo;s sphericity test results were 0.725 (\u0026gt;0.5) and 1750 (df=153, p=0.000), respectively, indicating that statistically links were present among the variables and the results of PCA were reliable. Five factors with eigenvalue \u0026gt;1, explaining 84.12% of the total variance, were extracted using the PCA/FA with varimax rotation (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Table 2 goes here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLoadings of each parameter in the five PCs were shown in Fig.6 and Table 2. PC1 showed strong positive loadings (\u0026gt;0.75) on EC, TDS, Na\u003csup\u003e+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e, accounting for 32.51% of the total variance. These indicators represent the composition of dissolved matter in rivers and natural sources of the ionic groups of salts (Varol, 2020). The dissolution load in rivers is generally controlled by rock weathering, atmospheric deposition, and evaporation-fractional crystallization process (Gibbs, 1970). In arid areas, evaporation is generally more intense, and riverine dissolved matter is more susceptible to the influence of evaporite, characterized by high TDS content. While this study area is located in a subtropical humid region, where evaporation-fractional crystallization process is usually weak. Additionally, the average TDS content in the river is 188.19mg/L, which is much lower than the average TDS content of rivers controlled by evaporites in arid areas (732mg/L), indicating that evaporation-fractional crystallization process has a relatively small impact on river dissolved matter (Wu, 2016). Previous studies have reported that waters in the Danjinagkou Reservoir basin are controlled by carbonate weathering (Zhang et al., 2020). What\u0026rsquo;s more, TDS content is an important indicator to measure the strength of rock weathering in a watershed, and the TDS content of water controlled by atmospheric deposition is usually low. The TDS content in this study is much higher than the average TDS content in world rivers (65mg/L) (Wu et al., 2016), suggesting relatively strong weathering in the watershed. Therefore, PC1 can be termed as rock weathering.\u003c/p\u003e\n\u003cp\u003ePC2, accounting for 22.59% of the total variance, showed strong positive loadings on Cl\u003csup\u003e-\u003c/sup\u003e, WT and TP, strong negative loadings on DO, and moderate positive loadings on K\u003csup\u003e+\u003c/sup\u003e and DOC. River water chemistry characteristics is not only controlled by natural geochemical process, but also by anthropogenic perturbations (Liu et al., 2019). Chloride can be an effect indicator of several anthropogenic activities such as domestic sewage and agriculture (Gholizadeh et al., 2016). K\u003csup\u003e+\u003c/sup\u003e could reflect the influence of the fertilizers use and represent agricultural runoff (Varol et al., 2011). TP, DO and DOC are often regarded as indicators of organic pollution, and anthropogenic activities such as wastewater discharge and intensive agricultural are the main sources of surface water organic pollution (Anh et al., 2023). Low levels of DO are primarily attributed to the discharge of organic matter (Santos et al., 2024) and population growth, domestic sewage discharge, and fertilizer application can lead to the increase of DOC content in rivers (Wen et al., 2021). Extensive use of detergents containing phosphorus significantly increases the phosphorus content in domestic wastewater (Gao et al., 2023), and previous studies have reported that TP in surface water might be ascribed to the domestic sewage (Liu et al., 2020; Zhang et al., 2022). The TP content increased significantly in the monitoring section (G6-G8), which is near Guanshan Town, further proving that domestic sewage discharge is an important source of TP in the river. Existing data indicates that the rural population in the upper reaches of Danjiangkou Reservoir accounts for 60%, and the agricultural industry accounts for 46% (Zhai et al., 2023). The average fertilizer application intensity in Shiyan City, where the watershed is located, is 343.07 kg\u0026middot;hm\u003csup\u003e-2\u003c/sup\u003e, which is 1.52 times of the upper limit of environmental safety standard of fertilizer application in developed countries (Gong et al., 2022). What\u0026rsquo;s more, large scale cultivation along rivers is quite common according to our field survey.\u0026nbsp;Therefore, excessive phosphorus can be lost from the surrounding farmland through surface runoff in intensive agricultural areas, resulting in large-scale non-point source pollution of surface water and promoting eutrophication. Previous studies have reported that in the Danjiangkou Reservoir area, which is based on agriculture, 65.6% of the surrounding farmland is at high risk of phosphorus loss (Li et al., 2020; Zhang et al., 2019). Therefore, PC2 may be termed as mixed factor by domestic sewage discharge and agricultural non-point source pollution.\u003c/p\u003e\n\u003cp\u003ePC3 showed strong positive loadings on DC and DIC, accounting for 12.45% of the total variance. Generally, riverine DIC originates mostly from mineral weathering, dissolved soil CO\u003csub\u003e2\u003c/sub\u003e, degradation of organic matter, and biological respiration (Ni and Li, 2022; Yin et al., 2020). When carbonate rocks are weathered with soil CO\u003csub\u003e2\u003c/sub\u003e, half of the carbon in the weathering product HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e is derived from the soil CO\u003csub\u003e2\u003c/sub\u003e, and the other half is derived from carbonate rocks, whereas all the carbon produced by silicate rock weathering is derived from the soil CO\u003csub\u003e2\u003c/sub\u003e (Yin et al., 2020). Considering the widespread development of silicate rocks in the Guanshan River Basin, dissolved soil CO\u003csub\u003e2\u003c/sub\u003e may be an important source of riverine DC and DIC. Additionally, the high forest coverage rate (93%) and large biomass in the Guanshan River Basin, leading to significantly contribution of soil CO\u003csub\u003e2\u003c/sub\u003e to DIC. What\u0026rsquo;s more, soil-derived CO\u003csub\u003e2\u003c/sub\u003e is reported to accounted for 67% of DIC in the world\u0026rsquo;s rivers (Tye et al., 2022), indicating that dissolved soil CO\u003csub\u003e2\u003c/sub\u003e is another important source of DIC. The concentration of DIC in the Guanshan River was obviously higher than that of DOC, and there was no significant relationship between riverine DIC and DOC, indicating that DIC released by organic matter degradation in the river contributed little to the DIC pool. To sum up, PC3 can be inferred to dissolved soil CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eIn the PC4 (10.04% of the total variance), there was a strong positive loading on pH and a strong negative loading on ORP. Generally, pH in natural water is mainly influenced by temperature (Chen et al., 2022) and significant correlations among pH, ORP and WT were found. Therefore, the PC4 can be interpreted as the seasonal factor, reflecting the physiochemical source of variability (Varol, 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePC5 showed strong positive loading on TN and strong negative loading on DSi, accounting for 6.54% of the total variance. TN concentration is usually closely connected with intensive agricultural activities in rural areas (Anh et al., 2023). In Qinba Mountain area, where this study area is located, agricultural activities are frequent and nitrogen-based fertilizer are widely used, leading to the soil nutrient content greatly increased. Fertilizers in soil may migrate into river with surface runoff, becoming one of the important sources of nitrogen (Dong et al., 2022). Additionally, free-range livestock in the Guanshan River basin is prevalent, leading rivers become home to farming wastewater. Several researches have proved that agricultural activities around the Danjiangkou Reservoir are main sources of atmospheric nitrogen deposition and have an influence on water quality (Guo et al., 2022; Zhang et al., 2024b).\u0026nbsp;What\u0026rsquo;s more, agricultural activities have also been confirmed to be an anthropogenic source of silicon (Bu et al., 2015). Maavara et al. (2020b) also found that the global supply of silicon to rivers has been reduced by human activities. Therefore, DSi has a negative load on PC5 and further proves that PC5 may represent an anthropogenic source. In the whole, PC5 might present agricultural non-point source pollution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Fig. 6 goes here]\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e3.4.2 Source apportionment\u003c/h3\u003e\n\u003cp\u003eTo quantify the contributions of each pollution source to water quality indices, an APCS-MLR receptor model was established based on the identified pollution sources. Predicted/observed plots of APCA-MLR model were shown in Fig.7 and Fig.S1. The correlation coefficients (R\u003csup\u003e2\u003c/sup\u003e) of all parameters were higher than 0.5, ranged from 0.63-0.96, indicating that the consistency between observed and predicted values was good and the results of source apportionment were relatively reliable (Zhang et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Fig. 7 goes here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig.8 shows the contributions of different sources to the studied water quality parameters. Rock weathering (VF1) was the primary source, with an average contribution of 38.96%. The other four sources with average contribution rates of 12.34% (VF2), 13.54% (VF3), 23.95% (VF4), and 11.21% (VF5). VF1 made significant contributions to EC (74.84%), TDS (74.67%), K\u003csup\u003e+\u003c/sup\u003e (54.21%), Na\u003csup\u003e+\u003c/sup\u003e (72.95%), Ca\u003csup\u003e2+\u003c/sup\u003e (77.03%), Mg\u003csup\u003e2+\u003c/sup\u003e (64.26%) and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e (67.64%). The contributions of domestic sewage discharge and agricultural non-point source pollution (VF2) ranged from 0.66% (EC) to 40.12% (TP) for the 18 water quality indices. Besides, VF2 also accounted for 38.32% of DO and 33.36% of Cl\u003csup\u003e-\u003c/sup\u003e. This contribution of dissolved soil CO\u003csub\u003e2\u003c/sub\u003e (VF3) is manifested by the high contribution of DC (57.15%) and DIC (44.48%). Contribution of seasonal change (VF4) for different variable were between 3.43% (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e) and 86.06% (pH). In addition, VF4 also accounted for 75.59% of ORP, 57.56% of Cl\u003csup\u003e-\u003c/sup\u003e, and 44.53% of WT. The contribution of VF5 is manifested by the high contribution of TN (47.35%), DSi (43.52%), and TP (25.40%).\u003c/p\u003e\n\u003cp\u003eOn the whole, natural factors such as rock weathering and seasonal variation are the main sources of water quality indexes in Guanshan River Basin. The contributions of natural sources include rock weathering and dissolved soil CO\u003csub\u003e2\u003c/sub\u003e to DC and DIC were 78.26% and 77.36%, respectively. For DOC, season factor contributed the most (34.82%). Seasonal variation may affect endogenous DOC by affecting microbial activity in river. Additionally, the difference of soil erosion degree in different seasons also affected the exogenous DOC (Chaplot and Mutema, 2021). However, for nutrients such as TN and TP, anthropogenic sources such as sewage discharge and agricultural non-point source pollution should not be ignored, which accounted for 51.44% of TN, and 65.53% of TP, respectively. Agricultural non-point source contributed the most for DSi (43.52%), followed by rock weathering (29.86%). Generally, DSi is mainly derived from phytolith dissolution and silicate weathering (Ma et al., 2017), which were not comprehensively considered in this study, indicating that this receptor model has certain limitations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Fig. 8 goes here]\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e3.5 Implications of water environment protection\u003c/h2\u003e\n\u003cp\u003ePollution sources in the upstream basin are the main sources of nitrogen and phosphorus in Danjiangkou Reservoir. Guanshan River basin is located in the upstream of Danjiangkou Reservoir and classified as a water safety guarantee zone. Therefore, water quality safety in this basin is very important. However, several water quality sections with strong human disturbance still enriched with nutrients, highlighting reasonable management and control measures should be taken to ensure water quality safety.\u003c/p\u003e\n\u003cp\u003eFirstly, this study highlighted that TN and TP are important parameters of water quality assessment. Therefore, strengthening water environment monitoring in areas with intensive activities such as Guanshan Town, and closely tracking the changes of water quality parameters such as TN and TP, are essential to avoid eutrophication. Secondly, some effective measures should be adopted to control pollutants emissions since domestic sewage discharge and agricultural non-point source pollution are the main anthropogenic sources. For example, strengthening the awareness of environmental protection of residents along rivers and increasing efforts in domestic sewage collection and treatment to avoid direct discharge of sewage. Besides, optimizing the structure of agricultural industry, improving planting pollution technology and fertilizer utilization rate are also effective measures to reduce exogenous nutrient load in rivers.\u003c/p\u003e\n\u003cp\u003eThirdly, silicon plays a crucial role in global biogeochemical cycles (Hawking et al., 2018), while it usually receives less attention than N and P (Wang et al., 2016), making limited understanding on the biogeochemical cycling mechanism of riverine silicon. In order to strength the management of nutrient elements in rivers, in the future, continuous and increased monitoring of riverine silicon, determination of silicon in other forms such as biogenic silicon and using isotope tracer can be carried out to explore the long-term trend of silicon concentration and form. Exploring the coupling biogeochemical cycling mechanisms of silicon and other biogenic elements in mountainous river is also necessary. The distribution of DSi is usually controlled by natural conditions, but anthropogenic interference should not be ignored. Fourthly, with the continuous development of urbanization and agriculture, many pollutants such as plastics, heavy metals and organochlorine pesticides exist widely in river systems and have a direct impact on the biogenic elements in the ecosystem (Chen et al., 2024c). While this study only focused on the change characteristics and main sources of biogenic elements. It is essential to explore the relationship between biogenic elements and other pollutants, in order to evaluate the geochemical characteristics of biogenic elements more accurately and comprehensively.\u003c/p\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eIn this study, the Guanshan River, extremely sensitive to the impact of the middle route of the South-to-North Water Diversion project, was systematically studied. Spatial and temporal distribution characteristics of the biogenic element concentrations were analyzed, the water quality was comprehensively evaluated, and the main sources of biogenic elements were identified and quantified by the multivariate statics. The conclusions can be summarized as follows:\u003c/p\u003e \u003cp\u003e(1) The concentrations of biogenic elements in the Guanshan River basin were generally low, and most parameters showed significant seasonal differences. Spatially, the variation trends of biogenic elements concentration were similar in different seasons. The concentrations of DIC, DOC, TN and TP were all higher in the middle and lower reaches, while DSi concentration was higher in the upper reaches.\u003c/p\u003e \u003cp\u003e(2) WQI is a useful method for comprehensive evaluation of water quality, and the water quality of Guan River was generally good based on WQI assessment results, indicating that the water environment treatment carried out in recent years are effective. But the water quality in wet season was slightly worse than that in dry season. The proposed WQI\u003csub\u003emin\u003c/sub\u003e model indicated that TN was the most important parameters for water quality assessment.\u003c/p\u003e \u003cp\u003e(3) Five potential sources of water quality indexes were revealed by APCS-MLR model. The mean contributions of rock weathering, mixed sources of domestic sewage discharge and agricultural non-point source pollution, dissolved soil CO\u003csub\u003e2\u003c/sub\u003e, seasonal factors and agricultural non-point source pollution were 38.96%, 12.34%, 13.54%, 23.95% and 11.21%, respectively. Each source contributed to each water quality variable differently. Riverine DIC was mainly derived from natural sources such as rock weathering and dissolved soil CO\u003csub\u003e2\u003c/sub\u003e, and seasonal factor contributed the most to riverine DOC. The contribution of sewage discharge and agricultural non-point source pollution to TN and TP reached 51.44% and 65.53%, respectively. The contribution of rock weathering and agricultural non-point source pollution to DSi were 29.86% and 43.53%, respectively.\u003c/p\u003e \u003cp\u003e(4) Strengthening the concentration monitoring of biogenic elements and controlling sewage discharge and agricultural non-point source pollution are recommended to ensure water ecological security. Exploring the relationship between biogenic elements and other pollutants is likely to be a crucial focus for future research.\u003c/p\u003e \u003cp\u003eThis study enhances the understanding of spatiotemporal distribution characteristics and main sources of biogenic elements in the Guanshan River basin, and these results are of great importance for the scientific management of river water environment and the exploration of geochemical characteristics of biogenic elements.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u003c/h2\u003e\n\u003cp\u003eAll authors have given their consent to participate in submitting this manuscript to this journal.\u003c/p\u003e\n\u003ch2\u003eConsent to publish\u003c/h2\u003e\n\u003cp\u003eAll authors were informed and consented to publish the article.\u003c/p\u003e\n\u003ch3\u003eAuthor contributions\u003c/h3\u003e\n\u003cp\u003eYihang Wu: sampling, methodology, statistical analyses, writing-original draft. Qianzhu Zhang: conceptualization, sampling, data curation, writing-review and editing, funding acquisition. Yuan Luo: sampling, methodology, statistical analyses. Ke Jin: sampling, data curation. Qian He: sampling, data curation. Yang Lu: supervision, funding acquisition.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis work was funded by the National Natural Science Foundation of China (42407108), Knowledge Innovation Program of Wuhan-Shuguang (2022020801020245), Water Conservancy Key Scientific Research Project of Hubei Province (HBSLKY202405), the Central Public-interest Scientific Institution Basal Research Fund of China (CKSF2023299/CQ).\u003c/p\u003e\n\u003ch3\u003eCompeting Interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch3\u003eData availability statement\u003c/h3\u003e\n\u003cp\u003eThe full dataset that supporting the findings of this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnh NT, Can LD, Nhan NT et al (2023) Influences of key factors on river water quality in urban and rural areas: A review. 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East China Normal University. (in Chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang YY, Xu H, Zhao XC et al (2025) Rainfall impacts on nonpoint nitrogen and phosphorus dynamics in an agricultural river in subtropical montane reservoir region of southeast China. J Environ Sci 149:551\u0026ndash;563\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei MJ, Duan PF, Gao PC et al (2020) Exploration and application of hydrochemical characteristics method for quantification of pollution sources in the Danjiangkou Reservoir area. J Hydrol 590:125291\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen ZD, Song KS, Shang YX et al (2021) Natural and anthropogenic impacts on the DOC characteristics in the Yellow River continuum. Environ Pollut 287:117231\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu WE (2016) Hydrochemistry of inland rivers in the north Tibetan Plateau: Constraints and weathering rate estimation. 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J Yangtze River Sci Res Inst 39(3):38\u0026ndash;46 (in Chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhai WL, Tang J, Wang CG et al (2023) Simulation of non-point source pollution in Danjiangkou Reservoir basin based on coupled improved output coefficient method and SWAT model. J Yangtze River Sci Res Inst 40(07):50\u0026ndash;58 (in Chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang YN, Zhang GS, Pan JF et al (2019) Soil organic carbon distribution in relation to terrain \u0026amp; land use a case study in a small watershed of Danjiangkou reservoir area, China. Global Ecology and Conservation, 2019, 20, e00731\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang QZ, Zhou HM, Lu Y et al (2020) The research on riverine hydrochemistry and controlling factors in the Danjiangkou Reservoir. 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(in Chinese)\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Descriptive statistics of physicochemical parameters\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eparameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003emin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003emax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eCV/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" style=\"width: 76px;\"\u003e\n \u003cp\u003ewet season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e8.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e8.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eWT/(℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e26.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e27.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e20.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e29.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e9.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eEC/(\u0026mu;S/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e269.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e271.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e159.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e419.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e19.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e52.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDO/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e8.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e8.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eTDS/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e174.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e175.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e19.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e34.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eORP/(mV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e220.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e221.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e167.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e299.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e13.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e29.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDC/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e18.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e18.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e32.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e25.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDOC/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e15.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e66.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDIC/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e14.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e14.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e18.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e21.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eTP/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e74.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eTN/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e46.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDSi/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e19.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" style=\"width: 76px;\"\u003e\n \u003cp\u003edry season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e9.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eWT/(℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e9.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e13.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e23.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eEC/(\u0026mu;S/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e310.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e294.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e168.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e447.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e23.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e71.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDO/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e11.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e11.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e13.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eTDS/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e201.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e191.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e108.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e289.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e23.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e46.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eORP/(mV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e225.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e227.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e181.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e260.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e19.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDC/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e16.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e16.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e26.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e33.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDOC/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDIC/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e15.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e15.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e24.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e32.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eTP/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.0051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.0037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e92.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eTN/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e85.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDSi/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e39.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" style=\"width: 76px;\"\u003e\n \u003cp\u003eWhole study period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e8.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eWT/(℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e17.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e17.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e29.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e51.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e9.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eEC/(\u0026mu;S/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e289.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e285.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e159.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e447.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e22.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e65.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDO/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e10.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e10.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e13.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e17.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eTDS/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e188.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e185.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e103.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e289.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e22.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e42.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eORP/(mV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e223.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e224.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e167.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e299.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e11.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e24.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDC/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e17.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e17.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e32.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e29.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDOC/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e15.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e96.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDIC/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e14.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e14.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e24.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e28.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eTP/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e112.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eTN/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e64.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDSi/(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e100.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Varimax rotated factor loadings based on PCA/FA\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 110px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 532px;\"\u003e\n \u003cp\u003eComponents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003ePC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003ePC5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e-0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.839\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.874\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eDO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.841\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.953\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eORP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e-0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.824\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n 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\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"biogenic elements, water quality assessment, source apportionment, APCS-MLR, Danjiangkou reservoir","lastPublishedDoi":"10.21203/rs.3.rs-4818908/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4818908/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDanjiangkou Reservoir has been widely concerned as the water source of the world\u0026rsquo;s longest cross basin water transfer project. Biogenic elements are the foundation of material circulation and key factors affecting water quality. However, there is no comprehensive study on the biogenic elements in tributaries of Danjiangkou Reservoir, hindering a detailed understanding of geochemical cycling characteristics of biogenic elements in this region. Guanshan River, one of the main tributaries that directly enter the Danjiangkou Reservoir, was token as the research object. Spatiotemporal distribution characteristics of basic water quality parameters and biogenic elements were studied. Water quality was comprehensively evaluated through water quality index (WQI). Absolute principal component score-multiple linear regression (APCS-MLR) model was adopted to explore the main sources of biogenic elements. Results showed that, in terms of season, the concentrations of TN, TP, and DOC were significantly higher in wet season than in dry season, while no significant differences were found for DIC and DSi. Spatially, the concentrations of DC, DIC, TN and TP in the middle and lower reaches were higher than that in the upstream. DOC concentration peaked in the middle reaches, while DSi showed higher concentrations in the upstream. WQI values indicated that the river water quality was between good and excellent, although the water quality in wet season was slightly worse than that in the dry season. PCA extracted five potential sources, which accounting for 84.12% of the total variance, including rock weathering, mixed source of sewage discharge and agricultural non-point source pollution, dissolved soil CO\u003csub\u003e2\u003c/sub\u003e, seasonal factor and agricultural non-point source pollution. These sources contributed 38.96%, 12.33%, 13.54%, 23.95% and 11.21% to river water quality parameters, respectively. Strengthening the monitoring of biogenic elements, controlling pollutant discharge and exploring the relationship between biogenic elements and other pollutants are important for the water environment management in this basin.\u003c/p\u003e","manuscriptTitle":"Spatial and temporal distribution characteristics and source apportionment of biogenic elements using APCS-MLR model in the main inlet tributary of Danjiangkou Reservoir","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 21:09:00","doi":"10.21203/rs.3.rs-4818908/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2025-01-02T11:31:54+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-12-16T18:05:30+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-16T17:09:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2024-12-06T04:06:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-12-05T02:19:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a9242a33-d467-4bca-b96c-2caae6883a96","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-27T16:02:31+00:00","versionOfRecord":{"articleIdentity":"rs-4818908","link":"https://doi.org/10.1007/s11356-025-35898-3","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2025-01-20 15:57:45","publishedOnDateReadable":"January 20th, 2025"},"versionCreatedAt":"2024-12-18 21:09:00","video":"","vorDoi":"10.1007/s11356-025-35898-3","vorDoiUrl":"https://doi.org/10.1007/s11356-025-35898-3","workflowStages":[]},"version":"v1","identity":"rs-4818908","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4818908","identity":"rs-4818908","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}