Hydrochemical characteristics and water quality evaluation for irrigation and drinking purposes of Bangong Co Lake Watershed

Hydrochemical characteristics and water quality evaluation for irrigation and drinking purposes of Bangong Co Lake Watershed | 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 Hydrochemical characteristics and water quality evaluation for irrigation and drinking purposes of Bangong Co Lake Watershed Yuxiang Shao, Buqing Yan, Baiyang Liu-Lu, Gang Feng, Kun Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2747303/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jul, 2023 Read the published version in Water → Version 1 posted You are reading this latest preprint version Abstract In order to explore the hydrochemical characteristics, influencing factors, and water quality of various water bodies in Bangong Co Lake Watershed, 60 water samples were collected from lake, river, groundwater, glacier water bodies in the watershed. Piper diagram, Gibbs’ diagrams, ion ratio analysis, statistical methods, and principal component analysis were used to study the hydrochemical characteristics and its influencing factors. Drinking water quality index (DWQI) and USSL classification were applied to assess the groundwater quality suitability for agricultural and drinking purposes. The hydrochemical characteristics show the differences among water bodies and their spatial distribution. Analyzed groundwater and surface water samples such as river water and glaciers mainly presented Ca-HCO 3 type, and lake water mainly presented Na-Cl type and a small number of Na-HCO 3 ·Cl type. The lake water chemical components are mainly affected by evaporative karst decomposition. The main mineralization process of groundwater and river water was related to the dissolution of reservoir minerals such as dolomite and calcite, and halite. The DWQI indicates that 79% of the groundwater samples in the study area showed a good quality for drinking. For irrigation water quality, the electrical conductivity (EC), calculated Sodium adsorption ratio (SAR), Magnesium hazardous ratio (MHR) showed that more than 13% of the total samples were not suitable for irrigation. USSL classification indicated that glacier and river water are relatively suitable for irrigation. And part of the groundwater and lake water has very high alkalinity or salinity which is alarming when considered for irrigation. Bangong Co Lake Watershed Hydrochemical characteristics Water quality Surface water Groundwater Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction The Qinghai-Tibet Plateau, known as the roof of the world and the water tower of Asia, is the birthplace of the Yellow River, the Yangtze River, the Ganges River, the Mekong River, the Indus River and other rivers, and has the largest, highest, and most densely distributed alpine lake cluster in the world (Shi. 1990; Wang et al. 2016 ). In the environment of Qinghai-Tibet Plateau, lakes play an important role in regulating the ecosystem, and hydrochemistry and are the vital indicator of water-rock interaction and environmental change in basins (Yan et al. 2016 ; Zhang R et al. 2021 ). In the last few years, many academics have conducted investigation in lakes on the Tibetan Plateau, which mainly focused on hydrochemical characteristics and its spatial distribution (Jin et al. 2010 ; Sun and Jin. 2020), hydrochemical types (), typical water pollutants (Ren et al. 2016 ), water quality assessment (Wang et al. 2021 ; Zhang Y B et al. 2021), and hydrochemistry evolution and sources (Li et al. 2015 ; Wang et al. 2013 ). Owing to the high altitude and inconvenience of transportation, hydrochemical characteristics and water quality assessment of many typical lakes on the Qinghai-Tibet Plateau have not been investigated. Natural factors such as geological structure, hydrogeological conditions, vegetation, climate, rock weathering, terrain, and human activities have a great impact on the chemical composition of water (He et al. 2011 ). Many scholars have learned about the soil salinization, regional geological background, element migration and enrichment laws, and physical weathering process of rocks through the study of ion characteristics in the watershed (Xiao et al. 2010 ). Sun and Jin ( 2020 ) pointed out the differences in chemical characteristics and evolution between tectonic lakes and glacial lakes on the plateau based on the analysis of physicochemical parameters and ion concentrations of lakes on the Qinghai-Tibet Plateau. Li et al. ( 2022 ) have made a discussion on the relationship between lake change and climate in recent decades and clarify that lakes are important supporter of water cycle and environmental change. Bazova and Moiseenko ( 2021 ) analyzed samples of ores, air pollutants, lake water in northwest Russia and used spatial analysis methods, suggested that the anthropogenic enrichment of Cu, Ni, Mn, Zn, Pb and As in lake water bodies was caused by flue gas discharge from metallurgical enterprises. Baranov et al. ( 2020 ) studied the chemical composition, natural geochemistry and human factors of dissolved effluent in the northern Valdai Mountains by using statistical methods. The results show that soil mineral composition, rainfall intensity and biogeochemical processes had a great influence on the chemical composition of water bodies in the aeration zone. Therefore, it is of great scientific significance to study the hydrochemical characteristics of rivers and lakes for the water cycle process and water resources environmental protection in plateau lakes. Due to the high evaporation, low rainfall, and less recharge in the plateau arid area, the surface water and shallow groundwater are mostly saline or brackish water, which is not conducive to plant growth, human life and industrial activities (Jiang et al. 2022). Water and its quality assessment is a vital resource for maintaining social development and economic stability and ecological balance in the arid regions of the plateau.[1,2] Previously, there were a series of studies related to lake water quality assessment on the plateau arid area such as Qinghai Lake (Xu et al. 2010 ), Ebinur Lake Watershed (Zhu et al. 2019 ), Dal Lake in Kashmir Valley (Kumar et al. 2022 ), Mapam Yumco (Sun et al. 2012 ), Nam Co (Guo and Kang. 2012), and some other lakes, which justify that water quality assessment is necessary for plateau lake. Bangong Co Lake is a long and narrow tectonic lake with a nearly east-west distribution In the western part of the Tibetan Plateau, which is characterized by dry and cold climate with strong evaporation (Fontes et al. 1996 ). As the main source of replenishment for the Lake Bangong Co watershed, glacial meltwater is insufficient to balance evaporation in the area. Likewise, affected by evaporation in the whole watershed and river water supply in the east of Bangong Co Lake, the salinity of the lake gradually increases from east to west (Xiao et al. 2015 ). The water cycle system and ecosystem in this plateau arid watershed are very fragile and sensitive, which are susceptible to climate change and human activities The domestic water and irrigation water in the study area are mainly shallow aquifers in the lakeshore zone and river valley, or supplemented by snowmelt water and river water. However, there are few relevant studies on the various water bodies of Bangong Co Lake Watershed. Only some studies on the lake groups on the Tibetan Plateau have presented the data of the lake water in the east side of Bangong Lake and the corresponding lake sediments, which indicated that the lake water type on the east side of Bangong Lake is Cl-Na or CO 3 Cl-MgNa type (Hu et al. 2019 ; Wen et al. 2006 ). Lin et al. ( 2021 ) has carried out some research on the information of toxic organic pollutants and metals of Bangong Co Lake. It indicates that ΣPAH and ΣPAE concentrations in lake water have no relationship with hydrochemical parameters and organic pollutants have the main source for domestic waste related to regional increasing human activities in recent years. Therefore, the hydrochemical characteristic and its controlling factors of the river water, lake water, groundwater and other water bodies in the watershed of Bangong Co Lake is still unknown. And there is no available information on water quality assessment in this region. The present investigation was performed to explore the glaciers, rivers, lakes and groundwater along the lakeshore in Bangong Co Lake watershed. Statistical analysis, a Gibbs diagram, a Piper diagram, and an ion ratio analysis were used to analyze the hydrochemical characteristics and formation mechanism of different water bodies. Drinking water quality index (DWQI) and irrigation water quality parameters were used to assess the water quality and its suitability in the study area. The aim of this work is to explain the hydrochemical characteristics in this area, and supply the research data in this area and provide useful data for the protection and rational utilization of the water environment in the basin. 2 Materials And Methods 2.1 Overview of the study area Bangong Co Lake Watershed is located in the northwest of Tibet, China. Based on ArcGIS hydrological analysis module and DEM data, it is calculated that the watershed area of Bangong Co Lake is about 33 000km 2 , with the location between 32°40'- 34°30' N and 78°10'- 81°15' E and the elevation range is 3736-6771m. The watershed reaches the Kanakoram Mountains in the north, and the Gangdises Mountains in the south. The highest point is located at the eastern mountains of the Zecuo Lake, and the lowest point is located in the Bangong Co Lake in the middle of the area (Fig. 1 ). The geomorphology of the watershed belongs to plateau lakebasin geomorphic type, which has been fully eroded by snow and water for a long time. The watershed covers the Bangong Lake replenishment river basin and several small intermountain lake basins such as Spangour Co, Zecuo, Rebangcuo, Aiyongcuo and Sharda Co (Yang et al. 2003 ). This region has cold and dry climate, where the annual average temperature is 0.1-2℃. The annual rainfall is about 70-80mm with high variability, and mostly concentrated in July or August (Xie et al. 2004 ). The main source of water in the study area is glacial meltwater. Under the action of gravity, the water mainly flows along the slope or gully to feed rivers or veins of rock, and drain into the lake eventually. Because the sediments in river valley at the middle or lower reaches and the sediments in lake shore are thick and loose, the river water is likely to penetrate, forming relatively stable shallow groundwater in the alluvial fan or valley area. Geologically, the study area is located in the nearly east-west Bangong-Nujiang docking zone, with Qiangtang Terrane to the north and Lhasa Terrane to the south (Yin and Harrison. 2000; Zhao et al. 2021 ). Qiangtang Terrane in the region is mainly composed of ophiolite melange, carbonate rocks, sandstone and slate rocks from shamuluo Group (J 3 K 1 s) and Mugagangri Group (J 1 − 2 M), and Lhasa Terrane in the study area is mainly composed of ophiolite mélange, Mesozoic granites and clastic rocks, and sandwiched limestone, flysch sediments from Mugagangri Group (J 1 − 2 M), Jielu Group(J 3 ), Shamuluo Group (J 3 K 1 s) (Li et al. 2016 ; Xie, Xiao, Xiao, Cao, OY, Niao and Feng. 2004). Quaternary sediments are mainly distributed in the sides of lake and river valleys. 2.2 Sampling and Measurement In early June 2022, field investigation was carried out in Bangong Lake Basin, and a total of 60 water samples were collected, including 14 lake water samples, 19 groundwater samples, 24 river water samples and 3 glacier water samples (Fig. 1 ). Among them, river samples were mainly distributed in Makazangbu and Doma Rivers, and groundwater samples were mainly collected from wells on alluvial fan bodies in lakeshore zone. Water samples were mostly collected from at a depth of 0.1-0.5m, and filtered through 0.45µm cellulose acetate membrane and stored in 1500mL cleaned polyethylene bottles. Before collection, the collection containers were rinsed with sampling water for 2 to 3 times. The lake water samples were taken from a location about 1-2m away from the lake. In addition, water electrical conductivity (EC) and pH were measured in the field using a multi-parameter portable water analyzer (In-situ SMARTROL, USA), with the pH accuracy was ± 0.1pH and the conductivity accuracy was ± 1µS/cm. All water samples were sent to the Xizang Shengyuan Environmental Engineering Co., LTD for testing. The content of HCO 3 − was measured by acid-base titration. Water salinity (TDS) was determined by dry weight method at 105℃. Total hardness (TH) was determined by EDTA method. The main cations (Na + , K + , Ca 2+ , Mg 2+ ) concentration and trace heavy metals (Fe, Mn, Zn, Cu and Cr) levels were determined by inductively coupled plasma emission spectrometer (Agilent5110, USA). The total As content was detected by atomic fluorescence (AFS-933,USA) with detection limits of 0.09 mg/L. Anion concentration (SO 4 2− , Cl − , NO 3 − ) was determined by chromatography (LC-10ADvp, Japan). F − was determined by ion chromatography system(ECO IC, Switzerland). Quality control was carried out in the test process, and all the measured parameters were determined by blank values. Also, the test results were tested by ion balance test, which were within ± 5%. 2.3 Data Processing SPSS v22.0 was used to perform the average (Mean), maximum (Max), minimum (Min), and coefficient of variation (CV). The piper diagrams (Piper. 1944), principal component analysis, Gibbs’ diagrams (Gibbs. 1970) and ion ratio analysis (Meybeck. 1987) were implemented in Origin v2021 and Microsoft Excel v2013 to explore classification and main controlling factors of the nature water in Lake Bangong Co Watershed. International standards (WHO. 2017), drinking water quality index (Adimalla et al. 2018 ; Sarkar et al. 2022 ), USSL classification (Richards. 1954) and other irrigation water indicators are used to evaluate groundwater suitability for drinking and irrigation. ArcGIS v10.4 and CorelDRAW Graphics Suite v18 are used for mapping and modification. 3 Results And Discussion 3.1 Chemical Composition of Different Water Bodies in Bangong Lake Basin 3.1.1 Statistical characteristics of parameters The parameters of each water body in Bangong Co Watershed are shown in Table 1 . The pH of glacier, lake water, river water and shallow groundwater along the lakeshore in the study area indicated weak alkalinity, ranging from 6.6 to 8.5. The pH of Bangong Co lake water was relatively high (pH = 8.1–8.5), and the pH of glacier was relatively low (pH = 6.6–7.6). The CV% for water pH of each water bodies was < 10%, which showed weak variability. TDS and EC are parameters to determine the salinity of water (Wu et al. 2017 ). The TDS of glacier, river water, groundwater, and lake samples ranged from 13–25 mg/L, 81–438 mg/L, 124–972 mg/L, 206-4091mg/L, respectively. The TDS values of most lake samples and some groundwater were above the WHO standard limits (WHO. 2017). The average TDS of river water is 282 mg/L, which was also higher than the global average and values for other large rivers in China (Xiao et al. 2016 ). Similarly, the EC of glacier water, river water, groundwater, and lake samples ranged from 23–43µS/cm, 145–779 µS/cm, 220–1706 µS/cm, 385–7861 µS/cm, respectively. Water hardness is a very important parameter in the growth and reproduction of aquatic organisms (Kim et al. 2015 ). The TH of glacier, groundwater, river, and lake are in the range of 23-43mg/L, 27–750 mg/L and 58–467 mg/L, and 258–2854 mg/L, respectively. With the decrease of altitude, the value of TH, EC, TDS increased gradually, which were found the highest around the Bangong Co Lake. The CV% for TH, EC, and TDS of different water bodies were between 10% and 66%, which showed medium variability. Based on TH and TDS (Ganguli et al. 2022 ), lake water is hard-brackish water, glacier is soft-fresh water, and river water changes from soft-fresh water to hard-fresh water along the stream (Fig. 2 ). Table 1 Major ions and heavy metal compositions of water samples from the Bangong Co Lake Watershed. Sample type pH TDS TH EC K + Na + Ca 2+ Mg 2+ Cl − SO 4 2− HCO 3 − NO 3 − F − Fe Mn As glacier (n = 3) min 6.6 13 9 23 0.15 0.25 3.08 0.22 1.04 1.96 6.67 0.18 ND ND ND ND max 7.6 25 12 43 0.26 0.66 3.66 0.58 1.25 2.83 8.00 0.64 ND ND ND ND Ave 7.2 20 10 35 0.20 0.49 3.44 0.36 1.12 2.38 7.56 0.35 - - - - CV% 6 27 10 24 24 36 7 43 8 15 8 60 - - - - river water (n = 24) min 7.1 81 58 145 0.41 1.41 10.40 3.23 1.76 7.63 52.20 ND ND ND ND ND max 8.5 438 467 779 6.31 65.60 88.50 52.06 50.60 132.62 427.50 63.83 0.7 0.138 0.398 0.011 Ave 7.8 282 196 498 2.28 26.98 39.93 19.89 27.65 31.89 211.56 7.68 0.2 0.044 0.115 0.005 CV% 5 39 47 39 52 61 50 54 54 90 47 213 87 82 143 68 lake water (n = 14) min 8.1 406 258 385 1.88 46.30 6.91 14.60 3.35 38.50 168.0 ND ND ND ND 0.002 max 8.5 4491 2854 7861 83.10 1089.0 39.80 266.40 1163.60 1004.00 1092.0 1.02 ND 0.091 0.010 0.044 Ave 8.4 2606 1573 4546 37.85 638.31 15.84 134.06 618.50 605.25 700.64 0.10 - 0.043 0.003 0.020 CV% 1 62 43 61 73 68 56 58 69 65 52 278 - 60 76 63 groundwater (n = 19) min 6.8 124 79 220 0.94 3.10 12.33 7.43 3.40 0.33 66.50 1.09 ND ND ND ND max 8.4 972 754 1706 23.44 184.0 180.00 93.50 256.00 528.00 581.70 68.57 1.5 0.224 0.441 0.003 Ave 7.7 414 302 729 5.79 45.75 66.79 32.98 49.52 92.43 287.19 28.83 0.5 0.060 0.127 0.002 CV% 5 63 61 62 94 96 75 70 132 134 53 68 90 124 117 16 WHO(2017)limit - 1000 500 200 250 250 50 1.5 0.3 0.1 0.01 The units for ion concentration are mg/L, for EC, µs/cm, and for TDS, mg/L. CV% coefficient of variation. ND not detected. The ion diagrams (Fig. 3 ) depict the milligram equivalent and spatial variation of anions and cations in the different water bodies across the basin. In the water samples of groundwater, river water and glacier, the cations were dominated by Ca 2+ , and the abundance order was Ca 2+ >Na + >K + >Mg 2+ . However, the main cation of lake water was Na + , with the cation abundance order was Na + >Mg 2+ >K + >Ca 2+ . The main anion in groundwater, river water and glacier samples was HCO 3 − , with an abundance order of HCO 3 − >SO 4 2− >Cl − >NO 3 − , whereas the main for lake samples anion was Cl − , with an abundance order of Cl − >HCO 3 − >SO4 2 − >NO 3 − . The strong coefficient of variation was found in Na + , Cl − and SO 4 2− of groundwater, and NO 3 − of river and lake water, which was between 1.15 and 2.85, indicating that the spatial distribution of these ionic components was unstable and may be affected by human activities. The coefficient of variation of most chemical parameters of different water bodies in the area is less than 1 (Table 1 ), which is weak or medium variation. Spatially, Doma River and Maka Zangbo River showed a gradual increasing trend of ions from downstream to downstream. The main ions concentration in the lake increases gradually from east to west (Fig. 3 c). However, for lake samples S7 to S5, the ions and TDS increased slowly compared to the other sections, which may due to a water exchange barrier caused by the narrow body of the lake (Wang et al. 2011 ). According to Fig. 3 (d), there is no obvious spatial rule of groundwater in the lakeshore zone. Al, Cr, Pb, Cd, Hg, Cu and Ni were below the limit of quantification. Besides, Fe, Mn, As and F − were found at very insignificant concentrations. Table 1 shows the summary of the datasets of trace metals. Concentrations of Fe of various water bodies varied from 0.005 to 0.224 mg/L, which was within the WHO tolerable limit (0.3mg/L). As concentrations in lake samples varied from 0.002 to 0.035 mg/L, with the mean concentration of 0.021 ± 0.012 mg/L, most of which exceeded the WHO tolerable limits (0.01mg/L). Mn concentrations in river and groundwater ranges from 0.004 to 0.398mg/L and 0.010 to 0.441mg/L with the mean concentration of 0.044mg/L and 0.127mg/L ,which exceeded the WHO limit (0.1mg/L). However, little lake water sample had Mn content exceeding the WHO standard. F − concentrations of various water bodies were all below the acceptable limit according to WHO standards. 3.2 Hydrochemical classification and Cause Analysis of Hydrochemical Characteristics 1) Piper diagram Water hydrochemical characteristics can be classified by the piper diagram, which shows scatter plots of cations (Na + , K + , Ca 2+ , and Mg 2+ ) and anions (HCO 3 − , Cl − , and SO 4 2− ) (Piper. 1944; Sarkar, Pal and Islam. 2022). According to Fig. 4 , water samples in the Bangong Co Lake watershed were identified into three water types. River samples, glacier, and most of the groundwater corresponded to zone 2 of the piper diagram, which is characterized as Ca-HCO 3 type. The lake samples were distributed in zone 5, which demonstrates saline water with high Cl − or SO 4 2− and Na + . Due to Cl − is the main cation of lake samples, lake water is classified as Na-Cl type. Only a few groundwater samples corresponded to zone 3, which represents contaminated water with high Cl − and Ca 2+ concentrations. Eastern water samples are in the transition between the river point and the lake point, indicating those water samples exhibit ion exchange and simple dissolution or mixing. 2) Gibbs’ diagrams Based on the analysis of lots of global hydrochemical compositions of the surface waters, such as large lake, river and precipitation water samples, the diagrams were proposed by Gibbs to figure out the features of ionic distribution in different natural water bodies (Gibbs. 1970). Rock weathering, atmospheric precipitation, and evaporation–crystallization process were pointed out to be three major factors which controlled the surface water chemistry in Gibbs’ plots (Kammoun et al. 2022 ; Wang, Shang, Shen, Wu and Xiao. 2013). In this paper, TDS-Na + /(Na + +Ca 2+ ) diagram and TDS-Cl − /(Cl − +HCO 3 − ) diagram were used to distinguish the ionic characteristics of water bodies in the Bangong Co Lake Watershed. According to the Gibbs’ diagrams (Fig. 5 ), the river, groundwater and some of the lake water samples are in the zone that is controlled by rock weathering. And the lake water samples which are located in the west side of the Bangong Co Lake come under or near the evaporation dominance zone, whereas the eastern lake water and some groundwater samples are in the transition zone controlled by evaporation–crystallization and rock weathering (Fig .5b, 5c). This indicates that the dominant ions of various water bodies were significantly affected by rock weathering and evaporation-crystallization may have an effect on the western lake water and some groundwater. Duoma and Makazangbu rivers which were the main sources of replenishment joined Bangong Co lake on the east side, resulting in a characteristic transition between evaporation and rock weathering in the eastern lake. Glacier water is considered to be precipitation samples in the Bangong Co lake area. The glacier water samples come under the end element of rock weathering and slightly closer to the rain zone in Gibbs’ diagrams. Therefore, the dissolved ions in the rainwater of Bangong Co lake watershed are slightly affected by ocean evaporation and are mainly controlled by the dissolution of atmospheric CaCO 3 particles. 3) Ion ratio analysis The cations and anions dissolved by chemical weathering of different rocks contribute to the combination of water in nature (Fadili et al. 2016 ). The characteristics of ion ratio can provide a further judgement of the influence of rock weathering on the chemical composition of groundwater and surface water (Meybeck. 1987). In the study area, large areas of carbonate rocks, sandstones, slates, and granites are developed in the area through which the Doma River and Maka Zangbu flow. Na + and K + are mainly derived from evaporite or silicate, and Ca 2+ and Mg 2+ are mainly derived from carbonate and silicate weathering and evaporative dissolution (Zhang et al. 2019 ). Thus, when the ratio of γ(Na + +K + ) and γ(Cl − ) is close to or above the 1:1 line, which means dissolution of evaporate is the leading role for Na + , K + in water chemical evolution. In this paper, the relationship between γ(Na + +K + ) and γ(Cl − ) showed that the lake samples, river water, groundwater and glacier water are almost on the upper side of the 1:1 line (Fig. 6 a), indicating that the rock salt (NaCl) and potassium salt (KCl) are the main sources of Na + , K + and Cl − , and Cl − content is not enough to balance the content of Na + and K + in water. The excess Na + and K + may be derived from weathering and dissolution of silicate rock. Carbonates, evaporates, or silicates containing calcium and magnesium are the main sources of Ca 2+ and Mg 2+ ions provided by natural water (Guo and Kang. 2012). Most of the samples fall below or near the milligram equivalent ratio of γ(Ca 2+ + Mg 2+ ) /γ(HCO 3 − + SO 4 2− ) = 1 (Fig. 6 b), manifesting that the Ca 2+ and Mg 2+ may originate from the weathering dissolution of silicate rocks, evaporites, and carbonates. The ratio of γ(Ca 2+ +Mg 2+ ) and γ(HCO 3 − ) can be used to further study the source of Ca 2+ and Mg 2+ (Sun and Jin. 2020). In Fig. 5 c samples from these sources plot almost near or up on the 1:1 line of γ(Ca 2+ + Mg 2+ )/γ(HCO 3 − ), indicating that Ca 2+ , Mg 2+ and HCO 3 − in the river water and groundwater and other water bodies mainly derived from the dissolution of dolomite (CaMg(CO 3 ) 2 ) and calcite (CaCO 3 ) rather than gypsum(CaSO 4 ). Generally, silicates are more difficult to weather than carbonates, thus the ratio of (Ca 2+ + Mg 2+ )/(Na + + K + ) or γ(HCO 3 − )/γ(Na + + K + ) in the water can be used as a symbol for judging the main types of weathered rocks in nature water (Guo and Kang. 2012). The high ratios of γ(Ca 2+ +Mg 2+ )/γ(Na + +K + ) of the river water and groundwater manifest the characteristic of flowing through the carbonate weathering regions (Fig. 6 d). However, lake samples were located on the lower side of γ(Ca 2+ + Mg 2+ )/γ(Na + + K + ) 1:1 line, which confirmed that silicate dissolution or evaporate dissolution contributes to the main ion characteristics (Fig. 6 d). 4) Principal component analysis Principal component analysis (PCA) can reduce the dimension of data set by converting original variable into new and uncorrelated variables which are generated by retaining original information (Gu et al. 2014 ). Many studies have used PCA technique to identify the important parameters that determine the water quality (Ahmad et al. 2020 ; Şehnaz et al. 2019 ). Before component analysis, all data have passed the Kaiser–Meyer–Olkin (KMO) value (0.704) and Bartlett’s test of sphericity statistics (p < 0.05) by using Origin2022 to evaluate the feasibility of PCA for source apportionment (Wang and Shi. 2019). Table 2 Loadings of each variable on principal component (PC) comparison of Bangong Co Lake Watershed Elements Component PC1 PC2 K + 0.305 0.041 Na + 0.317 0.012 Ca 2+ -0.081 0.676 Mg 2+ 0.312 0.115 Cl − 0.317 0.034 SO 4 2− 0.311 0.102 HCO 3 − 0.300 0.172 NO 3 − -0.079 0.636 TDS 0.317 0.041 EC 0.318 0.041 TH 0.302 0.047 pH 0.182 -0.248 As 0.289 -0.126 Eigenvalue 9.801 1.767 Variance (%) 75.393 13.593 By filtering principal components(PCs) with eigenvalue greater than 1 (Lin, Dong, Wang, Li, Liu, Li and Crittenden. 2021), only two PCs were extracted from the scree plot, and explained 89.0% of the total variance (Table 2 and Fig. 7). K + , Na + , Mg 2+ , HCO 3 − , Cl − , SO 4 2− , TDS, As, and EC have a relatively high loading on the principal component PC1, accounting for 75.4% of the total variance, indicating that it is possibly affected by halite dissolution. PC2 explained 13.6% of the total variance, and has a strong positive loading for Ca 2+ , NO 3 − and negative weak loading for pH, indicating that carbonate weathering, animal husbandry activities or human activities are the possible causes of these ions. The distribution of factor scores on PC1 and PC2 is shown in Fig. 7(b). Sampling points are divided into two categories related to the geological or environmental background of the watershed. The contribution of lake water samples to PC1 increased gradually from the east to the west, indicating that PC1 was mainly affected by evaporation. Rivers and groundwater contribute to PC2 rather than lake water. PC2 comprised Ca 2+ , NO 3 − and pH. NO 3 − in the groundwater of lakeshore zone and in the middle and lower reaches of rivers is easily affected by human activities. At the same time, Ca 2+ is mainly produced by weathering of carbonate flowing through the rivers. Therefore, there is further certain that PC2 reflects anthropogenic activity and carbonate karst decomposition. The middle and lower reaches of Doma River and Makazangbu River are important animal husbandry areas, agricultural irrigation areas and human living areas, indicating that agricultural fertilization and human activities have a certain impact on the water supply environment. 3.3 Water quality evaluation 1) Assessment of Groundwater Suitability for Drinking Purposes A drinking water quality index (DWQI) method was used to assess the suitability of groundwater for drinking (Adimalla, Li and Venkatayogi. 2018; Sarkar, Pal and Islam. 2022). The DWQI is a water quality assessment method proposed by Horton RK which has been widely used (Horton. 1965). In this technique, the weighted index method was used to change a large number of water quality parameters into a single index that can be compared, effectively providing a comprehensive groundwater quality evaluation model (Ali et al. 2020 ). The calculation of DWQI can be divided into several steps: (a) assignment of each parameter weight according to its relative importance (values from 2 to 5), (b) calculation of the relative weight to each index (W i ), (c) the rate calculation of the quality parameter (Q i ), and (d) the water quality index computation of each sample (DWQI). These data formulas of are calculated by following equations: $${\text{W}}_{\text{i}}={\text{w}}_{\text{i}}/{\sum }_{\text{i}=1}^{\text{n}}{\text{w}}_{\text{i}}$$ $${\text{Q}}_{\text{i}}=\left({\text{C}}_{\text{i}}-{\text{C}}_{\text{i}\text{p}}\right)/\left({\text{S}}_{\text{i}}-{\text{C}}_{\text{i}\text{p}}\right)\times 100$$ $$\text{W}\text{Q}\text{I}=\sum \left({\text{Q}}_{\text{i}}\times {\text{W}}_{\text{i}}\right)$$ Where, W i is the relative weight, w i is the weight of each parameter and n is the total number of parameters considered for the WQI calculation, C i is the concentration of each parameter(mg/L); C ip is the ideal value of the parameter in pure water; S i is the standard value of each parameter. In this study, an analysis of eleven water quality parameters, namely pH, TH, TDS, Cl − , SO 4 2− , NO 3 − , F − , Na + , As, and Mn, was used to evaluate the suitability of groundwater for drinking. The relative weight of each parameter and its weight used in these calculations are presented in Table 3 . Based on DWQI values, the groundwater quality status can be categorized into five types (Adimalla, Li and Venkatayogi. 2018; Kammoun, Abidi and Zairi. 2022; Kumari and Rai. 2020) which were excellent water ( 125). In the Bangong Co Lake Watershed, the computed DWQI values vary from 12 to 222 with an average value of 44 ± 46. According to the DWQI classification, 67% (n = 14) of the total groundwater samples come under the excellent category, and 5% (n = 1) are classified as good water category, respectively. About 19% (n = 4) of groundwater samples fell under moderate water quality, and 5% (n = 1) come under unsuitable for drinking (Fig. 8 ). Figure 8 shows the spatial distribution of the DWQI, which illustrates that the chemical characteristics of groundwater in the lakeshore zone have no regular distribution in the east-west direction. While those wells located near lakeshore part have higher DWQI values than those away from lakeshore, indicating that groundwater near the lakeshore may be affected by lake water (Fig. 5 ). Table 3 The relative weight to each index and weight assigned for DWQI. Parameters Weight Relative weight Na + 2 0.047 Cl − 4 0.093 SO 4 2− 3 0.070 NO 3 − 5 0.116 TDS 5 0.116 pH 3 0.070 As 5 0.116 TH 3 0.070 Fe 4 0.093 Mn 4 0.093 F − 5 0.116 2) Water Evaluation for Irrigation In the plateau and semi-arid region of Bangong Co Lake Watershed, agriculture and livestock husbandry are the main occupations, and the main crops in the region are barley, rape, wheat, peas (Huntington. 1906; Yan. 2022). Agriculture is the basic department of regional economic and social development. In addition, afforestation is an initiative advocated by the local government to improve the ecological environment (Yan. 2022). Due to the fact that water quality plays an important role in crop, survival of tree planting and soil characteristics in the study area,, groundwater irrigation suitability evaluation is quite necessary. To better evaluate the suitability of regional surface water and groundwater, this study uses the USSL classification proposed by Richards ( 1954 ) and several other methods, such as EC, SAR, PI, MHR (Ayers and Westcot. 1976; Doneen. 1964; Richards. 1954). Table 4 shows the calculation methods and sources of these parameters. Table 4 Equations used for irrigation and irrigation water quality assessment IWQ parameters and equations Irrigation problem Degree of restriction on use Number of samples (%) Reference None Slight to moderate Severe None Slight to moderate Severe EC(µS/cm) Salinty 3000 35(58%) 17(28%) 8(13%) Richards ( 1954 ) SAR= \(\frac{{\text{N}\text{a}}^{+}}{\sqrt{0.5\times （{\text{C}\text{a}}^{2+}+{\text{M}\text{g}}^{2+}）}}\) Permeab-ility > 700 700 ~ 200 < 200 51(85%) 1(2%) 8(13%) Bouwer ( 1978 ) Richards ( 1954 ) PI= \(\left(\frac{{\text{N}\text{a}}^{+}+\sqrt{{\text{H}\text{C}\text{O}}_{3}^{-}}}{{\text{C}\text{a}}^{2+}+{\text{M}\text{g}}^{2+}+{\text{N}\text{a}}^{+}}\right)\times 100\) 75 19(32%) 41(68%) 0 Doneen ( 1964 ) MHR= \(\left(\frac{{\text{M}\text{g}}^{2+}}{{\text{C}\text{a}}^{2+}+{\text{M}\text{g}}^{2+}}\right)\times 100\) 50 33(55%) - 27(45%) Ayers and Westcot ( 1976 ) High salinity and high sodium concentration in irrigation water are the main causes of soil salinization, which affects the growth of plants and crops (Rajmohan et al. 2021 ). The sodium adsorption ratio (SAR), recommended by Richards (Richards. 1954), is one of the important indices to calculate the harm of sodium in irrigation water. In Bangong Co Lake Watershed, the SAR value ranged from 0.03 to 16.72 with the mean value of 3.05 ± 4.92. On the basis of the classification of SAR values (Richards. 1954; Wilcox. 1955), water samples in Bangong Co Lake Watershed are classified into good (n = 51, 85%), poor (n = 1, 2%), and unsuitable (n = 8, 13%). EC is the salinity parameter of irrigation water, ranges from 23 to 7861 µS/cm with an average value of 1493 ± 2179 µS/cm. According to EC value classification, water samples in Bangong Co Lake Watershed come under good (n = 35, 58%), poor (n = 17, 28%), and unsuitable (n = 8, 13%). Doneen ( 1964 ) pointed out that poor soil permeability is usually caused by irrigation water with high Na + and HCO 3 − concentrations. Thus, Permeability index (PI) is often used to evaluate the harm of high Na ion concentration in soil in irrigation water. In this study, PI ranged from 36 to 181, with an average of 71 ± 26. According to Wilcox's classification, watershed water samples were classified as good (n = 49, 32%), suitable (n = 41, 68%) (A. Saleh. 1999; Wilcox. 1955). Ayers and Westcot ( 1976 ) noticed that irrigation water with high Mg 2+ can leads to soil Ca 2+ deficiency and crop yields reduction. In this study, Magnesium hazardous ratio (MHR) ranged from 10 to 98, with a mean of 54 ± 23. The results show that 55% of water samples are suitable for irrigation (MHR < 50), and the rest of the water samples (n = 27, 45%) are unsuitable for irrigation (MHR ≥ 50). Richards ( 1954 ) developed the USSL classification for assessing the suitability of irrigation water which combined both SAR and EC parameters. In the USSL diagram (Fig. 8 ), all glacier (n = 3, 100%), some groundwater samples (n = 2, 11%), and some river samples (n = 3, 13%) and fell into C1-S1 region, indicating relatively low alkalinity and salinity hazards of water samples; most of groundwater samples (n = 10, 53%) and river samples (n = 19, 79%) plotted on C2-S1 region. Some groundwater samples (n = 7, 37%) and eastern lake samples are classified into C3-S1 region. These low-sodium (S1) and medium or high alkalinity (C2, C3) water samples basically meet the requirements of irrigation water. Only one lake sample plotted on C4-S2 region, and western lake samples almost plotted on C5-S4 region, which are not suitable for crops and soil irrigation. In conclusion, glacial meltwater and river water are more suitable for irrigation than lake and part of groundwater. 4 Conclusions The hydrochemical characteristics of surface water and lakeshore groundwater in Bangong Lake Watershed, Northwest Tibet, China were analyzed by using multivariate statistical method, piper diagrams Gibbs’ diagrams, ion ratio method and PCA analysis. At the same time, DWQI parameters, irrigation water indexes and spatial analysis were used to evaluate the quality of irrigation water and drinking water. The main conclusions are summarized below. Different water bodies in the study area are found to be alkaline (pH > 7; 80%). Glacier water is categorized as soft-fresh and lake water is classified as hard-brackish. Groundwater and river water are categorised as soft-fresh and hard-fresh. Na-Cl is the dominant water type for lake water, and Ca-HCO 3 type is the dominant water type for glacier, river water and groundwater. Only 78% of groundwater samples are Ca-Cl types. Ionic concentrations of river waters have increased trend from upstream to downstream in spatial distribution, and high concentrations are found in the coastal region. For spatial patterns of water in the Bangong Co Lake, ionic concentrations increase from east to the west. There is no obvious spatial distribution of groundwater ion concentrations in the lakeshore zone in the east-west direction For drinking water quality, the DWQI value classifies groundwater as excellent (67%), good (5%), poor (19%). The spatial distribution of DWQI value map demonstrates that the shallow groundwater quality has an obvious downward trend when it’s the closer to the lakeshore line. For irrigation water quality, the IWQ parameters show that 52%, 49%, and 55% of total samples have SAR 75, and MHR < 50, respectively, which are belong to good to permissible classes for irrigation. USSL classification suggests that glacier, river, and some of the groundwater samples are suitable for irrigation, and eastern lake samples and part of groundwater samples only for salt-tolerant crops and high permeability soil with good drainage. While, western lake water is classified to be unsuitable irrigation water. The research will be definitely helpful for the Bangong Co Lake area to utilize water resources in a sustainable way. . Declarations Acknowledgements The authors gratefully acknowledge the technical staff at the laboratory of Xizang Shengyuan Environmental Engineering Co., LTD for their constant help. We also thank the contributions of the staff members of Center of Applied Geological Survey, China Geological Survey. Author contributions Yuxiang Shao, Buqing Yan and Baiyang Liu-Lu contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yuxiang Shao, Kang Gong and Kun Zhang. 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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-2747303","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":187338899,"identity":"8be3ab7e-20ac-4352-9e0c-4370eae473c3","order_by":0,"name":"Yuxiang Shao","email":"","orcid":"","institution":"China Geological Survey","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Shao","suffix":""},{"id":187338900,"identity":"18615865-af5c-416d-bc00-101a8fdf0579","order_by":1,"name":"Buqing 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Survey","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kang","middleName":"","lastName":"Gong","suffix":""}],"badges":[],"createdAt":"2023-03-28 13:29:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2747303/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2747303/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3390/w15142655","type":"published","date":"2023-07-22T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":35050695,"identity":"beefe69a-bc4e-4202-b8db-915e3af93c1a","added_by":"auto","created_at":"2023-03-30 15:59:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1906126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSampling locations and geology of the study area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2747303/v1/a032d607a67d6e6876e14cd8.jpg"},{"id":35048914,"identity":"612810ef-bb57-45d9-beb9-e97472373e9b","added_by":"auto","created_at":"2023-03-30 15:35:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1165270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClassification of water in Bangong Co Lake Watershed based on TH and TDS.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2747303/v1/5cd823459bd1940a4a1b5329.jpg"},{"id":35050337,"identity":"6e460fa5-473e-4384-8b7a-a51f7d80809d","added_by":"auto","created_at":"2023-03-30 15:51:01","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2821059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe spatial distribution of anions and cations in different water samples.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2747303/v1/44e0437a112a7ef3af0e9caf.jpg"},{"id":35049843,"identity":"d644d11f-b7ec-4582-a2b6-15b1e0324b5f","added_by":"auto","created_at":"2023-03-30 15:43:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2361600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePiper plot of water samples.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2747303/v1/d91220effe006fe4e1b9c184.jpg"},{"id":35049846,"identity":"cc281da4-457f-4b8b-8da5-53663a111731","added_by":"auto","created_at":"2023-03-30 15:43:01","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1972978,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGibbs plot: (a) comparison of natural processes that define the water chemistry of water on the Gibbs (1970) diagram; (b) TDS versus Na\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e/(Na\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e + Ca\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e); (c) TDS versus Cl\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e−\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e/(Cl\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e−\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e + HCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e−\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2747303/v1/bd7def4fd5a03dcdc1ea3178.jpg"},{"id":35049842,"identity":"ba9053a4-760e-4d09-b091-3c1bd026689c","added_by":"auto","created_at":"2023-03-30 15:43:01","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2016388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistributions of ionic ratios in water samples: (a) γ(Na\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e + K\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e) versus γ(Cl\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e−\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e); (b) γ(Ca\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e + Mg\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e) versusγ(HCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e- \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e+ SO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e2−\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e); (c) γ(Ca\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e + Mg\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e) versus γ(HCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e−\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e); (d) γ(Ca\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e + Mg\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e) versus γ(Na\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e + K).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2747303/v1/5a35303473c8ee18e4504643.jpg"},{"id":35048922,"identity":"04347415-b25c-40bf-ba5c-aceeec2cb7ee","added_by":"auto","created_at":"2023-03-30 15:35:01","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1385038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal component analysis diagram of major ions in water samples from the Lake Bangong Co Lake catchment\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2747303/v1/c46ed2c641e533f3299ac9e2.jpg"},{"id":35048916,"identity":"7d1b1154-ed1a-489b-b8e4-a13c8a1c7c99","added_by":"auto","created_at":"2023-03-30 15:35:01","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1054964,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal component analysis diagram of major ions in water samples from the Lake Bangong Co Lake catchment\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2747303/v1/f3ed13745dcaf5603fe8fb23.jpg"},{"id":35048920,"identity":"db73fa8f-29b7-4d42-bbb2-76df24f87a80","added_by":"auto","created_at":"2023-03-30 15:35:01","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1319705,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUSSL diagram showing the suitability of groundwater for irrigation purposes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2747303/v1/b57303e475557cb83ab600d3.jpg"},{"id":43196903,"identity":"2590430a-7250-4b3b-8fe7-e53a6d15635f","added_by":"auto","created_at":"2023-09-15 14:28:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1749526,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2747303/v1/cb1be85e-48f8-486d-80d8-0768a5875d06.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hydrochemical characteristics and water quality evaluation for irrigation and drinking purposes of Bangong Co Lake Watershed","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe Qinghai-Tibet Plateau, known as the roof of the world and the water tower of Asia, is the birthplace of the Yellow River, the Yangtze River, the Ganges River, the Mekong River, the Indus River and other rivers, and has the largest, highest, and most densely distributed alpine lake cluster in the world (Shi. 1990; Wang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the environment of Qinghai-Tibet Plateau, lakes play an important role in regulating the ecosystem, and hydrochemistry and are the vital indicator of water-rock interaction and environmental change in basins (Yan et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang R et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the last few years, many academics have conducted investigation in lakes on the Tibetan Plateau, which mainly focused on hydrochemical characteristics and its spatial distribution (Jin et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sun and Jin. 2020), hydrochemical types (), typical water pollutants (Ren et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), water quality assessment (Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang Y B et al. 2021), and hydrochemistry evolution and sources (Li et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Owing to the high altitude and inconvenience of transportation, hydrochemical characteristics and water quality assessment of many typical lakes on the Qinghai-Tibet Plateau have not been investigated.\u003c/p\u003e \u003cp\u003eNatural factors such as geological structure, hydrogeological conditions, vegetation, climate, rock weathering, terrain, and human activities have a great impact on the chemical composition of water (He et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Many scholars have learned about the soil salinization, regional geological background, element migration and enrichment laws, and physical weathering process of rocks through the study of ion characteristics in the watershed (Xiao et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Sun and Jin (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) pointed out the differences in chemical characteristics and evolution between tectonic lakes and glacial lakes on the plateau based on the analysis of physicochemical parameters and ion concentrations of lakes on the Qinghai-Tibet Plateau. Li et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have made a discussion on the relationship between lake change and climate in recent decades and clarify that lakes are important supporter of water cycle and environmental change. Bazova and Moiseenko (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) analyzed samples of ores, air pollutants, lake water in northwest Russia and used spatial analysis methods, suggested that the anthropogenic enrichment of Cu, Ni, Mn, Zn, Pb and As in lake water bodies was caused by flue gas discharge from metallurgical enterprises. Baranov et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) studied the chemical composition, natural geochemistry and human factors of dissolved effluent in the northern Valdai Mountains by using statistical methods. The results show that soil mineral composition, rainfall intensity and biogeochemical processes had a great influence on the chemical composition of water bodies in the aeration zone. Therefore, it is of great scientific significance to study the hydrochemical characteristics of rivers and lakes for the water cycle process and water resources environmental protection in plateau lakes.\u003c/p\u003e \u003cp\u003eDue to the high evaporation, low rainfall, and less recharge in the plateau arid area, the surface water and shallow groundwater are mostly saline or brackish water, which is not conducive to plant growth, human life and industrial activities (Jiang et al. 2022). Water and its quality assessment is a vital resource for maintaining social development and economic stability and ecological balance in the arid regions of the plateau.[1,2] Previously, there were a series of studies related to lake water quality assessment on the plateau arid area such as Qinghai Lake (Xu et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Ebinur Lake Watershed (Zhu et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Dal Lake in Kashmir Valley (Kumar et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Mapam Yumco (Sun et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), Nam Co (Guo and Kang. 2012), and some other lakes, which justify that water quality assessment is necessary for plateau lake.\u003c/p\u003e \u003cp\u003eBangong Co Lake is a long and narrow tectonic lake with a nearly east-west distribution In the western part of the Tibetan Plateau, which is characterized by dry and cold climate with strong evaporation (Fontes et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). As the main source of replenishment for the Lake Bangong Co watershed, glacial meltwater is insufficient to balance evaporation in the area. Likewise, affected by evaporation in the whole watershed and river water supply in the east of Bangong Co Lake, the salinity of the lake gradually increases from east to west (Xiao et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The water cycle system and ecosystem in this plateau arid watershed are very fragile and sensitive, which are susceptible to climate change and human activities\u003c/p\u003e \u003cp\u003eThe domestic water and irrigation water in the study area are mainly shallow aquifers in the lakeshore zone and river valley, or supplemented by snowmelt water and river water. However, there are few relevant studies on the various water bodies of Bangong Co Lake Watershed. Only some studies on the lake groups on the Tibetan Plateau have presented the data of the lake water in the east side of Bangong Lake and the corresponding lake sediments, which indicated that the lake water type on the east side of Bangong Lake is Cl-Na or CO\u003csub\u003e3\u003c/sub\u003eCl-MgNa type (Hu et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Lin et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) has carried out some research on the information of toxic organic pollutants and metals of Bangong Co Lake. It indicates that ΣPAH and ΣPAE concentrations in lake water have no relationship with hydrochemical parameters and organic pollutants have the main source for domestic waste related to regional increasing human activities in recent years. Therefore, the hydrochemical characteristic and its controlling factors of the river water, lake water, groundwater and other water bodies in the watershed of Bangong Co Lake is still unknown. And there is no available information on water quality assessment in this region.\u003c/p\u003e \u003cp\u003eThe present investigation was performed to explore the glaciers, rivers, lakes and groundwater along the lakeshore in Bangong Co Lake watershed. Statistical analysis, a Gibbs diagram, a Piper diagram, and an ion ratio analysis were used to analyze the hydrochemical characteristics and formation mechanism of different water bodies. Drinking water quality index (DWQI) and irrigation water quality parameters were used to assess the water quality and its suitability in the study area. The aim of this work is to explain the hydrochemical characteristics in this area, and supply the research data in this area and provide useful data for the protection and rational utilization of the water environment in the basin.\u003c/p\u003e"},{"header":"2 Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Overview of the study area\u003c/h2\u003e \u003cp\u003eBangong Co Lake Watershed is located in the northwest of Tibet, China. Based on ArcGIS hydrological analysis module and DEM data, it is calculated that the watershed area of Bangong Co Lake is about 33 000km\u003csup\u003e2\u003c/sup\u003e, with the location between 32\u0026deg;40'- 34\u0026deg;30' N and 78\u0026deg;10'- 81\u0026deg;15' E and the elevation range is 3736-6771m. The watershed reaches the Kanakoram Mountains in the north, and the Gangdises Mountains in the south. The highest point is located at the eastern mountains of the Zecuo Lake, and the lowest point is located in the Bangong Co Lake in the middle of the area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The geomorphology of the watershed belongs to plateau lakebasin geomorphic type, which has been fully eroded by snow and water for a long time. The watershed covers the Bangong Lake replenishment river basin and several small intermountain lake basins such as Spangour Co, Zecuo, Rebangcuo, Aiyongcuo and Sharda Co (Yang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This region has cold and dry climate, where the annual average temperature is 0.1-2℃. The annual rainfall is about 70-80mm with high variability, and mostly concentrated in July or August (Xie et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The main source of water in the study area is glacial meltwater. Under the action of gravity, the water mainly flows along the slope or gully to feed rivers or veins of rock, and drain into the lake eventually. Because the sediments in river valley at the middle or lower reaches and the sediments in lake shore are thick and loose, the river water is likely to penetrate, forming relatively stable shallow groundwater in the alluvial fan or valley area.\u003c/p\u003e \u003cp\u003eGeologically, the study area is located in the nearly east-west Bangong-Nujiang docking zone, with Qiangtang Terrane to the north and Lhasa Terrane to the south (Yin and Harrison. 2000; Zhao et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Qiangtang Terrane in the region is mainly composed of ophiolite melange, carbonate rocks, sandstone and slate rocks from shamuluo Group (J\u003csub\u003e3\u003c/sub\u003eK\u003csub\u003e1\u003c/sub\u003es) and Mugagangri Group (J\u003csub\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/sub\u003eM), and Lhasa Terrane in the study area is mainly composed of ophiolite m\u0026eacute;lange, Mesozoic granites and clastic rocks, and sandwiched limestone, flysch sediments from Mugagangri Group (J\u003csub\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/sub\u003eM), Jielu Group(J\u003csub\u003e3\u003c/sub\u003e), Shamuluo Group (J\u003csub\u003e3\u003c/sub\u003eK\u003csub\u003e1\u003c/sub\u003es) (Li et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xie, Xiao, Xiao, Cao, OY, Niao and Feng. 2004). Quaternary sediments are mainly distributed in the sides of lake and river valleys.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling and Measurement\u003c/h2\u003e \u003cp\u003eIn early June 2022, field investigation was carried out in Bangong Lake Basin, and a total of 60 water samples were collected, including 14 lake water samples, 19 groundwater samples, 24 river water samples and 3 glacier water samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among them, river samples were mainly distributed in Makazangbu and Doma Rivers, and groundwater samples were mainly collected from wells on alluvial fan bodies in lakeshore zone. Water samples were mostly collected from at a depth of 0.1-0.5m, and filtered through 0.45\u0026micro;m cellulose acetate membrane and stored in 1500mL cleaned polyethylene bottles. Before collection, the collection containers were rinsed with sampling water for 2 to 3 times. The lake water samples were taken from a location about 1-2m away from the lake. In addition, water electrical conductivity (EC) and pH were measured in the field using a multi-parameter portable water analyzer (In-situ SMARTROL, USA), with the pH accuracy was \u0026plusmn;\u0026thinsp;0.1pH and the conductivity accuracy was \u0026plusmn;\u0026thinsp;1\u0026micro;S/cm.\u003c/p\u003e \u003cp\u003eAll water samples were sent to the Xizang Shengyuan Environmental Engineering Co., LTD for testing. The content of HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e was measured by acid-base titration. Water salinity (TDS) was determined by dry weight method at 105℃. Total hardness (TH) was determined by EDTA method. The main cations (Na\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e) concentration and trace heavy metals (Fe, Mn, Zn, Cu and Cr) levels were determined by inductively coupled plasma emission spectrometer (Agilent5110, USA). The total As content was detected by atomic fluorescence (AFS-933,USA) with detection limits of 0.09 mg/L. Anion concentration (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) was determined by chromatography (LC-10ADvp, Japan). F\u003csup\u003e\u0026minus;\u003c/sup\u003e was determined by ion chromatography system(ECO IC, Switzerland). Quality control was carried out in the test process, and all the measured parameters were determined by blank values. Also, the test results were tested by ion balance test, which were within \u0026plusmn;\u0026thinsp;5%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Processing\u003c/h2\u003e \u003cp\u003eSPSS v22.0 was used to perform the average (Mean), maximum (Max), minimum (Min), and coefficient of variation (CV). The piper diagrams (Piper. 1944), principal component analysis, Gibbs\u0026rsquo; diagrams (Gibbs. 1970) and ion ratio analysis (Meybeck. 1987) were implemented in Origin v2021 and Microsoft Excel v2013 to explore classification and main controlling factors of the nature water in Lake Bangong Co Watershed. International standards (WHO. 2017), drinking water quality index (Adimalla et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sarkar et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), USSL classification (Richards. 1954) and other irrigation water indicators are used to evaluate groundwater suitability for drinking and irrigation. ArcGIS v10.4 and CorelDRAW Graphics Suite v18 are used for mapping and modification.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results And Discussion","content":"\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e3.1 Chemical Composition of Different Water Bodies in Bangong Lake Basin\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec8\"\u003e\n \u003ch2\u003e3.1.1 Statistical characteristics of parameters\u003c/h2\u003e\n \u003cp\u003eThe parameters of each water body in Bangong Co Watershed are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The pH of glacier, lake water, river water and shallow groundwater along the lakeshore in the study area indicated weak alkalinity, ranging from 6.6 to 8.5. The pH of Bangong Co lake water was relatively high (pH\u0026thinsp;=\u0026thinsp;8.1\u0026ndash;8.5), and the pH of glacier was relatively low (pH\u0026thinsp;=\u0026thinsp;6.6\u0026ndash;7.6). The CV% for water pH of each water bodies was \u0026lt;\u0026thinsp;10%, which showed weak variability. TDS and EC are parameters to determine the salinity of water (Wu et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The TDS of glacier, river water, groundwater, and lake samples ranged from 13\u0026ndash;25 mg/L, 81\u0026ndash;438 mg/L, 124\u0026ndash;972 mg/L, 206-4091mg/L, respectively. The TDS values of most lake samples and some groundwater were above the WHO standard limits (WHO. 2017). The average TDS of river water is 282 mg/L, which was also higher than the global average and values for other large rivers in China (Xiao et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, the EC of glacier water, river water, groundwater, and lake samples ranged from 23\u0026ndash;43\u0026micro;S/cm, 145\u0026ndash;779 \u0026micro;S/cm, 220\u0026ndash;1706 \u0026micro;S/cm, 385\u0026ndash;7861 \u0026micro;S/cm, respectively. Water hardness is a very important parameter in the growth and reproduction of aquatic organisms (Kim et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). The TH of glacier, groundwater, river, and lake are in the range of 23-43mg/L, 27\u0026ndash;750 mg/L and 58\u0026ndash;467 mg/L, and 258\u0026ndash;2854 mg/L, respectively. With the decrease of altitude, the value of TH, EC, TDS increased gradually, which were found the highest around the Bangong Co Lake. The CV% for TH, EC, and TDS of different water bodies were between 10% and 66%, which showed medium variability. Based on TH and TDS (Ganguli et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), lake water is hard-brackish water, glacier is soft-fresh water, and river water changes from soft-fresh water to hard-fresh water along the stream (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMajor ions and heavy metal compositions of water samples from the Bangong Co Lake Watershed.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSample type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFe\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMn\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eglacier (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eriver water (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e427.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003elake water (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1089.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1163.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1004.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1092.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e638.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e618.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e605.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e700.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003egroundwater (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e528.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e581.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWHO(2017)limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe units for ion concentration are mg/L, for EC, \u0026micro;s/cm, and for TDS, mg/L. CV% coefficient of variation. ND not detected.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe ion diagrams (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) depict the milligram equivalent and spatial variation of anions and cations in the different water bodies across the basin. In the water samples of groundwater, river water and glacier, the cations were dominated by Ca\u003csup\u003e2+\u003c/sup\u003e, and the abundance order was Ca\u003csup\u003e2+\u003c/sup\u003e\u0026gt;Na\u003csup\u003e+\u003c/sup\u003e\u0026gt;K\u003csup\u003e+\u003c/sup\u003e\u0026gt;Mg\u003csup\u003e2+\u003c/sup\u003e. However, the main cation of lake water was Na\u003csup\u003e+\u003c/sup\u003e, with the cation abundance order was Na\u003csup\u003e+\u003c/sup\u003e\u0026gt;Mg\u003csup\u003e2+\u003c/sup\u003e\u0026gt;K\u003csup\u003e+\u003c/sup\u003e\u0026gt;Ca\u003csup\u003e2+\u003c/sup\u003e. The main anion in groundwater, river water and glacier samples was HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, with an abundance order of HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026gt;SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u0026gt;Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026gt;NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, whereas the main for lake samples anion was Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, with an abundance order of Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026gt;HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026gt;SO4 \u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026gt;NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e. The strong coefficient of variation was found in Na\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e of groundwater, and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e of river and lake water, which was between 1.15 and 2.85, indicating that the spatial distribution of these ionic components was unstable and may be affected by human activities. The coefficient of variation of most chemical parameters of different water bodies in the area is less than 1 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), which is weak or medium variation. Spatially, Doma River and Maka Zangbo River showed a gradual increasing trend of ions from downstream to downstream. The main ions concentration in the lake increases gradually from east to west (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). However, for lake samples S7 to S5, the ions and TDS increased slowly compared to the other sections, which may due to a water exchange barrier caused by the narrow body of the lake (Wang et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). According to Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e(d), there is no obvious spatial rule of groundwater in the lakeshore zone.\u003c/p\u003e\n \u003cp\u003eAl, Cr, Pb, Cd, Hg, Cu and Ni were below the limit of quantification. Besides, Fe, Mn, As and F\u003csup\u003e\u0026minus;\u003c/sup\u003e were found at very insignificant concentrations. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the summary of the datasets of trace metals. Concentrations of Fe of various water bodies varied from 0.005 to 0.224 mg/L, which was within the WHO tolerable limit (0.3mg/L). As concentrations in lake samples varied from 0.002 to 0.035 mg/L, with the mean concentration of 0.021\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012 mg/L, most of which exceeded the WHO tolerable limits (0.01mg/L). Mn concentrations in river and groundwater ranges from 0.004 to 0.398mg/L and 0.010 to 0.441mg/L with the mean concentration of 0.044mg/L and 0.127mg/L ,which exceeded the WHO limit (0.1mg/L). However, little lake water sample had Mn content exceeding the WHO standard. F\u003csup\u003e\u0026minus;\u003c/sup\u003e concentrations of various water bodies were all below the acceptable limit according to WHO standards.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e3.2 Hydrochemical classification and Cause Analysis of Hydrochemical Characteristics\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003e1) Piper diagram\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWater hydrochemical characteristics can be classified by the piper diagram, which shows scatter plots of cations (Na\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e, and Mg\u003csup\u003e2+\u003c/sup\u003e) and anions (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) (Piper. 1944; Sarkar, Pal and Islam. 2022). According to Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, water samples in the Bangong Co Lake watershed were identified into three water types. River samples, glacier, and most of the groundwater corresponded to zone 2 of the piper diagram, which is characterized as Ca-HCO\u003csub\u003e3\u003c/sub\u003e type. The lake samples were distributed in zone 5, which demonstrates saline water with high Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e or SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e. Due to Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e is the main cation of lake samples, lake water is classified as Na-Cl type. Only a few groundwater samples corresponded to zone 3, which represents contaminated water with high Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e concentrations. Eastern water samples are in the transition between the river point and the lake point, indicating those water samples exhibit ion exchange and simple dissolution or mixing.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2) Gibbs\u0026rsquo; diagrams\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBased on the analysis of lots of global hydrochemical compositions of the surface waters, such as large lake, river and precipitation water samples, the diagrams were proposed by Gibbs to figure out the features of ionic distribution in different natural water bodies (Gibbs. 1970). Rock weathering, atmospheric precipitation, and evaporation\u0026ndash;crystallization process were pointed out to be three major factors which controlled the surface water chemistry in Gibbs\u0026rsquo; plots (Kammoun et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang, Shang, Shen, Wu and Xiao. 2013). In this paper, TDS-Na\u003csup\u003e+\u003c/sup\u003e/(Na\u003csup\u003e+\u003c/sup\u003e+Ca\u003csup\u003e2+\u003c/sup\u003e) diagram and TDS-Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e/(Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e+HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) diagram were used to distinguish the ionic characteristics of water bodies in the Bangong Co Lake Watershed.\u003c/p\u003e\n \u003cp\u003eAccording to the Gibbs\u0026rsquo; diagrams (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), the river, groundwater and some of the lake water samples are in the zone that is controlled by rock weathering. And the lake water samples which are located in the west side of the Bangong Co Lake come under or near the evaporation dominance zone, whereas the eastern lake water and some groundwater samples are in the transition zone controlled by evaporation\u0026ndash;crystallization and rock weathering (Fig .5b, 5c). This indicates that the dominant ions of various water bodies were significantly affected by rock weathering and evaporation-crystallization may have an effect on the western lake water and some groundwater. Duoma and Makazangbu rivers which were the main sources of replenishment joined Bangong Co lake on the east side, resulting in a characteristic transition between evaporation and rock weathering in the eastern lake. Glacier water is considered to be precipitation samples in the Bangong Co lake area. The glacier water samples come under the end element of rock weathering and slightly closer to the rain zone in Gibbs\u0026rsquo; diagrams. Therefore, the dissolved ions in the rainwater of Bangong Co lake watershed are slightly affected by ocean evaporation and are mainly controlled by the dissolution of atmospheric CaCO\u003csub\u003e3\u003c/sub\u003e particles.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3) Ion ratio analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe cations and anions dissolved by chemical weathering of different rocks contribute to the combination of water in nature (Fadili et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The characteristics of ion ratio can provide a further judgement of the influence of rock weathering on the chemical composition of groundwater and surface water (Meybeck. 1987). In the study area, large areas of carbonate rocks, sandstones, slates, and granites are developed in the area through which the Doma River and Maka Zangbu flow.\u003c/p\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e are mainly derived from evaporite or silicate, and Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e are mainly derived from carbonate and silicate weathering and evaporative dissolution (Zhang et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, when the ratio of \u0026gamma;(Na\u003csup\u003e+\u003c/sup\u003e+K\u003csup\u003e+\u003c/sup\u003e) and \u0026gamma;(Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e) is close to or above the 1:1 line, which means dissolution of evaporate is the leading role for Na\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e in water chemical evolution. In this paper, the relationship between \u0026gamma;(Na\u003csup\u003e+\u003c/sup\u003e+K\u003csup\u003e+\u003c/sup\u003e) and \u0026gamma;(Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e) showed that the lake samples, river water, groundwater and glacier water are almost on the upper side of the 1:1 line (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea), indicating that the rock salt (NaCl) and potassium salt (KCl) are the main sources of Na\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e content is not enough to balance the content of Na\u003csup\u003e+\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e in water. The excess Na\u003csup\u003e+\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e may be derived from weathering and dissolution of silicate rock.\u003c/p\u003e\n \u003cp\u003eCarbonates, evaporates, or silicates containing calcium and magnesium are the main sources of Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e ions provided by natural water (Guo and Kang. 2012). Most of the samples fall below or near the milligram equivalent ratio of \u0026gamma;(Ca\u003csup\u003e2+\u003c/sup\u003e + Mg\u003csup\u003e2+\u003c/sup\u003e) /\u0026gamma;(HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e+ SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;1 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb), manifesting that the Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e may originate from the weathering dissolution of silicate rocks, evaporites, and carbonates. The ratio of \u0026gamma;(Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e) and \u0026gamma;(HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) can be used to further study the source of Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e (Sun and Jin. 2020). In Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec samples from these sources plot almost near or up on the 1:1 line of \u0026gamma;(Ca\u003csup\u003e2+\u003c/sup\u003e+ Mg\u003csup\u003e2+\u003c/sup\u003e)/\u0026gamma;(HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), indicating that Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e and HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e in the river water and groundwater and other water bodies mainly derived from the dissolution of dolomite (CaMg(CO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e) and calcite (CaCO\u003csub\u003e3\u003c/sub\u003e) rather than gypsum(CaSO\u003csub\u003e4\u003c/sub\u003e).\u003c/p\u003e\n \u003cp\u003eGenerally, silicates are more difficult to weather than carbonates, thus the ratio of (Ca\u003csup\u003e2+\u003c/sup\u003e+ Mg\u003csup\u003e2+\u003c/sup\u003e)/(Na\u003csup\u003e+\u003c/sup\u003e + K\u003csup\u003e+\u003c/sup\u003e) or \u0026gamma;(HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e)/\u0026gamma;(Na\u003csup\u003e+\u003c/sup\u003e + K\u003csup\u003e+\u003c/sup\u003e) in the water can be used as a symbol for judging the main types of weathered rocks in nature water (Guo and Kang. 2012). The high ratios of \u0026gamma;(Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e)/\u0026gamma;(Na\u003csup\u003e+\u003c/sup\u003e+K\u003csup\u003e+\u003c/sup\u003e) of the river water and groundwater manifest the characteristic of flowing through the carbonate weathering regions (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ed). However, lake samples were located on the lower side of \u0026gamma;(Ca\u003csup\u003e2+\u003c/sup\u003e + Mg\u003csup\u003e2+\u003c/sup\u003e)/\u0026gamma;(Na\u003csup\u003e+\u003c/sup\u003e + K\u003csup\u003e+\u003c/sup\u003e) 1:1 line, which confirmed that silicate dissolution or evaporate dissolution contributes to the main ion characteristics (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ed).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4) Principal component analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePrincipal component analysis (PCA) can reduce the dimension of data set by converting original variable into new and uncorrelated variables which are generated by retaining original information (Gu et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Many studies have used PCA technique to identify the important parameters that determine the water quality (Ahmad et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Şehnaz et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Before component analysis, all data have passed the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) value (0.704) and Bartlett\u0026rsquo;s test of sphericity statistics (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) by using Origin2022 to evaluate the feasibility of PCA for source apportionment (Wang and Shi. 2019).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLoadings of each variable on principal component (PC) comparison of Bangong Co Lake Watershed\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eElements\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEigenvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eBy filtering principal components(PCs) with eigenvalue greater than 1 (Lin, Dong, Wang, Li, Liu, Li and Crittenden. 2021), only two PCs were extracted from the scree plot, and explained 89.0% of the total variance (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. 7). K\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, TDS, As, and EC have a relatively high loading on the principal component PC1, accounting for 75.4% of the total variance, indicating that it is possibly affected by halite dissolution. PC2 explained 13.6% of the total variance, and has a strong positive loading for Ca\u003csup\u003e2+\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and negative weak loading for pH, indicating that carbonate weathering, animal husbandry activities or human activities are the possible causes of these ions.\u003c/p\u003e\n \u003cp\u003eThe distribution of factor scores on PC1 and PC2 is shown in Fig.\u0026nbsp;7(b). Sampling points are divided into two categories related to the geological or environmental background of the watershed. The contribution of lake water samples to PC1 increased gradually from the east to the west, indicating that PC1 was mainly affected by evaporation. Rivers and groundwater contribute to PC2 rather than lake water. PC2 comprised Ca\u003csup\u003e2+\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and pH. NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e in the groundwater of lakeshore zone and in the middle and lower reaches of rivers is easily affected by human activities. At the same time, Ca\u003csup\u003e2+\u003c/sup\u003e is mainly produced by weathering of carbonate flowing through the rivers. Therefore, there is further certain that PC2 reflects anthropogenic activity and carbonate karst decomposition. The middle and lower reaches of Doma River and Makazangbu River are important animal husbandry areas, agricultural irrigation areas and human living areas, indicating that agricultural fertilization and human activities have a certain impact on the water supply environment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e3.3 Water quality evaluation\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003e1) Assessment of Groundwater Suitability for Drinking Purposes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eA drinking water quality index (DWQI) method was used to assess the suitability of groundwater for drinking (Adimalla, Li and Venkatayogi. 2018; Sarkar, Pal and Islam. 2022). The DWQI is a water quality assessment method proposed by Horton RK which has been widely used (Horton. 1965). In this technique, the weighted index method was used to change a large number of water quality parameters into a single index that can be compared, effectively providing a comprehensive groundwater quality evaluation model (Ali et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The calculation of DWQI can be divided into several steps: (a) assignment of each parameter weight according to its relative importance (values from 2 to 5), (b) calculation of the relative weight to each index (W\u003csub\u003ei\u003c/sub\u003e), (c) the rate calculation of the quality parameter (Q\u003csub\u003ei\u003c/sub\u003e), and (d) the water quality index computation of each sample (DWQI). These data formulas of are calculated by following equations:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equa\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$${\\text{W}}_{\\text{i}}={\\text{w}}_{\\text{i}}/{\\sum }_{\\text{i}=1}^{\\text{n}}{\\text{w}}_{\\text{i}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Equation\" id=\"Equb\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$${\\text{Q}}_{\\text{i}}=\\left({\\text{C}}_{\\text{i}}-{\\text{C}}_{\\text{i}\\text{p}}\\right)/\\left({\\text{S}}_{\\text{i}}-{\\text{C}}_{\\text{i}\\text{p}}\\right)\\times 100$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Equation\" id=\"Equc\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\text{W}\\text{Q}\\text{I}=\\sum \\left({\\text{Q}}_{\\text{i}}\\times {\\text{W}}_{\\text{i}}\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, W\u003csub\u003ei\u003c/sub\u003e is the relative weight, w\u003csub\u003ei\u003c/sub\u003e is the weight of each parameter and n is the total number of parameters considered for the WQI calculation, C\u003csub\u003ei\u003c/sub\u003e is the concentration of each parameter(mg/L); C\u003csub\u003eip\u003c/sub\u003e is the ideal value of the parameter in pure water; S\u003csub\u003ei\u003c/sub\u003e is the standard value of each parameter.\u003c/p\u003e\n \u003cp\u003eIn this study, an analysis of eleven water quality parameters, namely pH, TH, TDS, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, F\u003csup\u003e\u0026minus;\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, As, and Mn, was used to evaluate the suitability of groundwater for drinking. The relative weight of each parameter and its weight used in these calculations are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Based on DWQI values, the groundwater quality status can be categorized into five types (Adimalla, Li and Venkatayogi. 2018; Kammoun, Abidi and Zairi. 2022; Kumari and Rai. 2020) which were excellent water (\u0026lt;\u0026thinsp;50), good water (50\u0026ndash;75), poor water (75\u0026ndash;100), very poor water (100\u0026ndash;125) and water unsuitable for drinking (\u0026gt;\u0026thinsp;125). In the Bangong Co Lake Watershed, the computed DWQI values vary from 12 to 222 with an average value of 44\u0026thinsp;\u0026plusmn;\u0026thinsp;46. According to the DWQI classification, 67% (n\u0026thinsp;=\u0026thinsp;14) of the total groundwater samples come under the excellent category, and 5% (n\u0026thinsp;=\u0026thinsp;1) are classified as good water category, respectively. About 19% (n\u0026thinsp;=\u0026thinsp;4) of groundwater samples fell under moderate water quality, and 5% (n\u0026thinsp;=\u0026thinsp;1) come under unsuitable for drinking (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e shows the spatial distribution of the DWQI, which illustrates that the chemical characteristics of groundwater in the lakeshore zone have no regular distribution in the east-west direction. While those wells located near lakeshore part have higher DWQI values than those away from lakeshore, indicating that groundwater near the lakeshore may be affected by lake water (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe relative weight to each index and weight assigned for DWQI.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRelative weight\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2) Water Evaluation for Irrigation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn the plateau and semi-arid region of Bangong Co Lake Watershed, agriculture and livestock husbandry are the main occupations, and the main crops in the region are barley, rape, wheat, peas (Huntington. 1906; Yan. 2022). Agriculture is the basic department of regional economic and social development. In addition, afforestation is an initiative advocated by the local government to improve the ecological environment (Yan. 2022). Due to the fact that water quality plays an important role in crop, survival of tree planting and soil characteristics in the study area,, groundwater irrigation suitability evaluation is quite necessary. To better evaluate the suitability of regional surface water and groundwater, this study uses the USSL classification proposed by Richards (\u003cspan class=\"CitationRef\"\u003e1954\u003c/span\u003e) and several other methods, such as EC, SAR, PI, MHR (Ayers and Westcot. 1976; Doneen. 1964; Richards. 1954). Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the calculation methods and sources of these parameters.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEquations used for irrigation and irrigation water quality assessment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIWQ parameters\u003c/p\u003e\n \u003cp\u003eand equations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIrrigation problem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDegree of restriction on use\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eNumber of samples (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlight to moderate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlight to moderate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC(\u0026micro;S/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSalinty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e700\u0026thinsp;~\u0026thinsp;3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichards (\u003cspan class=\"CitationRef\"\u003e1954\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAR=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{\\text{N}\\text{a}}^{+}}{\\sqrt{0.5\\times （{\\text{C}\\text{a}}^{2+}+{\\text{M}\\text{g}}^{2+}）}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePermeab-ility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e700\u0026thinsp;~\u0026thinsp;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51(85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBouwer (\u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eRichards (\u003cspan class=\"CitationRef\"\u003e1954\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePI=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(\\frac{{\\text{N}\\text{a}}^{+}+\\sqrt{{\\text{H}\\text{C}\\text{O}}_{3}^{-}}}{{\\text{C}\\text{a}}^{2+}+{\\text{M}\\text{g}}^{2+}+{\\text{N}\\text{a}}^{+}}\\right)\\times 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026thinsp;~\u0026thinsp;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDoneen (\u003cspan class=\"CitationRef\"\u003e1964\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMHR=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(\\frac{{\\text{M}\\text{g}}^{2+}}{{\\text{C}\\text{a}}^{2+}+{\\text{M}\\text{g}}^{2+}}\\right)\\times 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAyers and Westcot (\u003cspan class=\"CitationRef\"\u003e1976\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eHigh salinity and high sodium concentration in irrigation water are the main causes of soil salinization, which affects the growth of plants and crops (Rajmohan et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The sodium adsorption ratio (SAR), recommended by Richards (Richards. 1954), is one of the important indices to calculate the harm of sodium in irrigation water. In Bangong Co Lake Watershed, the SAR value ranged from 0.03 to 16.72 with the mean value of 3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92. On the basis of the classification of SAR values (Richards. 1954; Wilcox. 1955), water samples in Bangong Co Lake Watershed are classified into good (n\u0026thinsp;=\u0026thinsp;51, 85%), poor (n\u0026thinsp;=\u0026thinsp;1, 2%), and unsuitable (n\u0026thinsp;=\u0026thinsp;8, 13%). EC is the salinity parameter of irrigation water, ranges from 23 to 7861 \u0026micro;S/cm with an average value of 1493\u0026thinsp;\u0026plusmn;\u0026thinsp;2179 \u0026micro;S/cm. According to EC value classification, water samples in Bangong Co Lake Watershed come under good (n\u0026thinsp;=\u0026thinsp;35, 58%), poor (n\u0026thinsp;=\u0026thinsp;17, 28%), and unsuitable (n\u0026thinsp;=\u0026thinsp;8, 13%).\u003c/p\u003e\n \u003cp\u003eDoneen (\u003cspan class=\"CitationRef\"\u003e1964\u003c/span\u003e) pointed out that poor soil permeability is usually caused by irrigation water with high Na\u003csup\u003e+\u003c/sup\u003e and HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e concentrations. Thus, Permeability index (PI) is often used to evaluate the harm of high Na ion concentration in soil in irrigation water. In this study, PI ranged from 36 to 181, with an average of 71\u0026thinsp;\u0026plusmn;\u0026thinsp;26. According to Wilcox\u0026apos;s classification, watershed water samples were classified as good (n\u0026thinsp;=\u0026thinsp;49, 32%), suitable (n\u0026thinsp;=\u0026thinsp;41, 68%) (A. Saleh. 1999; Wilcox. 1955).\u003c/p\u003e\n \u003cp\u003eAyers and Westcot (\u003cspan class=\"CitationRef\"\u003e1976\u003c/span\u003e) noticed that irrigation water with high Mg\u003csup\u003e2+\u003c/sup\u003e can leads to soil Ca\u003csup\u003e2+\u003c/sup\u003e deficiency and crop yields reduction. In this study, Magnesium hazardous ratio (MHR) ranged from 10 to 98, with a mean of 54\u0026thinsp;\u0026plusmn;\u0026thinsp;23. The results show that 55% of water samples are suitable for irrigation (MHR\u0026thinsp;\u0026lt;\u0026thinsp;50), and the rest of the water samples (n\u0026thinsp;=\u0026thinsp;27, 45%) are unsuitable for irrigation (MHR\u0026thinsp;\u0026ge;\u0026thinsp;50).\u003c/p\u003e\n \u003cp\u003eRichards (\u003cspan class=\"CitationRef\"\u003e1954\u003c/span\u003e) developed the USSL classification for assessing the suitability of irrigation water which combined both SAR and EC parameters. In the USSL diagram (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), all glacier (n\u0026thinsp;=\u0026thinsp;3, 100%), some groundwater samples (n\u0026thinsp;=\u0026thinsp;2, 11%), and some river samples (n\u0026thinsp;=\u0026thinsp;3, 13%) and fell into C1-S1 region, indicating relatively low alkalinity and salinity hazards of water samples; most of groundwater samples (n\u0026thinsp;=\u0026thinsp;10, 53%) and river samples (n\u0026thinsp;=\u0026thinsp;19, 79%) plotted on C2-S1 region. Some groundwater samples (n\u0026thinsp;=\u0026thinsp;7, 37%) and eastern lake samples are classified into C3-S1 region. These low-sodium (S1) and medium or high alkalinity (C2, C3) water samples basically meet the requirements of irrigation water. Only one lake sample plotted on C4-S2 region, and western lake samples almost plotted on C5-S4 region, which are not suitable for crops and soil irrigation. In conclusion, glacial meltwater and river water are more suitable for irrigation than lake and part of groundwater.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThe hydrochemical characteristics of surface water and lakeshore groundwater in Bangong Lake Watershed, Northwest Tibet, China were analyzed by using multivariate statistical method, piper diagrams Gibbs\u0026rsquo; diagrams, ion ratio method and PCA analysis. At the same time, DWQI parameters, irrigation water indexes and spatial analysis were used to evaluate the quality of irrigation water and drinking water. The main conclusions are summarized below.\u003c/p\u003e \u003cp\u003eDifferent water bodies in the study area are found to be alkaline (pH\u0026thinsp;\u0026gt;\u0026thinsp;7; 80%). Glacier water is categorized as soft-fresh and lake water is classified as hard-brackish. Groundwater and river water are categorised as soft-fresh and hard-fresh.\u003c/p\u003e \u003cp\u003eNa-Cl is the dominant water type for lake water, and Ca-HCO\u003csub\u003e3\u003c/sub\u003e type is the dominant water type for glacier, river water and groundwater. Only 78% of groundwater samples are Ca-Cl types. Ionic concentrations of river waters have increased trend from upstream to downstream in spatial distribution, and high concentrations are found in the coastal region. For spatial patterns of water in the Bangong Co Lake, ionic concentrations increase from east to the west. There is no obvious spatial distribution of groundwater ion concentrations in the lakeshore zone in the east-west direction\u003c/p\u003e \u003cp\u003eFor drinking water quality, the DWQI value classifies groundwater as excellent (67%), good (5%), poor (19%). The spatial distribution of DWQI value map demonstrates that the shallow groundwater quality has an obvious downward trend when it\u0026rsquo;s the closer to the lakeshore line. For irrigation water quality, the IWQ parameters show that 52%, 49%, and 55% of total samples have SAR\u0026thinsp;\u0026lt;\u0026thinsp;6, PI\u0026thinsp;\u0026gt;\u0026thinsp;75, and MHR\u0026thinsp;\u0026lt;\u0026thinsp;50, respectively, which are belong to good to permissible classes for irrigation. USSL classification suggests that glacier, river, and some of the groundwater samples are suitable for irrigation, and eastern lake samples and part of groundwater samples only for salt-tolerant crops and high permeability soil with good drainage. While, western lake water is classified to be unsuitable irrigation water. The research will be definitely helpful for the Bangong Co Lake area to utilize water resources in a sustainable way.\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eThe authors gratefully acknowledge the technical staff at the laboratory of Xizang Shengyuan Environmental Engineering Co., LTD for their constant help. We also thank the contributions of the staff members of Center of Applied Geological Survey, China Geological Survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eYuxiang\u0026nbsp;Shao, Buqing Yan and Baiyang Liu-Lu\u0026nbsp;contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yuxiang\u0026nbsp;Shao, Kang\u0026nbsp;Gong\u0026nbsp;and Kun\u0026nbsp;Zhang. The first draft of the manuscript was written by Yuxiang\u0026nbsp;Shao\u0026nbsp;and all authors commented on previous versions of the manuscript. All authors read and approved the final versions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eNo Funding\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. Saleh FA, M. Shehata (1999) Hydrogeochemical processes operating within the main aquifers of Kuwait. Journal of Arid Environments\u003cem\u003e.\u003c/em\u003e 42(3): 195-209. https://doi.org/10.1006/jare.1999.0511\u003c/li\u003e\n\u003cli\u003eAdimalla N, Li P, Venkatayogi S (2018) Hydrogeochemical evaluation of groundwater quality for drinking and irrigation purposes and integrated interpretation with water quality index studies. 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Water\u003cem\u003e.\u003c/em\u003e 11(10): 2067-2078. https://doi.org/10.3390/w11102067\u003c/li\u003e\n\u003c/ol\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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bangong Co Lake Watershed , Hydrochemical characteristics , Water quality , Surface water , Groundwater","lastPublishedDoi":"10.21203/rs.3.rs-2747303/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2747303/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to explore the hydrochemical characteristics, influencing factors, and water quality of various water bodies in Bangong Co Lake Watershed, 60 water samples were collected from lake, river, groundwater, glacier water bodies in the watershed. Piper diagram, Gibbs’ diagrams, ion ratio analysis, statistical methods, and principal component analysis were used to study the hydrochemical characteristics and its influencing factors. Drinking water quality index (DWQI) and USSL classification were applied to assess the groundwater quality suitability for agricultural and drinking purposes. The hydrochemical characteristics show the differences among water bodies and their spatial distribution. Analyzed groundwater and surface water samples such as river water and glaciers mainly presented Ca-HCO\u003csub\u003e3\u003c/sub\u003e type, and lake water mainly presented Na-Cl type and a small number of Na-HCO\u003csub\u003e3\u003c/sub\u003e·Cl type. The lake water chemical components are mainly affected by evaporative karst decomposition. The main mineralization process of groundwater and river water was related to the dissolution of reservoir minerals such as dolomite and calcite, and halite. The DWQI indicates that 79% of the groundwater samples in the study area showed a good quality for drinking. For irrigation water quality, the electrical conductivity (EC), calculated Sodium adsorption ratio (SAR), Magnesium hazardous ratio (MHR) showed that more than 13% of the total samples were not suitable for irrigation. USSL classification indicated that glacier and river water are relatively suitable for irrigation. And part of the groundwater and lake water has very high alkalinity or salinity which is alarming when considered for irrigation.\u003c/p\u003e","manuscriptTitle":"Hydrochemical characteristics and water quality evaluation for irrigation and drinking purposes of Bangong Co Lake Watershed","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-30 15:34:56","doi":"10.21203/rs.3.rs-2747303/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db0cd7b8-2052-41cc-af0a-884643817f99","owner":[],"postedDate":"March 30th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-15T14:28:30+00:00","versionOfRecord":{"articleIdentity":"rs-2747303","link":"https://doi.org/10.3390/w15142655","journal":{"identity":"water","isVorOnly":true,"title":"Water"},"publishedOn":"2023-07-22 00:00:00","publishedOnDateReadable":"July 22nd, 2023"},"versionCreatedAt":"2023-03-30 15:34:56","video":"","vorDoi":"10.3390/w15142655","vorDoiUrl":"https://doi.org/10.3390/w15142655","workflowStages":[]},"version":"v1","identity":"rs-2747303","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2747303","identity":"rs-2747303","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}