Abstract

The Tibetan Plateau is very important as it provides water resources for about 40% of the world's population and the runoff-yield area of the Yellow rivers. In this paper, the water quality in Xiahe County, located in the northeast Tibetan Plateau, was investigated. Six parameters (chloride, chemical oxygen demand, ammonia nitrogen, nitrate, fluoride, sulfate) were selected to assess the quality and health status of surface water in Xiahe County. The main types of hydrochemical in the surface water were considered to be Mg2+-Ca2+-HCO3-Cl and Mg2+-Ca2+-HCO3. The cations and anions were mainly from weathering and dissolution of carbonate rock. Fuzzy comprehensive evaluation (FCE) results showed that the water quality in all 69 sampling sites was all class I. The integrated health status was higher than 0.95 and the health rate was 100%. Although ammonia nitrogen was recognized as the main contaminant, it had little effect on the entire body of water. Overall, the surface water qualities of most samples in Xiahe County were found to be in good condition.

INTRODUCTION

The Tibetan Plateau is a water source for 40% of the world's population (Cao & Zhang 2015) and is called the ‘Water Tower of Asia’ because it feeds the Indus, Ganges, Brahmaputra, Yangtze and Yellow rivers (Huang et al. 2009; Zhou et al. 2013a, 2013b). Water, like in the 20th century, will become an important factor restricting the development of the global economy (Aly et al. 2016; Ren et al. 2016). Although climate warming has resulted in increased amounts of meltwater from the Tibetan Plateau, much of the water cannot be used. This reduces the availability of water for downstream applications, thus limiting the water consumption of these areas (Barnett et al. 2005; Gao et al. 2013; Cao & Zhang 2015). In recent years, studies have been carried out on the runoff (Chen et al. 2006) and precipitation of the plateau (Zhou et al. 2013a) and alpine lake water of the Tibetan Plateau (Song et al. 2014). However, most studies of Yangtze Estuary and Tibetan surface water quality have focused on major ions in saline lakes (Wu 2005).

Surface water is the most precious resource for people's daily life, irrigation and industry in the Tibetan Plateau pastoral area of Gansu Province. It consists of water that flows in the form of rivulets, springs, streams and rivers or that is collected to form ponds, lakes and sea discharge (Chidya et al. 2011). Surface water quality does not meet drinking water quality criteria. Severe drought and desertification at the end of the 20th century have created significant issues in water quality. Groundwater is now considered as one of the key methods to resolve the drinking water problem (Ding et al. 2007). Furthermore, the quality of the insufficient surface water is susceptible to decline. Firstly, surface water can be contaminated by anthropogenic activities (Venkateswaran & Deepa 2015; Sun et al. 2016) from non-point sources and point sources. The non-point-source pollutants are washed from the earth's surface by storm runoff and enter water bodies of their own accord (Cheng et al. 2007), whereas the point-source pollutants are directly released into water bodies in man-made pipes (Zampella et al. 2007; Gyawali et al. 2013). Secondly, agricultural activities degrade water quality directly or indirectly because of the ever-increasing mass of fertilizers, pesticides and dairy manure in croplands (Yu et al. 2013; Giri & Qiu 2016). As a result, surface water quality is a matter of serious concern, being vital to human health, for quality of crops (and thus grains) may be affected by the soil and contaminated environment (Zhang et al. 2012; Voyslavov et al. 2013). Surface water quality and availability have deteriorated due to ongoing increase in population, industrialization, and anthropogenic activities (Erturk et al. 2010; Cao & Zhang 2015; Effendi 2016). Increase in water pollution affects not only water quality but also endangers human health, ecological balance, economic development, and social prosperity (Voyslavov et al. 2013). Consequently, water quality assessment is important for the protection of people's health, agriculture, industry, recreation, tourism and ecosystems (Dinka et al. 2015).

Currently, studies on hydrochemical characteristics and water quality assessment of water are mainly concentrated on river, estuary wetland, and lake areas (Cui & Li 2014a, 2014b). However, surface water study in the Tibetan Plateau pastoral area has been neglected. Xiahe County not only belongs to the northeast Tibetan Plateau and the National Nature Reserve of the Three Rivers Source but also the runoff-yield area of the Yellow River. This paper took Xiahe County as an example to study the hydrochemical characteristics and quality of surface water of the northeast Tibetan Plateau pastoral area. In this study, 69 surface water sampling sites were selected to analyze the hydrochemical characteristics and water quality of surface water in the northeast Tibetan Plateau of China. The fuzzy comprehensive evaluation (FCE) method, field research, multivariate statistical methods, and the Piper diagram were applied to analyze the hydrochemical characteristics and quality of the surface water. Based on the above analysis, the main objectives of this study were: (1) to reveal the correlation between surface water samples of anions and cations; (2) to analyze the hydrochemical type of surface water; (3) to assess the quality of surface water in Xiahe County.

STUDY AREA

Xiahe County (101°54′–103°25′E, 34°32′–35°34′N) is located in the southern part of Gansu Province and the northeast edge of the Qinghai–Tibetan Plateau, China (Li et al. 2011). It is a pure animal husbandry county and the most important grazing land of Gannan Tibetan Autonomous Prefecture, Gansu Province. The population, composed primarily of herdsmen, exceeds 80,000, who live in an area of 6,274 square kilometres (Li et al. 2015). The average annual temperature is 2.6 °C with the highest 28.9 °C and the lowest −24.6 °C. The average annual precipitation is approximately 516 mm, and is concentrated in the months of July and August (Hou et al. 2013). Many abundant storm water tributaries, the snow-capped mountains and lakes constitute an integral water conservation system of the Yellow River, as a ‘reservoir’ (Wang et al. 2012). The Daxia and Tao rivers, which flow through Xiahe County, also belong to the Yellow River ‘reservoir’.

MATERIALS AND METHODS

Water Sampling

Sixty-nine surface water samples were collected from Xiahe County in the northeast Tibetan Plateau pastoral area, on 15 July 2012. The spatial distribution map of surface water sampling sites in Xiahe County is shown in Figure 1. Water samples were collected with plastic bottles (500 ml). Each sample had three replicates and a detention time of 1 min. Water samples were taken at a depth of approximately 30 cm below the water surface. The water was stored at a temperature below 4 °C after bottling in the laboratory.

Figure 1

Distribution map of surface water sampling sites in Xiahe County.

Figure 1

Distribution map of surface water sampling sites in Xiahe County.

Analytical techniques

The water quality parameters monitored for each sample included the physical properties of water samples, including turbidity, pH, and electric conductivity (EC). The chemical composition was Na+, NH4+, K+, Mg2+, Ca2+, Cl, SO42−, HCO3. The toxicological indicators of F, NO3. EC and pH were analyzed immediately in the field, and other parameters were analyzed in the laboratory. These samples were filtered through a 0.45 μm membrane filter before analysis. Analytical methods and parameters, which were based on the methods outlined in the Chinese National Quality Standards for Drinking Water (GB/T 5749-2006 and GB/T 5750-2006), are summarized in Table 1.

Table 1

Analytical methods used for the analysis of surface water

Quality parameterSymbol of method used
Turbidity Turbidity Turbidity meter 
pH pH pH meter 
Electrical conductivity EC Electrical conductivity meter 
Sodium Na+ Inductively coupled plasma optical emission spectroscopy (ICP-OES) 
Calcium Ca2+ 
Potassium K+ 
Magnesium Mg2+ 
Ammonium NH4+ Nesslers reagent photometry 
Chloride Cl AgNO3 titration 
Sulfate SO42− Barium chromate indirect atomic absorption spectrophotometry 
Bicarbonate HCO3 Acid–base titration 
Chemical oxygen demand COD Potassium dichromate method 
Fluorine F Fluoride ion electrode 
Nitrate nitrogen NO3 Ion selective spectrometry 
Quality parameterSymbol of method used
Turbidity Turbidity Turbidity meter 
pH pH pH meter 
Electrical conductivity EC Electrical conductivity meter 
Sodium Na+ Inductively coupled plasma optical emission spectroscopy (ICP-OES) 
Calcium Ca2+ 
Potassium K+ 
Magnesium Mg2+ 
Ammonium NH4+ Nesslers reagent photometry 
Chloride Cl AgNO3 titration 
Sulfate SO42− Barium chromate indirect atomic absorption spectrophotometry 
Bicarbonate HCO3 Acid–base titration 
Chemical oxygen demand COD Potassium dichromate method 
Fluorine F Fluoride ion electrode 
Nitrate nitrogen NO3 Ion selective spectrometry 

Assessment methods of surface water

The FCE is a quantitative scientific evaluation method and has been widely applied in many fields, such as environmental (Liu et al. 2010), agriculture (Cheng & Tao 2010), and engineering (Piplani & Wetjens 2007). FCE is used to determine changes in water quality upstream/downstream of sites, i.e, the extent of contaminants from anthropogenic activities. The advantages and disadvantages of water quality can be observed visually. The principal procedure of FCE includes establishing the evaluation factor set  
formula
(1)
and grading level set  
formula
(2)
of evaluated objects. This membership function with each category is expressed as follows (Bi et al. 2015; Xie et al. 2017).
The membership function of level 1 is:  
formula
(3)
The membership function of level j is:  
formula
(4)
The membership function of level m is:  
formula
(5)
where ri1, rij, and rim are is the fuzzy memberships of indicator i to classes 1, j, and m, respectively; ci is the monitoring value; sij is the allowable value of the water quality indicator.
The fuzzy evaluation matrix R is the comprehensive survey of the index of safety evaluation of surface water and will have i rows and j lines, that is to say R= [rij] (Zhou et al. 2013a, 2013b):  
formula
(6)
Then determine a weighting factor W and the assessment index weighting factor is determined as in Equation (7):  
formula
(7)
where W is the weighting factor; W1, W2, W3, W4, and W5 are the weights for the evaluation parameters.  
formula
(8)
where B is the FCE matrix of membership of each water quality class. B is normalized using Equation (9) and the final FCE matrix B′ is obtained as shown in Equation (10). A water sample is classified to the class with the maximized membership (Zhang et al. 2012):  
formula
(9)
 
formula
(10)

The chloride, COD, ammonia nitrogen, nitrate, fluoride, and sulfate were used as assessment parameters to establish an evaluation factor set U (n = 6) that depended on 69 surface water sample sites from Xiahe County. An evaluation criteria set V (m = 5) was also determined (Table 2) and water quality classification according to the Environmental Quality Standards for Surface Water of China (GB3838-2002).

Table 2

Standard of quality classification for surface water

Parameter/mg L−1IIIIIIIVV
Chloride 250 250 250 250 250 
 15 15 20 30 40 
Ammonia nitrogen 0.15 0.5 1.0 1.5 2.0 
Nitrate 10 10 20 20 25 
Fluoride 1.0 1.0 1.0 1.5 >1.5 
Sulfate 250 250 250 250 250 
Parameter/mg L−1IIIIIIIVV
Chloride 250 250 250 250 250 
 15 15 20 30 40 
Ammonia nitrogen 0.15 0.5 1.0 1.5 2.0 
Nitrate 10 10 20 20 25 
Fluoride 1.0 1.0 1.0 1.5 >1.5 
Sulfate 250 250 250 250 250 

RESULTS AND DISCUSSION

characteristics of surface water

Descriptive statistics

Descriptive statistics, which include the maximum, minimum, variation coefficient (VC), range, arithmetic mean, variance, skewness and standard deviation (SD), were used for the hydrochemical characteristics of surface water as shown in Table 3. Factors with skewness values lower than 2, including K+, Mg2+, Cl, pH, COD, and EC, showed normal distributions. Na+, F, and SO42− were large and especially for Ca2+, showed heterogeneous concentration distributions. It may be due to the excessively high concentration in some samples (XH17, XH49). For Mg2+, Cl, pH, and EC the VCs were lower than 0.5, while those of K+, Na+, Ca2+, SO42− and NO3 were higher than 0.5 (range from 0.5 to 0.9). Low VCs may result from a natural source and high VCs may be impacted by anthropogenic activities (Bu et al. 2016). HCO3 ranged from 24.62 to 237.86 mg L−1, Cl ranged from 11.70 to 45.73 mg L−1, and sulfate ranged from 6.02 to 50.80 mg L−1 (with mean of 13.78 mg L−1). Therefore the major anions of the surface water were mainly dominated by HCO3. The HCO3 in the surface water was from carbonate weathering and dissolution by carbonic acid (Gautam et al. 2015). Mg2+ ranged from 5.43 to 40.64 mg L−1, Ca2+ ranged from 2.79 to 74.39 mg L−1, sodium ranged from 2.91 to 48.52 mg L−1, and K+ ranged from 0.87 to 6.20 mg L−1 (with mean of 2.01 mg L−1). Thus the major cations were mainly dominated by Ca2+ and Mg2+. Na+ and K+ derived from the dissolution of silicate minerals. SO42− mainly came from CaSO4 and MgSO4 through evaporation of saline mineral solution.

Table 3

Descriptive statistics of surface water samples in Xiahe County

ParameterRangeMinMaxMeanSDVCVarianceSkewness
K+ / mg L−1 5.34 0.87 6.20 2.01 1.04 0.52 1.08 1.78 
Ca2+/ mg L−1 71.6 2.80 74.4 13.1 8.51 0.65 72.4 5.53 
Na+/ mg L−1 45.6 2.90 48.5 7.69 6.89 0.90 47.4 3.82 
Mg2+/ mg L−1 35.2 5.43 40.6 14.1 6.04 0.43 36.1 1.92 
F/ mg L−1 0.30 0.06 0.36 0.13 0.06 0.48 0.004 2.05 
Cl/ mg L−1 34 11.7 45.7 23.8 6.62 0.28 43.8 0.36 
SO42−/ mg L−1 44.8 6.02 50.8 13.8 9.97 0.72 99.4 2.21 
NO3/ mg L−1 6.76 0.46 7.22 1.50 1.05 0.71 1.10 3.34 
HCO3/ mg L−1 213 24.6 238 73.6 36.8 0.50 1,351 2.2 
pH 1.49 7.16 8.65 8.09 0.29 0.04 0.09 −1.07 
COD/ mg L−1 16.6 2.08 18.7 6.43 3.72 0.58 13.8 1.43 
EC / μS cm−1 596 130 726 353 111 0.32 1,230 0.7 
Turbidity / NTU 752 0.17 752 70.6 162 2.3 2631 3.34 
ParameterRangeMinMaxMeanSDVCVarianceSkewness
K+ / mg L−1 5.34 0.87 6.20 2.01 1.04 0.52 1.08 1.78 
Ca2+/ mg L−1 71.6 2.80 74.4 13.1 8.51 0.65 72.4 5.53 
Na+/ mg L−1 45.6 2.90 48.5 7.69 6.89 0.90 47.4 3.82 
Mg2+/ mg L−1 35.2 5.43 40.6 14.1 6.04 0.43 36.1 1.92 
F/ mg L−1 0.30 0.06 0.36 0.13 0.06 0.48 0.004 2.05 
Cl/ mg L−1 34 11.7 45.7 23.8 6.62 0.28 43.8 0.36 
SO42−/ mg L−1 44.8 6.02 50.8 13.8 9.97 0.72 99.4 2.21 
NO3/ mg L−1 6.76 0.46 7.22 1.50 1.05 0.71 1.10 3.34 
HCO3/ mg L−1 213 24.6 238 73.6 36.8 0.50 1,351 2.2 
pH 1.49 7.16 8.65 8.09 0.29 0.04 0.09 −1.07 
COD/ mg L−1 16.6 2.08 18.7 6.43 3.72 0.58 13.8 1.43 
EC / μS cm−1 596 130 726 353 111 0.32 1,230 0.7 
Turbidity / NTU 752 0.17 752 70.6 162 2.3 2631 3.34 

The coefficient variation of turbidity was the largest, which could be due to effluents from agricultural return flow (Dinka et al. 2015). It did not meet the WHO (1996) guideline for drinking water which was set at 5 NTU (Hoko 2005). EC ranged from 130 to 726 μS cm−1 (with a mean of 596 μS cm−1) and 24.6% samples exceeded the desirable limit of 500 μS cm−1. Geochemical processes along with evaporation, silicate weathering, sulphate reduction, oxidation process and ion exchange were the main contributors to the large variation in EC (Mohapatra et al. 2009). However, in the study area, the variation of EC could be primarily due to anthropogenic activities and agricultural activities.

According to the WHO (2009), the standard pH values for drinking water range from 6.5 to 8.5 and that of irrigation water from 6.5 to 8.4 (El-Sayed & Salem 2015). Most of the surface water samples were found to have pH values ranging from 7.16 to 8.65 which indicated that they were suitable for drinking and irrigation.

Piper diagram

The Piper diagram is useful for geochemical evaluation and is a graphical presentation of the major ions to quickly determine the hydrochemical facies of the surface water in the study area (Srinivasamoorthy et al. 2014; Venkateswaran & Deepa 2015; Ebrahimi et al. 2016). However, the main weakness of the Piper diagram is that it shows the chemical character of surface water based on the relative concentration of its constituents rather than the absolute concentrations. As shown in Figure 2, the major cation and anion concentrations were demonstrated in the bottom left and right triangles, respectively. The dispersion of HCO3 dominated the waters, while the majority of samples were mainly concentrated in the Mg2+ and Ca2+ fields, accounting for more than 70% of the cations. This suggested that the hydrochemicals in the surface water were dominated mainly by Mg2+-Ca2+-HCO3-Cl with Mg2+-Ca2+-HCO3 being secondary. This was consistent with the hydrochemical types of Qinghai Lake Basin (Cui & Li 2014a, 2014b) and Bukan basin, in the northwest of Iran (Pazand & Hezarkhani 2012). The anions and cations in the surface water could mainly come from the weathering and dissolution of carbonate rock, or be associated with anthropogenic activities (Chidya et al. 2011). In addition, Li et al. (2007) found that Ca2+ and HCO3 accounted for 59% of the total ions in rainfall and this suggested that precipitation affects their concentration since heavy precipitation can wash crystallized aerosols out of the atmosphere.

Figure 2

Piper diagram showing the hydrochemical compositions of surface water in Xiahe County.

Figure 2

Piper diagram showing the hydrochemical compositions of surface water in Xiahe County.

Spearman's correlation coefficient

Spearman's correlation coefficient is a moment correlation coefficient performed on the ranks of the data rather than the raw data (Puth et al. 2015). The analyzed parameters are shown in Table 4. A significant correlation was found between pH on the one hand and concentrations of K+, turbidity, and F on the other. A significant positive correlation was established between concentrations of K+, Ca2+, Na+, Mg2+, HCO3, and NO3 and the sulfate content of the surface water. Concerning the principal ions, a correlation was also found between Mg2+, Na+, and HCO3. The content of F and K+ was increased with the decrease in pH. At the same time, the concentrations of Ca2+, Mg2+, and Na+ were increased with increasing concentrations of HCO3 and showed a significant positive correlation (r = 0.41, 0.75, 0.64, respectively). The significant positive correlation suggests that the leading cations such as Mg2+, Ca2+, Na+ and K+ were from weathering of different rocks. For example, Mg2+ and Ca2+ were supplied by the silicates and carbonates (Xiao et al. 2015), Na+ and K+ by the weathering of silicates (Xiao et al. 2015). In general, HCO3 values were found to be the highest in samples from all seasons and sites indicating that weathering of rock plays a major role (Gautam et al. 2015). Ion exchange may be one of the other important processes influencing water geochemistry in semiarid/arid areas (Xiao et al. 2015).

Table 4

Correlation matrices of hydrochemical parameters of surface water in Xiahe County

 K+Ca2+Na+Mg2+SO42−ClHCO3NO3FpHTurbidityCODEC
K+             
Ca2+ 0.04            
Na+ 0.73** −0.02           
Mg2+ 0.38** −0.17 0.62**          
SO42− 0.64** 0.33** 0.81** 0.53**         
Cl 0.07 −0.07 0.16 0.13 0.03        
HCO3 0.47** 0.41** 0.64** 0.75** 0.69** −0.16       
NO3 0.64** −0.14 0.85** 0.50** 0.74** 0.18 0.40**      
F 0.65** 0.15 0.53** 0.34** 0.47** 0.10 0.46** 0.49**     
pH −0.33** −0.12 −0.11 −0.09 −0.20 −0.06 −0.11 −0.10 −0.33**    
Turbidity 0.31* −0.09 −0.06 0.03 0.02 0.13 −0.09 0.07 0.49** −0.32**   
COD 0.32** −0.09 0.19 0.03 0.13 0.08 −0.02 0.17 0.17 −0.12 −0.01  
EC 0.53** −0.02 0.67** 0.80** 0.62** 0.08 0.72** 0.51** 0.41** −0.16 0.01 0.19 
 K+Ca2+Na+Mg2+SO42−ClHCO3NO3FpHTurbidityCODEC
K+             
Ca2+ 0.04            
Na+ 0.73** −0.02           
Mg2+ 0.38** −0.17 0.62**          
SO42− 0.64** 0.33** 0.81** 0.53**         
Cl 0.07 −0.07 0.16 0.13 0.03        
HCO3 0.47** 0.41** 0.64** 0.75** 0.69** −0.16       
NO3 0.64** −0.14 0.85** 0.50** 0.74** 0.18 0.40**      
F 0.65** 0.15 0.53** 0.34** 0.47** 0.10 0.46** 0.49**     
pH −0.33** −0.12 −0.11 −0.09 −0.20 −0.06 −0.11 −0.10 −0.33**    
Turbidity 0.31* −0.09 −0.06 0.03 0.02 0.13 −0.09 0.07 0.49** −0.32**   
COD 0.32** −0.09 0.19 0.03 0.13 0.08 −0.02 0.17 0.17 −0.12 −0.01  
EC 0.53** −0.02 0.67** 0.80** 0.62** 0.08 0.72** 0.51** 0.41** −0.16 0.01 0.19 

*Significant at P < 0.05, **significant at P < 0.01, N = 69.

Water quality assessment

Spatial analysis of surface water quality

In order to investigate the surface water quality of Xiahe County, maps of pH, turbidity, ammonia nitrogen, fluoride, sulfate, and chloride were interpolated by using ArcGIS 10.2. As shown in Figure 3, the pH values are almost stable around 8, which may be due to high concentrations of NH4+ and Ca2+ present in rainfall (Li et al. 2016). The complex patterns of pH reflect the spatial heterogeneity of the geology (Chang 2008). Turbidity was often related to flow rate and an indirect measure of water clarity. Higher flows may decrease water clarity owing to the increased amount of suspended material. There is a high concentration of ammonia in downstream sites and low concentrations in the central and upstream sites. The fluoride and sulfate showed similar patterns – high in the upper eastern part and low in the lower western part of Xiahe County. The concentration of chloride was relatively high in the entire region, which may indicate that Cl was initiated from the agricultural activity in the region, accumulated in the subsurface over time and eventually washed down toward the water body (Baram et al. 2014).

Figure 3

Spatial distribution of surface water about pH, turbidity, ammonia nitrogen, sulfate, fluoride, and chloride in Xiahe County.

Figure 3

Spatial distribution of surface water about pH, turbidity, ammonia nitrogen, sulfate, fluoride, and chloride in Xiahe County.

High concentrations in downstream sites from their point-source loads are mainly derived from human activities and the nutrient inputs from agriculture which involves the extensive use of fertilizers (Kilonzo et al. 2014). Detailed spatial distribution of water quality may supply significant information for ecological water demand and thus the environmental safety of the Plateau pastoral area.

Fuzzy comprehensive evaluation

The FCE results are shown in Table 5. We found that the surface water quality was all class I, and the integrated health status exceeded 0.95, which means that water quality for most samples was good and suitable for drinking and irrigation. Some of the concentrations of ammonia nitrogen were between class I and class III, with the exception of six samples (XH34, XH36, XH39, XH40, XH59, XH60) which were class IV and three samples (XH41, XH42, XH51) which exceeded class V. XH34 and XH51 were located in Ganjia and Quao townships indicating that human activities could have a direct effect on water quality. The water quality of XH41 and XH42 could be affected by agricultural activities since these two sites are surrounded by villages and croplands. XH59 and XH60 were located in depopulated zone. XH36, XH39, XH40 and other sites were mainly located in a suburban regions which focused on pasture husbandry. Animals drink directly from the surface water, and may have contributed to the increasing concentrations of the parameters. Thus, ammonia nitrogen was found to be the main pollutant from the following sources: (a) biodegradable waste, animal waste and plant residues from agricultural waste production and increasing industrial activities in the Plateau pastoral area (Liu et al. 2014); (b) local herders' and tourists' garbage and waste (Cong et al. 2009; Hu et al. 2015); (c) cattle breeding and fertilizer applications that could release a mass of NH3, which is converted to aerosol NH4+ or directly scavenged by rainwater (Li et al. 2016). Additionally, excessive use of fertilizers, manure and pesticides is likely to be harmful to water quality, although they are used for improved production and protection of crops (Darko et al. 2008; Liu et al. 2014).

Table 5

Results of fuzzy comprehensive evaluation

Sampleb1b2b3b4b5WQFCESampleb1b2b3b4b5WQFCE
XH1 0.863 0.105 0.032 0.966 XH36 0.961 0.009 0.028 0.978 
XH2 0.887 0.038 0.077 0.964 XH37 0.993 0.007 0.999 
XH3 0.979 0.021 0.996 XH38 0.985 0.015 0.997 
XH4 0.960 0.040 0.992 XH39 0.938 0.053 0.009 0.973 
XH5 0.989 0.011 0.998 XH40 0.951 0.023 0.026 0.975 
XH6 0.978 0.022 0.996 XH41 0.974 0.022 0.004 0.984 
XH7 0.985 0.168 0.997 XH42 0.967 0.030 0.003 0.980 
XH8 0.974 0.026 0.995 XH43 0.886 0.114 0.977 
XH9 0.990 0.010 0.998 XH44 0.980 0.020 0.996 
XH10 0.978 0.022 0.996 XH45 0.981 0.019 0.996 
XH11 0.972 0.028 0.998 XH46 0.972 0.028 0.994 
XH12 0.968 0.032 0.994 XH47 0.981 0.019 0.996 
XH13 0.914 0.058 0.028 0.977 XH48 0.874 0.033 0.093 0.956 
XH14 0.981 0.019 0.996 XH49 1.000 1.000 
XH15 0.997 0.003 0.999 XH50 1.000 1.000 
XH16 0.870 0.097 0.033 0.967 XH51 0.984 0.007 0.009 0.989 
XH17 0.981 0.019 0.996 XH52 0.935 0.062 0.003 0.973 
XH18 0.977 0.023 0.995 XH53 0.932 0.010 0.058 0.975 
XH19 0.984 0.016 0.997 XH54 0.927 0.023 0.050 0.975 
XH20 0.949 0.051 0.990 XH55 0.935 0.003 0.062 0.975 
XH21 0.910 0.090 0.982 XH56 0.936 0.001 0.063 0.975 
XH22 0.977 0.023 0.995 XH57 0.985 0.015 0.997 
XH23 0.968 0.032 0.994 XH58 1.000 1.000 
XH24 0.985 0.015 0.997 XH59 0.959 0.010 0.031 0.977 
XH25 0.961 0.039 0.992 XH60 0.949 0.030 0.021 0.975 
XH26 0.968 0.032 0.994 XH61 0.917 0.053 0.030 0.977 
XH27 0.913 0.061 0.026 0.977 XH62 0.974 0.026 0.997 
XH28 0.972 0.028 0.994 XH63 0.930 0.070 0.986 
XH29 0.986 0.014 0.997 XH64 0.993 0.007 0.999 
XH30 0.975 0.025 0.995 XH65 0.997 0.003 0.999 
XH31 0.972 0.028 0.994 XH66 0.997 0.003 0.999 
XH32 0.981 0.019 0.996 XH67 0.955 0.045 0.991 
XH33 1.000 1.000 XH68 0.997 0.003 0.999 
XH34 0.942 0.035 0.023 0.972 XH69 0.993 0.007 0.999 
XH35 0.948 0.052 0.990          
Sampleb1b2b3b4b5WQFCESampleb1b2b3b4b5WQFCE
XH1 0.863 0.105 0.032 0.966 XH36 0.961 0.009 0.028 0.978 
XH2 0.887 0.038 0.077 0.964 XH37 0.993 0.007 0.999 
XH3 0.979 0.021 0.996 XH38 0.985 0.015 0.997 
XH4 0.960 0.040 0.992 XH39 0.938 0.053 0.009 0.973 
XH5 0.989 0.011 0.998 XH40 0.951 0.023 0.026 0.975 
XH6 0.978 0.022 0.996 XH41 0.974 0.022 0.004 0.984 
XH7 0.985 0.168 0.997 XH42 0.967 0.030 0.003 0.980 
XH8 0.974 0.026 0.995 XH43 0.886 0.114 0.977 
XH9 0.990 0.010 0.998 XH44 0.980 0.020 0.996 
XH10 0.978 0.022 0.996 XH45 0.981 0.019 0.996 
XH11 0.972 0.028 0.998 XH46 0.972 0.028 0.994 
XH12 0.968 0.032 0.994 XH47 0.981 0.019 0.996 
XH13 0.914 0.058 0.028 0.977 XH48 0.874 0.033 0.093 0.956 
XH14 0.981 0.019 0.996 XH49 1.000 1.000 
XH15 0.997 0.003 0.999 XH50 1.000 1.000 
XH16 0.870 0.097 0.033 0.967 XH51 0.984 0.007 0.009 0.989 
XH17 0.981 0.019 0.996 XH52 0.935 0.062 0.003 0.973 
XH18 0.977 0.023 0.995 XH53 0.932 0.010 0.058 0.975 
XH19 0.984 0.016 0.997 XH54 0.927 0.023 0.050 0.975 
XH20 0.949 0.051 0.990 XH55 0.935 0.003 0.062 0.975 
XH21 0.910 0.090 0.982 XH56 0.936 0.001 0.063 0.975 
XH22 0.977 0.023 0.995 XH57 0.985 0.015 0.997 
XH23 0.968 0.032 0.994 XH58 1.000 1.000 
XH24 0.985 0.015 0.997 XH59 0.959 0.010 0.031 0.977 
XH25 0.961 0.039 0.992 XH60 0.949 0.030 0.021 0.975 
XH26 0.968 0.032 0.994 XH61 0.917 0.053 0.030 0.977 
XH27 0.913 0.061 0.026 0.977 XH62 0.974 0.026 0.997 
XH28 0.972 0.028 0.994 XH63 0.930 0.070 0.986 
XH29 0.986 0.014 0.997 XH64 0.993 0.007 0.999 
XH30 0.975 0.025 0.995 XH65 0.997 0.003 0.999 
XH31 0.972 0.028 0.994 XH66 0.997 0.003 0.999 
XH32 0.981 0.019 0.996 XH67 0.955 0.045 0.991 
XH33 1.000 1.000 XH68 0.997 0.003 0.999 
XH34 0.942 0.035 0.023 0.972 XH69 0.993 0.007 0.999 
XH35 0.948 0.052 0.990          

WQ: water quality.

To ensure improved quality of surface water there needs to be attention on the following items: (a) the residues and dung generated by plants and animals should be recycled; (b) domestic waste arising from tourism and daily life activities of local herdsmen should be separated, recovered and treated properly; (c) relevant government departments should work on improving awareness about environmental protection aims to reduce the impact on drinking water for the northeast pastoral population.

CONCLUSIONS

The 69 surface water samples were used to analyze the hydrochemical characteristics and quality of the surface water of Xiahe County. This research mainly found the following. (1) The hydrochemistry type in the surface water of Xiahe County was dominated mainly by Mg2+-Ca2+-HCO3-Cl and Mg2+-Ca2+-HCO3. (2) FCE showed that the surface water quality of nearly all samples was class I, and the integrated health status reached more than 0.95. This indicates that the water quality of most samples was in the good to excellent category and the water was suitable for irrigation directly but not for drinking unless treated appropriately. (3) Ammonia nitrogen was found to be the main pollutant. This document may serve as a guide for future researchers to evaluate the surface water conditions in the Tibetan Plateau pastoral area of China.

ACKNOWLEDGEMENTS

This work was supported by the Tibetan Plateau Community Protection and Rational Utilization of Natural Grassland Technology Research and Demonstration Project(201203006), the National Natural Science Foundation of China (No. 41571051), and the Fundamental Research Funds for the Central Universities in Lanzhou University (lzujbky-2015-150 and lzujbky-2016-261).

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