Abstract

The objective of the study is to assess the quality of the Shule River water for irrigational purposes. Surface water samples were collected along the course of the river in May and October 2012. The samples were analyzed for pH, electrical conductivity (EC), bicarbonate, chloride, sulphate, sodium, potassium, calcium and magnesium. Surface water was generally alkaline (average pH 8.17) and water pH and total dissolved solids in May were higher than those in October. EC ranged from 0.24 to 2.15 mS cm−1. Sodium was identified as the dominant cation, sulphate was identified as the dominant anion in May for both samples of river water but, in October, the dominant anions are respectively sulphate, bicarbonate and chloride from the upper region to the lower region. The total dissolved solids, chloride and sodium were found to exceed the permissible limits for irrigation water in the lower region. According to the principal factor analysis results, among water quality parameters measured in this study, chloride is the best indicator for monitoring water quality. The results revealed a deteriorating water quality in the lower region of the river.

INTRODUCTION

Water resource shortage and sustainable utilization of limited water resources has become a major problem to be solved in the world (e.g. Hasiniaina et al. 2010; Ghrair et al. 2015; Bharathi et al. 2016). Water quality obviously plays a critical role in this relation (Ntengwe 2006), as it is crucial to maintain a well-balanced environment. Salinity and salt ions of irrigation water affect soil properties and plants. Irrigation water could explain 29% of soil salinity (Darwish et al. 2005). Inappropriate management may decrease soil quality by accelerating the processes of degradation (Lal 1993). Therefore, it is necessary to determine the chemical components of water for effective water resource management and protection (Jeong 2001; Tweed et al. 2005; Perea & Rodriguez-Rodriguez 2009; Moosavirad et al. 2013; Dragon & Gorski 2015).

The Shule River is located in the arid region in the northwest of China, it is one of the three largest inland rivers in Hexi Corridor in Gansu Province (Ji et al. 2006), it has many important uses, such as irrigation, drinking water, industrial, and energy production, which considerably depend on water quality. The shortage of water resources and improper exploitation and utilization led to acute change of the regional hydrologic situation as well as a series of ecological and environmental problems, such as flow cutoff, steppe degradation, soil salinization, and desertification (Wang et al. 2015).

The chemical composition of river water is an important feature of the river valley and can indicate the climate and environment of the river flow through the area (Gibbs 1970). Unfortunately, several studies such as Zhou et al. (2014) and Zhao et al. (2015) have assessed the quality of river water and groundwater in the Shule River restricted to only certain smaller locations. Therefore, it is important to study the spatial and seasonal variation in the quality of water. The present investigation was made with an objective of evaluating the quality of river water along the entire stretch of the Shule River, and for identifying the influence of geogenic and anthropogenic sources.

MATERIALS AND METHODS

Study area

The Shule River originates from the Qilian Mountains and vanishes in the Lop Nor, now disappearing in the desert of Dunhuang (Figure 1). It is approximately 670 km long from south to north and covers a total area of 41,300 km2. The upper region of the river lies within the boundaries of Changma Gorge and Qilian Mountains and it is 346 km long and covers an area of 13,300 km2, the altitude gradually goes down from 4,787 m to 1,870 m and the average annual precipitation ranges from 400 to 50 mm, and it lies within the alpine semiarid climate zone. The middle region of the river lies within the boundaries of Changma Gorge and Shuangta Reservoir and it is 129 km long and covers an area of 12,000 km2. The lower region of the river lies within the boundaries of Shuangta Reservoir and the Lop Nor and it is 195 km long and covers an area of 16,000 km2. The middle and lower regions is characterized by the flat terrain, and it lies within the warm temperate arid zone.

Figure 1

Surface water sampling locations along the river course. The Shule River lies in the northwest in Gansu Province of China (in the bottom right map). The map is created by Dr. Dongbo (Dry Land Agriculture Institute, Gansu Academy of Agricultural Sciences), the ArcGIS 10.3 software is used, and the creation date is on June 12, 2018.

Figure 1

Surface water sampling locations along the river course. The Shule River lies in the northwest in Gansu Province of China (in the bottom right map). The map is created by Dr. Dongbo (Dry Land Agriculture Institute, Gansu Academy of Agricultural Sciences), the ArcGIS 10.3 software is used, and the creation date is on June 12, 2018.

The Shule valley, which lies within the plains of the Hexi Corridor, is in the center of the study area at an elevation of around 1,200 m, this region is a warm temperate arid zone (Wang et al. 2015). July is the hottest and January is the coldest. The average highest and lowest temperatures and precipitation in Yumen town (52436 station) were collected by China Meteorological Data Sharing Service System from 1955 to 2011 (Figure 2). The soils are mainly skeletal gray brown desert soils and brown desert soils derived from weathered sandstone.

Figure 2

Average highest and lowest temperatures and precipitations in Yumen town (52436 station) from 1955 to 2011. Climate data were obtained from China Meteorological Data Sharing Service System.

Figure 2

Average highest and lowest temperatures and precipitations in Yumen town (52436 station) from 1955 to 2011. Climate data were obtained from China Meteorological Data Sharing Service System.

Agricultural production is one of the primary economic activities in the study area. Intensive irrigated agriculture has started in this region since the 1960s. The irrigation water source in the study area is mainly from the Shule River. The main crops in the study area are cash crops, including cotton (Gossypium), Hami melon (Cucumis melo var. saccharinus), grapes (Vitis vinifera L.), Goji (Lycium barbarum L.) and cumin (Cuminum cyminum L.).

Sampling and analysis methods

Based on criteria such as the location of dams, the confluence of tributaries, major industries, as well as logistical constraints, sampling points were chosen at intervals ranging from 5 to 80 km. In total, 12 sampling sites at all flow stages were marked with Arabic numbers from 1 to 12 (Figure 1), each of the 12 sites has been sampled 3 times. Sampling was carried out in May and October 2012 based on spring runoff and autumn runoff.

The water samples were stored at 4 °C in a portable refrigerator and immediately transported to the laboratory. The pH was directly measured using a pH electrode. Electrical conductivity (EC) was measured using a conductance instrument (DDS-11A; Shanghai Shengci Instrument Limited Company). Total dissolved solids (TDS) were calculated using the method proposed by Lloyd & Heathcote (1985). The concentrations of sodium (Na) and potassium (K) were measured by flame photometry. The concentrations of calcium (Ca), magnesium (Mg), and sulphate (SO4) were measured with ethylenediaminetetraacetic acid (EDTA) titration methods (Lu 2000), respectively. The concentrations of carbonate (CO3) and bicarbonate (HCO3) were measured by double indicator titration and the concentration of chloride (Cl) was measured by titration with silver nitrate (Lu 2000).

Data processing

Water quality samples were analyzed using the RockWare Aq. QA (Version 1.1). The data were analyzed with SPSS 20.0. Principal component analysis (PCA) was performed with the PROC PRINCOMP (SAS, version 8.0) based on a matrix composed of water chemical properties (pH, EC, TDS, HCO3, Cl, SO4, Ca, Mg, K, and Na). A principal component provides information on the most meaningful parameters, which describes a whole data set, affording data reduction with a minimum loss of the original information (Yu et al. 2003; Shrestha & Kazama 2007).

RESULTS

Descriptive statistical chemical characters of surface water

The results in Table 1 show the statistical values of pH, EC, TDS, HCO3, SO4, Cl, Ca, Mg, K, and Na in surface water in the Shule River. Surface water was generally alkaline (average pH 8.17). The alkalinity was caused by the parent rock material in the valley. EC values ranged from 0.24 to 2.15 mS cm−1. The average TDS value of surface water was 2,011.7 mg L−1, which was higher than that of tap water (TDS 600–1,100 mg L−1). This increase in TDS was mainly ascribed to dissolved inorganic salts in soils in the lower region the river.

Table 1

Descriptive statistical test results for pH and salt ions in surface water in the Shule River valley in May and October 2012. (n = 72 samples)

  Minimum Maximum Mean Standard deviation Skewness CV(%) 
pH 7.51 8.39 8.17 0.204 −1.699 2.50 
EC (mS cm−10.24 2.15 0.62 0.459 2.429 73.9 
TDS (mg·L−1695.6 9,274.5 2,011.7 1,667.4 3.863 82.9 
HCO3 (mg·L−199.6 1,393.7 362.0 350.7 1.569 96.9 
SO4 (mg·L−140.5 388.6 110.8 84.1 2.250 75.9 
Cl (mg·L−142.5 1,361.3 404.6 427.9 1.127 105.8 
Ca (mg·L−128.0 128.0 55.3 28.9 1.444 52.2 
Mg (mg·L−116.8 98.4 45.4 20.1 1.294 44.2 
K (mg·L−113.0 148.9 58.9 27.3 1.490 46.3 
Na (mg·L−1364.5 8,324.0 969.6 1,590.3 4.675 164.0 
  Minimum Maximum Mean Standard deviation Skewness CV(%) 
pH 7.51 8.39 8.17 0.204 −1.699 2.50 
EC (mS cm−10.24 2.15 0.62 0.459 2.429 73.9 
TDS (mg·L−1695.6 9,274.5 2,011.7 1,667.4 3.863 82.9 
HCO3 (mg·L−199.6 1,393.7 362.0 350.7 1.569 96.9 
SO4 (mg·L−140.5 388.6 110.8 84.1 2.250 75.9 
Cl (mg·L−142.5 1,361.3 404.6 427.9 1.127 105.8 
Ca (mg·L−128.0 128.0 55.3 28.9 1.444 52.2 
Mg (mg·L−116.8 98.4 45.4 20.1 1.294 44.2 
K (mg·L−113.0 148.9 58.9 27.3 1.490 46.3 
Na (mg·L−1364.5 8,324.0 969.6 1,590.3 4.675 164.0 

In statistics, skewness is a measure of the asymmetry of the probability distribution of a random variable about its mean. The skewness values of EC, TDS, HCO3, SO4, Cl, Ca, Mg, K, and Na were positive and greater than 1, indicating that the frequency distributions of them were skewed strongly towards the lower concentrations. However, the skewness value of pH was negative and less than −1, indicating that the frequency distribution of pH was skewed strongly towards the higher value. The variations of Cl and Na were statistically characterised by extremely high CV values (Table 1). The CV values indicated that Cl and Na varied more rapidly than HCO3, SO4, Ca, Mg, and K.

Spatial and temporal variations of hydrochemical types of surface water

According to the values of primary and secondary ions, hydrochemical types are classified into four main types. If the milligram equivalent value of an ion accounts for 45 to 70% of the sum of milligram equivalent values of unipolar ions, the ion is classified as the primary ions. If the milligram equivalent value of an ion accounts for 25 to 45% of the sum of milligram equivalent values of unipolar ions, the ion is classified as the secondary ions. If the milligram equivalent value of an ion accounts for less than 25% of the sum of milligram equivalent values of unipolar ions, the ion is not considered. If the milligram equivalent value of an ion accounts for more than 70% of the sum of milligram equivalent values of unipolar ions, other ions are not considered and the hydrochemical type of water is named after the ion.

From the upper region to the lower regions, most of the water samples are classified as Mg-Na-SO4 type, followed by Mg-Na-Cl-HCO3-SO4 and Mg-Ca-Na-Cl-SO4 type in May (Table 2). The dominant anions are always SO4. However, in October, most of the water samples are classified as Mg-Na-HCO3-SO4, followed by Na-SO4-Cl-HCO3 and Na-HCO3-Cl type and the dominant anions are SO4, HCO3, and Cl from the upper region to the lower regions, respectively.

Table 2

Chemical types of surface water in the Shule River Valley in May and October, 2012

Valley region Sampling sites Hydrochemical type
 
May October 
Upper region Mg-Na-SO4 Mg-Na-HCO3-SO4 
Mg-Na-HCO3-SO4 Na-SO4-Cl-HCO3 
Mg-Na-HCO3-SO4 Na-Cl-SO4-HCO3 
Middle region Mg-Na-SO4 Na-SO4-Cl-HCO3 
Mg-Na-HCO3-Cl-SO4 Na-Cl-HCO3 
Mg-Na-Cl-HCO3-SO4 Na-Cl-HCO3 
Mg-Na-Cl-HCO3-SO4 Na-HCO3-Cl 
Na-Mg-Cl-HCO3-SO4 Na-Cl 
Mg-Na-Cl-HCO3-SO4 Na-Cl 
Lower region 10 Ca-Mg-Na-Cl-SO4 Na-Cl 
11 Ca-Mg-Na-Cl-SO4 Na-HCO3-Cl 
12 Mg-Ca-Na-Cl-SO4 Na-HCO3-Cl 
Valley region Sampling sites Hydrochemical type
 
May October 
Upper region Mg-Na-SO4 Mg-Na-HCO3-SO4 
Mg-Na-HCO3-SO4 Na-SO4-Cl-HCO3 
Mg-Na-HCO3-SO4 Na-Cl-SO4-HCO3 
Middle region Mg-Na-SO4 Na-SO4-Cl-HCO3 
Mg-Na-HCO3-Cl-SO4 Na-Cl-HCO3 
Mg-Na-Cl-HCO3-SO4 Na-Cl-HCO3 
Mg-Na-Cl-HCO3-SO4 Na-HCO3-Cl 
Na-Mg-Cl-HCO3-SO4 Na-Cl 
Mg-Na-Cl-HCO3-SO4 Na-Cl 
Lower region 10 Ca-Mg-Na-Cl-SO4 Na-Cl 
11 Ca-Mg-Na-Cl-SO4 Na-HCO3-Cl 
12 Mg-Ca-Na-Cl-SO4 Na-HCO3-Cl 

In order to clarify the study results, the water quality data were fitted with RockWare Aq. QA (Figure 3). The Durov diagram showed that surface water generally exhibited a few variations. In May (Figure 3(a)), in the cation and anion diagrams, the samples are shifted toward the Na and SO4 apex, respectively. TDS gradually increased from 700 and 9,300 mg L−1 from locations 1 to 12, except for location 11. It may be caused by location 11 on the floodplain, the velocity of water flow is low and the sedimentation is significant. In October (Figure 3(b)), the dominant anions varied with sampling location. Firstly, the primary anions were SO4 at location 1, HCO3 at locations 5, 6, and 7, and Cl at locations 8, 9, 10, 11 and 12. However, the dominant cations were sodium from locations 1 to 12 and TDS basically presented the increasing trend from locations 1 to 12 except for location 7. TDS in October was obviously lower than that in May. The water pH varied from 8.0 to 8.4 in May and from 7.5 to 8.3 in October, respectively.

Figure 3

Ionic proportions in the surface water from upstream to downstream of the Shule River valley in May (a) and October (b).

Figure 3

Ionic proportions in the surface water from upstream to downstream of the Shule River valley in May (a) and October (b).

Paired-samples T-test of salt and ions of surface water between May and October

To explore water quality variation with temporal, we examined the temporal difference of water quality between May and October with the paired-samples t-test at P= 0.05 or 0.01 by SPSS 20.0 software (Table 3). HCO3 and Cl showed significant differences between May and October (P < 0.01). However, pH, EC, TDS, SO4, Ca, Mg, K, or Na showed no significant differences between May and October (P > 0.05).

Table 3

T-test of salt and ions of the surface water between in May and October

Variable (May-Oct) Mean Std Dev Std Error t Value Pr > |t| 
pH 0.07 0.27 0.08 0.912 0.381 
EC (mS cm−10.30 0.56 0.16 1.855 0.091 
TDS (mg·L−1−0.3 2,093.8 604.4 0.000 1.000 
HCO3 (mg·L−1−550.4 380.8 109.9 −5.007 0.000* 
SO4 (mg·L−171.5 112.6 32.5 2.200 0.050 
Cl (mg·L−1−475.2 365.4 105.5 −4.506 0.001* 
Ca (mg·L−123.1 39.6 11.4 2.023 0.068 
Mg (mg·L−1918.6 2,180.6 629.4 1.459 0.172 
K (mg·L−13. 7 41.8 12. 1 0.304 0.767 
Na (mg·L−13.5 26.7 7.7 0.454 0.658 
Variable (May-Oct) Mean Std Dev Std Error t Value Pr > |t| 
pH 0.07 0.27 0.08 0.912 0.381 
EC (mS cm−10.30 0.56 0.16 1.855 0.091 
TDS (mg·L−1−0.3 2,093.8 604.4 0.000 1.000 
HCO3 (mg·L−1−550.4 380.8 109.9 −5.007 0.000* 
SO4 (mg·L−171.5 112.6 32.5 2.200 0.050 
Cl (mg·L−1−475.2 365.4 105.5 −4.506 0.001* 
Ca (mg·L−123.1 39.6 11.4 2.023 0.068 
Mg (mg·L−1918.6 2,180.6 629.4 1.459 0.172 
K (mg·L−13. 7 41.8 12. 1 0.304 0.767 
Na (mg·L−13.5 26.7 7.7 0.454 0.658 

Note: *denote a significant difference at p < 0.01.

PCA of surface water chemical properties

The PCA was used to explore the most important factors determining the spatial variations in chemical parameters of the Shule River. A total of four principal components (PCs) were obtained with eigenvalues more than one which accounted for around 85% of the total variance in the 10 chemical parameters of the Shule River. The contributions of the first, second, third, and fourth PCs to the variation were respectively 41%, 20%, 13%, and 12%, which accounted for 85% of the total variation (Table 4).

Table 4

The eigenvalue and cumulative contribution rate of the correlation matrix

Item PRIN1 PRIN2 PRIN3 PRIN4 
Eigenvalue 4.12 1.95 1.26 1.17 
Proportion of variable (%) 41 20 13 12 
Proportion of cumulative variable (%) 41 61 73 85 
Item PRIN1 PRIN2 PRIN3 PRIN4 
Eigenvalue 4.12 1.95 1.26 1.17 
Proportion of variable (%) 41 20 13 12 
Proportion of cumulative variable (%) 41 61 73 85 

When the accumulative variation of the PCs is above 85% of the total variation, these components reflect the total variation and can interpret well the changes in the chemical properties of water. The comprehensive score was the highest for Cl, followed by TDS, Na, EC, HCO3, Ca, Mg and SO4, and the lowest for K, indicating that among water parameters measured in this study, Cl and K are respectively the best and worst indicators for monitoring water quality (Table 5). The analysis output would provide valuable information for the decision making in the river basin management.

Table 5

Comprehensive and general scores of PCs for pH, EC and salty ions

Item Score of PRIN1 Score of PRIN2 Score of PRIN3 Score of PRIN4 General score Order 
pH 0.07 0.08 −0.54 0.68 0.29 
EC 0.42 −0.10 0.09 0.14 0.55 
TDS 0.42 0.09 0.39 0.05 0.95 
HCO3 −0.00 0.60 0.12 −0.25 0.47 
SO4 −0.11 −0.33 0.38 0.48 0.42 
Cl 0.08 0.61 0.07 0.28 1.04 
Ca 0.41 0.11 −0.18 −0.13 0.21 
Mg 0.45 0.08 −0.16 0.10 0.47 
0.30 −0.30 −0.45 −0.33 −0.78 
Na 0.41 −0.19 0.36 0.01 0.59 
Item Score of PRIN1 Score of PRIN2 Score of PRIN3 Score of PRIN4 General score Order 
pH 0.07 0.08 −0.54 0.68 0.29 
EC 0.42 −0.10 0.09 0.14 0.55 
TDS 0.42 0.09 0.39 0.05 0.95 
HCO3 −0.00 0.60 0.12 −0.25 0.47 
SO4 −0.11 −0.33 0.38 0.48 0.42 
Cl 0.08 0.61 0.07 0.28 1.04 
Ca 0.41 0.11 −0.18 −0.13 0.21 
Mg 0.45 0.08 −0.16 0.10 0.47 
0.30 −0.30 −0.45 −0.33 −0.78 
Na 0.41 −0.19 0.36 0.01 0.59 

DISCUSSION

Surface water quality is affected by both natural processes and anthropogenic activities. Generally, natural surface water quality varies from place to place, depending on climatic variations, and with the types of soils and rocks. A variety of human activities such as agricultural activities, industrial and urban development, and recreation significantly alter the quality of natural waters and change the water use potential.

Spatial variations of water pH

From the upper region to the lower regions, water pH ranged from 7.5 to 8.4. The pH in lower reaches of the river was normally high, where highly alkaline water may be attributed to the dissolved sodium in soils in the lower region of the river. Generally, the pH values in this study were indicative of poor water quality. The average pH values were beyond the acceptable range of regulatory limits (World Health Organization 2004). Hoq et al. (2006) reported that river water of the Sundarbans was characterized by slightly alkaline pH and that water pH was neutral to alkaline (7.4 to 8.1). Water pH in this study area was similar to river water of the Sundarbans.

Spatial variations of salt ions in surface water

EC is a good indicator in assessing water quality (RamyaPriya & Elango 2018). Irrigation waters are divided into four classes with respect to conductivity, the dividing points between classes being at 0.25, 0.75, and 2.25 mS cm−1, those classes are respectively known as low-salinity water, medium-salinity water, high-salinity water and very high-salinity water (U.S. Salinity Lab. 1954). EC and TDS presented the increasing trend with fluctuations. In May, EC values ranged from 0.24 to 2.15 mS cm−1 and TDS content ranged from 700 to 9,300 mg L−1, in October, EC values ranged from 0.31 to 0.81 mS cm−1 and TDS content ranged from 1,000 to 2,900 mg L−1 (Figure 3). The increases in EC and TDS are mainly ascribed to dissolved inorganic salts in soils in the middle and lower regions of the river. According to the recommendations from the Food and Agriculture Organization (FAO), water samples with ECiw > 0.75 mS cm−1 had the increasing risk of salinity (Ayers & Westcot 1985).

The content of Cl gradually increased from 42.5 mg L−1 to 425.4 mg L−1 in May and from 74.4 mg L−1 to 1,361.3 mg L−1 in October (Figure 3). According to the results by James et al. (1982), irrigation water with a Cl concentration above 20 mg L−1 was considered to be unsuitable for irrigation. Marschner (1995) also reported that the concentration of Cl above 710 mg L−1 in the external solution led to Cl toxicity to sensitive plant species.

Sodium was the dominant cation in the river. The observed highest concentration of Na was 8,324.0 mg L−1, which was much higher than the maximum permissible level in irrigation water. The higher salty ions concentration in the present study indicated the deterioration of water quality over time. Therefore, such irrigation water should not be used in Na-sensitive or Cl-sensitive crops.

Temporal variations of salt ions in surface water

The Durov diagrams in Figure 3(a) and 3(b) indicated that TDS and major ions in surface waters showed significant temporal variations between May and October. The content of TDS in surface water in May was obviously higher than that in October. HCO3 and Cl showed significant differences between May and October. No significant differences were observed for major cations (K, Na, Ca, and Mg) between May and October. Major anions in surface water of the river showed significant seasonal variations, but major cations showed no significant seasonal variation. This is further showed by the Durov diagrams which show the dominance of Na among the cations as well as Cl and SO4 among the anions.

CONCLUSIONS

This work investigated water chemistry of surface based on pH, EC, TDS and several salt ions. Surface water was generally alkaline and water pH and TDS in May were higher than those in October. EC, TDS, Cl, Ca, Mg, K, and Na showed the increasing trends with spatial variations. In the lower region of the river, the high contents of TDS, Cl, and Na were found in the study area. Therefore, irrigation water should not be used in Na-sensitive or Cl-sensitive crops.

From the upper region to the lower region, most of the water samples are classified as Mg-Na-SO4 type, followed by Mg-Na-Cl-HCO3-SO4 and Mg-Ca-Na-Cl-SO4 types and major anions was SO4 and showed no spatial variations in the river in May. However, in October, most of the water samples are classified as Mg-Na-HCO3-SO4 type, followed by Na-SO4-Cl-HCO3 and Na-HCO3-Cl types and major anions were SO4 in the upper region of the river, HCO3 in the middle region, and Cl in the lower region. Major anions in surface water in the river showed significant spatial variations, but major cations showed no spatial variation. This reflects the evolutionary regularity of surface water in the river from piedmont Gobi Belt of Qilian in the upper region to silt deposit area in the middle region, and then the desert area in the lower region. According to the principal factor analysis results, among water parameters measured in this study, Cl and K are respectively the best and worst indicators for monitoring water quality. The data from this study provide important information for water resource utilization and management.

ACKNOWLEDGEMENTS

The study was financially supported by National Natural Science Foundation of China (Nos. 41163002, 41363004 and 31460630) and Innovation Team of Gansu Academy of Agricultural Sciences (2015GAAS03).

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