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Article

Study on Water Quality Change Trend and Its Influencing Factors from 2001 to 2021 in Zuli River Basin in the Northwestern Part of the Loess Plateau, China

by
Zhenghong Zhang
,
Fu Zhang
*,
Zhengzhong Zhang
and
Xuhu Wang
Forestry College of Gansu Agricultural University, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(8), 6360; https://doi.org/10.3390/su15086360
Submission received: 2 March 2023 / Revised: 22 March 2023 / Accepted: 31 March 2023 / Published: 7 April 2023
(This article belongs to the Section Sustainable Water Management)
Browse Figures
Review Reports Versions Notes

Abstract

:
The Zuli River is in the northwest of the Loess Plateau. As an important center of production and domestic water source, variations in the water quality of this basin and their influencing factors are important considerations for improving the river water environment. In order to identify and predict changes in the water quality of the watershed, the following water quality indicators, namely, dissolved oxygen content (DO), five-day biological oxygen demand (BOD5), ammonia nitrogen concentration (NH3-N), the high-manganese salt index (CODMn), volatile phenol concentration (VP), total phosphorus (TP), fluoride concentration (F−), and nitrite nitrogen concentration (NO3-N), were studied together with their change trends, influencing factors, and main variation cycles in the basin from 2001 to 2021. The results were as follows: (1) All the water quality indicators except for DO and F- showed an increasing trend before 2011, and DO showed an extreme, significant downward trend. There was an increase in the content of pollutants in the water caused by chemical fertilizer and aquaculture, resulting in a decrease in the DO content. (2) There was an extreme, significant upward trend in DO after 2011, while a significant downward trend was observed in the other water quality indicators except for NO3-N. (3) According to the main variation cycle of the other water quality indicators, the DO will remain in its peak period, while the other water quality indicators except for NO3-N will remain in a trough period (which began in 2021). The increase in precipitation and runoff reduced the content of pollutants in the water. Therefore, the overall water quality of the Zuli River Basin gradually improved after 2011. This may be due to (1) increased precipitation and runoff, thereby diluting the concentration of pollutants in the river, or (2) a decreased concentration of pollutants entering the river with the decrease in soil erosion.

1. Introduction

Water resources are an important basis for the survival of terrestrial organisms, but the amount of water available for biological use is very low [1]. More importantly, water resources also show regional imbalances across the world, especially in arid and semi-arid areas, and are very scarce [2,3]. The permanent surface area of the water of downstream countries affected by water scarcity has decreased over time in Central Asia [4]. The research conducted by Rockstroem et al. showed that by 2050, approximately 60% of the world’s population could face surface water shortages in relation to water stemming from lakes, rivers, and reservoirs [5]. The question of how to manage water resources has become one of the major challenges in the 21st century [6]. In fact, global population growth and economic development have had serious impacts on the aquatic environment [7]. In recent decades, the development of industry and agriculture has caused surface water pollution [8,9,10]. The shortage of water resources and water pollution are interrelated in that water pollution reduces the supply of water [11,12]. The pollution of rivers and lakes in the United States generally originates from agriculture, which mainly increases the content of nitrogen and phosphorus [13]. The excessive use of chemical fertilizers and pesticides has caused serious non-point source water pollution [14]. Industrially polluted rivers have increased concentrations of heavy metal ions, sulfides, organic compounds, etc. [10,15]. The same process has occurred in other parts of the world [16,17,18]. In addition, climate change precipitates water quality problems, accentuates water level fluctuations and loss of biodiversity, and induces the destruction of habitats, eventually leading to the death of fish [19]. In sub-basins, land use patterns and landscape patterns have significant impacts on water quality changes [20]. Water pollution has exacerbated water conflicts in arid areas [21]. Therefore, water pollution remains one of the key focuses of global interest and research.
China is a country with scarce water resources and serious water pollution [21,22]. The results of research have indicated that most of its regions mainly rely on surface water, which accounts for less than 50% of the surface water supply situated in only four provinces of China [23]. As its water bodies became increasingly polluted, studies found that in some parts of China, the pollution level exceeded the national drinking water standard for nitrate (45 mg/L) [24]. According to the statistics of the Ministry of Ecology and Environment of China (http://mee.gov.cn/ (accessed on 28 May 2022)), water pollution mainly originates from agricultural production, and the main pollutants are measured by chemical oxygen demand (COD) and include nitrogen and phosphorus. The emissions of COD, total nitrogen, and total phosphorus in China’s water bodies were 1324.09 × 104 t, 270.46 × 104 t, and 28.47 × 104 t, respectively, in 2010. This research has shown that the total amount of agricultural N emissions in water bodies in the Haihe Basin was 1079 Gg N in 2012, of which cropland contributed 54%, while the total amount of agricultural P emissions in these water bodies was 208 Gg P, of which livestock contributed 78% [25]. Facing severe water pollution, the Chinese government has taken active measures to reduce the discharge of pollutants [26,27]. Compared with those in 2010, the total amounts of water pollution emissions of COD, nitrogen, and phosphorus decreased by 19.4%, 47.79%, and 25.53, respectively, in 2017. Pan et al. (2022) found that China’s Environmental Protection Interview led to an average reduction in water pollution of 14.5%, and this effect persisted in the long term [28]. However, the research of Wu et al. (2022) demonstrated that in the Yongan River watershed of eastern China, although chemical N fertilizer use decreased by 49% and the number of domestic animals decreased by 73% from 2000–2019, flow-normalized riverine TN and NO3− concentrations progressively increased by 161% and 232%, respectively, from 1980–2019 [29]. Therefore, in general, China’s water environment is improving, but there is still a risk of water quality deterioration in some areas.
The Yellow River Basin covers a total of 752,400 km2, spanning nine provinces in northern China, and it is an important area of economic development and ecological protection. Similarly, with the development of industry and agriculture, the shortage of water resources and water pollution in the Yellow River Basin has become increasingly prominent [30]. The research of Lu et al. (2021) found that the polymeric state of dissolved copper in the Yellow River tends to be stable [31]. Xia et al. (2022) concluded that nitrate concentrations have a pronounced influence of human activities in the Yellow River Delta [32]. Hong et al. (2020) found that the concentration of heavy metals in the Yellow River was affected by runoff, and the concentration of heavy metals was the lowest in the wet season [33]. Wang et al. (2009) found that there was a significant difference in water pollution between the tributaries and the main stream of the Yellow River that and SO42−, NO3−, and Cl− were present in the main stream [34]. In the Lanzhou section of the upper reaches of the Yellow River, the concentrations of total PAHs in the porewaters ranged from 48.2 to 206 μg/L and were positively correlated with the sediment content in the water [35]. In addition, some studies showed that the water quality in the upper reaches of the Yellow River Basin was better than that in the lower reaches [36]. One study also showed that the water pollution in the Yellow River Basin is due to agricultural (planting, animal husbandry, and aquaculture), industrial, and domestic pollution [37]. The research of Zuo et al. (2016) showed that there is a mid- to high-risk level of Cd in the upper Yellow River [38]. In the small areas where the tributaries of the middle and upper reaches of the Yellow River Basin flow, agriculture is the main industry. Therefore, water pollution mainly originates from agriculture in these regions. Kang et al. (2015) suggested that there was non-point source pollution in the Anjiagou watershed in the upper reaches of the Yellow River, and the main pollutants were CODMn, BOD5, and TP derived from agricultural land and forested land [39].
As illustrated by the abovementioned studies, water pollution has become a problem that cannot be ignored with regard to the utilization of water resources in the Yellow River Basin. Changes in water quality in the Yellow River Basin are very important for the social development and ecological protection of the region. Therefore, it is necessary to analyze changes in water quality in the Yellow River Basin and study the factors affecting the changes in water pollutants. In this study, we selected the Zuli River Basin in the upper reaches of the Yellow River Basin as an area to study temporal changes in surface water quality. The goals of this study were to (1) analyze the change trend of pollutants in the Zuli River Basin from 2001 to 2021; (2) detect the change cycle of water pollutants in the Zuli River Basin; and (3) identify the factors affecting the changes in water pollutants in the Zuli River Basin. This study will contribute to the protection, scientific development, and utilization of water resources in the upper reaches of the Yellow River Basin.

2. Materials and Methods

2.1. Study Area Description

The Zuli River is a tributary of the Yellow River located in the northwestern part of the Loess Plateau (104°13′–105°35′ E, 35°16′–36°34′ N), China (Figure 1). The total length of the main stream is 220 km, and it is part of the fifth sub-region of the hilly and gully region of the Loess Plateau. The Zuli River originates from the northern foot of Huajialing Mountain in the south of Huining County and merges with the Yellow River in Jingyuan County. The entire drainage area of the Zuli River is 10,653 km2, covering seven counties (Tongwei, Longxi, Huining, Anding, Yuzhong, Pingchuan, and Jingyuan) in Gansu Province and a portion of the Ningxia Hui Autonomous Region. Most of the river basin is in Gansu Province, which accounts for 94.4% of the total area of the river basin. The remaining 5.6% of the total area is in the Ningxia Autonomous Region. The soil types in the basin are mainly gray calcite soil, yellow loessal soil, and black loessal soil [40]. The average annual runoff is 6625.81 × 108 m3, and the sediment content in the Zuli River is 1198.62 × 104 t.

2.2. Climatic Conditions

The climates of the Zuli River Basin are arid and semi-arid, with hot summers and dry, cold winters. The average annual precipitation in the Zuli River Basin is 365.75 mm (2001–2021), concentrated in the July–September period, which accounts for 60–80% of the annual precipitation, and the mean annual air temperature is 6.3 °C with an extreme high of 30.5 °C and an extreme low of −20.1 °C [41].

2.3. Water Quality Assessment Standards

The hydrological data, including those related to precipitation, runoff and sediment, and water quality, were all selected from the hydrological observation station of Zuli River Basin. The length of the data sequence ranged from 2001 to 2021. According to the Environmental Quality Standard for Surface Water (GB3838-2002), the following water quality indicators were selected for analysis in Zuli River Basin: DO, BOD5, NH3-N, CODMn, VP, TP, F−, and NO3-N [20,42].

2.4. Presentation of Pollution Sources

Water pollution in the Zuli River Basin is caused by agricultural non-point source pollution [42]. Therefore, we collected data on the amount of fertilizer used, the number of large livestock, the number of pigs, and the number of sheep in the Zuli River Basin from the yearbooks of Gansu Province and Ningxia Autonomous Region to analyze the impacts of social factors on the water pollution changes in the Zuli River. The data on these social factors were weighted according to the proportion of the area of each county in the Zuli River Basin from 2001 to 2021.

2.5. Statistical Analyses

The coefficient of linear regression was used to analyze the change trends of the water quality indicators in the Zuli River Basin from 2001 to 2021. The positive coefficient of linear regression indicated whether the data showed an increase in this period or vice versa. The significance of this change trend was also tested. In addition, some indicators of classical statistics, such as the mean, maximum, minimum, standard deviation (STD), and coefficient of variation (CV), were also used to analyze water quality changes.
In addition, wavelet (Mexican hat) analysis [43,44,45,46] was used to study the temporal variations in water quality indicators in the Zuli River from 2001 to 2021. Wavelet coefficients, after Mort transformation, can reflect the change period of data. High wavelet coefficients represent peaks and vice versa. The main change period can be determined by examining the wavelet variance. Using the results of wavelet analysis, we identified the cycles of water quality variation and predict whether the content of water pollutants in the Zuli River will increase in the future.

3. Results

3.1. Change Trend of Water Quality in Zuli River Basin from 2001 to 2021

The change trend of water quality in the Zuli River Basin from 2001 to 2021 is shown in Figure 2. It was divided into two time periods (2001–2011 and 2011–2021) to better analyze the change trend of the water quality. There was a downward trend in DO and F-, while there was an upward trend in the other water quality indicators before 2011. However, it was very clear that most of the water quality indicators showed a downward trend after 2011, except for DO. This means that the water quality in the Zuli River Basin was relatively poor before 2011 and gradually improved after 2011. The upward trend in DO indicates a reduction in the content of microorganisms in the surface water after 2011, which also demonstrates that the water quality improved.
As shown in Table 1, the water quality varied greatly between 2001 and 2021. The CV of more than half of the water quality indicators exceeded 50%, and the values of BOD5, TP, and VP were more than 90%. This large CV indicates that the interannual variation range of the water quality indicators was large. For example, the maximum level of BOD5 (32.7 mg/L) was more than 50 times the minimum (0.63 mg/L). In addition, only DO and BOD5 showed significant changes before 2011, of which there was an extreme, significant downward trend in DO at a rate of 0.34 mg/L × a−1 and a significant upward trend in BOD5 at a rate of 1.1 mg/L × a−1. However, most of the water quality indicators, except for TP and NO3-N, showed significant changes after 2011. There were extreme, significant variations in DO, CODMn, VP, and F- between 2011 and 2021, with variation rates of 0.63 mg/L × a−1, −1.21 mg/L × a−1, −0.0021 mg/L × a−1, and −0.056 mg/L × a−1, respectively. Additionally, there were significant downward trends in BOD5 at a rate of 2.3 mg/L × a−1 and NH3-N at a rate of 0.55 mg/L × a−1. More importantly, the absolute value of the variation rate of the water quality indicators during 2011–2021, except for NO3-N, was greater than that during 2001–2011. Therefore, the water quality in the Zuli River Basin had a significant trend of improvement after 2011, and the rate of improvement was greater than the rate of deterioration before 2011.
The fluctuations in the wavelet coefficients reflect the variation cycle of the water quality indicators in the Zuli River Basin from 2001 to 2021. The wavelet coefficient also reflects the changes in water quality in different time periods, and the variation cycle was determined by the size of the wavelet variance. Generally, the larger the wavelet variance, the more evident the variation cycle. In Figure 3a, there is a relatively clear variation cycle of 10 years regarding the content of DO. The DO had a clear high–low–high pattern of variation in this variation cycle of 10 years, which was basically consistent with the actual changes in DO (Figure 2a). Therefore, we can infer that the content of DO may still be in a peak period after 2021. On the contrary, there was a clear variation cycle of 10 years for the content of BOD5 with a low–high–low change trend, and the concentration of BOD5 may enter a trough period after 2021. According to the main variation cycle of the other water quality indicators from 2001 to 2021, the concentration of most of the water quality indicators, except for DO and NO3-N, may be in a trough period after 2021. Therefore, the water quality of the Zuli River Basin may gradually improve in the short term after 2021.

3.2. Impact of Three Environmental Factors on Water Quality

As is well-known, the water quality of rivers is affected by environmental factors [47,48]. In this study, three environmental factors, including precipitation, runoff, and sediment, were chosen to analyze their relationships with water quality in the Zuli River Basin. As shown in Figure 4 and Table 2, there was a clear downward trend in precipitation, runoff, and sediment with rate of −6.84 mm × a−1, −61.44 × 108 m3 × a−1, and −94.2 × 104 t × a−1 before 2011, while there was an upward trend in precipitation and runoff with rate of 10.99 mm × a−1, 321.04 × 108 m3 × a−1 and −51.33 × 104 t × a−1 after 2021. The overall variation trend of precipitation and runoff in the two time periods was similar to that of DO and contrasted with that of most of the remaining water quality indicators. It seems that the variations in precipitation and runoff had impacts on the variation in water quality in the Zuli River Basin between 2001 and 2021.
As shown in Figure 5, the main variation cycles of the three environmental factors (precipitation, runoff, and sediment) in the Zuli River Basin from 2001 to 2021 were determined. Precipitation had a clear variation cycle of 4 years and a low–high–low–high pattern of variation after 2010 (Figure 5a). There was a relatively clear variation cycle of 10 years for runoff, with a high–low–high pattern of variation after 2012 (Figure 5b). Compared with precipitation and runoff, sediment had a variation cycle of 10 years before 2010, but in general, there was a more evident cycle of 3 years (Figure 5c). Therefore, precipitation and sediment had relatively short variation cycles, and their high–low changes were frequent in the past 21 years, whereas runoff had a relatively long variation cycle.
Spearman correlation analysis was used to determine the relationship between three environmental factors (precipitation, runoff, and sediment) and water quality in the Zuli River Basin (Table 3 and Table 4). It was found that precipitation had a significant positive relationship with the DO and a significant negative relation with the TP between 2001 and 2011. Although there was a relatively high degree of correlation between TP and runoff, it did not pass the significance test. In Table 3, it is evident that the absolute values of most of the correlation coefficients are small. This means that decreased levels of precipitation, runoff, and sediment may not be the cause of the poor water quality in the Zuli River Basin between 2001 and 2011. Compared with the low absolute correlation coefficients from 2001–2011, the absolute values of the correlation coefficients were relatively high between 2011 and 2021, which means that there were relatively significant positive or negative relationships between the environmental factors and water quality. However, only five groups of correlations passed the significance test. For example, F− showed an extreme, significant, negative relationship with precipitation, and there was a significant negative relationship between precipitation and NH3-N. Therefore, the impacts of precipitation and runoff on the water quality of the Zuli River Basin after 2011 were relatively high. In addition, some studies have shown that sediment has a good adsorption effect on phosphorus and polycyclic aromatic hydrocarbons [46]. In this study, there was a significant negative correlation between sediment and TP before 2011 and a significant positive correlation with VP after 2011. Before 2011, due to the reductions in sediment content and phosphorus adsorption in the water, the content of phosphorus in the water increased; however, the VP in the water did not increase with the decrease in the content of sediment after 2011. More importantly, overall, the correlation coefficients between sediment and the other water quality indicators were small. Therefore, compared with precipitation and runoff, sediment did not seem to be a main factor affecting water quality changes in the Zuli River Basin from 2001 to 2021.

3.3. Impact of Quantity of Social Factors on Water Quality

Water quality is also affected by social factors, such as the growth of populations, the development of industry and agriculture, etc. [48,49]. The Zuli River Basin mainly encompasses seven counties in Gansu Province, which are mostly agricultural areas. The agricultural practices in these areas mainly include planting and aquaculture. The impact of planting on water quality originates from the use of chemical fertilizers, while the impact of aquaculture is mainly due to animal manure. The use of fertilizer and livestock manure leads to changes in water quality, and the excessive use of fertilizer and unreasonable use of livestock manure usually lead to a deterioration of surface water quality [21,29,37]. Therefore, we selected the amount of fertilizer used and the quantities of the main livestock types (including large cattle, pigs, and sheep) to analyze the impacts of social factors on the water quality of the Zuli River Basin.
Figure 6 shows the variations in social factors, including fertilizer, large cattle, pigs, and sheep, in the Zuli River Basin from 2001 to 2021. The amount of fertilizer and the number of sheep maintained an increasing trend from 2001 to 2021. The number of large cattle showed a clear downward trend between 2001 and 2021. There was slight variation in the number of pigs. The results in Table 5 show that the four environmental factors selected for our paper had small annual variations, which shows that the nature of planting and aquaculture in the Zuli River Basin was relatively stable. In addition, the average number of sheep in the Zuli River Basin was more than three times and two times that of large livestock and pigs, respectively; thus, sheep raising is the main pillar of the breeding industry in the region, and the number of sheep showed significant upward trends in 2001–2011 and 2011–2021, respectively, at rates of 1.21 × 104 × a−1 and 1.19 × 104 × a−1.
The cycles of the four social factors were calculated (Figure 7). In Figure 7a, the wavelet coefficient of fertilizer is relatively small, and periodic change is not apparent. The result of the wavelet variance of fertilizer showed a relatively prominent 10-year cycle, indicating that it generally had a low–high pattern of its variation trend from 2001 to 2021. There was a variation cycle of 7 years for large cattle, and it showed a low–high–low pattern of variation after 2010 (Figure 7b). The pattern for pigs was very similar (Figure 7c). The variation cycle of sheep functioned corresponding to a 10-year scale (Figure 7d). As can be inferred from their main variation cycles, the amount of fertilizer and the number of lager cattle may be in a trough period after 2021, while the numbers of pig and sheep may be in a peak period.
Spearman correlation was used to detect the relationships between the four social factors (fertilizer, large cattle, pigs, and sheep) and water quality in the Zuli River Basin (Table 6 and Table 7). The results of the correlation analysis showed that most of the water quality indicators were not strongly related to social factors before 2011. From 2001–2011, the amount of fertilizer showed an extreme, significant correlation with the DO and BOD5; the number of large cattle showed an extreme, significant correlation with the CODMn and F−; the number of pigs showed an extreme, significant correlation with the F−; and the number of sheep showed an extreme, significant correlation with the DO and BOD5 (Table 6). By comparing the change trends of the water quality indicators and social factors before 2011 (Figure 3 and Figure 7), it is evident that these results are consistent with the conclusion that the application of fertilizer and the increase in the number of livestock may precipitate poor water quality. Fertilizer and animal manure increase the content of pollutants in water, thus intensifying nitrification and oxidation and reducing the content of DO in water [50]. Generally, the F− in water is derived from rock and soil differentiation, mining, metallurgy, etc. [51], and shows little correlation with animal manure. In this study, the F− was significantly positively correlated with animal feces, which may be caused by the daily drinking of water with high content of F−.
However, the correlation results in Table 7 show that the water quality indicators were only related with the number of sheep, which had extreme, negative, significant relationships with most of the quality indicators except for DO and NO3-N after 2011. The other social factors showed little correlation with the water quality indicators. Compared to the change trend of the water quality indicators and sheep after 2011 (Figure 3 and Figure 6d), the relationship in Table 7 shows that the water quality improved with a greater number of sheep, which does not conform to the hypothesis dictating that aquaculture will lead to a deterioration of the water quality.

4. Discussion

To better explain the relationships between water quality and environmental factors, the main variation cycles of the water quality indicators were compared with those of three environmental factors (precipitation, runoff, and sediment) in the Zuli River Basin from 2001 to 2021 (Figure 8). In Figure 8, because the cycles of most of the water quality indicators (except for TP) and runoff are relatively similar, the variations in runoff and the water quality indicators show obvious similarities or dissimilarities. For instance, the variation trends of DO and runoff were similar, while those of BOD5 and runoff were in opposition to the main variation cycle. Additionally, in relation to the main variation cycle, the variation trends of most of the water quality indicators except for DO, TP, and NO3-N were opposite to those of runoff after 2010. It seems that the variation in runoff may have affected the water quality of the Zuli River, especially after 2010. Precipitation and runoff may increase or decrease the level of pollutants in rivers through the transportation of surface pollutants into rivers via surface runoff. Surface pollutants adhere to the soil and enter the river through heavy precipitation and surface runoff [52]; however, increased precipitation can increase runoff, and increased runoff enhances the potency of dilution and reduces the residence time of pollutants in rivers [31,53,54,55]. Therefore, the quality of water in a river also depends on other environmental factors, such as landscape patterns and land use types [56]. For example, due to the low vegetation coverage of the surface during the dry season, surface runoff after rain is more likely to transport pollutants from soil into the Changle River watershed [57]. Forest land was found to reduce the content of pollutants in rivers compared to cultivated land in the Jiulong River Basin [58]. In the Zuli River Basin, more vegetation measures have been applied for soil erosion control in the watershed area, thereby reducing the erosion of soil and its transfer into rivers through surface runoff, since the 1980s [39,40]. In Figure 4, one can see that the sediment content in the Zuli River has continued to decline, indicating that the soil erosion in the entire basin has decreased; thus, the pollutants accompanying the soil may have also decreased from 2001 to 2021. Therefore, reduced soil erosion and increased runoff may be the main reasons for the decrease in the content of pollutants in the Zuli River Basin after 2011.
Agricultural production and livestock breeding can increase the number of pollutants in surface water. The use of chemical fertilizers increases the content of nitrogen and phosphorus in surface water, resulting in poor water quality [59,60]. Farmed animal waste not only increases the content of pollutants in surface water but also leads to an increase in chemical oxygen demand, leading to poor water quality [61]. However, in this study, we found that an increase in the number of sheep had a significant negative correlation with the concentration of pollutants in the water of the Zuli River Basin after 2011. In order to identify the reason for this trend, the main variation cycles of water quality indicators were compared with those of four social factors (fertilizer, large cattle, pigs, and sediment) in the Zuli River Basin from 2001 to 2021 (Figure 9). It was found that the number of sheep and the concentrations of DO and NO3-N were at high peaks, while the other water quality indicators were at a low peak after 2011. This may be the reason for the extremely significant negative correlation between the number of sheep and water quality indicators (except for DO and NO3-N) presented in Table 7. The reason why there was little correlation between the social factors and water quality indicators after 2011 may be that (1) the slow growth of fertilizer use and aquaculture in the Zuli River Basin and the implementation of more environmentally friendly operation methods did not increase the discharge of pollutants or that (2) the increase in runoff reduced the content of pollutants in the river.

5. Conclusions

In this study, the water quality indicators of DO, BOD5, NH3-N, CODMn, VP, TP, F−, and NO3-N were selected for an analysis of the Zuli River Basin. We studied the change trends and main variation cycles of these water quality indicators and analyzed the factors affecting them. The results are as follows:
Six water quality indicators, excepting DO and F−, showed an increasing trend before 2011, of which NO3-N and BOD5 had the fastest growth rates, reaching 3.83 mg/L × a−1 and 1.1 mg/L × a−1, respectively. DO showed an extreme, significant downward trend, with a rate of 0.34 mg/L × a−1. The water quality of the Zuli River Basin continued to deteriorate from 2001–2011.
There was an extreme, significant upward trend in DO at a rate of 0.63 mg/L × a−1 after 2011, while there were extreme, significant downward trends in CODMn, VP, and F- at rates of 1.21 mg/L × a−1, 0.0021 mg/L × a−1, and 0.056 mg/L × a−1, respectively. BOD5 and NH3-N significantly decreased at rates of 2.3 mg/L × a−1 and 0.55 mg/L × a−1, respectively. The water quality of the Zuli River Basin gradually improved after 2011.
DO, BOD5, VP, and NO3-N all had 10-year main variation cycles; NH3-N and CODMn both had 7-year main variation cycles; TP had a 3-year main variation cycle; and F− had an 8-year main variation cycle. According to the main variation cycles of the other water quality indicators, the water quality of the Zuli River Basin may gradually improve in the short term after 2021.
Through Spearman correlation analysis and a comparison of the major periodic changes, it was found that the reasons for these water quality changes in the Zuli River Basin are complex. Before 2011, social factors may have been the main reasons for the poor water quality in the Zuli River Basin, especially the increase in the content of pollutants in the water caused by chemical fertilizers and aquaculture, resulting in a decrease in the DO content. In contrast, environmental factors may be the main reasons for the improvement of the water quality in the Zuli River Basin after 2011, mainly the increase in precipitation and runoff, which has reduced the content of pollutants in the water.
Therefore, the overall water quality of the Zuli River Basin has gradually improved since 2011. This may be due to (1) increased precipitation and runoff, diluting the concentrations of pollutants in the river, or (2) decreased content of pollutants entering the river with decreasing soil erosion.

Author Contributions

Conceptualization, Z.Z. (Zhenghong Zhang) and F.Z.; Data curation, Z.Z. (Zhengzhong Zhang); Funding acquisition, F.Z.; Investigation, Z.Z. (Zhenghong Zhang); Methodology, Z.Z. (Zhenghong Zhang) and X.W.; Resources, Z.Z. (Zhenghong Zhang) and F.Z.; Software, X.W.; Supervision, Z.Z. (Zhengzhong Zhang) and X.W.; Writing—original draft, Z.Z. (Zhengzhong Zhang); Writing—review and editing, F.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Gansu Provincial Department of Education University Innovation Capacity Improvement Project in 2020 (2020B-323).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manusscript; or in the decision to publish the results.

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Figure 1. Location of Zuli River Basin.
Figure 1. Location of Zuli River Basin.
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Figure 2. The change trend of water quality ((a) DO, (b) BOD5, (c) NH3-N, (d) CODMn, (e) VP, (f) TP, (g) F− and (h) NO3-N) in Zuli River Basin from 2001 to 2021 (black dots and broken lines indicate actual changes, and red dashed lines indicate change trends).
Figure 2. The change trend of water quality ((a) DO, (b) BOD5, (c) NH3-N, (d) CODMn, (e) VP, (f) TP, (g) F− and (h) NO3-N) in Zuli River Basin from 2001 to 2021 (black dots and broken lines indicate actual changes, and red dashed lines indicate change trends).
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Figure 3. Wavelet analysis of the water quality indicators ((a) DO, (b) BOD5, (c) NH3-N, (d) CODMn, (e) VP, (f) TP, (g) F− and (h) NO3-N) in Zuli River Basin from 2001 to 2021. The color chart is the wavelet transform. The color gradient bar reflects the variations in wavelet coefficients. Red indicates high levels and blue color indicates low levels. The curve in the left figure is the contour of the wavelet coefficient, and the blue bar graph on the right is the wavelet variance.
Figure 3. Wavelet analysis of the water quality indicators ((a) DO, (b) BOD5, (c) NH3-N, (d) CODMn, (e) VP, (f) TP, (g) F− and (h) NO3-N) in Zuli River Basin from 2001 to 2021. The color chart is the wavelet transform. The color gradient bar reflects the variations in wavelet coefficients. Red indicates high levels and blue color indicates low levels. The curve in the left figure is the contour of the wavelet coefficient, and the blue bar graph on the right is the wavelet variance.
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Figure 4. The change trend of three environmental factors ((a) precipitation, (b) runoff, and (c) sediment) in Zuli River Basin from 2001 to 2021 (black dots and lines indicate actual changes, and red dashed lines indicate change trends).
Figure 4. The change trend of three environmental factors ((a) precipitation, (b) runoff, and (c) sediment) in Zuli River Basin from 2001 to 2021 (black dots and lines indicate actual changes, and red dashed lines indicate change trends).
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Figure 5. Wavelet analysis of three environmental factors ((a) precipitation, (b) runoff, and (c) sediment) in Zuli River Basin from 2001 to 2021. The color chart represents the wavelet transform. The color gradient bar reflects the variation in wavelet coefficients. Red indicates high levels and blue indicates low levels. The curve in the left figure is the contour of the wavelet coefficient, and the blue bar graph on the right represents the wavelet variance.
Figure 5. Wavelet analysis of three environmental factors ((a) precipitation, (b) runoff, and (c) sediment) in Zuli River Basin from 2001 to 2021. The color chart represents the wavelet transform. The color gradient bar reflects the variation in wavelet coefficients. Red indicates high levels and blue indicates low levels. The curve in the left figure is the contour of the wavelet coefficient, and the blue bar graph on the right represents the wavelet variance.
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Figure 6. The change trend of social factors ((a) fertilizer, (b) large cattle, (c) pigs, and (d) sheep) in Zuli River Basin from 2001 to 2021 (black dots and broken lines indicate actual changes, and red dashed lines indicate change trends).
Figure 6. The change trend of social factors ((a) fertilizer, (b) large cattle, (c) pigs, and (d) sheep) in Zuli River Basin from 2001 to 2021 (black dots and broken lines indicate actual changes, and red dashed lines indicate change trends).
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Figure 7. Wavelet analysis of four social factors ((a) fertilizer, (b) large cattle, (c) pigs, and (d) sheep) in Zuli River Basin from 2001 to 2021. The color chart represents the wavelet transform. The color gradient bar reflects the variation in wavelet coefficients. Red color denotes high levels and blue color denotes low levels. The curve in the left figure is the contour of the wavelet coefficient. The blue bar graph on the right is the wavelet variance.
Figure 7. Wavelet analysis of four social factors ((a) fertilizer, (b) large cattle, (c) pigs, and (d) sheep) in Zuli River Basin from 2001 to 2021. The color chart represents the wavelet transform. The color gradient bar reflects the variation in wavelet coefficients. Red color denotes high levels and blue color denotes low levels. The curve in the left figure is the contour of the wavelet coefficient. The blue bar graph on the right is the wavelet variance.
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Figure 8. Main variation cycles of water quality indicators and three environmental factors in Zuli River Basin ((a) DO at 10-year scale, (b) BOD5 at 10-year scale, (c) NH3-N at 7-year scale, (d) CODMn at 7-year scale, (e) VP at 10-year scale, (f) TP at 3-year scale, (g) F− at 8-year scale, (h) NO3-N at 10-year scale, precipitation at 4-year scale, runoff at 10-year scale, and sediment at 3-year scale).
Figure 8. Main variation cycles of water quality indicators and three environmental factors in Zuli River Basin ((a) DO at 10-year scale, (b) BOD5 at 10-year scale, (c) NH3-N at 7-year scale, (d) CODMn at 7-year scale, (e) VP at 10-year scale, (f) TP at 3-year scale, (g) F− at 8-year scale, (h) NO3-N at 10-year scale, precipitation at 4-year scale, runoff at 10-year scale, and sediment at 3-year scale).
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Figure 9. Main variation cycles of water quality indicators and four social factors in Zuli River Basin ((a) DO on a 10-year scale, (b) BOD5 on a 10-year scale, (c) NH3-N on a 7-year scale, (d) CODMn on a 7-year scale, (e) VP on a 10-year scale, (f) TP on a 3-year scale, (g) F− on an 8-year scale, (h) NO3-N on a 10-year scale, fertilizer on a 10-year scale, large cattle on a 7-year scale, pigs on a 7-year scale, and sheep on a 10-year scale).
Figure 9. Main variation cycles of water quality indicators and four social factors in Zuli River Basin ((a) DO on a 10-year scale, (b) BOD5 on a 10-year scale, (c) NH3-N on a 7-year scale, (d) CODMn on a 7-year scale, (e) VP on a 10-year scale, (f) TP on a 3-year scale, (g) F− on an 8-year scale, (h) NO3-N on a 10-year scale, fertilizer on a 10-year scale, large cattle on a 7-year scale, pigs on a 7-year scale, and sheep on a 10-year scale).
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Table
Table 1. The statistics of water quality indicators in Zuli River Basin.
Table 1. The statistics of water quality indicators in Zuli River Basin.
Mean (mg/L)Max (mg/L)Min (mg/L)STD (mg/L)CVcr1 (mg/L × a−1)cr2 (mg/L × a−1)
DO5.497.82.21.6730.46%−0.34 **0.63 **
BOD59.7832.70.639.2294.25%1.1 *−2.3 *
NH3-N4.3111.80.722.8165.28%0.33−0.55 *
CODMn9.3720.23.924.4947.95%0.125−1.21 **
VP0.0050.0210.00050.006114.82%0.00085−0.0021 **
TP0.230.780.0170.2293.08%0.018−0.041
F−1.0481.470.490.2423.57%−0.007−0.056 **
NO3-N43.2912516.822.2451.37%3.830.87
cr1 represents the change rate from 2001 to 2011, and cr2 represents the change rate from 2011 to 2021. ** means that the change rate passed the 99% significance test (α = 0.01), and * means that the change rate passed the 95% significance test (α = 0.05).
Table
Table 2. The statistics of three environmental factors in Zuli River Basin from 2001 to 2021.
Table 2. The statistics of three environmental factors in Zuli River Basin from 2001 to 2021.
MeanMaxMinSTDCVcr1cr2
Precipitation365.75 (mm)491.19 (mm)275.89 (mm)65.98 (mm)18%−6.84 (mm × a−1)10.99 (mm × a−1)
Runoff6625.81 (108 m3)10,090 (108 m3)4274 (108 m3)1544.75 (108 m3)23%−61.44 (108 m3 × a−1)321.04 (108 m3 × a−1)
Sediment1198.62 (104 t)3010 (104 t)101 (104 t)808.52 (104 t)67%−94.2 (104 t × a−1)−51.33 (104 t × a−1)
Cr1 represents the change rate from 2001 to 2011, and cr2 represents the change rate from 2011 to 2021.
Table
Table 3. Spearman correlation results of three environmental factors and water quality indicators in Zuli River Basin between 2001 and 2011.
Table 3. Spearman correlation results of three environmental factors and water quality indicators in Zuli River Basin between 2001 and 2011.
DOBOD5NH3-NCODMnVPTPF−NO3-N
Precipitation0.41−0.460.020.23−0.22−0.170.45−0.24
Runoff0.48−0.380.04−0.080.20−0.600.420.19
Sediment0.61 *−0.45−0.050.050.15−0.61 *0.450.09
* means that the change rate passed the 95% significance test.
Table
Table 4. Spearman correlation results of three environmental factors and water quality indicators in Zuli River Basin between 2011 and 2021.
Table 4. Spearman correlation results of three environmental factors and water quality indicators in Zuli River Basin between 2011 and 2021.
DOBOD5NH3-NCODMnVPTPF−NO3-N
Precipitation0.47 −0.48 −0.68 * −0.57 −0.44 −0.28 −0.75 ** 0.58
Runoff0.55 −0.55 −0.57 −0.64 * −0.47 −0.38 −0.62 * 0.55
Sediment−0.23 0.31 0.35 0.34 0.61 *0.15 0.22 0.48
** means that the change rate passed the 99% significance test and * means that the change rate passed the 95% significance test.
Table
Table 5. The statistics of four social factors in Zuli River Basin from 2001 to 2021.
Table 5. The statistics of four social factors in Zuli River Basin from 2001 to 2021.
MeanMaxMinSTDCVcr1cr2
Fertilizer3.58 (104 t)4.38 (104 t)2.60 (104 t)0.65 (104 t)18.10%0.093 (104 t × a−1) **0.029 (104 t × a−1)
Large cattle9.80 × 10411.32 × 1046.78 × 1041.20 × 10412.27%−0.025 (104 × a−1)−2.13 (104 × a−1)
Pig13.16 × 10420.09 × 1048.80 × 1042.08 × 10415.80%−0.054 (104 × a−1)0.11 (104 × a−1)
Sheep28.17 × 10456.55 × 10415.71 × 1048.72 × 10430.97%1.21 (104 × a−1) **1.19 (104 × a−1) **
cr1 represents the change rate from 2001 to 2011 and cr2 represents the change rate from 2011 to 2021. ** means that the change rate passed the 99% significance test (α = 0.01).
Table
Table 6. Spearman correlation results of four social factors and water quality indicators in Zuli River Basin between 2001 and 2011.
Table 6. Spearman correlation results of four social factors and water quality indicators in Zuli River Basin between 2001 and 2011.
DOBOD5NH3-NCODMnVPTPF−NO3-N
Fertilizer−0.81 **0.73 *0.490.070.320.05−0.090.35
Large cattle0.100.090.350.81 **0.35−0.130.74 **−0.29
Pig0.18−0.06−0.030.510.27−0.360.61 *0.03
Sheep−0.79 **0.85 **0.570.250.480.010.180.30
** means that the change rate passed the 99% significance test, and * means that the change rate passed the 95% significance test.
Table
Table 7. Spearman correlation results of four social factors and water quality indicators in Zuli River Basin between 2011 and 2021.
Table 7. Spearman correlation results of four social factors and water quality indicators in Zuli River Basin between 2011 and 2021.
DOBOD5NH3-NCODMnVPTPF−NO3-N
Fertilizer0.210.310.140.08−0.18−0.030.05−0.22
Large cattle−0.050.430.360.400.17−0.040.62 *−0.23
Pig0.100.05−0.26−0.10−0.08−0.470.25−0.19
Sheep0.92 **−0.74 **−0.85 **−0.91 **−0.91 **−0.68 **−0.80 **0.27
** means that the change rate passed the 99% significance test, and * means that the change rate passed the 95% significance test.
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Zhang, Z.; Zhang, F.; Zhang, Z.; Wang, X. Study on Water Quality Change Trend and Its Influencing Factors from 2001 to 2021 in Zuli River Basin in the Northwestern Part of the Loess Plateau, China. Sustainability 2023, 15, 6360. https://doi.org/10.3390/su15086360

AMA Style

Zhang Z, Zhang F, Zhang Z, Wang X. Study on Water Quality Change Trend and Its Influencing Factors from 2001 to 2021 in Zuli River Basin in the Northwestern Part of the Loess Plateau, China. Sustainability. 2023; 15(8):6360. https://doi.org/10.3390/su15086360

Chicago/Turabian Style

Zhang, Zhenghong, Fu Zhang, Zhengzhong Zhang, and Xuhu Wang. 2023. "Study on Water Quality Change Trend and Its Influencing Factors from 2001 to 2021 in Zuli River Basin in the Northwestern Part of the Loess Plateau, China" Sustainability 15, no. 8: 6360. https://doi.org/10.3390/su15086360

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