The River Chief System (RCS) is an institutional innovation launched to prevent and control water pollution. Its implementation has attracted much attention because it is a government-led effort to solve China's complex water problems. This study analyzed the characteristics and trends of four water quality (WQ) parameters, including pH, dissolved oxygen (DO), permanganate index (CODMn), and ammonia nitrogen (NH3-N), which were determined weekly from samples collected at 150 WQ monitoring stations in the Chinese rivers and lakes. The minimum WQ index (WQImin), Mann–Kendall test, wavelet analysis, and ArcGIS software were applied to evaluate the spatiotemporal variation of WQ before and after the implementation of the RCS, taking the main second-order basins and lakes in China as the research unit. The results demonstrated that CODMn and NH3-N were the main factors exceeding WQ. After the performance of the RCS, the WQ of each sub-basin and lake was improved, basically reaching the Class Ⅲ standard threshold; the WQImin values of sub-basins and lakes were above the ‘very bad’ level, where the proportion of ‘good’ was 68.09%, an increase of 52.38%. The spatial distribution of the trend coefficient of WQImin showed an upward trend, with the maximum trend coefficient being 4.99/a.

  • The WQImin was used to analyze and assess the River Chief System.

  • River Chief System has promoted water quality reaching the Class Ⅲ.

  • CODMn and NH3-N were the main factors in exceeding water quality.

  • The trend coefficient of WQImin showed an upward trend.

  • Measures taken by the River Chief System were summarized for reference.

Rivers and lakes are humanity's most important water source, and their health is crucial to survival (Alver, 2019). Affected by human activities, WQ degradation has become one of the most severe problems in the world (Vorosmarty et al., 2010). As a developing country, China's rapid industrialization and urbanization have increased environmental risks (Zhang et al., 2017; Cao et al., 2019; An et al., 2023), faced with water shortage (Liu et al., 2020b; Zhang et al., 2021; Wang et al., 2021a), ecological water destruction (Wang & Yang, 2016), excessive carbon emissions (Li et al., 2023), river and lake water pollution (Gao & Liang, 2016; Xu et al., 2017; Yu et al., 2018), and so on. Water pollution control of rivers and lakes is a typical, complex, changeable and highly uncertain transboundary pollution control problem (Li et al., 2020a). River basin pollution involves multiple up-down streams, left-right banks, different administrative regions and industries, the coexistence of diffuse source pollution and point source pollution, and difficulty determining the responsibility, so it is difficult to harness the river basin (Wardropper et al., 2015; Wang et al., 2019a; Wang & Chen, 2019b).

Facing the increasingly severe water environment, establishing effective management mechanisms is a major concern for governments worldwide (Tang et al., 2020). According to the organization and task classification, there are three basic management modes: basin authorities (e.g., the Tennessee Valley Authority in the USA), basin coordinating committees (e.g., the Murray–Darling Basin Committee in Australia), and basin committees (e.g., Great Lakes Commission in the USA) (Xu & Wang, 2017). For a long time, China has implemented a unified management model with hierarchical and sectoral management of river and lake resources (Tang et al., 2020). Since the first ‘Water Law’ was promulgated in 1988, China has implemented Integrated Water Resources Management (IWRM) (Wang & Chen, 2019b). However, the application of IWRM in China proved ineffective and inadequate, as had similar applications in other parts of Asia and Latin America (Biswas, 2008). To overcome the complex water resources problem, the Chinese government made a breakthrough in designing the watershed management system (Guo et al., 2014; Zuo & Liu, 2015). In August 2007, Wuxi became the first city in China to implement the RCS in response to a massive cyanobacteria outbreak in Taihu Lake (Liu & Richards, 2019; Wu et al., 2021a). In 2016, the Chinese government issued ‘The views on fully implementing River Chief System,’ establishing the RCS as China's national institutional water resource management model (Zhang et al., 2022b). With the widespread implementation of RCS, discussing the effectiveness of RCS implementation has gradually become a hot topic (Li et al., 2021b). She et al. (2019) and Liu et al. (2019) argued that RCS solves the problem of watershed environmental governance. Some studies also pointed out that RCS strongly supported reducing water pollution, improving management information and ecological environment, and upgrading lake functions (Liu et al., 2019, 2020a; She et al., 2019; Wang & Chen, 2019b). However, some studies believe that the function of the RCS is limited (Li et al. 2020b). Some researchers think that RCS, like other watershed governance policies, failed to solve the problems of regional coordination and vertical integration in China's watershed governance (Zhou et al., 2021). Most existing studies have analyzed the policy effect of RCS from the theoretical level, and there is a lack of quantitative evaluation of the WQ change of the implementation effect of RCS.

Monitoring river WQ and its temporal and spatial variations is essential for water resource conservation and management (Zhang et al., 2018). WQ evaluation is the premise of water environment governance and management and is an important index to make up for the implementation effect of the RCS among scientists, decision-makers, and the public (Zhao et al., 2016). Due to different regions and purposes, WQ assessment methods can be divided into the following categories: single factor evaluation method (Wang et al., 2021b), numerous pollution index method (Sutadian et al., 2016), Water Quality Index (WQI) (Nong et al., 2020), artificial neural network method (Ighalo et al., 2021), and so on. Compared with other WQ assessment methods, the WQI integrates information from multiple WQ parameters, converts a large number of WQ data into a single numerical mathematical tool (Stambuk-Giljanovic, 1999), and provides a general mechanism for people (Bordalo et al., 2001; Cude, 2001), which can also assess and classify WQ changes (Uddin et al., 2021). WQI has been widely used in surface water (Qu et al., 2020; Ma et al., 2021) and groundwater (Mokarram et al., 2020) throughout the world, such as in China (Gao et al., 2022), India (Rajkumar et al., 2022), and Malaysia (Khozani et al., 2022). Based on the WQI, the Universal WQI (Bor & Elci, 2022), Department of Environment-WQI (Bati et al., 2022), WQ index deterioration (WQI-DET) (Dai et al., 2022; Pang et al., 2022), CCME-WQI (Zhao et al., 2020b; Chong et al., 2022; Fartas et al., 2022), WQImin (Nong et al., 2020; Wu et al., 2021b), and other methods were proposed.

The objectives of this study were (1) to analyze the changes of WQ in sub-basins and lakes in China before and after the implementation of the RCS (2007–2018); (2) to evaluate the WQ using the WQImin; (3) to investigate the spatial distribution of WQImin using the M–K test and trend coefficient; and (4) to reveal the periodic of WQImin based on wavelet analysis. This study will provide the first comprehensive report on the WQ status and changes after the implementation of RCS, thus providing a useful and reliable example for answering whether the RCS can positively impact watershed pollution control and contribute to the global management of the same projects.

Study area and data sources

A basin is a unique natural resource centered on a river, surrounded by a watershed line, and is complete, independent, and highly integrated from its source to its mouth (Zhang et al., 2022b). A complete watershed covers different physical geographical conditions, ecological environment types, and socio-economic development models and has significant spatial differences (Jiang et al., 2020b). Figure 1(a) shows the main Chinese rivers. There are 10 first-order basins (Figure 1(b)) in mainland China (Sun et al., 2021). This study analyzed 37 second-order basins (referred to as sub-basins hereinafter) (Figure 1(c)) and 10 lakes (Figure 1(d)).
Fig. 1

Map of Chinese rivers (a), first-order basins (b), water quality monitoring sites in the second-order basins (c), and lakes (d).

Fig. 1

Map of Chinese rivers (a), first-order basins (b), water quality monitoring sites in the second-order basins (c), and lakes (d).

Close modal

WQ data used in this article were collected from the China National Environmental Monitoring Centre (http://www.cnemc.cn/sssj/szzdjczb/). Weekly dates were carried out from week 44 in 2007 to week 52 in 2018. Four WQ parameters, pH, DO, CODMn, and NH3-N were used. The second-order basins and lakes have 150 WQ monitoring sites (Table 1).

Table 1

Number of WQ monitoring sites.

First-order basinSecond-order basin (abbreviation)Num.First-order basinSecond-order basin (abbreviation)Num.
Songhua River Heilongjiang River (HLR) Pearl River Xijiang River (XR) 
Argun River (AR) Beijiang River (BR) 
Songhua River (SR) 11 Coastal River (CR) 
Wusulijiang River (WSR) Pearl River Delta (PRD) 
Tumenjiang River (TR) Southeast Rivers Hainan Island Rivers (HIR) 
Liao River Liao River (LR) Minjiang River (MJR) 
Yalujiang River (YLR) Qiantangjiang River (QR) 
Hai River Chaobai, Beiyun and Jiyun River (CBJR) Southwest Rivers Yarlung Zangbo River (YZR) 
Yongdinghe River (YDR) Nujiang River (NR) 
Daqinghe River (DR) Lancangjiang River (LCR) 
Ziyahe River (ZR) Yuanjiang River (YJR) 
Zhangweinan Canal River (ZCR) Northwest Rivers Irtysh River (IR) 
Huai River Huai River (HR) 20 Ili River (ILR) 
Yishusi River (YSR) Lake Num. 
Yellow River Yellow River (YR) Taihu Lake (THL) 
Fenhe River (FR) Chaohu Lake (CHL) 
Weihe River (WR) Dianchi Lake (DCL) 
Yangtze River Yalongjiang River (YLJR) Dongting Lake (DTL) 
Minjiang River (MR) Poyang Lake (PYL) 
Jialingjiang River (JR) Liangzi Lake (LZL) 
Hanjiang River (HJR) Fuxian Lake (FXL) 
Yangtze River (YTR) Erhai Lake (EHL) 
Dongting Lake Basin (DLB) Xingkai Lake (XKL) 
Poyang Lake Basin (PLB) Beier Lake (BEH) 
First-order basinSecond-order basin (abbreviation)Num.First-order basinSecond-order basin (abbreviation)Num.
Songhua River Heilongjiang River (HLR) Pearl River Xijiang River (XR) 
Argun River (AR) Beijiang River (BR) 
Songhua River (SR) 11 Coastal River (CR) 
Wusulijiang River (WSR) Pearl River Delta (PRD) 
Tumenjiang River (TR) Southeast Rivers Hainan Island Rivers (HIR) 
Liao River Liao River (LR) Minjiang River (MJR) 
Yalujiang River (YLR) Qiantangjiang River (QR) 
Hai River Chaobai, Beiyun and Jiyun River (CBJR) Southwest Rivers Yarlung Zangbo River (YZR) 
Yongdinghe River (YDR) Nujiang River (NR) 
Daqinghe River (DR) Lancangjiang River (LCR) 
Ziyahe River (ZR) Yuanjiang River (YJR) 
Zhangweinan Canal River (ZCR) Northwest Rivers Irtysh River (IR) 
Huai River Huai River (HR) 20 Ili River (ILR) 
Yishusi River (YSR) Lake Num. 
Yellow River Yellow River (YR) Taihu Lake (THL) 
Fenhe River (FR) Chaohu Lake (CHL) 
Weihe River (WR) Dianchi Lake (DCL) 
Yangtze River Yalongjiang River (YLJR) Dongting Lake (DTL) 
Minjiang River (MR) Poyang Lake (PYL) 
Jialingjiang River (JR) Liangzi Lake (LZL) 
Hanjiang River (HJR) Fuxian Lake (FXL) 
Yangtze River (YTR) Erhai Lake (EHL) 
Dongting Lake Basin (DLB) Xingkai Lake (XKL) 
Poyang Lake Basin (PLB) Beier Lake (BEH) 

Minimum water quality index

WQImin was calculated by the following equation (Pesce & Wunderlin, 2000):
formula
(1)
where is the WQ value after normalization (Table 2); CpH, CDO, CCODMn, and CNH3-N are the pH, DO, CODMn, and NH3-N after normalization, respectively. Pi is the relative weight assigned to each parameter (Table 2); 1, 4, 3, and 3 represent the relative weight, respectively (Pesce & Wunderlin, 2000). The range of WQImin is from 0 to 100. Five categories, including excellent (91 ≤ WQImin ≤ 100), good (71 ≤ WQImin < 91), medium (51 ≤ WQImin < 71), bad (26 ≤ WQImin < 51), and very bad (0 ≤ WQImin < 26) as suggested by Kannel et al. (2007) were delimited.
Table 2

Parameters considered for WQImin calculation.

ParameterUnitsPiNormalization factor
1009080706050403020100
pH pH unit 7–8 7–8.5 7–9 6.5–7 6–9.5 5–10 4–11 3–12 2–13 1–14 
DO mg/L ≥7.5 ≥7.0 ≥6.5 ≥6.0 ≥5.0 ≥4.0 ≥3.5 ≥3.0 ≥2.0 ≥1.0 <1.0 
CODMn mg/L <5 <10 <20 <30 <40 <50 <60 <80 <100 <150 >150 
NH3-N mg/L <0.01 <0.05 <0.1 <0.2 <0.3 <0.4 <0.5 <0.75 <1.00 <1.25 >1.25 
ParameterUnitsPiNormalization factor
1009080706050403020100
pH pH unit 7–8 7–8.5 7–9 6.5–7 6–9.5 5–10 4–11 3–12 2–13 1–14 
DO mg/L ≥7.5 ≥7.0 ≥6.5 ≥6.0 ≥5.0 ≥4.0 ≥3.5 ≥3.0 ≥2.0 ≥1.0 <1.0 
CODMn mg/L <5 <10 <20 <30 <40 <50 <60 <80 <100 <150 >150 
NH3-N mg/L <0.01 <0.05 <0.1 <0.2 <0.3 <0.4 <0.5 <0.75 <1.00 <1.25 >1.25 

Data processing methodology

The Mann–Kendall (M–K) trend test was recommended by the WMO to be applied to the trend analysis of sudden changes in environmental data time series, which is often used to analyze the changing trend of WQ, water temperature, precipitation, runoff, etc. (Abebe et al., 2022). Wavelet analysis was widely used to study multi-time scale change characteristics (Yuan et al., 2022). A one-way analysis of variance was used to verify the differences between each parameter (Wen et al., 2022). ArcGIS software was often used to plot the spatial distribution of variables (Raheja et al., 2022).

WQ characteristic

Table 3 shows the statistical results of the four WQ parameters at the sub-basins and lakes from 2007 to 2018. The pH and DO in sub-basins and lakes met the Class Ⅲ standard threshold (Class Ⅲ hereinafter). However, the situation of CODMn and NH3-N was not optimistic. The maximum CODMn and NH3-N in the sub-basins were 2.18 and 2.96 times the Class Ⅲ, respectively, and 1.28 and 1.77 times in the lakes. The CV% revealed the stable characteristic of the WQ parameters. The NH3-N and pH were the lowest and highest CV%, respectively.

Table 3

Analysis of WQ parameters of sub-basins and lakes from 2007 to 2018 (avg.: average; S.D.: standard deviation; CV%: coefficient of variation).

AreaParametersThresholds of the Class Ⅲ standardsaAvg. ± S.D.CV%MinMax
Sub-basin pH 6–9 7.64 ± 0.07 0.01 7.48 7.83 
DO (mg/L) ≥5 7.86 ± 0.85 0.11 6.22 9.96 
CODMn (mg/L) ≤6 4.7 ± 1.9 0.39 2.6 13.1 
NH3-N (mg/L) ≤1 0.87 ± 0.50 0.58 0.19 2.96 
Lake pH 6–9 7.98 ± 0.15 0.02 7.38 8.46 
DO (mg/L) ≥5 7.93 ± 1.31 0.17 4.96 11.27 
CODMn (mg/L) ≤6 4.8 ± 1.0 0.21 3.1 7.7 
NH3-N (mg/L) ≤1 0.42 ± 0.27 0.64 0.15 1.77 
AreaParametersThresholds of the Class Ⅲ standardsaAvg. ± S.D.CV%MinMax
Sub-basin pH 6–9 7.64 ± 0.07 0.01 7.48 7.83 
DO (mg/L) ≥5 7.86 ± 0.85 0.11 6.22 9.96 
CODMn (mg/L) ≤6 4.7 ± 1.9 0.39 2.6 13.1 
NH3-N (mg/L) ≤1 0.87 ± 0.50 0.58 0.19 2.96 
Lake pH 6–9 7.98 ± 0.15 0.02 7.38 8.46 
DO (mg/L) ≥5 7.93 ± 1.31 0.17 4.96 11.27 
CODMn (mg/L) ≤6 4.8 ± 1.0 0.21 3.1 7.7 
NH3-N (mg/L) ≤1 0.42 ± 0.27 0.64 0.15 1.77 

aStandards from the Environmental Quality Standards for Surface Water (China, 2002).

The pH of lakes ranged from 7.38 to 8.46, with a mean of 7.98. There was no significant difference in the sub-basin's pH values, which ran from 7.48 to 7.83, with an average of 7.64 (P < 0.05). DO in sub-basins varied between 6.22 and 9.96 mg/L with an average of 7.86 mg/L. This value was almost equal to the lakes, which ranged from 4.96 to 11.27 mg/L and an average of 7.93 mg/L (P < 0.05). The CODMn of the sub-basin ranged from 2.6 to 13.1 mg/L, with an average of 4.7 mg/L. This value was similar to the lakes, where values went from 3.1 to 7.7 mg/L and averaged 4.8 mg/L (P < 0.05). NH3-N showed the highest variability among the four parameters. The NH3-N values varied from 0.19 to 2.96 mg/L, with an average of 0.87 mg/L for the sub-basins. The NH3-N values ranged from 0.15 to 1.77 mg/L, with an average of 0.42 mg/L for lakes (P < 0.05). In general, CODMn and NH3-N were the main factors of exceeding WQ. NH3-N was the factor with the most significant difference between sub-basins and lakes among the four WQ parameters. The WQ in the sub-basins was superior to that in the lake.

Comparison of WQ

Figure 2 shows the changes in pH, DO, NH3-N and CODMn before and after the implementation of the RCS. The WQ in each sub-basin and lake was alkaline, and the pH value changed little after the implementation of the RCS. Before the implementation of the RCS, the DO of AR, DR, ZCR, FR, WR, and PRD were lower than Class Ⅲ. The CODMn in the Songhua River, Liao River, Hai River, Huai River, Yellow River, Northwest Rivers, and lakes (DCL and BEH) exceeded Class Ⅲ, and the pollution of FR and ZCR was the most serious. The NH3-N in the Hai River and the Yellow River exceeded Class Ⅲ, and the pollution of FR and ZCR was the most serious. After implementing the RCS, the DO met Class Ⅲ, and some even met Class I. There was a significant decline in CODMn and NH3-N; the FR's mean of CODMn and NH3-N decreased by 57.14 and 52.37%, respectively. CODMn and NH3-N were the most severe pollution factors among the four parameters. FR had the worst WQ and should be of great concern. After the implementation of the RCS, the WQ of each sub-basin and lake was improved, basically reaching Class Ⅲ.
Fig. 2

Average concentration and standard deviations for four WQ parameters without RCS and with RCS.

Fig. 2

Average concentration and standard deviations for four WQ parameters without RCS and with RCS.

Close modal

Evaluation of WQ using the WQImin method

The WQImin result showed the WQ variations in the sub-basins and lakes before and after the implementation of the RCS (Figure 3). Before the implementation of the RCS (2007–2016), the WQ of the FR and ZCR was classified as a ‘very bad’ level; WQI values in sub-basins varied from ‘very bad’ to ‘excellent,’ while those in lakes were generally above ‘medium.’ The proportion of ‘good,’ ‘medium,’ ‘bad,’ and ‘very bad’ in sub-basins and lakes was 44.68, 44.68, 6.38, and 4.26%, respectively; most areas were classified as ‘medium’ and ‘good’ level, accounting for 89.36%. After the implementation of the RCS (2017–2018), no study area was at a ‘very bad’ level; the WQImin values of sub-basins and lakes were above a ‘very bad’ level. The proportion of ‘good’ was 68.09%, an increase of 52.38%. The ‘medium’ and ‘bad’ proportions were 2.13 and 29.79%, respectively, with a decrease of 66.67 and 33.33%. Overall, there was an apparent change in WQImin value before and after the RCS. The RCS played a positive and promoting role in improving the WQ of various sub-basins and lakes in China.
Fig. 3

Variations of the WQImin in the sub-basins and lakes without the RCS and with the RCS: (a) no RCS and (b) RCS.

Fig. 3

Variations of the WQImin in the sub-basins and lakes without the RCS and with the RCS: (a) no RCS and (b) RCS.

Close modal

Spatial distribution of WQImin

From the spatial distribution of the WQImin (Figure 4), it can be seen that the upward trend of WQImin was regional; the trend coefficient was between −1.47/a and 4.99/a, with the increasing trend and the decreasing trend accounting for 74.5 and 25.5%, respectively (Figure 4), that is, the upward trend was the main one. The spatial difference of WQImin in different sub-basins and lakes was noticeable. For sub-basins, there were 13 sub-basins with significant increases, and FR and ZR were the sub-basins with the most significant and smallest increased trend, with values of 4.99/a and 0.04/a, respectively. There were 17 sub-basins with no significant increase, and LR and JR were the sub-basins with the largest and smallest increased trends, with values of 2.08/a and 0.04/a, respectively. There were seven sub-basins with no significant decreases, and WSR and NR were the sub-basins with the largest and smallest decreases, with values of 0.7/a and 0.04/a, respectively. For lakes, there were two sub-basins with a significant increase, three with no significant increase, and five with no significant decrease. The maximum rise trend was 1.65/a, and the minimum decline was −1.47/a. Although some sub-basins or lakes had not significantly decreased, it can be seen in Figure 3 that these sub-basins still had a high WQImin.
Fig. 4

Spatial distribution of WQImin in the Chinese sub-basins and lakes from 2007 to 2018.

Fig. 4

Spatial distribution of WQImin in the Chinese sub-basins and lakes from 2007 to 2018.

Close modal

Periodic analysis of WQImin based on wavelet analysis

Continuous wavelet transform reflects the time-scale transformation characteristics of the signal itself. The time characteristic of the WQImin was analyzed by continuous wavelet transform (Figure 5). At the sub-basin, there were periods of 1–8, 1–16, 2–4, 2–6, 2–8, 2–14, 3–6, 3–15, 4–8, 4–14, 6–10, 6–16, 7–9, 8–12, 8–16 and 10–14 months. At the lake scale, there were periods of 2–6, 4–8, 4–14, 8–12, 8–16, and 10–12 months. In general, there were different periods in different sub-basins and lakes, which explained the variation of WQ in other regions of China from the periodicity.
Fig. 5

Continuous wavelet power spectrum of the WQImin.

Fig. 5

Continuous wavelet power spectrum of the WQImin.

Close modal

Analysis of WQ parameters and its influencing sources

After the implementation of the RCS, the WQ has been significantly improved. Factors affecting pH, DO, CODMn, and NH3-N were analyzed based on existing research results (Table 4). Animals and plants can live in the proper pH range of water so that pH can indicate deterioration of the aquatic environment (Jin et al., 2022). The pH value of Chinese rivers and lakes is above 7, which suggests that the water body under study is generally alkaline. The pH may be related to the combined effects of anthropogenic sources, discharge, and carbonate rocks (Safari et al., 2021). Carbonate and bicarbonate precipitates remove some contaminants, and a more alkaline pH is desirable in river water (Clark et al., 2014). Aquatic organisms are sensitive to fluctuations in dissolved oxygen levels in the water body, significantly decreasing DO (Zhu & Heddam, 2020). DO is related to the survival of aquatic organisms and ecological water balance (Jin et al., 2022). The lower DO limit of marine microorganisms and drinking water is 4 and 6 mg/L, respectively (Alam et al., 2007). Although DO supersaturation is not associated with contaminated water, low DO is related to dirty water, which can cause the water to take on an unpleasant and black odor (Li et al., 2016).

Table 4

Comparison of WQ with other studies.

AreaDate (sampling frequency)pHDO (mg/L)CODMn (mg/L)NH3-N (mg/L)
This study (sub-basins) Oct. 2007 to Dec. 2018 (Weekly) 7.64 ± 0.07 7.86 ± 0.85 4.7 ± 1.9 0.87 ± 0.50 
This study (lakes) 7.98 ± 0.15 7.93 ± 1.31 4.8 ± 1.0 0.42 ± 0.27 
Jialing Rivea Jan. 2010 to Dec. 2015 (Monthly) 7.86 ± 0.27 7.89 ± 1.98 3.81 ± 2.50 1.24 ± 2.42 
Ge Lakeb both May and Sept. between 2008 and 2012 (Monthly) 8.14 ± 0.67 7.18 ± 1.28 5.44 ± 1.01 0.6 ± 0.15 
Ningxia Section of the Yellow Riverc Jan. 2016 to Dec. 2020 (Monthly) 7.8–8.4 7.5–9.4 6.4–10.7 0.1–0.6 
Daihai Lake, Chinad The dry season (May) and wet season (Jul.) from 2008 to 2017 8.72 ± 0.25 5.99 ± 1.72 13.12 ± 2.01 0.29 ± 0.14 
Changsha-Zhuzhou-Xiangtan section of Xiangjiang River, Chinae Jan. to Dec. 2016 (Monthly) 7.5 ± 0.3 7.26 ± 0.5 2.10 ± 0.62 0.26 ± 0.12 
Tuojiang Riverf Jan. 2012 to Dec. 2018 (Monthly) 7.5–8.6 6.57 ± 0.34 3.48 ± 0.6 0.55 ± 0.13 
Middle Yangtze Riverg Jan. 2010 to Dec. 2019 (Monthly) 7.76–8.01 6.2–11.8 0.13–0.63 0.15–1.2 
Hai Riverh Jan. 2010 to Sept. 2014 (Monthly) 7.89 ± 0.38 6.75 ± 3.50 11 ± 16 8.10 ± 14.55 
South-to-North Water Diversion Project of Chinai Mar. 2016 to Feb. 2019 (Monthly) 8.22 ± 0.15 9.48 ± 1.34 1.91 ± 0.16 0.038 ± 0.015 
AreaDate (sampling frequency)pHDO (mg/L)CODMn (mg/L)NH3-N (mg/L)
This study (sub-basins) Oct. 2007 to Dec. 2018 (Weekly) 7.64 ± 0.07 7.86 ± 0.85 4.7 ± 1.9 0.87 ± 0.50 
This study (lakes) 7.98 ± 0.15 7.93 ± 1.31 4.8 ± 1.0 0.42 ± 0.27 
Jialing Rivea Jan. 2010 to Dec. 2015 (Monthly) 7.86 ± 0.27 7.89 ± 1.98 3.81 ± 2.50 1.24 ± 2.42 
Ge Lakeb both May and Sept. between 2008 and 2012 (Monthly) 8.14 ± 0.67 7.18 ± 1.28 5.44 ± 1.01 0.6 ± 0.15 
Ningxia Section of the Yellow Riverc Jan. 2016 to Dec. 2020 (Monthly) 7.8–8.4 7.5–9.4 6.4–10.7 0.1–0.6 
Daihai Lake, Chinad The dry season (May) and wet season (Jul.) from 2008 to 2017 8.72 ± 0.25 5.99 ± 1.72 13.12 ± 2.01 0.29 ± 0.14 
Changsha-Zhuzhou-Xiangtan section of Xiangjiang River, Chinae Jan. to Dec. 2016 (Monthly) 7.5 ± 0.3 7.26 ± 0.5 2.10 ± 0.62 0.26 ± 0.12 
Tuojiang Riverf Jan. 2012 to Dec. 2018 (Monthly) 7.5–8.6 6.57 ± 0.34 3.48 ± 0.6 0.55 ± 0.13 
Middle Yangtze Riverg Jan. 2010 to Dec. 2019 (Monthly) 7.76–8.01 6.2–11.8 0.13–0.63 0.15–1.2 
Hai Riverh Jan. 2010 to Sept. 2014 (Monthly) 7.89 ± 0.38 6.75 ± 3.50 11 ± 16 8.10 ± 14.55 
South-to-North Water Diversion Project of Chinai Mar. 2016 to Feb. 2019 (Monthly) 8.22 ± 0.15 9.48 ± 1.34 1.91 ± 0.16 0.038 ± 0.015 

Existing studies (Table 4) and Figure 2 show that CODMn and NH3-N are important pollutant indicators. Stubborn pollutants mainly cause CODMn concentrations from wastewater treatment plants, industrial operations, and domestic sewage (Schliemann et al., 2021; Li et al., 2021a). Usually, during the rainy season, heavy rainfall promotes the accumulation of non-point source pollution generated by surface, household waste, and farmland and then washes into river channels through rainwater, resulting in a high CODMn (Chong et al., 2022). In addition, CODMn is positively correlated with discharge and sediment in the river (Xiang et al., 2021). Animal feedlots and crop production are significant sources of NH3-N load (Xin et al., 2020). The water environment is more acidic during the rainy season, which is conducive to nitrification, resulting in lower NH3-N in the rainy season (Pak et al., 2021). Increased water level, discharge, and precipitation reduce NH3-N and CODMn concentrations (Wang et al., 2020). Long et al. (2022) pointed out that soil erosion and non-point source pollution are the key factors of WQ deterioration of the Jialing River in Chongqing. Xu et al. (2022) found that the increase in agricultural (planting, animal husbandry, and aquaculture) and industrial and domestic pollution cause WQ deterioration; the industry remains the primary source of environmental water pollution in China, followed by domestic and agricultural sources, of which agrarian authorities have the most negligible impact on WQ. The origins of river water pollutants include industrial wastewater, domestic sewage, livestock wastewater, and agricultural non-point source pollution (Zhang et al., 2022a). Bai et al. (2021) found that in the Tuojiang River, urban domestic point sources contributed the most to COD and NH3-N, followed by rural domestic point sources.

Selection of key parameters and WQ evaluation of WQImin

WQI can classify and compare WQ conditions in specific areas at different locations or timelines (Wu & Chen, 2013). Wu et al. (2021b) constructed the WQImin using turbidity, DO, NH3-N, NO3-N, and CODMn, and the difference between WQI and WQI was only 6.02%. Varol et al. (2022) found no significant difference between the WQImin, composed of 10 key parameters, and the WQI, composed of all 17 key parameters, when evaluating the Karasu River's WQ. The discriminant analysis method indicated that temperature, electrical conductivity, Chla, potassium, calcium, NO3-N, and COD were the variables affecting the temporal change of river WQ. Xu et al. (2022) established a cost-effective WQ evaluation model based on WQImin in Poyang Lake Basin and found that NH3-N, total nitrogen, CODMn, DO, fecal coliform, and total phosphorus could represent the overall WQ. To evaluate the WQ of Danjiangkou Reservoir and the lower Hanjiang River, Zhao et al. (2020a) calculated the WQImin value of the reservoir and the lower Hanjiang River WQ using four WQ parameters, including pH, DO, CODMn, and NH3-N. Nong et al. (2020) used the WQI to evaluate the seasonal and spatial changes in WQ; a WQImin method of total phosphorus, fecal coliform, mercury, water temperature, and DO was established. In general, the construction of WQImin by pH, DO, CODMn, and NH3-N in this study has a wide literature basis.

The WQI-DET increased from −550.11 to 48.67 from 2005 to 2019 in the Yongding River Basin (Dai et al., 2022). CCME-WQI values increased from 76.5 (general) in 2010 to 80.5 (good) in 2019 in the middle Yangtze River (Chong et al., 2022). The WQI-DET ranged from −322 to 58.06 between 2016 and 2021 in the Fenjiang River, belonging to the Pearl River Basin (Pang et al., 2022). In the Pearl River Estuary, CCME-WQI increased from 54.75 to 68.79 in 2008 to 83.50–88.15 in 2017 (Zhao et al., 2020b). The current research results and Figure 3 showed that the WQ improved after the RCS implementation.

Practical and feasible measures to improve the WQ

Reducing pollution sources is an important measure to enhance the quality of water. The spatial distribution of pollutant discharge can be divided into non-point source (diffused) and point source pollution (Gu et al., 2020; Ding & Feng, 2022). Based on existing studies (Qiu et al., 2019, 2020; Yi et al., 2020; Liu et al., 2021; Wang et al., 2021c; Ding & Feng, 2022; Hou et al., 2022; Thompson-Saud & Wenger, 2022), this paper summarizes the measures of point and non-point sources pollution (Figure 6) aiming to provide a reference for selecting control measures when reducing pollution sources. There were 19 and 18 pollution control measures in recent years. In practical application, appropriate measures are selected according to specific problems.
Fig. 6

Practical and feasible measures of reducing pollution.

Fig. 6

Practical and feasible measures of reducing pollution.

Close modal

Deficiencies and prospects

This study analyzed the WQ changes of major river sub-basins and lakes from 2007 to 2018, especially the data of the 2 years after the implementation of the RCS. In terms of the time scale of the data, a more extended series of data were needed. WQ parameters mainly include pH, water temperature, permanganate index, fecal coliform, dissolved oxygen, total phosphorus, total nitrogen, NH3-N, fluoride, sulfate, mercury, arsenic, copper, zinc, selenium, 5-day biochemical oxygen demand, and so on (Kannel et al., 2007; Nong et al., 2020). Only pH, DO, CODMn, and NH3-N were studied. To better analyze the WQ changes before and after the implementation of the RCS, more extended series and more data are needed for further research and analysis combined with WQI and WQImin.

China's River Chief System has been promoted throughout the country as an institutional innovation in river management and water environment ecological protection. Most studies focus on qualitative analysis of this policy at the institutional level. At the same time, there is relatively little quantitative research on the effectiveness of the River Chief System in improving water quality. This study evaluated the spatiotemporal variation of water quality before and after the implementation of the River Chief System, taking the main sub-basins and lakes in China as the study unit. After the implementation of the River Chief System, the water quality of each sub-basin and lake has been improved and meets the standards. There is a significant improvement in time and space in the WQImin values before and after the River Chief System. The River Chief System plays a positive role in improving the water quality of various sub-basins and lakes in China. This study only analyzes the water quality data for the 2 years after the implementation of the River Chief System, and further data are needed to demonstrate the effectiveness of the implementation of the River Chief System. The River Chief System is an innovation of the Chinese government in river and lake management, providing a good model for other countries to explore basin collaborative management.

The work is supported by the National Natural Science Foundation of China (52309092), the Yellow River Water Science Research Joint Fund (U2243214), the Science and Technology Development Fund of the Yellow River Institute of Hydraulic Research (202310), the National Key R&D Program of China (2021YFC3200402), the Yellow River Basin Ecological Protection and High-quality Development Joint Study (Phase I) (2022-YRUC-01-0202).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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