Evaluation of spatiotemporal variation of water quality in China: a study on the effectiveness of the River Chief System

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), WQI_min (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 WQI_min; (3) to investigate the spatial distribution of WQI_min using the M–K test and trend coefficient; and (4) to reveal the periodic of WQI_min 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.

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, COD_Mn, and NH₃-N were used. The second-order basins and lakes have 150 WQ monitoring sites (Table 1).

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).

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 COD_Mn and NH₃-N was not optimistic. The maximum COD_Mn 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.

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 COD_Mn 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, COD_Mn 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.

After the implementation of the RCS, the WQ has been significantly improved. Factors affecting pH, DO, COD_Mn, 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).

Existing studies (Table 4) and Figure 2 show that COD_Mn and NH₃-N are important pollutant indicators. Stubborn pollutants mainly cause COD_Mn 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 COD_Mn (Chong et al., 2022). In addition, COD_Mn 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 COD_Mn 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.

WQI can classify and compare WQ conditions in specific areas at different locations or timelines (Wu & Chen, 2013). Wu et al. (2021b) constructed the WQI_min using turbidity, DO, NH₃-N, NO₃-N, and COD_Mn, and the difference between WQI and WQI was only 6.02%. Varol et al. (2022) found no significant difference between the WQI_min, 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 WQI_min in Poyang Lake Basin and found that NH₃-N, total nitrogen, COD_Mn, 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 WQI_min value of the reservoir and the lower Hanjiang River WQ using four WQ parameters, including pH, DO, COD_Mn, and NH₃-N. Nong et al. (2020) used the WQI to evaluate the seasonal and spatial changes in WQ; a WQI_min method of total phosphorus, fecal coliform, mercury, water temperature, and DO was established. In general, the construction of WQI_min by pH, DO, COD_Mn, 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.

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, COD_Mn, 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 WQI_min.

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 WQI_min 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.