1. Introduction
At present, most river and lake ecosystems worldwide have encountered problems such as water pollution and degradation of ecological functions, that have seriously affected the sustainable development of economic societies [
1]. A survey by the United States Environmental Protection Agency showed that non-point source (NPS) pollution caused ~60% of rivers and lakes water quality to be substandard [
2]. Surveys in the United States, Japan, and other countries show that even if all point source pollution achieved zero emissions, the river water, lake water, and sea water quality compliance rates would only be 65%, 42%, and 78%, respectively. Eutrophication of lakes and reservoirs mainly comes from NPS pollution [
3]. In recent years, point source pollution has been gradually controlled, and it was found that the main cause of the deterioration of water environmental quality is the increasing NPS, especially NPS pollution caused by human agricultural activities [
4,
5]. The composition of pollution sources is currently rapidly changing in China, and the proportion of NPS pollution load is gradually increasing [
6]. The uncertainty in NPS pollution emissions and pathways has caused NPS to harm agricultural production, water resources, watershed hydrological processes, and the habitat of aquatic organisms [
7]. NPS pollution affects the quality of the environment on which humans depend and can directly or indirectly affect public health [
8,
9].
In the area of basin water environment management, the main purpose of dividing control units is to deconvolve complex basin water environmental problems into each control unit, so that specific water basin water environmental management measures and policies can be effectively implemented to improve the water quality of the river basin [
10]. The concept of the control unit was originally derived from the Total Maximum Daily Loads (TMDL) technical guidelines in the United States. It is understood that if the problem in the water body originates from the lower reaches of the basin, such as lakes and reservoirs, the entire target water body is regarded as a TMDL control unit. However, if the problem water body is distributed throughout the watershed, the latter is divided into several sub-watersheds according to the catchment area, and each sub-watershed is a control unit for research purposes [
11].
The United States agencies usually study and manage the pollution in control units taking the basin as the control unit and the control unit’s water quality as the goal. The concept of China’s control unit was developed in researching water environmental capacity and total amount control technology during the “Sixth Five-Year Plan” and “Seventh Five-Year Plan”, and the concept of three-level management of the planning area, control area, and control unit was gradually proposed [
12]. However, this type of control unit does not have a clear responsibility body, which makes planning management measures challenging to implement efficiently.
The soil and water assessment tool (SWAT) is a long-period distributed watershed hydrological model, which can predict the production and pollution of different regions in the basin under different soil types, land use types, and management measures. It has gradually become an indispensable tool in water resources and water environmental protection management planning and is commonly used to assess the long-term impact of land management models on water flows, sediment, and agricultural nutrients in complex watersheds [
13]. With the application and development of model research, the SWAT model has gradually become an important tool for simulating runoff processes, NPS pollution loads, spatio-temporal characteristics, and critical source areas and evaluating different measures at the basin scale [
14]. Best Management Practices (BMPs) were a series of measures proposed by the United States in the mid-1970s to generate environmentally beneficial natural processes such as hydrology, soil erosion, ecology, and nutrient cycling in river basins, to avoid pollution in water environments of the river basin caused by agricultural production [
15,
16,
17]. SWAT models were proposed to evaluate the effects of BMPs. Panagopoulos et al. developed an efficient and user-friendly decision support tool to determine the best location for agricultural BMPs and trade-offs between multiple targets to economically and effectively control diffuse pollution across a river basin [
18,
19]. However, the SWAT model pollution load output results are based on the sub-basin or hydrologic response unit (HRU) as a unit, and only subsequently the critical source areas of the pollution source in the basin are identified. BMPs are usually established based on critical source areas, which only consider the “quantity” of the pollution load, that is, they are only located in heavily polluted areas. If the areas include more than two administrative areas, they will not be conducive to implementing the measures to the responsible body.
In view of this problem, this study introduces a set of control unit division methods, and according to these methods, the control units of the Guishui River Basin in Northern China are divided. The Guishui River Basin is located in the Yanqing District of Beijing and is located in the Beijing-Tianjin-Hebei eco-economic development zone. In 2022, the Yanqing District will host the World Winter Olympics; thus, higher requirements are imposed on the quality of the water environment. In this study, we used the SWAT model to analyze the distribution characteristics of NPS pollution in the Guishui River Basin based on the control unit, and established five BMP scenarios to evaluate the environmental and cost benefits of each scenario. Based on the conclusions of this study, the critical source area of NPS pollution is located in the control unit, and management decision makers can adopt BMPs for the critical source area to provide technical support for management decision makers in identifying and regulating NPS pollution in the basin.
4. Conclusions
The novelty of the study is included in the Introduction in terms of mentioning the limitations of the existing approach. However, the SWAT model pollution load output results are based on the sub-basin or hydrologic response unit (HRU) as a unit, and only subsequently the critical source areas of the pollution source in the basin are identified. BMPs are usually established based on critical source areas, which only consider the “quantity” of the pollution load; that is, they are only located in heavily polluted areas. If the areas include more than two administrative areas, they will not be conducive to implementing the measures to the responsible body.
This study systematically applies a well-known methodology to non-point source pollution study for the Guishui River Basin, and tests various methodologies for reducing it. Because of the systematic design of the study, the outcomes are well suited for policy implementation. Based on the control unit scale, the SWAT model was used to analyze the cost-effectiveness of BMP implementation in the Guishui River Basin, and the scenarios and measures were simulated and analyzed from environmental benefits and economic costs perspective:
(1) This study introduces the division method of the river basin control unit. The division of the control unit can effectively implement the measures to the responsible subjects. The control unit analyzes the pollution load of the river basin and clarifies the responsible party.
(2) The SWAT model verified by parameter calibration shows good applicability to the simulation of NPS pollution in the Guishui River Basin. Based on the control unit scale, the spatial and temporal distribution of NPS pollution in the river basin is analyzed. The results show that the time distribution of pollutants in the river basin is extremely uneven, and the pollutant output is mainly distributed in the rainy season. Rainfall runoff is the main influencing factor NPS pollution output. Similarly, there are differences in the spatial distribution of pollutant output. In 2016, the TN loss intensity of the Lushui River Basin was between 0.24 and 0.36 kg/ha, and the TP loss intensity was between 0.078 and 0.146 kg/ha. The lower reach of the Guishui River Basin (control units 4, 8, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 35, 41, 43, and 44) is a critical source area for pollutant loss. Therefore, the implementation of governance measures should take into account the spatiotemporal distribution of pollutants, which can be adjusted in time to control units.
(3) This study established five scenarios, including structural and nonstructural measures. The SWAT model was used to simulate the pollutant reduction in each scenario. Simulation results show that the reduction efficiency of structural measures is much higher than that of nonstructural measures, of which S2 pollutants have the highest reduction effect. The average reduction rates of TN and TP were 63.4% and 62.6%, respectively. The S5 pollutant reduction effect was the lowest, and the average reduction rates of TN and TP were only 0.2% and 0.8%. Similarly, under the condition of economic cost, the CE value of structural measures set in this study is much higher than nonstructural measures. S3 is the most cost-effective measure, the values for TN and TP are 1798.13 g/€ and 601.56 g/€, Compared with S4, S3 shows a better pollutant reduction rate when using fewer land resources.
In the selection of BMPs in the river basin, in consideration of environmental and economic benefits, factors such as land resources, government development planning and technological level must also be considered, as decision makers are required to take all factors into consideration. Therefore, selecting a variety of effective BMPs reasonably, and providing long-term and effective plans for local environmental protection and sustainable development is key.
In addition, the SWAT model was based on the parameters for the study area only and did not verify those for different areas of the same watershed. This is likely to cause errors in the calculation of runoff and pollution in each sub-watershed. Further research should be conducted on the parameters of each sub-basin in the future.