China has experienced rapid urbanization since the 1990s [1
]. Unfortunately, urban population growth and land use changes have led to the degradation of flowing watercourses and the discharge of excessive wastewater [2
]. Such water pollution, which threatens drinking water, agricultural irrigation and the ecological landscape, has become a key issue restricting urban development [3
]. Consequently, the Chinese government has regarded urban water pollution with great importance and has invested substantial amounts of energy and money accordingly [4
]. Nevertheless, some cities still face serious water pollution problems, especially rapidly developing cities where land use patterns are changing fast and sewage management and treatment are lacking [6
]. To further control and improve the water quality situation in such cities, accurately recognizing of the sources of pollutants is of considerable significance.
In general, water pollution is caused mainly by point sources and non-point sources (NPS) pollution. The former, such as industrial and domestic pollution, is relatively easy to collect into the pipe networks and process in the sewage treatment facilities, whereas the latter, which is widely distributed and originates from multiple-source, is more difficult to monitor and control and has tremendous impact on the water quality of lakes and rivers [9
]. Appropriately, many studies have focused on the assessment of NPS pollution loads. The most common and concerning sources of NPS pollution primarily include urban runoff (UR) [12
], agricultural runoff (AR) [13
], livestock and poultry breeding (LPB) [14
] and aquaculture [15
]. Ongley et al. [13
] estimated the pollution statuses of agricultural and rural areas and their contribution to the total water pollution throughout China. Li et al. [16
] simulated the NPS pollution load in the city of Baoding and analysed the effects of NPS pollution on Baiyangdian Lake. Due to the complexity of NPS components, empirical methods are often used to comprehensively evaluate all NPS pollutants, but these methods broadly lack detailed spatial and temporal descriptions [11
]. Some studies have attempted to simulate the temporal variation in and the spatial distribution of pollutants, but most research has focused mainly on a certain type of NPS pollution, such as UR or AR [8
]. In addition, because LPB and aquaculture do not correspond exactly to a specific type of land use, their spatial distributions are relatively difficult to describe [15
Accurate estimations of NPS pollution are important for the processing of NPS pollution loads [3
]. The most common methods for evaluating NPS pollution loads include empirical evaluation methods and physics-based models [17
]. The export coefficient model, which is based on a number of statistical materials to calculate pollution load, is the most common empirical method [23
]. The methodology behind the export coefficient model is easy to understand, but this approach relies heavily on the accuracy of the empirical data and cannot explain the dynamic processes responsible for generating the pollution loads. Moreover, the use of empirical data rather than real-time data means that the results obtained using this method usually have a large time step (monthly, seasonal or annual). In contrast, physical-based models are employed primarily to establish the relationship of rainfall runoff pollution loads and simulate the NPS pollution loads during the rainfall runoff process [7
]. These models generally contain good physical mechanisms, and some can provide information regarding the spatial and temporal distribution characteristics of NPS pollution loads [7
]. Some common NPS models are shown in Table 1
Most of these models described above are expert in simulating specific pollution sources or pollutions of certain land use patterns. Urban rainstorm events are often the focus of attention in urban areas; for this purpose, models such as SWMM and STORM are commonly used [18
]. Additionally, some models, such as SWAT and HSPF, are well suited to the evaluation of NPS pollution in a watershed or farmland [17
], but are not applicable to urbanized areas. These NPS evaluation models generally encounter problems with insufficient water quantity or quality modules. Lee et al. [26
] compared the water quality simulation effects of SWMM and HSPF in urban areas and found that SWMM is suitable for almost only urban areas, whereas HSPF can be applied to only homogenous land uses. Some studies have tried to improve upon existing models or to couple multiple models to form a new model; the resulting models can acquire better simulation results in diversified land use patterns. Lin et al. [30
] assessed the effects of NPS phosphorus on soil by integrating the sediment delivery distributed (SEDD) and pollution load (PLOAD) models. Yang et al. [31
] coupled the Xinanjiang model and SWAT to assess the NPS pollution load around Songtao Reservoir on Hainan Island. Nevertheless, land uses in urbanized areas encompass the characteristics of both urban and rural areas, and, thus, a variety of NPS types need to be considered. In addition, most of these models were developed in the United States or Europe; hence, considering the climate and soil distribution differences between these regions and China and other regions, some improvements must be implemented when using these models.
This study investigated a typical urbanized area, the Dafeng Basin. The study area has experienced rapid development with an urban population which has more than doubled in recent decades, but little attention has been paid to the water pollution therein, which seriously affects the quality of people’s lives and hinders sustainable social and economic development within the basin. The most problematic pollutant is domestic sewage, although NPS pollution is also significant; both sources aggravate the existing burden on the water environment. In recent years, the local government has increased its investments in water pollution control and management; however, measured data from monitoring sites shows that despite the many measures that have already been implemented, the levels of TN and TP pollutants still do not meet the requirement of water quality standards (GB 3838-2002 standard in China). Since there is no TN standard for rivers in this standard, we use ammonia nitrogen standard to replace it. In this study, we evaluated the spatiotemporal dynamics of NPS pollution of the Dafeng Basin. On the basis of an evaluation of the previous literature, no model was found to be completely applicable to this type of region. Therefore, we combined multiple lumped models to develop an NPS assessment model that would be well suited to the study area. The main NPS pollutants in the study area were considered, including UR, AR, LPB, aquaculture and others, and their connections with land use were established. The combined model consists of two modules: water quantity and water quality, focusing on TN and TP, and considering the dynamic simulation of pollutant accumulation and erosion of pollutants under different land use conditions. Via the proposed model based on land use type, the spatiotemporal variation in the NPS pollution load were simulated and used to provide a scientific basis for regional water pollution control.
5.1. Comparison with Other Studies
Water pollution has received widespread attention, and many studies have explored the spatial and temporal variations in sediment and pollutants. Table 8
shows the simulation results of some of these studies from China. The simulated results of the sediment, TN and TP loads in our study area are comparable to the results reported in these case studies. In particular, the sediment load was above 30 tons km−2
in agricultural areas, e.g., the Sanjiang Plain, Hainan Island and our research area. However, there were some differences in the TN loads of different regions; nevertheless, the loads were generally greater than 0.3 tons km−2
in urban areas and agricultural areas. Additionally, the TP loads of different regions varied greatly; in some regions, the TP loads was as low as 0.04 tons km−2
, while the TP load reached 1.7 tons km−2
in other regions.
5.2. Suggestions for Water Pollution Control
This study calculated various types of pollution loads. Among them, domestic pollution was the main source of pollutants, followed by NPS pollution. However, domestic pollution can be addressed by building pipe networks and corresponding sewage treatment plants. In contrast, NPS pollution is relatively difficult to control.
(1) Pollution load control priority
According to the simulation results of NPS pollution emissions, we prioritized the processing of pollution loads. The TN emission intensity decreased in the order of UR > LPB > AR > other, and the TP emission intensity decreased in the order UR > other > LPB > AR (Table 7
and Figure 10
). Evidently, UR and LPB were the main sources of TN and TP emissions. Therefore, we should first consider reducing UR and LPB. To reduce the UR pollution load, it is fundamental to improve the urban ecological environment by increasing the urban greening rate and constructing sponge cities [42
]. For LPB, the management strategy could be further optimized by returning waste to farmlands as much as possible, based on the pollution load carrying capacity of the land [43
]. In addition, farmland strategies require improving the utilization rate of fertilizers [21
] to reduce the pollution emissions. For aquaculture, some aquatic plants can be planted to absorb NPS pollutants [19
The concentrations of NPS TN and TP in rivers were simulated (Figure 11
). The concentrations of TN in sub-basin 37 exceeded the fourth category standard during the dry season, while that of TP below the same standard all year round (Figure 11
c). The load reduction pressure of TN was greater than that of TP; hence, for farmlands, the focus should be on reducing TN. In addition, the concentrations of TN and TP emissions were relatively high during the dry season. Thus, the reduction of pollutants during the dry season should be of particular concern.
(2) Measures of water quality improvement
Measures to improve water quality mainly include reducing pollution emissions and diluting pollution loads. The water quality compliance requirements in the study area are fourth category—that is, TN < 1.5 mg L−1 and TP < 0.3 mg L−1. Based on the discharged pollutant loads in 2018, it is necessary to reduce TN by at least 193.54 tons and TP by at least 20.39 tons to meet these standards. If we adjust the supply water instead of reducing pollutants, at least 1.29 × 108 m3 of pure water will need to be supplied to meet this standard. If the water used to dilute the pollutants satisfies the third category standard, it will be necessary to supply at least 3.87 × 108 m3 of water. Among them, the amount of water needed to dilute NPS pollution with pure water or the third category standard water accounted for 16.9%. Certainly, this issue is serious and very common in the control of polluted rivers in many cities throughout China.
5.3. Uncertainties and Limitations
The study area is a man-made river basin, the boundaries of which are controlled by water conservancy engineering, and the river flow is controlled artificially. Unfortunately, only observation of the water level and not the flow volume are available. Therefore, a comparison of the simulated runoff with measured data cannot be provided. Here, as an alternative, we performed water balance analysis and the rationality analysis of the runoff coefficient to demonstrate the rationality of the simulation results. For such a rain-sewage confluence area, many sewage outlets lack measured (let alone continuously monitored) data, which makes it very difficult to verify the simulated results directly and therefore introduces some uncertainties. In this paper, an on-site investigation was conducted to mine available information, including the layout of sewage networks and their operation situations, the numbers of the overflow sewage outlets, and the discharges of industrial wastewater. The simulated NPS pollution results, in addition to the surveys of domestic pollution sources, online monitoring of industrial pollution sources, and laboratory tests of sediment contamination, were compared with the measured water quality. The simulated average concentration was consistent with the measured water quality. In addition, we compared the simulated results with those of the export coefficient model; their differences were less than 10% for the simulated total amount and for each component. Although it is impossible to provide further validation, these analyses still represent progress for a developing city with such a complex situation and limited data.
The four sources identified in this paper, namely UR, LPB, AR and other sources, were the four main NPS pollutants in the study area. The source data employed in this paper were obtained mainly from reference materials and local investigations, and some of these data were at monthly or annual scales. Although we considered the pollution load factors and made some adjustments for the pollution sources at different times, some uncertainties in the hourly and daily data remained. Of course, the temporal uncertainty in source data has certain impacts on hourly and daily simulated results but has little effect on monthly and annual-scale results. Therefore, the monthly and annual-scale simulation results are reliable.
Many parameters were involved in the model. Among them, the runoff generation module of the Xinanjiang model included 13 parameters (Table 3
), which were mainly set based on the relevant literature. Some parameters significantly affected the water quantity simulation. During the parameter debugging process of the Xinanjiang model, we found that W0
, K and IMP were relatively sensitive. Table 4
shows several important parameters in the water quality simulation process. The vegetation cover and management factor and the maintenance measure factor were both key parameters for calculating the sediment and sorbed pollution production. Moreover, the ground wash coefficients of TN and TP (kN
) had considerable influences on the dissolved pollution load. Finally, the surface water treatment coefficients (FNS and FPS) and groundwater treatment coefficients (FNG and FPG) affected the total amounts of simulated NPS loads into rivers.
In this paper, a combination model was proposed and used to simulate the NPS TN and TP pollution load in the study area. We evaluated the spatiotemporal distribution of NPS pollution load. According to the results, we discovered the following:
(1) Ponds and farmlands had higher TN and TP production intensities than the other land use types, and the unit emissions in the northwestern region, which is mainly urban land and contains a great deal of aquaculture and LPB, were relatively higher;
(2) The variations in the monthly and interannual TN and TP loadings were consistent with the variations in rainfall. The emissions of TN and TP accounted for 56.2% and 58.0%, respectively, of the total in summer;
(3) NPS TN pollution was more serious than NPS TP pollution in the study area, especially in farmlands, and the concentrations of TN and TP emissions were relatively higher during the dry season;
(4) UR and LPB were the main sources of NPS TN and TP emissions in the study area. If NPS pollution cannot be removed from the study area, at least 2.19 × 107 m3 of pure water or 6.56 × 107 m3 of the third category standard water needs to be supplied to dilute the current rivers to meet the required fourth category standard.