Abstract
Affected by urban construction and industrial discharge, the diffuse source pollution of river water environment in urban sections is becoming more and more serious. The use of numerical models can accurately and efficiently simulate the process of diffuse source pollution and analyze its evolution. Based on MIKE21, the urban river water environment model of the Bai river is constructed. After parameter calibration, it is proved that the model has high accuracy. Based on the discharge of this river section in 2018, the river diffuse source pollution is simulated under three hydrological conditions, and the BP neural network is used to simulate the river diffuse source pollution. Network assessment of water quality in dry years. The results showed that the four water quality indicators showed the spatial distribution characteristics of upstream>urban section>downstream, and the river water quality in the year showed the temporal distribution characteristics of flood season better than a non-flood season. The mean value of the coefficient of certainty R2 reaches 0.94 when comparing the simulated and measured values, which indicates that the model has high applicability and satisfactory fit. If the water quality of the water year does not meet the standard, the diffuse source pollution discharge should be reduced from May to October. This study can provide reference and guidance for urban river diffuse source pollution control.
HIGHLIGHT
A two-dimensional hydrodynamic model was developed using MIKE software and validated by field test data to determine the two-dimensional model parameters, including flow rate, flow velocity, eddy viscosity coefficient, boundary conditions, roughness, etc.
Graphical Abstract
INTRODUCTION
In recent years, urban river water pollution has been aggravated due to human activities, and diffuse source pollution is the main source of urban pollution discharge. Therefore, accurately analyzing the process of diffuse source pollution and putting forward improvement suggestions based on this is a major scientific problem that needs to be solved in the development and management of the water environment. The randomness of water flow and the complexity of pollutant diffusion make diffuse source pollution analysis difficult. Common water pollution research methods include the empirical formula method, physical model method, and numerical calculation method. The numerical calculation model of water environment has been widely used in the fields of water pollution control and water environment impact assessment due to its unique characteristics of high efficiency and high precision (Li et al. 2021a, 2021b; Shahady & Cleary 2021).
Domestic and foreign researchers have done a lot of research on urban river water environment simulation. Xue et al. (2021) used a coupled model to model the water quantity and water quality of a typical agricultural plain watershed in North China. Yang et al. (2021) applied the MIKE 21 model to set different working conditions and studied the impact of water diversion in typical urban artificial lakes on water quality. Taking Wuhan East Lake as the research area, Li et al. (2020) proposed a model framework that coupled the physics-based model and MIKE 21 and evaluated the change process of its water quality. Zhu et al. (2021) used MIKE11 to analyze the effect of water diversion from the Yangtze River on the improvement of water quality in the receiving area of the eastern route of the South-to-North Water Diversion. Lin & Li (2015) used the spatial data management function of GIS and the water environment simulation function of the distributed hydrological model to construct a water environment management system for the watershed and took the Jiulong River Basin as the experimental watershed to realize the water environment of diffuse source pollution discharge. Simulation, assessment of environmental effects of non-diffuse source pollution management measures, and release of water environment information. Based on the MIKE SHE, Li et al. (2021a, 2021b) established the response relationship of the Yanghe water environment to the drainage in the basin, simulated and predicted the temporal and spatial variation of pollutants in the mainstream of the Yanghe River, and compared and analyzed the water quality status of key sections under different water pollution control schemes, to provide technical support for the improvement of water environment in Zhangjiakou City. Zhang et al. (2020) artificially solved the problems of poor river water quality and water environment degradation, constructed a coupled model of watershed hydrology, hydrodynamics, and water quality, and further evaluated the improvement efficiency of river water quality under different control measures.
BP neural network has good durability, real-time performance, high self-learning, self-adaptation, and fault tolerance. The model also has the advantage of a simple structure, which is very convenient and fast in actual operation. A series of studies have been carried out at home and abroad in applying artificial neural networks (ANNs) to water quality evaluation. Sun et al. (2004) conducted a further discussion based on the traditional ANN method, based on the BP algorithm, and adjusted the ANN output structure to make it more adaptable. The improved ANN was applied to the evaluation and classification of groundwater quality and compared with the evaluation results of fuzzy comprehensive evaluation. Wang et al. (2020) used the fuzzy BP neural network method to evaluate the water quality of the Liaohe Estuary wetland in different periods and regions and combined the fuzzy mathematics comprehensive evaluation method to analyze the membership degree of the output results. An optimization algorithm combining particle swarm optimization (PSO), genetic algorithm (GA), and BP neural network is proposed by Yan et al. (2019) and applied to predict water quality in Beijing Haihu Lake and achieved the desired results. Chen et al. (2020) established a new type of BP neural network, the IABC-BP network, and found through practice that the model has more predictive capability and is approximately 25% more accurate than the traditional BP neural network.
Although scholars have achieved fruitful results in water environment simulation and water quality assessment, there is a lack of research in some key aspects. Researchers usually only consider water environment simulation unilaterally and ignore the evaluation and analysis of calculation results, or only evaluate the water quality of rivers without studying the changes in river water environment caused by changes in hydrological conditions. The innovation of this study lies in the targeted simulation and analysis of the water quality results of diffuse source pollution in urban rivers under different hydrological conditions, and at the same time, the water quality grade of the results is evaluated by combining machine learning methods, which can well provide new insights for future diffuse source pollution research. ideas. The structure of the research content of this paper is as follows: Taking the urban section of Bai river as the research object (1) Establish the hydrodynamic and water quality model of the Yangcheng section of the Bai river, and calibrate the model parameters. (2) Based on the relevant hydrology and water quality data, the ECO module in MIKE21 is selected for simulation calculation, and the biochemical process of each state variable is carried out through differential equations, and the numerical solution of the study area is carried out. (3) Use BP neural network to evaluate the water quality grade, analyze the water environment characteristics of Bai river Basin based on water quality evaluation, and give reasonable suggestions. The research results can provide a theoretical basis and reference for urban river water environment improvement and diffuse source pollution control.
MODEL PRINCIPLE
The Mike21 model is a plane two-dimensional numerical simulation model based on hydrodynamic and water quality research. Compared with empirical formulas and physical models, this model greatly improves the calculation speed and the accuracy of the results. Compared with other numerical models, the Mike21 model is developed and evolved based on a lot of engineering experience, and it has better performance in the research of large-scale water flow fields and water quality changes and is widely used. The Mike21 water environment model mainly includes the hydrodynamic HD and water quality Ecolab simulation process, which can well simulate the diffuse source pollution process.
Hydrodynamic governing equations
Water quality control equation
Model step
Model evaluation
Result evaluation
The evaluation standard adopted in this study is China Surface Water Environmental Quality Standard (GB3838-2002), see Table 1 for details.
serial number . | state variables . | I . | II . | III . | IV . | V . |
---|---|---|---|---|---|---|
1 | DO≥ | 7.5 | 6 | 5 | 3 | 2 |
2 | NH3-N≤ | 0.15 | 0.5 | 1 | 1.5 | 2 |
3 | TP≤ | 0.02 | 0.1 | 0.2 | 0.3 | 0.4 |
4 | COD | 15 | 15 | 20 | 30 | 40 |
serial number . | state variables . | I . | II . | III . | IV . | V . |
---|---|---|---|---|---|---|
1 | DO≥ | 7.5 | 6 | 5 | 3 | 2 |
2 | NH3-N≤ | 0.15 | 0.5 | 1 | 1.5 | 2 |
3 | TP≤ | 0.02 | 0.1 | 0.2 | 0.3 | 0.4 |
4 | COD | 15 | 15 | 20 | 30 | 40 |
Error reverse transfer: Use the gradient descent method to correct the weight values of all layers. The learning algorithm of the weight values will be introduced in detail below.
CASE ANALYSIS
Study area and data source
Study area
Bai River is an important mainstream of the Yangtze River in China (Aviation et al. 2021; Kumar & Hong 2021). Bai River has a total length of 264 kilometers and a drainage area of 12,270 square kilometers. It is located in Nanyang City, Henan Province, central China. It has a high strategic position in the optimal allocation of water resources, flood control, and waterlogging prevention in the Yangtze River Basin. This research area selects the Nanyang urban section of Bai River as the research object, with a length of 46.3 km. The proportion of water functional areas with water quality above Class II in the Bai River Basin is 27.8%. The proportion of III water quality is 38.9%, accounting for the largest proportion. The proportion of IV water quality is 22.2%, and the proportion of V water quality is 11.1%. As the main urban landscape water body in Nanyang City, with the urbanization process and rapid social and economic development of the Bai River Basin, the deterioration of water quality will greatly affect the urban landscape function and the living environment of urban residents (Zuo et al. 2020). Therefore, it is of great significance to study the water pollution characteristics of urban rivers, establish a mathematical model of the water environment in the urban section of Bai River, and conduct an overall analysis of the characteristics of the water environment in the study area.
Data sources
The data of hydrological stations and corresponding water quality monitoring sections selected in this study are from China Hydrological Information Network (http://xxfb.mwr.cn/sq_dxsk.html). The locations of hydrological stations and water quality sections are shown in Figure 3. The elevation data of the study area were obtained from the China National Satellite Data Network (https://www.gscloud.cn).
Model building
Project settings
In this study, three calculation conditions were set for the water quality numerical simulation (Table 2), with the Duck river mouth section as the upstream boundary of the model and Wadian as the downstream boundary of the model.
Working condition setting . | Type . | Traffic at guaranteed rate m3/s . | Inlet concentration . |
---|---|---|---|
Working condition 1 | P = 10% guaranteed Rate (high flow year) | 59.24 | sequentially |
Working condition 2 | P = 50% Guaranteed Rate (average water year) | 23.38 | |
Working condition 3 | P = 90% Guaranteed Rate (dry year) | 14.12 |
Working condition setting . | Type . | Traffic at guaranteed rate m3/s . | Inlet concentration . |
---|---|---|---|
Working condition 1 | P = 10% guaranteed Rate (high flow year) | 59.24 | sequentially |
Working condition 2 | P = 50% Guaranteed Rate (average water year) | 23.38 | |
Working condition 3 | P = 90% Guaranteed Rate (dry year) | 14.12 |
Mesh division
Parameter setting
The simulation selects high flow year, average water year, and dry year as the simulation time, the total number of time steps is 730 steps, and the step size is set to 86,400 s. Through debugging, the dry water depth of the model is hdry = 0.006 m, the submerged water depth is hflood = 0.058 m, and the wet water depth is hwet = 0.13 m. The state variables of the water quality model are selected as dissolved oxygen (DO), biochemical oxygen demand (BOD), and ammonia nitrogen (NH3-N). The parameter settings of the model after calibration are shown in Table 3 below.
Parameter . | Value . | Unit . |
---|---|---|
precipitation evaporation | Measured sequence value | mm |
wind field | Measured sequence value | m/s |
Coriolis force | 7.92 × 10−5 | s−1 |
Eddy viscosity | 0.3 | |
salinity | 0 | |
water temperature | 17 | °C |
Diffusion coefficient | 0.35 | |
COD degradation rate | 0.25 | /day |
BOD degradation rate | 0.32 | /day |
NH3-N nitrification rate | 0.12 | /day |
DO oxygen utilizaton rate | 0.04 | /day |
Parameter . | Value . | Unit . |
---|---|---|
precipitation evaporation | Measured sequence value | mm |
wind field | Measured sequence value | m/s |
Coriolis force | 7.92 × 10−5 | s−1 |
Eddy viscosity | 0.3 | |
salinity | 0 | |
water temperature | 17 | °C |
Diffusion coefficient | 0.35 | |
COD degradation rate | 0.25 | /day |
BOD degradation rate | 0.32 | /day |
NH3-N nitrification rate | 0.12 | /day |
DO oxygen utilizaton rate | 0.04 | /day |
Model validation
Hydrodynamic model validation
It is generally considered that after the accuracy reaches 0.8, the model accuracy is acceptable. It can be seen from the table that R2 is greater than 0.80, indicating that the calculated value of the model can guarantee the accuracy of the state variable. The R2 for each model is shown in Table 4.
Water quality model validation
RESULTS
Using the Ecolab water quality model that has been calibrated, the spatial and temporal distribution characteristics of each water quality index in a typical hydrological year are respectively explored. And set three monitoring points A1, A2, A3 in the model (Figure 3).
Result analysis of working condition I
Result analysis of working condition II
Result analysis of working condition III
Hydrological stations . | Duck River mouth . | Nanyang penyao . | Wadian . |
---|---|---|---|
R2 | 0.89 | 0.92 | 0.91 |
Hydrological stations . | Duck River mouth . | Nanyang penyao . | Wadian . |
---|---|---|---|
R2 | 0.89 | 0.92 | 0.91 |
State variables . | COD . | DO . | NH3-N . | TP . |
---|---|---|---|---|
R2 | 0.90 | 0.95 | 0.96 | 0.94 |
State variables . | COD . | DO . | NH3-N . | TP . |
---|---|---|---|---|
R2 | 0.90 | 0.95 | 0.96 | 0.94 |
BP network water quality evaluation
Based on the surface water environmental quality standard (GB3838-2002) Table 6, the rand function is used to generate the input layer sample data, and the upper limit of DO is set as 14.64 mg/L. Convert the decimal 1–6 into binary as a standard output sample: 001 means class I water, 010 means class II water, 011 means class III water, 100 means class IV water, 101 means class V water, 110 means water lower than class V. The tang excitation function is used between the input layer and the hidden layer, (Wen & Yuan 2020) and the pure excitation function is used between the hidden layer and the output layer. Because the data is too small to meet the training requirements, linear interpolation is used to expand the training samples to 5*170. In the sample data, 5*120 are selected as training data, and 5*50 are selected as test data.
COD . | NH3-N . | DO . | TP . | Water quality grade . |
---|---|---|---|---|
2 | 0.15 | 7.5 | 0.01 | I |
4 | 0.5 | 6 | 0.025 | II |
6 | 1 | 5 | 0.05 | III |
10 | 1.5 | 3 | 0.1 | IV |
15 | 2 | 2 | 0.2 | V |
COD . | NH3-N . | DO . | TP . | Water quality grade . |
---|---|---|---|---|
2 | 0.15 | 7.5 | 0.01 | I |
4 | 0.5 | 6 | 0.025 | II |
6 | 1 | 5 | 0.05 | III |
10 | 1.5 | 3 | 0.1 | IV |
15 | 2 | 2 | 0.2 | V |
CONCLUSION
In view of the complexity of water quality analysis, this study uses the high-precision MIKE21 hydrodynamic and water quality coupling model to simulate the water environment in the urban section of the Bai river Basin, and combines the BP neural network to evaluate the regional water quality, and explores the calculation results and self-purification capacity.
- (1)
In terms of spatial distribution, the water quality in the urban section of the modeling area is worse than that in the upper reaches of the river. The main reason is that there are industrial sewage outlets in the modeling area, and the confluence of pollution sources has caused obvious pollution in the river area. The four water quality indicators showed the spatial distribution characteristics of upstream > urban segment > downstream.
- (2)
In terms of temporal distribution, the water quality of rivers during the year showed that the temporal distribution characteristics of the flood season were better than that of the non-flood season. The main reason is that the upstream inflow water volume is small in non-flood seasons, and the reduction of the flow rate will directly weaken the carrying effect of the water body on pollutants. The failure of water body to degrade in time has a great impact on water quality.
- (3)
Due to the limitation of verification data collection and time, the factors considered in the water environment model are relatively single, and only the main regional excess factors such as COD, NH3-N, total phosphorus, and DO are considered, and other pollution factors such as heavy metals are not included in the model. Therefore, the later research can expand and improve the model through supplementary data, so that the water environment simulation model can be more comprehensive. In this paper, a two-dimensional hydrodynamic model of the urban section of Bai River is established. Because the study area is too narrow and long, the vertical scale of the water body is not considered in this paper. But in fact, there is a pivot reservoir area in the upper reaches of the river, so the hydrodynamic and water quality characteristics of the river and the reservoir area can be further studied at a three-dimensional scale.
AUTHOR CONTRIBUTION
All the authors contributed to the conception and design of the study. Writing and editing: Xianqi Zhang and Xilong Wu; Preliminary data collection: Tao Wang; Chart editing: Guoyu Zhu Haiyang Chen; All authors have read and approved the final manuscript.
FUNDING
This work was supported by the Key Scientific Research Project of Colleges and Universities in Henan Province (CN) [grant number 17A570004].
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.